Volume 14 | Issue 4

 Abstract

This paper employs a dual shift-share analysis (SSA) applied simultaneously to Gross Value Added (GVA) and employment data to decompose Uttar Pradesh's (UP) regional economic performance into its National Growth Effect (NGE), Industrial Mix Effect (IME), and Regional Competitive Effect (RCE) across five chronologically demarcated sub-periods from 2000-01 to 2024-25. The analysis reveals that UP has generated a positive Regional Competitive Effect in all three broad sectors- agriculture, industry, and services when assessed over the full twenty-five-year horizon, in both output and employment dimensions. The services sector registers the highest GVA-based RCE, while industry achieves a rare triple-positive alignment across all three shift-share components in both GVA and employment. A critical divergence between GVA-based and employment-based RCEs during Phase 3 is identified and interpreted as evidence of Lewisian disguised unemployment and low-productivity labour absorption a configuration of jobful but productivity-poor growth. Labour productivity analysis confirms that UP's GVA per worker has grown at a marginally faster pace than the national average, though the absolute productivity gap remains substantial across all sectors. The paper concludes with a three-pillar policy architecture centred on agricultural employment diversification, deepening secondary-sector industrial competitiveness, and geographic broadening of the services economy into eastern and central UP.

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 Keywords

Keywords: Shift-Share Analysis, Regional Competitiveness, Structural Transformation, Uttar Pradesh, Gross Value Added, Labour Productivity, Disguised Unemployment, Lewis Model, Industrial Mix Effect, Regional Competitive Effect

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The Existing grievance handling systems in educational institutions often lack privacy, effective tracking, and timely resolution, which discourages students from reporting issues. To address these challenges, a Smart Grievance Portal is proposed as a secure, web-based solution for efficient complaint management. The system supports dual-mode complaint submission, allowing users to choose between anonymous and protected identity options, along with role-based access control for students, teachers, and administrators. It incorporates a structured workflow for complaint processing, real-time tracking, and an automated escalation mechanism to ensure timely resolution. Additionally, an audit trail and feedback system enhance transparency and accountability. The system was evaluated using 40-50 users in a controlled environment, demonstrating significant improvements in performance. Results indicate a reduction in complaint resolution time by up to 40-50 percent, along with improved tracking efficiency and user participation. The proposed system enhances communication, builds user trust, and provides a scalable solution for modern grievance management in educational institutions.

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 Keywords

Grievance Management, Role-Based Access Control, RBAC, SLA Escalation, Privacy, Privacy-Preserving Systems, Web Application, Complaint Tracking, Digital Governance

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 Abstract

braj ka Samajik Jeevan evam Lokgeeton ka Adhyayan"

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braj ka Samajik Jeevan evam Lokgeeton ka Adhyayan"

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Social media has become a powerful force in shaping political campaigns and elections in India. With the rapid growth of internet access and smartphone usage, platforms such as Facebook, Twitter (X), Instagram, WhatsApp, and YouTube have transformed how political parties communicate with voters. These platforms allow direct interaction between leaders and citizens, making political communication faster, more personalized, and more engaging. Political parties now use data analytics and targeted messaging to influence different groups of voters based on their interests, age, and location. At the same time, social media plays a key role in increasing political awareness and participation, especially among youth. It helps in mobilizing voters, spreading information, and encouraging democratic engagement. However, the growing influence of social media also brings challenges such as the spread of fake news, misinformation, political polarization, and concerns about data privacy. The use of advanced technologies like artificial intelligence and digital campaigning strategies has further increased the complexity of modern elections in India. Overall, social media has significantly changed the nature of political campaigns and electoral behavior in India. While it strengthens democracy by increasing participation and access to information, it also raises serious concerns that require proper regulation and responsible use.

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 Keywords

Artificial intelligence in politics, Digital campaign strategy, Digital democracy, Electoral process in India, Fake news, Miss information, Political communication, Political polarization, Social media, Voter behavior, Youth participation.

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This paper presents the design and implementation of smart glasses for visually impaired individuals using Arduino Nano and ultrasonic sensor technology. The system detects obstacles in front of the user and provides real-time feedback through a buzzer and vibration motor. The ultrasonic sensor measures distance by calculating the time taken for sound waves to reflect from nearby objects. When an obstacle is detected within a predefined range, the system activates audio and tactile alerts to ensure safe navigation. The proposed solution is cost-effective, compact, wearable, and easy to implement. This assistive device enhances independence, mobility, and safety for blind users, especially in indoor environments.

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Arduino Nano, Ultrasonic Sensor, Assistive Technology, Smart Glasses, Obstacle Detection, Vibration Motor, Buzzer.

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Creative Commons Attribution 4.0 and The Open Definition

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Skin diseases affect millions of people every year, and diagnosing them manually can take a lot of time and effort. Most existing methods focus on just one type of skin disease at a time. This project proposes using advanced computer technology, like Convolutional Neural Networks (CNN) and VGG, to quickly and accurately detect different types of skin diseases. The CNN model examines skin images, automatically learning patterns that indicate various conditions, and can provide instant suggestions for treatment, making diagnosis faster and more reliable.

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Skin Disease Detection, Deep Learning, CNN, VGG16, Image Classification, Medical Imaging, Transfer Learning

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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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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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 Abstract

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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 Abstract

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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 Abstract

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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 Keywords

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

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 Abstract

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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 Keywords

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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Landslides are dangerous natural disasters caused by heavy rainfall, soil saturation, ground movement, and environmental instability, where early detection is difficult due to continuously changing conditions. This project proposes a Smart IoT-Based Real-Time Landslide Monitoring and Prediction System using ESP32 and Artificial Intelligence. Multiple sensors monitor soil moisture, rainfall, ground vibration, and accelerometer values in real time, while cloud integration enables remote monitoring. Sensor data is transmitted through Wi-Fi to the Blynk cloud platform for continuous data collection and analysis. A Random Forest-based AI model, along with a Weighted Risk Score mechanism, analyzes real-time and historical data to classify regions as safe or landslide-prone. When high-risk conditions are detected or when sensor values exceed safe thresholds, the system immediately generates visual alerts and sends real-time notifications to users through a web dashboard and mobile application. Enabling early prediction of landslides, reducing response time, and significantly improving disaster preparedness and safety in vulnerable areas.

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Creative Commons Attribution 4.0 and The Open Definition

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IoT, Landslide Monitoring, ESP32, Blynk Cloud, Artificial Intelligence, Random Forest Algorithm, Weighted Risk Score, Real-Time Monitoring, Hazard Prediction, Early Warning System

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Creative Commons Attribution 4.0 and The Open Definition

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Micro, Small and Medium Enterprises (MSMEs) form an essential part of India's economic activity, yet banks still face difficulties in assessing their creditworthiness due to inconsistent documentation, limited financial history, and high variability in business performance. Traditional machine-learning models and rule-based systems often fail to capture these complex patterns. This study aims to build a comparative deep-learning framework that evaluates multiple neural-network architectures--such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid models--to identify the most suitable model for MSME loan approval prediction. A large MSME-related dataset containing financial, behavioural, and operational impact factors is used for training and evaluation. The study follows a structured workflow: data preprocessing, feature selection, model development, model comparison, and feature-importance analysis. Performance is measured using accuracy, F1-score, and ROC-AUC. The goal is not only to select the best-performing network but also to highlight the factors that influence MSME loan approval outcomes. This research is expected to help financial institutions adopt more reliable and data-driven credit assessment techniques for MSME borrowers

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MSME Loan Approval, Deep Learning, ANN, CNN, LSTM, Credit Risk Prediction, Neural Network Comparison.

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 Abstract

Academic stress has emerged as a major psychological concern in higher education, significantly affecting students' well-being and academic performance. Sports students represent a unique subgroup within the student population, as they are required to simultaneously manage academic responsibilities and athletic commitments. This dual-role engagement often leads to increased stress due to time constraints, physical fatigue, and performance expectations.

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Creative Commons Attribution 4.0 and The Open Definition

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Academic stress, sports students, student-athletes, higher education, dual-role conflict, Bengaluru

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Creative Commons Attribution 4.0 and The Open Definition

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Traffic congestion and road accidents are major challenges in modern urban environments, leading to delays, increased fuel consumption, and safety risks. This case study explores the design, implementation, and effectiveness of a Traffic Alert System that utilizes real-time data collection, communication technologies, and intelligent algorithms to inform road users about traffic conditions. The system integrates data from sensors, GPS devices, and mobile applications to detect congestion, accidents, and route disruptions. Alerts are then transmitted to users through mobile notifications, digital signboards, and navigation systems. The study evaluates system performance based on response time, accuracy, and user satisfaction. Results indicate that the Traffic Alert System significantly improves traffic flow, reduces travel time, and enhances road safety. The case study also highlights challenges such as data reliability, infrastructure limitations, and privacy concerns, while suggesting future improvements using artificial intelligence and smart city integration.

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Creative Commons Attribution 4.0 and The Open Definition

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Traffic Alert System , Intelligent Transportation Systems (ITS) , Real-Time Traffic Monitoring ,GPS Technology ,Road Safety, Traffic Congestion , Smart Cities, Mobile Notifications, Data Analytics Accident Detection

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Creative Commons Attribution 4.0 and The Open Definition