The problem of immigrant migration risk analysis is deeply studied and there are number of issues identified in the selection of country. There exists number of approaches available in the selection of countries towards migration. However, the methods do not produce effective results in the selection of migrant country. To handle this issue, an efficient real-time migrant risk analysis model (RMAM) is presented in this article. The method uses the traces of previos migration histories and data set maintained (...) by the model. Using the migration data set, the method preprocesses the data set for noise removal and normalization. With the normalized data set, the method applies Nation Centric Religious Support Analysis and Psychological Support Analysis. The nation centric religious support analysis algorithm measure the risk factors on religious features by computing National Religious Support (NRS) and the Psychological Support Analysis algorithm computes the value of Psychological Support (PS) for different countries. Based on the result of different analysis, the method computes Migration Support (MS) for various countries towards the selection of migration countries. The proposed model improves the performance of risk analysis and migration control. (shrink)

This paper presents a machine learning-based framework for real-time short-term snowfall forecasting by integrating atmospheric and topographic data. The model uses real-time meteorological data such as temperature, humidity, and pressure, along with terrain data like elevation and land cover, to predict snowfall occurrence within a 12-hour forecast window. Random Forest (RF) and Support Vector Machine (SVM) models are employed to process these multi-source inputs, demonstrating a significant improvement in prediction accuracy over traditional methods. Experimental results (...) show that the RF model achieved a root mean square error (RMSE) of 3.2 cm, while traditional regression models achieved an RMSE of 5.1 cm. The proposed framework provides a more reliable and accurate tool for real-time snowfall prediction, especially in mountainous regions, and can be deployed for operational weather forecasting applications. (shrink)

The "Smart and Effective Real-time Management of Street Parking" project is designed to enhance urban parking enforcement through the use of advanced machine learning and computer vision technologies. The system leverages CCTV cameras to continuously monitor parking spaces, detecting their availability and instances where vehicles are incorrectly parked. By analyzing video feeds, the system identifies parking violations and extracts license plate numbers using Optical Character Recognition (OCR). Notifications are promptly sent to drivers regarding their parking status, ensuring timely (...) enforcement of parking regulations. This approach focuses on effective management of parking resources and compliance with regulations. The system’s real-time capabilities contribute to a significant reduction in urban congestion by promoting orderly parking practices and efficient use of parking resources. Implementation of this technology supports enhanced urban planning initiatives by providing valuable data insights into parking patterns and violations, thus fostering a more organized, sustainable, and efficient urban environment. (shrink)

Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a framework for (...) Optimized Machine Learning Algorithms designed to analyze ECG signals in real time within IoT networks. The proposed system leverages lightweight machine learning models, including support vector machines (SVM) and convolutional neural networks (CNNs), optimized to run efficiently on low-power IoT devices while maintaining high accuracy. The system addresses the computational limitations of IoT devices by employing edge computing techniques that distribute the processing load between IoT devices and edge servers. Additionally, data compression and feature extraction techniques are applied to reduce the size of the data transmitted over the network, thereby minimizing latency and bandwidth usage. This paper reviews the current advancements in real-time ECG analysis, explores the challenges posed by IoT environments, and presents the optimized machine learning algorithms that enhance real-time monitoring of heart health. The system is evaluated for its performance in terms of accuracy, energy efficiency, and data transmission speed, showing promising results in improving real-time ECG signal analysis in resource-constrained IoT networks. (shrink)

The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future (...) enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (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)

As technology improves, artificial systems are increasingly able to behave in human-like ways: holding a conversation; providing information, advice, and support; or taking on the role of therapist, teacher, or counsellor. This enhanced behavioural complexity, we argue, encourages deeper forms of affective engagement on the part of the human user, with the artificial agent helping to stabilise, subdue, prolong, or intensify a person’s emotional condition. Here, we defend a fictionalist account of human/AI interaction, according to which these encounters involve (...) an elaborate practise of imaginative pretence: a make-believe in which the artificial agent is attributed a life of its own. We attend, specifically, to the temporal characteristics of these fictions, and to what we imagine artificial agents are doing when we are not looking at them. (shrink)

In today's data-driven business environment, organizations increasingly rely on advanced analytics and decision support systems to gain a competitive edge. Azure AI and Machine Learning (ML) provide powerful tools for predictive analytics, enabling businesses to forecast trends, optimize processes, and make more informed decisions. By leveraging the capabilities of Microsoft Azure, businesses can integrate AI and ML into their decision-making processes, enhancing productivity and improving strategic outcomes. This paper explores how Azure's AI and ML tools can be applied to (...) predictive analytics and decision support systems, with a focus on real-time decision-making, risk mitigation, and operational efficiency. We will discuss the key Azure services like Azure Machine Learning, Azure Cognitive Services, and Azure Databricks, as well as real-world use cases that highlight their impact on business performance. The paper will also highlight the challenges associated with implementing these technologies and suggest best practices for organizations aiming to integrate AI and ML into their systems. (shrink)

In "The Epistemic Significance of Perceptual Learning," Elijah Chudnoff (this issue) argues that cases from perceptual learning show that perception not only generates reasons for beliefs but also preserves those reasons over time in perceptual learning cases. In this paper, we dispute the idea that perceptual learning enables the preservation of perceptual reasons. We then argue for an alternative view, viz. the view that perceptual learning is epistemically significant insofar as it modifies our perceptual system in such a way (...) as to make us capable of perceiving subtle low-level properties (e.g. lightness) and high-level properties (e.g. chess configurations). Acquiring the capacity to perceive these properties is what enables us to achieve expertise in a variety of subject matters (e.g. chicken sexing, chess playing, language fluency). Along the way, we argue against two main points in Chudnoff’s paper. The first is that, pace Chudnoff, perceptual learning does not result in the acquisition of new facts. It only results in the acquisition of a new perceptual capacity. The second is that experiences resulting from perceptual learning can always serve as immediate justifiers of beliefs and hence do not need supporting background information in order to serve as reasons. (shrink)

When someone is prepunished, they are punished for a predicted crime they will or would commit. I argue that cases of prepunishment universally assumed to be merely hypothetical—including those in Philip K. Dick’s “The Minority Report”— are equivalent to some instances of the real-life punishment of attempt offenses. This conclusion puts pressure in two directions. If prepunishment is morally impermissible, as philosophers argue, then this calls for amendments to criminal justice theory and practice. At the same time, if (...) prepunishment is not imaginary, then the philosophers who reject it cannot claim that their view is supported by common sense. (shrink)

This paper addresses the challenge of efficiently handling a diverse array of customer queries by proposing the development of an innovative web-based customer support chatbot. The objectives encompass creating a versatile system capable of interpreting and resolving a spectrum of customer complaints, enhancing support staff efficiency, and facilitating knowledge base updates. The proposed methodology employs the MERN stack for web app development and integrates Generative AI and pre-trained Large Language Models (LLMs), specifically OpenAI's prebuilt models, for intelligent responses. (...) The pseudo code outlines the interactions between the chatbot, support staff, and administrators, ensuring a seamless process. Results indicate the chatbot's commendable ability to accurately interpret queries, showcasing language versatility in various Indian and foreign languages. Efficient handling of varied phrasings and also accommodating grammatical errors. The use of prebuilt models is highlighted as a practical and efficient solution, reducing implementation time compared to custom models. Additionally, the implementation is recognized for its scalability, positioning it as a practical and efficient solution for evolving customer support needs in the near future. (shrink)

The kinds of real or natural kinds that support explanation and prediction in the social sciences are difficult to identify and track because they change through time, intersect with one another, and they do not always exhibit their properties when one encounters them. As a result, conceptual practices directed at these kinds will often refer in ways that are partial, equivocal, or redundant. To improve this epistemic situation, it is important to employ open-ended classificatory concepts, to understand (...) when different research programs are tracking the same real kind, and to maintain an ongoing commitment to interact causally with real kinds to focus reference on those kinds. A tempting view of these non-idealized epistemic conditions should be avoided: that they signal an ontological structure of the social world so plentiful that it would permit ameliorated classificatory schemes to achieve their normative aims regardless of whether they defer to real-kind classificatory schemes. To ground these discussions, the essay appeals to an overlooked convergence in the systematic naturalistic frameworks of Richard Boyd and Ruth Millikan. (shrink)

Dredging operations are essential for maintaining navigable waterways, but determining the ideal time to dredge requires a multifaceted approach, incorporating both environmental and operational variables. This paper presents the Dredging Analysis and Decision Support System (DADSS), a data-driven solution that employs historical sedimentation data, weather patterns, and water flow statistics to optimize dredging decisions. The system leverages Random Forest Classifier and Regressor models to predict the need for dredging and estimate associated costs. Key inputs include sedimentation depth, water (...) flow rate, sediment type, and precipitation levels. By providing real-time insights and cost-effective guidance without reliance on physical sensors, this solution enhances waterway management efficiency while reducing environmental impact. Results demonstrate a clear optimization in dredging operations with a reduced frequency of unnecessary dredging. (shrink)

Newton supported the idea of absolute time, unlike Leibniz, for which time is only a relation between events and cannot be expressed independently, a statement in concordance with the relativity of space-time. Eternalism claims that the past and the future exist in a real sense, going to the idea that time is a dimension similar to spatial dimensions, that future and past events are "present" on the axis of time, but this view is challenged. (...) On four-dimensional vision, the universe is an existing space-time topology, containing everything that has happened, everything that happens and everything that's going to happen. It follows that there is no singular moment to be considered as insignificant as present. Time travel is possible if the four-dimensional vision including the time is correct, but it is not possible if presentism is true. William Godfrey-Smith says that "the metaphysical image underlying the discussion of time travel is that of the universe block, in which the world is conceived as extended in time as it is in space." DOI: 10.13140/RG.2.2.18575.64163. (shrink)

The "Udyog Saarthi App" is a progressive web-based application developed to enhance job coaching and career readiness for adults with disabilities, specifically focusing on the 4% reservation opportunities provided by NIEPMD and similar institutions in India. This innovative platform addresses the unique needs of users by allowing them to select their specific disability during registration, which customizes the website’s layout for improved accessibility. The application offers a comprehensive listing of real-time job opportunities that align with users' skills and (...) qualifications, empowering them to pursue suitable career paths. In addition, it provides personalized learning resources tailored to each job role, ensuring that users are equipped with the necessary skills to excel. The app also includes periodic mock assessments designed to evaluate users' preparedness for employment. These assessments employ natural language processing (NLP) techniques to calculate scores based on various parameters such as readability scores and the SMOG index, promoting objective and effective evaluations. Moreover, the Udyog Saarthi App features an interactive dashboard that enables users to track their progress and analytics, fostering a sense of accomplishment and motivation. To promote community engagement and support, the application includes a friend recommender system that connects users with similar disabilities, facilitating peer networking and encouragement. Incorporating multilingual support through the Google Translate API, the platform aims to reach a diverse user base, ensuring that language barriers do not hinder accessibility. Ultimately, the Udyog Saarthi App strives to empower individuals with disabilities by providing essential tools and resources, enabling them to become self-sufficient and industry-ready in the competitive job market. (shrink)

plant watering and monitors crop health to enhance farming efficiency and sustainability. The first component of the system is an automatic plant watering system, which leverages soil moisture sensors to monitor real-time soil conditions. When the soil moisture level falls below a predefined threshold, the system triggers an automatic irrigation process via a water pump. This system can be remotely controlled and monitored through a smartphone application or web interface, ensuring optimal water usage and preventing over-watering or under-watering. (...) The second component, the crop health indicator, uses a combination of environmental sensors (temperature, humidity, soil pH, light intensity) and advanced imaging technologies such as multispectral cameras or drones to assess crop health. These sensors detect critical factors affecting plant growth and yield, such as disease, pest infestation, or nutrient deficiencies. The data collected from these sensors is processed in real-time and analyzed to provide actionable insights and timely alerts to farmers, enabling them to take appropriate actions to address crop stress and optimize yield. The system is designed to be user-friendly, providing farmers with easy access to critical data and control through mobile and web interfaces. The integration of these technologies aims to reduce resource consumption, enhance crop productivity, and support sustainable agricultural practices. (shrink)

his paper presents a Multilingual Translation Engine designed to automate public announcements in multiple languages. It leverages Google Translate for real-time translation and gTTS (Google Text-to-Speech) for speech synthesis. The system supports over 90 languages, enhancing accessibility in transportation hubs. The engine's architecture is built using Flask for the backend, integrated with APIs to handle translation and audio generation. Additionally, the system supports custom profiles for recurring announcements. Future enhancements could include speech recognition and advanced scheduling.

The present work consists of a discussion about the concept of real number in Frege. It is intended to explain how Frege tries to define the real numbers, highlighting the importance that the notion of “magnitude-ratio” plays in this definition. In Part III, in Volume II of the Grundgesetze der Arithmetik (1903) (from now on only: Grundgesetze), Frege presents his theory of real numbers, which is followed by a long criticism of the real number theories in (...) force at that time, such as by Cantor, Dedekind, Thomae and Weierstrass. In view of this, to fulfill our purpose, we will examine the text only where Frege presents his own theory of real numbers (the informal presentation) (§§156-164). Frege's criticisms of previous theories will not be examined in detail, unless an introduction to his criticism of formalism, in order to elucidate such criticism. Thus, only the part in which the philosopher presents his own conception of real numbers will be analyzed. To support the discussion, authors Peter Simons, Eric Snyder and Stewart Shapiro will be used. (shrink)

Monitoring soil pH levels forms the backbone of precision agriculture with regard to maximizing crop health and yield. The paper discusses an IoT-based solution, specifically designed for continuous soil pH testing in digital farming. It has pH sensors placed strategically around the agricultural fields such that the information regarding the acidity or alkalinity of the soil is available in real-time. The pH information will be transmitted to the central IoT gateway. Here, the information is processed, and via communication (...) of cloud-based platforms, it is further stored and analyzed. So, by checking variations in soil pH, the system provides actionable information to farmers. Thus, by adjusting soil treatments like lime or fertilizers better, it enhances crop performance. The response uses mobile and web interfaces that support automated pH testing with automatically designed data visualization. This allows farmers the chance to decide from remote sites about imminent and proper fluctuations in soil pH by the use of historical data analysis and models from machine learning. One part of the effectuality of this proactive approach would be that soil quality is improved while unnecessary chemical use is avoided, therefore reducing the resulting costs of farming with sustainability considerations. By limiting itself to pH value testing, this solution increases the farmland's productivity with less wastage of resources. (shrink)

The hard problem – focusing essentially on vision here – is in fact the problem of the origin of our image of the external world. This formulation in terms of the “image” is never seen stated, for the forms populating our image of the world are considered computable, and not considered qualia – the “redness” of the cube is the problem, not the cube as form. Form, however, cannot be divorced from motion and hence from time. Therefore we must (...) examine the classical, spatial metaphysic of space and time, for practical purposes initiated by Galileo, wherein the real has been equated with the quantitative and wherein quality has been stripped from the material world. In this metaphysic, which sees form as quantitative or computable, the origin of qualia is problematic, with a problem of even greater primacy becoming the “memory” that supports the transforming events of perception, e.g., rotating cubes, buzzing flies, twisting leaves. It is this memory, supporting time-extended, flowing events, that necessarily supports all qualia. The concept of storage of “snapshots” of time-flowing events, a notion which the classic metaphysic engenders, is unworkable as a solution to the perception of these flows. Form, in fact being dynamic and defined over flowing fields, equally is a quality, equally requires this memory, and since forms populate the image, the origin of the entire image is indeed a problem. The counter-proposal becomes Bergson’s temporal metaphysic wherein motion is indivisible (or non-differentiable), the global motion of the universal field itself then carrying an intrinsic form of memory. In this framework, with this field viewed as holographic, Bergson provides a unique solution – one that leaves the problem of representation behind – as to how the brain specifies the qualitative image of the dynamically transforming external world. (shrink)

The most trending behavioral approach of mass people nowadays hovers to acquire a specific area to live on for their mental satisfaction. It is the person registered home to live on the next days of life. This issue has firmly increased due to the rapid and mostly uncontrolled increase of population within most of the countries. Now that, it is the conscious craving for men to settle up for a property that has the highest credential service and maintenance ease possibility; (...) it leads them to the housing provider with the highest product quality offering. The easiest way is to redirect them to go for a trustworthy name in the REM providers. Now, narrowing down to a developing country such as Sri Lanka, its Real estate developers have rewarded investors with strong returns post 2009. It is the time when Sri Lanka saw the end of the civil conflict. When the real estate sector has always been a major factor in the development of the economy in every country. Regionally, Sri Lanka remains attractive as an emerging market, when considering the ROI on Real Estate. For an effective discussion, this paper states the linkage of consumer behavior to the highest quality housing provider offers to them. The paper discusses all the necessary factors to prove the enhanced purchase of supreme quality property company dealers like Prime LnadPvt. Ltd is a leading real estate company in Sri Lanka. However, the valuation of property is most vital for the real estate industry. It depends on the type of artifact, the real estate sector offers to its clients. For this industry, infrastructure quality is the most important factor. All the contributing factors, analyses reveal this lesson. This truth is supported by many theories and analyses to lead today’s provider, meeting the original demand factor of the mass people. (shrink)

The philosophy of superdeterminism is based on a single scientific fact about the universe, namely that cause and effect in physics are not real. In 2020, accomplished Swedish theoretical physicist, Dr. Johan Hansson published a physics proof using Albert Einstein’s Theory of Special Relativity that our universe is superdeterministic meaning a predetermined static block universe without cause and effect in physics. The philosophy of superdeterminism is also supported by recent research in quantum physics experimentally verifying the reality of negative (...) time delay. The exiting of a photon peak prior to the entrance of the same peak into a medium is best explained by this apparent effect-like event occurring prior to and correlated with the cause-like event in a predetermined static block universe without cause and effect in physics. Our superdeterministic universe can be predetermined to exhibit everything a quantum field theory interpretation of negative time delay predicts without any reliance on unobservable quantum fields. (shrink)

The proposed sensor device for dragline operator is designed to enhance environmental safety by detecting and alerting operators to climate change impacts and potential hazards. Utilizing advanced sensor technology, this device continuously monitors weather conditions, such as heavy rain, strong winds, and extreme temperatures. When a hazard is detected, the device provides real-time alerts through an intuitive interface, enabling operators to take immediate action to mitigate risks. This proactive approach ensures operators are well-informed about their surroundings, significantly reducing (...) the risk of accidents and promoting a safer working environment. Additionally, the sensor device seamlessly integrates with existing dragline systems, ensuring compatibility and ease of adoption. By enhancing situational awareness and facilitating timely responses to environmental threats, this sensor device represents a critical advancement in the safety and efficiency of dragline operations in the mining and construction indus. (shrink)

Over the past few years we have seen an increasing number of legal proceedings related to inappropriately implemented technology. At the same time career paths have diverged from the foundation of statistics out to Data Scientist, Machine Learning and AI. All of these new branches being fundamentally branches of statistics and mathematics. This has meant that formal training has struggled to keep up with what is required in the plethora of new roles. Mathematics as a taught subject is still (...) based in decades old teaching specifications and has not been updated centrally as a curriculum to include new technologies, coding or ethics. This subject area is firmly split between ICT and Mathematics in secondary school, continuing on to be split between Computer Science and Mathematics at University. As we move forwards with technology we see these once seperate fields becoming increasingly intertwined. We propose that existing provision for concepts such as ethics and societal responsibility in analysis currently exist but have not been incorporated into the mainstream curriculum of School or University. This is partially due to the split between fields in an educational setting but also the speed with which education is able to keep up with Industry and its requirements. Principles and frameworks of socially responsible modelling beginning at school level means that ethics and real-life modelling are introduced much earlier than normal. Integrating these concepts with philosophical principles of society and politics ensures a suitable background for future modellers and users of technology to draw on. Modelling is currently undertaken in technical sciences at University but the Subject Benchmark Statements are not current (Subject Benchmark Statements describe the nature of study and the academic standards expected of graduates in specific subject areas. They show what graduates might reasonably be expected to know, do and understand at the end of their studies). Even in 2019 the UK did not yet have Benchmark Statements that discuss the learning to be done in Higher Education around AI and advanced Machine Learning. Where there is provision for AI and Data Science within degree courses ethics is not generally highlighted as a key concept. As such there can be a lack of focus on the teaching of modelling or ethics as specific skills. The skills required to use a basic statistical model, for example would not be sufficient to start from scratch and build an ethical model reflecting real-world scenarios with which to inform policy or organisational decision making. This is a skill in itself and includes such aspects as awareness of data quality, ethics, user implementation problems, context and an understanding of the environment that the model is being created in. As the field of analytics has progressed so quickly in the last decade modules such as those covering AI have simply been bolted on to Maths or Computing degrees rather than being fully detailed as key areas of study or driving new, more integrated courses of study. This paper posits that gaps at primary, secondary and tertiary educational levels need to be addressed. Implementing and integrating key concepts from school level is essential so that areas such as assumptions, caveats, quality assurance and answering the right questions with constructive challenge become a cultural fixture. This not only helps developers of technology but users of rapidly developing technology also. In addition, leadership and soft skills as part of this education will ensure that a cultural shift can take place and promote continuous improvement in analysis within organisations. The addition of key concepts throughout the educational system and the updating of potentially outdated curriculums is key to ensuring a functional society. A society where every citizen is a user of tech and those that develop it can be ethical and socially responsible in its development. (shrink)

This project centers on developing a geo-tagging and monitoring system for plantations within the catchment area of a hydro project. Designed for the forest department, it provides real-time insights into environmental conditions to support the sustainable management of forest resources. The system comprises an Arduino Uno microcontroller that integrates GPS, DHT11 (temperature and humidity sensor), and LoRa modules to track and communicate critical data, such as the geographic location, temperature, and humidity around each plantation site. Powered by (...) a 7.4V Li-ion battery and regulated through an LM7805 voltage regulator for consistent 5V output, the system is optimized for low power consumption to ensure longevity in remote settings. Using the LoRa module, the system transmits data over long distances to a central receiver, which could be stationed at a monitoring hub. This data transmission operates independently of internet connectivity, making it ideal for isolated forest areas where network coverage is limited or non-existent. Each plant or designated plantation area can be geo-tagged and monitored through data collected by the sensors and sent through LoRa communication. The data is then processed and can be visualized on a server or application interface, giving forest officials an overview of the environmental conditions in each monitored area. This innovative system offers a scalable solution for remote environmental monitoring, allowing for expansion with additional sensors or wider deployment across forest areas. Furthermore, it lays a foundation for enhanced forest management through features like environmental alerts and trend monitoring, potentially aiding in conservation efforts by ensuring optimal conditions for plantation growth. By leveraging IoT technology in an eco-friendly, power-efficient setup, this project presents a modern approach to sustainable forestry and plantation monitoring. (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. (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)

This paper introduces an AI-based IT training system that enhances the learning process through personalized, real-time support and automated assessment mechanisms. Leveraging AI technologies such as chatbots and NLP, the system addresses the challenges posed by traditional IT training platforms that offer standardized content and delayed feedback. By automating assessments and providing context-aware assistance, the system improves learner engagement, supports skill development, and ensures timely feedback, paving the way for a scalable, interactive, and effective learning environment.

The philosophy of superdeterminism is based on a single scientific fact about the universe, namely that cause and effect in physics are not real. In 2020, accomplished Swedish theoretical physicist, Dr. Johan Hansson published a physics proof using Albert Einstein’s Theory of Special Relativity that our universe is superdeterministic meaning a predetermined static block universe without cause and effect in physics. In the absence of cause and effect in physics, past, present and future events must all exist equally in (...) logical relationship. Our experience of time must be an illusion, because our experience of change in our world is not actually caused in time. Rather, the change we experience in our world is caused by the changing appearances in our static block universe in the static fourth dimension of time. We should think of time as a distance in the fourth dimension of time from our present position in the dimension of time to another position either towards or away from the beginning of the universe. Because physical reality is not actually caused to change in our static block universe, then static physical reality is actually timeless. But, because very good evidence supports the creation of timeless physical reality, then non-temporal causation of timeless physical reality outside of time is real. (shrink)

The Metaverse is poised to transform the digital landscape by providing immersive, interactive, and persistent 3D virtual worlds. For the Metaverse to achieve its full potential, it requires robust, scalable, and low-latency cloud infrastructure to deliver seamless and immersive experiences to users. This paper explores the design considerations for cloud infrastructure tailored to the Metaverse, including scalability, low-latency networking, and high-performance computing. We examine key challenges such as real-time rendering, massive concurrent user support, data storage, and security. (...) Furthermore, we analyze the use of edge computing, content delivery networks (CDNs), and specialized cloud services to address these challenges. Through case studies of current metaverse platforms and cloud providers, we highlight emerging solutions and technologies that can support the Metaverse’s growing demands. Finally, the paper presents future directions in cloud architecture that will facilitate the widespread adoption of the Metaverse, while ensuring an immersive, scalable, and secure environment for users. (shrink)

issues in real-time, allowing authorities to address concerns swiftly. Additionally, the integration of IoT-based monitoring, utilizing ESP32/ARDUINO devices, will enable the collection of real-time data, providing alerts on water supply status and system malfunctions. The technology stack comprises JavaScript, PHP, and PostgreSQL with PostGIS for spatial data management, emphasizing secure, scalable data handling. The platform is designed for continuous improvement through user feedback, ensuring it evolves to meet the dynamic needs of the Jal Jeevan Mission. Through (...) innovative technology and a user-centric approach, this project aims to support equitable access to safe water across rural areas. (shrink)

We present data and argument to show that in Tetris - a real-time interactive video game - certain cognitive and perceptual problems are more quickly, easily, and reliably solved by performing actions in the world rather than by performing computational actions in the head alone. We have found that some translations and rotations are best understood as using the world to improve cognition. These actions are not used to implement a plan, or to implement a reaction; they are (...) used to change the world in order to simplify the problem-solving task. Thus, we distinguish pragmatic actions ñ actions performed to bring one physically closer to a goal - from epistemic actions - actions performed to uncover information that is hidden or hard to compute mentally. To illustrate the need for epistemic actions, we first develop a standard information-processing model of Tetris-cognition, and show that it cannot explain performance data from human players of the game - even when we relax the assumption of fully sequential processing. Standard models disregard many actions taken by players because they appear unmotivated or superfluous. However, we describe many such actions that are actually taken by players that are far from superfluous, and that play valuable roles in improving human performance. We argue that traditional accounts are limited because they regard action as having a single function: to change the world. By recognizing a second function of action - an epistemic function - we can explain many of the actions that a traditional model cannot. Although, our argument is supported by numerous examples specifically from Tetris, we outline how the one category of epistemic action can be incorporated into theories of action more generally. (shrink)

. The increasing frequency of infectious disease outbreaks highlights the urgent need for efficient and accessible health management systems. Existing platforms often lack real-time updates, regional specificity, and user-focused features, leaving individuals underprepared to address emerging health threats. This research presents Pranasetu, a web-based health wellness improvement application designed to enhance public awareness and personal health insights through technology. The platform incorporates real-time outbreak updates, a dynamic map of India for disease tracking, location-based alerts, and an (...) interactive symptom-based questionnaire supported by a predictive disease model. Multilingual support ensures accessibility across diverse user groups, while chatbot assistance enhances user engagement and usability. Developed with React.js, the application focuses on a simplified, intuitive interface for seamless navigation. Methodologically, Pranasetu integrates real-time data aggregation, predictive analytics, and interactive design to deliver actionable health insights tailored to individual users and regions. The results demonstrate significant improvements in user awareness, accessibility, and responsiveness to health risks. Pranasetu's innovative features address critical gaps in existing systems by providing localized, real-time data and personalized health assessments, making it a valuable tool for improving public health outcomes. This research underscores the potential of leveraging modern web technologies to empower individuals and communities with actionable health information, fostering better preparedness and proactive health management. (shrink)

The internet of things is increasingly spreading into the domain of medical and social care. Internet-enabled devices for monitoring and managing the health and well-being of users outside of traditional medical institutions have rapidly become common tools to support healthcare. Health-related internet of things (H-IoT) technologies increasingly play a key role in health management, for purposes including disease prevention, real-time tele-monitoring of patient’s functions, testing of treatments, fitness and well-being monitoring, medication dispensation, and health research data collection. (...) H-IoT promises many benefits for health and healthcare. However, it also raises a host of ethical problems stemming from the inherent risks of Internet enabled devices, the sensitivity of health-related data, and their impact on the delivery of healthcare. This paper maps the main ethical problems that have been identified by the relevant literature and identifies key themes in the on-going debate on ethical problems concerning H-IoT. (shrink)

The current assessment of behaviors in the inventories to diagnose autism spectrum disorders (ASD) focus on observation and discrete categorizations. Behaviors require movements, yet measurements of physical movements are seldom included. Their inclusion however, could provide an objective characterization of behavior to help unveil interactions between the peripheral and the central nervous systems. Such interactions are critical for the development and maintenance of spontaneous autonomy, self-regulation and voluntary control. At present, current approaches cannot deal with the heterogeneous, dynamic and stochastic (...) nature of development. Accordingly, they leave no avenues for real-time or longitudinal assessments of change in a coping system continuously adapting and developing compensatory mechanisms. We offer a new unifying statistical framework to reveal re-afferent kinesthetic features of the individual with ASD. The new methodology is based on the non-stationary stochastic patterns of minute fluctuations (micro-movements) inherent to our natural actions. Such patterns of behavioral variability provide re-entrant sensory feedback contributing to the autonomous regulation and coordination of the motor output. From an early age, this feedback supports centrally driven volitional control and fluid, flexible transitions between intentional and spontaneous behaviors. We show that in ASD there is a disruption in the maturation of this form of proprioception. Despite this disturbance, each individual has unique adaptive compensatory capabilities that we can unveil and exploit to evoke faster and more accurate decisions. Measuring the kinesthetic re-afference in tandem with stimuli variations we can detect changes in their micro-movements indicative of a more predictive and reliable kinesthetic percept. Our methods address the heterogeneity of ASD with a personalized approach grounded in the inherent sensory-motor abilities that the individual has already developed. (shrink)

It is often suggested that we are equipped with a set of cognitive tools that help us to filter out unreliable testimony. But are these tools effective? I answer this question in two steps. Firstly, I argue that they are not real-time effective. The process of filtering, which takes place simultaneously with or right after language comprehension, does not prevent a particular hearer on a particular occasion from forming beliefs based on false testimony. Secondly, I argue that they (...) are long-term effective. Some hearers sometimes detect false testimony, which increases speakers’ incentives for honesty and stabilizes the practice of human communication in which deception is risky and costly. In short, filtering prevents us from forming a large number of beliefs based on false testimony, not by turning each of us into a high-functioning polygraph but by turning the social environment of human communication into one in which such polygraphs are not required. Finally, I argue that these considerations support strong anti-reductionism about testimonial entitlement. (shrink)

This volume examines two main questions: What is linguistics about? And how do the results of linguistic theorizing bear on inquiry in related fields, particularly in psychology? The book develops views that depart from received wisdom in both philosophy and linguistics. With regard to questions concerning the subject matter, methodological goals, and ontological commitments of formal syntactic theorizing, it argues that the cognitive conception adopted by most linguists and philosophers is not the only acceptable view, and that the arguments in (...) its favor collapse under scrutiny. Nevertheless, as the book shows, a detailed examination of the relevant psycholinguistic results and computational models does support the claim that the theoretical constructs of formal linguistics are operative in real-time language comprehension. These constructs fall into two categories: mental phrase markers and mental syntactic principles. Both are indeed psychologically real, but in importantly different ways. The book concludes by drawing attention to the importance of the often-elided distinction between personal and subpersonal psychological states and processes, as well as the logical character of dispositional and occurrent states. By clarifying these concepts, particularly by reference to up-and-running psychological and computational models, the book yields a richer and more satisfying perspective on the psychological reality of language. (shrink)

One key aspect in the design of autonomous vehicles is the implementation of real-time object detection which includes identifying and classifying obstacles present in the environment of the vehicle . The system will also be able to capture live or recorded videos and accurately identify and classify the objects within the video streams in very active situations requiring low turnaround times so that the vehicle is able to make the necessary safe driving critical choices . more recent state (...) of the art deep learning frameworks that tackle the object detection problem are Region Based Convolutional Nural network R-CNN and its variants fast R-cnn and faster R-cnn more approximately Fixes models’ Execution Speed. Another object detection system presented in the year 2015 is known as the YOLO system which is focused on fast predictive capabilities and for use in motion pictures. BDD100K dataset for video based deep driving understanding in 100k videos and 10 tasks which are designed to assess how far the algorithm gets in its objective of Image Recognition in driverless cars. The dataset has geographical, environment and weather variability, which is beneficial for training models that have a high likelihood of encountering drastic changes. (shrink)

Low back pain (LBP) carries a high risk of chronicization and disability, greatly impacting the overall demand for care and costs, and its treatment is at risk of scarce adherence. This work introduces a new scenario based on the use of a mobile health tool, the Dress-KINESIS, to support the traditional rehabilitation approach. The tool proposes targeted self-manageable exercise plans for improving pain and disability, but it also monitors their efficacy. Since LBP prevention is the key strategy, the tool (...) also collects real-patient syndromic information, shares valid educational messages and fosters self-determined motivation to exercise. Our analysis is based on a comparison of the performance of the traditional rehabilitation process for non-specific LBP patients and some different scenarios, designed by including the Dress-KINESIS’s support in the original process. The results of the simulations show that the integrated approach leads to a better capacity for taking on patients while maintaining the same physiotherapists’ effort and costs, and it decreases healthcare costs during the two years following LBP onset. These findings suggest that the healthcare system should shift the paradigm towards citizens’ participation and the digital support, with the aim of improving its efficiency and citizens’ quality of life. (shrink)

Digital Twins are conceptualised in the academic technical discourse as real-time realistic digital representations of physical entities. Originating from product engineering, the Digital Twin quickly advanced into other fields, including the life sciences and earth sciences. Digital Twins are seen by the tech sector as the new promising tool for efficiency and optimisation, while governmental agencies see it as a fruitful means for improving decision-making to meet sustainability goals. A striking example of the latter is the European Commission (...) who wishes to delegate a significant role to Digital Twins in addressing climate change and supporting Green Deal policy. As Digital Twins give rise to high expectations, ambitions, and are being entrusted important societal roles, it is crucial to critically reflect on the nature of Digital Twins. In this article, we therefore philosophically reflect on Digital Twins by critically analysing dominant conceptualisations, the assumptions underlying them, and their normative implications. We dissect the concept and argue that a Digital Twin does not merely fulfil the role of being a representation, but is in fact a steering technique used to direct a physical entity towards certain goals by means of multiple representations. Currently, this steering seems mainly fuelled by a reductionist approach focused on efficiency and optimisation. However, this is not the only direction from which a Digital Twin can be thought and, consequently, designed and deployed. We therefore set an agenda based on a critical understanding of Digital Twins that helps to draw out their beneficial potential, while addressing their potential issues. (shrink)

Human creativity generates novel ideas to solve real-world problems. This thereby grants us the power to transform the surrounding world and extend our human attributes beyond what is currently possible. Creative ideas are not just new and unexpected, but are also successful in providing solutions that are useful, efficient and valuable. Thus, creativity optimizes the use of available resources and increases wealth. The origin of human creativity, however, is poorly understood, and semantic measures that could predict the success of (...) generated ideas are currently unknown. Here, we analyze a dataset of design problem-solving conversations in real-world settings by using 49 semantic measures based on WordNet 3.1 and demonstrate that a divergence of semantic similarity, an increased information content, and a decreased polysemy predict the success of generated ideas. The first feedback from clients also enhances information content and leads to a divergence of successful ideas in creative problem solving. These results advance cognitive science by identifying real-world processes in human problem solving that are relevant to the success of produced solutions and provide tools for real-time monitoring of problem solving, student training and skill acquisition. A selected subset of information content (IC Sánchez–Batet) and semantic similarity (Lin/Sánchez–Batet) measures, which are both statistically powerful and computationally fast, could support the development of technologies for computer-assisted enhancements of human creativity or for the implementation of creativity in machines endowed with general artificial intelligence. (shrink)

This paper describes an AFOSR-supported basic research program that focuses on developing a new framework for combining hard with soft data in order to improve space situational awareness. The goal is to provide, in an automatic and near real-time fashion, a ranking of possible threats to blue assets (assets trying to be protected) from red assets (assets with hostile intentions). The approach is based on Conceptual Spaces models, which combine features from traditional associative and symbolic cognitive models. While (...) Conceptual Spaces are revolutionary, they lack an underlying mathematical framework. Several such frameworks have attempted to represent Conceptual Spaces, but by far the most robust is the model developed by Holender. His model utilizes integer linear programming in order to obtain an overall similarity value between observations and concepts that support the formation of hypotheses. This paper will describe a method for building Conceptual Spaces models for threats that utilizes ontologies as a means to provide a clear semantic foundation for this inferencing process; in particular threat ontologies and space domain ontologies are developed and employed in this approach. A space situational awareness use-case is presented involving a kinetic kill scenario and results are shown to assess the performance of this fusion-based inferencing framework. (shrink)

Microsoft Fabric represents a significant leap forward in analytics services, combining data management, analytics, and machine learning into a unified platform. This paper offers a detailed examination of Microsoft Fabric, covering its architecture, prominent features, advantages, and potential applications. Engineered to streamline data processes and foster collaboration among data experts, Microsoft Fabric supports real-time analytics, scalable data storage, advanced machine learning capabilities, and robust security protocols. Future developments for the platform include deeper AI integration, expanded data connectivity, enhanced (...) user experience, fortified security measures, sustainability initiatives, and collaborative tools. By harnessing Microsoft Fabric, organizations can gain comprehensive data insights and drive innovation effectively. (shrink)

Migraine is a complex neurological disorder characterized by recurrent moderate to severe headaches, accompanied by additional symptoms such as nausea, sensitivity to light and sound, and visual disturbances. Accurate and timely diagnosis of migraines is crucial for effective management and treatment. However, the diverse range of symptoms and overlapping characteristics with other headache disorders pose challenges in the diagnostic process. In this research, we propose the development of an expert system for migraine diagnosis using artificial intelligence and the CLIPS (C (...) Language Integrated Production System) framework. The expert system utilizes a rule-based inference engine to analyze patient-reported symptoms and provide reliable diagnoses or probability scores indicating the likelihood of migraine. The knowledge base of the expert system is designed based on expert knowledge obtained from medical professionals specializing in migraines. The collected knowledge is translated into a structured format suitable for the CLIPS inference engine, incorporating rules and facts to represent the diagnostic criteria and associated symptoms. The system prompts users to provide relevant information about their symptoms, medical history, and potential triggers. It applies the defined rules and facts to evaluate the likelihood of migraine and generate accurate diagnoses or probability scores. Preliminary evaluation results demonstrate the potential of the expert system as a valuable tool for diagnosing migraines. A dataset of anonymized patient records with confirmed migraine cases was used to test the system. The diagnoses generated by the expert system were compared against the known diagnoses, and a high level of accuracy was observed, with 90% of cases correctly diagnosed as migraines. These results highlight the effectiveness and reliability of the system in assisting medical professionals in the diagnosis of migraines. The proposed expert system offers several advantages for migraine diagnosis. It leverages the collective knowledge and expertise of experienced migraine specialists, providing a standardized and consistent approach to diagnosis. The system can handle large amounts of patient data and effectively analyse complex relationships between symptoms, risk factors, and diagnostic criteria. Furthermore, it offers real-time feedback and recommendations, supporting medical professionals in their clinical decision-making process. Future work involves refining the expert system based on feedback from medical experts, expanding the knowledge base to encompass a wider range of symptoms and risk factors, and conducting further evaluations to enhance its accuracy and applicability in clinical settings. The development of an expert system for migraine diagnosis has the potential to improve the diagnostic process, leading to more effective management and treatment strategies for individuals suffering from migraines. (shrink)

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. The scientific significance of this project lies in its potential (...) to contribute to biodiversity conservation efforts. By enabling efficient monitoring of bird populations, it provides valuable data that can inform policymaking and conservation planning. Moreover, the project highlights the intersection of technology and ecology, demonstrating how AI can address real-world environmental challenges. (shrink)

SMS spam has become a widespread issue, leading to significant inconvenience and security risks for users. Detecting and filtering out such spam messages is crucial for enhancing the user experience and ensuring privacy. TThe dataset used for training and testing the model consists of labeled SMS messages, which are processed using feature extraction techniques such as TF-IDF and word tokenization. Several machine learning algorithms, including Naive Bayes, Support Vector Machine (SVM), and Random Forest, are evaluated to determine the best-performing (...) model for spam detection. The system is trained and tested using a variety of performance metrics, including accuracy, precision, recall, and F1-score. The results show that machine learning models, particularly Naive Bayes, exhibit high accuracy in distinguishing spam from legitimate messages. This system can be implemented in real-time applications such as mobile phones and email services to improve spam detection and reduce unwanted content. By automating the spam filtering process, the system enhances the efficiency and reliability of communication systems. (shrink)

In critical medical emergencies, timely and accurate drug recommendation is essential for saving lives and reducing complications. This project proposes a Drug Recommendation System utilizing Machine Learning (ML) techniques to assist healthcare professionals in making quick and accurate drug selections based on patient symptoms, medical history, and emergency condition. The system integrates data from diverse medical databases, including symptoms, diseases, patient demographics, and prior medical records, to recommend the most appropriate drugs or treatments in real-time. The ML model (...) is trained on historical medical data, utilizing algorithms such as Decision Trees, Random Forests, and Neural Networks to predict optimal drug choices for different emergency situations. The system not only considers the immediate medical needs but also factors in contraindications, potential drug interactions, and patient-specific conditions, ensuring safer drug administration. This approach aims to reduce human errors in emergency drug prescriptions, enhance the speed of medical response, and improve the overall quality of care during emergencies. By leveraging data-driven insights, the system supports healthcare professionals in making well-informed decisions, even in high-pressure situations. Ultimately, the Drug Recommendation System serves as a valuable tool in healthcare, enhancing emergency medical care and optimizing treatment outcomes. (shrink)

A case study in historical cognitive science, this paper addresses two claims made by radical proponents of new dynamical approaches. It queries their historical narrative, which sees embodied, situated cognition as correcting an individualist, atemporal framework originating in Descartes. In fact, new Descartes scholarship shows that 17th-century animal spirits neurophysiology realized a recognizably distributed model of memory; explicit representations are patterns of spirit flow, and memory traces are changes left by experience in connections between brain pores. This historical sketch supports (...) the second dynamicist claim, that connectionists' stress on the cognitive importance of pattern-recreation needs supplementing by dynamicists' real-time focus and attention to the active roles of body and environment. Animal spirits theory exhibits just the 'continuous reciprocal causation' between brain, body, and environment which Andy Clark sees as dynamicism's central contribution, and allows for the embedding of brains in culture as well as the physical world. (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)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), (...) the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi-cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)