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

Air pollution is a significant environmental concern that affects human health, ecosystems, and climate change. Effective monitoring and management of outdoor air quality are crucial for mitigating its adverse effects. This paper presents an advanced approach to outdoor pollution measurement utilizing Internet of Things (IoT) technology, combined with optimization techniques to enhance system efficiency and data accuracy. The proposed framework integrates a network of IoT sensors that continuously monitor various air pollutants, such as particulate matter (PM), carbon monoxide (CO), (...) sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃), across different geographic locations. 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. (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)

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)

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)

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)

The rapid advancement of intelligent computing has revolutionized the field of economic forecasting, providing unprecedented capabilities for developing, implementing, and analyzing advanced prediction models. This paper explores the comprehensive process of harnessing intelligent computing for economic forecasting, emphasizing the critical stages of model development, integration, and evaluation. Initially, it discusses data collection and preprocessing techniques essential for building robust models, followed by the selection of suitable statistical, machine learning, and deep learning algorithms. The paper then outlines the practical aspects (...) of model implementation, including real-time data integration, deployment strategies, and scalability considerations. Further, it delves into the continuous monitoring and maintenance required to ensure model accuracy and reliability over time. The analysis section evaluates model performance using various accuracy metrics, scenario analyses, and visualization techniques to provide actionable insights. Through case studies across the financial sector, government policy, and business planning, the paper illustrates the transformative impact of intelligent computing on economic forecasting. The findings underscore the potential of advanced prediction models to enhance decision-making, anticipate economic trends, and respond proactively to dynamic economic environments. (shrink)

The primary objective of this research is to enhance the precision and speed of stock handling while minimizing human intervention and error. Our design incorporates state-of-the-art sensors, real-time tracking systems, and autonomous robots programmed with advanced algorithms for object identification, gripping, and movement. We propose a systematic workflow for automating the storage and retrieval process, starting from the identification of the stock to its precise placement and retrieval within the storage facility. The design also addresses potential challenges such (...) as robot mobility, collision avoidance, and space optimization. Performance metrics, including accuracy, time efficiency, and system scalability, are measured using simulation-based experiments in a controlled environment. The results show significant improvements in operational efficiency compared to traditional stock management approaches. This integration paves the way for future advancements in fully automated warehouses, reducing the need for human labor and increasing reliability. Finally, we discuss potential enhancements, including AI-based decision-making algorithms, multi-robot collaboration, and integration with Internet of Things (IoT) for real-time data analysis and continuous system improvement. (shrink)

We propose a systematic workflow for automating the storage and retrieval process, starting from the identification of the stock to its precise placement and retrieval within the storage facility. The design also addresses potential challenges such as robot mobility, collision avoidance, and space optimization. Performance metrics, including accuracy, time efficiency, and system scalability, are measured using simulation-based experiments in a controlled environment. The results show significant improvements in operational efficiency compared to traditional stock management approaches. This integration paves the (...) way for future advancements in fully automated warehouses, reducing the need for human labor and increasing reliability. Finally, we discuss potential enhancements, including AI-based decision-making algorithms, multi-robot collaboration, and integration with Internet of Things (IoT) for real-time data analysis and continuous system improvement. (shrink)

The results show significant improvements in operational efficiency compared to traditional stock management approaches. This integration paves the way for future advancements in fully automated warehouses, reducing the need for human labor and increasing reliability. Finally, we discuss potential enhancements, including AI-based decision-making algorithms, multi-robot collaboration, and integration with Internet of Things (IoT) for real-time data analysis and continuous system improvement. Key words: Robotic aut.

The design also addresses potential challenges such as robot mobility, collision avoidance, and space optimization. Performance metrics, including accuracy, time efficiency, and system scalability, are measured using simulation-based experiments in a controlled environment. The results show significant improvements in operational efficiency compared to traditional stock management approaches. This integration paves the way for future advancements in fully automated warehouses, reducing the need for human labor and increasing reliability. Finally, we discuss potential enhancements, including AI-based decision-making algorithms, multi-robot collaboration, and (...) integration with Internet of Things (IoT) for real-time data analysis and continuous system improvement. (shrink)

Social media has transformedevery communication and posting modality into sharing and expression of opinions. Everyday, an enormous amount of data shared on Twitter, Facebook, Reddit, and Instagram portrays varied emotions that range between positive endorsements and negative criticisms. This is the very aspect of sentiments that make up the core basis through which marketing strategies are developed, along with improvement of customer services and the promotion of brand loyalty for businesses and organizations. Traditional approaches to sentiment analysis will (...) struggle with dynamic and voluminous data from social media, which makes it require very high tools that would be able to analyze them in real-time. (shrink)

The research outlines a workflow that incorporates data collection, preprocessing, model training, and optimization. Real-world datasets from retail and e-commerce sectors are utilized to validate the proposed methodology, showcasing substantial improvements in model performance. The results indicate that optimized models not only provide better predictions of consumer behaviour but also enhance customer segmentation and targeting strategies. The study concludes with recommendations for future research, including the exploration of hybrid optimization techniques and the application of these methods in (...) class='Hi'>real-time analytics. (shrink)

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm (...) and Random Forest over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model. (shrink)

Accelerated computing leverages specialized hardware and software techniques to optimize the performance of computationally intensive tasks, offering significant speed-ups in scientific, engineering, and data-driven fields. This paper presents a comprehensive study examining the role of accelerated computing in enhancing processing capabilities and reducing execution times in diverse applications. Using a custom-designed experimental framework, we evaluated different methodologies for parallelization, GPU acceleration, and CPU-GPU coordination. The aim was to assess how various factors, such as data size, computational complexity, and (...) task concurrency, impact processing efficiency. (shrink)

: One of the most active study fields in natural language processing, web mining, and text mining is sentiment analysis. Big data is an important research component in education that is used to advance the value of education by watching students' performance and understanding their learning habits. Real-time student feedback will enable teachers and students to understand teaching and learning challenges in the most user-friendly manner for students. By linking learning analytics to grounded theory, the proposed (...) Deep Convolutional Neural Network (DCNN) analyses students' sentiments and emotions through feedback using a Lexicon-based emotional analysis approach. The sentiment analysis approach is a computer procedure that identifies and classifies subjective information from the source material as good, negative, or neutral. (shrink)

This paper explores the application of AI-driven HR analytics in shaping workforce agility, focusing on how real-time data collection, analysis, and modeling foster an adaptable workforce. It highlights the role of predictive analytics in forecasting workforce needs, identifying skill gaps, and optimizing talent deployment. Additionally, the paper discusses how AI enhances strategic decision-making by providing precise metrics and insights into employee behavior, productivity, and satisfaction. The integration of AI into HR systems ultimately shifts HR from a (...) traditionally reactive to a highly proactive, data-driven function. (shrink)

This study presents a neural network-based model for predicting smoke potential in a specific area using a Kaggle-derived dataset with 15 environmental features and 62,631 samples. Our five-layer neural network achieved an accuracy of 89.14% and an average error of 0.000715, demonstrating its effectiveness. Key influential features, including temperature, humidity, crude ethanol, pressure, NC1.0, NC2.5, SCNT, and PM2.5, were identified, providing insights into smoke occurrence. This research aids in proactive smoke mitigation and public health protection. The model's accuracy and feature (...) analysis empower decision-makers, with potential applications in real-time smoke event monitoring and preparedness strategies. This work contributes to the field of air quality forecasting and environmental stewardship, offering a data-driven approach to address smoke-related challenges and enhance community well-being. (shrink)

This paper explores the application of AI-driven HR analytics in shaping workforce agility, focusing on how real-time data collection, analysis, and modeling foster an adaptable workforce. It highlights the role of predictive analytics in forecasting workforce needs, identifying skill gaps, and optimizing talent deployment. Additionally, the paper discusses how AI enhances strategic decision-making by providing precise metrics and insights into employee behavior, productivity, and satisfaction. The integration of AI into HR systems ultimately shifts HR from a (...) traditionally reactive to a highly proactive, data-driven function. (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 "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)

For centuries, fiction—particularly fables—has seamlessly combined storytelling, moral lessons, and societal reflections to engage readers on both emotional and intellectual levels. Despite extensive research on the benefits of reading and the emotional responses it evokes, a critical gap remains in understanding what drives readers to form deep emotional connections with specific works. This study seeks to identify the characteristics of a book that foster such connections. Using Bayesian Mindsponge Framework analytics, we analyzed a dataset of 129 Amazon reviews of Wild (...) Wise Weird, a collection of 42 fables that intertwine traditional storytelling with contemporary sensibilities, offering life lessons, humor, and social commentary. Of these reviews, approximately 66% expressed love for the book. The findings reveal that readers who describe the book as unique, whimsical, quirky, or innovative are more likely to express affection for it. Similarly, readers who are drawn to the book’s illustrations and characters tend to form stronger emotional connections. The cultural richness and representativeness of the book also emerged as significant factors in fostering readers’ love. The book successfully captures and conveys the depth, charm, and moral essence of Vietnamese culture to a global audience. Readers have expressed heartfelt gratitude after engaging with the book, underscoring its success in fostering meaningful emotional connections. Thus, insights from this analysis offer actionable recommendations for stakeholders in the literary ecosystem, from authors and publishers to marketers, to enhance readers’ emotional attachment and commitment to books. (shrink)

The relationship between philosophy and science in contemporary times is closer than ever. From the methodology perspective, scientific and philosophical research has a clear sequential relationship. It is highlighted in the following aspects: 1. the methodology of scientific research, including theoretical assumptions and data modeling, parallels with apparent similarities in conceptual analysis and logical deduction in philosophy;2. consistency of analytical argumentation methods in scientific research and philosophical research;3. naturalism is currently a research approach that both scientific and philosophical (...) research adopt. There has been a significant dispute over the relationship between philosophy and science among contemporary philosophers. This dispute focuses on the impact of scientific development on philosophy research. Nowadays, no one would deny the existence of this impact, but there is no consensus on the impact's nature, scope, and role. Wittgenstein's conception of certainty can provide valuable insights to understand the relationship between philosophy and science. In addition, we conclude the pragmatic turn in cognitive science because we misinterpret the cognitive scientists' emphasis on action as a practical claim and interpret the cognitive scientists' views with pragmatism. The theoretical analysis and research suggestions philosophers provide are becoming essential focuses in contemporary artificial intelligence research, including the interpretability of computer technology, the ethical norms of artificial intelligence technology, and the possible conception of human beings in the future. The interdisciplinary study of science and philosophy will achieve more significant development. In sum, understanding the nature of philosophy can be given a new explanation from a future perspective. The future outlook here refers to the starting point from possible world scenarios that can be conceived and traced back to all the facts happening in the present world, thereby giving an appropriate explanation to the real world. (shrink)

The relationship between philosophy and science in contemporary times is closer than ever. From the methodology perspective, scientific and philosophical research has a clear sequential relationship. It is highlighted in the following aspects: 1. the methodology of scientific research, including theoretical assumptions and data modeling, parallels with apparent similarities in conceptual analysis and logical deduction in philosophy;2. consistency of analytical argumentation methods in scientific research and philosophical research;3. naturalism is currently a research approach that both scientific and philosophical (...) research adopt. There has been a significant dispute over the relationship between philosophy and science among contemporary philosophers. This dispute focuses on the impact of scientific development on philosophy research. Nowadays, no one would deny the existence of this impact, but there is no consensus on the impact's nature, scope, and role. Wittgenstein's conception of certainty can provide valuable insights to understand the relationship between philosophy and science. In addition, we conclude the pragmatic turn in cognitive science because we misinterpret the cognitive scientists' emphasis on action as a practical claim and interpret the cognitive scientists' views with pragmatism. The theoretical analysis and research suggestions philosophers provide are becoming essential focuses in contemporary artificial intelligence research, including the interpretability of computer technology, the ethical norms of artificial intelligence technology, and the possible conception of human beings in the future. The interdisciplinary study of science and philosophy will achieve more significant development. In sum, understanding the nature of philosophy can be given a new explanation from a future perspective. The future outlook here refers to the starting point from possible world scenarios that can be conceived and traced back to all the facts happening in the present world, thereby giving an appropriate explanation to the real world. (shrink)

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

The study outlines the technical challenges encountered during migration, including data compatibility issues, schema conversion, and security compliance, as well as strategies to mitigate these obstacles. Testing and validation techniques are applied throughout the migration, highlighting essential checkpoints to confirm data accuracy and optimal performance in the Snowflake environment. Postmigration performance metrics are evaluated to illustrate improvements in query execution, scalability, and overall system efficiency compared to the legacy system. The results underscore the advantages of Snowflake’s architecture in (...) handling high-volume, concurrent queries without compromising speed or accuracy. (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)

Purpose. The purpose of this study is to explore vectorization as an engineering technique to improve the performance and readability of Python code, particularly in data processing tasks. We aim to demonstrate the benefits of vectorization through practical examples involving the handling of missing data. Design / Method / Approach. To achieve the research goals, we performed a comparative analysis between loop-based and vectorized implementations. Specifically, two versions of a function were developed to identify columns containing missing (...) values within a dataset. These implementations were tested on two real-world datasets. We compared execution time and code readability. Findings. The findings showed that vectorization resulted in substantial performance improvements, reducing execution time by hundreds of times compared to traditional loop-based methods. Additionally, the vectorized code was more compact, leading to greater readability and ease of maintenance. Theoretical Implications. Vectorization provides a higher level of abstraction for performing operations on data structures. This allows developers to focus on algorithmic logic rather than managing iterative control structures, contributing to broader discussions on optimizing computational efficiency in Python. Practical Implications. For data engineers and analysts, vectorization represents a highly effective solution for optimizing Python code. It significantly accelerates data-intensive tasks, such as missing data imputation, data analysis, and machine learning, making it an essential tool for enhancing productivity in data-driven environments. Originality / Value. This study presents a practical approach to optimizing Python code through vectorization. It is valuable for professionals seeking to improve efficiency in their workflows. Research Limitations / Future Research. The limitation of this research lies in its focus on a single problem – missing data imputation. Future research should expand the scope to other computational areas, such as image processing and simulation modeling, or examine the use of vectorization alongside Just-In-Time (JIT) compilation using tools like Numba to further boost Python's performance. Paper Type. Practitioner Paper. (shrink)

The main objective of this project is to improve an existing real-time vehicle tracking system by improving its control circuit with GPS and a microcontroller. It uses GPS technology to track the vehicle location; the position data of the vehicle is updated in a real-time manner so that monitoring is done effectively. also features remind that can be set to alert when entering or leaving a chosen virtual perimeter of the car. The purpose of the (...) project is to offer the consumer an accurate and inexpensive solution of vehicle tracking that allows to determine location, perform geofencing, and be used for security or planning, both for commercial fleets and individuals. (shrink)

There is broad agreement among social researchers and social ontologists that the project of dividing humans into social kinds should be guided by at least two methodological commitments. First, a commitment to what best serves moral and political interests, and second, a commitment to describing accurately the causal structures of social reality. However, researchers have not sufficiently analyzed how these two commitments interact and constrain one another. In the absence of that analysis, several confusions have set in, threatening to (...) undermine shared goals for the responsible modeling of social kinds of humans. This essay first explains the source and substance of these confusions. Then, by distinguishing different value-laden investigative questions into the classification of social kinds of humans, it sets out specific relations of dependence and constraint between empirically-driven investigations and value-driven investigations into social kinds of humans. The result is a more detailed and fruitful framework for thinking about the classification of social kinds that respects both normative interests and mind-independent causal regularities. (shrink)

Abstract: The Spotify Status Dataset is a valuable resource that provides real-time insights into the operational status and performance of Spotify, a popular music streaming platform. This dataset contains a wide array of information related to server uptime, user activity, service disruptions, and more, serving as a critical tool for both Spotify's internal monitoring and the broader data analysis community. As digital services like Spotify continue to play a central role in music consumption, understanding the platform's (...) status becomes crucial for ensuring a seamless user experience. This dataset offers numerous research and analysis opportunities, whether you're interested in exploring service availability trends or investigating user behavior patterns during service disruptions. This study highlights the significance of the Spotify Status Dataset, its potential applications, and the valuable insights it can offer to various stakeholders, from Spotify engineers working to enhance service reliability to researchers studying the effects of downtime on user engagement. It invites readers on a journey through this dataset to uncover the secrets of Spotify's operational status and its impact on music enthusiasts worldwide. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...) behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural Language Processing (NLP), Optimization Techniques. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...) behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. T. (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)

We present the scheduling algorithms of Distributed Real-time Systems in this analysis paper. A real-time system is a type of software where, under a given time period, we have to execute the process with a specific result. Whereas, in the general context, there is no fixed deadline. In reality, scheduling implies performing the operation according to its characteristics and scheduling is done on different processors, one is Uni-processor and one is Multiprocessor and can be (...) performed on the distributed machine as well. Make span length, processor utilization, cache performance, power consumption, task balance, and scheduling penalties are the criteria typically used to evaluate the various scheduling algorithms. The scheduling of real-time assignments consists of 2 sub-problems in the distributed and multiprocessor network: CPU role distribution and scheduling tasks on single processors. The allocation of tasks may be static or interactive. For successful task execution, we'll discuss numerous task allocation algorithms. (shrink)

Many experienced runners consider the use of wearable devices an important element of the training process. A key techno-utopic promise of wearables lies in the use of proprietary algorithms to identify training load errors in real-time and alert users to risks of running-related injuries. Such real-time ‘knowing’ is claimed to obviate the need for athletes’ subjective judgements by telling runners how they have deviated from a desired or optimal training load or intensity. This realist-contoured perspective is, (...) however, at odds with sociological research indicating that users of wearables engage in active ‘data sense-making’ that is highly contextualised. To investigate how athletes use (or not) algorithmic analysis to understand, make sense of, and improve their performance in real-time, we undertook qualitative interviews with distance runners to explore lived experiences of running with wearables. The runners described how they actively interpreted data from wearables, drawing on their own experience, ‘somatic knowledge’, and embodied ways of knowing. This allowed them to assess the relevance and usefulness of data in relation to their own goals, intentions, and feelings. Our findings challenge the techno-utopic promises of real-time and predictive analytics. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...) behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. (shrink)

Each wagon is equipped with sensors that continuously capture and transmit weight data to the central server via Node MCUs, enabling real-time analysis through the Blynk app. By monitoring the load conditions, the system ensures wagons remain within safe weight limits, preventing overloading, which can cause damage and safety hazards, or under-loading, which reduces operational efficiency. A key feature of the system is its use of machine learning algorithms to detect patterns and anomalies related to load (...) distribution. When potential risks are identified, the system generates automated alerts, prompting interventions such as load adjustments, wagon rerouting, or even halting operations when necessary. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this (...) elusive feat. Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

Road abnormalities, such as speed breakers and potholes, pose significant risks to traffic safety, contributing to accidents, vehicle damage, and driver discomfort. Traditional methods for detecting these issues are primarily manual and reactive, leading to inefficiencies in maintenance and increased hazards for road users. To address this urgent need, the proposed project aims to develop a computer vision-based system for the automatic detection of these road anomalies. By utilizing techniques such as image processing and analysis through OpenCV, the system (...) will allow users to input either images or videos for real time analysis and evaluation. This capability not only automates the detection process but also enhances the speed and accuracy of identifying road conditions. The system will continuously improve its performance as it processes more data, ensuring that it remains effective in various environments. This proactive approach aims to facilitate timely maintenance interventions, thereby enhancing overall road safety and user comfort. The successful implementation of this project could serve as a model for future developments in automated road condition assessment, ultimately contributing to smarter transportation systems and safer roadways for all users. (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)

Reciprocal Ethics is a novel ethical framework rooted in praxeology, the study of purposeful action. It represents an entirely new paradigm in moral philosophy, placing interaction at the core of universal ethics. Traditional ethical theories often divorce thought from action. Reciprocal Ethics contends that they are two aspects of the same phenomenon in the human experience, removing the traditional boundary between theoretical and practical ethics. The system categorizes all social interaction as either “self-directed” or “other-directed”, and by introducing the concept (...) of "normative signaling" it also exerts legitimate prescriptive power. The theory is a self-consistent, purely descriptive approach to ethics, that suggests that one's actions serve as semiotic signs that signal one's ethical stance in social interaction. Hence actions do not only define individual moral profiles, but also the moral ecosystem within which one actually operates. The theory operates in real time, adapting to the complexity of reality by taking into account uncertainty, incomplete data and social asymmetries. Reconciling elements of consequentialism and deontological theories, Reciprocal Ethics delivers a streamlined, yet comprehensive ethical system for social interaction, offering tangible insights for navigating complex moral dilemmas. By synthesizing descriptive metaethics and normative moral philosophy into one comprehensive formal theory, a bold assertion arises: Reciprocal Ethics becomes the Formal Science of Ethics. (shrink)

In the real world there are people who enter the homes and steal everything they find valuable. In the virtual world there are individuals who penetrate computer systems and "steal" all your valuable data. Just as in the real world, there are uninvited guests and people feel happy when they steal or destroy someone else's property, the computer world could not be deprived of this unfortunate phenomenon. It is truly detestable the perfidy of these attacks. For if (...) it can be observed immediately the apparent lack of box jewelry, penetration of an accounting server can be detected after a few months when all clients have given up the company services because of the stolen data came to competition and have helped it to make best deals. Cybercrime is a phenomenon of our time, often reflected in the media. Forensic investigation of computer systems has a number of features that differentiate it fundamentally from other types of investigations. The computer itself is the main source of information for the investigator. CONTENTS: Computing systems and storage media - Computing devices - - Peripheral devices - - External drives for media storage - Typology of data stored on specific supports – File systems - - Program that allows working with ” inactive” space - Information that can be obtained from the computing system environment Computer networks - Copper wire in computer networks - Optical fibers - Wireless LAN - Internet and Intranet Software and services - Client/server architecture - Protocols and Standards - Internet Services - - e-Mail - - - Spam - - HTTP - - Web address - URL - - Web browsers - - - Browser cookies - - Working with web pages - - - Choosing your favorite web pages - - - Keeping track of visited web pages - - - Saving web pages - - Proxy servers - - Privacy on the Internet - FTP - Instant Messaging - Peer-to-peer networks Vulnerabilities - The first attacks on the Internet - Cybercrime - - Typologies of cyber attackers - - - Classification of cyber attackers according to their skills and objectives - Classification of risks and incidents in cyberworld - - Classification as a list of terms - - List of categories - - Categories of results - - Empirical lists - Events, attacks and incidents - Online security events, actions, and targets - - Actions - - Targets - Attacks - - Tools - - Vulnerabilities - - Unauthorized results Cybercrime laws - The concept of "cybercrime" Investigations - Computer forensic investigations - Digital evidence - Digital sampling during investigations - The suspect - Witnesses in cybercrime - Transporting of samples in laboratory - Analysis of samples - Preparing team members - Computer tools Convention on Cybercrime - Preamble - Chapter I – Use of terms - Chapter II – Measures to be taken at the national level - - Section 1 – Substantive criminal law - - - Title 1 – Offences against the confidentiality, integrity and availability of computer data and systems - - - Title 2 – Computer-related offences - - - Title 3 – Content-related offences - - - Title 4 – Offences related to infringements of copyright and related rights - - - Title 5 – Ancillary liability and sanctions - - Section 2 – Procedural law - - - Title 1 – Common provisions - - - Title 2 – Expedited preservation of stored computer data - - - Title 3 – Production order - - - Title 4 – Search and seizure of stored computer data - - - Title 5 – Real-time collection of computer data - - Section 3 – Jurisdiction - Chapter III – International co-operation - - Section 1 – General principles - - - Title 1 – General principles relating to international co-operation - - - Title 2 – Principles relating to extradition - - - Title 3 – General principles relating to mutual assistance - - - Title 4 – Procedures pertaining to mutual assistance requests in the absence of applicable international agreements - - Section 2 – Specific provisions - - - Title 1 – Mutual assistance regarding provisional measures - - - Title 2 – Mutual assistance regarding investigative powers - - - Title 3 – 24/7 Network - Chapter IV – Final provisions Recommendation No. R (95) 13 - Appendix to Recommendation No. R (95) 13 - - I. Search and seizure - - II. Technical surveillance - - III. Obligations to co-operate with the investigating authorities - - IV. Electronic evidence - - V. Use of encryption - - VI. Research, statistics and training - - VII. International co-operation Rules for obtaining digital evidence by police officers Standards in the field of digital forensics Principles in digital evidence Procedures model for the forensic examination - Hard disk examination Code of Ethics Sources and references About - Nicolae Sfetcu - - By the same author - - Contact Publishing House - MultiMedia Publishing . (shrink)

The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time. The classification system was evaluated using benchmark phishing datasets, and the results demonstrated a significant improvement in detection (...) accuracy and reduced false positives. (shrink)

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

In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more Comprehensive investigation, we develop an approach for revealing, that is, uncovering, (...) Lacanian register relationships. Objectives of this work include the wide and general application of our methodology. This methodology is strongly based on the “letting the data speak” Correspondence Analysis analytics Platform of Jean-Paul Benzécri, that is also the geometric data analysis, both qualitative and quantitative analytics, developed by Pierre Bourdieu. (shrink)

Introduction: There is a general agreement among previous studies that gender, merit and catchment area criteria allows for access to university education, but the pattern of these variables over the years has not been proven in these studies. Purpose: This study used a times series approach to evaluate the enrolment patterns in federally owned universities in South-South Zone, Nigeria, based on the gender, merit and catchment area criteria. Methodology: The descriptive survey design was adopted for this study. A purposive sampling (...) technique was adopted to choose three universities based on the generation of their establishment. The evaluation covered from 2010 to 2019 using stationary time-bound data collected from the academic planning units of the institutions through a checklist. Future enrolment forecasts were made to 2031 using the moving average forecasting technique. Results: A gender-based difference in enrolment, in favour of males was recorded between 2010 and 2019; there were notable fluctuations in the enrolment pattern based on the merit criterion from 2010 to 2019; the enrolment pattern in terms of the catchment area criterion was consistent from 2010 – 2019. In terms of the projection, it was predicted that enrolment at the aggregate level would rise continuously across federal universities, but not at the institutional level. more women than men are expected to be enrolled in academic programmes at federal institutions, while at the institutional level, gender-based enrolment can fluctuate. Recommendations/Classroom Implications: Based on these findings, implications are discussed for future enrolment decisions at both the institutional and national levels. (shrink)

The increasing demand for reliable power sources has made diesel generators essential in various industries. However, traditional monitoring methods often rely on manual inspections, resulting in inefficiencies, unplanned downtimes, and higher operational costs. This project proposes a digital monitoring system for diesel generators that leverages advanced sensor technologies and real-time data analytics to overcome these challenges. The system will utilize Internet of Things (IoT) devices to collect key operational parameters, such as fuel levels, temperature, vibration, and performance (...) metrics, enabling comprehensive monitoring of generator health. By integrating machine learning algorithms, the system will analyze historical and real-time data to predict potential failures and optimize maintenance schedules, shifting from reactive to proactive management. A user-friendly interface will allow operators to visualize generator performance and receive instant alerts for detected anomalies, enhancing decision- making processes, reducing downtime, and lowering maintenance costs—ultimately increasing the reliability and efficiency of power supply. Additionally, the system facilitates remote monitoring, providing continuous oversight regardless of location. This project not only aims to enhance the operational efficiency of diesel generators but also lays the groundwork for smarter energy management solutions in the future. The successful implementation of this digital monitoring system could serve as a model for similar applications across various sectors, contributing to sustainability and operational excellence in energy management. Through this innovative approach, we aim to ensure diesel generators operate at optimal performance levels while minimizing environmental impact. Keywords. Diesel Generators, Digital Monitoring, IoT Devices, Real-time Data Analytics, Machine Learning. (shrink)

This study aimed to identify real-life experts working for a port authority and lay people (students) who played The Sustainable Port, a serious game aiming to simulate the dynamics occurring in a port area. To achieve this goal, we analyzed eye gaze data collected noninvasively using low-grade webcams from 28 participants working for the port authority of the Port of Rotterdam and 66 students. Such data were used for a classification task implemented using a MiniRocket classifier, an (...) algorithm used for time-series classification. The classifier reached an F1 score of 0.75 (SD = 0.07), a PR AUC of 0.73 (SD = 0.14), and an ROC AUC of 0.75 (SD = 0.15) providing evidence that it is possible to identify real-life experts about maritime port management using data that can be obtained from a webcam. We speculate that the gaze direction used to train the MiniRocket may contain relevant information about the cognitive processes and decisions occurring throughout the gameplay. We suggest that the methods here presented not only can be used to detect experts playing simulations, such as serious games, but also to identify experts tackling screen-presented tasks. (shrink)

If science disputes the validity or authenticity of religious knowledge it is because both the scientist and the rational man assume that every object of knowledge there is or can be exists as a material percept in time and space. If we assume that knowledge of material objects is definite knowledge – an assumption itself suspect considering that the latest WMAP data indicates that 95.4% of the total matter in our universe is dark matter and dark energy – (...) all scientific knowledge (confined as it is to knowledge of 4.6% of the visible universe) is definite knowledge; but because it’s knowledge is confined to a miniscule fraction of all knowledge there is or can be had in our material universe, it can scarcely be said – as Sir Bertrand Russell claims – that all definite knowledge is scientific knowledge. There are other reasons why all definite knowledge is not in the domain of science. Parts of the universe are; the universe as a whole isn’t. The sentient body is; the Self that is clothed by that body isn’t. Analysis of both these wholes reveals the possibility of the existence of a third entity that is beyond scientific knowledge. This humanity calls God. A detailed investigation not conducted but outlined here reveals that (a) Russell’s claim that science holds a monopoly of definite knowledge is false: he probably meant that science has a monopoly of objectively verifiable knowledge; (b) as objective verifiability is contingent upon objectively real existence, science’s monopoly is over entities that are objectively real; (c) all objects of knowledge are not material objects in time and space: there are objects that, because they are not percepts, are not objectively verifiable. There is nothing indefinite about knowledge of such entities. There is nothing subjective about the knowledge of such entities either: so it is not open to science to dismiss knowledge of such entities as subjective, arising from the state of mind of the observer. Space and time are aspects of objective reality and not of subjective reality. The second part of this paper briefly examines the nature of dimensions. Space, time and Self consciousness are three types of dimensions considered. The third part of this paper speaks of a concept called immutable wholes. This is a logical culmination of the paper because dimensions are the state-giving norms of the wholes that these dimensions define and characterize. Spiritual seekers make these entities (sub specie aeternitatis) objects of their realization. Such entities as the Universe as a Whole, the Self, and God belong to this class of existents. If Universe as a Whole {not parts of it (study of which is in the domain of science), no matter how large those parts are, but the whole. Only a true seeker can tell science why the whole ≠ sum of all its parts – but that would be the subject of another paper}. (shrink)