Volume 14 | Issue 4 |

 Abstract

The concept of NigrahasthAna (points of defeat) occupies a central place in the NyAya system's theory of debate, as articulated in the NyAyas?tra and elaborated in its BhA?ya. These points of defeat signify the conditions under which a debater is considered logically or procedurally defeated in a philosophical disputation. Far from being merely rhetorical devices, NigrahasthAnas function as normative principles that ensure intellectual rigor, clarity, and fairness in dialogue. The NyAyas?tra enumerates various forms of defeat, such as self-contradiction, incoherence, irrelevance, failure to respond, and improper reasoning. The BhA?ya further refines these categories by explaining their practical application in debate and emphasizing their epistemological significance. Together, they provide a systematic framework to identify errors not only in argumentation but also in cognitive processes underlying reasoning.

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POINTS OF DEFEAT IN CLASSICAL INDIAN LOGIC: A STUDY OF NIGRAHASTHANA IN THE NYAYASUTRA AND BHASYA

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In Ayurveda, personalized nutrition is based on an individual's constitution (Prakriti), seasonal variations (Ritu), and digestive strength (Agni), which play a crucial role in maintaining health and preventing diseases. However, existing Artificial Intelligence (AI)-based Ayurvedic systems mainly focus on diagnostic analysis and lack effective implementation of constitution-specific dietary recommendations. This research proposes VedAahar, a machine learning-based Ayurvedic meal recommendation system that integrates Random Forest multi-class classification with rule-based filtering derived from Ahara Vidhi principles. The system utilizes structured datasets, including vegetarian, non-vegetarian, and fasting meal categories, and considers features such as prakriti, ritu, health goals, and meal slots to generate personalized recommendations. Additionally, Top-K probabilistic ranking and history-based de-duplication techniques are applied to ensure diverse and relevant meal suggestions. Experimental results show that the model achieves an accuracy of 69.14%, demonstrating its effectiveness in providing constitution-aware dietary guidance. Overall, the proposed system bridges traditional Ayurvedic knowledge with modern AI techniques, contributing to the development of intelligent and personalized preventive healthcare solutions.

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 Keywords

Ayurveda , Food Recommendation, Random forest, Prakriti, Hybrid Recommendation System, Dosha Balancing, AI in Healthcare

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The rapid evolution of web development and cloud computing produced many full-stack platforms that provide front end and back-end environments in one place. Even with these advancements, developers, educators, and learners struggle to set up, maintain, and sync their development environments. Presented here is CodeNova, a cloud based full-stack coding environment, powered by Docker. CodeNova assists in the setting up development environment, offers scalability, and security by virtue of its isolated execution environments. The platform includes the MERN stack (MongoDB, Express.js, React.js, and Node.js) with Docker and CI/CD automation to create a collaborate environment that serves the needs of the community and is easy to reproduce. We analyze the evolution of browser based IDEs, the role of Docker in cloud development, and the use of containerized environments for education. We present data from comparative analysis and experimentation revealing that environments created with Docker run faster, are more scalable, and have better quality than traditional IDEs.

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Full Stack Development, Docker, Containerization, Online IDE, Cloud Computing, DevOps, Web-based Platforms.

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This project aims to create an AI-based healthcare chatbot that offers primary medical assistance through various input methods, such as text, voice, and images. The system uses natural language processing for text queries, speech recognition for audio inputs, and computer vision for analyzing medical images. It provides real-time diagnostic guidance and predicts diseases. Unlike traditional rule-based chatbots, this system utilizes Groq-powered AI models to mimic consultations with doctors and give structured medical responses. It acts as a scalable first-level triage assistant that can manage multiple patients at once, improving access, efficiency, and affordability in healthcare. The chatbot's design prioritizes privacy and features a modular architecture, making it easy to implement in hospitals, telemedicine platforms, and mobile apps. This supports the shift to digital healthcare and raises awareness about early disease detection.

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Keywords--AI healthcare, multimodal chatbot, disease prediction, speech recognition, image analysis ,telemedicine

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Medical and paramedical professionals frequently employ transcutaneous electric nerve stimulation (TENS), a non-pharmacological technique, to manage pain and to give them insight into the clinical research evidence supporting the use of TENS as an analgesic and non-analgesic in both paediatric and adult patients with dental-related conditions. In an effort to increase knowledge, a brief discussion of the history of therapeutic electricity, the mechanism of action of TENS, the components of tens equipment, types, administration ways, benefits, and contraindications among dentists is also attempted. It is utilised in dentistry because of its dental applications 1 Keywords: Transcutaneous Electrical nerve stimulation, Pain, Orofacial pain 2

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Keywords: Transcutaneous Electrical nerve stimulation, Pain, Orofacial pain 2

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This paper is about a movie recommendation system that uses machine learning to suggest movies to people. The system is made up of a few parts, including content-based filtering, collaborative filtering and emotion-based filtering, which all work together to give users personalized movie suggestions. To make this system we first took a dataset and cleaned it up and organized it. Then we used something called feature extraction to make special matrices that show how similar things are to each other.We did all of this work in something called Jupyter Notebook and then we took the data we had prepared and put it into a web application made with Streamlit so people can use it in time.When people use this application they can pick a movie they like. Say how they are feeling and the system will suggest other movies that are similar or that might match their mood. Content-based filtering is when the system suggests movies that're a lot like the one the person picked and collaborative filtering is when the system looks at what other people with similar tastes have liked.We also have emotion-based filtering, which suggests movies that fit the persons mood, like happy or sad movies.The system can also get information about movies from outside like what people think of them what kind of movie it's who is in it and what the poster looks like which makes the experience better for the user.Our system does a job of solving some big problems like making sure the movies are really personalized and that the information is always up to date.It shows that using ways of recommending movies and putting them into a web application that people can interact with can make the whole system work better and be more useful.The results we got from testing the system were good. People seemed to like the suggestions it made. This work is important because it shows how machine learning and web technology can be used together to make things that're useful and work well. The movie recommendation system is an example of this and it can be used in other areas too.The system is made to be practical and to work for a lot of people. It can be used to make other kinds of recommendation systems too.This is a deal because it can help people find things they like and make their lives more fun and interesting. The movie recommendation system is a start and it can be used to make even more things, like it.

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 Keywords

Movie recommendation system, machine learning, hybrid recommendation, content-based filtering, collaborative filtering, personalized recommendation, Python, Streamlit, Scikit-learn, TMDb API.

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Although Large Language Models (LLMs) have shown great promise in automated content creation, they are still vulnerable to factual inconsistencies and hallucinations in knowledge-intensive jobs. By adding outside knowledge sources to the generating process, Retrieval-Augmented generating (RAG) improves factual accuracy and contextual grounding. In order to create richer content, multimodal generative models allow for the simultaneous integration of text and picture synthesis. Despite these developments, the majority of current systems rely on cloud-based deployment, which raises issues with infrastructure reliance, data privacy, and regulatory compliance. In order to generate PowerPoint automatically, this research suggests a safe, completely offline multimodal AI platform that combines a locally installed LLM with diffusion-based picture synthesis and a vector-based retrieval system. The proposed architecture is designed to be deployable on-site without depending on any costly external APIs or GPUs. Based on experimental evaluation, it is found that augmentation in retrieval improves factual grounding and structural coherence over LLM-only baselines, while multimodal integration improves user-perceived slide quality. This proposed architecture is a solution for institutional and commercial environments that require secure document automation using AI technology.

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Large Language Models, Retrieval-Augmented Generation, Multimodal AI, Offline Deployment, Automated Presentation Generation, Hallucination Mitigation.

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Abstract : Alex Haley's Roots: The Saga of an American Family, is highly regarded for its evocative depiction of slavery while also offering a rigorous examination of how cultural memory endures through oral cultural transmission, within a societal framework where people in bondage were barred from literacy and archival practices, and even their original names. Storytelling becomes evident as the primary vehicle for transmitting identity, heritage, and historical knowledge across generations. According to Haley, oral recounting is not a cultural practice that can be applied as a simple ritual in culture; rather, it serves as an important channel through which memory is transmitted. Pivotal to this process is the prime character of Kunta Kinte who was compelled to leave his homeland in Gambia and was given a new name by his enslavers. Kunta Kinte resolutely preserves his African heritage, remembering his parents, village customs, Mandinka language, and ancestral lineage. These memories are carefully preserved through oral transmission to his daughter, Kizzy. By recounting his history through spoken words, Kunta Kinte ensures that his identity remains alive through subsequent generations. As a result, oral narrative functions as an indispensable method for preserving both personal and cultural identity. This development does not happen without Kizzy, who dutifully listens to the stories told by her father and thereby passes them down to her son, Chicken George. The slave masters attempted to erase the African identity of culture by introducing new names, obstructing, and bifurcating families. However, oral tradition gives the characters a sense of connection to their origins, and their dignity. Every personal event in the story of Kunta Kinte makes them even more ethnic and proud of their ethnicity. Roots ascertains that cultural memory has the ability to become perpetuated through displacement and oppression when passed on through oral stories. Storytelling appears to be a powerful tool of family continuity, as history, who he was, and how cultural heritage will be transmitted to the next generations, no matter how the external forces are attempting to disseminate it.

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Oral Narrative, Cultural Memory, Identity and Heritage, Intergenerational Transmission, Neo-Slave Narrative, Oral Tradition, Trauma and Memory

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This study explores the evolution of human knowledge in the digital age by examining the role of Digital Humanities and Artificial Intelligence (AI) through the philosophical perspectives of G. W. F. Hegel and Martin Heidegger. It argues that Digital Humanities is not merely a technical advancement but a transformative intellectual framework that enhances human intelligence by integrating computational tools with humanistic inquiry. Using a conceptual and analytical model, the study investigates the impact of computer knowledge, AI usage, and digital literacy on the Digital Humanities Capability Index. The findings reveal a significant positive relationship between these variables, indicating that technological engagement strengthens cognitive abilities, reshapes knowledge production, and fosters interdisciplinary understanding. From a Hegelian perspective, this transformation represents a dialectical progression of human consciousness, while Heidegger's notion of technology as a mode of revealing highlights its potential to uncover deeper layers of meaning when critically applied. The study concludes that Digital Humanities and AI function as collaborative forces that expand human autonomy and intellectual capacity, marking a significant shift in the nature and structure of knowledge in the contemporary digital era.

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Digital Humanities, Artificial Intelligence, Human Intelligence, Digital Literacy, Knowledge Transformation, G. W. F. Hegel, Martin Heidegger, Philosophy of Technology

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This study is about moving from traditional cloud storage to a new way of storing data called Peer-to-Peer networks. Traditional cloud storage is easy to use. It has some big problems. One company is in control of all the data. It costs a lot of money to keep all the servers running. We think we have found a way to make a system. We want to use something called Web Real-Time Communication and a special kind of encryption to let browsers store data safely on their own. Our main goal is to figure out how to move a lot of data on this new system and make sure the data is safe for a long time. We need to have a way to check that the data is still there which we call "Proofs of Retrievability" to make sure the data does not get lost over time. Peer-, to-Peer networks and cloud storage are the things we are looking at in this study.

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P2P Storage: Refers to the decentralized distribution of data across multiple peer nodes. WebRTC: The core protocol enabling browser-native, real-time communication and data exchange. DataChannel: The specific WebRTC component used for transferring arbitrary file segments. NAT Traversal: The process of bypassing network firewalls using STUN and TURN utilities. Decentralization: The structural shift away from a single point of failure toward a distributed network. Data Integrity: Ensuring th

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