given the increasing threats to data confidentiality and integrity. The project titled "Anonymization and Revocation of User Identity in CPABE-Based Ring Networks" addresses these concerns by integrating CiphertextPolicy Attribute-Based Encryption (CPABE) into a ring network environment. This novel approach effectively hides user identities during communications, mitigating risks of unauthorized data access and preventing potential misuse of sensitive information. A key feature of this project is the implementation of a secure revocation mechanism that ensures once a user's permissions are revoked, they (...) no longer have access to protected data, thereby strengthening the overall security framework. By combining CPABE’s robust access control with the anonymity provided by ring signatures, the project establishes a solution that guarantees both privacy preservation and secure data access control in networked environments. The successful integration of anonymization and secure revocation into CPABE-based systems represents a significant contribution to the field of secure communication networks, offering a reliable method for safeguarding user identities and maintaining data integrity. This approach can greatly enhance the security and privacy aspects of digital communications in a world where privacy breaches are increasingly prevalent. (shrink)

Abstract. The increasing adoption of IPv6 has introduced new security concerns, including the threat of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks leads service disruptions, financial losses, and reputational damage. A Denial of Service (DoS) attack is when a single computer or device sends a large amount of traffic to a network or website. Whereas A Distributed Denial of Service (DDoS) launched from multiple devices or computers, making it harder to block and more powerful. (...) We detect these attacks using the Wireshark ,Kali Linux and Bashtop. (shrink)

With the growing sophistication and frequency of cyberattacks, there is a critical need for effective systems that can detect and prevent breaches in real time. The AI/ML-based Network Intrusion Detection System (NIDS) addresses this need by analyzing traffic patterns to identify security breaches in firewalls, routers, and network infrastructures. By integrating machine learning algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest—the system is able to detect both known cyber threats and previously unseen attack vectors. Unlike traditional methods that (...) rely heavily on predefined indicators of compromise (IoCs), this system utilizes anomaly detection techniques, allowing it to identify new and emerging threats. As cyberattacks evolve, organizations must adopt adaptive methods to secure their networks. This system achieves high accuracy in classifying network traffic, with real-time alerts that provide early warnings of suspicious activities. It also includes intuitive visualizations, helping network administrators gain insights into the nature and scope of attacks. With the rise of increasingly complex and frequent cyberattacks, this NIDS offers a robust solution for enhancing network security and response capabilities. (shrink)

What happens to the inner light of consciousness with the death of the individual body and brain? Reductive materialism assumes it simply fades to black. Others think of consciousness as indicating a continuation of self, a transformation, an awakening or even alternatives based on the quality of life experience. In this issue, speculation drawn from theoretic research are presented. -/- Table of Contents Epigraph: From “The Immortal”, Jorge Luis Borges iii Editor’s Introduction: I Killed a Squirrel the Other Day, Gregory (...) M. Nixon iv-xi Research Essays The Tilde Fallacy and Reincarnation: Variations on a "Skeptical" Argument Teed Rockwell 862-881 Death, Consciousness, and Phenomenology, Steve Bindeman 882-899 The Idealist View of Consciousness After Death, Bernardo Kastrup 900-909 Consciousness, a Cosmic Phenomenon—A Hypothesis, Eva Déli 910-930 The Theory of a Natural Afterlife: A Newfound, Real Possibility for What Awaits Us at Death, Bryon K. Ehlmann 931-950 Near-Death Cases Desegregating Non-Locality/Disembodiment via Quantum Mediated Consciousness: An Extended Version of the Cell-Soul Pathway, Contzen Pereira & J Shashi Kiran Reddy 951-968 On the Possible Existence of Quantum Consciousness After Brain Death, Massimo Pregnolato & Alfredo Pereira Jr. 969-991 Science and Postmortem Survival, Edward F. Kelly 992-1011 Explorations ISS Theory: Cosmic Consciousness, Self, and Life Beyond Death in a Hyperdimensional Physics, Chris H. Hardy 1012-1035 Does the Consciousness End, Remain Awake, or Transform After Death? Radivoj Stankovich (with Micho Durdevich) 1036-1050 Big Bang Spirituality, Life, and Death, Ken Bausch 1051-1063 Death, Consciousness and the Quantum Paradigm, Ronald Peter Glasberg 1064-1077 Living With Limits: The Continuum of Consciousness, Donald Brackett 1078-1098 Mysticism, Consciousness, Death, Mike Sosteric 1099-1118 What Dies? Eternalism and the Afterlife in William James, Jonathan Bricklin 1119-1140 Theories of Consciousness and Death: Does Consciousness End, Continue, Awaken, or Transform When the Body Dies? Roger Cook 1141-1153 It’s the Other Way Around: Matter is a Form of Consciousness and Death is the End of the Illusion of Life in the World, James P. Kowall & Pradeep B. Deshpande 1154-1208 Statements A Feminine Vision for the World Consciousness, & a New Outrageous Ontology, Lorna Green 1209-1217 The Mask of Eternity: The Quest for Immortality and the Afterlife, Iona Miller 1218-1228 Are We Really “such stuff as dreams are made on”? Chris Nunn 1229-1225 Is the Afterlife a Non-Question? (Let's Hope Not), Deepak Chopra 1226-1230 Life After Death? An Improbable Essay, Stuart Kauffman 1231-1236. (shrink)

CVDs continue to be the number one cause of death, therefore early detection and treatment is crucial. Currently, the application of Artificial Intelligence (AI) in the cardiac image analysis has become popular due to increased accuracy, productivity, and modelling. In this paper, the use of the AI system to study the echocardiogram, CT, MRI, and other images of the heart and blood vessels to view the risk factors for cardiovascular diseases is discussed. We focus on the current methods of artificial (...) intelligence, how it is implemented in clinical practice, problems, and prospects for AI development. (shrink)

Generative AI has proven itself as an efficient innovation in many fields including writing and even analyzing data. For spatial computing, it provides a potential solution for solving such issues related to data manipulation and analysis within the spatial computing domain. This paper aims to discuss the probabilities of applying generative AI to graph-based spatial computing; to describe new approaches in detail; to shed light on their use cases; and to demonstrate the value that they add. This technique thus incorporates (...) graph theory, generative models to model spatial relations, generate new spatial forms and improve on spatial decision-making processes. The paper surveys such methods, describes typical applications, and outlines further development of the subject. (shrink)

Maritime transport plays a pivotal role in global trade, yet it faces challenges due to corrosion, which deteriorates metallic surfaces of vessels, leading to potential safety hazards and financial burdens. Traditional corrosion detection methods such as visual inspections are inefficient, time-consuming, and often subjective. This paper proposes a deep learning-based solution utilizing Convolutional Neural Networks (CNNs) to detect and assess corrosion on marine vessel surfaces. Our proposed solution not only automates the detection process but also enhances accuracy, ensuring early identification (...) and effective management of corrosion. Through rigorous experimentation, the model demonstrated high accuracy, significantly improving the corrosion detection process for the maritime industry. (shrink)

To develop a Unified Portal to collaborate across educational stakeholders to formulate and maintain curriculum standards for Technical Education in India. In that AICTE plays a pivotal role in shaping the landscape of technical education in India. We are going to design a portal to streamline the curriculum design process for educators. This portal incorporates various curriculum design tools to facilitate a more efficient and effective curriculum creation experience.

The generative AI system is being adopted across the several fields to provide novel solutions for text generation, image synthesis, and decision-making. But when they are used in multi-agent and multi-cloud systems, they are expensive in terms of computation and finance. Regarding the aforementioned factors, this paper aims to examine methods of reducing such costs while achieving system efficiency. Such measures as dynamic workload distribution, resource scaling, as well as cost-conscious model selection is described. Through the examples of case studies (...) and simulations, we show that incorporating these strategies can drastically decrease expenses and ensure immediate and accurate scalability across clouds of different ecosystems. (shrink)

Cloudbursts pose a significant threat in India, especially during the South-West Monsoon season that commences in June. India's diverse climate regions, including the northern Himalayan region, Indo-Gangetic Plain, southern peninsula, and coastal areas, experience sporadic cloudbursts, with only 31 recorded instances, mainly in Himachal Pradesh, Uttarakhand, and Jammu and Kashmir. To address the lack of comprehensive Indian cloudburst data, we've curated a dataset, incorporating meteorological factors for cloudburst prediction. This dataset encompasses variables such as Temperature, Wind Gust, Wind Gust Speed, (...) Humidity, Monsoon patterns, Air Pressure, and Cloud Density. Our goal is to improve preparedness and mitigation strategies, safeguarding lives, and property in cloudburst prone areas. Employing optimized machine learning algorithms, our model analyzes these parameters alongside prevailing weather conditions, facilitating cloudburst event prediction. We evaluate the prediction performance of machine learning algorithms, including KNN. The KNN algorithm outperformed others with an accuracy of 86.18%. Moreover, we provide graphical insights into the correlation between humidity and cloudburst occurrence, emphasizing the importance of weather variables in prediction models. This research contributes to cloudburst forecasting, even with limited Indian data, and highlights the potential of utilizing diverse machine learning techniques for improved accuracy. (shrink)

The application leverages gamification and personalized recommendations to encourage ecofriendly behaviors, track environmental impact, and reward users for their commitment to protecting the planet. By integrating features such as daily challenges, educational content, and a community leaderboard, the app seeks to empower individuals to make positive changes in their daily lives while building a community of like-minded environmental enthusiasts.

Wool production industries rely heavily on accurate demand forecasting to manage supply chains and production schedules effectively. Predicting wool demand allows companies to avoid overproduction and resource wastage while meeting market needs efficiently. Traditional forecasting models often struggle with the seasonality and variability of wool demand. The ARIMA (Auto Regressive Integrated Moving Average) model, a time series forecasting technique, is particularly suited for this task due to its ability to capture both trends and seasonal fluctuations in historical data. The data (...) for this research was collected from official records of wool demand across various Indian states for 2016-2023. After data pre-processing (cleaning, sorting, and organizing), the ARIMA model was implemented using Python for analysis and forecasting. Flask was employed to create a web-based interface, allowing users to interact with the predictive model easily. The model parameters (p, d, q) were fine-tuned to fit the data and generate accurate forecasts. The ARIMA model demonstrated a strong ability to predict wool demand, achieving a low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The model's forecasts were closely aligned with actual demand figures across different states, capturing both long-term trends and seasonal variations. Graphical representations showed minimal deviation between predicted and actual values, validating the accuracy of the model. The implementation of the ARIMA model for wool demand forecasting has significant practical implications for production planning in the textile industry. Companies can use the system to optimize production schedules, reduce costs, and minimize wastage. This methodology can be extended to other commodities where seasonal demand plays a critical role, offering a versatile tool for various sectors. (shrink)

Today e-commerce has had tremendous growth in the past years primarily due to changes in technology and customer’s buying behavior. One of the big shifts in the process has been the use of ML in advertising which has the capability to transform the marketing domain together with consumer interactions. This paper discusses the viability of using machine learning for designing realistic models of advertising to increase effectiveness of target and personalized advertising, as well as conversion rates in e-commerce. Several techniques (...) are explored in the study, such as supervised and unsupervised learning, recommender systems, and content optimization with natural language processing. By using case studies and experimental models, we discuss how and to what extent ML is beneficial in e-commerce advertising transformation. The results presented in this paper indicate that using machine learning in advertising has a potential of dramatically improving the customer experience while simultaneously increasing brand recognition and sales. (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 health industry is experiencing change, the newest forerunner of which is being propelled by spatial computing and generative AI. Spatial computing simply refers to the ability to interface with physical space through computation and digital devices; on the other hand, generative AI means using advanced machine learning to generate new output. This paper examines the roles and the combined possibilities of these two technologies with the view of transforming health care and diagnostics in the field of patient care. Precision (...) medicine unveils new opportunities of integrating precise spatial computing components and generative artificial intelligence to provide a personalized treatment for patients and data visualization for surgeons. The paper examines major use cases, concerns, and future of this convergence specifically in the fields of medicine. (shrink)

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally (...) accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies. (shrink)

How do we know what we know? • How do we perceive ourselves and the world around? • Is what we perceive the only reality that exists? (or) • Is there a different reality other than what we perceive? (Not referring to the source of C, but to the CoC, in the very limit of our physical structure).

Migrating legacy database applications to modern cloud-based solutions, such as Snowflake, is becoming essential for organizations aiming to leverage scalable, efficient, and cost-effective data solutions. Legacy databases, typically bound to on-premises infrastructure, often lack the flexibility required by contemporary data analytics and storage needs. Snowflake, a cloud-native data platform, provides a versatile environment that enables efficient data storage, sharing, and processing. This research examines a structured methodology for migrating legacy database applications to Snowflake, addressing key stages from initial assessment to (...) data transformation, integration, and performance optimization. A systematic, step-by-step migration process is implemented to minimize risks, reduce downtime, and ensure data integrity. (shrink)

In this paper, we present a case of a boy – Noah Wall, who till today surprises the world of neuroscience with his will to grow his brain and survive. The case presented in this study sets a stepping stone in understanding the advent of the will to make a choice, from a neuro-quantum mechanics interpretation. We propose that besides our internal states of choices (neurogenesis, neuroplasticity, cell differentiation, etc.) we also relate with external states of choices (love, compassion, empathy, (...) emotions, etc.) that contributes to its emergence. Quantum uncertainty seems to support the existence of a fundamental property based on which the universe functions; which means that even the nothing of free space has a small chance of containing something. Outcomes are not determined by prior or random events but by consciousness that gives rise to these outcomes. This provides us a lead into understanding the existence of the will and the origin of choice when we look deeper into the realms of the implausible interpretations of quantum mechanics. Free will is the ability for the mind to choose between possible outcomes. Willful power is therefore not only a psychological intervention but also a biological and quantum intervention, where we have the capacity to make choices about what direction we will take, making a change to the systematic functioning of our body. (shrink)

Origin of life on earth transpired once and from then on, it emerges as an endless eternal process. Matter and energy are constants of the cosmos and the hypothesis is that the origin of life is a moment when these constants intertwined or interacted. Energy from the cosmos interacted with inorganic matter to support matter with retention of this riveted energy, as energy to be circulated within the primitive channelized structures to conserve energy by the materialization of the proton homeostasis (...) mechanisms developed from the obtainable inorganic matter. The driver for these processes as we now confirm, exists in the quantum world and through quantum phenomenal processes could have combined these constants to create the magic of life. Primitive earth was a chemical reactive system that triggered a macromolecular evolution by means of open thermodynamic systems, driven by cyclic gradients of temperature, electromagnetic radiation and chemical potentials which sustained life and proto-consciousness in the first life forms driven by the quantum processes. The origin of life is always an intriguing topic but the purpose for finding the cause should never be inclined towards obliterating it; for if that is the case, the further we seek, the farther it will go. (shrink)

News Rakshak is an advanced web application that monitors, analyzes, and verifies news related to government schemes in regional media across multiple platforms, including 200+ websites, e-papers, and YouTube channels. The application leverages Natural Language Processing (NLP) for sentiment analysis and integrates Artificial Intelligence (AI) to identify and flag fake news and videos. Sentiments are classified as positive, neutral, or negative, rated on a scale from 0 to 5, providing Public Information Bureau (PIB) officers with actionable insights. The system uses (...) Optical Character Recognition (OCR) for extracting text from image-based news formats and delivers real-time alerts to officials via multiple channels. The user-friendly interface, equipped with filtering options, enables effective trend visualization. News Rakshak serves as a comprehensive tool for tracking and verifying government-related news sentiment and authenticity, ensuring accurate feedback from regional media. (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)

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. Our findings reveal that implementing accelerated computing can achieve substantial improvements, often reducing computation times by more than 60% compared to traditional sequential methods. This paper details the experimental setup, including algorithm selection and parallelization techniques, and discusses the role of memory bandwidth and latency in achieving optimal performance. Based on the analysis, we propose a streamlined methodology to guide the deployment of accelerated computing frameworks in various industries. Concluding with a discussion on future directions, we highlight potential advancements in hardware architectures and software optimizations that could further augment computational efficiency and scalability in accelerated computing. (shrink)

Generative AI is reshaping sectors like banking and healthcare by enabling innovative applications such as personalized service offerings, predictive analytics, and automated content generation. In banking, generative AI drives customer engagement through tailored financial advice, fraud detection, and streamlined customer service. Meanwhile, in healthcare, it enhances medical imaging analysis, drug discovery, and patient diagnostics, significantly impacting patient care and operational efficiency. This paper presents an experimental study examining the implementation and effectiveness of generative AI in these sectors.

. Thyroid diseases represent a significant global health concern, necessitating accurate and timely diagnostic methods for effective treatment. Traditional diagnostic approaches often rely on complex blood tests and imaging techniques that can be challenging to interpret. This paper explores the application of machine learning (ML) and deep learning (DL) techniques, particularly Vision Transformers (ViT), for thyroid disease detection. We conducted a comprehensive literature survey that highlights various studies employing ML and DL models, revealing high accuracy rates but also significant limitations (...) such as small sample sizes and dataset imbalances. Our research methodology involved creating a custom dataset, preprocessing images, and developing robust models using both ML algorithms and advanced DL architectures. Furthermore, we discuss the implications of our findings for clinical practice and propose future research directions to enhance diagnostic capabilities. This study underscores the potential of leveraging AI technologies to improve the accuracy of thyroid disease detection while addressing existing challenges in traditional diagnostic methods, ultimately contributing to better patient outcomes in thyroid health management. (shrink)

Dermatological conditions affect a significant portion of the global population, with delayed diagnoses often leading to worsening conditions. This project introduces an AI-based web application that uses deep learning algorithms and artificial neural networks to analyse skin images and provide rapid preliminary diagnoses. The system can identify various dermatological conditions, assess severity, and offer accuracy metrics, thereby facilitating early detection and improving healthcare outcomes. This tool addresses the growing gap between patients and dermatological care, especially in areas with limited specialist (...) availability. (shrink)

This paper presents the design and development of a Fitness App that integrates workout plans, dietary recommendations, and motivational features to help users maintain a healthy lifestyle. The app aims to provide users with personalized workout routines, meal plans, and motivational content to keep them engaged throughout their fitness journey. The goal is to create a comprehensive app that empowers users to take control of their health by providing them with the tools and resources they need to achieve their fitness (...) goals effectively. (shrink)

This paper extensively develops and thoroughly creates a highly centralized and extremely efficient log collection system utilizing and leveraging the robust syslog server and comprehensively collects and gathers log data from the numerous hosts residing within the network, meticulously analyses and scrutinizes the detailed log files and promptly sends and transmits a critical alert message to the central host through secure and reliable SMS. This paper thoroughly and comprehensively addresses and tackles the increasingly growing and rapidly expanding need for extremely (...) robust and highly reliable cybersecurity measures and solutions in the vulnerable and sensitive power sector. (shrink)

Recent studies show that racism still exists in the American medical profession, the fact of which legitimizes the historically long-legacy of mistrust towards medical profession and health authorities among African Americans. Thus, it was suspected that the participation of black patients in end-of-life care has always been significantly low stemmed primarily from their mistrust of the medical profession. On the other hand, much research finds that there are other reasons than the mistrust which makes African Americans feel reluctant to the (...) end-of-life care, such as cultural-religious difference and genuine misunderstanding of the services. If so, two crucial questions are raised. One is how pervasive or significant the mistrust is, compared to the other factors, when they opt out of the end-of-life care. The other is if there is a remedy or solution to the seemingly broken relationship. While no studies available answer these questions, we have conducted an experiment to explore them. The research was performed at two Philadelphia hospitals of Mercy Health System, and the result shows that Black patients’ mistrust is not too great to overcome and that education can remove the epistemic obstacles as well as overcome the mistrust. (shrink)

This paper presents Kaizen, a sensor-driven mobile application developed to track and encourage physical activity through goal-setting, gamification, and personalized diet recommendations, tailored specifically to the Indian dietary context. Kaizen uses accelerometers, GPS, and gyroscopes to monitor user movements in real-time, offering an alternative to traditional fitness tracking methods that often lack cultural dietary considerations or personalized motivation systems. With a ranking system and interactive challenges, Kaizen aims to enhance user engagement and promote continuous improvement in health and fitness. Early (...) user testing indicates high responsiveness and motivational efficacy, suggesting broad potential for adoption and further development. (shrink)

The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus on (...) developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy. (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)

This article examines the rapidly evolving landscape of cybersecurity, focusing on emerging threats and innovative defense mechanisms. We analyze four key threat vectors: Advanced Persistent Threats (APTs), ransomware, Internet of Things (IoT) vulnerabilities, and social engineering attacks. These threats pose significant risks to organizations, including data breaches, financial losses, and operational disruptions. In response, we explore cutting-edge defense mechanisms such as Artificial Intelligence and Machine Learning for threat detection, Zero Trust Architecture for access control, blockchain for data integrity, behavioral analytics (...) for insider threat detection, and quantum cryptography for enhanced encryption. The article also outlines best practices for cybersecurity, including regular software updates, strong password policies, employee training, and security audits. By synthesizing current research and industry trends, this article provides a comprehensive overview of the cybersecurity landscape, emphasizing the need for continuous adaptation and vigilance in the face of evolving cyber threats. (shrink)

The advent of cloud computing has fundamentally transformed various business operations, with Human Capital Management (HCM) standing out as a significant beneficiary. Cloud-based HCM solutions offer unparalleled scalability, accessibility, and integration capabilities, enabling organizations to manage their workforce more efficiently and strategically. This paper explores the revolutionary impact of cloud computing on HCM, highlighting key advancements such as enhanced data analytics, improved employee engagement, cost-effectiveness, and support for remote and hybrid work models. Through a comprehensive literature review and analysis of (...) case studies from diverse industries, the study demonstrates how cloud computing facilitates real-time data access, seamless system integration, and the implementation of advanced technologies like artificial intelligence (AI) and machine learning (ML) within HCM frameworks. Additionally, the research addresses challenges associated with cloud-based HCM adoption, including data security, privacy concerns, and change management. Future research directions are proposed to further leverage cloud computing capabilities, emphasizing the integration of emerging technologies and the continuous evolution of cloud-based HCM tools. The findings underscore the pivotal role of cloud computing in enhancing organizational agility, optimizing human capital, and driving sustainable business growth. (shrink)

In the modern business landscape, effective Human Capital Management (HCM) is pivotal for organizational success. The integration of HCM tools into existing HR systems is a strategic endeavor that can enhance workforce management, streamline HR processes, and foster employee engagement and productivity. This paper explores best practices for integrating HCM tools, highlighting key considerations such as system compatibility, data migration, user training, and change management. Through an extensive literature review and analysis of real-world case studies, the study demonstrates how successful (...) integration can lead to significant improvements in HR operations and overall organizational performance. Additionally, the paper identifies common challenges encountered during the integration process and proposes strategies to mitigate them. The findings underscore the importance of a structured approach to HCM tool integration, emphasizing the role of leadership, stakeholder engagement, and continuous evaluation. Future research directions are suggested to further enhance the integration frameworks, incorporating advancements in artificial intelligence (AI) and machine learning (ML) to drive more intelligent and adaptive HCM systems. The insights provided aim to equip HR professionals and organizational leaders with the knowledge to navigate the complexities of HCM tool integration, ultimately fostering a more agile, efficient, and engaged workforce. (shrink)

The integration of cloud architecture into Human Capital Management (HCM) systems has revolutionized the way organizations manage their workforce. Cloud-based HCM solutions offer enhanced scalability, flexibility, and accessibility, enabling organizations to streamline HR processes, improve data management, and foster greater employee engagement. This paper explores the impact of cloud architecture on HCM processes, highlighting key benefits such as cost-effectiveness, real-time data access, and seamless integration with other business systems. Through a comprehensive literature review and analysis of case studies from diverse (...) industries, the study demonstrates how cloud-based HCM systems optimize talent acquisition, performance management, learning and development, and workforce analytics. Additionally, the research addresses the challenges associated with transitioning to cloud-based HCM solutions, including data security, privacy concerns, and change management. Best practices for implementing cloud architecture in HCM are discussed, emphasizing the importance of strategic planning, stakeholder engagement, and continuous evaluation. The findings underscore the pivotal role of cloud architecture in enhancing organizational efficiency, agility, and competitive advantage in today’s dynamic business environment. (shrink)

The Grievance Management System is a web-based platform designed to enhance the efficiency and transparency of handling public grievances. By introducing role-based access for users, moderators, and government officials, the system ensures that grievances are systematically reviewed, prioritized, and resolved. Users can submit grievances, track their status, and receive notifications regarding updates. Moderators are tasked with verifying the validity of each grievance and assigning it a priority level before passing it on to government officials for action. Government officials, in turn, (...) have the ability to filter grievances by priority and submission date, helping them manage and resolve issues more effectively. The system integrates technologies such as Flask for backend logic, MySQL for database management, and natural language processing (NLP) to classify grievances into relevant departments. It also prevents rejected grievances from being re- submitted and delivers real-time notifications to users, ensuring an efficient and transparent grievance handling process. This project aims to improve the accountability and responsiveness of government departments in addressing public complaints. (shrink)

Due to risks like tampering, corruption, and illegal access, the rapid rise in cloud storage usage makes data integrity a top priority. As a way to verify the correctness of data stored in the cloud, we develop a "Data Integrity Verification Scheme" in this project which involves the use of third-party auditing (TPA). The system produces hash values for files both during upload and retrieval using cryptographic hashing methods, assuring consistency between both. Without needing a link to the real file (...) contents, the third-party auditor independently confirms the accuracy of the data that was stored. The Google Cloud SDK and 2 Python-based encryption libraries, hashlib and pycryptodome, are utilized in the project. Our approach is meant to be safe and efficient, providing real-time verification of integrity for data stored in cloud. . (shrink)

The New Evil Demon problem has been hotly debated since the case was introduced in the early 1980’s (e.g. Lehrer and Cohen 1983; Cohen 1984), and there seems to be recent increased interest in the topic. In a forthcoming collection of papers on the New Evil Demon problem (Dutant and Dorsch, forthcoming), at least two of the papers, both by prominent epistemologists, attempt to resist the problem by appealing to the distinction between justification and excuses. My primary aim here is (...) to critically evaluate this new excuse maneuver as a response to the New Evil Demon problem. -/- Their response attempts to give us reason to reject the idea that victims of the New Evil Demon have justification for believing as they do. I shall argue that this approach is ultimately unsuccessful, however much of value can be learned from these attempts. In particular, progress in the debate can be made by following those who advance the excuse maneuver and make explicit the connection between epistemic justification and epistemic norms. By doing so, the questions being debated are clarified, as is the methodology being used to attempt to answer them. (shrink)

The acquisition of a new skill usually proceeds through five stages, from novice to expert, with a sixth stage of mastery available for highly motivated performers. In this chapter, we re-state the six stages of the Dreyfus Skill Model, paying new attention to the transitions and interrelations between them. While discussing the fifth stage, expertise, we unpack the claim that, “when things are proceeding normally, experts don’t solve problems and don’t make decisions; they do what normally works” (Dreyfus & Dreyfus, (...) 1988, pp. 30 – 31). This leads us to offer an account of the “perspectival deliberation” that arises for experts and masters and that is distinct from the calculative deliberation characteristic of the lower stages of skillfulness. (shrink)

The Alumni Association platform is designed to connect alumni and current students of an educational institution. It provides tools for alumni networking, professional development, and community engagement. The primary goal is to facilitate meaningful communication between alumni and students, helping students access job opportunities, mentorship, and collaborative spaces for startup ideas. Alumni can be easily located through a search feature, enabling personalized networking. Event management tools support the organization of alumni events, reunions, and workshops, fostering ongoing involvement. Announcements and updates (...) keep users informed of key events and opportunities. This portal ultimately aims to build a dynamic and supportive ecosystem, enhancing the educational experience and creating lasting bonds within the alumni community. (shrink)

In the wake of the advent of genome editing technology CRISPR-Cas9 (clustered regularly interspaced palindromic repeat (CRISPR)-associated protein 9), there has been a global debate around the implications of manipulating the human genome. While CRISPR-based germline gene editing is new, the debate about the ethics of gene editing is not – for several decades now, scholars have debated the ethics of making heritable changes to the human genome. The arguments that have been raised both for and against the use of (...) genetic technologies in human reproduction reiterate much of the arguments made in the pre-CRISPR debate. As such, it is instructive for South Africa to reflect on these arguments now, in considering our position on the regulation of the use of this novel biotechnology. There are two dominant schools of thought in this area, bioliberalism and bioconservatism. Bioconservatives raise concerns about the risks of genetic manipulation, and argue that it ought to be limited or prohibited to avert these risks to human health and human nature. Bioliberal scholars are more open to the prospect of genetic manipulation, because of its potential utility. In this article, I conclude that in liberal democracies such as our own, bioliberal arguments ought to be seriously considered when formulating policy on human genome editing because of the extent to which they resonate with our Constitutional values and human rights. I further suggest that there is a need for an enquiry into the relevance of African perspectives on the ethical questions that arise concerning germline genome editing. (shrink)

Early Heidegger argues that a “homogenous space of nature” can be revealed by stripping away the intelligibility of Dasein's everyday world, a process he calls “deworlding.” Given this, some interpreters have suggested that Heidegger, despite not having worked out the details himself, is also committed to a notion of deworlded time. Such a “natural time” would amount to an endogenous sequentiality in which events are ordered independently of Dasein and the stand it takes on its being. I show that Heidegger (...) was indeed committed to such a temporal realism even though his treatment of these issues is somewhat scattered and pulled in different directions. In the course of my reconstruction, I renew an interpretation of Heidegger that stresses Dasein's thrownness into nature and I answer William Blattner's powerful interpretation of Heidegger as a failed temporal idealist who was unable to derive the sequentiality of ordinary time from Dasein's non-sequential originary temporality. Heidegger did not attempt to derive sequentiality; instead, he understood it as a built-in feature of the natural universe by which Dasein's activities are constrained. World-time turns out to be a co-production of Dasein's non-sequential originary temporality and the endogenous sequentiality of events in nature. (shrink)

In its most basic form, Simple Reliabilism states that: a belief is justified iff it is formed as the result of a reliable belief-forming process. But so-called New Evil Demon cases have been given as counterexamples. A common response has been to complicate reliabilism from its simplest form to accommodate the basic reliabilist position, while at the same time granting the force of NED intuitions. But what if despite initial appearances, Simple Reliabilism, without qualification, is compatible with the NED intuition? (...) What we can call the Dispositionalist Response to the New Evil Demon problem is fascinating because it contends just that: Simple Reliabilism is fully compatible with the NED intuition. It is claimed that all we need to do to recognize their compatibility is appreciate that reliability is a dispositional property. In this paper I shall critically evaluate the Dispositionalist proposal. (shrink)

In common with traditional forms of epistemic internalism, epistemological disjunctivism attempts to incorporate an awareness condition on justification. Unlike traditional forms of internalism, however, epistemological disjunctivism rejects the so-called New Evil Genius thesis. In so far as epistemological disjunctivism rejects the New Evil Genius thesis, it is revisionary. -/- After explaining what epistemological disjunctivism is, and how it relates to traditional forms of epistemic internalism / externalism, I shall argue that the epistemological disjunctivist’s account of the intuitions underlying the New (...) Evil Genius thought experiment is at best incomplete. As presented, therefore, epistemological disjunctivism is unable to accommodate the core guiding intuitions of epistemic internalism. Given the stated aim of not being revisionary on this score, the view is at a dialectical disadvantage over the traditional forms of epistemic internalism the position is meant to replace. Unfortunately, therefore, at present, the impasse between internalism and externalism remains. (shrink)

In addition to the notion of defeat, do we need to expand the epistemological repertoire used in accounting for the context dependence of justification? It has recently been argued that we ought to admit a hitherto unrecognized fundamental epistemic kind called ‘disqualifiers’. Disqualifiers are taken to be not reducible to any other epistemic notion. Rather, they are meant to be primitive. If this is correct, it is a surprising and novel discovery, and so it is worthy of further epistemological investigation. (...) In this paper I shall first argue that the cases given do not motivate positing the notion of a disqualifier. Conclusions drawn about the existence of disqualifiers do not follow from the considerations advanced. Second, I shall directly argue that an essential core claim of those who would posit disqualifiers, that so-called disqualifiers actually do prevent epistemic bases from conferring justification, is false. In sum, I shall argue that there are no disqualifiers. (shrink)

In this article I argue that the value of epistemic justification cannot be adequately explained as being instrumental to truth. I intend to show that false belief, which is no means to truth, can nevertheless still be of epistemic value. This in turn will make a good prima facie case that justification is valuable for its own sake. If this is right, we will have also found reason to think that truth value monism is false: assuming that true belief does (...) have value, there is more of final epistemic value than mere true belief. (shrink)

This case study is a retrospection of my family’s experiences that highlight several ELE facets. Following a description, we attempt to explain some of these phenomena from the perspectives of science and other germane disciplines.

Influential cases have been provided that seem to suggest that one can fail to have knowledge because of the social environment. If not a distinct kind of social defeater, is there a uniquely social phenomenon that defeats knowledge? My aim in this paper is to explore these questions. I shall argue that despite initial appearances to the contrary, we have no reason to accept a special class of social defeater, nor any essentially social defeat phenomenon. We can explain putative cases (...) of social defeat with our existing epistemological apparatus. (shrink)

Do psychological traits predict philosophical views? We administered the PhilPapers Survey, created by David Bourget and David Chalmers, which consists of 30 views on central philosophical topics (e.g., epistemology, ethics, metaphysics, philosophy of mind, and philosophy of language) to a sample of professional philosophers (N = 314). We extended the PhilPapers survey to measure a number of psychological traits, such as personality, numeracy, well-being, lifestyle, and life experiences. We also included non-technical ‘translations’ of these views for eventual use in other (...) populations. We found limited to no support for the notion that personality or demographics predict philosophical views. We did, however, find that some psychological traits were predictive of philosophical views, even after strict correction for multiple comparisons. Findings include: higher interest in numeracy predicted physicalism, naturalism, and consequentialism; lower levels of well-being and higher levels of mental illness predicted hard determinism; using substances such as psychedelics and marijuana predicted non-realist and subjectivist views of morality and aesthetics; having had a transformative or self-transcendent experience predicted theism and idealism. We discuss whether or not these empirical results have philosophical implications, while noting that 68% of our sample of professional philosophers indicated that such findings would indeed have philosophical value. (shrink)