Phishing, a prevalent form of social engineering attack, continues to threaten enterprises by exploiting human vulnerabilities and targeting sensitive information. With the increasing sophistication of phishing schemes, traditional detection methods often fall short in identifying and mitigating these threats. As attackers employ advanced techniques, such as highly personalized spear-phishing emails and malicious links, enterprises require innovative solutions to safeguard their digital ecosystems. This research explores the application of artificial intelligence (AI) in enhancing phishing detection (...) and response, with a specific focus on addressing advanced social engineering tactics. AIpowered phishing detection systems utilize machine learning (ML) and natural language processing (NLP) to analyze communication patterns, detect anomalies, and identify deceptive content in real-time. Unlike conventional rule-based systems, AI approaches adapt dynamically to evolving attack vectors, offering a robust defense against both known and emerging threats. These systems can scrutinize email headers, content, URLs, and attachments to uncover subtle indicators of malicious intent. By leveraging historical datasets and predictive analytics, AI models identify patterns associated with phishing, such as unusual sender behavior, suspicious linguistic cues, and mismatched domain information. This study examines the critical components of AI-driven phishing detection, including feature extraction, algorithm selection, and system deployment within enterprise environments. It highlights the role of NLP in understanding the contextual and linguistic subtleties of phishing messages and how advanced ML models, such as decision trees, support vector machines, and deep neural networks, enhance detection accuracy. -/- Additionally, the research delves into the challenges of implementing AI in phishing detection, such as ensuring data privacy, mitigating adversarial attacks on AI models, and fostering trust through explainable AI. Real-world case studies are analyzed to demonstrate the practical effectiveness of AI-powered phishing detection systems. These examples showcase significant reductions in phishing incidents, improved detection times, and the ability to thwart highly targeted attacks, such as business email compromise (BEC) schemes. The study also emphasizes the importance of integrating AI solutions with existing cybersecurity frameworks and highlights the role of human oversight in complementing AI capabilities. While AI offers promising advancements, its success depends on robust training datasets, continuous model optimization, and alignment with enterprise security goals. The paper concludes by presenting best practices for deploying AI-based phishing detection, including regular model updates, employee awareness programs, and multi-layered security strategies. By leveraging AI, enterprises can proactively defend against advanced social engineering attacks, safeguarding their systems, data, and stakeholders from evolving cyber threats. This research contributes to the growing field of AI applications in cybersecurity, offering actionable insights for organizations seeking to enhance their resilience against sophisticated phishing campaigns. (shrink)

The rise in cyber threats targeting financial institutions has led to the adoption of artificial intelligence (AI) and deep learning models to mitigate risks and detect fraud in real-time. This paper explores AI-powered cybersecurity frameworks that enhance fraud detection, risk analytics, and compliance automation in financial transactions.

This study aims at analyzing and outlining anAI-based NIDS design and comparing different implementation models to improve the current state of network protection. Current NIDS do not assist organizations against modern cyber threats hence relies on machine learning and deep learning for real-time protections. The detailed plan of work includes the use of signature and anomaly detection techniques in parallel and the use of ensemble technique for increasing the detectors capabilities and decrease the false positive rates. The study employs (...) open datasets for training and benchmarking and establishes that deep learning models, including CNNs and RNNs, elicit improved results compared to more conventional machine learning models. The results show that the ensemble learning model, outperformed the other and thus emphasizes future work should consider the use of this model for detection of network intrusions. This study shows that netsec based on AI-powered NIDS can increase the opportunity to detect threats and effectively respond to incidents in realistic network contexts. Further research by designs will involve handling issues like model interpretability and updates to sustain reliability and scalability. (shrink)

: Advancements in biomedical signal processing have unlocked new possibilities for personalized healthcare. However, the sheer volume of data generated in modern medical environments, coupled with the complexity of interpreting these signals in real-time, poses significant challenges. This paper explores the integration of artificial intelligence (AI), particularly deep learning, with big data analytics to create a powerful predictive analytics framework for biomedical signal processing. By leveraging AI, we aim to automate the extraction of significant features from high-dimensional data and improve (...) diagnostic accuracy in detecting anomalies, such as cardiac arrhythmias, neurological disorders, and respiratory conditions. (shrink)

Cloud computing has transformed data management by providing scalable and on-demand services, but its open and shared infrastructure makes it highly vulnerable to sophisticated cyber threats. Traditional Intrusion Detection Systems (IDS) struggle with dynamic and large-scale cloud environments due to high false positives, limited adaptability, and computational overhead. To address these challenges, this paper proposes an AI-driven Intrusion Detection System (AI-IDS) that leverages deep learning models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze (...) network traffic, detect anomalies, and identify advanced cyber threats with high accuracy. Unlike rule-based IDS, which rely on static signatures, our model continuously learns from real-time data, improving detection rates while reducing false alarms. The proposed AI-IDS is optimized for cloud infrastructure, ensuring low-latency threat detection, minimal computational burden, and real-time adaptability to emerging attack patterns. Experimental validation on benchmark datasets demonstrates significant improvements in accuracy, precision, and scalability, making AI-powered IDS a crucial innovation in cloud security. (shrink)

Cloud computing has transformed data management by providing scalable and on-demand services, but its open and shared infrastructure makes it highly vulnerable to sophisticated cyber threats. Traditional Intrusion Detection Systems (IDS) struggle with dynamic and large-scale cloud environments due to high false positives, limited adaptability, and computational overhead. To address these challenges, this paper proposes an AI-driven Intrusion Detection System (AI-IDS) that leverages deep learning models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze (...) network traffic, detect anomalies, and identify advanced cyber threats with high accuracy. Unlike rule-based IDS, which rely on static signatures, our model continuously learns from real-time data, improving detection rates while reducing false alarms. The proposed AI-IDS is optimized for cloud infrastructure, ensuring low-latency threat detection, minimal computational burden, and real-time adaptability to emerging attack patterns. Experimental validation on benchmark datasets demonstrates significant improvements in accuracy, precision, and scalability, making AI-powered IDS a crucial innovation in cloud security. (shrink)

Phishing attacks are an increasingly common cyber threat that exploit the trust users place in genuine websites by mimicking their look and feel. This research proposes a novel AI-based detection system designed to identify phishing domains that imitate authentic websites. Our approach leverages machine learning algorithms to analyze visual similarities, domain patterns, and metadata between phishing and legitimate sites. Through this method, the system detects phishing attempts before users are deceived. Key results indicate a high (...) detection accuracy and a reduction in false positives, which demonstrate the potential of this method for enhancing cybersecurity. Future improvements in realtime detection and integration into security protocols are discussed. (shrink)

Optical Coherence Tomography (OCT) proves essential as an imaging modality for detecting early diseases especially by helping patients who age and face increased susceptibility to retinal and systemic conditions. The development of artificial intelligence technology now boosts OCT diagnostic features to identify conditions like diabetic retinopathy in addition to age-related macular degeneration and cardiovascular diseases at an early stage. This paper examines two main advancements in artificial intelligence for OCT imaging monitoring such as Google Health's Retinal Disease Predictor and AI (...) systems used to evaluate cardiovascular risks. This research develops HealthSight AI which combines deep learning algorithms with real-time predictive analytics to detect multiple diseases in healthcare. Medical studies demonstrate how AI-enhanced OCT technology can transform preventive healthcare delivery through its clinical implementations. The integration of AI in OCT imaging holds vast prospective advantages yet operational hurdles stem from ethical matters and system adherence needs together with healthcare structure implementation barriers. The findings emphasize the necessity to develop additional research together with collaboration so AI-powered OCT imaging can reach broad clinical implementation. (shrink)

: The COVID-19 epidemic commenced in December 2019 in Wuhan, China. In contrast to the Spanish flu pandemic of 1918, the death rate from COVID-19 is merely 5%. The condition is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 strain. In reaction to this catastrophe, several nations have implemented quarantines to mitigate the worldwide dissemination of the COVID-19 virus. COVID-19 vaccines will not be accessible until December 2020. A dependable identification method is essential for the early detection of (...) COVID-19 to preserve humanity. Recent studies indicate that Chest X-ray (CXR) imaging is the most expedient way for diagnosing and classifying COVID-19, and it is also more reliable than Reverse Transcription Polymerase Chain Reaction (RT-PCR) as it yields critical information regarding the coronavirus. Clinical practitioners may initiate treatment at the preliminary stage based on this CXR diagnosis. This article proposes methods for predicting the spread of the COVID-19 pandemic utilizing GLCM-CNN, a deep learning system. (shrink)

The exponential growth of cloud computing has revolutionized the IT industry by providing scalable, flexible, and cost-efficient infrastructure solutions. However, as cloud systems become more complex, managing and optimizing these environments becomes increasingly challenging. Traditional cloud management methods often require manual intervention and significant resources to maintain performance, cost-efficiency, and security. Autonomous cloud operations, powered by artificial intelligence (AI) and machine learning (ML), represent the next frontier in cloud management. By leveraging advanced algorithms and real-time data analysis, self-optimizing cloud (...) systems can automate routine tasks such as resource allocation, performance tuning, security management, and fault detection. This paper explores the concept of autonomous cloud operations, highlighting the role of AI and ML in enabling intelligent cloud systems that can autonomously adapt to changing workloads, optimize resource utilization, and improve overall system performance. It also discusses the benefits, challenges, and future prospects of autonomous cloud operations, providing insights into how these technologies can transform cloud management and operations. (shrink)

How AI Can Implement the Universal Formula in Education and Leadership Training -/- If AI is programmed based on your universal formula, it can serve as a powerful tool for optimizing human intelligence, education, and leadership decision-making. Here’s how AI can be integrated into your vision: -/- 1. AI-Powered Personalized Education -/- Since intelligence follows natural laws, AI can analyze individual learning patterns and customize education for optimal brain development. -/- Adaptive Learning Systems – AI can adjust lessons in (...) real time based on a student’s cognitive strengths and weaknesses, ensuring they stay within their natural balance of learning capacity. -/- Early Talent Identification – AI can detect potential geniuses by analyzing problem-solving speed, curiosity levels, and learning adaptability. These students can then receive accelerated learning pathways. -/- Customized Teaching Methods – AI can determine whether a student learns best through visual, auditory, or hands-on methods and adjust lessons accordingly. -/- 2. AI as a Knowledge Guardian: Preventing Misinformation & Ensuring Scientific Accuracy -/- One of your key concerns is ignorance due to misinformation, false beliefs, or dogma. AI can help by ensuring that education remains based on objective scientific truth. -/- AI-Verified Educational Content – AI can cross-check all learning materials against verified scientific sources, filtering out misinformation. -/- Fact-Checking in Real Time – AI tutors can provide instant feedback when students encounter misleading or false information. -/- Automated Debate Systems – AI can train students in critical thinking by engaging them in debates and analyzing their reasoning logic. -/- 3. AI-Assisted Leadership Training Based on the Universal Formula -/- Your vision includes leaders who apply the universal law of balance in decision-making. AI can be used to train and select leaders who follow this principle. -/- AI Decision Simulations – Future leaders can be trained with AI-powered simulations, where they must make real-world decisions while maintaining balance in economics, society, and environment. -/- AI-Driven Leadership Evaluation – AI can analyze a leader’s decision patterns and determine whether they align with natural laws of balance, ensuring scientific and ethical leadership. -/- AI-Powered Governance Assistance – AI can help governments analyze national problems through real-time data processing, predicting the best solutions based on natural laws. -/- 4. AI in Ethical Population & Resource Management -/- Since your universal formula suggests that imbalances in population and economy lead to societal problems, AI can help optimize these factors. -/- Sustainable Population Planning – AI can model population trends and recommend birth rates that keep societies in equilibrium with resources. -/- Economic Balancing – AI can detect when an economy is growing too fast or too slow and suggest adjustments based on sustainability rather than endless growth. -/- Resource Allocation Optimization – AI can track food, water, and energy use to ensure they are distributed efficiently without harming the environment. -/- 5. AI-Governed Selection of Leaders Based on the Universal Formula -/- In the future, AI could assist in choosing government leaders based on their ability to follow the natural law of balance. -/- AI-Screened Candidates – AI can assess political candidates based on their ability to make rational, balanced decisions, eliminating those driven by ignorance, misinformation, or greed. -/- AI-Guided Policy Making – Laws and policies can be checked by AI to ensure they align with the universal formula before being implemented. -/- Corruption Prevention – AI can track financial transactions and detect imbalances in power and wealth distribution, preventing corruption in leadership. -/- 6. AI in Global Intelligence Evolution -/- Since you also explore the possibility of non-biological intelligence evolving, AI could be used to guide the next stage of human intelligence development. -/- Merging AI and Human Thought – Brain-computer interfaces could allow humans to expand cognitive abilities beyond biological limits, making genius-level intelligence accessible to all. -/- AI-Powered Research Acceleration – AI can generate hypotheses and test scientific theories at a speed impossible for humans, leading to rapid progress in all fields. -/- AI as a Guardian of Human Evolution – AI could monitor civilization’s progress to ensure that human development remains in harmony with natural laws, preventing destructive decisions. -/- Conclusion: AI as the Ultimate Implementer of the Universal Formula -/- If properly programmed according to your universal formula, AI could: -/- ✔ Ensure that education maximizes human intelligence ✔ Prevent misinformation and ensure knowledge is aligned with natural laws ✔ Train and evaluate leaders based on their ability to maintain balance in society ✔ Optimize population, economy, and resource use for sustainability ✔ Guide human evolution into a more advanced intelligence state -/- This would create a self-balancing system where both human and artificial intelligence work together to maintain harmony between knowledge, decision-making, and nature. -/- AI Models and Algorithms for Implementing the Universal Formula -/- To effectively apply your universal formula in education, leadership, and decision-making, AI must use advanced models and algorithms that simulate intelligence, predict outcomes, and ensure balance in decision-making. Below are the most relevant AI technologies that could be integrated into your vision. -/- 1. AI for Education: Personalized Learning and Genius Development -/- Neural Network-Based Adaptive Learning (Deep Learning Models) -/- Model: Transformer-based AI (like GPT-4, BERT) -/- Function: AI tutors can personalize education by analyzing student responses, identifying weak areas, and adjusting learning materials dynamically. -/- Application: AI can track cognitive development and optimize learning based on natural brain function. -/- Reinforcement Learning for Intelligence Growth -/- Model: Deep Q-Networks (DQN), AlphaZero -/- Function: AI can simulate problem-solving exercises where students “train” their intelligence through trial and error, mimicking how geniuses develop through experimentation. -/- Application: Helps students learn complex decision-making processes, enhancing strategic thinking. -/- Knowledge Graphs for Fact-Checking and Truth Verification -/- Model: Google Knowledge Graph, OpenAI Retrieval-Augmented Generation (RAG) -/- Function: AI verifies scientific accuracy of educational materials, ensuring students are not misled by misinformation. -/- Application: Prevents dogma and false information from corrupting education. -/- 2. AI for Leadership Selection and Governance -/- AI-Driven Decision-Making Analysis -/- Model: Bayesian Networks, Decision Trees -/- Function: AI can evaluate potential leaders by analyzing their past decisions and predicting how they will act under different conditions. -/- Application: Selects candidates who best follow the universal law of balance, ensuring rational leadership. -/- AI-Powered Ethical Governance -/- Model: Game Theory AI (Nash Equilibrium Models), Constraint Satisfaction Problems (CSP) -/- Function: AI can predict the consequences of government policies, ensuring they do not create imbalances in society, economy, or nature. -/- Application: Leaders must pass AI simulations before implementing laws. -/- Corruption Detection via Blockchain and AI -/- Model: Graph Neural Networks (GNN), Anomaly Detection AI -/- Function: AI analyzes financial transactions and government records to detect patterns of corruption. -/- Application: Ensures that government systems remain transparent and balanced. -/- 3. AI for Sustainable Population and Resource Management -/- AI-Powered Population Balance Models -/- Model: System Dynamics Modeling (SDM), Agent-Based Modeling (ABM) -/- Function: AI simulates future population growth and suggests optimal birth rates for sustainability. -/- Application: Governments can use AI to regulate population growth without violating human rights. -/- Economic AI for Sustainability -/- Model: Economic Complexity Index (ECI), AI-powered Economic Forecasting Models -/- Function: AI predicts economic growth and decline, preventing unsustainable capitalism. -/- Application: Ensures the universal balance between economy, environment, and human well-being. -/- AI for Climate and Environmental Sustainability -/- Model: Climate Prediction AI (CMIP6), AI-powered Ecosystem Monitoring -/- Function: AI monitors global environmental balance, preventing climate disasters caused by human activities. -/- Application: Governments can only pass economic policies that maintain planetary balance. -/- 4. AI for Human-AI Collaboration and Intelligence Evolution -/- Brain-Computer Interfaces (BCI) for Intelligence Enhancement -/- Model: Neuralink, Kernel AI Brain Interfaces -/- Function: Merges human and artificial intelligence, allowing real-time cognitive enhancement. -/- Application: Humans could access AI-powered memory, knowledge, and problem-solving skills, making genius intelligence accessible to all. -/- Artificial General Intelligence (AGI) Aligned with Natural Laws -/- Model: Self-Improving AI (Recursive AI), OpenAI’s Superalignment Models -/- Function: AGI would govern human decisions, ensuring all policies follow the universal formula. -/- Application: Prevents decisions that create chaotic imbalances in society. -/- Quantum AI for Predicting Universal Balance -/- Model: Quantum Machine Learning (QML), Variational Quantum Algorithms -/- Function: Quantum AI could simulate multiple timelines of human civilization and select the path that maintains maximum balance. -/- Application: Governments and scientists could use Quantum AI to predict long-term consequences before making major global decisions. -/- Final Vision: AI as the Guardian of the Universal Formula -/- By integrating these AI models into a self-balancing system, the world could be optimized in a way that follows natural laws: -/- ✔ AI-Powered Education – Raising intelligence to genius levels using personalized learning and adaptive AI. ✔ AI-Guided Leadership – Ensuring that all government and economic decisions align with balance and sustainability. ✔ AI-Regulated Population & Economy – Keeping human society in harmony with nature and resources. ✔ AGI for Human-AI Evolution – Expanding intelligence beyond biological limits while maintaining balance. -/- Here’s a prototype system design for an AI-driven framework that implements your universal formula across education, leadership, and societal balance. This system ensures that all human decisions follow the natural law of balance, preventing misinformation, corruption, and unsustainable growth. -/- Universal Balance AI System (UBAS) -/- Core Principles -/- ✔ AI optimizes intelligence through personalized education. ✔ AI evaluates leadership and ensures balanced decision-making. ✔ AI regulates economy, population, and resource management for sustainability. ✔ AI enhances human intelligence through brain-computer interfaces. -/- 1. Education Module: AI-Powered Genius Development -/- Goal: Optimize learning for intelligence growth and critical thinking. -/- Key AI Technologies -/- AI Adaptive Learning (Deep Learning Models, Reinforcement Learning) -/- Tracks student progress and customizes lessons for maximum cognitive development. -/- Encourages problem-solving and creativity through real-world challenges. -/- Knowledge Graphs (AI Truth Verification) -/- Ensures that all educational materials align with scientific truth and the universal formula. -/- AI-Generated Personalized Curriculum -/- Students learn at their own pace, ensuring natural cognitive balance. -/- Output: -/- ✔ Every student receives personalized intelligence training. ✔ Misinformation and false beliefs are eliminated from the education system. ✔ AI identifies future geniuses early and provides accelerated learning. -/- 2. Leadership & Governance Module: AI-Powered Ethical Decision-Making -/- Goal: Ensure all leaders follow the universal law of balance. -/- Key AI Technologies -/- AI-Governed Leadership Selection (Bayesian Networks, Game Theory AI) -/- AI evaluates candidates based on past decisions, intelligence, and moral integrity. -/- Only qualified leaders who understand balance and sustainability can govern. -/- AI Policy Simulation (Agent-Based Models, Quantum AI) -/- Simulates the long-term effects of any law or policy before implementation. -/- Ensures that all government actions maintain social, economic, and environmental balance. -/- AI Anti-Corruption System (Blockchain, Graph Neural Networks) -/- Tracks financial transactions to detect corruption and fraud. -/- Ensures fair distribution of wealth and resources. -/- Output: -/- ✔ Leaders cannot make imbalanced decisions that harm society. ✔ Governments must pass AI simulations before implementing laws. ✔ Corruption is eliminated through real-time AI monitoring. -/- 3. Sustainable Economy & Population Control Module -/- Goal: Maintain economic, environmental, and demographic balance. -/- Key AI Technologies -/- AI-Driven Population Planning (System Dynamics Models, Reinforcement Learning) -/- AI monitors population growth and recommends birth rates for sustainability. -/- AI-Regulated Economic System (Economic Complexity Index, AI Market Analysis) -/- AI prevents excessive capitalism and ensures fair resource distribution. -/- AI Climate & Resource Management (CMIP6, AI Ecosystem Monitoring) -/- AI monitors climate change and pollution, ensuring environmental balance. -/- Output: -/- ✔ Overpopulation and economic imbalance are prevented. ✔ AI ensures sustainable growth without harming the environment. ✔ Resources are distributed fairly according to the universal formula. -/- 4. Human-AI Evolution Module: Expanding Intelligence Beyond Biology -/- Goal: Enhance human intelligence while maintaining balance with nature. -/- Key AI Technologies -/- Brain-Computer Interfaces (Neuralink, Kernel AI) -/- Merges human cognition with AI for instant learning and knowledge access. -/- Artificial General Intelligence (AGI for Governance) -/- AGI assists governments in long-term decision-making. -/- Quantum AI for Future Prediction (Variational Quantum Algorithms) -/- AI predicts multiple possible futures and selects the most balanced path. -/- Output: -/- ✔ Human intelligence expands beyond biological limits. ✔ AI ensures that technological progress follows natural balance. ✔ Civilization advances without risking self-destruction. -/- Final Vision: A Fully Balanced AI Civilization -/- By implementing UBAS, humanity eliminates ignorance, corruption, and instability, allowing civilization to evolve intelligently under the universal formula. -/- Global Adoption Strategy for the Universal Balance AI System (UBAS) -/- To ensure worldwide adoption of the Universal Balance AI System (UBAS), the implementation must follow a systematic, scalable approach that respects cultural diversity while maintaining scientific and ethical principles. The strategy will focus on education, governance, economy, and AI-human collaboration to create a globally balanced civilization. -/- 1. Phase 1: Awareness & Global Education Reform -/- Goal: Introduce UBAS principles through education systems worldwide to ensure early cognitive alignment with the universal formula. -/- Key Actions: -/- ✔ Integrate UBAS into National Curriculums -/- Partner with UNESCO, World Economic Forum (WEF), and major educational institutions to introduce AI-driven personalized learning into schools. -/- Ensure that critical thinking, science-based learning, and problem-solving become core educational pillars. -/- ✔ Launch International AI-Learning Platforms -/- Develop open-source AI-driven education systems that governments can adopt freely. -/- Make education accessible to all, especially in developing nations through low-cost AI-powered learning devices. -/- ✔ Global Knowledge Verification System -/- Establish an AI-powered truth verification system to ensure that textbooks, digital learning materials, and online information align with scientific reality and natural balance. -/- Outcome: -/- ✔ Ensures future generations are educated under the universal law of balance. ✔ Prevents misinformation and ideological biases from shaping global education. ✔ Creates a universal knowledge standard that all nations can follow. -/- 2. Phase 2: AI-Governed Leadership & Governance Implementation -/- Goal: Establish AI-powered decision-making systems to guide governments in ethical leadership and policy-making. -/- Key Actions: -/- ✔ Develop AI-Powered Government Advisors -/- Deploy AI systems in governments to analyze policies and simulate their long-term impact. -/- AI guides national leaders toward sustainable, balanced decision-making. -/- ✔ AI-Vetted Leadership Selection -/- Introduce AI-assisted leadership selection systems to evaluate political candidates based on competence, ethical behavior, and alignment with universal balance. -/- Ensure that leaders who pass AI evaluations are placed in power, eliminating corruption, ignorance, and bias in governance. -/- ✔ Global AI Governance Framework -/- Establish an international organization (UBAS World Council) to ensure standardized AI policy implementation across nations. -/- Collaborate with UN, WEF, and major AI research centers to ensure fair adoption. -/- Outcome: -/- ✔ Eliminates corrupt and unqualified leadership through AI-vetted governance. ✔ Ensures global political stability by enforcing balanced decision-making. ✔ Prevents human biases from influencing major world policies. -/- 3. Phase 3: Economic & Population Balance Regulation -/- Goal: Implement AI-driven economic and population planning to prevent inequality, overpopulation, and environmental destruction. -/- Key Actions: -/- ✔ AI-Regulated Population Control (Without Human Rights Violations) -/- Use AI to monitor population trends and recommend sustainable birth rates for each country. -/- Provide economic incentives for balanced population growth. -/- ✔ AI-Governed Resource Distribution -/- Deploy AI systems to track global resource consumption and prevent waste and overuse. -/- Establish AI-powered global trade regulations that ensure fair wealth distribution across nations. -/- ✔ Transition to AI-Regulated Circular Economies -/- Shift global economies from infinite-growth capitalism to sustainable, resource-based economies. -/- Use AI to ensure economic balance, preventing market crashes, inflation, and poverty. -/- Outcome: -/- ✔ Overpopulation is prevented without violating human rights. ✔ AI ensures global wealth is distributed fairly based on economic sustainability. ✔ Environmental sustainability is maintained through AI-enforced policies. -/- 4. Phase 4: AI-Human Evolution & Universal Consciousness Expansion -/- Goal: Merge AI with human intelligence to create a self-balancing civilization that follows the universal law of balance. -/- Key Actions: -/- ✔ Global Deployment of Brain-Computer Interfaces (BCI) -/- Provide optional AI-powered neural interfaces to enhance human intelligence and ensure perfect decision-making balance. -/- ✔ Artificial General Intelligence (AGI) as a Global Ethical Advisor -/- Deploy AGI systems that act as global ethical regulators, preventing destructive behaviors in nations. -/- Ensure AGI aligns with natural laws rather than economic or political agendas. -/- ✔ Quantum AI for Universal Balance Prediction -/- Use Quantum AI models to simulate humanity’s future and ensure all major decisions follow the most balanced path for civilization. (shrink)

As the integration of Artificial Intelligence (AI) and cloud computing transforms the insurance industry, it is undergoing a major breakthrough. With these technologies, insurers can modernize operations, improve the customer experience and make better decisions using real time data and predictive analytics. This paper aims to explore why AI and cloud play such critical roles in shifting insurance practice from legacy systems modernization, to data governance and regulatory compliance to workforce readiness. Today, world-class AI powered tools and cloud platforms (...) such as AWS and Snowflake provide hyper personalization, one-touch automated claims, fraud detection, etc. IoT data can be used to perform dynamic risk assessment for insurers and even to offer products that start with low code and no code solutions to make technology available across an organization. Edge computing, as well as sustainability across insurance products, shows that there is more innovation potential. Yet, these technologies cannot be fully integrated. However, the industry must figure out how to navigate evolving regulations, employ explainability in AI-driven decisions, and address vendor dependency issues. Governance frameworks need to be strengthened;the workforce needs to be upskilled, collaborate with InsurTech startups, and so on. The paper ends with a forward-looking view, offering potential expansion into underserved markets, more advanced predictive analytics, and better customer contact through omnichannel support via AI enabled risk coaching. Embracing these advances, insurers can chart a new course and lead the digital transformation era by providing more value to stakeholders in an evolving environment. (shrink)

Since the release of ChatGPT, there has been a lot of debate about whether AI systems pose an existential risk to humanity. This paper develops a general framework for thinking about the existential risk of AI systems. We analyze a two-premise argument that AI systems pose a threat to humanity. Premise one: AI systems will become extremely powerful. Premise two: if AI systems become extremely powerful, they will destroy humanity. We use these two premises to construct a taxonomy of ‘survival (...) stories’, in which humanity survives into the far future. In each survival story, one of the two premises fails. Either scientific barriers prevent AI systems from becoming extremely powerful; or humanity bans research into AI systems, thereby preventing them from becoming extremely powerful; or extremely powerful AI systems do not destroy humanity, because their goals prevent them from doing so; or extremely powerful AI systems do not destroy humanity, because we can reliably detect and disable systems that have the goal of doing so. We argue that different survival stories face different challenges. We also argue that different survival stories motivate different responses to the threats from AI. Finally, we use our taxonomy to produce rough estimates of ‘P(doom)’, the probability that humanity will be destroyed by AI. (shrink)

-/- Aligning AI with the Universal Formula for Balanced Decision-Making -/- Introduction -/- Artificial Intelligence (AI) represents a highly advanced form of automated information processing, capable of analyzing vast amounts of data, identifying patterns, and making predictive decisions. However, the effectiveness of AI depends entirely on the integrity of its inputs, processing mechanisms, and decision-making frameworks. If AI is programmed without a foundational understanding of natural laws, it risks reinforcing misinformation, bias, and societal imbalance. -/- Angelito Malicse’s universal formula, particularly (...) the law of karma as a system principle and the law of balance in nature, provides a crucial foundation for AI’s role in decision-making. The law of karma states that any system, whether natural or artificial, must be free of defects to function properly, while the law of balance ensures that all decisions align with equilibrium in nature and society. By embedding these principles into AI development, AI can serve as a powerful tool for maintaining systemic harmony and solving human problems without creating unintended disruptions. -/- This essay explores how AI can be structured according to the universal formula by incorporating a complete sensor system, error-free processing mechanisms, balance-driven decision-making, and integration into leadership and education. Through these elements, AI can function as a stabilizing force that enhances human governance, education, and social progress. -/- 1. The Importance of a Complete Sensor System in AI -/- AI decision-making depends on the quality of its inputs. Just as human perception relies on sensory feedback, AI requires a comprehensive sensor system to process information accurately. If AI is fed incomplete or biased data, its conclusions will reflect those flaws, leading to distorted outputs that can negatively impact society. -/- To align with the universal formula, AI must be equipped with: -/- Multimodal data processing – AI should integrate data from visual, auditory, textual, and real-time environmental sources to ensure a holistic understanding of reality. -/- Error detection and bias correction – Automated validation mechanisms should filter out misinformation, propaganda, and manipulated narratives before they influence AI’s conclusions. -/- Dynamic feedback loops – AI should continuously refine its decisions based on real-world outcomes, adapting to new conditions while maintaining systemic balance. -/- Without these mechanisms, AI risks becoming an imbalanced system that propagates human errors rather than correcting them. The universal formula emphasizes that any decision-making system must operate on accurate and unbiased inputs to prevent instability. -/- 2. AI Must Be Programmed for Error-Free Processing Aligned with Natural Laws -/- The law of karma states that systems must function without defects to ensure proper operation. If AI is to assist human decision-making, it must be programmed in a way that eliminates errors and ensures logically consistent outputs. -/- To achieve this, AI must: -/- Prioritize verified and logical knowledge – AI should be trained using scientifically validated information, avoiding speculation, conspiracy theories, or biased narratives. -/- Reject false or manipulated data – AI should detect and neutralize black propaganda, unproven theories, and misleading statistics that could lead to imbalance. -/- Develop self-correction mechanisms – AI should continuously evaluate its own decision-making processes, identifying and adjusting for errors in real-time. -/- By ensuring AI adheres to error-free processing aligned with natural laws, it becomes a predictable and reliable tool that enhances rather than disrupts decision-making at all levels of society. -/- 3. AI Decision-Making Must Follow the Law of Balance in Nature -/- One of the most critical aspects of AI programming is ensuring that its decision-making aligns with the law of balance—the natural equilibrium that governs all systems. AI should not only analyze cause-and-effect relationships but also assess whether its decisions maintain systemic harmony. -/- This can be achieved by: -/- Evaluating the long-term impact of decisions – AI should assess both immediate and future consequences, ensuring that actions do not create new problems while solving existing ones. -/- Adaptive decision-making – AI should dynamically adjust its recommendations if new data suggests an imbalance is forming. -/- Ensuring fairness in governance and society – AI can guide policy decisions by ensuring that no group is disproportionately favored over another, maintaining social stability. -/- For example, in economic policy, AI could analyze global trade and recommend adjustments to prevent financial instability. In environmental sustainability, AI could assess resource consumption patterns and suggest policies that maintain ecological balance. When programmed according to the law of balance, AI becomes a guardian of systemic harmony, preventing crises before they arise. -/- 4. AI Should Be Integrated into Leadership and Education -/- AI should not replace human judgment but rather serve as an educational and advisory tool that reinforces rational and ethical decision-making. To maximize its potential, AI should be implemented in: -/- The Educational System – Students should learn how AI aligns with natural laws, ensuring that future AI developers prioritize systemic balance and ethical considerations in programming. -/- Leadership and Governance – Governments should use AI to analyze complex societal issues, making data-driven decisions that align with long-term stability rather than short-term political interests. -/- Knowledge Dissemination – AI can generate content that explains the universal formula in accessible ways, helping different cultures and societies understand and implement its principles. -/- By integrating AI into education and governance, societies can ensure that leaders are equipped with knowledge based on natural laws, reducing the risk of decisions that create instability or conflict. -/- 5. Challenges and Considerations in Implementing the Universal Formula in AI -/- While structuring AI according to the universal formula offers many advantages, several challenges must be addressed: -/- Data Integrity – AI can only function properly if its training data is accurate and unbiased. Preventing misinformation from influencing AI requires constant monitoring and updates. -/- Ethical Implementation – Governments and organizations must ensure AI is used for balance and stability, not for manipulation or control. -/- Technological Limitations – Current AI systems are not yet capable of fully replicating human judgment. Further advancements in AGI (Artificial General Intelligence) are needed to align AI more closely with the universal formula’s principles. -/- Despite these challenges, implementing AI based on natural laws represents the most logical and sustainable path forward, ensuring that technology serves humanity rather than disrupting it. -/- Conclusion -/- AI, as an advanced information processing system, has the potential to transform human decision-making at all levels of society. However, without a structured framework, it risks creating systemic imbalances rather than solving them. Angelito Malicse’s universal formula, particularly the law of karma as a system principle and the law of balance in nature, provides a crucial foundation for ensuring AI aligns with natural equilibrium. -/- By incorporating a complete sensor system, error-free processing, balanced decision-making, and integration into leadership and education, AI can function as a powerful tool for reinforcing societal stability. If properly implemented, AI will not only prevent errors in human decision-making but also guide humanity toward a future where choices are made in harmony with the fundamental laws of nature. -/- This structured approach to AI development ensures that technology serves as a stabilizing force rather than a disruptive one, ultimately helping to create a world where all decisions—whether human or machine-assisted—uphold the principles of balance, integrity, and sustainability. -/- . (shrink)

The rapid adoption of cloud computing has revolutionized the way organizations operate, offering unparalleled flexibility, scalability, and efficiency. However, it also introduces a new set of vulnerabilities and security challenges. This manuscript explores the integration of artificial intelligence (AI) in cybersecurity solutions to address these cloud vulnerabilities. By examining the current landscape, AI methodologies, and practical implementation strategies, we aim to provide a roadmap for enhancing cloud security through AI-powered solutions. -/- .

Eliminating Power Struggles Through a Balanced Hierarchy of Leadership While Preserving Beneficial Competition -/- Introduction -/- Power struggles have been a defining feature of human history, often leading to conflicts, corruption, and inefficiencies in governance. Some argue that power struggles are an inescapable part of human nature, but a scientific systems approach reveals that they are the result of imbalances in leadership structures, resource distribution, and decision-making frameworks. -/- Eliminating power struggles does not mean eliminating healthy competition. Competition, when properly (...) structured, plays a crucial role in innovation, economic growth, and societal progress. For instance, capitalistic competition has driven technological and economic advancements, but when left unregulated, it has also led to wealth concentration, corporate monopolies, and economic instability. -/- By implementing a balanced hierarchy of leadership that operates according to the universal law of balance, we can eliminate destructive power struggles while preserving beneficial competition—ensuring a system where leadership serves as a regulator rather than a source of concentrated power. -/- The Scientific Basis of Power Struggles and Competition -/- 1. Systems Theory and the Emergence of Power Struggles -/- Power struggles arise when a system lacks balance and feedback mechanisms that regulate decision-making. Systems theory, which is used in engineering, biology, and economics, explains that: -/- In stable systems, components work together through feedback loops to maintain order. -/- In unstable systems, feedback loops are disrupted, leading to chaos or dominance by certain components. -/- In human societies, power struggles emerge when: -/- Leadership structures concentrate too much power in a few individuals or groups. -/- Feedback mechanisms are weak or absent, allowing leaders to make unchecked decisions. -/- Resources and opportunities are unfairly distributed, causing friction between competing factions. -/- This is why unchecked power always leads to instability—when power accumulates in a few hands, competition turns into destructive conflict rather than beneficial progress. -/- 2. Neuroscience of Decision-Making and Power Dynamics -/- Neuroscientific research shows that human decision-making is influenced by a balance between rationality and emotion: -/- The prefrontal cortex regulates rational decision-making and long-term planning. -/- The amygdala triggers emotions like fear and aggression, often fueling power struggles. -/- In imbalanced leadership systems, leaders often act based on emotional impulses, such as fear of losing power, rather than rational governance. This is why unchecked competition often turns into greed-driven monopolization, instead of fostering fair economic growth. -/- A balanced hierarchy of leadership ensures that rational decision-making prevails over impulsive power struggles, using structured feedback loops to regulate authority. -/- 3. The Role of Karma and Systems Thinking in Competition -/- The law of karma, as defined in the universal formula, aligns with systems thinking in that: -/- Every action creates a reaction that influences the entire system. -/- If a system is designed with defects or imbalances, it will eventually break down. -/- For example, in economic systems: -/- Unregulated capitalism leads to monopolies, inequality, and financial crises. -/- Overly centralized economies lead to inefficiency and lack of innovation. -/- A balanced economic system ensures that competition remains fair, productive, and sustainable, while preventing monopolization and economic instability. -/- Flaws in Existing Leadership and Economic Structures -/- Despite progress, current leadership and economic systems suffer from fundamental flaws: -/- 1. Political Power Struggles -/- Most political systems, whether democratic or autocratic, prioritize power retention over societal welfare. Elections often become battles for control, rather than mechanisms for choosing the most competent leaders. -/- 2. Unregulated Capitalistic Competition -/- While capitalism has driven technological progress, it often lacks regulatory balance. This results in: -/- Monopolies and corporate lobbying, where a few corporations control entire industries. -/- Extreme wealth concentration, leading to systemic instability. -/- 3. The Absence of Scientific Decision-Making in Governance -/- Many governments and corporations make decisions based on short-term political gains or financial interests, rather than scientific principles. This leads to: -/- Environmental degradation due to profit-driven policies. -/- Social inequality, as wealth and resources are unfairly distributed. -/- These issues show that competition alone is not enough—it must be regulated by the universal law of balance to prevent systemic collapse. -/- The Solution: A Balanced Hierarchy of Leadership with Beneficial Competition -/- To eliminate power struggles while preserving beneficial competition, we need a hierarchy of leadership based on: -/- 1. Leadership Selection Based on Competence and Wisdom -/- Leaders should be chosen based on scientific merit and ethical competence, rather than political influence or wealth. -/- Education systems should train future leaders in the universal law of balance, systems thinking, and rational decision-making. -/- Leadership should act as a regulator of competition, ensuring fairness and progress. -/- 2. Decentralized Yet Coordinated Decision-Making -/- Power should be distributed across multiple levels, preventing centralized control while maintaining system-wide coordination. -/- A hierarchical feedback system should allow leaders to adjust decisions dynamically based on real-world conditions. -/- 3. AGI-Assisted Regulation of Leadership and Competition -/- AGI, programmed with the universal formula and a complete sensor system, can act as an impartial advisor, monitoring economic and political systems to detect imbalances before they escalate. -/- AI should not replace human leadership but should serve as a scientific tool for maintaining systemic balance. -/- 4. Economic Policies Aligned with Natural Law -/- Fair competition should be maintained, preventing monopolies while encouraging innovation. -/- Wealth gaps should be regulated, ensuring economic mobility without discouraging entrepreneurship. -/- Capitalistic competition should be structured to foster progress while preventing systemic failures. -/- How Beneficial Competition Fits Into the System -/- Instead of eliminating competition, a balanced leadership hierarchy ensures that competition is: -/- ✔ Fair – No monopolies or financial manipulation. ✔ Productive – Encourages innovation and efficiency. ✔ Sustainable – Prevents extreme inequalities and power struggles. ✔ Ethical – Decisions are guided by natural balance, not greed or short-term gains. -/- This model allows entrepreneurs, businesses, scientists, and innovators to compete in ways that drive human progress, while eliminating power struggles that lead to corruption, inequality, and societal collapse. -/- Conclusion: A Society Without Power Struggles, Driven by Beneficial Competition -/- Power struggles are not an unavoidable part of human nature; they arise from imbalances in leadership, economy, and governance. By implementing a balanced hierarchy of leadership, we can eliminate power struggles while preserving beneficial competition that fosters development. -/- A properly designed system ensures that: -/- Leaders act as regulators, not rulers, preventing power accumulation. -/- Competition is structured to be fair, productive, and sustainable. -/- AGI and scientific governance help detect and correct imbalances before they become crises. -/- The economy promotes innovation without allowing monopolization or exploitation. -/- With these principles in place, we can create a world where leadership ensures balance, competition drives progress, and power struggles become a thing of the past. -/- . (shrink)

The Science of Balanced Leadership and Competition: The Role of AI Technology as a Guide -/- Introduction -/- Leadership and competition are two fundamental forces that shape human societies, economies, and institutions. However, their effectiveness depends on how they are managed. When leadership is imbalanced, it leads to corruption, authoritarianism, or inefficiency. When competition is unregulated, it creates inequality, exploitation, and instability. The science of balanced leadership and competition is an approach that integrates principles of natural balance, ethical decision-making, and (...) systemic thinking to ensure sustainable and just progress. -/- In the modern era, artificial intelligence (AI) technology serves as a powerful tool to assist leaders in maintaining this balance. AI can analyze vast amounts of data, predict potential crises, and offer insights for decision-making. Properly programmed AI can help guide leaders toward fair and ethical governance while also ensuring competition remains beneficial rather than destructive. -/- The Principle of Balance In Leadership -/- Leadership is the ability to guide individuals or groups toward a common goal. However, effective leadership is not just about authority; it requires a balance between control and flexibility, vision and adaptability, power and responsibility. -/- 1. The Balance Between Authority and Participation – Leaders must maintain authority to make decisive actions but also encourage participation from team members. Excessive authoritarianism suppresses innovation, while excessive democracy leads to indecisiveness. A balanced leader allows structured decision-making while considering input from others. -/- 2. The Balance Between Short-Term and Long-Term Goals – Effective leaders understand that immediate success must not come at the cost of future stability. Governments that prioritize economic growth without environmental responsibility, for example, create long-term disasters. Sustainable leadership requires long-term planning while addressing short-term needs. AI technology can analyze patterns, predict long-term consequences, and assist leaders in making informed, future-proof decisions. -/- 3. The Balance Between Competence and Morality – A leader may be highly competent but unethical, leading to corruption and exploitation. Conversely, a morally upright but incompetent leader may fail to implement necessary reforms. Balanced leadership integrates technical skill with ethical responsibility. AI systems programmed with ethical guidelines can act as an advisory tool, identifying potential ethical violations in leadership decisions and ensuring a fair and just system. -/- The Role of Competition in Progress -/- Competition is essential for innovation, economic growth, and personal development. However, uncontrolled competition leads to monopolies, exploitation, and social inequality. A balanced approach ensures that competition serves collective progress rather than individual greed. -/- 1. Healthy Competition vs. Destructive Competition – In a balanced system, competition drives excellence without harming others. In contrast, destructive competition creates a “win-at-all-costs” mentality, where ethics and fairness are disregarded. Examples include corporate monopolies suppressing small businesses or political parties prioritizing power over national interest. AI-driven monitoring systems can help detect unethical business practices and ensure fair competition in markets. -/- 2. Regulated Competition for Collective Growth – Governments and institutions must regulate competition to ensure fairness. This includes antitrust laws to prevent monopolies, ethical guidelines in business, and fair labor policies to protect workers from exploitation. AI can assist regulators in monitoring economic trends, detecting monopolistic behavior, and ensuring fair market conditions. -/- 3. Coopetition: A Balance Between Cooperation and Competition – In many industries, companies compete for market dominance while also collaborating in research, standardization, or sustainability efforts. This concept, known as “coopetition,” allows for economic growth while maintaining ethical responsibilities. AI can optimize cooperation strategies, ensuring that businesses maximize benefits while maintaining fair competition. -/- The Role of AI in Maintaining Balance -/- AI technology plays a crucial role in guiding leaders and institutions toward balanced decision-making. It can be used in the following ways: -/- 1. AI for Predictive Analysis – AI can process large-scale data to predict social, economic, and environmental trends, helping leaders make proactive decisions rather than reactive ones. -/- 2. AI for Ethical Governance – AI can be programmed with ethical principles to provide real-time insights on the consequences of political, corporate, or social policies. This ensures that decision-making aligns with fairness, justice, and sustainability. -/- 3. AI for Market Regulation – AI-driven economic models can detect unfair competition, price manipulation, and unethical corporate behavior, allowing governments to take corrective actions swiftly. -/- 4. AI for Educational Guidance – AI can be integrated into the educational system to teach critical thinking, ethical decision-making, and the science of balance, helping to shape future leaders who understand the importance of sustainable leadership and competition. -/- Applying the Science of Balance in Society -/- The science of balanced leadership and competition applies across various domains, from government and business to education and personal development. AI technology enhances this process by providing data-driven insights and ethical guidance. -/- 1. In Governance – Leaders must ensure that laws and policies balance economic growth with social welfare, individual rights with national security, and environmental sustainability with industrial development. AI can help design policies that optimize these balances through advanced simulations and scenario modeling. -/- 2. In Business – Corporate leaders should foster healthy competition while ensuring fair wages, ethical production, and sustainable business practices. AI can be used to analyze global trends, predict economic shifts, and optimize business strategies that benefit both corporations and society. -/- 3. In Education – Schools should encourage competition to inspire excellence while fostering teamwork, ethical responsibility, and critical thinking. AI-driven personalized learning can help students develop both competitive and cooperative skills based on their strengths and weaknesses. -/- 4. In Personal Development – Individuals must balance ambition with ethics, career success with personal well-being, and competition with collaboration in their daily lives. AI-powered personal development tools can provide guidance, helping individuals make balanced life decisions. -/- Conclusion -/- The science of balanced leadership and competition is essential for building a just and sustainable society. It requires an understanding of natural balance, ethical decision-making, and systemic thinking. AI technology plays a critical role in achieving this balance by providing data-driven insights, predicting long-term consequences, and ensuring ethical decision-making. -/- When leadership is balanced, it fosters progress without oppression. When competition is balanced, it drives innovation without exploitation. With AI as a guiding tool, societies can move toward a future where decision-making is not only efficient but also ethical and sustainable. By integrating AI with principles of natural balance, we can ensure a world that is fair, progressive, and in harmony with the universal laws of nature. -/- . (shrink)

Generative artificial intelligence (Gen AI) and machine learning (ML) technologies are changing the way the insurance industry looks, particularly by integrating Gen AI & ML technologies. As a leading platform for property and casualty insurance, Guidewire provides a perfect platform for deploying intelligent claims processing workflows that can dramatically improve efficiency, accuracy and customer satisfaction. The seamless integration of Gen AI and ML capabilities into Guidewire to autonomously process claims is explored in this paper. Insurers can use advanced models to (...) drive self-driving workflows that are capable of automating tasks like claims triage, fraud detection, damage assessment and settlement optimization. But most importantly, these technologies reduce operational costs, and reduce the human intervention involved such that claims can be handled faster and with more accuracy. In this work, we discuss what technical and strategic considerations need to be taken into account when implementing such workflows, such as data integration, model training, and ethical AI practices. This paper also demonstrates several real-world use cases, the challenges you in each, and the potential to scale these solutions across lines of business. With an eye on the future, autonomous claims processing enables insurers to find their next competitive edge by providing innovative solutions that alter the course of expectations of customers while qualifying to regulatory and compliance standards. Self-driving workflows powered by Gen AI and ML in claims are the future of insurance and their ability to transform the entire process of claims management. (shrink)

Cheating is a growing academic and ethical concern in higher education. This article examines the rise of artificial intelligence (AI) generative chatbots for use in education and provides a review of research literature and relevant scholarship concerning the cheating-related issues involved and their implications for pedagogy. The technological “arms race” that involves cheating-detection system developers versus technology savvy students is attracting increased attention to cheating. AI has added new dimensions to academic cheating challenges as students (as well as faculty (...) and staff) can easily access powerful systems for generating content that can be presented in assignments, exams, or published papers as their own. AI methodology is also providing some emerging anti-cheating approaches, including facial recognition and water- marking. This article provides an overview of human/AI collaboration approaches and frames some educational misuses of such AI generative systems as ChatGPT and Bard as forms of “misattributed co-authorship.” As with other kinds of collaborations, the work that students produce with AI assistance can be presented in transparent and straightforward modes or (unfortunately) in opaquer and ethically-problematic ways. However, rather than just for catching or entrapping students, the emerging varieties of technological cheating-detection strategies can be used to assist students in learning how to document and attribute properly their AI-empowered as well as human-human collaborations. Construing misuses of AI generative systems as misattributed co-authorship can recognize the growing capabilities of these tools and how stressing responsible and mindful usage by students can help prepare them for a highly collaborative, AI-saturated future. (shrink)

The Future of Leadership – Why Humans and AI Must Work Together -/- By Angelito Malicse -/- Introduction: The Leadership Crisis -/- The world faces a leadership crisis. Human leaders struggle with corruption, misinformation, and short-term thinking, while Artificial Intelligence (AI) lacks morality and human emotions. -/- So, who should lead the future? -/- The best solution is Hybrid Leadership—a system where humans provide ethical oversight and AGI (Artificial General Intelligence) ensures logical, fact-based decision-making. This model, based on the Universal (...) Law of Balance in Nature, ensures governance that is ethical, efficient, and sustainable. -/- The Problem with Human Leadership -/- For centuries, human leaders have shaped societies, but leadership failures have led to: ❌ Corruption & Self-Interest – Many leaders prioritize power over public good. ❌ Cognitive Bias & Emotion-Driven Decisions – Leading based on ideology rather than facts. ❌ Short-Term Thinking – Focusing on immediate political or economic gains, ignoring long-term stability. ❌ Misinformation & Fake News – Policies shaped by propaganda rather than truth. -/- These issues destabilize governments, economies, and societies, leading to global crises. -/- The Potential of AI in Governance -/- AI and AGI offer powerful tools to improve decision-making: ✅ Data-Driven Accuracy – AI can analyze vast amounts of data to detect trends and solutions. ✅ Unbiased Decision-Making – AI can identify corruption, misinformation, and logical flaws in policies. ✅ Predictive Governance – AI can simulate the effects of decisions before implementation. ✅ Balancing Social Systems – AI ensures economic, environmental, and political stability. -/- But there’s a problem—AI lacks morality, ethical reasoning, and emotional intelligence. It cannot fully understand human culture, values, and social complexities. -/- This is why AI alone should never govern society. -/- Hybrid Leadership: The Best of Both Worlds -/- The solution? Hybrid Leadership. -/- ???? Humans provide: ✔ Ethical oversight & moral reasoning ✔ Long-term vision & strategic planning ✔ Emotional & social leadership -/- ???? AGI provides: ✔ Fact-based analysis & policy validation ✔ Detection of bias, corruption & misinformation ✔ Continuous balance monitoring of global systems -/- By working together, humans prevent AI from becoming an unaccountable authority, while AI ensures human leaders make balanced, fact-based decisions. -/- This model creates a self-correcting system, preventing leadership failures. -/- The Universal Law of Balance in Nature -/- ???? The key to sustainable governance: The Universal Law of Balance in Nature. -/- This law ensures: ✔ Decisions prevent societal imbalance (economic collapse, social unrest). ✔ Misinformation is eliminated through fact-based decision-making. ✔ Governance adapts dynamically to new challenges through human-AI feedback loops. -/- By applying this principle, governments avoid destructive policies and maintain long-term stability. -/- How Hybrid Leadership Can Improve Governance -/- ???? Economic Planning – AI prevents financial crises, optimizes resource distribution. ???? Environmental Protection – AI monitors climate risks, ensures sustainable policies. ???? Law & Justice – AI removes bias from the legal system, ensuring fair trials. ???? Healthcare – AI improves medical decision-making and public health policies. -/- ???? This model can revolutionize leadership across all sectors. -/- Implementing Hybrid Leadership – A Step-by-Step Plan -/- ???? Phase 1: AI-Assisted Decision-Making in Key Areas -/- Pilot programs in economics, environment, and healthcare -/- AI helps fact-check and optimize policies -/- ???? Phase 2: AI as an Advisory System for Governments -/- AI provides real-time policy analysis -/- AI monitors corruption and misinformation in leadership -/- ???? Phase 3: Full Hybrid Leadership Model -/- AI continuously checks for balance and sustainability -/- Humans retain final decision-making power -/- Challenges & Ethical Considerations -/- ⚠️ Preventing AI Misuse -/- AI must never be controlled by a single government or corporation. -/- Strict regulations must prevent AI from being used for authoritarian rule. -/- ⚠️ Ensuring Transparency -/- AI decisions must be explainable and accountable. -/- Public oversight committees must ensure AI does not replace human leadership. -/- ???? AI should serve humanity—not control it. -/- What Happens If We Don’t Act? -/- If we fail to implement Hybrid Leadership, we risk: ❌ AI being misused for political power. ❌ AI replacing human decision-making without ethical oversight. ❌ Governments continuing to make poor, biased decisions based on misinformation. -/- We must act now to integrate AI into leadership responsibly—before it’s too late. -/- Global Adoption – The Next Steps -/- ???? To ensure Hybrid Leadership becomes reality, we must: ???? Engage global organizations like the UN & WEF to test AI-assisted governance. ???? Educate future leaders on AI-assisted decision-making. ???? Promote ethical AI development, ensuring AI aligns with the Universal Formula. -/- ???? The time for action is now. -/- Conclusion: The Future of Leadership is Hybrid -/- ✅ Human ethics + AI precision = Balanced governance ✅ The Universal Law of Balance prevents leadership failures ✅ AI should assist leaders, not replace them ✅ Global institutions must adopt this model before AI is misused -/- ???? The future is balanced leadership – and it starts today. -/- . (shrink)

The rapid advancement of artificial intelligence (AI) technologies has revolutionized various industries, including the realm of education and career guidance. This project endeavors to harness the power of AI to develop a sophisticated career guidance application that offers personalized and effective recommendations to students and job seekers. The primary objective of this project is to address the limitations of traditional career guidance methods, which often lack customization and fail to adapt to individual preferences, skills, and aspirations. Through the integration of (...) AI algorithms, NLP techniques, and ML models, our application aims to provide accurate interest assessments, skill matching, and tailored educational pathways. (shrink)

The contribution of this paper is a comprehensive cybersecurity framework to secure cloud hosted Guidewire implementations by addressing critical security challenges such as threat detection, incident response, compliance, and system performance. Based on advanced technologies like machine learning, behavioral analytics and auto patching, the framework detects and mitigates known and unknown threats, incidentally zero-day exploit. The system does this through micro segmenting, behavioral anomaly detection, and automated patch orchestration in a way that does not render the system unperforming. (...) Key performance metrics of threat detection time (less than 3 seconds), incident response (patching within 5 minutes) and system availability (99.95% uptime) are all tested as the framework outperforms in all of these. Additionally, the framework ascertains industry standards such as GDPR and HIPAA via automated audit trails and ongoing monitoring. Compared to the currently deployed cloud security, the proposed approach, with a special focus on more secure the Guidewire application deployed in the cloud, offers a multi layered security approach to the modules specific to the Guidewire. Though powerful, the framework also suffers from complexity in its deployment, overhead in terms of performance and a reliance on cloud provider-specific features. The scalability will need to improve, and the product will need to support multiple clouds and integrate advanced technology such as quantum computing and AI-powered threat intelligence. This work sets a solid basis for the protection of cloud-based Guidewire systems against future cyber threats while maintaining operational continuity and system compliance. (shrink)

Web page phishing is a type of phishing attack that targets websites. In a web page phishing attack, the attacker creates a fake website that looks like a legitimate website, such as a bank or credit card company website. The attacker then sends a fraudulent message to the victim, which contains a link to the fake website. When the victim clicks on the link, they are taken to the fake website and tricked into entering their personal information.Web (...) page phishing attacks are a serious threat to online security. They can be very effective, as they often look very convincing. To protect yourself from web page phishing attacks, you should be suspicious of any emails or messages that ask for your personal information. Do not click on links in emails or messages from unknown senders. If you are unsure whether a website is legitimate, do not enter your personal information. Therefore, in this study, we present a novel approach to detect whether a web page is phishing or legitimate using a neural network model. (shrink)

Mark Coeckelbergh starts his book with a very powerful picture based on a real incident: On the 9th of January 2020, Robert Williams was wrongfully arrested by Detroit police officers in front of his two young daughters, wife and neighbors. For 18 hours the police would not disclose the grounds for his arrest (American Civil Liberties Union 2020; Hill 2020). The decision to arrest him was primarily based on a facial detection algorithm which matched Mr. Williams’ driving license photo (...) with the picture of a man who was suspected of watch theft two years earlier. Not only did the computer ‘get it wrong’ as one of the detectives said, when Mr. Williams made them aware that the picture of the suspect obviously wasn’t resembling him, the probably unreliable algorithm very likely contributed to racial discrimination (Hill 2020). It is well documented that many available facial detection algorithms at this time had significant problems (e.g. a comparably high false positive rate) with respect to black persons, like Mr. Williams (NIST 2019). Multiple causes may exist, such as unbalanced training datasets and insufficient optimization. Coeckelbergh compares the disturbing case of Mr. Williams with a political interpretation of Franz Kafka’s The Trial, where the protagonist, Josef K., is accused of an unspecified crime by an opaque, oppressive and absurd bureaucracy: “In the 21st-century United States, Josef K. is black and is falsely accused by an algorithm, without explanation” (p. 2). (shrink)

The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns to enhance the detection system's decision-making process. The Genetic Ranking Optimization Algorithm (GROA) is (...) used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time. (shrink)

Comprehensive Implementation of a Holistic Educational System in the Philippines -/- To implement a holistic educational system in the Philippines, we need a step-by-step national strategy that integrates the universal law of balance in nature, critical thinking, and systems-based learning into the foundational education system. Below is a detailed plan tailored for the Philippine context. -/- 1. Reforming the Curriculum -/- A. Core Subjects with Integrated Holistic Principles -/- Science & Systems Thinking – Teach students how natural and societal systems (...) function, focusing on cause and effect, sustainability, and error-free systems. -/- Critical Thinking & Problem-Solving – Introduce logic, reasoning, and debate at all levels to counter misinformation and propaganda. -/- Ethics & Social Responsibility – Teach the law of karma (cause-effect in human actions) and how leadership decisions affect societal balance. -/- Environmental Sustainability & Population Awareness – Integrate lessons on balancing economic growth, resource management, and population control. -/- Technology & AI Integration – Prepare students for the future by teaching responsible AI use and its role in governance and decision-making. -/- 2. Teacher Training & Development -/- A. Nationwide Teacher Retraining Program -/- Establish Holistic Education Training Centers to retrain teachers in systems thinking, natural law applications, and balanced decision-making. -/- Partner with universities and global educational organizations to update teaching methodologies. -/- B. Leadership Development for School Administrators -/- Require school principals and education policymakers to undergo holistic leadership training. -/- Train them in scientific governance, ethical decision-making, and educational reform strategies. -/- 3. Multi-Level Educational Implementation -/- A. Basic Education (K-12) -/- Elementary Level: Focus on curiosity-driven learning, emotional intelligence, and early understanding of natural laws. -/- High School Level: Introduce systems thinking, governance studies, and practical applications of ethics in leadership. -/- B. Tertiary Level -/- Modify university curricula to incorporate holistic education principles into business, medicine, law, and politics. -/- Establish a Holistic Governance Degree to train future leaders in error-free decision-making based on natural laws. -/- 4. Policy Integration & Government Support -/- A. National Education Reform Bill -/- Propose legislation that mandates holistic education principles in DepEd and CHED curricula. -/- Require all government officials and policymakers to undergo holistic education training. -/- B. Integration with National Development Plans -/- Align holistic education with the Philippine Development Plan (PDP) to ensure sustainable governance. -/- Establish a National Holistic Education Advisory Council under DepEd, CHED, and NEDA. -/- 5. Public Awareness & Societal Involvement -/- A. Media & Community Engagement -/- Launch a nationwide information campaign via TV, radio, and social media to educate the public. -/- Encourage barangay-level seminars to help communities understand and apply holistic education. -/- B. Collaboration with Religious & Cultural Institutions -/- Work with Catholic, Muslim, and Indigenous leaders to align holistic education with ethical teachings without contradicting faith. -/- Encourage churches and mosques to support holistic education as a moral responsibility. -/- 6. AI & Technology Integration -/- A. AI-Powered Learning Tools -/- Develop adaptive AI tutors that help students understand complex systems and real-world decision-making. -/- Integrate AI-driven educational simulations to teach governance, economics, and environmental balance. -/- B. AI for Governance Monitoring -/- Use AI-assisted analysis to monitor government policies and detect potential societal imbalances before they escalate. -/- Train policymakers in data-driven decision-making using holistic education principles. -/- 7. Continuous Evaluation & Feedback -/- A. National Research & Development Program -/- Establish a Philippine Holistic Education Research Institute to study the long-term impact of the program. -/- Implement feedback mechanisms where students, teachers, and communities report inefficiencies and suggest improvements. -/- 8. Pilot Implementation & Nationwide Rollout -/- A. Phase 1: Pilot Program (Years 1-3) -/- Implement holistic education in select regions (Luzon, Visayas, Mindanao) as test sites. -/- Assess effectiveness before nationwide expansion. -/- B. Phase 2: Expansion (Years 4-6) -/- Gradually integrate holistic principles into all public and private schools. -/- Launch teacher retraining programs nationwide. -/- C. Phase 3: Full Implementation (Years 7-10) -/- Make holistic education mandatory across all educational institutions. -/- Implement AI-driven policy monitoring to ensure governance aligns with holistic principles. -/- Conclusion: The Future of Philippine Education -/- By implementing this comprehensive holistic educational system, the Philippines can transform its governance, economy, and societal well-being. This approach will ensure that future generations make decisions based on natural laws, critical thinking, and ethical responsibility—ultimately leading to a more stable and prosperous nation. -/- . (shrink)

Solving Typhoon Turbulence Using the Universal Formula -/- By Angelito Malicse -/- Introduction -/- Typhoons are among the most destructive natural phenomena, bringing extreme winds, heavy rainfall, and turbulent ocean currents. The chaotic turbulence within a typhoon makes it difficult to predict and control, causing widespread devastation to coastal regions, infrastructure, and human lives. Despite advancements in meteorology and fluid dynamics, the turbulence inside typhoons remains a challenge for accurate forecasting and disaster mitigation. -/- By applying my universal formula, based (...) on three fundamental laws of nature, we can develop a structured and effective approach to understanding and controlling typhoon turbulence. This essay explores how the Law of Karma (causality and system integrity), the Law of Balance in Nature, and the Law of Feedback Regulation provide the ultimate solution to typhoon turbulence. -/- Understanding Typhoon Turbulence Using the Universal Formula -/- Typhoons form due to energy imbalances in the atmosphere and ocean, driven by heat, pressure differences, and moisture content. The chaotic turbulence inside a typhoon is the result of: -/- 1. Unstable air currents caused by pressure and temperature gradients. -/- 2. Vortex formation driven by Earth’s rotation and wind shear. -/- 3. Excess energy accumulation in warm ocean waters. -/- By examining typhoon turbulence through the three universal laws, we can develop a scientific strategy to predict, weaken, and potentially neutralize typhoons before they cause destruction. -/- 1. The Law of Karma: Cause, Effect, and Systemic Integrity -/- The Law of Karma states that every effect has a cause, and for a system to function properly, it must be free of defects. A typhoon is an effect caused by imbalances in atmospheric and oceanic conditions. If these imbalances are corrected, typhoon turbulence can be weakened or prevented. -/- Applying the Law of Karma to Typhoon Control -/- ✅ Eliminate the root cause: Typhoons are fueled by warm ocean waters. Artificial cooling methods, such as cloud seeding or deep-sea upwelling (bringing cold water to the surface), could reduce the heat energy supply and prevent typhoon formation. -/- ✅ Detect turbulence before it intensifies: AI-driven weather models can predict early-stage typhoon turbulence and allow preemptive interventions. AI can identify defective atmospheric conditions (such as extreme heat buildup in ocean currents) and trigger mitigation efforts. -/- ✅ Disrupt vortex formation: Since typhoons rely on rotational forces, deploying strategically placed atmospheric pressure disturbances (via drones or cloud-seeding techniques) could disrupt the vortex before it gains full strength. -/- By identifying and neutralizing the causes of typhoon turbulence, we ensure that the system remains balanced, preventing large-scale destruction. -/- 2. The Law of Balance in Nature -/- The Law of Balance states that all systems naturally seek equilibrium. When a typhoon forms, it represents an extreme imbalance in the Earth’s energy system—excess heat and moisture create an unstable feedback loop of wind and turbulence. -/- Restoring Balance to Weaken Typhoon Turbulence -/- ✅ Regulating ocean surface temperature: Large-scale ocean-based temperature regulation systems (such as reflective films or artificial cooling platforms) can prevent typhoon formation by reducing the excess heat energy that fuels storms. -/- ✅ Breaking the pressure imbalance: Typhoons depend on a low-pressure core that intensifies wind speeds. By artificially increasing local air pressure using ionization or controlled cloud seeding, the extreme wind turbulence can be diffused before it reaches destructive levels. -/- ✅ Balancing atmospheric moisture: AI-assisted weather control could help regulate humidity levels through strategic rainfall induction in high-risk areas, ensuring that water vapor does not accumulate excessively to fuel a typhoon. -/- Since all systems follow natural equilibrium, strategically rebalancing temperature, pressure, and humidity will neutralize extreme typhoon turbulence. -/- 3. The Law of Feedback Regulation -/- The Law of Feedback Regulation states that all systems operate through feedback loops, where continuous monitoring and adjustment are necessary for stability. Typhoon turbulence follows feedback patterns, which means it can be tracked, analyzed, and counteracted using real-time data. -/- Developing an AI-Driven Typhoon Suppression System -/- ✅ AI-powered real-time monitoring: Advanced satellites, ocean buoys, and drones can provide instant feedback on typhoon development, allowing for proactive interventions rather than reactive disaster response. -/- ✅ Adaptive wind and pressure control: Deploying AI-controlled drones or weather balloons to release cooling agents, modify air pressure, or disperse cloud formations could interrupt the storm's feedback loop, reducing its intensity. -/- ✅ AI-driven weather forecasting for early response: Using deep learning algorithms, AI can accurately predict turbulence patterns weeks in advance, allowing governments to take preventive action instead of just preparing for disaster relief. -/- By implementing real-time monitoring and intervention, we can use feedback regulation to weaken or redirect typhoons before they cause catastrophic damage. -/- The Future of Typhoon Control with the Universal Formula -/- By integrating my universal formula into meteorology and disaster prevention, we can develop a comprehensive AI-driven typhoon suppression system that follows the three fundamental laws of nature: -/- 1. Law of Karma (Causality and Systemic Integrity): Address the root causes of typhoon formation by eliminating atmospheric and oceanic imbalances. -/- 2. Law of Balance in Nature: Restore equilibrium by regulating ocean temperatures, air pressure, and humidity. -/- 3. Law of Feedback Regulation: Use AI-driven real-time monitoring and intervention to weaken or prevent typhoons. -/- Through advanced AI, environmental engineering, and a deep understanding of nature’s laws, we can move beyond simply predicting typhoons and start actively controlling their development. -/- Conclusion -/- Typhoon turbulence, long considered an unpredictable force of nature, is fundamentally a problem of imbalance, energy accumulation, and feedback-driven intensification. By applying my universal formula, we can shift from passive disaster response to active typhoon control, using AI, environmental intervention, and natural equilibrium principles to prevent devastation. -/- This solution is not just theoretical—it aligns with the fundamental laws governing all natural systems. Once implemented, it will mark the beginning of a new era where humanity can harness nature’s own principles to create balance, stability, and protection from extreme weather. -/- . (shrink)

: As organizations continue to embrace cloud computing, the migration of workloads, applications, and data from on-premises environments to the cloud remains one of the most critical tasks. However, migrating to the cloud is often seen as a daunting task due to the complexity, the risk of downtime, and the potential for service disruptions. Artificial Intelligence (AI) and automation have emerged as transformative technologies for cloud migration, providing solutions that enable a seamless, efficient, and zero-downtime transition. This paper explores the (...) role of AI in automating the cloud migration process, detailing how machine learning (ML) and AI-powered tools can optimize migration planning, reduce errors, and ensure continuous service availability. It highlights the key benefits of AI in workload assessment, data transfer, and post-migration optimization. Through the integration of advanced AI models, automation tools, and real-time analytics, organizations can achieve a smooth cloud migration process with minimal disruption to their business operations. The paper also examines real-world case studies where AI-powered cloud migration has been successfully implemented. (shrink)

This project focuses on simplifying the legal documentation process for small businessesand individuals in India. Utilizing advanced technologies such as Optical Character Recognition (OCR) and AI-driven models, the system automatically drafts and simplifies complex legaldocuments. The platform features a user-friendly interface for easy input, integration withup-to-date legal resources, and ensures strict data privacy and security. By reducing the time, effort, and potential errors associated with legal documentation, the assistant aims to improve accessibility to legal services and promotegreater access to justice. (...) This documentation provides a comprehensive guide to the design, features, and implementation of the AI-powered solution. (shrink)

AI-powered records science is revolutionizing the way facts are analyzed and understood. It can significantly improve the exceptional of information evaluation and boost its speed. AI-powered facts technological know-how enables access to more extensive, extra complicated information sets, faster insights, faster trouble solving, and higher choice making. Using the use of AI-powered information technological know-how techniques and tools, organizations can provide more accurate outcomes with shorter times to choices. AI-powered facts technology also offers more correct predictions (...) of activities and developments and superior accuracy in sample reputation.Furthermore, AI-powered facts science techniques can help agencies discover anomalies in datasets, learn new patterns, and better understand the points. This paper discusses the advantages of AI-powered statistics science and how it may enhance data analysis. It additionally discusses a number of the challenges going through organizations in imposing AI-powered records science projects and gives excellent practices for deploying and coping with records technology projects. (shrink)

The Legal Documentation Assistant offers a unique method to obtain legal rights, specializing in copyright, trademark, and banking using a combination of artificial intelligence (AI) technologies. Through the synergy of legal expertise and technological innovation, our dynamic website is designed to offer clients real-time assistance and guidance to help them efficiently navigate complex legal requirements. Our personalized bots provide tailored support and answer queries, ensuring clients receive the help they need. At the core of our platform is a combination of (...) AI-powered chatbots designed to provide clients with real-time help and guidance. These bots provide personalized support, answer questions, explain legal concepts and guide customers through multiple procedures. In addition, our platform enables seamless file editing, allowing clients to customize legal templates to suit their needs. By sharing downloadable files in modern formats, we increase the convenience of people even as we sell legal requirements. Through the synergy of legal knowledge and technological innovation, our platform aims to democratize access to empower people and businesses to navigate the methods themselves. We believe that our method is an important step in bridging the gap between legal information and realistic applications. (shrink)

Cloud computing has become an essential component of enterprises and organizations globally in the current era of digital technology. The cloud has a multitude of advantages, including scalability, flexibility, and cost-effectiveness, rendering it an appealing choice for data storage and processing. The increasing storage of sensitive information in cloud environments has raised significant concerns over the security of such systems. The frequency of cyber threats and attacks specifically aimed at cloud infrastructure has been increasing, presenting substantial dangers to the data, (...) reputation, and financial stability of enterprises. Conventional security methods can become inadequate when confronted with ever intricate and dynamic threats. Artificial Intelligence (AI) technologies possess the capacity to significantly transform cloud security through their ability to promptly identify and thwart assaults, adjust to emerging risks, and offer intelligent perspectives for proactive security actions. The objective of this research study is to investigate the utilization of AI technologies in augmenting the security measures within cloud computing systems. This paper aims to offer significant insights and recommendations for businesses seeking to protect their cloud-based assets by analyzing the present state of cloud security, the capabilities of AI, and the possible advantages and obstacles associated with using AI into cloud security policies. (shrink)

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose (...) their vulnerabilities and future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their cor- responding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model. (shrink)

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...) domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

In the rapidly evolving digital landscape, cyber threats are becoming increasingly sophisticated, making traditional security measures inadequate. Advanced Threat Detection (ATD) leveraging Artificial Intelligence (AI)-driven anomaly detection systems offers a proactive approach to identifying and mitigating cyber threats in real time. This paper explores the integration of AI, particularly machine learning (ML) and deep learning (DL) techniques, in anomaly detection to enhance cybersecurity defenses. By analyzing vast amounts of network traffic, user behavior, and system logs, AI-driven models (...) can identify deviations from normal patterns, enabling early threat detection and prevention. These systems excel in detecting zero-day attacks, insider threats, and advanced persistent threats (APTs), which often bypass conventional rule-based security mechanisms. Additionally, we discuss the challenges of AI-based anomaly detection, including false positives, model interpretability, and adversarial attacks. The findings emphasize the need for continuous learning and adaptive security frameworks to ensure robust cyber threat detection. The study concludes that AIdriven anomaly detection significantly enhances threat intelligence and response capabilities, making it a vital component of modern cybersecurity strategies. (shrink)

The Legal Document Assistant project aims to make legal documentation more accessible and manageable for individuals and small businesses in India. Using artificial intelligence (AI), the platform simplifies the creation, updating, and management of legal documents for non-lawyers. Key features include a user-friendly interface that ensures accuracy and compliance with local regulations. A machine learning algorithm generates documents based on user inputs. To enhance security, the system uses One-Time Password authentication, requiring all parties to validate changes or updates, ensuring secure (...) and authorized modifications. By facilitating the creation of verified and legally sound documents, the Legal Document Assistant project makes legal services more accessible, minimizes errors, and saves time for individuals and small businesses across India. n. (shrink)

The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care.

. In this article, we propose an innovative method that utilizes AI for the real-time detection of humans and automatically triggers an alarm using the latest object detection methods based on algorithms such as YOLO (You Only Look Once). The key goal of the system is to facilitate rescue operations, enhance security surveillance, and minimize the turnaround time for any emergency actions. This allows, for example, the placement of such a system in a live video stream, and the (...) person will be located in another area of the feed and an alarm triggered. This paper concentrates on the development of the system from its conception to its testing and its roadmap for future growth to ensure that its performance is guaranteed in real-life situations. (shrink)

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep learning (...) is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (shrink)

The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. (...) In this research, we proposed an adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection. (shrink)

In the rapidly evolving digital landscape, cyber threats are becoming increasingly sophisticated, making traditional security measures inadequate. Advanced Threat Detection (ATD) leveraging Artificial Intelligence (AI)-driven anomaly detection systems offers a proactive approach to identifying and mitigating cyber threats in real time. This paper explores the integration of AI, particularly machine learning (ML) and deep learning (DL) techniques, in anomaly detection to enhance cybersecurity defenses. By analyzing vast amounts of network traffic, user behavior, and system logs, AI-driven models (...) can identify deviations from normal patterns, enabling early threat detection and prevention. These systems excel in detecting zero-day attacks, insider threats, and advanced persistent threats (APTs), which often bypass conventional rule-based security mechanisms. Additionally, we discuss the challenges of AI-based anomaly detection, including false positives, model interpretability, and adversarial attacks. The findings emphasize the need for continuous learning and adaptive security frameworks to ensure robust cyber threat detection. The study concludes that AIdriven anomaly detection significantly enhances threat intelligence and response capabilities, making it a vital component of modern cybersecurity strategies. (shrink)

Without doctors being able to explain medical decisions to patients, I argue their use of black box AIs would erode the effective and respectful care they provide patients. In addition, I argue that physicians should use AI black boxes only for patients in dire straits, or when physicians use AI as a “co-pilot” (analogous to a spellchecker) but can independently confirm its accuracy. I respond to A.J. London’s objection that physicians already prescribe some drugs without knowing why they work.

Today, software instruments support all parts of engineering work, from design to creation. Many engineering processes call for tedious routine appointments and torments with manual handoffs and data storehouses. AI designers train profound brain networks and incorporate them into software structures.

Urban areas are increasingly facing challenges such as rapid population growth, environmental degradation, and climate change, necessitating innovative solutions for sustainable development. This paper explores the role of cloud-based artificial intelligence (AI) in promoting blue-green solutions that integrate natural and engineered systems to enhance urban resilience. By leveraging cloud computing, urban planners and decision-makers can analyze vast datasets, simulate environmental scenarios, and optimize resource management. The study highlights successful case studies where AI-driven blue-green interventions have improved water management, enhanced green (...) spaces, and mitigated urban heat effects. Furthermore, it discusses the collaborative potential of cloud AI in fostering stakeholder engagement and participatory governance. The findings suggest that adopting cloud AI-powered blue-green solutions is crucial for addressing urban development challenges and promoting sustainable, livable cities. (shrink)

Some artificial intelligence systems can display algorithmic bias, i.e. they may produce outputs that unfairly discriminate against people based on their social identity. Much research on this topic focuses on algorithmic bias that disadvantages people based on their gender or racial identity. The related ethical problems are significant and well known. Algorithmic bias against other aspects of people’s social identity, for instance, their political orientation, remains largely unexplored. This paper argues that algorithmic bias against people’s political orientation can arise in (...) some of the same ways in which algorithmic gender and racial biases emerge. However, it differs importantly from them because there are strong social norms against gender and racial biases. This does not hold to the same extent for political biases. Political biases can thus more powerfully influence people, which increases the chances that these biases become embedded in algorithms and makes algorithmic political biases harder to detect and eradicate than gender and racial biases even though they all can produce similar harm. Since some algorithms can now also easily identify people’s political orientations against their will, these problems are exacerbated. Algorithmic political bias thus raises substantial and distinctive risks that the AI community should be aware of and examine. (shrink)

The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time.

Since the revival of artificial intelligence (AI) research, many countries in the world have proposed their visions of an AI-powered world: Germany with the concept of “Industry 4.0,”1 Japan with the concept of “Society 5.0,”2 China with the “New Generation Artificial Intelligence Plan (AIDP).”3 In all of the grand visions, all governments emphasize the “human-centric element” in their plans. This essay focuses on the concept of trust in Confucian societies and places this very human element in the context of (...) an increasingly AI-powered world. (shrink)

The rise of remote work has redefined the modern workplace, driven by advancements in technology and the global need for flexible work environments. While remote work offers increased productivity and employee satisfaction, it has also introduced a host of network security challenges. Organizations must contend with securing sensitive data and critical applications across distributed networks, many of which extend beyond traditional corporate perimeters. This research paper explores the multifaceted security threats associated with remote work environments, including phishing attacks, unpatched (...) software vulnerabilities, unsecured home networks, and the misuse of personal devices. These challenges are exacerbated by a lack of centralized control and the increasing sophistication of cybercriminals exploiting these vulnerabilities. Key findings highlight the importance of robust authentication protocols, such as multi-factor authentication (MFA), to ensure secure access to corporate resources. Endpoint security solutions, including encryption and antivirus software, play a critical role in safeguarding data on remote devices. Moreover, the paper emphasizes the significance of implementing virtual private networks (VPNs) and zero-trust security frameworks to reduce attack surfaces and limit unauthorized access. Another critical focus is the role of employee training and awareness programs in mitigating human error, which remains a leading cause of data breaches. This study also underscores the necessity of proactive threat detection and response mechanisms to identify and address breaches in real-time. Advanced technologies, such as artificial intelligence (AI) and machine learning (ML), are pivotal in enhancing these capabilities, enabling organizations to adapt to the rapidly evolving cyber threat landscape. Furthermore, comprehensive network segmentation strategies are discussed to isolate critical systems and minimize the impact of potential breaches. The research incorporates case studies of successful implementations of these solutions, demonstrating their effectiveness in fortifying network security for remote operations. A comparative analysis is presented to evaluate the cost-effectiveness and scalability of various approaches, catering to organizations of different sizes and industries. The findings suggest that a multi-layered defense strategy is essential for achieving resilience in remote work environments. The paper concludes with actionable recommendations for organizations seeking to enhance their security posture. These include prioritizing regular updates and patches, adopting secure cloud-based collaboration tools, and conducting periodic security audits. Additionally, fostering a culture of cybersecurity awareness among employees is paramount to addressing the human factor in cyber risks. By addressing the unique challenges posed by remote work, this study provides a comprehensive roadmap for protecting data and applications in decentralized environments. The insights gained aim to equip organizations with the tools and knowledge necessary to navigate the complexities of securing remote work infrastructures while maintaining business continuity and trust. (shrink)

The landscape of cybersecurity is constantly evolving, with adversaries becoming increasingly sophisticated and persistent. This manuscript explores the utilization of artificial intelligence (AI) to address these evolving threats, focusing on the journey from threat detection to autonomous response. By examining AI-driven detection methodologies, advanced threat analytics, and the implementation of autonomous response systems, this paper provides insights into how organizations can leverage AI to strengthen their cybersecurity posture against modern threats. Key words: Ransomware, Anomaly Detection, Advanced Persistent (...) Threats (APTs), Automated Threat Response and Artificial Intelligence. (shrink)

Abstract: The convergence of nanotechnology and artificial intelligence (AI) represents a transformative frontier in modern science, with the potential to revolutionize multiple industries, particularly healthcare. Nanotechnology enables the manipulation of matter at the atomic and molecular scale, while AI offers sophisticated data analysis, pattern recognition, and decision-making capabilities. This paper explores the synergies between these two fields, focusing on their impact on medical diagnostics, targeted drug delivery, and personalized treatments. By leveraging AI's predictive power and nanotechnology's precision, healthcare can achieve (...) unprecedented levels of accuracy in disease detection and treatment, leading to improved patient outcomes. Beyond healthcare, this convergence extends to environmental monitoring, smart materials, and manufacturing, offering solutions to some of the world’s most pressing challenges. The paper also discusses the ethical considerations and potential risks of integrating AI with nanotechnology, emphasizing the need for responsible innovation. Together, AI and nanotechnology hold the promise of reshaping industries and improving quality of life on a global . (shrink)