Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic analysis for (...) better performances. These previously suggested techniques for network traffic analysis typically need voluminous information on network usages. Hence, this work proposes OFedeMWOUAA (optimal federated learning-based UAA technique with Meadow Wolf Optimisation) and DNN (deep Neuron Networks) for minimizing risks of data leakages in MWNs (Mobile Wireless Networks). In the proposed OFedeMWOUAA, the need to submit data to cloud servers does not arise because it trains DLTs locally and only uploads model gradients or knowledge weights. The OFedeMWOUAA approach effectively decreases dangers to data privacies with very minor performance losses in simulations. (shrink)

Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic analysis for (...) better performances. These previously suggested techniques for network traffic analysis typically need voluminous information on network usages. Hence, this work proposes OFedeMWOUAA (optimal federated learning-based UAA technique with Meadow Wolf Optimisation) and DNN (deep Neuron Networks) for minimizing risks of data leakages in MWNs (Mobile Wireless Networks). In the proposed OFedeMWOUAA, the need to submit data to cloud servers does not arise because it trains DLTs locally and only uploads model gradients or knowledge weights. The OFedeMWOUAA approach effectively decreases dangers to data privacies with very minor performance losses in simulations. (shrink)

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge (...) data set, the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is done.Numerous bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware specimens have been collected from the Mailing database for testing purposes. The experimental findings show that the suggested technique for measuring cyber security hazards in IoT has superior classification results to existing methods. Hence to detect the attack prevention in IoT process Whale with Grey wolf optimization (WGWO) and deep convolution network is used. (shrink)

Optimization algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to determine the optimal aggregation and transmission schedules, taking into account factors such as network topology, node energy levels, and data urgency. The proposed approach is validated through extensive simulations, demonstrating significant improvements in energy consumption, packet delivery ratio, and overall network performance. The results suggest that the optimized aggregated packet transmission method can effectively extend the lifespan of duty-cycled WSNs while ensuring reliable data (...) communication. Future work will explore the integration of machine learning techniques for adaptive scheduling and the application of the proposed method to heterogeneous WSNs. (shrink)

Federated Learning (FL) is a machine learning paradigm that enables model training across decentralized devices while preserving data privacy. However, FL faces two significant challenges: privacy concerns and scalability issues. Privacy concerns arise from potential vulnerabilities in aggregating updates, whereas scalability issues stem from the increasing number of edge devices and the computational overhead required for communication and model updates. This paper explores cutting-edge advancements aimed at addressing these challenges, including advanced encryption techniques, differential privacy mechanisms, federated (...) class='Hi'>optimization methods, and decentralized training architectures. We also discuss strategies for managing communication costs, improving convergence speeds, and ensuring robustness in heterogeneous environments. By integrating novel approaches to privacy and scalability, next-generation federated learning can provide a more secure, efficient, and scalable framework for a wide range of applications, from healthcare to autonomous vehicles. (shrink)

Federated Learning (FL) is a machine learning paradigm that enables model training across decentralized devices while preserving data privacy. However, FL faces two significant challenges: privacy concerns and scalability issues. Privacy concerns arise from potential vulnerabilities in aggregating updates, whereas scalability issues stem from the increasing number of edge devices and the computational overhead required for communication and model updates. This paper explores cutting-edge advancements aimed at addressing these challenges, including advanced encryption techniques, differential privacy mechanisms, federated (...) class='Hi'>optimization methods, and decentralized training architectures. We also discuss strategies for managing communication costs, improving convergence speeds, and ensuring robustness in heterogeneous environments. By integrating novel approaches to privacy and scalability, next-generation federated learning can provide a more secure, efficient, and scalable framework for a wide range of applications, from healthcare to autonomous vehicles. (shrink)

The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. The system's performance is evaluated through simulations and real-world experiments, demonstrating its capability to deliver reliable and timely pollution data. (...) Future work will explore the integration of machine learning techniques for predictive analytics and the expansion of the sensor network for broader coverage. (shrink)

There are various aspects to Internet of Things security, such as guaranteeing the safety of both the devices and the Internet of Things networks to which they connect. Many other types of equipment, including industrial robots, smart grids, construction automation systems, entertainment gadgets, and many more, are included in this, despite the fact that they were not designed with network security in mind. When it comes to securing systems, networks, and data, IoT device security must be able to resist a (...) wide range of IoT security assaults. One of the most important issues in the field of data security is the creation of intrusion detection systems (IDSs) for the Internet of Things. Client devices (edge devices) in Federated Learning utilize local data to train the machine learning model, and then send the updated model parameters to a cloud server so that they may be aggregated (rather than raw data).This paper proposes a machine learning system that employs federated learning to detect intrusions in the IoT. The FedAVG algorithm is employed to aggregate models. Models are trained locally by nodes. The models are trained and validated using machine learning methods, including Random Forest, ID3, and Support Vector Machine. The NSL KDD data set is employed to undertake experiments. (shrink)

Any properly designed network coding technique can result in increased throughput and reliability of multi-hop wireless networks by taking advantage of the broadcast nature of wireless medium. In many inter-flow network coding schemes nodes are encouraged to overhear neighbour’s traffic in order to improve coding opportunities at the transmitter nodes. A study of these schemes reveal that some of the overheard packets are not useful for coding operation and thus this forced overhearing increases energy consumption dramatically. In this (...) paper, we formulate network coding aware sleep/wakeup scheduling as a semi Markov decision process (SMDP) that leads to an optimal node operation. In the proposed solution for SMDP, the network nodes learn when to switch off their transceiver in order to conserve energy and when to stay awake to overhear some useful packets. One of the main challenges... (shrink)

The proliferation of various wireless communication technologies and devices has ushered in the big data era in large-scale wireless networks. Researchers face new challenges when working with big data from large-scale wireless networks compared to traditional computer systems. This is because big data has four essential characteristics: high value, real-time velocity, immense variety, and great volume. The goal of this article is to survey all the new stuff about big data analytics (BDA) methods for massive wireless (...) networks. Data collecting, data preprocessing, data storage, and data analytics are the four distinct phases that make up the BDA life cycle. We next provide an exhaustive review of the technical solutions that have been created to address the difficulties associated with BDA for large-scale wireless networks, organised according to each stage of the BDA life cycle. We also explore the research questions that remain unanswered and provide an overview of the potential future possibilities in this exciting field. (shrink)

The World’s Leading Research and Development Institutions and Companies -/- Introduction -/- Research and Development (R&D) is the backbone of global innovation, driving technological progress, economic growth, and scientific discoveries. Across the world, top institutions and corporations invest billions of dollars into R&D to push the boundaries of human knowledge and create groundbreaking technologies. This essay explores the most influential research institutions and companies shaping the future through their contributions in science, engineering, medicine, and technology. -/- The Role of Research (...) Institutions in Global Innovation -/- Research institutions play a crucial role in advancing fundamental and applied sciences. Many of these institutions are publicly funded universities and government research agencies that focus on a wide range of disciplines, from medicine and engineering to artificial intelligence and space exploration. -/- 1. Leading Research Institutions -/- Some of the most prestigious research institutions include: -/- National Institutes of Health (NIH) – USA: The primary agency of the U.S. government responsible for biomedical and public health research. -/- National Aeronautics and Space Administration (NASA) – USA: A global leader in aerospace and space exploration, NASA continues to innovate in space technology and planetary science. -/- Centre National de la Recherche Scientifique (CNRS) – France: The largest governmental research organization in France, covering all scientific disciplines. -/- Max Planck Society – Germany: A network of research institutes in Germany, known for excellence in science and technology. -/- Chinese Academy of Sciences (CAS) – China: China’s premier research institution focuses on science and technology innovation across various fields. -/- Harvard University – USA: Leads in biomedical research, social sciences, and humanities. -/- Massachusetts Institute of Technology (MIT) – USA: At the forefront of engineering, robotics, artificial intelligence, and quantum computing research. -/- Stanford University – USA: Known for its deep ties to Silicon Valley, Stanford plays a key role in tech innovation. -/- University of Oxford & University of Cambridge – UK: Both institutions are among the oldest and most respected in the world, conducting high-impact research in medicine, engineering, and the humanities. -/- Swiss Federal Institute of Technology Zurich (ETH Zurich) – Switzerland: Excels in engineering and natural sciences research. -/- University of Tokyo – Japan: Japan’s leading university, known for its research in science and technology. -/- California Institute of Technology (Caltech) – USA: A leader in space science and physics. -/- Imperial College London – UK: Specializes in engineering, medical, and business research. -/- University of Toronto – Canada: A top institution in AI, robotics, and engineering. -/- 2. Engineering and Technology Research Leaders -/- Engineering research institutions contribute to infrastructure, automation, and digital transformation. Some of the most prominent ones include: -/- University of California, Berkeley – USA: Known for its research in engineering, computer science, and environmental science. -/- University of Pennsylvania – USA: Invests in innovative engineering projects. -/- University of Michigan – USA: Known for technological advancements in multiple fields. -/- Swiss Federal Institute of Technology Lausanne (EPFL) – Switzerland: Excels in engineering and digital research. -/- Tsinghua University – China: A leader in AI, renewable energy, and industrial automation research. -/- The Role of Private Companies in Research and Development -/- Private corporations play a major role in R&D, investing heavily in emerging technologies, pharmaceuticals, consumer electronics, and industrial innovations. Many of the world’s biggest companies spend billions to maintain leadership in their respective fields. -/- 1. Leading Technology Companies in R&D -/- Amazon ($73.2B in R&D spending, 2022): Focuses on cloud computing, AI, logistics, and automation. -/- Alphabet (Google) ($39.5B in R&D, 2022): Invests in AI, quantum computing, and search algorithms. -/- Meta (Facebook) ($35.3B in R&D, 2022): Focuses on AI, VR, and the Metaverse. -/- Apple ($27.7B in R&D, 2022): Leads in consumer electronics and semiconductor technology. -/- Microsoft ($26.6B in R&D, 2022): Invests in AI, cloud computing, and software innovation. -/- 2. Engineering and Industrial Companies in R&D -/- Volkswagen ($19.88B in R&D, 2022): A leader in automotive engineering and electric vehicles. -/- Samsung ($19.29B in R&D, 2022): Develops advanced semiconductors and electronics. -/- Intel ($17.53B in R&D, 2022): Focuses on microprocessors and AI hardware. -/- Huawei ($24B in R&D, 2022): Leads in telecommunications and 5G development. -/- Siemens – Germany: A global leader in industrial automation, medical technology, and smart infrastructure. -/- Nokia – Finland: Focuses on telecommunications infrastructure and technology development. -/- Sony – Japan: Invests in consumer electronics, gaming, and entertainment technologies. -/- Boeing – USA: Develops aerospace technology, defense systems, and commercial aircraft innovations. -/- General Electric (GE) – USA: Focuses on energy, aviation, and industrial automation R&D. -/- 3. Pharmaceutical and Biotech Research Leaders -/- Roche ($14.72B in R&D, 2022): A leading pharmaceutical company specializing in diagnostics and biotech innovations. -/- Pfizer: Known for vaccine development, including the COVID-19 vaccine. -/- Moderna: A pioneer in mRNA technology for medical treatments. -/- Johnson & Johnson: Invests heavily in medical devices and pharmaceuticals. -/- Bristol-Myers Squibb – USA: Focuses on biopharmaceutical research, including immunology and oncology. -/- AstraZeneca – UK/Sweden: Invests in pharmaceutical research, particularly in oncology and cardiovascular diseases. -/- The Impact of R&D on Society and the Future -/- The advancements driven by R&D have a profound impact on global society. Some of the most significant areas of innovation include: -/- Healthcare: Research in biotechnology, gene therapy, and medical AI is revolutionizing healthcare, leading to personalized medicine and new treatments for previously incurable diseases. -/- Artificial Intelligence: AI research in institutions and companies is driving automation, machine learning, and deep learning applications. -/- Sustainable Energy: Engineering research is focusing on renewable energy, electric vehicles, and smart grid technologies to combat climate change. -/- Space Exploration: Organizations like NASA, SpaceX, and ESA are expanding human exploration beyond Earth. -/- Telecommunications: Companies like Huawei, Nokia, and Qualcomm are leading the development of 5G and future wireless communication technologies. -/- Automotive Industry: Electric vehicles, hydrogen fuel cells, and autonomous driving are key research areas for companies like Tesla, Volkswagen, and Toyota. -/- Conclusion -/- The world’s leading R&D institutions and companies are shaping the future through their groundbreaking work in science, engineering, medicine, and technology. Universities and research centers provide the foundation for discovery, while corporations drive commercial applications of these innovations. As investments in R&D continue to grow, humanity will witness even greater advancements in fields such as artificial intelligence, biotechnology, and space exploration, ultimately improving lives and pushing the boundaries of what is possible. -/- . (shrink)

Integrating machine learning (ML) into network management and orchestration has revolutionized edgebased networking paradigms, where real-time decision-making is critical. Traditional network management approaches often struggle with edge environments' dynamic and resource-constrained nature. By leveraging ML algorithms, networks at the edge can achieve enhanced efficiency, automation, and adaptability in areas such as traffic prediction, resource allocation, and anomaly detection (Wang et al., 2021). Supervised and unsupervised learning techniques facilitate proactive network optimization, reducing latency and improving quality of service (QoS) (Li (...) & Zhang, 2020). Furthermore, reinforcement learning (RL) models enable autonomous network orchestration, allowing edge devices to adapt intelligently to fluctuating workloads and environmental conditions (Patel et al., 2019). However, challenges such as data privacy, computational overhead, and model interpretability remain key concerns in deploying ML-driven network orchestration at the edge (Chen et al., 2022). This paper explores state-of-the-art advancements, key challenges, and future research directions in ML-based edge network management, highlighting its potential to drive the next generation of intelligent, self-optimizing networks. (shrink)

Internet network optimization techniques require immediate expansion because users require fast latency performance alongside improved data transmission speed. Dynamic traffic systems operate with Machine learning algorithms that belong to the Artificial Intelligence category to power their fundamental operational tools. Through real-time data processing, AI systems can modify network pathways in operation thus generating enhanced performance together with outstanding user interface quality. Using reinforcement learning and neural networks developed by artificial intelligence enables better traffic prediction along with response abilities (Zhang (...) et al., 2020). The effectiveness of networks improves from dynamic routing since it addresses congestion issues to boost overall system data transfer speeds according to Lee et al. (2019). Large real-time data stream processing through AI systems promotes network flexibility that leads to enhanced operational results for unpredictable internet traffic (Kumar & Rao, 2020). Before displaying internet infrastructure network applications this research evaluates multiple AI traffic optimization approaches to determine their latency performance measurements including throughput rates. (shrink)

The article suggests an unsupervised model for featuring student’s learning patterns in an open-ended learning scenario. The article proceeds by generating powerful metrics to characterize the learner’s behavior and efficacy through Coherence investigation. Then, the selected features are combined through a Gaussian Hybrid Fuzzy Clustering (GHFC) that categorizes students based on their learning patterns. The proposed system features the essential behaviors of every group and associate the behaviors with ability to develop right models to gauge the learning gains between pre- (...) and post-test scores. Also, this article explains the deployment of behavior characterization to be developed as a adaptive framework of learning behaviors. (shrink)

The changing conditions for the accumulation and transmission of knowledge in the age of multimedia networks make it inevitable that old philosophical problems become formulated in a new light. Above all, the problem of the unity of knowledge is once again a topical issue. The situation-dependent acquisition of knowledge that is made possible by mobile learning transcends the boundaries of traditional disciplines, linking the domains of text, diagram, and picture. Database integration and multimedia search become central problems in the (...) epistemology of the 21st century, while handheld devices are emerging as vital technologies for supporting collaborative learning. -- Kristóf Nyíri has published widely on Wittgenstein, Austrian intellectual history, and the philosophy of communication. He directs the interdisciplinary research program COMMUNICATIONS IN THE 21ST CENTURY, conducted jointly by the Institute for Philosophical Research of the Hungarian Academy of Sciences and Westel Mobile Telecommunications, Budapest. (shrink)

The broadcast nature of communication in transmission media has driven the rise of network coding’s popularity in wireless networks. Numerous benefits arise from employing network coding in multi-hop wireless networks, including enhanced throughput, reduced energy consumption, and decreased end-to-end delay. These advantages are a direct outcome of the minimized transmission count. This paper introduces a comprehensive framework to employ network coding in these networks. It refines decision-making at coding and decoding nodes simultaneously. The coding-nodes employ optimal stopping theory (...) to find optimal moments for packet transmission. Meanwhile, the decoding-nodes dynamically decide, through SMDP (Semi Markov Decision Process) problem formulation, whether to conserve energy by deactivating radio units or to stay active for improved coding by overhearing packets. The proposed framework, named ENCODE, enables nodes to learn how and when to use network coding over time. Simulation results compare its performance with existing approaches. Our simulation results shed new light on when and how to use network coding in wireless multi-hop networks more effectively. (shrink)

In the privacy preserving data mining, the utility mining casts a very vital part. The objective of the suggested technique is performed by concealing the high sensitive item sets with the help of the hiding maximum utility item first (HMUIF) algorithm, which effectively evaluates the sensitive item sets by effectively exploiting the user defined utility threshold value. It successfully attempts to estimate the sensitive item sets by utilising optimal threshold value, by means of the grey wolf optimisation (GWO) algorithm. (...) The optimised threshold value is then checked for its performance analysis by employing several constraints like the HF, MC and DIS. The novel technique is performed and the optimal threshold resultant item sets are assessed and contrasted with those of diverse optimisation approaches. The novel HMUIF considerably cuts down the calculation complication, thereby paving the way for the enhancement in hiding performance of the item sets. (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)

There is an increase in the use of in-door wireless networking solutions via Wi-Fi and this increase infiltrated and utilized Wi-Fi enable devices, as well as smart mobiles, games consoles, security systems, tablet PCs and smart TVs. Thus the demand on Wi-Fi connections increased rapidly. Rule Based System is an essential method in helping using the human expertise in many challenging fields. In this paper, a Rule Based System was designed and developed for diagnosing the wireless connection problems (...) and attain a precise decision about the cause of the problem. SL5 Object expert system language was used in developing the rule based system. An Evaluation of the rule based system was carried out to test its accuracy and the results were promising. (shrink)

This project, Bird Species Identification Using Deep Learning, proposes an advanced system leveraging the power of deep learning to accurately identify bird species from images. The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks. A dataset comprising diverse bird species images is preprocessed and augmented to enhance model robustness and generalization. The model architecture is designed to extract intricate features, enabling accurate identification even in challenging scenarios such as varying lighting conditions, occlusions, or (...) similar species appearances. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, ensuring comprehensive validation. R. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. (...) The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized (...) through techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

A common characteristic of all popular multi-path routing algorithms in Mobile Ad-hoc networks, such as AOMDV, is that the end to end delay is reduced by utilization of parallel paths. The competition between the neighboring nodes for obtaining a common channel in those parallel paths is the reason for end to end delay increment. In fact, due to medium access mechanism in wireless networks, such as CSMA/CA, data transmissions even through two Node Disjoint paths are not completely independent (...) and each path will affect the other one. In this paper we have modified the AODV protocol which results in selection of zone-disjoint paths, to the extent feasible, and as a result we achieve less end to end delay. The efficiency of the proposed protocol has been evaluated on different scenarios and there has been a noticeable improvement in the packet delivery ratio and also in the reduction of end-to-end delay comparing to AOMDV. (shrink)

The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks. A dataset comprising diverse bird species images is preprocessed and augmented to enhance model robustness and generalization. The model architecture is designed to extract intricate features, enabling accurate identification even in challenging scenarios such as varying lighting conditions, occlusions, or similar species appearances. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, ensuring comprehensive validation. Results indicate significant accuracy improvements (...) over traditional machine learning approaches, demonstrating the potential of deep learning in species identification. This project holds promise for applications in wildlife monitoring, ecological research, and educational tools, promoting awareness and conservation efforts. Future work may include integrating the system into mobile applications or deploying it for real-time bird species identification in field conditions. (shrink)

t: Wireless Sensor Networks (WSNs) have become increasingly prevalent in various applications, ranging from environmental monitoring to smart cities. However, the limited energy resources of sensor nodes pose significant challenges in maintaining network longevity and data transmission efficiency. Duty-cycled WSNs, where sensor nodes alternate between active and sleep states to conserve energy, offer a solution to these challenges but introduce new complexities in data transmission.

rd species identification plays a vital role in biodiversity conservation and ecological studies, offering insights into habitat health and species distribution. Traditional methods for identifying bird species are time-intensive and prone to human error, necessitating automated solutions. This project, Bird Species Identification Using Deep Learning, proposes an advanced system leveraging the power of deep learning to accurately identify bird species from images. The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks. A dataset comprising (...) diverse bird species images is preprocessed and augmented to enhance model robustness and generalization. (shrink)

Abstract: Age and gender classification has been around for a long time, and efforts are still being made to improve the findings. This has been the case since the inception of social media platforms. Visible understanding has become more important in the computer vision society with the emergence of AI increase in performance and help train a model to achieve age and gender classification. Although these networks built for the mobile platform are not always as accurate as the larger, (...) more resource- intensive networks we've come to know and love, they stand out when it comes to the accuracy trade-off. Despite the importance of these attributes in our daily lives, the ability to estimate them efficiently from face images is still far from meeting the requirements of commercial applications. (shrink)

Efficient rake scheduling and demand forecasting in railway operations are essential to address the complexities of passenger demand, minimize delays, and enhance utilization. This project uses advanced machine learning methods, specifically LSTM (Long Short-Term Memory) networks and GBM (Gradient Boosting Machine), to predict demand and optimize rake scheduling dynamically. Integrating a user-friendly web interface allows realtime data monitoring, enabling railway operators to make informed decisions. By leveraging real-time data sources, including rake movement, schedules, weather, and traffic conditions, this project aims (...) to improve operational efficiency and responsiveness. Keywords. Railway Scheduling, Rake Optimization, Demand Forecasting, LSTM, GBM, Machine Learning, RealTime Data. (shrink)

Some of the routing algorithms in mobile ad hoc networks use multiple paths simultaneously. These algorithms can attempt to find node-disjoint paths to achieve higher fault tolerance capability. By using node-disjoint paths, it is expected that the end-to-end delay in each path should be independent of each other. However, because of natural properties of wireless media and medium access mechanisms in ad hoc networks, the end-to-end delay between any source and destination depends on the pattern of communication in (...) the neighborhood region. In this case some of the intermediate nodes should be silent to reverence their neighbors and this matter increases the average of end-to-end delay. To avoid this problem, multi-path routing algorithms can use zone-disjoint paths instead of node-disjoint paths. Two routes with no pair of neighbor nodes are called zone-disjoint paths. In this paper we propose a new multi-path routing algorithm that selects zone-disjoint paths, using omni-directional antenna. We evaluate our algorithm in several different scenarios. The simulation results show that the proposed approach is very effective in decreasing delay and packet loss. -/- . (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)

Wireless Sensor Networks (WSNs) have become increasingly prevalent in various applications, ranging from environmental monitoring to smart cities. However, the limited energy resources of sensor nodes pose significant challenges in maintaining network longevity and data transmission efficiency. Duty-cycled WSNs, where sensor nodes alternate between active and sleep states to conserve energy, offer a solution to these challenges but introduce new complexities in data transmission. This paper presents an optimized approach to aggregated packet transmission in duty-cycled WSNs, utilizing advanced (...) class='Hi'>optimization techniques to enhance energy efficiency, reduce latency, and improve network throughput. By aggregating data packets from multiple nodes before transmission, the proposed method minimizes the number of transmissions, thereby conserving energy. Optimization algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to determine the optimal aggregation and transmission schedules, taking into account factors such as network topology, node energy levels, and data urgency. T. (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)

Abstract: Wireless security is the avoidance of unlawful access or impairment to computers using wireless networks. Securing wireless network has been a research in the past two decades without coming up with prior solution to which security method should be employed to prevent unlawful access of data. The aim of this study was to review some literatures on wireless security in the areas of attacks, threats, vulnerabilities and some solutions to deal with those problems. It was (...) found that attackers (hackers) have different mechanisms to attack the networks through bypassing the security trap developed by organizations and they may use one weak pint to attack the whole network of an organization. However the author suggested using firewall in each wireless access point as the counter measure to protect data of the whole organization not to be attacked. (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)

This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.

The increasing complexity and demands of next-generation networks necessitate the integration of Artificial Intelligence (AI) to enhance their efficiency and security. This article explores advancements in AI-driven communication systems, focusing on optimizing network performance, ensuring robust security measures, and addressing the challenges of scalability and real-time adaptability. By analyzing case studies, emerging technologies, and recent research, this study highlights AI's transformative potential in redefining communication systems for future applications.

Urban transportation systems face significant challenges due to increasing congestion, inefficient routes, and fluctuating passenger demand. Traditional public transportation networks often struggle to adapt dynamically to these challenges, leading to delays, overcrowding, and environmental inefficiencies. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban mobility by enabling real-time route optimization, demand forecasting, and passenger flow management. By integrating data from GPS trackers, fare collection systems, and environmental sensors, cities can reduce travel times, enhance commuter satisfaction, (...) and promote sustainable transportation. Experimental results demonstrate improvements in route efficiency, passenger load balancing, and operational costs, offering a blueprint for AI-driven smart urban mobility. (shrink)

Modernizing workflows is imperative to address labor-intensive tasks that hinder productivity and efficiency. Convolutional Neural Networks (CNNs), a prominent technique in Artificial Intelligence, offer transformative potential for automating complex processes and streamlining operations. This study explores the application of CNNs in building accurate classification models for diverse datasets, demonstrating their ability to significantly enhance decision-making processes and operational efficiency. By leveraging a dataset of images, an optimized CNN model has been developed, showcasing high accuracy and reliability in classification tasks. The (...) findings underscore the ability of AI-powered approaches to reduce manual efforts, improve productivity, and support sustainable modernization across various domains. This study is relevant to professionals seeking to embed AI solutions into conventional workflows, offering a pathway to enhanced innovation and efficiency. (shrink)

The convergence of quantum computing and machine learning is poised to revolutionize the field of artificial intelligence (AI). Quantum computing offers the potential to exponentially speed up computations, which can be leveraged to overcome the limitations of classical computing in training and inference for machine learning models. Quantum algorithms promise to enhance machine learning tasks, such as optimization, data processing, and pattern recognition, by solving problems that are computationally infeasible for classical machines. This paper explores the synergy between quantum (...) computing and machine learning, focusing on the quantum-enhanced capabilities in AI. We review current research on quantum machine learning (QML) algorithms, discuss their theoretical underpinnings, and present promising applications in areas such as optimization, natural language processing, and drug discovery. Furthermore, we address the challenges and future directions in merging quantum computing with AI, highlighting the potential for accelerated AI solutions and transformative advancements in various industries. (shrink)

By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, the (...) system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on mobile devices for ease of use in rural areas. The potential future applications of this system include integration with precision agriculture techniques and expansion to a wider range of crops. (shrink)

The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random Forest, (...) Support Vector Machines, and Neural Networks, that have been tuned using strategies including hyper parameter selection, crossvalidation, and feature selection. (shrink)

This project, titled Bird Species Identification Using Deep Learning, aims to develop a robust system that can identify bird species from images with high precision. The core of this project involves training a CNN model on a diverse dataset of bird images. This dataset includes species from various geographical locations and environments, capturing a wide range of appearances, postures, and behaviors. By preprocessing and augmenting the dataset, the model is designed to handle challenges such as variations in lighting, background noise, (...) and partial occlusions. The implementation of this project has numerous practical applications. For instance, it can be integrated into mobile or web-based platforms to provide real-time bird identification tools for researchers, birdwatchers, and environmental agencies. It can also be utilized in wildlife monitoring programs to track species distribution and migration patterns. Furthermore, the system can support educational initiatives, allowing individuals to learn about bird species and their significance in the ecosystem. (shrink)

—One of the promising applications of wireless sensor networks (WSNs) is monitoring of the human body for health concerns. For this purpose, a large number of small sensors are implanted in the human body. These sensors altogether provide a network of wireless sensors (WBANs) and monitor the vital signs and signals of the human body; these sensors will then send this information to the doctor. The most important application of the WBAN is the implementation of the monitoring network (...) for patient safety in the hospital environment. In this case, supporting patients’ mobility is one of the basic needs, which has been underestimated in recent studies. The problem that involves providing the required energy for the units used in this type of network is challenging; for this reason, sent/ received units with very low power consumption and with a very small radius are used in order to save energy. The resulting small sending range, leads to the lack of support for patients' mobility. In this paper, the AD HOC mode is suggested for use to establish a network and a multi-path routing algorithm, for the purpose of importing patients’ mobility in hospital setting. The results of the simulation show that in addition to supporting patients' mobility, the use of the proposed idea instead of previously presented protocols, reduces delays in data transmission and energy consumption; and it also increases the delivery rate depending on the destination and the lifetime of the network, while on the other hand, it increases routing overhead. (shrink)

The state of the availability of digital resources and institutional compliance to COVID-19 mitigation measures was evaluated by the researchers in this study. Informed by the need to answer two research questions, the study adopted the descriptive survey design. A sample of 409 participants was drawn from a population of 2,410 academic staff at the University of Calabar, leveraging the multistage sampling process. “Availability of digital resources and institutional compliance with COVID-19 mitigation measures questionnaire” was used for data collection. After (...) administration, we recovered 397 copies of the instrument successfully. We used descriptive statistics to answer the research questions. Our findings indicated a great extent in the availability of digital resources such as computers (91.18%), human capacity (63.48%), network printers (67.76%), projectors (59.19%), websites (92.95%), and wireless networks (86.65%). There was a low extent in the availability of digital resources such as local area network (5.79%), cloud storage systems (2.27%), digital libraries (7.56%), firewalls (1.26%), Internet service (48.36%), routers (23.69%), and Zoom videoconferencing platform (23.93%). The unavailability of digital resources such as host/servers (0%) and virtual private networks (0%) in the higher education institution (HEI). Further evidence shows a low extent of institutional compliance with COVID-19 mitigation measures generally and specifically for all indicators. In conclusion, there is a high degree of unreadiness to promote e-learning when flexible learning systems should operate in the institution. Among others, we recommend that the management of the HEI should apportion proceeds from internally generated funds to procure digital resources to increase the institution's capacity to respond to COVID-19 or similar cases in the future. (shrink)

Bird species identification plays a vital role in biodiversity conservation and ecological studies, offering insights into habitat health and species distribution. Traditional methods for identifying bird species are time-intensive and prone to human error, necessitating automated solutions. This project, Bird Species Identification Using Deep Learning, proposes an advanced system leveraging the power of deep learning to accurately identify bird species from images. The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks. A dataset comprising (...) diverse bird species images is preprocessed and augmented to enhance model robustness and generalization. The model architecture is designed to extract intricate features, enabling accurate identification even in challenging scenarios such as varying lighting conditions, occlusions, or similar species appearances. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, ensuring comprehensive validation. Results indicate significant accuracy improvements over traditional machine learning approaches, demonstrating the potential of deep learning in species identification. This project holds promise for applications in wildlife monitoring, ecological research, and educational tools, promoting awareness and conservation efforts. Future work may include integrating the system into mobile applications or deploying it for real-time bird species identification in field conditions. (shrink)

: Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as (...) Genetic Algorithms (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. Data from multiple sources, including camera feeds and wearable sensors, are used to train and validate the models, ensuring a comprehensive understanding of drowsiness indicators. (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 rapid evolution of the financial sector, particularly in banking and fintech, necessitates continuous innovation in financial product development and testing. However, challenges such as data privacy, regulatory compliance, and the limited availability of diverse datasets often hinder the effective development and deployment of new products. This research investigates the transformative potential of AI-driven synthetic data generation as a solution for accelerating innovation in financial product development. Synthetic data, generated through advanced AI techniques such as Generative Adversarial Networks (GANs), Variational (...) Autoencoders (VAEs), and Transformer-based models, can simulate real-world financial scenarios with a high degree of fidelity while preserving privacy and compliance standards. The use of synthetic data enables financial institutions and fintech companies to conduct rigorous testing, modeling, and validation of new products and services without relying on sensitive customer data. By generating realistic yet artificial datasets, organizations can explore a broader range of scenarios, including rare or extreme market conditions, thus enhancing the robustness and reliability of their financial models. This paper provides a comprehensive analysis of the underlying methodologies for synthetic data generation, focusing on their application to financial product development. It delves into the specific architectures and frameworks used in generating synthetic data, including GANs, VAEs, and synthetic minority over-sampling techniques (SMOTE), and examines their respective advantages and limitations. The paper also addresses the critical issue of ensuring the quality and utility of synthetic data, emphasizing metrics such as statistical similarity, privacy preservation, and applicability to real-world use cases. The discussion extends to the ethical and regulatory implications of deploying AI-driven synthetic data in finance, highlighting the need for transparent and explainable AI models to ensure trust and compliance. Moreover, the research explores practical case studies where financial institutions and fintech firms have successfully implemented synthetic data to develop and test new products, demonstrating significant reductions in time-to-market and development costs. One of the key contributions of this research is the exploration of how AI-driven synthetic data generation can facilitate the development of innovative financial products such as algorithmic trading strategies, risk management tools, credit scoring models, and fraud detection systems. By simulating diverse market behaviors and customer interactions, synthetic data enables the fine-tuning of algorithms and models to achieve higher accuracy and performance. Additionally, the paper discusses the integration of synthetic data generation into existing financial data ecosystems, proposing a framework for leveraging hybrid datasets that combine synthetic and real data to optimize model training and validation. The potential for synthetic data to drive collaborative innovation in finance is also considered, as it allows multiple stakeholders, including banks, fintech startups, and regulators, to share and analyze data without compromising confidentiality or privacy. The research also addresses the limitations and challenges associated with synthetic data generation in the financial domain, including issues related to data representativeness, overfitting, and the potential misuse of synthetic datasets. It emphasizes the need for ongoing research to develop more sophisticated algorithms that can generate highly realistic and diverse financial data. Furthermore, it identifies areas for future exploration, such as the use of federated learning and differential privacy techniques to enhance the security and privacy of synthetic data generation processes. The findings of this paper underscore the importance of AI-driven synthetic data generation as a catalyst for innovation in banking and fintech, providing a secure, scalable, and cost-effective means to develop, test, and validate new financial products and services. As the financial industry continues to evolve, the role of synthetic data in shaping the future of financial product development will become increasingly critical, paving the way for more efficient and innovative financial solutions. (shrink)

Mechanisms for QoS provisioning in communication networks range from flow-based resource reservation schemes, providing QoS guarantees, through QoS differentiation based on reservation aggregation techniques to adaptation of applications, compensating for incomplete reservations. Scalable, aggregation-based reservations can also be combined with adaptations for a more flexible and robust overall QoS provisioning. Adaptation is particularly important in wireless networks, where reservations schemes are more difficult to realize. It is widely accepted that usability of Cellular or Mobile IP can be largely (...) improved if adaptation is incorporated. Adaptation, in turn, depends on coding techniques involved and on user requirements. Those impose certain preferences and ordering relations on QoS hierarchies or degradation paths to be followed along when adaptation is performed. With the advent of MPEG4 coding techniques, more sophisticated adaptation schemes are more likely to be supported in the future. (shrink)