India dropped its target of 500 GW of renewable energy capacity fossil fuel sources by 2030. Its responsibilities the United Nations Framework Convention Climate Change [UNFCCC],and reducing radiations by one billion tonnes by the end of the decade at the COP26 conference, held in Glasgow in November 2022. Researchers are continually searching for inexhaustible and reasonable energy sources. Solar energy is one of the greenest sources of energy and is also one of the cleanest. The most important factor in using (...) solar energy is the location of the solar power plant. The main objective of this study is to find the best location for a new solar power plant in a specific region called Bundelkhand region of Uttar Pradesh in India. Here we offer an extension of ELECTRE III method as two-phase Pythagorean neutrosophic elimination and choice translating reality PN-ELECTRE-III) method to adapt with fuzzy, ambiguous, unsure, and indeterminate criteria. The Pythagorean neutrosophic numbers [PNNs] used by the group decision support system of PN-ELECTRE III to measure performance of the alternatives. The options are entirely outclassed in the subsequent stage in view of the past stage's evaluations of them. By defining PNN we describe the technique of indifference threshold functions, preference treshold and veto threshold functions, which provide a more stable basis to drop outranking relations. By calculating the concordance credibility, discordance credibility and net credibility degrees of each alternative, the ranking module of the PN-ELECTRE III approach is made simpler. In order to confirm the applicability of the strategy suggested in this paper, the location selection problem for solar plants is finaly solved. (shrink)

The rapid advancement of Artificial Intelligence (AI) and the developments of Large Language Models (LLMs) like Generative Pretrained Transformers (GPTs) have significantly influenced content creation in scholarly communication and across various fields. This paper presents a comparative analysis of the content reliability between human-generated and GPT-generated scholarly articles. Recent developments in AI suggest that GPTs have become capable in generating content that can mimic human language to a greater extent. This highlights and raises questions about the quality, accuracy, and reliability (...) of such content, especially in academic contexts. Statistical evaluations and quantitative assessments conducted in this study uncover key differences in content accuracy, coherence, citation usage, and overall reliability between human and GPT-generated articles. The paper also examines the potential biases, the role of context, and the implications of AI-generated content for the future of scholarly communication. The study concludes with framework for predictive model indicating the potential future impact of GPT-driven content creation and recommendations for ensuring content quality and ethical considerations in AI usage. (shrink)

Cloud computing plays a pivotal role in the development and operation of Internet of Things (IoT) ecosystems, enabling efficient data storage, processing, and real-time analysis. By integrating cloud infrastructure with IoT devices, organizations can unlock significant benefits such as scalability, flexibility, and cost-efficiency. This paper explores the synergistic relationship between cloud computing and IoT, focusing on how cloud services enhance IoT capabilities, optimize resource utilization, and provide a robust framework for large-scale IoT deployments. We discuss key challenges, security concerns, and (...) emerging trends in the integration of cloud and IoT, and propose solutions to mitigate these issues. The paper concludes with insights into the future trajectory of cloud-enabled IoT ecosystems and their impact on industries. (shrink)

This fourteenth volume of Collected Papers is an eclectic tome of 87 papers in Neutrosophics and other fields, such as mathematics, fuzzy sets, intuitionistic fuzzy sets, picture fuzzy sets, information fusion, robotics, statistics, or extenics, comprising 936 pages, published between 2008-2022 in different scientific journals or currently in press, by the author alone or in collaboration with the following 99 co-authors (alphabetically ordered) from 26 countries: Ahmed B. Al-Nafee, Adesina Abdul Akeem Agboola, Akbar Rezaei, Shariful Alam, Marina Alonso, Fran Andujar, (...) Toshinori Asai, Assia Bakali, Azmat Hussain, Daniela Baran, Bijan Davvaz, Bilal Hadjadji, Carlos Díaz Bohorquez, Robert N. Boyd, M. Caldas, Cenap Özel, Pankaj Chauhan, Victor Christianto, Salvador Coll, Shyamal Dalapati, Irfan Deli, Balasubramanian Elavarasan, Fahad Alsharari, Yonfei Feng, Daniela Gîfu, Rafael Rojas Gualdrón, Haipeng Wang, Hemant Kumar Gianey, Noel Batista Hernández, Abdel-Nasser Hussein, Ibrahim M. Hezam, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Muthusamy Karthika, Nour Eldeen M. Khalifa, Madad Khan, Kifayat Ullah, Valeri Kroumov, Tapan Kumar Roy, Deepesh Kunwar, Le Thi Nhung, Pedro López, Mai Mohamed, Manh Van Vu, Miguel A. Quiroz-Martínez, Marcel Migdalovici, Kritika Mishra, Mohamed Abdel-Basset, Mohamed Talea, Mohammad Hamidi, Mohammed Alshumrani, Mohamed Loey, Muhammad Akram, Muhammad Shabir, Mumtaz Ali, Nassim Abbas, Munazza Naz, Ngan Thi Roan, Nguyen Xuan Thao, Rishwanth Mani Parimala, Ion Pătrașcu, Surapati Pramanik, Quek Shio Gai, Qiang Guo, Rajab Ali Borzooei, Nimitha Rajesh, Jesús Estupiñan Ricardo, Juan Miguel Martínez Rubio, Saeed Mirvakili, Arsham Borumand Saeid, Saeid Jafari, Said Broumi, Ahmed A. Salama, Nirmala Sawan, Gheorghe Săvoiu, Ganeshsree Selvachandran, Seok-Zun Song, Shahzaib Ashraf, Jayant Singh, Rajesh Singh, Son Hoang Le, Tahir Mahmood, Kenta Takaya, Mirela Teodorescu, Ramalingam Udhayakumar, Maikel Y. Leyva Vázquez, V. Venkateswara Rao, Luige Vlădăreanu, Victor Vlădăreanu, Gabriela Vlădeanu, Michael Voskoglou, Yaser Saber, Yong Deng, You He, Youcef Chibani, Young Bae Jun, Wadei F. Al-Omeri, Hongbo Wang, Zayen Azzouz Omar. (shrink)

This eleventh volume of Collected Papers includes 90 papers comprising 988 pages on Physics, Artificial Intelligence, Health Issues, Decision Making, Economics, Statistics, written between 2001-2022 by the author alone or in collaboration with the following 84 co-authors (alphabetically ordered) from 19 countries: Abhijit Saha, Abu Sufian, Jack Allen, Shahbaz Ali, Ali Safaa Sadiq, Aliya Fahmi, Atiqa Fakhar, Atiqa Firdous, Sukanto Bhattacharya, Robert N. Boyd, Victor Chang, Victor Christianto, V. Christy, Dao The Son, Debjit Dutta, Azeddine Elhassouny, Fazal Ghani, Fazli Amin, (...) Anirudha Ghosha, Nasruddin Hassan, Hoang Viet Long, Jhulaneswar Baidya, Jin Kim, Jun Ye, Darjan Karabašević, Vasilios N. Katsikis, Ieva Meidutė-Kavaliauskienė, F. Kaymarm, Nour Eldeen M. Khalifa, Madad Khan, Qaisar Khan, M. Khoshnevisan, Kifayat Ullah,, Volodymyr Krasnoholovets, Mukesh Kumar, Le Hoang Son, Luong Thi Hong Lan, Tahir Mahmood, Mahmoud Ismail, Mohamed Abdel-Basset, Siti Nurul Fitriah Mohamad, Mohamed Loey, Mai Mohamed, K. Mohana, Kalyan Mondal, Muhammad Gulfam, Muhammad Khalid Mahmood, Muhammad Jamil, Muhammad Yaqub Khan, Muhammad Riaz, Nguyen Dinh Hoa, Cu Nguyen Giap, Nguyen Tho Thong, Peide Liu, Pham Huy Thong, Gabrijela Popović‬‬‬‬‬‬‬‬‬‬, Surapati Pramanik, Dmitri Rabounski, Roslan Hasni, Rumi Roy, Tapan Kumar Roy, Said Broumi, Saleem Abdullah, Muzafer Saračević, Ganeshsree Selvachandran, Shariful Alam, Shyamal Dalapati, Housila P. Singh, R. Singh, Rajesh Singh, Predrag S. Stanimirović, Kasan Susilo, Dragiša Stanujkić, Alexandra Şandru, Ovidiu Ilie Şandru, Zenonas Turskis, Yunita Umniyati, Alptekin Ulutaș, Maikel Yelandi Leyva Vázquez, Binyamin Yusoff, Edmundas Kazimieras Zavadskas, Zhao Loon Wang.‬‬‬‬. (shrink)

This volum includes 37 papers of mathematics or applied mathematics written by the author alone or in collaboration with the following co-authors: Cătălin Barbu, Mihály Bencze, Octavian Cira, Marian Niţu, Ion Pătraşcu, Mircea E. Şelariu, Rajan Alex, Xingsen Li, Tudor Păroiu, Luige Vlădăreanu, Victor Vlădăreanu, Ştefan Vlăduţescu, Yingjie Tian, Mohd Anasri, Lucian Căpitanu, Valeri Kroumov, Kimihiro Okuyama, Gabriela Tonţ, A. A. Adewara, Manoj K. Chaudhary, Mukesh Kumar, Sachin Malik, Alka Mittal, Neetish Sharma, Rakesh K. Shukla, Ashish K. Singh, Jayant (...) Singh, Rajesh Singh, V.V. Singh, Hansraj Yadav, Amit Bhaghel, Dipti Chauhan, V. Christianto, Priti Singh, and Dmitri Rabounski. (shrink)

: Natural Language Processing (NLP), a subset of Artificial Intelligence (AI), has emerged as a powerful tool in analyzing human language to detect underlying mental health issues such as depression and suicidal tendencies. By leveraging techniques in machine learning and deep learning, NLP systems can process and interpret textual data from diverse sources like social media, forums, and online journals to identify patterns and linguistic markers associated with mental health conditions. These markers include the use of emotionally charged words, changes (...) in writing style, expressions of hopelessness, isolation, or distress, which are critical indicators of a person's psychological state. The process typically involves data preprocessing, feature extraction using models such as TF-IDF or word embeddings (e.g., Word2Vec, GloVe), and classification using supervised learning techniques or deep learning models like BERT or LSTM. Understanding human emotions is a critical aspect of human-computer interaction, mental health diagnostics, and customer service applications. This project focuses on leveraging Natural Language Processing (NLP) techniques to analyze and interpret emotions expressed in textual data. By utilizing state-of-the-art machine learning models, including deep learning architectures such as recurrent neural networks (RNNs) and transformers, the project aims to develop models capable of detecting and classifying emotional states such as joy, sadness, anger, fear, and surprise from written content. The research investigates various aspects of emotion detection, including sentiment analysis, contextual understanding, and the role of linguistic features in emotion recognition. Additionally, it explores challenges like sarcasm, ambiguity, and cultural differences in emotional expression. The goal is to create an emotionaware system that can improve applications in fields such as automated customer support, mental health monitoring, and social media analysis, enabling more empathetic and responsive interactions between humans and machines. Through this work, we seek to enhance the understanding of emotions in textual communication, providing valuable insights into emotional dynamics and improving human-computer interactions. (shrink)

Natural Language Processing (NLP), a subset of Artificial Intelligence (AI), has emerged as a powerful tool in analyzing human language to detect underlying mental health issues such as depression and suicidal tendencies. By leveraging techniques in machine learning and deep learning, NLP systems can process and interpret textual data from diverse sources like social media, forums, and online journals to identify patterns and linguistic markers associated with mental health conditions. These markers include the use of emotionally charged words, changes in (...) writing style, expressions of hopelessness, isolation, or distress, which are critical indicators of a person's psychological state. The process typically involves data preprocessing, feature extraction using models such as TF-IDF or word embeddings (e.g., Word2Vec, GloVe), and classification using supervised learning techniques or deep learning models like BERT or LSTM. The application of NLP for mental health detection is particularly impactful in healthcare and public safety. It enables early identification and intervention for at-risk individuals by providing clinicians and support organizations with actionable insights from large volumes of textual content. On social media platforms, it facilitates automated monitoring to detect crisis situations and initiate timely support. Additionally, in educational institutions and workplaces, such systems can promote mental well-being by identifying signs of distress and enabling appropriate counseling or outreach efforts. However, this field faces notable challenges, including data privacy concerns, ethical considerations, the risk of false positives or misdiagnosis, and ensuring the cultural and linguistic diversity of datasets to avoid bias. Ongoing research aims to improve model accuracy and interpretability while integrating multimodal data for more robust detection. The goal is to develop responsible and sensitive AI systems that assist in understanding and supporting mental health, ultimately contributing to a safer, more empathetic digital environment. (shrink)

The rapid urbanization of cities globally has brought about significant challenges in terms of resource management, sustainability, and quality of life for residents. To address these issues, the concept of Smart Cities has emerged, where technologies like the Internet of Things (IoT) are harnessed to integrate various sensors and data systems to enhance urban services and infrastructure. Smart Cities leverage IoT devices such as sensors, actuators, and connected systems to collect and analyze real-time data, which enables efficient decision-making, optimized resource (...) allocation, and improved services for citizens Incorporating IoT into city infrastructure allows for better management of essential services such as water, electricity, waste management, transportation, and healthcare. This integration fosters urban sustainability by reducing energy consumption, minimizing waste, and enhancing the quality of life through the application of data-driven solutions. The data collected through IoT systems also plays a crucial role in predictive maintenance, risk management, and climate adaptation strategies, ensuring that cities become more resilient to the challenges of climate change and population growth. This paper explores the integration of IoT in Smart Cities, focusing on the role of sensors and data collection for urban sustainability. The paper provides an overview of the technological framework that supports IoT in Smart Cities and discusses how these technologies are transforming urban life. It also highlights key challenges, such as data privacy, security concerns, and infrastructure costs, while proposing strategies for overcoming these challenges. Furthermore, the paper discusses successful case studies and outlines future trends in IoT deployment for urban sustainability. (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.

The rapid expansion of digital data has made metadata crucial in organizing, managing, and retrieving information effectively. Machine learning (ML) offers powerful tools to automate and enhance metadata processing, leading to improved accuracy, scalability, and efficiency. This paper explores how ML algorithms are applied to metadata extraction, classification, annotation, and enrichment. We review current research, examine the methodologies employed, and present a comparative analysis of techniques. Our findings suggest that supervised learning models, especially deep learning architectures, outperform traditional rule-based systems (...) in most scenarios. However, challenges remain in terms of interpretability, bias, and data quality. (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)

Scalability has emerged as a defining challenge and opportunity in deploying deep learning models. As datasets grow and models become more complex, traditional computing infrastructure often falls short in providing the necessary resources for training and inference. Cloud infrastructure offers a compelling solution, enabling the deployment of deep learning models at scale through dynamic resource provisioning, GPU and TPU acceleration, and integrated services. This paper investigates the role of cloud platforms—such as AWS, Microsoft Azure, and Google Cloud—in making scalable AI (...) a practical reality. We examine the infrastructure elements that support deep learning workflows, including containerization, orchestration, distributed computing, and model monitoring. The research includes a comprehensive literature review, methodological framework, and analysis of existing deployment architectures and tools like TensorFlow Serving, TorchServe, and Kubernetes-based solutions. Key findings reveal that cloud-native services significantly reduce deployment complexity, offer robust failover and scaling capabilities, and support DevOps and MLOps pipelines. A typical cloud-based workflow is discussed, highlighting stages such as data ingestion, model training, containerization, orchestration, and monitoring. The paper further explores the advantages of cloud deployment, including elasticity, cost-effectiveness, and global accessibility, while also identifying disadvantages such as vendor lock-in, data privacy risks, and latency in edge applications. Future directions include exploring serverless AI, green AI, and federated learning to improve sustainability, cost control, and data governance. (shrink)

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)

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)

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.

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)

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 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)

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 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)

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)

The paper presents the outlines of an ontology of plans and guidelines, which is then used as the basis for a framework for implementing guideline-based systems for the management of workflow in health care organizations. The framework has a number of special features, above all in that it enables us to represent in formal terms assignments of work-items both to individuals and to teams and to tailor guideline to specific contexts of application in health care organizations. It is designed also (...) to enable implementations to do justice to the fact that the processes carried out in health care organizations may deviate in different ways from the norms set forth in corresponding guideline definitions. This means that implementations built in conformity with the framework will be marked by a type of flexibility that might make them more likely to be accepted by healthcare professionals than are standard guideline-based management systems. (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)

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)

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)

The goal of this project is to create a prototype smart farm using Internet of Things (IoT) technology. Developing an Internet of Things feeding control system and designing and implementing environmental monitoring and control systems for farm housing are the primary objectives. The development method uses open-source hardware and software and is conducted in pig farms in the Thai region of Nakhon Si Thammara. An ESP8266 Wi-Fi microcontroller is crucial to the system design, allowing several sensors to be connected to (...) the server and actuators to be controlled. The MQTT protocol is used to transmit data over a secure wireless local network. The data flow is designed using Node-RED, and storage is handled using a NoSQL database such as InFluxDB. The feeding control system achieved a 100% compliance rate by following instructions continuously. End customers can utilise a web application on their PCs or cellphones to obtain real-time data and operate the system. Furthermore, the collected data is processed and analysed as big data, which facilitates the analysis of climate change trends in the vicinity of the study site and forecasting. The overall performance rating of the system received great marks from users, averaging 4.40 out of 5 with a 0.46 standard deviation. As a result, the created IoT system functions as a prototype that may be further enhanced for use in bigger farms or other agricultural environments. (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)

Customer Relationship Management has become one of the most valuable tools in business, with the help of predictive analyzes organizations can identify their customer’s needs and improve business results. This research focuses on using of predictive analytics in Salesforce, the prominent CRM system that helps to gain insights from large volumes of data with the help of deep learning approaches. Using current advanced models like Recurrent Neural Networks and the transformer connections, companies can gain deep insights into such patterns and (...) trends as customer buying behavior, sales anticipation, and customer lifetime value. It is not only effective in improving decision making but also useful in the customization of the marketing strategy involving the delivery of messages that the customer might be interested. Integrating deep learning into Salesforce’s analytics is a revolution in the CRM system function. Former approaches in data analysis could not deal with unstructured data or analyses customer paths, as mentioned before. However, thanks to deep learning, large-scale customer data and aspects of previously hidden patterns have become changer for business interpretation. This research demonstrates the potential of using deep learning for predictive analytics to inform customer-oriented initiatives and increase the proportion of customers amongst leads and stability of client base. Thus, by focusing on Salesforce as an example, this study gives a clear vision of how artificial intelligence revolutionizes the approach to CRM, serves as the reference point for data-driven businesses in the digital environment. (shrink)

The Internet of Things (IoT), Artificial Intelligence (AI), and Big Data are shaping the new evolving customer experience landscape. This transition points to the very important role that Salesforce, the world’s leading Customer Relationship Management (CRM) platform, will play. This paper talks about how Salesforce uses these technologies to reinvent customer engagement, personalize and predict. And by connecting devices and systems in real time, IoT lets businesses collect actionable data about their customer’s behavior and preferences. With the combination of AI (...) inside Salesforce, delivered via tools like Einstein, processes can be automated and predictive analytics; recommendations are provided by this data. It also allows us to integrate big data and analyze massive datasets to find trends, improve decision making, and deliver hyper personalization experiences. The practical applications considered in the paper include predictive maintenance, real time customer support, and targeted marketing campaigns. Second, it discusses the problems of including IoT, AI, and Big Data in existing systems, including data privacy issues, technological complexities and organizational blockades. This paper features key trends, takes stock of innovations and serves as a roadmap for organizations looking to redefine customer experiences in the hyper digital world. It’s no wonder Salesforce plays a pivotal marketing role in this future of customer-centric strategies. (shrink)

The Internet of Things (IoT) wireless LAN in healthcare has moved away from traditional methods that include hospital visits and continuous monitoring. The Internet of Things allows the use of certain means, including the detection, processing and transmission of physical and biomedical parameters. With powerful algorithms and intelligent systems, it will be available to provide unprecedented levels of critical data for real-time life that are collected and analyzed to guide people in research, management and emergency care. This chapter provides a (...) quick overview of IoT features and how they relate to wireless discovery and technology to deploy the medical applications you need. In the world, the revolution in any industry is to connect your products and devices to the Internet and make them independent and remotely connected, so that anyone can use and view them from anywhere and anytime. The Internet of Things provides us with a home automation system that uses smart devices to overcome this obstacle, allowing us to easily manage our appliances. A smart city is a vision to integrate a variety of information and communication solutions for residents with essential services, such as smart parking on all streets. The main motivation for using the Internet for parking objects is simply collecting data to get free parking. The IoT-based RTSSPS architecture is divided into three parts: a WSN-based smart street parking module, an IoT-based smart street parking module, and an IoT-based smart street parking module. IoT-based cloud with street parking algorithm, rating and future directions. (shrink)

Customer relationship management has been signed up as an integral part of the organization and is widely implemented within Salesforce, which operates globally. Such modern CRM systems are a must-have for data governance and consent management. As such, they must follow regulations such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the like. To remedy this problem, in this paper, we will discuss the intricacies of data governance as well as the other side of (...) obtaining consent in Salesforce, how regulatory laws play into the situation, and what challenges exist with regard to Salesforce for data governance and consent management and what does not exist to replace them in terms of data governance and consent management provided by Salesforce specifically. We then describe how we approach the IAC and describe a structured approach using automated consent tracking, role-based access control, data classification, encryption mechanisms, and audit logs. We also dive deep into how Artificial Intelligence (AI) and Machine Learning (ML) influence salesforce data governance. Industry case studies show empirical results of improvement in compliance and operational efficiency. Finally, this paper provides recommendations and future directions to those organizations trying to build resilient data governance for their Salesforce organization. (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)

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 examines three main applications within different domains that utilize graph theory such as finding shortest paths, optimizing network flow throughput and performing clustering operations in massive network structures. The research demonstrates the significance of graph-based models which helps tackle real-world difficulties between logistics operations and traffic systems and biological systems.

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)

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)

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)

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)

Gross Domestic Product is an total estimate of country’s economy, in terms of total productive output. It is of great importance to policy makers and central banks to know its trend and predict possible changes. It is of huge importance to coverage policy makers and central banks to know its trend and predict possible changes. This gives accurate assessment of the future state of the economy – whether it is heading toward expansion or contraction – such that preemptive actions can (...) be taken as appropriate. Reliable forecasting of GDP is a very important tool in the macro- economic toolbox. Unfortunately, national economies are very complicated and it isn't clean to version them accurately, not to mention expect their behavior. GDP forecast are very uncertain and unavoidably short-term. In this work we plan to use decision tree, bootstrap bagging and random forest. The motive for using these algorithms is that the random forest combines trees with the belief of an ensemble. Thus, the trees are weak learners and the random forest is a strong learner. The runtime of random forest is pretty fast, and they are able to handle an unbalanced and missing data. Bootstrap bagging reduces variance and helps to avoid over fitting. And by through the use of decision tree we can predict the classes for our training data. In this work we plan to implement a system that will help in GDP prediction. Manually it is very difficult and time consuming, tedious work to analyze data of whole country’s economical progress based on total production in country and all investments. We can take help of Machine Learning in this task. This will make our job easy and will give results in much less time. (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.

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)

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.

Formalisms based on one or other flavor of Description Logic (DL) are sometimes put forward as helping to ensure that terminologies and controlled vocabularies comply with sound ontological principles. The objective of this paper is to study the degree to which one DL-based biomedical terminology (SNOMED CT) does indeed comply 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 these 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 the phenomenon of multiple inheritance. The advantages and limitations of our approach are also discussed. (shrink)

Abstract: The emergence of women entrepreneurs and their contribution to the national economy is quite visible in India. Women’s entrepreneurship has been recognized during the last decade as an important untapped source of economic growth. According to of statistics women in India 2010, proportion of female main workers to total population in percentage is 16.65 in rural areas and 9.42 in urban areas this shows overall less contribution of women in work but more percentage of women workers in rural areas. (...) According to the quick result of the Fourth All India Census of MSMEs (2006-07), there are 26 million MSMEs in India which provides employment to about 60 million people. The sector contributes about 40 % GDP, beside 45% to the total manufacturing output and 40 % to the exports from the country. There could be many opportunities identification hidden inside the challenges for small business concerns. This paper examines various opportunities and challenges for Women Entrepreneurship Development in Micro Small and Medium Enterprise. More precisely the paper explores, rewards for starting a small business, myths about small businesses, new opportunities, four forms of entrepreneurship, identification practices among first generation and major challenges in small business. (shrink)

Using Agentic AI in concert with AWS Full Stack Development, this study offers a technically solid and scalable method to securely process financial data. AWS Lambda for serverless computation, Amazon S3 for scalable storage, AWS Glue for data classification and transformation, Athena for SQL-like querying, and QuickSight for interactive visualisation comprise the fundamental elements of the system. While Agentic AI modules enable autonomous decision-making abilities to regulate data integrity, recognise abnormalities, and adaptably react to compliance violations, Custom AWS configuration rules (...) are used for real-time compliance monitoring. Tight guidelines like PCI-DSS and GDPR enable secure cloud migration, improved auditability, and policy execution. These guidelines open the path for real-time, low-latency analytics on enormous financial data. Combining data-driven insight production with intelligent control will help to build a transparent and strong environment fit for modern financial activities, as the suggested approach shows. (shrink)