Higher Education has been considered as a site of knowledge, and it is a place, where one can pursue it. But, the distribution of knowledge and acquiring knowledge is controlled by various factors. For example, caste, class, language, region, religion, gender, race, etc. The two principal factors, i.e. language and caste, determine one's access and then survival in higher educational institutions. The Hegemony of English language becomes a very problematic for non-English background students in the higher educational classroom in India. (...) The hegemony of one language itself achieves the status of dominant or prestigious. The caste, class and gender also determine who are going to use the English language as a medium of instruction. In the era of modernization and globalization, caste, class and gender dynamic work inherently in the higher education. In this context, this paper gives a broader insight of language and caste-based discrimination, and it's a mechanism in the higher education set-ups. (shrink)

For a Dalit,especially from a rural background, it is not easy to survive in the higher education system in India because it is overwhelmingly dominated by the upper caste, class, and English-speaking people. It is not uncommon for Dalit learners like us to face multiple discrimination, and even exclusion in higher educational institutions. Intersectionality between these three factors abounds in institutions of higher learning. The transition from native language to English has not been an easy task for me, for in (...) my educational journey, I have discovered that English is not just a language but also a commodity. It is becoming increasingly easy for economically well-off people to acquire education in English and dominate the spheres of educational institutions in India. They are usually considered as knowledgeable and intellectual persons. On the other hand, Dalit students also want to take education in English but, most of them are not able to do so because of their caste background and rampant discrimination. This study is based on my own experience and fieldwork at the University of Delhi through a semi-structured questionnaire. (shrink)

Education is essential to ensure the overall development of individuals. However, there exists a symbiotic relationship between language and education: the language of education determines one’s socio-economic position. Notably, knowledge production and dissemination in South Africa is dominated by English and vernacular languages are neglected. This discriminatory binary exists from primary schools to universities. It creates challenges for black, underprivileged, rural and semi-urban students, as they lack proficiency in English (known as the language of elites). Along with cultural and racial (...) discrimination, students from South Africa are victims of linguicism. Similarly, the modern Indian state has made education mandatory as a policy measure but students from marginalised sections face a language barrier. Consequently, students feel physiological trauma including inferiority complexes, humiliation. (shrink)

Since security threats in IoT-enabled smart cities may not appear clear and present to detection mechanisms, efforts have been made to use artificial intelligence methods for anomaly detection. Anomaly detection has been performed using unsupervised learning approaches (Autoencoders, GANs, One Class SVMs) in turn, with these instances considered security threats. In addition, an element for patches and traffic redirection in real time is included in the framework. Results show that the AI detection in general has much more security resilience, decreasing (...) possible attack vectors. This makes the integration of various features like AI, blockchain, and IDS for a solid IoT security a must. Since security threats in IoT-enabled smart cities may not appear clear and present to detection mechanisms, efforts have been made to use artificial intelligence methods for anomaly detection. Anomaly detection has been performed using unsupervised learning approaches (Autoencoders, GANs, One-Class SVMs) in turn, with these instances considered security threats. In addition, an element for patches and traffic redirection in real time is included in the framework. Results show that the AI detection in general has much more security resilience, decreasing possible attack vectors. This makes the integration of various features like AI, blockchain, and IDS for a solid IoT security a must. (shrink)

An explicit formal-ontological representation of entities existing at multiple levels of granularity is an urgent requirement for biomedical information processing. We discuss some fundamental principles which can form a basis for such a representation. We also comment on some of the implicit treatments of granularity in currently available ontologies and terminologies (GO, FMA, SNOMED CT).

The Unified Medical Language System and the Gene Ontology are among the most widely used terminology resources in the biomedical domain. However, when we evaluate them in the light of simple principles for wellconstructed ontologies we find a number of characteristic inadequacies. Employing the theory of granular partitions, a new approach to the understanding of ontologies and of the relationships ontologies bear to instances in reality, we provide an application of this theory in relation to an example drawn from the (...) context of the pathophysiology of hypertension. This exercise is designed to demonstrate how, by taking ontological principles into account we can create more realistic biomedical ontologies which will also bring advantages in terms of efficiency and robustness of associated software applications. (shrink)

In today’s rapidly evolving cloud landscape, enterprises are increasingly adopting multi-cloud strategies to enhance availability, avoid vendor lock-in, and optimize operational costs. However, this architectural shift introduces significant challenges in synchronizing data across heterogeneous cloud platforms in real time. Traditional batch synchronization techniques fail to meet the low-latency and high-throughput demands of modern applications. This paper presents a robust, scalable, and real-time data synchronization architecture leveraging Kafka Stream Processing to ensure consistency and continuity of data across multiple cloud environments.

Predictive analytics, empowered by generative AI and cloud technologies, has the potential to revolutionize educational practices by facilitating early intervention and proactive support for students. This chapter explores the integration of predictive analytics in educational settings, focusing on how datadriven insights can identify at- risk students and tailor interventions to their specific needs. By leveraging generative AI algorithms, educators can analyze vast amounts of data, including academic performance, engagement levels, and socio- emotional factors, to predict potential challenges before they escalate. (...) The chapter highlights case studies that demonstrate successful implementations of predictive analytics in schools and universities, showcasing improved student outcomes, enhanced engagement, and more personalized learning experiences. Additionally, it discusses the ethical considerations and challenges associated with data privacy and bias in AI systems. (shrink)

Natural Language Processing (NLP) is a rapidly growing field at the intersection of artificial intelligence and linguistics, aiming to enable machines to understand, interpret, and generate human language. This paper explores various aspects of NLP and its applications, from basic language processing to advanced machine learning models. The focus is on the evolution of NLP techniques, challenges, and current trends, with a particular emphasis on deep learning methods that have revolutionized the field. The paper also highlights significant NLP applications, including (...) sentiment analysis, machine translation, and conversational AI systems. (shrink)

As the systematic revolution converges computing technologies with Artificial Intelligence (AI) applications, various industries have been transformed, ranging from healthcare to finance. This paper presents an extensive review of the progress achieved through this synergy, alongside the challenges and possible future perspectives. The methodology used in this study is a literature review supported by a theoretical model illustrating the integration of AI with computing. The findings reveal that computational capabilities, data processing speeds, and AI model efficiencies have drastically improved. However, (...) significant roadblocks persist, particularly in data privacy, ethical considerations, and hardware limitations. These challenges underscore the need for addressing the field’s boundless potential for breakthroughs. Existing hurdles, such as the lack of robust regulations, call for timely intervention. As we move forward, this paper emphasizes the necessity for ongoing innovation and the development of comprehensive policies to navigate the evolving landscape. Ultimately, our conclusion highlights the critical importance of preparing for these challenges to fully realize the transformative potential of the AI and computing synergy. (shrink)

The International Classification of Functioning, Disability and Health provides a classification of human bodily functions, which, while exhibiting non-conformance to many formal ontological principles, provides an insight into which basic functions such a classification should include. Its evaluation is an important first step towards such an adequate ontology of this domain. Presented at the 13th Annual North American WHO Collaborating Center Conference on the ICF, 2007.

In today’s data-intensive world, real-time insights have become essential for businesses and systems that demand timely and intelligent decision-making. Traditional batch processing techniques are increasingly inadequate for handling high-velocity data streams generated from IoT devices, social media platforms, financial transactions, and industrial systems. This paper presents an architectural approach to real-time data stream processing using Apache Kafka integrated with AI models to enable dynamic analytics and automated responses. The system leverages Kafka's distributed messaging capabilities for reliable data ingestion and delivery, (...) while AI models trained on historical and real-time data are used to extract meaningful patterns and predictions. The proposed solution is designed to ensure low-latency inference, scalable processing, and seamless integration with existing data infrastructure. (shrink)

As organizations increasingly adopt cloud-native architectures, Kubernetes has emerged as the leading container orchestration platform. However, monitoring such dynamic and distributed environments remains a significant challenge. -/- Traditional observability tools often fall short in detecting real- time anomalies and understanding contextual issues across -/- multiple clusters. This study introduces an enhanced Kubernetes monitoring system based on Distributed Event Processing (DEP). The proposed system integrates telemetry collection, event stream processing, and complex event correlation to offer intelligent and scalable monitoring capabilities. It (...) enables proactive identification of issues such as pod failures, network delays, and performance bottlenecks by analyzing real-time event data from Kubernetes clusters. The architecture supports multi-cluster observability using distributed tools like Apache Kafka, Flink, Prometheus, and Fluentd. Empirical results demonstrate improved detection speed, reduced false positives, and faster recovery time, making this approach highly effective for modern DevOps workflows. (shrink)

Cloud computing is revolutionizing modern computing infrastructure, but as the demand for cloud services grows, so does the energy consumption of the data centers that support them. Data centers, which house the servers that power cloud computing, are significant contributors to global carbon emissions. This paper explores strategies to achieve net-zero carbon emissions in data centers, focusing on energy efficiency, renewable energy integration, and carbon offsetting practices. The paper reviews current technologies, practices, and policies aimed at reducing the carbon footprint (...) of cloud computing and discusses emerging innovations such as green data centers, server optimization techniques, and the use of artificial intelligence (AI) for energy management. By highlighting case studies from leading companies, this paper presents a comprehensive approach to achieving sustainability in the cloud computing ecosystem. The paper concludes by emphasizing the importance of collaboration among industry stakeholders, policymakers, and consumers in accelerating the transition to net-zero emissions in the digital infrastructure sector. (shrink)

The rapid evolution of digital banking, e-commerce, and financial technologies has led to an unprecedented volume of online financial transactions. While this digital transformation has improved convenience and efficiency, it has also exposed systems to increasingly sophisticated fraud schemes. Traditional rule-based detection methods often fall short in identifying complex and adaptive fraudulent behaviors. This paper proposes an AI-powered framework for real-time fraud detection in financial transactions, leveraging advanced machine learning and deep learning models to identify anomalies with high accuracy and (...) low latency. The proposed system integrates real-time data stream processing, behavioral analytics, and model explainability to ensure prompt and reliable fraud mitigation. Additionally, it includes adaptive learning mechanisms that enable continuous improvement based on new fraud patterns. The architecture is designed to scale across distributed environments, making it suitable for high-throughput, mission-critical financial platforms. Experimental results demonstrate the system’s superior performance compared to conventional techniques in terms of detection accuracy, response time, and false positive reduction. (shrink)

In modern distributed systems, real-time data handling and message delivery are crucial, especially in mission-critical environments such as healthcare, finance, and industrial control systems. Apache Kafka, widely adopted for high-throughput message streaming, lacks built-in mechanisms to enforce message prioritization, often treating all messages equally in terms of delivery order and processing urgency. This limitation can lead to significant delays or failures in delivering high-priority messages, resulting in critical service degradation. This paper proposes a novel approach to enable efficient message queue (...) prioritization in Kafka-based architectures. By designing a priority-aware framework that involves custom producer-consumer logic, modified topic-partition strategies, and dynamic scheduling algorithms, we demonstrate improved latency and timeliness for high-priority messages. The system is evaluated under various workloads and failure conditions, and the results reveal significant improvements in processing efficiency and delivery guarantees for priority messages without compromising overall system performance. The -/- proposed model enhances Kafka's usability for real-time, high- stakes applications where differentiated message handling is -/- vital. ,. (shrink)

We provide a methodology for the creation of ontological partitions in biomedicine and we test the methodology via an application to the phenomenon of blood pressure. An ontology of blood pressure must do justice to the complex networks of intersecting pathways in the organism by which blood pressure is regulated. To this end it must deal not only with the anatomical structures and physiological processes involved in such regulation but also with the relations between these at different levels of granularity. (...) For this purpose our ontology offers a variety of distinct partitions � of substances, processes and functions � and integrates these together within a single framework via transitive networks of part-whole and dependence relations among the entities in each of these categories. The paper concludes with a comparison of this methodology with the approaches of GOTM, KEGG, DIP and BIND and provides an outline of how the methodology is currently being applied in the field of biomedical database integration. (shrink)

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)

In recent years, the financial industry has undergone a transformative shift fueled by artificial intelligence and data-driven personalization. At the heart of this evolution lies Natural Language Understanding (NLU), a subfield of Natural Language Processing (NLP), which enables machines to comprehend and respond to human language with contextual awareness. Personalized financial services—such as investment advice, spending insights, budgeting support, and credit recommendations—require a deep understanding of user intent, transactional behavior, and conversational cues. This paper presents a comprehensive study of the (...) integration of state-of-the-art NLU models into financial service ecosystems, focusing on their ability to enhance user experience through contextual, accurate, and secure interactions. (shrink)

Ontological principles are needed in order to bridge the gap between medical and biological information in a robust and computable fashion. This is essential in order to draw inferences across the levels of granularity which span medicine and biology, an example of which include the understanding of the roles of tumor markers in the development and progress of carcinoma. Such information integration is also important for the integration of genomics information with the information contained in the electronic patient records in (...) such a way that real time conclusions can be drawn. In this paper we describe a large multi-granular datasource built by using ontological principles and focusing on the case of colon carcinoma. (shrink)

The rapid digitalization of financial services has driven the adoption of artificial intelligence (AI) for automating decision-making, fraud detection, risk assessment, and personalized financial offerings. However, these AI models often require access to sensitive user data, leading to significant concerns around data privacy, security, and regulatory compliance. Federated Learning (FL) has emerged as a transformative approach to address these concerns by enabling collaborative model training across decentralized data sources without exposing raw data to central servers. This paper explores the application (...) of federated learning techniques specifically tailored for secure AI model training in the FinTech domain. We present an in-depth analysis of FL architecture, system design considerations, privacy-preserving mechanisms, and optimization strategies for heterogeneous and non-IID data distributions. Real-world use cases are studied to demonstrate the feasibility of integrating FL with existing financial systems. The evaluation also compares FL’s effectiveness against centralized models, considering parameters like model accuracy, data leakage resistance, and convergence performance. This work aims to provide FinTech developers and researchers with a practical blueprint for implementing secure and scalable federated AI systems that meet evolving data protection standards. (shrink)

There are a number of existing classifications and staging schemes for carcinomas, one of the most frequently used being the TNM classification. Such classifications represent classes of entities which exist at various anatomical levels of granularity. We argue that in order to apply such representations to the Electronic Health Records one needs sound ontologies which take into consideration the diversity of the domains which are involved in clinical bioinformatics. Here we outline a formal theory for addressing these issues in a (...) way that the ontologies can be used to support inferences relating to entities which exist at different anatomical levels of granularity. Our case study is the colon carcinoma, one of the most common carcinomas prevalent within the European population. (shrink)

In recent years, the banking sector has undergone a radical transformation driven by advancements in Artificial Intelligence (AI), particularly Generative AI technologies. With the growing expectations of digital-savvy customers, traditional banking interfaces are no longer sufficient to provide the level of personalization and engagement users demand. Generative AI models, such as Large Language Models (LLMs), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs), have the potential to revolutionize how financial institutions interact with users by dynamically customizing content, services, and interfaces (...) based on individual preferences and behavioral patterns. This paper explores the application of Generative AI in designing personalized user experiences in mobile and web-based banking applications. It investigates the working principles, architectural designs, and implementation strategies required to leverage synthetic content generation, natural language processing, and multimodal AI to create hyper-personalized financial services. Additionally, it examines case studies from leading banks, analyzes the effectiveness of these technologies in increasing user engagement, and discusses the ethical and regulatory considerations surrounding user data and content generation. The paper concludes with insights on future -/- enhancements, including emotion-aware personalization, cross- platform customization, and the integration of Generative AI -/- with voice banking interfaces. (shrink)

Arguments attempting to debunk moral beliefs, by showing they are unjustified, have tended to be global, targeting all moral beliefs or a large set of them. Popular debunking arguments point to various factors purportedly influencing moral beliefs, from evolutionary pressures, to automatic and emotionally-driven processes, to framing effects. We show that these sweeping arguments face a debunker’s dilemma: either the relevant factor is not a main basis for belief or it does not render the relevant beliefs unjustified. Empirical debunking arguments (...) in ethics can avoid this predicament, but only if they are refocused on highly selective classes of moral belief. Experimental data can combine with familiar consistency reasoning to reveal that like cases are not being treated alike. Selective debunking arguments are unlikely to yield sweeping sceptical conclusions, but they can lead to rational moral change. (shrink)

This paper explores the intersection between Advaita Vedanta's non-dualistic conception of consciousness and the evolving field of quantum physics, offering a dialogue between ancient metaphysical insights and modern scientific thought. Advaita Vedanta, an Indian philosophical tradition, posits that consciousness is the fundamental reality, transcending the material world, which it views as an illusion, or Maya. Similarly, contemporary developments in quantum physics challenge the classical materialistic worldview by revealing that reality, at its most fundamental level, is interconnected, probabilistic, and observer-dependent. This (...) philosophical dialogue aims to bridge these two domains by examining how quantum phenomena, such as wave-particle duality, non-locality, and the observer effect, resonate with Advaita’s concept of Brahman as the ultimate, non-dual consciousness. By exploring parallels between these seemingly disparate fields, this paper questions the limitations of materialistic interpretations of consciousness and proposes that quantum physics could offer a scientific framework to support Advaita’s assertion that consciousness is the substratum of reality. The discussion further investigates how the observer effect in quantum mechanics might align with the Advaitic principle that reality is shaped by consciousness. This study contributes to contemporary debates in the philosophy of mind and philosophy of science by proposing a unified model of consciousness that transcends dualistic divisions between mind and matter, advancing the possibility of a deeper metaphysical understanding of the universe. (shrink)

Can reasoning improve moral judgments and lead to moral progress? Pessimistic answers to this question are often based on caricatures of reasoning, weak scientific evidence, and flawed interpretations of solid evidence. In support of optimism, we discuss three forms of moral reasoning (principle reasoning, consistency reasoning, and social proof) that can spur progressive changes in attitudes and behavior on a variety of issues, such as charitable giving, gay rights, and meat consumption. We conclude that moral reasoning, particularly when embedded in (...) social networks with mutual trust and respect, is integral to moral progress. (shrink)

: The rise of digital platforms for job recruitment has brought with it a growing threat of fraudulent job postings, undermining the trust and safety of online hiring systems. This paper proposes an advanced fraud detection system based on the ALBERT (A Lite BERT) model to identify fraudulent job postings. The system will utilize a dataset created by merging job postings from multiple sources to better capture both legitimate and fraudulent job listings. A comprehensive pre-processing pipeline, including data cleaning and (...) feature engineering, will be applied to prepare the data for model training. The system will incorporate various techniques, such as SMOTE (Synthetic Minority Over-sampling Technique), to address class imbalance. The ALBERT model will then be fine-tuned to classify job postings, and the system will be evaluated using standard performance metrics like accuracy, precision, recall, and F1-score. The goal is to provide an effective and scalable solution for detecting fraudulent job postings in real-time, enhancing the overall security and trustworthiness of online recruitment platforms. (shrink)

How can we make moral progress on factory farming? Part of the answer lies in human moral psychology. Meat consumption remains high, despite increased awareness of its negative impact on animal welfare. Weakness of will is part of the explanation: acceptance of the ethical arguments doesn’t always motivate changes in dietary habits. However, we draw on scientific evidence to argue that many consumers aren’t fully convinced that they morally ought to reduce their meat consumption. We then identify two key psychological (...) mechanisms—motivated reasoning and social proof—that lead people to resist the ethical reasons. Finally, we show how to harness these psychological mechanisms to encourage reductions in meat consumption. A central lesson for moral progress generally is that durable social change requires socially-embedded reasoning. (shrink)

An important part of the Unified Medical Language System (UMLS) is its Semantic Network, consisting of 134 Semantic Types connected to each other by edges formed by one or more of 54 distinct Relation Types. This Network is however for many purposes overcomplex, and various groups have thus made attempts at simplification. Here we take this work further by simplifying the relations which involve the three Semantic Types – Diagnostic Procedure, Laboratory Procedure and Therapeutic or Preventive Procedure. We define operators (...) which can be used to generate terms instantiating types from this selected set when applied to terms designating certain other Semantic Types, including almost all the terms specifying clinical tasks. Usage of such operators thus provides a useful and economical way of specifying clinical tasks. The operators allow us to define a mapping between those types within the UMLS which do not represent clinical tasks and those which do. This mapping then provides a basis for an ontology of clinical tasks that can be used in the formulation of computer-interpretable clinical guideline models. (shrink)

The implementation of artificial intelligence in digital transformation has transformed multiple business sectors yet introduces considerable moral issues about system transparency as well as prejudice and managerial control. This paper evaluates the moral frameworks and principles which control AI development stages and deployment processes while defining proper AI implementation methods. The research evaluates practical examples of ethical AI deployment as well as the functions of organizations together with governments in maintaining AI governance standards. The establishment of fair regulations combined with (...) public trust and business-policy-cooperation stands needed for creating ethical AI governance systems. Research demonstrates the current developments in AI regulation combined with existing ethical issues in AI application as well as the requirement to create effective policies that protect both innovation and ethical principles. The research presents recommendations for policy development together with recommendations regarding future research that will improve both AI governance and ethical digital transformation. (shrink)

Rural Green Marketing: Challenges and Opportunities in India -/- Author / Authors : Indal Kumar Page no.194-203 Discipline : Applied Economics/ Management/ Commerce Script/language : English/Roman Category : Research paper Keywords: Rural Green Product, Environmentally safe of rural market, opportunities and challenges of Green Marketing.

Anti-gay attitudes have declined in the U.S. The magnitude, speed, and demographic scope of this change have been impressive especially in comparison with prejudice against other marginalized groups. We develop a philosophically-informed psychological account of the unique decline in anti-gay bias in the context of important cultural and political conditions. We highlight two key psychological mechanisms: interpersonal connection and social category classification. First, many people have discovered that a close friend, family member, or admired individual is gay, motivating them to (...) identify the harm and discrimination faced by the individual they know, and catalyzing moral consistency reasoning such that they generalize this interpersonal insight to strangers. Second, many people have taken an essentialist stance toward social categories, including sexual orientation, leading them to infer that being gay is genetically determined and not subject to free choice or moral responsibility, nor mutable and worth attempting to change. We contrast this with the relationship between essentialism and attitudes toward women and people of color, and provide an account of the difference. This psychological account has implications for the future decline of anti-gay attitudes, in the U.S. and other countries, along with the nascent decline of anti-trans attitudes. (shrink)

This chapter discusses contemporary scientific research on the role of reason and emotion in moral judgment. The literature suggests that moral judgment is influenced by both reasoning and emotion separately, but there is also emerging evidence of the interaction between the two. While there are clear implications for the rationalism-sentimentalism debate, we conclude that important questions remain open about how central emotion is to moral judgment. We also suggest ways in which moral philosophy is not only guided by empirical research (...) but continues to guide it. (shrink)

Security is a major concern on the internet, and Distributed Denial of Service (DDoS) attacks are a significant threat. These attacks overwhelm network resources and use up bandwidth, making it difficult for legitimate users to access services. One challenge in dealing with DDoS attacks is telling the difference between a sudden increase in traffic from real users, known as a "flash crowd," and actual attack traffic. This paper looks at different existing solutions for detecting DDoS attacks and explains how these (...) methods help reduce their impact. As more businesses move to cloud-based services, the risk of DDoS attacks has increased. These attacks can seriously disrupt organizations by overloading their network resources and making important services unavailable. Traditional defense methods, like firewalls, are no longer enough to handle the scale and complexity of modern DDoS attacks. To address this issue, We propose a new approach that utilizes the implementation of deep learning models to detect DDoS attacks in cloud environments. Our method uses various features from network traffic to train machine learning algorithms, aiming to effectively identify DDoS attacks in real-time with high accuracy. (shrink)

Formalisms such as description logics (DL) are sometimes expected to help terminologies ensure compliance with sound ontological principles. The objective of this paper is to study the degree to which one DL-based biomedical terminology (SNOMED CT) complies with such principles. We defined seven ontological principles (for example: each class must have at least one parent, each class must differ from its parent) and examined the properties of SNOMED CT classes with respect to these principles. Our major results are: 31% of (...) the classes have a single child; 27% have multiple parents; 51% do not exhibit any differentiae between the description of the parent and that of the child. The applications of this study to quality assurance for ontologies are discussed and suggestions are made for dealing with multiple inheritance. (shrink)

Moral arguments against the consumption of animal products from factory farms are traditionally categorical. The conclusions require people to eliminate from their diets all animal products (veganism), all animal flesh (vegetarianism), all animals except seafood (pescetarianism), etc. An alternative “reducetarian” approach prescribes progressive reduction in one's consumption of animal products, not categorical abstention. We articulate a much-needed moral defense of this more ecumenical approach. We start with a presumptive case in favor of reducetarianism before moving on to address three objections—that (...) it falls short of our obligations to address such an egregious practice, is a rationalization of the status quo, and cannot fix systemic injustices in animal agriculture. We conclude that reducetarianism is a defensible approach for many people and is a promising route to moral progress on factory farming. (shrink)

Railway asset change detection through Artificial Intelligence (AI) technology has transformed infrastructure monitoring by providing better efficiency combined with predictive maintenance functions and improved accuracy. The paper studies the development of railway asset monitoring throughout history while it moved from traditional manual inspections to AI-powered solutions. The study recognizes three main barriers which include irregular data acquisition practices along with restricted sensor abilities and imprecise AI model precision and difficulties applying them to current railway control platforms. The research investigates novel (...) data collection approaches besides studying hybrid AI model deployment and finding optimal solutions for railway operational AI integration. The research findings establish critical requirements for railway administrators and policy makers and developers of AI systems regarding standardized regulatory structures and enhanced AI interpretation capabilities and collaborative advancements. Music research will further AI railway asset monitoring by integrating edge computing technology and 5G connectivity to process real-time data streams. The presented study enhances railway infrastructure management systems through its advances in AI methodology development along with solutions to implementation hurdles. (shrink)

The auto insurance industry is plagued by fraudulent claims, resulting in significant financial losses and increased premiums for policyholders. To combat this issue, insurers are turning to advanced analytics and machine learning techniques to identify and prevent fraudulent activity. By analyzing a vast array of data points, including claimant information, vehicle details, and accident reports, insurers can build predictive models that detect patterns and anomalies indicative of fraud. These models can identify red flags such as inconsistent or suspicious claims, exaggerated (...) damage, or fake accidents. Moreover, they can also uncover organized criminal rings and scams, helping insurers to take proactive measures to prevent and investigate fraudulent claims. By leveraging data analytics and machine learning. (shrink)

The article discusses the implementation of AI within cartridge inventory systems to achieve improved inventory control with reduced operational expenses and superior efficiency results. Research shows that AI solutions succeed in practice because they eliminate stock shortages while managing excess inventory to achieve better supply chain operational results throughout real-world situations. The research approach examined existing AI usage in inventory management and documented success stories about HP and Canon when they applied AI technology to their cartridge supply operations. The research (...) confirms how AI technologies' predictive analytics and machine learning capabilities deliver better demand forecasting accuracy and improved resource allocation while reducing business costs. Implementing AI solutions provides business opportunities to industries dependent on cartridges since they can gain operational efficiency with enhanced inventory precision while cutting overhead expenses. This research paper ends with propositional suggestions about investigating how AI technology integrates with IoT systems for advanced real-time inventory monitoring. (shrink)

Modern warehouse operations are increasingly confronted with critical inventory management issues such as stockouts, overstocking, and delays in restocking—all of which stem largely from the inefficiencies of manual inventory systems. To address these challenges, this project proposes a comprehensive smart warehouse management solution that combines real-time inventory tracking with advanced predictive analytics and intelligent stock alert mechanisms. Central to this system is a user-friendly web application that empowers both administrators and customers to manage products efficiently, monitor stock levels dynamically, and (...) automate reordering processes. The solution leverages ARIMA-based time series forecasting models to analyze historical inventory and sales data, enabling the system to accurately predict future stock requirements. This predictive capability ensures proactive inventory replenishment, minimizes the risk of supply chain disruptions, and enhances overall operational reliability. Additionally, the platform incorporates visual dashboards that display key insights such as product demand patterns, sales trends, and inventory turnover rates, supporting data-driven decision-making. By automating critical inventory functions, the system significantly reduces human error and administrative overhead. It enhances transparency and responsiveness throughout the supply chain, ensuring that stock levels are always optimized to meet customer demand without unnecessary surplus. The integration of intelligent alerts provides timely notifications for low-stock or high- demand items, enabling swift corrective actions. In summary, the proposed smart warehouse management system transforms conventional inventory handling into a proactive, intelligent, and efficient process. It offers a scalable and adaptive framework that not only addresses current warehouse challenges but also supports future growth and digital transformation initiatives in inventory management. (shrink)

As organizations increasingly adopt cloud-native architectures, the demand for flexible, efficient, and scalable orchestration solutions across multi-cloud environments has grown significantly. Kubernetes, as a leading container orchestration platform, has become the de facto standard for managing workloads across heterogeneous cloud infrastructures. However, orchestrating workloads across multiple cloud providers introduces complex challenges related to resource optimization, workload portability, latency management, inter-cluster communication, and security. This paper presents a comprehensive framework for Scalable Kubernetes Workload Orchestration in Multi-Cloud Environments, aiming to optimize resource (...) utilization, ensure high availability, and maintain seamless workload distribution. The proposed approach integrates advanced workload schedulers, federated Kubernetes clusters, and cloud-agnostic deployment strategies to address limitations in native Kubernetes scalability across clouds. Our model leverages container placement algorithms, latency-aware scheduling, and policy-based workload migration to dynamically balance workloads based on system health, network performance, and cost-effectiveness. Additionally, integration with Infrastructure-as-Code (IaC) tools and CI/CD pipelines ensures consistent deployment patterns and version control across cloud vendors. (shrink)

Evidence-based medicine relies on the execution of clinical practice guidelines and protocols. A great deal of of effort has been invested in the development of various tools which automate the representation and execution of the recommendations contained within such guidelines and protocols by creating Computer Interpretable Guideline Models (CIGMs). Context-based task ontologies (CTOs), based on standard terminology systems like UMLS, form one of the core components of such a model. We have created DAML+OIL-based CTOs for the tasks mentioned in the (...) WHO guideline for hypertension management, drawing comparisons also with other related guidelines. The advantages of CTOs include: contextualization of ontologies, providing ontologies tailored to specific aspects of the phenomena of interest, dividing the complexity involved in creating ontologies into different levels, providing a methodology by means of which the task recommendations contained within guidelines can be integrated into the clinical practices of a health care set-up. (shrink)

Recent work on the quality assurance of the Gene Ontology (GO, Gene Ontology Consortium 2004) from the perspective of both linguistic and ontological organization has made it clear that GO lacks the kind of formalism needed to support logic-based reasoning. At the same time it is no less clear that GO has proven itself to be an excellent terminological resource that can serve to combine together a variety of biomedical database and information systems. Given the strengths of GO, it is (...) worth investigating whether, by overcoming some of its weaknesses from the point of view of formal-ontological principles, we might not be able to enhance a version of GO which can come even closer to serving the needs of the various communities of biomedical researchers and practitioners. It is accepted that clinical and bioinformatics need to find common ground if the results of data-intensive biomedical research are to be harvested to the full. It is also widely accepted that no single method will be sufficient to create the needed common framework. We believe that the principles-based approach to life-science data integration and knowledge representation must be one of the methods applied. Indeed in dealing with the ontological representation of carcinomas, and specifically of colon carcinomas, we have established that, had GO (and related biomedical ontologies) followed some of the basic formal-ontological principles we have identified (Smith et al. 2004, Ceusters et al. 2004), then the effort required to navigate successfully between clinical and bioinformatics systems would have been reduced. We point here to the sources of ontologically-related errors in GO, and also provide arguments as to why and how such errors need to be resolved. (shrink)

In this paper current author proposes a need for studying intricate differences between psychological aspects, factors, cognitions, affect, behaviour and related dynamics. It can be promoted as a separate branch within the domain of Psychology as Intricacy Psychology. Case history taking for the new Intervention Meditation of current author is proposed in line with capturing such intricacies of the client. The current also proposes to study the intricacies belonging to ‘Self’ as an important factor within his new Intervention Meditation approach. (...) View of Self in line with current author’s new Intervention Meditation is also outlined. This paper is at the initial stage of the series of papers planned to develop his new Intervention Meditation approach which is entirely different from all other meditations in vogue. (shrink)

The integration of biomedical terminologies is indispensable to the process of information integration. When terminologies are linked merely through the alignment of their leaf terms, however, differences in context and ontological structure are ignored. Making use of the SNAP and SPAN ontologies, we show how three reference domain ontologies can be integrated at a higher level, through what we shall call the OBR framework (for: Ontology of Biomedical Reality). OBR is designed to facilitate inference across the boundaries of domain ontologies (...) in anatomy, physiology and pathology. (shrink)

The National Cancer Institute’s Thesaurus (NCIT) has been created with the goal of providing a controlled vocabulary which can be used by specialists in the various sub-domains of oncology. It is intended to be used for purposes of annotation in ways designed to ensure the integration of data and information deriving from these various sub-domains, and thus to support more powerful cross-domain inferences. In order to evaluate its suitability for this purpose, we examined the NCIT’s treatment of the kinds of (...) entities which are fundamental to an ontology of colon carcinoma. We here describe the problems we uncovered concerning classification, synonymy, relations and definitions, and we draw conclusions for the work needed to establish the NCIT as a reference ontology for the cancer domain in the future. (shrink)

The research investigates the historical development of graph theory from the 18th century as well as its basic concepts and extends applications across computer science and network analysis and biological and artificial intelligence fields. The study reviews modern developments along with prospective research avenues because graph theory continues its essential role in resolving practical situations.

Location analytics is key to get into the current market. It deals with movements data of customer which tells about the customer’s lifestyle, their behavior through understanding the visiting pattern. A geo-fence is a virtual boundary for a real-world geographic area. A geo-fence can be generated as per user’s need by considering different radius, or a geo-fence can be a predefined set of boundaries. Geo-fencing is use of geo-fence. Geo fencing is a feature that uses the global positioning system (GPS) (...) to define geographical boundaries. It has widely increasing scope. Very few applications are dealing with geo-notifications that are intended to inform the mobile/stationary user proactively about location specific information. The technology behind proactive location-based services is called Geo-fencing. (shrink)

Mathematics forms graph theory as its fundamental division to study relationships between objects which exist as nodes and edges. Euler's Seven Bridges of Königsberg research marked the origin of what transformed into an essential mathematical field that includes computer science and network design and biological applications and other computational domains. Future developments in the field and current innovations get attention in the study.

The digital transformation across industries has given rise to an ever-increasing reliance on cloud computing, enabling scalable, on-demand access to storage and computational resources. However, while cloud services have revolutionized the way data is managed and accessed, they also pose critical concerns related to trust, transparency, data privacy, and centralized control. Blockchain, with its decentralized and tamper-resistant ledger, emerges as a promising solution to overcome these limitations. Integrating blockchain with cloud computing is gaining significant traction in academia and industry as (...) it can ensure secure, transparent, and verifiable operations within cloud infrastructures. The convergence of these two technologies forms a new paradigm that enhances data integrity, authentication, and secure sharing in cloud-based environments. This paper presents a comprehensive survey of the integration of blockchain and cloud computing, highlighting recent research contributions, architectural models, security enhancements, and the challenges that must be addressed to realize their full potential. (shrink)