Volume 14 | Issue 4 |

 Abstract

This paper presents the design and implementation of an AI-driven campus assistant robot that integrates artificial intelligence, computer vision, speech processing, and embedded systems to provide interactive and context-aware assistance in educational environments. The system is developed using Python on a Raspberry Pi 5 and interfaces with an Arduino for navigation control. A Retrieval-Augmented Generation (RAG) framework powered by Google Gemini generates accurate responses from a predefined knowledge base, reducing hallucination and improving reliability. The system includes a graphical user interface built with Tkinter and real-time face recognition using OpenCV and face_recognition for personalized interaction. Voice-based communication is enabled through Speech Recognition and pyttsx3, facilitating natural human-robot interaction. A multi-threaded architecture maintains real-time responsiveness, while PySerial ensures reliable communication between processing and control units. Experimental results demonstrate that the proposed system is scalable, cost-effective, and practical for smart campus applications, including navigation, information retrieval, and personalized assistance.

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 Keywords

AI Assistant, Retrieval-Augmented Generation, Face Recognition, Smart Campus, Human-Robot Interaction.

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In this paper, SRAM cell topologies have been implemented on 22nm technology node with Tanner tool. Read power and write power dissipation, read delay, write delay, of all considered topologies have been determined out. Read and write actions of each SRAM cells have also been examined. Static Random Access Memory (SRAM) is a memory that is designed to provide high speed and low power applications. As the technology is shrinking down, the power supply is also scaled down which decreases the noise margin of the SRAM cells. The reduced noise margin further makes more leakage power in the SRAM cells. The main objective of this project is to deal with the power dissipation which occurs normally in the conventional Static Random Access Memory (SRAM) cells during the read and write operation. This problem can be solved by applying dual-threshold-voltage for 7T SRAM Cells. The respective power dissipation and delay of these cells are calculated and compared.

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 Keywords

Power Dissipation, Read Delay, Write delay, Dual Threshold Voltage, Write Power, Read Power

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The nursing profession has undergone significant transformation in modern healthcare systems. Nurses are no longer limited to basic caregiving but are actively involved in patient education, clinical decision-making, and evidence-based practice. This article explores how the expanded role of nurses contributes to improved patient outcomes. Drawing on existing literature, the paper highlights the impact of nurse-led interventions, effective communication, and patient-cantered care. The findings suggest that strengthening nursing capacity can enhance healthcare quality, reduce mortality rates, and improve patient satisfaction.

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 Keywords

Nursing, Patient Outcomes, Evidence-Based Practice, Patient Care, Healthcare Quality

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ABSTRACT Introduction: Hospitalization is a stressful experience for children and often leads to anxiety due to separation from family, unfamiliar surroundings, medical procedures, and fear of pain. Anxiety during hospitalization can negatively affect a child's emotional well-being, cooperation with treatment, and recovery. Non-pharmacological interventions such as play and activity-based therapies are effective, safe, and economical methods to reduce anxiety in children. Origami therapy, an art of paper folding, helps in diversion, relaxation, and emotional expression, thereby reducing anxiety levels among hospitalized children. Aim: The aim of the study was to evaluate the effectiveness of origami therapy on anxiety towards hospitalization among children admitted in the pediatric ward and SPICU of selected hospitals in India. Objectives: 1) To assess the pre-test level of anxiety towards hospitalization among children admitted in the pediatric ward and SPICU. 2) To implement origami therapy among hospitalized children. 3) To assess the post-test level of anxiety towards hospitalization after origami therapy. 4) To evaluate the effectiveness of origami therapy on anxiety towards hospitalization. 5)To find the association between pre-test anxiety levels and selected socio-demographic variables. Materials and Methods: A quantitative research approach with a pre-experimental one-group pre-test post-test design was adopted for the study. The study was conducted in the pediatric wards of selected hospitals of India. A sample of 30 children aged 3-6 years was selected using purposive sampling technique. Anxiety levels were assessed using the Hamilton Anxiety Rating Scale. Origami therapy was administered to the children for a specified duration during hospitalization. Pre-test and post-test anxiety scores were compared using descriptive and inferential statistics. Results and Findings: The findings revealed that before the intervention, the majority of children had moderate to severe levels of anxiety towards hospitalization. After the administration of origami therapy, there was a significant reduction in anxiety levels among the children. Statistical analysis showed a marked difference between pre-test and post-test anxiety scores, indicating that origami therapy was effective in reducing hospitalization-related anxiety. No significant association was found between anxiety levels and most socio-demographic variables. Conclusion: The study concluded that origami therapy is an effective, simple, low-cost, and non-pharmacological intervention for reducing anxiety towards hospitalization among children. Incorporating origami therapy into routine pediatric nursing care can help promote emotional well-being, improve cooperation, and enhance the overall hospital experience of children.

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 Keywords

Keywords: Origami Therapy, Anxiety, Hospitalization, Hospitalized Children, Pediatric Ward, Non-Pharmacological Intervention, Play Therapy

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Fraud detection has become a critical challenge in modern digital ecosystems due to the exponential growth of data and increasingly sophisticated fraudulent activities. Traditional rule-based systems are no longer sufficient to detect complex fraud patterns in real-time environments. This research paper explores the integration of Big Data Analytics and Machine Learning techniques to enhance fraud detection capabilities. The study focuses on scalable data processing frameworks such as Hadoop and Spark, combined with machine learning models like Decision Trees, Random Forest, and Logistic Regression. The proposed approach leverages large-scale structured and unstructured datasets to identify anomalies and predict fraudulent transactions with improved accuracy. Experimental analysis demonstrates that machine learning-based models significantly outperform traditional systems in terms of precision, recall, and scalability.

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Fraud Detection, Big Data Analytics, Machine Learning, Anomaly Detection, Credit Card Fraud, Apache Spark, Predictive Modeling, Neural Networks

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India's aspiration to become a global inspirator depends significantly on the development of a future-ready workforce that is not only skilled but also socially responsible and sustainable. Although initiatives such as the Skill India Mission and various vocational training programs have expanded opportunities for skill development, challenges such as low employability, rural-urban skill disparities, and a deficiency in value-based education persist. In this regard, Mahatma Gandhi's Nai Talim (Basic Education) philosophy presents a transformative approach by integrating productive work, ethical values, and community orientation into the educational framework. This paper re-examines Nai Talim as a model for skilling India, emphasising its fundamental principles of experiential learning, the dignity of labour, self-reliance, and the comprehensive development of Head, Heart, and Hand. It contends that Nai Talim's work-centric and value-integrated approach can effectively bridge the gap between education, employability, and sustainability. The study also analyses the role of the National Education Policy (NEP) 2020, which embodies Gandhian ideals by promoting experiential learning, vocational integration from Grade 6 onwards, local crafts, and entrepreneurship incubation. The NEP's emphasis on Indian Knowledge Systems, skill laboratories, and community-based training centres aligns with Gandhian thought, rendering it a robust framework for inclusive and sustainable workforce development. The paper concludes that a hybrid model, integrating Nai Talim's ethical foundation with contemporary vocational frameworks under NEP 2020, can assist India in achieving the dual objectives of economic growth and social equity. Such an approach would not only foster a skilled and innovative workforce but also cultivate responsible citizens dedicated to inclusive and sustainable national development.

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 Keywords

Nai Talim, Gandhian Philosophy, Skill Development, NEP 2020, Indian Knowledge Systems (IKS), Inclusive Growth, Workforce Sustainability, Experiential Learning, Sustainable Development Goals

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This paper introduces Beautex - a smart system made for salons and parlours that runs on both web and mobile devices, aiming to make daily tasks smoother while boosting how customers engage. Instead of locking features behind sign-ups, people can freely check out local spots, what services they offer, and how much things cost. Logging in only kicks in when booking a slot, which needs at least half the price paid upfront; going all-in early gives extra built-in rewards setup. The customer side helps users find services easily, see package deals, get auto alerts for appointments, while feedback only shows up after a real booking. Different ways to pay like scanning a code at the shop keep payments clear and smooth. On top of that, people can check out beauty items suggested by the salon, order them straight from the app, then grab an e-invoice right away, giving salons another way to earn. From day-to-day admin tasks, Beautex gives salon head one main screen to check money flow, track spending, keep tabs on stock, understand client habits, set staff shifts while also helping hire new team members. Instead of guessing, owners get smart tools that support quick choices plus big-picture strategy. A chatbot run by AI takes care of booking requests and answers common questions without delay. Coming updates will let salons handle refunds directly, launch an app for iPhones, even show clients how hairstyles might look using artificial intelligence making the project ready to grow alongside changing needs.

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 Keywords

Centralized platform for parlors and saloon , loyalty point system , chat bot

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The proposed project is about creating a complete machine learning system that helps industries predict how much energy they will use and also check how efficiently a smart grid system is working. The main aim of the project is to manage energy in a smarter and more efficient way. It combines energy prediction and efficiency checking into one connected system. This helps industries reduce energy waste and improve their overall performance. The system uses modern technologies like machine learning and data analysis to make accurate predictions. It is designed to be practical and useful in real industrial environments, especially in industries like steel manufacturing where energy usage is usually very high. By predicting energy usage early, companies can plan their operations better and avoid sudden increases in electricity demand. This also helps reduce electricity costs and maintain stable power systems. In the first stage of the project, machine learning models are trained using a dataset collected from a steel industry. This dataset contains information about how machines use electricity during different operations. The system uses two important models called Random Forest and Long Short-Term Memory (LSTM). These models are good at finding patterns in complex data and understanding how different factors affect energy usage. For example, they analyze values such as load type, power factor, voltage, current, and other electrical measurements. These values change over time, and the models learn how these changes influence energy consumption. The Random Forest model works by combining many small decision trees to produce accurate predictions. It is useful for handling large amounts of data and identifying the most important factors that affect energy usage. The LSTM model is a type of deep learning model that is specially designed to understand time-based data. It can remember past information and use it to predict future values. This makes it very suitable for predicting energy consumption because energy usage usually follows patterns over time. Using both models together improves prediction accuracy. After training the models, the system predicts short-term energy consumption in units called kilowatt-hours (kWh). These predictions show how much energy the industry will likely use in the near future. The predicted energy value is then added to another dataset called a synthetic smart grid dataset, which represents how electricity flows through a smart grid system. A smart grid is a modern electricity network that uses digital technology to monitor and control energy distribution. In the next step, the system calculates an Energy Efficiency Score based on the predicted energy consumption. This score shows how efficiently the grid is using energy. A higher score means better efficiency, while a lower score means energy is being wasted. The system uses fixed threshold values to classify the performance as either efficient or inefficient. This makes it easy for users to quickly understand the condition of the grid. Another machine learning model called a Random Forest Classifier is then used to predict grid efficiency using factors such as power demand, renewable energy contribution, weather conditions, temperature, and environmental data. The model analyzes these factors and predicts whether the system will operate efficiently. It also shows which factors are most important in affecting energy efficiency, helping engineers make better decisions to improve performance. Finally, the entire system is connected to a web application . Users can view results through dashboards and charts in a simple and user-friendly interface. Overall, this project provides a smart and scalable solution for energy management by helping industries save energy, reduce costs, improve system stability, and support sustainable energy practices.

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 Keywords

Keywords: Energy Forecasting, Smart Grid, Machine Learning, Random Forest, LSTM, Energy Efficiency, Time Series Prediction, IoT, Predictive Analytics, Sustainable Energy

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This research presents a Hybrid Classical-Quantum Investment Recommendation Framework aimed at improving retail investor profitability through flexible, multi-asset portfolio structuring. To avoid the processing limits and fractional-share restrictions found in traditional models like Modern Portfolio Theory, this new system uses a Two Phase Dynamic Market Screener. This tool assesses high-momentum stocks alongside safer capital assets, specifically Digital Gold and Fixed Deposits. For accurate short-term price prediction, the platform employs a combination of classical machine learning algorithms, such as ARIMA and LSTM. At the same time, it integrates Generative AI (Gemini) to evaluate real-time macroeconomic news sentiment, turning complex market information into clear and understandable investment reasons. A key feature of this system is an automated Risk Profiler that continuously adjusts asset allocation based on user demographics. One example is a mandatory "Capital Protection Mode" aimed at protecting the investments of senior citizens. To handle complex portfolio construction, the framework uses the Quantum Approximate Optimization Algorithm (QAOA). By framing the allocation task as a 0-1 Knapsack combinatorial problem, the system ensures mathematically superior and fully executable whole share distributions. Supported by an integrated Smart Tax Advisor focused on automated tax loss harvesting, this system acts as a responsive and investor-oriented model for future FinTech ecosystems.

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 Keywords

Portfolio Optimization, Quantum Computing, Risk Analysis, Behavioral Finance, Hybrid Classical-Quantum Algorithms, Financial Forecasting.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid growth of online shopping has significantly transformed consumer behavior, introducing new trends and challenges in purchasing habits. This study examines the impact of online shopping on consumer behavior by analyzing key factors such as frequency of purchases, spending patterns, and customer satisfaction. A survey of 200 consumers from Nashik was conducted to explore demographic influences, including age and gender, on shopping preferences and habits. Findings indicate that convenience, product variety, and competitive pricing are major drivers of online shopping, while challenges such as delivery delays and lack of physical inspection affect consumer satisfaction. The study reveals a growing inclination toward online shopping across diverse age groups, with younger consumers demonstrating higher engagement levels. This research contributes to understanding the dynamic relationship between technology adoption and consumer decision-making, offering insights for businesses to tailor their strategies in an increasingly digital marketplace.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Online shopping behaviour, Consumer behaviour, E-commerce, Online shopping trends, Consumer preferences, Digital shopping, E-commerce platforms, Customer satisfaction, Shopping frequency, Consumer purchasing decisions, Shopping patterns, Product variety, Brand loyalty, Consumer trust, Payment methods, Delivery satisfaction, Consumer reviews and ratings, Product quality, Customer service, Return and exchange policies, Consumer complaints, Convenience in online shopping, Demographic factors, Consu

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Creative Commons Attribution 4.0 and The Open Definition