The author o ers a brief report of introducing the study of religion in India since 194 While doing so he refers to the Constitution of India, so-called Nehruvian Consensus, the Kothari Commission which made an important distinction between ‘religious education’ and ‘educa- tion about religion’, as well as several other bodies responsible for national policy on education, which gave a unique shape of Indian secularism.

A review of Arvind Sharma's Gandhi: A Spiritual Biography (New Haven: Yale University Press, 2013).

This comprehensive article explores the convergence of cloud computing and Java technologies, highlighting their synergistic relationship in modern software development. It examines key concepts in cloud computing, including virtualization, microservices architecture, CI/CD practices, and serverless computing. The article also discusses various cloud service models (SaaS, PaaS, and IaaS), their applications, and essential tools and technologies for cloud development. A financial institution's infrastructure modernization case study illustrates the practical implementation of these concepts, demonstrating how cloud technologies and Java can enhance agility, (...) reliability, cost-efficiency, and innovation in traditional industries. (shrink)

This paper encapsulates the debate as to whether or not tarka is an additional source of knowledge. In this regard, Jaina thinkers opine that they are, unlike Buddhists and Nyāya thinkers, an additional source of knowledge, for what we come to know through tarka is not known through any other means of knowledge. En route, Jaina’s understanding of tarka is put forth, thereafter their criticism of others’ understanding is supplied. Eventually, some recent discussions over this debate are intimated that seem (...) to go in support of Jaina understanding of tarka. These recent discussions hint at the direction in which Jaina thinkers need to advance their stand. (shrink)

गेटिअर के शोध-पत्र "Is Justified Treu Belief Knowledge" जो कि ANALYSIS में 1963 में प्रकाशित हुआ था उसका यह हिन्दी अनुवाद है। .

An increasing number of enterprises are using artificial intelligence (AI) to improve their cyber security and threat intelligence. AI is a type of AI that generates new data independently of preexisting data or expert knowledge. One emerging cyberthreat to systems that has been increasing is adversarial attacks. By generating fictitious accounts and transactions, adversarial attacks can interfere with and take advantage of decentralized apps that operate on the Ethereum network. Because fraudulent materials (such as accounts and transactions) used as malicious (...) payloads can be mistaken for legitimate data, detecting adversarial attacks can be difficult. This paper suggests a paradigm for cyber threat hunting in the Ethereum blockchain that makes use of Adversarial Networks (GAN) and Deep Recurrent Neural Networks (RNN). By considering a variety of sources and data points, this technology enables decision support systems to automatically and rapidly identify threats posed by hackers or other harmful actors. The likelihood of a successful assault can be further decreased by using AI to find weaknesses in an organization's infrastructure. Because security operations centers (SOCs) need to quickly identify threats and take defensive action, this technology is particularly wellsuited for them. AI can give businesses an extra line of defense against increasingly complex threats by integrating intriguing and useful data items that would have otherwise gone unnoticed . (shrink)

This document provides a brief report on initial research into how argument presentation affects susceptibility to confirmation bias as well as feelings toward political opponents.

With recent advancements in systems engineering and artificial intelligence, autonomous agents are increasingly being called upon to execute tasks that have normative relevance. These are tasks that directly—and potentially adversely—affect human well-being and demand of the agent a degree of normative-sensitivity and -compliance. Such norms and normative principles are typically of a social, legal, ethical, empathetic, or cultural nature. Whereas norms of this type are often framed in the abstract, or as high-level principles, addressing normative concerns in concrete applications of (...) autonomous agents requires the refinement of normative principles into explicitly formulated practical rules. This paper develops a process for deriving specification rules from a set of high-level norms, thereby bridging the gap between normative principles and operational practice. This enables autonomous agents to select and execute the most normatively favourable action in the intended context premised on a range of underlying relevant normative principles. In the translation and reduction of normative principles to SLEEC rules, we present an iterative process that uncovers normative principles, addresses SLEEC concerns, identifies and resolves SLEEC conflicts, and generates both preliminary and complex normatively-relevant rules, thereby guiding the development of autonomous agents and better positioning them as normatively SLEEC-sensitive or SLEEC-compliant. (shrink)

The adoption of multi-cloud environments has become a strategic necessity for organizations seeking scalability, flexibility, and operational efficiency. However, distributing workloads across multiple cloud providers introduces significant security challenges, including authentication vulnerabilities, inconsistent security policies, data breaches, and compliance risks. Traditional security approaches often fail to address the complexity of multi-cloud ecosystems, requiring a more comprehensive risk mitigation strategy. This paper analyses key security risks in multi-cloud architectures and evaluates industry-standard risk assessment frameworks to prioritize effective countermeasures. Our findings indicate (...) that authentication, access control, and secure cloud networking are the most critical areas requiring immediate attention. Threats such as identity mismanagement, insecure data transfers, and lack of unified monitoring further escalate security concerns. To mitigate these risks, we propose a combination of zero-trust architecture, robust identity and access management (IAM), encryption protocols, and AI-driven threat detection. Implementing these strategies can enhance data integrity, regulatory compliance, and overall cloud security posture. By adopting a proactive approach, IT leaders can optimize cybersecurity investments and ensure the resilience of multi-cloud environments. This study provides actionable insights to strengthen security in distributed cloud architectures, enabling organizations to defend against evolving cyber threats. (shrink)

Deep fakes, a technology enabling the creation of highly realistic fake images and videos through face-swapping, have sparked significant attention due to their potential for misuse. This paper explores the technologies behind deep fakes and categorizes them into four types: deep fake pornography, political campaigns, commercial uses, and creative content. The authors discuss the ethical and regulatory challenges associated with each category. Initially, deep fakes were used maliciously, such as in revenge porn and political manipulation, which led to widespread fear. (...) However, like many technologies before it, deep fakes may eventually become more widely accepted. The paper also examines the role of online platforms and governments in addressing these issues and offers an optimistic view of how the democratization of AI might help resolve the ethical concerns surrounding deep fakes. (shrink)

Misinformation has been a persistent and detrimental phenomenon in our society in many ways, including individuals' physical health and economic security. With the advent of short video platforms and associated applications, dissemination of multi-modal misinformation, including images, texts, audios, and videos, have increased these issues. The advent of generative AI models such as ChatGPT and Stable Diffusion has further enhanced the complexity, providing give rise to Artificial Intelligence Generated Content (AIGC) and posing new challenges in the detection and mitigation of (...) misinformation. Therefore, the conventional methods of misinformation detection and intervention have proved to be insufficient in this new paradigm. This paper examines the challenge posed by AIGC in the context of misinformation. It analyzes the problem from a psychological and societal point of view, and investigates the subtle manipulation traces in AIGC at signal, perceptual, semantic, and human levels. Through examining manipulation traces like signal manipulation, semantic inconsistency, logical incoherence, and psychological strategies, our goal is to address AI-generated misinformation and present a conceptual design of systematic explainable solution. Finally, we hope this paper will provide useful insights into the fight against misinformation, especially in the age of AIGC. (shrink)

The convergence of cloud computing, blockchain technology, and the emerging era of quantum computing presents significant challenges for data security. This research tackles these growing vulnerabilities by introducing a comprehensive security framework that integrates Quantum Key Distribution (QKD), CRYSTALS-Kyber, and Zero-Knowledge Proofs (ZKPs) to protect data in cloud-based blockchain systems. The primary goal is to safeguard information against quantum threats through QKD, a quantum-secure cryptographic protocol. To enhance resilience against quantum attacks, the framework employs CRYSTALSKyber, a lattice-based encryption mechanism. Additionally, (...) ZKPs are utilized to strengthen privacy and verification processes within cloud and blockchain ecosystems. A key aspect of this study is the performance evaluation of the proposed framework, focusing on encryption and decryption efficiency, quantum key generation rates, and overall system performance. The analysis examines practical considerations such as file size, response time, and computational overhead to assess the framework’s real-world applicability. The findings highlight the framework’s effectiveness in mitigating quantum threats and securing cloud-based blockchain storage. By addressing critical gaps in both theoretical research and practical implementation, this study provides valuable insights for organizations seeking quantum-resistant data security solutions. The framework’s efficiency and scalability demonstrate its feasibility, offering a roadmap for securing cloud environments against the evolving challenges posed by quantum computing and blockchain integration. (shrink)

As cloud-native architectures continue to evolve, microservices have become the foundation for scalable and resilient applications. However, the decentralized nature of microservices introduces significant security challenges, including service-to-service communication security, identity management, and traffic control. Service mesh technologies, such as Istio, Linkerd, and Consul, provide a powerful solution by offering decentralized security enforcement, mutual TLS (mTLS) encryption, fine-grained access control, and observability without modifying application code. This paper explores how service meshes enhance microservices security by implementing zero-trust policies, automatic traffic (...) encryption, and service authentication mechanisms. Through the integration of policy-based access control, workload identity, and anomaly detection, service mesh architectures significantly reduce the risk of unauthorized access, lateral movement attacks, and data breaches. Additionally, this study highlights best practices for deploying secure service meshes in cloud-native environments, ensuring compliance with industry security standards. The findings demonstrate that adopting a service mesh improves security posture while maintaining agility and performance in microservices-based applications. (shrink)

The rapid advancement of quantum computing poses a significant threat to classical cryptographic systems, particularly those based on RSA, ECC, and other public-key algorithms. With Shor’s algorithm capable of efficiently factoring large numbers and breaking current encryption standards, the transition to postquantum cryptography (PQC) has become a global priority. This paper explores the impact of quantum computing on cryptographic security, the need for quantum-resistant cryptographic algorithms, and ongoing standardization efforts led by organizations such as NIST. We analyze various post-quantum cryptographic (...) techniques, including lattice-based, hash-based, multivariate, and code-based cryptography, assessing their feasibility for real-world implementation. Additionally, we discuss the challenges associated with transitioning to PQC, including computational overhead, interoperability, and regulatory compliance. As the quantum era approaches, organizations must proactively adopt post-quantum cryptographic solutions to safeguard sensitive data and ensure long-term security in a quantum-capable world. (shrink)

The rapid advancement of quantum computing poses a significant threat to classical cryptographic systems, particularly those based on RSA, ECC, and other public-key algorithms. With Shor’s algorithm capable of efficiently factoring large numbers and breaking current encryption standards, the transition to postquantum cryptography (PQC) has become a global priority. This paper explores the impact of quantum computing on cryptographic security, the need for quantum-resistant cryptographic algorithms, and ongoing standardization efforts led by organizations such as NIST. We analyze various post-quantum cryptographic (...) techniques, including lattice-based, hash-based, multivariate, and code-based cryptography, assessing their feasibility for real-world implementation. Additionally, we discuss the challenges associated with transitioning to PQC, including computational overhead, interoperability, and regulatory compliance. As the quantum era approaches, organizations must proactively adopt post-quantum cryptographic solutions to safeguard sensitive data and ensure long-term security in a quantum-capable world. (shrink)

: Ransomware remains one of the most significant cybersecurity threats, evolving rapidly with new attack vectors, encryption techniques, and extortion models. As we enter 2018, ransomware attacks have become more sophisticated, leveraging artificial intelligence (AI), automation, and emerging technologies to bypass traditional security measures. This paper analyzes the latest ransomware trends, including targeted attacks on critical infrastructure, Ransomware-as-a-Service (RaaS), and double/triple extortion tactics. Additionally, it explores advanced mitigation techniques such as AI-driven anomaly detection, zero-trust architectures, blockchain-based security solutions, and proactive (...) threat intelligence frameworks. By examining real-world case studies and industry best practices, this study provides insights into effective countermeasures and future directions for securing digital ecosystems against ransomware threats. The findings aim to assist cybersecurity professionals, policymakers, and organizations in strengthening their defense mechanisms against evolving ransomware threats in 2018 and beyond. (shrink)

The assessment of cloud data loss prevention and encryption was the main emphasis of the current study. Cloud computing, another name for cloud-based technologies, boosts organizational effectiveness for appropriate data management procedures. By improving data visualization, cloud-based data loss or leakage prevention (DLP) helps businesses comprehend the risks and problems associated with appropriate data management. This study demonstrated how to handle data with encryption. The growth of company processes and the effective management of all activities through data management can benefit (...) from cloud loss prevention. Cloud computing that is IoT and AI based evaluates the underlying reasons and offers a comprehensive understanding of business issues. As a result, they may encourage ongoing development and enhance their data loss risk management offerings. -/- . (shrink)

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various machine learning techniques (...) in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. Keywords: Real-t. (shrink)

Cloud computing has transformed data management by providing scalable and on-demand services, but its open and shared infrastructure makes it highly vulnerable to sophisticated cyber threats. Traditional Intrusion Detection Systems (IDS) struggle with dynamic and large-scale cloud environments due to high false positives, limited adaptability, and computational overhead. To address these challenges, this paper proposes an AI-driven Intrusion Detection System (AI-IDS) that leverages deep learning models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze network traffic, (...) detect anomalies, and identify advanced cyber threats with high accuracy. Unlike rule-based IDS, which rely on static signatures, our model continuously learns from real-time data, improving detection rates while reducing false alarms. The proposed AI-IDS is optimized for cloud infrastructure, ensuring low-latency threat detection, minimal computational burden, and real-time adaptability to emerging attack patterns. Experimental validation on benchmark datasets demonstrates significant improvements in accuracy, precision, and scalability, making AI-powered IDS a crucial innovation in cloud security. (shrink)

Homomorphic encryption is a transformative cryptographic technique that enables secure cloud data processing by allowing computations on encrypted data without requiring decryption. Unlike traditional encryption methods, which protect data only at rest and in transit, homomorphic encryption ensures end-to-end security, even during computation. This capability is particularly vital for industries that rely on cloud computing while handling sensitive information, such as finance, healthcare, and government sectors. However, despite its strong security guarantees, the widespread adoption of homomorphic encryption remains limited due (...) to its high computational complexity and performance overhead. This paper examines the role of homomorphic encryption in secure cloud data processing by evaluating its advantages, challenges, and real-world applicability. First, a comprehensive literature review contrasts traditional and homomorphic encryption techniques, highlighting their security implications and efficiency trade-offs. Second, experimental simulations are conducted to assess computation time, scalability, and data transfer efficiency in cloud environments. The results underscore that while homomorphic encryption enhances data security, its feasibility depends on specific use-case requirements, balancing security, performance, and cost. Finally, we explore practical applications such as privacy-preserving data analytics and secure outsourcing of computations, demonstrating how homomorphic encryption can redefine cloud security paradigms. (shrink)

t. Intense discussions concerning the hazards and ethical ramifications of artificial intelligence were sparked by its introduction and broad societal adoption. Traditional discriminative machine learning carries hazards that are frequently different from these risks. A scoping review on the ethics of artificial intelligence, with a focus on big language models and text-to-image models, was carried out in order to compile the recent discourse and map its normative notions. Enforcing accountability, responsibility, and adherence to moral and legal standards will become more (...) challenging as artificial intelligence systems get more adept at making decisions on their own. Here, a user-centered, realism-inspired method is suggested to close the gap between abstract concepts and routine research procedures. It lists five particular objectives for the moral application of AI: 1) comprehending model training and output, including bias mitigation techniques; 2) protecting copyright, privacy, and secrecy; 3) avoiding plagiarism and policy infractions; 4) applying AI in a way that is advantageous over alternatives; and 5) employing AI in a transparent and repeatable manner. Every objective is supported by workable plans, real-world examples of abuse, and remedial actions. This paper will discuss the nature of an accountability framework and related concerns in order to enable the organized responsibility for assignment and proof of AI systems. The suggested architecture for regulating AI incorporates crucial components like transparency, human oversight, and adaptability to address the issues with accountability that have been brought to light. Some crucial suggestions for putting the framework into practice and growing it were also provided through industrial case studies, guaranteeing that companies increase compliance, trust, and responsible AI technology adoption. (shrink)

The increasing complexity and sophistication of cyber threats have rendered traditional perimeter-based security models insufficient for protecting modern digital infrastructures. Zero Trust Architecture (ZTA) has emerged as a transformative cybersecurity framework that operates on the principle of "never trust, always verify." Unlike conventional security models that rely on implicit trust, ZTA enforces strict identity verification, continuous monitoring, least-privilege access, and microsegmentation to mitigate risks associated with unauthorized access and lateral movement of threats. By integrating technologies such as artificial intelligence (AI), (...) machine learning (ML), and behavioral analytics, Zero Trust enhances threat detection, reduces attack surfaces, and ensures a proactive security posture across cloud, on-premises, and hybrid environments. This paper explores the core principles, implementation strategies, and benefits of Zero Trust Architecture, along with its challenges and future trends in cybersecurity. Furthermore, it highlights real-world applications and case studies that demonstrate the effectiveness of ZTA in protecting critical assets against advanced cyber threats. By adopting a Zero Trust approach, organizations can significantly improve their resilience to cyberattacks and ensure robust data protection in an evolving threat landscape. (shrink)

The adoption of multi-cloud environments has become a strategic necessity for organizations seeking scalability, flexibility, and operational efficiency. However, distributing workloads across multiple cloud providers introduces significant security challenges, including authentication vulnerabilities, inconsistent security policies, data breaches, and compliance risks. Traditional security approaches often fail to address the complexity of multi-cloud ecosystems, requiring a more comprehensive risk mitigation strategy. This paper analyses key security risks in multi-cloud architectures and evaluates industry-standard risk assessment frameworks to prioritize effective countermeasures. Our findings indicate (...) that authentication, access control, and secure cloud networking are the most critical areas requiring immediate attention. Threats such as identity mismanagement, insecure data transfers, and lack of unified monitoring further escalate security concerns. To mitigate these risks, we propose a combination of zero-trust architecture, robust identity and access management (IAM), encryption protocols, and AI-driven threat detection. Implementing these strategies can enhance data integrity, regulatory compliance, and overall cloud security posture. By adopting a proactive approach, IT leaders can optimize cybersecurity investments and ensure the resilience of multi-cloud environments. This study provides actionable insights to strengthen security in distributed cloud architectures, enabling organizations to defend against evolving cyber threats. (shrink)

We investigate the productivity impacts of a generative artificial intelligence technology—specifically, the assistive chatbot ChatGPT—within the realm of mid-level professional writing tasks. In a preregistered online experiment, we assigned occupation-specific, incentivized writing tasks to 444 college-educated professionals, with half of the participants randomly exposed to ChatGPT. Our findings reveal that ChatGPT significantly enhances average productivity: the time taken to complete tasks decreases by 0.8 standard deviations, and output quality improves by 0.4 standard deviations. Additionally, the use of ChatGPT reduces inequality (...) among workers by compressing the productivity distribution, primarily benefiting those with lower initial ability. ChatGPT primarily substitutes for worker effort rather than complementing worker skills, shifting the focus of tasks from rough-drafting to idea generation and editing. Furthermore, exposure to ChatGPT increases job satisfaction and self-efficacy while also heightening both concerns and excitement about automation technologies. (shrink)

Deep fakes, a technology enabling the creation of highly realistic fake images and videos through face-swapping, have sparked significant attention due to their potential for misuse. This paper explores the technologies behind deep fakes and categorizes them into four types: deep fake pornography, political campaigns, commercial uses, and creative content. The authors discuss the ethical and regulatory challenges associated with each category. Initially, deep fakes were used maliciously, such as in revenge porn and political manipulation, which led to widespread fear. (...) However, like many technologies before it, deep fakes may eventually become more widely accepted. The paper also examines the role of online platforms and governments in addressing these issues and offers an optimistic view of how the democratization of AI might help resolve the ethical concerns surrounding deep fakes. -/- . (shrink)

This article presents a comprehensive assessment of web services based on SOAP (Simple Object Access Protocol) and REST (Representational State Transfer) principles, focusing on their application in mobile environments and multimedia conference scenarios. As web services enable seamless communication and data exchange across diverse systems, understanding their performance characteristics in different contexts is crucial. SOAP, with its protocol-driven approach and emphasis on security and reliability, and REST, known for its simplicity, scalability, and efficiency, are evaluated using key performance metrics such (...) as latency, throughput, scalability, and reliability. This assessment aims to highlight the strengths and weaknesses of each approach, providing insights into their suitability for resource-constrained mobile applications and high-performance multimedia conferencing. The results and analysis offer valuable guidance for developers and decision-makers in selecting the most appropriate web service methodology to enhance application performance and user experience in these environments. (shrink)

This study investigates Docker-related topics and challenges through an analysis of Stack Overflow posts to identify prevalent issues and trends in Docker development. By leveraging both tag-based and content-based filtering methods, we compiled a comprehensive dataset of Docker discussions. Latent Dirichlet Allocation (LDA) topic modeling was employed to categorize these discussions, revealing that Application Development is the predominant focus, encompassing areas such as Framework Management, Coding Issues, Data Transfer, and Dockerspecific frameworks. This dominant category reflects developers' substantial interest in leveraging (...) Docker for building and managing applications, driven by Docker's significant market presence. The study provides valuable insights into the diverse technical and operational challenges faced by Docker developers, highlighting key areas of interest and difficulty within the Docker community. These findings offer guidance for future research, tool development, and educational resources, aiming to address the evolving needs of Docker practitioners and enhance the overall development experience. (shrink)

The convergence of cloud computing, blockchain technology, and the emerging era of quantum computing presents significant challenges for data security. This research tackles these growing vulnerabilities by introducing a comprehensive security framework that integrates Quantum Key Distribution (QKD), CRYSTALS-Kyber, and Zero-Knowledge Proofs (ZKPs) to protect data in cloud-based blockchain systems. The primary goal is to safeguard information against quantum threats through QKD, a quantum-secure cryptographic protocol. To enhance resilience against quantum attacks, the framework employs CRYSTALSKyber, a lattice-based encryption mechanism. Additionally, (...) ZKPs are utilized to strengthen privacy and verification processes within cloud and blockchain ecosystems. A key aspect of this study is the performance evaluation of the proposed framework, focusing on encryption and decryption efficiency, quantum key generation rates, and overall system performance. The analysis examines practical considerations such as file size, response time, and computational overhead to assess the framework’s real-world applicability. The findings highlight the framework’s effectiveness in mitigating quantum threats and securing cloud-based blockchain storage. By addressing critical gaps in both theoretical research and practical implementation, this study provides valuable insights for organizations seeking quantum-resistant data security solutions. The framework’s efficiency and scalability demonstrate its feasibility, offering a roadmap for securing cloud environments against the evolving challenges posed by quantum computing and blockchain integration. (shrink)

This paper presents the design and implementation of a pattern-based J2EE application development environment aimed at streamlining the development process, enhancing code quality, and improving maintainability. We begin with an exploration of the fundamental principles of J2EE and the role of design patterns in software engineering. This is followed by a detailed discussion on the selection and application of various design patterns specifically tailored for J2EE, such as Singleton, Factory, Data Access Object (DAO), and Model-View-Controller (MVC).

Traditional project risk management techniques are becoming less and less effective in the age of Industry 4.0, which is defined by sophisticated automation, data interchange, and cyber-physical systems. It suggests a new framework that improves risk detection, assessment, and mitigation techniques by utilising technology like artificial intelligence (AI), big data analytics, and the Internet of Things (IoT). This paper illustrates how Industry 4.0 is revolutionising risk management procedures by looking at actual case studies and industry implementations. The framework offers practical (...) insights for practitioners looking to adjust to the changing technological landscape by addressing important possibilities and obstacles. According to the research, implementing Industry 4.0 technologies can greatly enhance project results and risk management effectiveness, providing a means for businesses to handle the challenges of contemporary project environments. (shrink)

Speech Emotion Recognition (SER) is an essential component in human-computer interaction, enabling systems to understand and respond to human emotions. Traditional emotion recognition methods often rely on handcrafted features, which can be limited in capturing the full complexity of emotional cues. In contrast, deep learning approaches, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, offer more robust solutions by automatically learning hierarchical features from raw audio data. This paper reviews recent advancements in deep (...) learning-based speech emotion recognition, discusses the various architectures used, and evaluates the challenges in real-world applications. We focus on the application of deep learning models to enhance the accuracy and robustness of SER, particularly in noisy environments. The study also discusses future directions for research, including multimodal emotion recognition and transfer learning to address challenges such as small datasets and cross-domain applications. (shrink)

A study was carried out from October, 2020 to September, 2021to investigate the diversity of fishes and the conservation status of Chittaura Jheel (Bahraich), Uttar Pradesh. During the study period, 38 fish species belonging to 28 genera, 14 families and 7 orders have been identified. The order Cypriniformes was found the dominated order with 15 species(39.47%) followed by Siluriformes 10 species (26.31%), Perciformes 4 species (10.52%), Ophiocephaliformes 4 species (10.52%), Synbranchiformes2 species (5.26%), Osteoglossiformes 2 species (5.26%) and Clupiformes 1 species (...) (2.63 %), The present investigation showed that the wetland, Chittaura Jheel possesses rich fish biodiversity but proper conservation measures are required to maintain sustainability and richness of the fish species diversity of that wetland of U.P. According to IUCN, 30 were enlisted as Least Concern (LC), 2 species as near endangered (NE), 3 species as near threatened (NT), 2 listed as Endangered (EN) and 1 species was listed as Vulnerable (VU). There is a need of conservation of fish diversity of this natural wetland. The pressure is increasing day by day due to increasing population, leading to loss of fish diversity hence identifying the problem and making a better management plan is the way for conservation of the fish diversity of the Chittaura Jheel, a wetland of Bahraich district of Uttar Pradesh. (shrink)

The increasing complexity and sophistication of cyber threats have rendered traditional perimeter-based security models insufficient for protecting modern digital infrastructures. Zero Trust Architecture (ZTA) has emerged as a transformative cybersecurity framework that operates on the principle of "never trust, always verify." Unlike conventional security models that rely on implicit trust, ZTA enforces strict identity verification, continuous monitoring, least-privilege access, and microsegmentation to mitigate risks associated with unauthorized access and lateral movement of threats. By integrating technologies such as artificial intelligence (AI), (...) machine learning (ML), and behavioral analytics, Zero Trust enhances threat detection, reduces attack surfaces, and ensures a proactive security posture across cloud, on-premises, and hybrid environments. This paper explores the core principles, implementation strategies, and benefits of Zero Trust Architecture, along with its challenges and future trends in cybersecurity. Furthermore, it highlights real-world applications and case studies that demonstrate the effectiveness of ZTA in protecting critical assets against advanced cyber threats. By adopting a Zero Trust approach, organizations can significantly improve their resilience to cyberattacks and ensure robust data protection in an evolving threat landscape. (shrink)

Homomorphic encryption is a transformative cryptographic technique that enables secure cloud data processing by allowing computations on encrypted data without requiring decryption. Unlike traditional encryption methods, which protect data only at rest and in transit, homomorphic encryption ensures end-to-end security, even during computation. This capability is particularly vital for industries that rely on cloud computing while handling sensitive information, such as finance, healthcare, and government sectors. However, despite its strong security guarantees, the widespread adoption of homomorphic encryption remains limited due (...) to its high computational complexity and performance overhead. This paper examines the role of homomorphic encryption in secure cloud data processing by evaluating its advantages, challenges, and real-world applicability. First, a comprehensive literature review contrasts traditional and homomorphic encryption techniques, highlighting their security implications and efficiency trade-offs. Second, experimental simulations are conducted to assess computation time, scalability, and data transfer efficiency in cloud environments. The results underscore that while homomorphic encryption enhances data security, its feasibility depends on specific use-case requirements, balancing security, performance, and cost. Finally, we explore practical applications such as privacy-preserving data analytics and secure outsourcing of computations, demonstrating how homomorphic encryption can redefine cloud security paradigms. Keywords: Homomorphic encryption, cloud security, encrypted computation, privacy-preserving data processing, secure cloud computing. (shrink)

Generative Adversarial Networks (GANs) have demonstrated significant potential in generating synthetic data for various applications, including those involving sensitive information like healthcare and finance. However, two major issues arise when GANs are applied to sensitive datasets: (i) the model may memorize training samples, compromising the privacy of individuals, especially when the data includes personally identifiable information (PII), and (ii) there is a lack of control over the specificity of the generated samples, which limits their utility for tailored use-cases. To address (...) these challenges, we propose a novel framework that integrates differential privacy with latent representation learning to ensure privacy while providing control over the specificity of generated data. Our approach ensures that the synthetic data does not reveal individual data points, and by learning effective latent codes, it allows for the generation of specific and meaningful data. We evaluate our method using the MNIST dataset, showing that it preserves privacy and demonstrates a privacy-utility trade-off, where increased privacy leads to decreased classification accuracy. Additionally, we highlight the computational challenges, as the training process incurs a tenfold increase in time compared to standard GAN models. Finally, we extend our approach to the CelebA dataset, demonstrating how privacy and specificity can be controlled to generate high-quality, private synthetic data. (shrink)

Generative Adversarial Networks (GANs) have demonstrated significant potential in generating synthetic data for various applications, including those involving sensitive information like healthcare and finance. However, two major issues arise when GANs are applied to sensitive datasets: (i) the model may memorize training samples, compromising the privacy of individuals, especially when the data includes personally identifiable information (PII), and (ii) there is a lack of control over the specificity of the generated samples, which limits their utility for tailored usecases. To address (...) these challenges, we propose a novel framework that integrates differential privacy with latent representation learning to ensure privacy while providing control over the specificity of generated data. Our approach ensures that the synthetic data does not reveal individual data points, and by learning effective latent codes, it allows for the generation of specific and meaningful data. We evaluate our method using the MNIST dataset, showing that it preserves privacy and demonstrates a privacy-utility trade-off, where increased privacy leads to decreased classification accuracy. Additionally, we highlight the computational challenges, as the training process incurs a tenfold increase in time compared to standard GAN models. Finally, we extend our approach to the CelebA dataset, demonstrating how privacy and specificity can be controlled to generate high-quality, private synthetic data. (shrink)

An increasing number of enterprises are using artificial intelligence (AI) to improve their cyber security and threat intelligence. AI is a type of AI that generates new data independently of preexisting data or expert knowledge. One emerging cyberthreat to systems that has been increasing is adversarial attacks. By generating fictitious accounts and transactions, adversarial attacks can interfere with and take advantage of decentralized apps that operate on the Ethereum network. Because fraudulent materials (such as accounts and transactions) used as malicious (...) payloads can be mistaken for legitimate data, detecting adversarial attacks can be difficult. This paper suggests a paradigm for cyber threat hunting in the Ethereum blockchain that makes use of Adversarial Networks (GAN) and Deep Recurrent Neural Networks (RNN). By considering a variety of sources and data points, this technology enables decision support systems to automatically and rapidly identify threats posed by hackers or other harmful actors. The likelihood of a successful assault can be further decreased by using AI to find weaknesses in an organization's infrastructure. Because security operations centers (SOCs) need to quickly identify threats and take defensive action, this technology is particularly wellsuited for them. AI can give businesses an extra line of defense against increasingly complex threats by integrating intriguing and useful data items that would have otherwise gone unnoticed. (shrink)

The exponential growth of healthcare data, along with its sensitive nature, has necessitated the development of innovative solutions for protecting patient privacy. Generative AI techniques, such as Generative Adversarial Networks (GANs), have shown promise in creating synthetic healthcare data that mirrors real-world patterns while preserving confidentiality. This paper proposes a privacy-enhanced generative AI framework for the creation of synthetic healthcare data. By incorporating differential privacy and federated learning, the system aims to enhance privacy while maintaining data utility for healthcare research (...) and machine learning tasks. The proposed framework not only safeguards patient information but also enables the creation of diverse, realistic synthetic datasets that can be leveraged for various healthcare applications. Results demonstrate that the synthetic data retains statistical integrity without compromising privacy, making it a viable solution for healthcare data sharing and analysis. (shrink)

Hybrid Electric Vehicles (HEVs) have gained significant popularity due to their reduced environmental impact and fuel efficiency. However, the complex integration of electrical and mechanical systems in HEVs presents significant cooling challenges. A robust cooling system is essential to maintain optimal performance and extend the lifespan of powertrains and battery systems. This paper explores the development of an advanced cooling system designed specifically for HEV powertrains, leveraging modern technologies such as heat exchangers, liquid cooling, and smart thermal management systems. The (...) proposed system uses adaptive cooling mechanisms that adjust based on real-time vehicle operating conditions to maximize efficiency and reduce energy consumption. Additionally, machine learning algorithms are integrated into the cooling system for predictive maintenance, identifying potential issues before they arise. By optimizing thermal management, the system ensures higher efficiency, improved vehicle performance, and a longer life for HEV components. This paper presents an analysis of the current cooling technologies in HEVs, identifies gaps in their functionality, and proposes a novel cooling framework that addresses these limitations, making HEVs more viable for long-term sustainability and performance. Keywords: Hybrid Electric Vehicle (HEV), Powertrain. (shrink)

The exponential growth of healthcare data, along with its sensitive nature, has necessitated the development of innovative solutions for protecting patient privacy. Generative AI techniques, such as Generative Adversarial Networks (GANs), have shown promise in creating synthetic healthcare data that mirrors real-world patterns while preserving confidentiality. This paper proposes a privacy-enhanced generative AI framework for the creation of synthetic healthcare data. By incorporating differential privacy and federated learning, the system aims to enhance privacy while maintaining data utility for healthcare research (...) and machine learning tasks. The proposed framework not only safeguards patient information but also enables the creation of diverse, realistic synthetic datasets that can be leveraged for various healthcare applications. Results demonstrate that the synthetic data retains statistical integrity without compromising privacy, making it a viable solution for healthcare data sharing and analysis. (shrink)

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various machine learning techniques (...) in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. -/- . (shrink)

Abstract: 1,3-dichloro-5,5-dimethylhydantoin (DCDMH) hydrolysed of this compounds leads to the formation of active positive halogen species like HOCl and H2O+Cl. The H2O+Cl in non-polar or less polar media, extensively used as a chlorinating and oxidizing agents in organic substrate. The rate of reaction is enhanced by the increasing [H+] and percentage of acetic acid. The main product of the oxidation is corresponding aldehydes. The thermodynamic parameters for the oxidation have been computed in the show step mechanism. The observed rate law (...) was in good conformity with the mechanism. (shrink)

Valmiki , The Sage of 5th century B.C wrote The Ramayana not to narrate the story of Rama in an absorbing style. Though the epic poem presents Rama’s Journey of life in enchanting poetry , the story and the enchanting poetry are sugar coating or honey to the organic medicine called Kundalini Yoga. Maharshi Valmiki wrote the Ramayana to spread / propagate Kundalini Yoga among the masses. Thus the soul of The Ramayana is Kundalini Yoga / Sri Vidya. Valmiki embedded (...) Kundalini Yoga in the Chapter titled “ Sundara Kanda” . Hanuman and Ravana are Kundalini Yogis of Samaya and Kaula Paths. And in Sundara Kanda , he inserted “ Trijata Swapna “ , dream sequence of a demon and in it embedded the Gayathri Mantra. The concepts of Vishnu and Avatar (reincarnation) were nonexistent during the Ramayana Period. Seshendra Sharma , Scholar - Poet in his Magnum Opus of Research “ Shodasi : Secrets of The Ramayana “ reveals these secrets lying hidden for thousands of years . (shrink)

Heidegger Being and Time, Sein und Zeit (1927): An Index -/- By -/- Daniel Fidel Ferrer -/- and -/- Ritu Sharma -/- 1. Heidegger, Martin, -- 1889-1976. 2. Heidegger, Martin, -- 1889-1976 -- Concordances. 3. Heidegger, Martin, -- 1889-1976 -- Indexes. 4). Metaphysics. 5). Philosophy, German. 6). Heidegger, Martin; -- Wörterbuch. 7). Sein und Zeit. English. 8). Ontology. 9). Space and time. 10). Being and Time [Sein und Zeit]. I. Ferrer, Daniel Fidel, 1952-. II. Sharma, Ritu. -/- Pages (...) for this volume 1-655. Total words indexed: 24,638. For the two volumes that are indexed: pages 1 to 1106. Total pages in this volume is 654. The actual texts are not included because of copyright. This is an experiment in to indexing two different languages: German and English in the same index. -/- The following is a machine index of two volumes written by Martin Heidegger. The first volume is the German edition of Sein und Zeit, GA 2. The second is the same volume only translated into English by Joan Stambaugh as Being and Time (1996). -/- Martin Heidegger’s Being and Time (German edition Sein und Zeit) published in 1927 is one of the most famous philosophical book published in the 20th century; and remains his most renowned book. Being and Time is listed in the overall ranking of the 100 most cited books of the 20th century. Boston Public Library ranks it as number 1 out of 100. Argument from authority, Being and Time is an incredibly influential book. Example from the Main Index: -/- Abandon[5], 1051, 1089, 1090, 1092 abandon[2], 679, 749 abandoned[5], 677, 808, 841, 970, 992 abandoning[2], 780, 1099 Abandonment[1], 1040 abandonment[4], 875, 945, 1098, 1099 abandonrnent[1], 1092 abandons[1], 972 Abart[1], 216 Abbiendung[1], 271. (shrink)

The examination of anomaly process detection using negative selection algorithms and classification techniques focuses on enhancing the ability to identify deviations from expected patterns within complex data sets. Negative selection algorithms, inspired by biological immune systems, offer a novel approach to anomaly detection by efficiently distinguishing between normal and anomalous data points. When combined with various classification techniques, these algorithms can improve the accuracy and robustness of anomaly detection systems. This abstract explores the integration of negative selection algorithms with traditional (...) and advanced classification methods to optimize anomaly detection processes. By leveraging these combined approaches, the study aims to address challenges such as false positives, detection latency, and adaptability to diverse data environments. The findings suggest that the synergy of negative selection algorithms and classification techniques can lead to more precise and reliable detection of anomalies, providing valuable insights for applications across cybersecurity, finance, healthcare, and other critical fields. (shrink)

The assessment of cloud data loss prevention and encryption was the main emphasis of the current study. Cloud computing, another name for cloud-based technologies, boosts organizational effectiveness for appropriate data management procedures. By improving data visualization, cloud-based data loss or leakage prevention (DLP) helps businesses comprehend the risks and problems associated with appropriate data management. This study demonstrated how to handle data with encryption. The growth of company processes and the effective management of all activities through data management can benefit (...) from cloud loss prevention. Cloud computing that is IoT and AI based evaluates the underlying reasons and offers a comprehensive understanding of business issues. As a result, they may encourage ongoing development and enhance their data loss risk management offerings. (shrink)

Factor Affecting Customer Service in Supply Chain Management of Small and Medium Enterprises: An Empirical Study of Jammu Region -/- Author / Authors : Sanjeev Lalhotra and Prof. B.C Sharma Page no.149-165 Discipline : Applied Economics/ Management/ Commerce Script/language : Roman/English Category : Research paper Keywords: Customer services, Supply Chain Management, Small and Medium Enterprises.