: 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)

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 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. The scope of the project encompasses the design, development, and implementation of AI-driven functionalities such as interest assessment, skill analysis, resume building, and personalized recommendations. The methodology involves data collection through user inputs, preprocessing of data for analysis, and the creation of a robust system architecture comprising frontend interfaces, backend servers, and database management. The implementation of the application involves a comprehensive technology stack, including Python for AI algorithms, TensorFlow for ML models, React.js for frontend development, Flask for backend services, and PostgreSQL for database management . (shrink)

Cognitive Optimization in the Age of AI: Enhancing Human Potential -/- Introduction -/- Cognitive optimization is the process of enhancing mental functions such as memory, learning, decision-making, and problem-solving to achieve peak intellectual performance. It is a multidisciplinary approach that integrates neuroscience, psychology, nutrition, lifestyle adjustments, and, increasingly, artificial intelligence (AI). In an era where information is abundant and rapid decision-making is crucial, optimizing cognitive abilities is more Important than ever. -/- AI-driven technologies, video games, mobile apps, and digital (...) platforms like YouTube are playing a transformative role in enhancing human cognition. YouTube, in particular, has become a powerful tool for self-directed learning, offering access to educational content, expert insights, and cognitive training exercises. -/- This essay explores cognitive optimization, key strategies involved, and how AI, video games, mobile apps, and platforms like YouTube are revolutionizing human intelligence. -/- The Science Behind Cognitive Optimization -/- Cognitive optimization is rooted in neuroplasticity—the brain’s ability to rewire itself through experience and learning. Several key factors contribute to cognitive enhancement: -/- 1. Neuroplasticity and Learning – Continuous learning strengthens neural connections, fostering intelligence and adaptability. Activities such as reading, solving complex problems, and learning new skills stimulate brain function. -/- 2. Nutrition and Brain Health – Diet plays a vital role in cognitive performance. Nutrients such as omega-3 fatty acids, antioxidants, and essential vitamins support brain function and protect against cognitive decline. -/- 3. Physical Exercise – Regular exercise increases blood flow to the brain, promotes neurogenesis (the growth of new neurons), and enhances memory and problem-solving skills. -/- 4. Sleep and Recovery – Sleep is essential for memory consolidation, emotional regulation, and mental clarity. Chronic sleep deprivation impairs cognitive function and decision-making. -/- 5. Mindfulness and Stress Management – Practices such as meditation, deep breathing, and yoga improve focus, reduce anxiety, and enhance mental resilience. -/- 6. Social Interaction and Collaboration – Engaging in meaningful discussions and debates strengthens cognitive agility and enhances problem-solving skills. -/- Beyond these traditional methods, digital tools such as AI-driven learning platforms, video games, mobile applications, and YouTube have expanded the possibilities for cognitive enhancement. -/- AI and Cognitive Optimization: A New Frontier -/- AI technology is revolutionizing cognitive optimization by providing personalized insights, accelerating learning, and even augmenting human intelligence. Below are several ways AI is transforming cognitive enhancement: -/- 1. AI-Powered Personalized Learning -/- AI-driven educational platforms such as adaptive learning systems customize lessons based on an individual’s learning style and pace. These systems use machine learning to analyze cognitive strengths and weaknesses, adjusting content dynamically to optimize learning efficiency. Platforms like Coursera, Duolingo, and Khan Academy utilize AI algorithms to provide personalized recommendations, ensuring users focus on areas that need improvement. -/- 2. Brain-Computer Interfaces (BCIs) -/- Brain-Computer Interfaces (BCIs) are AI-powered systems that enable direct communication between the human brain and computers. Companies like Neuralink and OpenBCI are developing BCIs that could enhance memory, restore lost cognitive functions, and even allow direct mental interaction with AI systems. This technology has the potential to revolutionize learning and decision-making by integrating human cognition with artificial intelligence. -/- 3. AI-Powered Cognitive Assistants -/- AI-driven virtual assistants such as ChatGPT, Google Assistant, and IBM Watson enhance cognition by providing instant access to information, summarizing complex topics, and even suggesting optimal decision-making strategies. These tools help individuals process vast amounts of information efficiently, reducing cognitive overload and enhancing productivity. -/- 4. AI in Mental Health and Stress Management -/- Mental health is a critical component of cognitive optimization. AI-driven mental health apps like Woebot and Wysa use machine learning to provide real-time emotional support, track mood patterns, and offer cognitive behavioral therapy (CBT) techniques. By reducing stress and anxiety, these AI-powered tools help maintain optimal cognitive function. -/- The Role of YouTube in Cognitive Optimization -/- YouTube has emerged as one of the most powerful platforms for cognitive enhancement. With millions of educational videos covering diverse subjects, YouTube serves as a free and accessible learning tool that fosters continuous cognitive growth. -/- 1. Learning New Skills and Expanding Knowledge -/- YouTube provides access to high-quality educational content in virtually every field. Channels like Veritasium, TED-Ed, and CrashCourse offer lessons on science, history, philosophy, and more. Learning new concepts regularly strengthens cognitive flexibility and enhances problem-solving skills. -/- 2. Enhancing Memory and Comprehension -/- Visual and auditory learning through YouTube helps reinforce memory retention. Watching documentaries, explainer videos, and step-by-step tutorials aids in deepening understanding and reinforcing neural pathways associated with knowledge acquisition. -/- 3. Improving Focus and Critical Thinking -/- Channels that discuss philosophy, logic, and critical thinking—such as Academy of Ideas or PBS Idea Channel—train the brain to analyze complex arguments, recognize biases, and develop sharper reasoning skills. Engaging in thought-provoking discussions through video content strengthens cognitive agility. -/- 4. AI-Generated Content for Personalized Learning -/- AI-powered recommendation algorithms help users discover personalized content based on their interests and learning habits. AI-curated video playlists allow individuals to engage in structured learning journeys, optimizing cognitive development in areas of personal interest. -/- 5. Interactive Learning and Problem-Solving -/- YouTube’s interactive features, such as quizzes, live Q&A sessions, and discussion threads, allow users to actively engage with educational content. This social interaction enhances cognitive engagement and retention. -/- By leveraging YouTube strategically, individuals can create personalized learning experiences that optimize cognitive function in a self-directed manner. -/- Video Games and Cognitive Optimization -/- Video games are increasingly recognized as tools for cognitive enhancement, providing interactive and engaging ways to improve various mental skills. -/- 1. Action Video Games for Reflexes and Decision-Making -/- Fast-paced action games like Call of Duty, Fortnite, and Counter-Strike train players to react quickly, improve hand-eye coordination, and develop strategic thinking under pressure. Studies suggest that such games enhance cognitive flexibility and decision-making speed. -/- 2. Puzzle and Strategy Games for Problem-Solving -/- Games like Portal, The Witness, and Tetris challenge players with complex puzzles that require logical thinking, spatial reasoning, and pattern recognition. -/- 3. Open-World and Role-Playing Games (RPGs) for Memory and Creativity -/- Games like The Legend of Zelda, Elden Ring, and The Witcher 3 encourage exploration, problem-solving, and strategic planning. -/- 4. Multiplayer and Cooperative Games for Social Cognition -/- Team-based games like League of Legends, Dota 2, and Among Us enhance communication skills, teamwork, and emotional intelligence. -/- The Future of Cognitive Optimization -/- The future of cognitive optimization lies in the seamless integration of AI, video games, mobile applications, and digital platforms like YouTube. Emerging technologies such as neuroprosthetics, AI-enhanced memory implants, and real-time brain augmentation could redefine human potential. -/- Furthermore, as AGI (Artificial General Intelligence) develops, AI could become an even more powerful tool in cognitive enhancement, potentially working alongside humans as cognitive partners. This could align with initiatives such as Angelito Malicse’s universal formula for decision-making, where AI-driven cognitive optimization ensures that human choices align with the natural law of balance. -/- Conclusion -/- Cognitive optimization is essential for maximizing human potential, and AI, video games, YouTube, and cognitive training apps are playing an increasingly significant role in this process. From personalized learning to interactive gaming and mobile applications, digital tools are transforming the way we enhance cognition. -/- By leveraging these tools responsibly and integrating them with fundamental cognitive enhancement strategies, humanity can unlock new levels of intelligence, creativity, and problem-solving ability—ensuring that the future of cognition is both powerful and sustainable. -/- . (shrink)

The EdTech revolution in India has emerged as a transformative force, particularly during and after the COVID-19 pandemic, when traditional education systems faced unprecedented disruptions. While digital technologies have unlocked new opportunities for teaching and learning, they have also exposed systemic inequities and deepened the existing digital divide. This paper examines how EdTech is reshaping India's education landscape by addressing these challenges, with a focus on both the opportunities it presents and the barriers it creates. The shift to digital (...) learning platforms has empowered students with access to a broader array of learning resources, interactive content, and personalized learning paths. However, millions of students, particularly those from rural areas or lower-income families, struggle to access these resources due to a lack of digital infrastructure, reliable internet connectivity, and affordability of devices. Furthermore, the role of teachers has evolved, requiring new pedagogical approaches and technical skills to effectively harness these digital tools. (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)

The EdTech revolution in India has emerged as a transformative force, particularly during and after the COVID-19 pandemic, when traditional education systems faced unprecedented disruptions. While digital technologies have unlocked new opportunities for teaching and learning, they have also exposed systemic inequities and deepened the existing digital divide. This paper examines how EdTech is reshaping India's education landscape by addressing these challenges, with a focus on both the opportunities it presents and the barriers it creates.

. In the evolving landscape of healthcare, the efficient processing of biomedical signals is critical for real-time diagnosis and personalized treatment. Conventional cloud-based AI systems for biomedical signal processing face challenges such as high latency, bandwidth consumption, and data privacy concerns. Edge computing, which brings data processing closer to the source, has emerged as a potential solution to these limitations. However, optimizing AI models for edge devices, which often have limited computational resources, remains a challenge. This paper proposes an (...) innovative approach to optimize AI models for biomedical signal processing by leveraging reinforcement learning (RL) techniques within edge computing environments. Reinforcement learning offers the potential to dynamically adapt model parameters based on real-time feedback from the edge devices, optimizing the trade-offs between model accuracy, resource utilization, and processing speed. The proposed system is designed to operate with high efficiency on edge devices, enabling faster signal processing while ensuring energy efficiency and maintaining diagnostic accuracy. A detailed review of existing literature highlights the benefits and challenges of edge computing in biomedical applications, the role of reinforcement learning in dynamic optimization, and the limitations of traditional AI models in constrained environments. The proposed system methodology integrates RL for model optimization, focusing on edge devices' unique constraints in terms of power, memory, and processing capacity. Simulation results demonstrate the improved efficiency and accuracy of biomedical signal processing with optimized AI models in edge computing setups, showing significant improvements in real-time diagnostic applications. (shrink)

This study explores the role of government policies, public-private partnerships, and emerging technologies in bridging the digital divide, ensuring inclusive and equitable access to quality education for all students. By analyzing case studies and statistical data, this research identifies key areas where the EdTech revolution can be leveraged to close gaps in the education system while fostering innovation in pedagogical practices. Finally, the paper presents recommendations for ensuring that the EdTech revolution contributes to systemic equity, rather than exacerbating existing disparities.

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)

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.

The Inefficiency of the Biological Brain and the Importance of AI for the Next Generation -/- The human brain, often considered the pinnacle of evolutionary design, is an extraordinary organ capable of creativity, critical thinking, and adaptation. However, despite its remarkable abilities, it is inherently inefficient when compared to artificial intelligence (AI) systems in certain domains. The inefficiencies of the biological brain, coupled with the rapid development of AI technology, underline why artificial general intelligence (AGI) holds immense promise for shaping (...) the future of the next generation. By complementing and extending human cognition, AI can address many of the limitations of the biological brain and equip young people with tools to solve the world’s most pressing challenges. -/- The Inefficiencies of the Biological Brain -/- The biological brain operates under significant constraints that limit its potential. Some of these inefficiencies include: -/- 1. Cognitive Biases and Emotional Interference The human brain is subject to cognitive biases, such as confirmation bias, which can hinder rational decision-making. Emotional states, while vital for human experience, can cloud judgment and lead to impulsive or irrational behavior. These limitations often result in flawed decisions, particularly in complex or high-stakes situations. -/- 2. Limited Processing Power and Memory While the brain is capable of parallel processing, it has finite processing power and memory storage. Tasks requiring large-scale computations, vast data analysis, or instantaneous recall of details often exceed its capabilities. For instance, analyzing terabytes of data to identify patterns or trends is beyond the capacity of any single individual. -/- 3. Slow Learning and Knowledge Retention Learning is a time-intensive process for humans. Acquiring expertise in a single domain can take years, and even then, the retention of knowledge often deteriorates over time. The brain also struggles to adapt quickly to entirely new fields or technologies without substantial effort. -/- 4. Physical Vulnerabilities The brain is biologically constrained by aging, disease, and trauma. These vulnerabilities can result in cognitive decline, memory loss, or even the complete inability to function. These natural limitations cannot yet be fully mitigated through biological means. -/- 5. Bias from Evolutionary Pressures The human brain evolved for survival in environments vastly different from the modern world. Traits like fear, aggression, or tribalism, which were once advantageous, can now lead to social conflicts, inefficiency, and short-term thinking. -/- Why AI and AGI Are Crucial for the Next Generation -/- Artificial intelligence has the potential to address the inefficiencies of the biological brain and create a brighter, more equitable future for the next generation. Its capabilities go far beyond traditional human cognition in several key areas: -/- 1. Enhanced Problem-Solving Abilities AI systems are capable of processing massive datasets, analyzing patterns, and generating solutions far more efficiently than the human brain. For example, AI is already transforming industries such as healthcare by diagnosing diseases with greater accuracy and speed. Future AGI could enable young people to tackle global challenges such as climate change, resource distribution, and poverty with unprecedented precision. -/- 2. Overcoming Human Biases Unlike the biological brain, which is prone to emotional and cognitive biases, well-designed AI systems can function based on objective logic and evidence. This could lead to more equitable policy-making, unbiased education systems, and transparent governance. -/- 3. Accelerated Learning and Knowledge Access AI-powered tools can revolutionize education by providing personalized learning experiences tailored to individual needs. With access to AI tutors and resources, young people can acquire skills and knowledge faster and more effectively than ever before, bridging educational gaps and fostering innovation. -/- 4. Augmentation of Human Potential By integrating AI with human cognition, individuals can expand their capabilities beyond natural limits. For example, brain-computer interfaces (BCIs) could allow people to directly interact with AI systems, enhancing memory, decision-making, and creativity. This symbiosis between humans and machines could redefine what it means to be human. -/- 5. Future-Proofing Against Global Challenges The next generation will face challenges that require solutions at a scale and complexity beyond human capacity. AI and AGI are indispensable for managing these challenges, from predicting pandemics and optimizing energy usage to navigating ethical dilemmas in an interconnected world. -/- Building a New Paradigm for the Next Generation -/- To ensure that AI benefits society as a whole, it is essential to integrate it into the foundational education and development of young people. Teaching students about AI, its capabilities, and ethical implications will empower them to use these tools responsibly. Furthermore, fostering collaboration between humans and AI, rather than fearing technological displacement, will cultivate a new paradigm where human creativity and machine efficiency coexist harmoniously. -/- Governments, educational institutions, and industries must work together to democratize access to AI technologies, ensuring that the benefits are shared equitably. Ethical frameworks should be developed to guide the use of AI, ensuring that it remains a tool for human progress rather than a source of inequality or harm. -/- Conclusion -/- The inefficiencies of the biological brain highlight the need for complementary technologies that can amplify human potential. AI and AGI represent the next frontier in human evolution, offering solutions to our cognitive and societal limitations. For the next generation, these technologies are not merely tools but essential companions in shaping a better, more sustainable world. By embracing AI and equipping young people with the skills to harness its power, humanity can transcend its limitations and unlock a future of unparalleled possibilities. -/- . (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.

Deep learning, a transformative force in computational biology, has reshaped biological data analysis and interpretation terrain. This review delves into the multifaceted role of deep knowledge in this field, exploring its historical roots, inherent advantages, and persistent challenges. It investigates explicitly its application in two pivotal domains: DNA sequence classification, where it has been used to identify disease-causing mutations, and protein structure prediction from sequence data, where it has enabled the accurate determination of protein tertiary structures. Moreover, it offers (...) a glimpse into the future trajectory of this dynamic field, sparking intrigue and excitement about the potential of deep learning. Deep learning, a powerful tool in computational biology, can be traced back to the inception of 'threshold logic,' a fusion of algorithmic principles and mathematical frameworks devised to mimic cognitive processes. This seminal breakthrough, which introduced the concept of a threshold function that could be used to model complex decision-making processes, paved the way for deep learning to decipher intricate patterns embedded within vast biological datasets. Deep learning presents many benefits, including precise disease diagnosis, novel drug discovery, and personalized medicine facilitation. Its prowess lies in handling vast, intricate datasets while enhancing generalization capabilities, a testament to its evolution and development that we can appreciate. (shrink)

The Holistic Governance Model (HGM): A Blueprint for the Future -/- Introduction -/- Governments today face increasing challenges, from economic instability and climate change to corruption and social inequality. No single government system has fully solved these issues, but by integrating the best aspects of existing models, we can create an optimal governance system. -/- The Holistic Governance Model (HGM) is a hybrid system that combines elements from Social Democracy, Technocracy, Semi-Direct Democracy, China’s Whole-Process People’s Democracy, and the Modified Westminster (...) System. It leverages AI-driven policy-making, technocratic expertise, direct citizen participation, and long-term national planning to create a balanced, efficient, and just government system. -/- 1. Core Foundations of the Holistic Governance Model -/- The HGM is built on six fundamental pillars, ensuring that governance is efficient, democratic, and sustainable. -/- 1.1. Economic System: A Hybrid of Capitalism and State Planning -/- Inspired by: Singapore (Technocracy), China (State-Controlled Development), Nordic Model (Social Welfare), and Switzerland (Financial Stability) ✅ Key Industries Under State Control: The government regulates healthcare, energy, and transportation while allowing free-market innovation. ✅ Smart Capitalism: Entrepreneurs and businesses thrive, but AI-driven economic planning ensures fair wealth distribution. ✅ Universal Basic Assets (UBA): Instead of universal basic income (UBI), citizens receive free housing, education, and healthcare, ensuring financial security. ✅ AI-Assisted Economy: AI predicts recessions, optimizes budgets, and prevents financial crises. -/- 1.2. Political System: A Dynamic Semi-Direct Democracy -/- Inspired by: Switzerland (Direct Democracy), Westminster System (Parliament), and Singapore (Meritocratic Leadership) ✅ Two-Tier Voting System: -/- General Elections: Citizens vote for representatives every 4-5 years. -/- National Referendums: Citizens directly vote on major policies twice a year. ✅ Technocratic Advisory Board (TAB): A non-political panel of scientists, economists, and AI experts advises the government. ✅ Decentralized Local Governance: Cities and regions have autonomy while aligning with national laws. -/- 1.3. Education & Human Development: AI-Powered Lifelong Learning -/- Inspired by: Nordic Model (Free Education), Singapore (STEM Focus), and China (AI Integration) ✅ Free Public Education for Life: Every citizen has access to continuous learning, including college and skill retraining. ✅ AI-Based Personalized Learning: Students use AI tutors that adapt to their learning pace. ✅ Ethics & Critical Thinking Curriculum: Courses in philosophy, logic, and decision-making prevent misinformation and ignorance. -/- 1.4. Healthcare & Social Welfare: AI-Driven Preventive Medicine -/- Inspired by: Nordic Model (Universal Healthcare), Singapore (Tech-Driven Healthcare), and China (Mass Coverage) ✅ Government-Funded Universal Healthcare: Citizens receive free basic healthcare, with private options available. ✅ AI & Robotics in Medicine: AI diagnoses diseases early, reducing medical costs. ✅ Preventive Healthcare Policy: Citizens receive incentives for maintaining good health (fitness programs, nutrition plans). -/- 1.5. Environmental & Energy Policies: The Green Industrial Revolution -/- Inspired by: China (Renewable Energy Expansion), Nordic Model (Sustainability Laws), and Singapore (Urban Planning) ✅ Mandatory Green Technology Adoption: Businesses must comply with strict eco-friendly policies. ✅ Carbon Credit Economy: Citizens and companies earn rewards for reducing carbon footprints. ✅ 100% Renewable Energy by 2050: The government funds solar, wind, and AI-optimized energy grids. -/- 1.6. Law & Order: AI-Governed Justice & Anti-Corruption Measures -/- Inspired by: Switzerland (Independent Judiciary), Singapore (Strict Law Enforcement), and China (AI in Governance) ✅ AI-Assisted Judiciary: AI helps judges analyze case laws for fair, unbiased rulings. ✅ Zero-Corruption Policy: Public officials caught in corruption face lifetime bans from government service. ✅ Balanced Civil Rights & Security: -/- Freedom of Speech: Citizens can express opinions, but disinformation is regulated. -/- AI Surveillance for Security: AI prevents crime while protecting privacy. -/- 2. Government Structure of HGM -/- 2.1. Executive Branch: The President, Prime Minister, and Technocrats -/- President (Elected by Public Vote) → Oversees national policies and international relations. -/- Prime Minister (Elected by Parliament) → Manages daily governance. -/- Technocratic Economic Council → Expert-led economic planning. -/- AI-Assisted Policy Bureau → Uses AI for healthcare, infrastructure, and environmental policies. -/- 2.2. Legislative Branch: Semi-Direct Parliament -/- Lower House (Elected Officials) → Creates laws based on citizen participation. -/- Upper House (Experts & Regional Representatives) → Reviews laws for scientific and ethical validity. -/- Referendum System → Citizens directly vote on key national issues. -/- 2.3. Judicial Branch: AI-Assisted Supreme Court -/- Independent Court System → Ensures justice and constitutional balance. -/- AI Legal Analysis → AI helps judges analyze previous cases and evidence. -/- 3. Implementation Strategy: How to Apply HGM Worldwide -/- To implement HGM, a phased approach is needed: -/- Phase 1 (0-5 Years): Research & AI Integration -/- ✅ Establish an HGM Global Research Center to develop AI governance models. ✅ Pilot AI-assisted economic planning in select countries. -/- Phase 2 (5-10 Years): Education & Public Awareness -/- ✅ Introduce HGM-based education systems (critical thinking, AI governance). ✅ Encourage political reforms to adopt hybrid governance models. -/- Phase 3 (10-20 Years): Nationwide Policy Implementation -/- ✅ Countries gradually adopt the HGM framework, with AI monitoring national progress. ✅ Global partnerships for climate change action and digital economy growth. -/- Phase 4 (Beyond 20 Years): Global Standardization -/- ✅ Establish an HGM Alliance for international cooperation. ✅ Implement AI-driven global policies on poverty, sustainability, and digital rights. -/- 4. Why the HGM is the Future of Governance -/- The Holistic Governance Model (HGM) offers the most advanced, efficient, and democratic government system by combining AI governance, citizen participation, economic stability, and social equality. -/- Conclusion: A Government System for the 21st Century -/- The Holistic Governance Model (HGM) combines the best government systems into a single, optimized model. By implementing AI, technocracy, direct democracy, and long-term planning, HGM ensures: ✅ Efficient economic management ✅ Social equality without excessive taxation ✅ Democratic participation with AI-driven transparency ✅ Sustainable environmental policies ✅ Corruption-free governance -/- If adopted globally, HGM could transform human civilization into a more just, advanced, and sustainable society. -/- . (shrink)

This article identifies and examines a tension in mental health researchers’ growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors or collections of data (...) points. The imperatives of the “participatory turn” in mental health research may be at odds with the (often unquestioned) assumptions and data collection methods of AI/ML approaches. This article aims to show why this is a problem and how it might be addressed. (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)

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)

Over the last few years, personal health monitoring wearable devices have emerged as innovative applications of Artificial Intelligence (AI) in the healthcare industry as they help in real time analysis and prediction of health standardized check-ups and health management. To navigate through the current trends, new technologies and developments, the prospects are as follows: The article also gives a logical look at the state of the art of such devices, enumerating the advantages and drawbacks, as well as outlining the main (...) ethical issues. In terms of method, it also discusses different AI approaches, data acquisition procedures, and those integrating systems used in the context of health monitoring. The finding and implication section discusses the findings of the current trend and the possibility of improvement in application and user acceptance. The last section provides a prognosis on what the area of personal health monitoring might look like in the future, as well as the position of AI in the optimisation of patient experience. (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. The scope of the project encompasses the design, development, and implementation of AI-driven functionalities such as interest assessment, skill analysis, resume building, and personalized recommendations. The methodology involves data collection through user inputs, preprocessing of data for analysis, and the creation of a robust system architecture comprising frontend interfaces, backend servers, and database management. (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)

Personalized learning experiences have undergone revolutionary transformations as a result of the use of artificial intelligence (AI) into education. The application of AI-driven adaptive learning methods designed especially for the Parakh assessment system's multiple-choice question (MCQ) selection is examined in this abstract. In order to dynamically evaluate a student's ability and modify the question complexity in real time, the suggested method makes use of machine learning models. The system carefully chooses questions that address each learner's unique (...) learning gaps and offer suitable challenge levels based on past performance data and question information. Preprocessing assessment data, extracting features from student interaction logs, and creating a recommendation system to improve the accuracy of MCQ selection are all part of the methodology. Evaluation measures like knowledge retention rates, time to completion, and response accuracy are utilized to evaluate the effectiveness of the system. By optimizing learning outcomes, this AI-based strategy seeks to provide a more stimulating and productive assessment environment. The method might completely change the way formative evaluations are carried out and guarantee that every student gets a customized education that is in line with their particular academic requirements. (shrink)

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)

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. The scope of the project encompasses the design, development, and implementation of AI-driven functionalities such as interest assessment, skill analysis, resume building, and personalized recommendations. T. (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)

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)

Generative AI is revolutionizing the landscape of creative learning by empowering students, educators, and creators to generate content, enhance project-based learning, and facilitate art generation. This paper explores the integration of generative AI tools in content creation, where learners can produce text, audio, and visual content with ease, fostering creativity and personalized learning experiences. In project-based learning, AI-driven platforms provide innovative approaches for hands-on projects, allowing students to simulate real-world scenarios, problem-solving, and interdisciplinary collaboration. Furthermore, (...) AI-generated art is reshaping the art world by enabling new forms of expression and democratizing the creative process. This study highlights the potential of generative AI to transform education through enhanced creativity, engagement, and personalized learning paths. (shrink)

Generative AI is revolutionizing the landscape of creative learning by empowering students, educators, and creators to generate content, enhance project-based learning, and facilitate art generation. This paper explores the integration of generative AI tools in content creation, where learners can produce text, audio, and visual content with ease, fostering creativity and personalized learning experiences. In project-based learning, AI-driven platforms provide innovative approaches for hands-on projects, allowing students to simulate real-world scenarios, problem-solving, and interdisciplinary collaboration. Furthermore, (...) AI-generated art is reshaping the art world by enabling new forms of expression and democratizing the creative process. This study highlights the potential of generative AI to transform education through enhanced creativity, engagement, and personalized learning paths. (shrink)

This project aims to provide an automated system for accurately estimating the calorie content of food and beverages using advanced deep learning algorithms. With the increasing demand for health-conscious individuals, there is a need for a reliable, efficient, and easy-to-use tool that can help users make informed dietary choices. The project utilizes image processing techniques and deep learning models, such as Convolutional Neural Networks (CNN), to analyze food images and predict the corresponding calorie content. The system works by (...) first capturing an image of the food or beverage, which is then processed and passed through a pre-trained deep learning model. The model is trained on a large dataset containing images of various food items along with their nutritional information. After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits. (shrink)

The rapid advancement of artificial intelligence (AI) and machine learning (ML) has led to significant innovations in the healthcare sector. One such development is AI-powered healthcare chatbots, which assist patients and medical professionals by providing medical guidance, symptom assessment, and appointment scheduling. This paper presents the design and implementation of an AI healthcare chatbot using machine learning techniques. The chatbot leverages natural language processing (NLP) and deep learning models to understand and respond to user queries effectively. (...) Experimental results demonstrate the chatbot's efficiency in delivering accurate and context-aware medical information. (shrink)

Abstract: The incorporation of Artificial Intelligence (AI) into educational technology has dramatically transformed learning through Intelligent Tutoring Systems (ITS). These systems utilize AI to offer personalized, adaptive instruction tailored to each student's needs, thereby improving learning outcomes and engagement. This paper examines the development and impact of ITS, focusing on AI technologies such as machine learning, natural language processing, and adaptive algorithms that drive their functionality. Through various case studies and applications, it illustrates how ITS have (...) revolutionized traditional educational methods and addresses the challenges and limitations, including data privacy and system biases. The discussion also covers future directions in ITS development, highlighting emerging trends and potential advancements. The goal is to provide a thorough understanding of how ITS use AI to enhance educational experiences and suggest avenues for future research in this evolving field. (shrink)

After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable (...) calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits. (shrink)

Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings, medical diagnoses and recruitment. Academic articles, policy texts, and popularizing books alike warn that such algorithms tend to be opaque: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation, I formulate a moral concern for opaque algorithms that is yet to receive (...) a systematic treatment in the literature: when such algorithms are used in life-changing decisions, they can obstruct us from effectively shaping our lives according to our goals and preferences, thus undermining our autonomy. I argue that this concern deserves closer attention as it furnishes the call for transparency in algorithmic decision-making with both new tools and new challenges. (shrink)

The model is trained on a large dataset containing images of various food items along with their nutritional information. After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track (...) their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits. (shrink)

The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating (...) habits. (shrink)

The model is trained on a large dataset containing images of various food items along with their nutritional information. After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track (...) their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits. (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)

Meta-learning, or learning to learn, has emerged as a powerful paradigm for creating adaptive models that can quickly adapt to new tasks with minimal data. In the context of personalized healthcare, meta-learning holds the potential to revolutionize precision medicine by enabling models that can personalize treatments based on individual characteristics. These models can leverage prior knowledge across multiple patients or conditions to provide rapid and accurate predictions for new patients, improving the efficiency and effectiveness of healthcare (...) delivery. This paper explores how meta-learning techniques can be applied to personalized healthcare, addressing the challenges of patient variability, data scarcity, and the need for individualized predictions. We also discuss several meta-learning strategies, such as Model-Agnostic Meta-Learning (MAML) and Prototypical Networks, and their integration into healthcare systems. Furthermore, we present case studies in areas like chronic disease management and treatment recommendation, highlighting the promise of meta-learning in precision medicine. (shrink)

The increasing computational demands of artificial intelligence (AI) workloads have significantly escalated energy consumption in data centers. AI-driven applications, including deep learning, natural language processing, and autonomous systems, require substantial computing power, primarily provided by Graphics Processing Units. These GPUs, while enhancing computational efficiency, contribute to significant power consumption and heat generation, necessitating advanced cooling strategies. This study provides a quantitative assessment of AI-specific hardware power usage, focusing on the NVIDIA H100 GPU. The analysis compares AI data center energy (...) consumption to the average US household power usage, demonstrating that a single AI rack consumes approximately 39 times the energy of a typical household. Additionally, a scalability analysis estimates that approximately 87 new hyper-scale data centers consume the electricity as much as consumed by New York City. This emphasizes that with rapid growth of AI Data Centers, the large-scale deployment could lead to an unprecedented rise in global energy demand. Furthermore, the study evaluates the impact of heat dissipation on cooling requirements, highlighting the need for energy- efficient cooling solutions, including liquid and immersion cooling techniques. Future research directions include energy- efficient AI models, renewable energy integration, sustainable AI accelerator designs, and intelligent workload optimization to mitigate the environmental impact of large-scale AI adoption. This research provides critical insights for designing more sustainable AI-driven data centers while maintaining high-performance computing efficiency. (shrink)

The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating (...) habits. (shrink)

This paper presents the development of an AI-driven emotional support chatbot designed to provide personalized and real-time mental health care. The chatbot leverages natural language processing and machine learning algorithms to respond to users' emotional needs, offering support in multiple languages to improve accessibility. The chatbot also integrates professional mental health resources to enhance its effectiveness. This study aims to bridge the gap between traditional mental health support systems and the growing demand for digital mental health tools. Key (...) results demonstrate increased user engagement, personalized care, and improved mental health outcomes. This approach highlights the chatbot’s potential to revolutionize mental health support systems. (shrink)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies that (...) provide personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. We also explore the ethical implications and challenges associated with deploying such systems, particularly regarding privacy concerns and the potential for misuse. Through extensive experiments, our model achieved an average accuracy rate of 92%, highlighting its robustness across different environments and user demographics. This research not only contributes to the growing field of affective computing but also lays the groundwork for future developments in emotionally intelligent systems. (shrink)

The development of conversational artificial intelligence (AI) has brought about new opportunities for improving the learning experience in education. ChatGPT, a large language model trained on a vast corpus of text, has the potential to revolutionize education by enhancing learning through personalized and interactive conversations. This paper explores the benefits of integrating ChatGPT in education in Thailand. The research strategy employed in this study was qualitative, utilizing in-depth interviews with eight key informants who were selected using purposive (...) sampling. The collected data was analyzed using content analysis and the software NVivo. The study's results indicated that ChatGPT can provide personalized learning experiences by adapting to individual student needs and preferences. Its ability to understand natural language and context can also facilitate more meaningful interactions between students and the system. Additionally, ChatGPT can assist with administrative tasks such as grading and feedback, allowing educators to focus on more personalized and meaningful interactions with students. Furthermore, ChatGPT can serve as a valuable tool for remote learning, providing students with the ability to access educational resources and support outside of traditional classroom settings. The paper also discusses potential ethical considerations in utilizing AI in education, such as data privacy and bias. Overall, this paper argues that the integration of ChatGPT in education has the potential to enhance the learning experience for students by providing personalized, interactive, and efficient support. (shrink)

This paper explores the integration of multiple intelligences and artificial intelligence (AI) in language learning, focusing on its potential to enhance personalization and engagement. Drawing from existing research and studies conducted in various contexts, including the Philippines, this study aims to contribute to the understanding of the benefits, challenges, and effectiveness of this integration. The paper begins with an introduction that highlights the background and significance of integrating multiple intelligences and AI in language learning, identifying research gaps, objectives, (...) research questions, and the theoretical framework. A literature review provides an overview of multiple intelligences theory by Howard Gardner, the role of AI in language learning, and identifies gaps in the existing literature. The methodology section outlines the research design and approach, participant selection, data collection methods, validity and reliability measures, and ethical considerations. Findings and results are presented through the analysis of qualitative data, exploring emergent themes and patterns. The discussion section critically examines the identified research gaps, discusses the validity and reliability of the study, addresses the scope and limitations, and explores the implications of the findings for theory, practice, and future research. The conclusion summarizes the key findings and contributions of the study, reflects on the achievement of research objectives, offers recommendations for further research, and provides final remarks tying together the main points of the study. This paper contributes to the existing body of knowledge by providing insights into the integration of multiple intelligences and AI in language learning and its impact on personalization, engagement, and language learning outcomes. (shrink)

This paper investigates how human interactions with AI-powered chatbots may offend human dignity. Current chatbots, driven by large language models (LLMs), mimic human linguistic behaviour but lack the moral and rational capacities essential for genuine interpersonal respect. Human beings are prone to anthropomorphise chatbots—indeed, chatbots appear to be deliberately designed to elicit that response. As a result, human beings’ behaviour toward chatbots often resembles behaviours typical of interaction between moral agents. Drawing on a second-personal, relational account of dignity, we (...) argue that interacting with chatbots in this way is incompatible with the dignity of users. We show that, since second-personal respect is premised on reciprocal recognition of second-personal moral authority, behaving towards chatbots in ways that convey second-personal respect is bound to misfire in morally problematic ways, given the lack of reciprocity. Consequently, such chatbot interactions amount to subtle but significant violations of self-respect—the respect we are dutybound to show for our own dignity. We illustrate this by discussing four actual chatbot use cases (information retrieval, customer service, advising, and companionship), and propound that the increasing societal pressure to engage in such interactions with chatbots poses a hitherto underappreciated threat to human dignity. (shrink)

Rapidly expanding data centers along with networks create a fundamental problem regarding resource allocation efficiency. Standard resource management systems prove unable to adapt dynamically to varying workloads so bandwidth allocation and computing utilization stays inefficient. Developers use recent advancements in artificial intelligence technology to build automatic optimization algorithms that instantly adjust resource distributions. Through the integration of machine learning with deep reinforcement learning systems organizations obtain predictive power to prepare resource distribution ahead of time without endangering operational efficiency. (...) According to Gandhi et al. (2012), both tested methods achieve enhanced energy efficiency while preventing delivery slowness. The study explores how AI addresses data facility network resource optimization by examining key techniques and discovering current trends and future development directions. (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)

We propose Qurio, which is our new model of pedagogy incorporating the principles of quantum mechanics with a curiosity AI called Curio AI equipped with a meta-curiosity algorithm. Curio has a curiosity profile that is in a quantum superposition of every possible curiosity type. We describe the ethos and tenets of Qurio, which we claim can create an environment supporting neuroplasticity that cultivates curiosity powered by tools that exhibit their own curiosity. We give examples of how to incorporate non-locality, (...) complementarity, and quantum lateral thinking into epistemology. We then futurecast the epistemology of curiosity and quantum pedagogy by way of curiosity’s event horizon. (shrink)

Morphic Resonance and the Spread of the Universal Formula for Free Will -/- Introduction -/- The challenge of implementing a universal formula for free will, based on the law of balance in nature, requires a mechanism that allows societies to naturally adopt and internalize its principles. One such mechanism may exist in morphic resonance, a concept proposed by Rupert Sheldrake. Morphic resonance suggests that patterns of thought, behavior, and structure become easier to replicate once they have been established in a (...) collective field of memory. -/- If this phenomenon applies to human decision-making and knowledge transmission, then the universal law of balance can spread more rapidly once a critical mass of individuals fully understands and applies it. This essay explores how morphic resonance can be harnessed to accelerate the adoption of the universal formula, ensuring that societies, leadership, education, and artificial intelligence (AI) naturally align with balance-based decision-making. -/- Understanding Morphic Resonance in Relation to the Universal Formula -/- Morphic resonance proposes that once a new behavior, skill, or knowledge is established within a group, it creates a morphic field that allows others to access and adopt the same pattern more easily. This idea aligns with the universal formula for free will, which states that all human decisions must conform to natural equilibrium to prevent instability, suffering, and disorder. -/- In this context, morphic resonance serves as an invisible feedback mechanism that reinforces balance-based thinking across societies. Once a significant number of people consistently apply the universal law of balance, it could lead to a self-reinforcing shift in collective decision-making, making it easier for others to adopt without requiring direct instruction. -/- The role of Morphic Resonance in Strengthening the Universal Formula -/- To ensure the widespread adoption of the universal formula, it is essential to establish a strong morphic field of balance-based decision-making. This requires an integrated approach combining education, leadership, AI, and mass media. -/- 1. Education as the Foundation of Morphic Resonance -/- Education is the primary vehicle for embedding balance-based decision-making into society. -/- Early Childhood Learning: Teaching children about natural balance, cause and effect, and systems thinking ensures that the universal formula becomes an instinctive part of their cognitive framework. -/- Critical Thinking Development: Encouraging students to apply rational analysis and evidence-based reasoning reinforces the habit of making balanced decisions. -/- Standardized Global Curriculum: If multiple nations implement a harmonized educational approach focused on natural balance, it could strengthen the morphic field, making it easier for future generations to intuitively grasp and apply the universal formula. -/- 2. Leadership and Governance as Catalysts of Resonance -/- Leaders play a crucial role in establishing decision-making norms that influence entire populations. -/- Governments must integrate balance-based policies that align with the universal formula, ensuring stability in economic, social, and environmental domains. -/- Leaders must undergo training in natural balance principles, reinforcing their ability to make rational, ethical, and sustainable decisions. -/- A coalition of balance-driven global leaders can create a powerful morphic resonance effect, allowing other nations to subconsciously recognize and adopt similar governance strategies. -/- 3. AI as an Amplifier of Morphic Fields -/- Artificial Intelligence (AI), particularly AGI (Artificial General Intelligence), has the potential to reinforce and spread the universal formula more effectively than human-led efforts alone. -/- AI systems programmed with balance-based decision-making will naturally guide policies, governance, and education in ways that amplify the morphic resonance effect. -/- AGI as a self-learning system can observe global patterns and autonomously promote equilibrium, ensuring that human civilization remains aligned with natural laws. -/- AI-assisted educational platforms can enhance the learning process by personalizing instruction on balance-based thinking, making it more intuitive for students to grasp and apply the universal formula. -/- 4. Mass Media and Cultural Influence in Strengthening the Field -/- To create a strong morphic field of balance, continuous exposure to balance-based thinking through media and culture is necessary. -/- Books, documentaries, podcasts, and social media campaigns can reinforce balance-based decision-making, making it a widely recognized concept. -/- Repetition of key principles in public discourse ensures that the universal formula becomes ingrained in collective consciousness. -/- Technological tools, such as virtual reality and AI-driven education, can create immersive experiences that allow individuals to engage deeply with the universal law of balance, further strengthening its resonance. -/- Achieving a Critical Mass for Morphic Resonance Activation -/- For morphic resonance to fully activate the global adoption of the universal formula, a critical percentage of individuals, institutions, and AI systems must consistently apply its principles. Once this tipping point is reached: -/- The concept of natural balance becomes self-perpetuating, influencing new generations without requiring intensive instruction. -/- Governments, economies, and institutions naturally shift toward balance-based policies. -/- Humanity instinctively aligns with the natural laws of decision-making, minimizing suffering, instability, and systemic errors. -/- Implications for the Future -/- If morphic resonance is successfully leveraged to spread the universal formula, it could lead to a new era of human civilization characterized by: -/- Sustainable governance and economic systems based on natural equilibrium. -/- A society where ethical decision-making is the norm, reducing corruption, conflict, and social instability. -/- The development of AI systems that reinforce balance-based logic, preventing technological risks associated with unchecked AI decision-making. -/- This transformation would not only impact individual behavior but also the collective consciousness of humanity, leading to a self-sustaining cycle of rational, ethical, and sustainable decision-making. -/- Conclusion -/- By integrating morphic resonance into the strategy for spreading the universal formula for free will, it becomes possible to accelerate its global adoption without solely relying on traditional education and policymaking. Once a critical mass of individuals, leaders, and AI systems adopt balance-based thinking, the morphic field of natural balance will allow future generations to intuitively align with these principles. -/- This ensures that the universal law of balance in nature is not just a theoretical framework but a living reality, guiding humanity, AI, and governance toward a stable, rational, and harmonious future. Through education, leadership, AI, and mass communication, the universal formula can become a self-reinforcing global shift, ensuring sustainability, wisdom, and long-term societal balance. -/- . (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)