This report describes a questionnaire-based study on 309 adult patients attending the Dental Outpatients Department of Bangladesh Medical College and Hospital, Dhaka during December 2000 to March 2001. The aim of the study was to determine the oral health knowledge of the patients in relation to their age, gender, economic and educational status. Almost two third (63.1%) of the subjects correctly said that pan chewing was bad for teeth. Three fourth (78.3%) of the subjects gave correct answer on question of (...) how to prevent teeth decay. When question was asked about cause of teeth decay, bleeding gum and action of fluoride on teeth, only 38.2%, 41.4% and 32.3% could give correct answer respectively. With a few exceptions, knowledge of oral health was comparatively poor among the older generation, females, less educated and less privileged group. (shrink)

This tenth volume of Collected Papers includes 86 papers in English and Spanish languages comprising 972 pages, written between 2014-2022 by the author alone or in collaboration with the following 105 co-authors (alphabetically ordered) from 26 countries: Abu Sufian, Ali Hassan, Ali Safaa Sadiq, Anirudha Ghosh, Assia Bakali, Atiqe Ur Rahman, Laura Bogdan, Willem K.M. Brauers, Erick González Caballero, Fausto Cavallaro, Gavrilă Calefariu, T. Chalapathi, Victor Christianto, Mihaela Colhon, Sergiu Boris Cononovici, Mamoni Dhar, Irfan Deli, Rebeca Escobar-Jara, Alexandru Gal, (...) N. Gandotra, Sudipta Gayen, Vassilis C. Gerogiannis, Noel Batista Hernández, Hongnian Yu, Hongbo Wang, Mihaiela Iliescu, F. Nirmala Irudayam, Sripati Jha, Darjan Karabašević, T. Katican, Bakhtawar Ali Khan, Hina Khan, Volodymyr Krasnoholovets, R. Kiran Kumar, Manoranjan Kumar Singh, Ranjan Kumar, M. Lathamaheswari, Yasar Mahmood, Nivetha Martin, Adrian Mărgean, Octavian Melinte, Mingcong Deng, Marcel Migdalovici, Monika Moga, Sana Moin, Mohamed Abdel-Basset, Mohamed Elhoseny, Rehab Mohamed, Mohamed Talea, Kalyan Mondal, Muhammad Aslam, Muhammad Aslam Malik, Muhammad Ihsan, Muhammad Naveed Jafar, Muhammad Rayees Ahmad, Muhammad Saeed, Muhammad Saqlain, Muhammad Shabir, Mujahid Abbas, Mumtaz Ali, Radu I. Munteanu, Ghulam Murtaza, Munazza Naz, Tahsin Oner, ‪Gabrijela Popović‬‬‬‬‬, Surapati Pramanik, R. Priya, S.P. Priyadharshini, Midha Qayyum, Quang-Thinh Bui, Shazia Rana, Akbara Rezaei, Jesús Estupiñán Ricardo, Rıdvan Sahin, Saeeda Mirvakili, Said Broumi, A. A. Salama, Flavius Aurelian Sârbu, Ganeshsree Selvachandran, Javid Shabbir, Shio Gai Quek, Son Hoang Le, Florentin Smarandache, Dragiša Stanujkić, S. Sudha, Taha Yasin Ozturk, Zaigham Tahir, The Houw Iong, Ayse Topal, Alptekin Ulutaș, Maikel Yelandi Leyva Vázquez, Rizha Vitania, Luige Vlădăreanu, Victor Vlădăreanu, Ștefan Vlăduțescu, J. Vimala, Dan Valeriu Voinea, Adem Yolcu, Yongfei Feng, Abd El-Nasser H. Zaied, Edmundas Kazimieras Zavadskas.‬‬. (shrink)

In the current digital environment, cyberattacks continue to pose a serious risk and difficulty. Internet of Things (IoT) devices are becoming more and more vulnerable due to security problems like ransomware, malware, poor encryption, and IoT botnets. These flaws may result in ransom demands, data tampering, illegal access, and system risks. Creating strong cybersecurity procedures for contemporary smart environments is essential to resolving these problems. This strategy uses proactive network traffic monitoring to spot any dangers in the Internet of Things (...) ecosystem. Our approach is to improve Smart Environments' security and awareness of potential threats. Two IoT gateways were used to examine the effectiveness and performance of a deep neural network (DNN) model. The results were promising: the model caused an average increase of less than 30 kb/s in network bandwidth and a mere 2% rise in CPU usage. Additionally, memory and power consumption were minimal, with 0.42 GB and 0.2 GB of memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device. The machine learning models achieved nearly 93% detection accuracy and a 92% F1 score on the datasets used. Our framework demonstrates an effective and efficient method for detecting malware and attacks in Smart Environments. (shrink)

In India, the comprehension of children's rights and legal literacy among students is often hindered by traditional educational methods that fail to effectively communicate complex legal concepts. This project aims to address this gap by developing an interactive, gamified platform designed to enhance the understanding of children's rights through engaging, choice-based stories. The platform provides a dynamic learning environment where students can explore various rights by making decisions within interactive scenarios, allowing them to see the real-world implications of their choices. (...) By making the content relatable and practical, this approach seeks to improve students' ability to apply their knowledge in real-life situations and advocate for their rights more effectively. The platform's innovative use of story-based learning and visualizations will transform the way children's rights are taught, ensuring a deeper, more impactful understanding for young learners across India. (shrink)

The purpose of this work is to compare the ways in which relational databases—more especially, Microsoft SQL Server—and non-relational databases—MongoDB—handle unstructured data in JSON format. Although a lot of study has been done comparing different database management systems in terms of security, performance, and other aspects, little is known about how these databases are evaluated according to the type of data they handle. The main goal of this study is to examine the various features that relational and non-relational databases provide (...) for handling JSON data. We will conduct a number of tests to do this, making sure that the relevant data doesn't need to be normalised. To draw a conclusion, the outcomes of these studies will thereafter be examined. (shrink)

In the context of mid-level professional writing jobs, we examine the productivity effects of a generative artificial intelligence technology, namely the assistive chatbot ChatGPT. We used ChatGPT to randomly expose half of the 444 college-educated professionals to occupation-specific, incentive-based writing tasks in a preregistered online experiment. Our results show that ChatGPT considerably increases average productivity: output quality improves by 0.4 standard deviations and task completion time drops by 0.8 standard deviations. By compressing the production distribution, ChatGPT also lessens worker inequality, (...) mainly helping individuals with lesser starting abilities. ChatGPT shifts the focus of activities from rough-drafting to idea generation and editing, thereby replacing worker effort rather than enhancing worker skills. Furthermore, exposure to ChatGPT increases job satisfaction and self-efficacy while also heightening both concerns and excitement about automation technologies. (shrink)

Through an examination of Stack Overflow posts, this study explores Docker-related subjects and difficulties in order to pinpoint common problems and patterns in Docker development. We created an extensive dataset of Docker conversations by utilising both tag-based and content-based filtering techniques. These conversations were categorised using Latent Dirichlet Allocation (LDA) topic modelling, which showed that the most common theme is Application Development, which includes topics like Framework Management, Coding Problems, Data Transfer, and Docker-specific frameworks. Because of Docker's strong market presence, (...) developers are very interested in using it to build and manage apps, as evidenced by this dominant category. The study highlights important areas of interest and difficulty within the Docker community and offers insightful information on the many technical and operational problems faced by Docker developers. These findings offer guidance for future research, tool development, and educational resources, aiming to address the evolving needs of Docker practitioners and enhance the overall development experience. (shrink)

Because of advanced automation, data exchange, and cyber-physical systems, traditional project risk management strategies are losing their effectiveness in the era of Industry 4.0. This study investigates how integrating Industry 4.0 technology into project risk management frameworks can have real-world implications. Using technologies like artificial intelligence (AI), big data analytics, and the Internet of Things (IoT), it proposes a new framework that enhances risk identification, assessment, and mitigation methods. In order to demonstrate how Industry 4.0 is transforming risk management practices, (...) this article examines real-world case studies and industry implementations. The framework addresses significant opportunities and challenges to provide practitioners with useful insights for adapting to the evolving technological context. The findings suggest that incorporating Industry 4.0 technologies can significantly improve risk management efficiency and project outcomes, offering a pathway for organizations to navigate the complexities of modern project environments. (shrink)

This paper presents an attempt to bridge the gap between logical and cognitive treatments of strategic reasoning in games. There have been extensive formal debates about the merits of the principle of backward induction among game theorists and logicians. Experimental economists and psychologists have shown that human subjects, perhaps due to their bounded resources, do not always follow the backward induction strategy, leading to unexpected outcomes. Recently, based on an eye-tracking study, it has turned out that even human subjects who (...) produce the outwardly correct ‘backward induction answer’ use a different internal reasoning strategy to achieve it. The paper presents a formal language to represent different strategies on a finer-grained level than was possible before. The language and its semantics help to precisely distinguish different cognitive reasoning strategies, that can then be tested on the basis of computational cognitive models and experiments with human subjects. The syntactic framework of the formal system provides a generic way of constructing computational cognitive models of the participants of the Marble Drop game. (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)

How do people reason about their opponent in turn-taking games? Often, people do not make the decisions that game theory would prescribe. We present a logic that can play a key role in understanding how people make their decisions, by delineating all plausible reasoning strategies in a systematic manner. This in turn makes it possible to construct a corresponding set of computational models in a cognitive architecture. These models can be run and fitted to the participants’ data in terms of (...) decisions, response times, and answers to questions. We validate these claims on the basis of an earlier game-theoretic experiment about the turn-taking game “Marble Drop with Surprising Opponent”, in which the opponent often starts with a seemingly irrational move. We explore two ways of segregating the participants into reasonable “player types”. The first way is based on latent class analysis, which divides the players into three classes according to their first decisions in the game: Random players, Learners, and Expected players, who make decisions consistent with forward induction. The second way is based on participants’ answers to a question about their opponent, classified according to levels of theory of mind: zero-order, first-order and second-order. It turns out that increasing levels of decisions and theory of mind both correspond to increasing success as measured by monetary awards and increasing decision times. Next, we use the logical language to express different kinds of strategies that people apply when reasoning about their opponent and making decisions in turn-taking games, as well as the ‘reasoning types’ reflected in their behavior. Then, we translate the logical formulas into computational cognitive models in the PRIMs architecture. Finally, we run two of the resulting models, corresponding to the strategy of only being interested in one’s own payoff and to the myopic strategy, in which one can only look ahead to a limited number of nodes. It turns out that the participant data fit to the own-payoff strategy, not the myopic one. The article closes the circle from experiments via logic and cognitive modelling back to predictions about new experiments. (shrink)

The art of prediction of stock market volatility has always been a most challenged interdisciplinary research problem among scientist due to its highly non- linear nature of market flow. This paper tries to analysis the historical data of BSE Sensex using extreme volatilities estimators, GARCH, ANN and new proposed Text Mining approach for stock market predictions. Finally experimental results illustrates that the new proposed Text model can able to predict the volatilities of the stock price better than other models.

The concept of a sales pipeline has gone through major changes due to the use of machine learning (ML) in managing sales pipelines. Analyzing intelligent sales pipelines that utilize ML algorithms for maximizing the deal closure in Salesforce environments constitutes the focus of this paper. The following discussion extends our prior work by specifically considering how the present approach of predictive modeling, NLP, and clustering contribute to more accurate predictions, better customer understanding, and enhanced targeting. To assess the performance of (...) the proposed approach we consider history data analysis, technological background of Salesforce platforms and proper integration of machine learning tools. This we illustrate with hypothetical business cases where the net deals closure rates have been achieved with an improvement in customer satisfaction by 30%. It also provides challenges like data quality, algorithms bias and scalability that have been discussed in this paper as well. Inferences made lay the foundation for organizations to employ intelligent pipelines to support credible sales development. (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)

The advancement of machine learning (ML) has revolutionized healthcare by enabling the early detection and diagnosis of multiple diseases. This paper presents a Multiple Disease Prediction System using machine learning algorithms to analyze patient data and predict the likelihood of diseases such as diabetes, heart disease, and kidney disease. The proposed model utilizes various ML classifiers, including Decision Trees, Random Forest, Support Vector Machines (SVM), and Neural Networks, to enhance prediction accuracy. The system aims to provide a costeffective, accurate, and (...) efficient tool for healthcare professionals and patients. (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)

This project aims to provide a comprehensive placement preparation platform designed for college campus placements, utilizing a modern tech stack to deliver a robust and efficient solution. Built with Node.js and integrated with MySQL Workbench for database management, this web application centralizes critical resources such as company-specific interview experiences, placement materials, preparation roadmaps, and eligibility criteria. Students can create an account and log in to access these resources, ensuring they have organized and up-to-date information to guide their preparation. With its (...) efficient backend powered by Node.js and structured data storage in MySQL, the platform delivers a seamless and responsive user experience. By addressing common challenges in placement preparation—fragmented information access, outdated resources, and lack of guided preparation paths—this application provides a centralized, user-friendly solution to enhance students' readiness and increase their chances of success in campus recruitment efforts. (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)

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 AWSAR Job Portal is developed to provide job seekers with a platform to register, log in, and apply for jobs with ease. The purpose of this project was to create a full-stack web application, ensuring smooth user experience and addressing common challenges such as responsiveness and compatibility across browsers. The project used technologies like React.js, Tailwind CSS for frontend development, and Node.js with Express.js for backend management. MongoDB was implemented for database storage. The key challenge faced was the unresponsiveness (...) of the 'Apply to a job' button post-login, which was successfully resolved through code optimization. This manuscript discusses the system's architecture, methodology, issues encountered, and future enhancements. (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)

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).

Understanding Terrorism in the context of Global Security -/- Author / Authors : Shreyasi Ghosh Page no. 89-106 Discipline : Political Science/Polity/ Democratic studies Script/language : Roman/English Category : Research paper Keywords: Terrorism, Violence, Threat, Global Security, Globalization.

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)

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)

This paper provisionally offers a way of addressing the predicament of a person who does not feel at home in her own concepts, because these concepts were once forced upon her by a colonial regime. If the goal for a person in such a circumstance is to overcome this alienation through intellectual means, then one way in which this might be accomplished would be to develop a hermeneutics that would enable her to ascertain the alienating aspects of her existing concepts. (...) To this end, I outline a hermeneutical strategy which requires that, in reading the colonizer’s textual tradition, the colonized/ex–colonized person must heuristically presuppose that her current concepts are entirely determined by this tradition unless these concepts can be shown to resist such determination on reflective–systematic grounds. (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)

When agents know a protocol, this leads them to have expectations about future observations. Agents can update their knowledge by matching their actual observations with the expected ones. They eliminate states where they do not match. In this paper, we study how agents perceive protocols that are not commonly known, and propose a semantics-driven logical framework to reason about knowledge in such scenarios. In particular, we introduce the notion of epistemic expectation models and a propositional dynamic logic-style epistemic logic for (...) reasoning about knowledge via matching agentsÊ expectations to their observations. It is shown how epistemic expectation models can be obtained from epistemic protocols. Furthermore, a characterization is presented of the effective equivalence of epistemic protocols. We introduce a new logic that incorporates updates of protocols and that can model reasoning about knowledge and observations. Finally, the framework is extended to incorporate fact-changing actions, and a worked-out example is given. © 2013 Elsevier B.V. (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)

My aim in this essay is to clarify certain issues relating to Kant’s definition of sensation. I will argue that even though sensation can occur in Kant’s system only if the subject is physiologically affected by some kind of object, Kant defines sensation non-referentially, that is, as relating entirely to the subject without reference to the affecting object. In the process, I will also demonstrate that sensation for Kant is the feeling accompanying the non-durational and obscurely conscious alteration or modification (...) in the representational state of the subject. (shrink)

In this essay, I investigate one aspect of Kant’s larger theory of the transcendental self. In the Prolegomena, Kant says that the transcendental self can be represented as a feeling of existence. In contrast to the view that Kant errs in describing the transcendental self in this fashion, I show that there exists a strand in Kant’s philosophy that permits us to interpret the representation of the transcendental self as a feeling of existence—as the obscurely conscious and temporally inaccessible modification (...) of the state of the discursive subject, which is built into all the representations of such a subject. I also provide an account of how the transcendental self can be legitimately understood both as an epistemic condition for the possibility of experience as well as the representation of a non-naturalistic feeling of existence. (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.

This research focuses on developing an advanced UAV detection system using state-of-theart YOLOv8 and YOLOv9 models. By training these models on diverse datasets, we aim to enhance real-time detection accuracy and precision under various environmental conditions. The system is designed to improve UAV safety and minimize bird collisions. Through rigorous benchmarking, we demonstrate significant improvements in detection performance, accuracy, and computational efficiency compared to existing approaches. A user-friendly web interface, built using HTML, CSS, and Flask, provides real-time visualization of detection (...) results and system performance. This innovative system holds immense potential for applications in defense, airspace safety, wildlife conservation, and other UAV-dependent sectors. (shrink)

The AICTE approval process for institutions plays a critical role in regulating technical education in India, ensuring quality and adherence to established standards. However, the current system is cumbersome, leading to delays and inefficiencies. This paper proposes the development of an AI-powered assistive portal that automates key stages of the approval process. The portal aims to reduce manual errors, provide real-time feedback, and enhance user experience for stakeholders, thereby improving overall system efficiency and transparency.

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 Legal Document Assistant project aims to make legal documentation more accessible and manageable for individuals and small businesses in India. Using artificial intelligence (AI), the platform simplifies the creation, updating, and management of legal documents for non-lawyers. Key features include a user-friendly interface that ensures accuracy and compliance with local regulations. A machine learning algorithm generates documents based on user inputs. To enhance security, the system uses One-Time Password authentication, requiring all parties to validate changes or updates, ensuring secure (...) and authorized modifications. By facilitating the creation of verified and legally sound documents, the Legal Document Assistant project makes legal services more accessible, minimizes errors, and saves time for individuals and small businesses across India. n. (shrink)

The project aims to address the critical need for a robust explosion risk assessment tool in the oil and gas industry, particularly in oil and gas refinery environments where safety is paramount. Traditional methods of threat assessment are often manual, subjective, and limited in their capacity to provide real-time insights, which poses significant challenges for industry professionals in managing potential hazards. Our project leverages real-time weather data and machine learning algorithms to develop an advanced web application designed to identify and (...) assess threat zones within oil and gas refineries. By utilizing refinery-specific datasets, real-time weather parameters, and machine learning models, the application enables users to upload or access refinery data, select industry-specific details, and receive accurate risk predictions along with stability class assessments. The application integrates the OpenWeatherMap API to provide up-to-date weather information, while a Random Forest classifier used for gas classification and some other parameters are calculated and trained on relevant features such as wind speed, cloud cover, and insolation levels to predict explosion risks. Designed with an intuitive, user-friendly interface, this tool will be invaluable for safety engineers, refinery managers, and environmental professionals, facilitating proactive risk management and informed decision-making. By addressing limitations in current risk assessment methods—such as limited access to real-time environmental data, variable risk levels, and lack of predictive analytics—our project aims to provide a comprehensive, data-driven solution for managing safety and mitigating risks in high-stakes industrial environments. (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)

In today’s competitive job market, job seekers often struggle to find suitable employment due to limited support and fragmented resources across various platforms. The PGRKAM Web Application addresses these challenges by centralizing employment data into a single, user-friendly platform that enhances job search efficiency. The application consolidates job listings from multiple sources, offers real-time notifications, and provides personalized job recommendations. It is structured with a two-tiered service model: a free version offering access to job listings and essential search features, and (...) a subscription-based version providing advanced support, including performance analysis, personalized training suggestions, and a structured feedback system to continuously improve employability. Optimized for web access and designed to support regional economic growth, the PGRKAM Web Application empowers job seekers with relevant, real-time data, ultimately increasing their chances of securing meaningful employment in Punjab. (shrink)

Cloud computing has revolutionized how businesses operate by providing scalable infrastructure, cost efficiency, and flexibility. However, as organizations increasingly migrate sensitive data and mission-critical applications to the cloud, securing these resources has become more complex. This paper explores the concept of building resilient security layers in the cloud, an essential approach to mitigate cyber threats and safeguard digital assets. We examine the various security challenges in cloud environments, including data breaches, misconfigurations, and insider threats, and offer solutions to address them (...) through a layered security approach. By leveraging technologies such as encryption, identity management, access control, threat intelligence, and security automation, organizations can create robust defenses against emerging cyber risks. The paper also highlights the role of advanced tools, including Artificial Intelligence (AI), Machine Learning (ML), and blockchain, in enhancing the resilience of cloud security frameworks. Through case studies and best practices, we provide insights into how organizations can implement these security layers effectively, ensuring that their cloud infrastructure remains secure and reliable in the face of evolving threats. (shrink)

Plant health monitoring is crucial for sustainable agriculture, as early detection of diseases can prevent significant crop losses. This study presents a deep learning-based approach using the YOLO (You Only Look Once) model for real-time plant disease detection. The model is trained on a curated dataset of diseased and healthy plant leaves, enabling accurate classification and identification of plant conditions. The methodology involves image preprocessing, feature extraction, and YOLO inference for detection. A webbased interface is developed using Streamlit, allowing users (...) to upload images of plant leaves for analysis. Upon detection of a disease, the system provides preventive measures and treatment suggestions to help farmers and agriculturists mitigate the impact. The integration of Streamlit ensures an accessible and userfriendly platform, with the potential for future deployment as a standalone application. The proposed system enhances precision in disease identification, offering an efficient, scalable, and practical solution for plant health monitoring. (shrink)

This project focuses on developing a community-based reporting and monitoring tool to improve women’s safety in colleges and universities. The tool allows users to anonymously report unsafe incidents, harassment, or suspicious activities, with features for immediate alerting to campus authorities and support groups. It integrates GPS tracking for emergencies and data visualization to monitor safety trends. Built using Java, XML, and Android Studio, the platform fosters collaboration between students, faculty, and administrators, encouraging proactive safety measures. By enabling discreet reporting and (...) real-time monitoring, the tool aims to create a safer campus environment for women. (shrink)

that can serve as a foundation for more refined ontologies in the field of proteomics. Standard data sources classify proteins in terms of just one or two specific aspects. Thus SCOP (Structural Classification of Proteins) is described as classifying proteins on the basis of structural features; SWISSPROT annotates proteins on the basis of their structure and of parameters like post-translational modifications. Such data sources are connected to each other by pairwise term-to-term mappings. However, there are obstacles which stand in the (...) way of combining them together to form a robust meta-classification of the needed sort. We discuss some formal ontological principles which should be taken into account within the existing datasources in order to make such a metaclassification possible, taking into account also the Gene Ontology (GO) and its application to the annotation of proteins. (shrink)

In today’s fast-paced supply chain environment, efficient inventory management is essential for small and medium-sized warehouses striving to reduce manual errors, avoid stockouts, and improve operational productivity. This paper presents the design and implementation of a smart warehouse system that leverages Internet of Things (IoT) technologies for real-time inventory monitoring and automated order fulfilment. The proposed system integrates RFID readers, barcode scanners, and various IoT sensors—including weight, temperature, and motion detectors—with a cloud-based inventory management platform. These components work together to (...) continuously monitor stock levels, detect item movement, and maintain environmental conditions for sensitive goods. When stock falls below a predefined threshold, the system automatically triggers restocking requests and can even notify or place orders with suppliers. A predictive analytics engine powered by machine learning analyses historical data to optimize reorder points, minimize waste, and forecast demand. The implementation demonstrates how IoTbased automation can enhance transparency, reduce costs, and increase scalability for small warehouse operations. Experimental results confirm improved efficiency, higher picking accuracy, and reduced operational downtime. This research offers a cost-effective and scalable solution tailored to meet the growing demands of inventory control in the digital era. (shrink)

Kant interpreters are divided on the question of whether determinate cognition plays a role in the harmony of the faculties in aesthetic judgement. I provide a ‘non-cognitive’ interpretation that allows Kant’s statements regarding judgements of natural beauty to cohere such that determinate cognition need not be taken to perform any role in such judgements. I argue that, in aesthetic harmony, judgement privileges the free activity of the imagination over the cognizing function of the understanding for the purpose of unifying the (...) object, although the free imagination cannot violate the obscure concepts and principles of ordinary common sense. (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)

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)

The present investigation was undertaken to investigate the acute toxicity of cadmium, a heavy metal widely detected in the aquatic environment due to natural effects and anthropogenic activities, in freshwater teleost, Channa punctata (Bloch). The experiments for the bioassay were performed in semi-static test condition according to the standard guidelines. The behavioural changes in the fish were observed for all tested concentrations of the metal. The data obtained for bioassay were analyzed for median lethal concentrations (LC ) of the metal (...) by SPSS computer software. The LC 50 50 values, estimated by SPSS, with 95% confidence level were found to be:20.589 (18.326-23.221), 17.133 (14.840- 19.734), 11.815 (9.696 – 13.850) mg/L and 9.908 (7.635-12381) mg/L for cadmium chloride at 24, 48, 72 and 96 h exposure, respectively. (shrink)

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)

In one of his earliest dialogues, the Soliloquia, Augustine identifies the liberal arts (disciplinae) with truth (veritas), and employs this somewhat puzzling identification as a premise in his infamous proof of the immortality of the soul (Sol. 2.24). In this paper, I examine Augustine’s argument for this peculiar identification. Augustine maintains both (1) that the constituent propositions of the liberal arts are true, and (2) that the liberal art of dialectic (disciplina disputandi) is the “truth through which all disciplines are (...) true” (Sol. 2.21). I argue that Augustine identifies the liberal arts with truth because (1) the constituent propositions of the liberal arts are learned, and (2) dialectic’s structure is essential to the liberal arts. I further suggest that the subject matter of each liberal art reflects a distinct segment of intelligible reality, that is, the realm of the intelligible Forms. (shrink)