Daily Archives: April 11, 2026

Authors: Dr. P.Vamsi krishna raja, Nama Venkata Bhaskara Sudheer

Abstract: Electric load forecasting plays a crucial role in efficient power system operation and energy management. Accurate prediction of electricity demand helps in reducing operational costs and improving system reliability. However, traditional forecasting methods often fail to handle complex and non-linear patterns present in real-world data. To address this issue, this paper proposes a machine learning–based approach using Extreme Gradient Boosting (XGBoost) for electric load forecasting. The proposed system utilizes historical load data along with important features such as time and temperature to train the model. Data preprocessing and feature selection techniques are applied to improve data quality and model performance. XGBoost, a powerful ensemble learning algorithm, is employed to capture complex relationships and enhance prediction accuracy. The model is evaluated using standard performance metrics, and the results demonstrate improved accuracy and efficiency compared to conventional methods. The proposed approach provides a reliable and scalable solution for electric load forecasting, supporting better decision-making in power system planning and management.

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Authors: K V Bharadwaj Karthik, V. Abhilash Naik, V Kowshik Chavali, G Suresh Babu

Abstract: This project focuses on the analysis of a three-phase star–delta step-down transformer using Altair Flux with emphasis on the no-load test and short- circuit test. The objective is to accurately evaluate core (iron) losses and copper (Joule) losses through finite element electromagnetic simulation. In the no-load test, rated voltage is applied to the primary winding while the secondary is kept open, enabling determination of magnetizing current, flux distribution, and core losses. The Bertotti loss model is employed within Altair Flux to separate hysteresis, eddy current, and excess losses in the core. Flux density distribution is examined to ensure operation below saturation limits. In the short-circuit test, the secondary winding is shorted and a reduced voltage is applied to circulate rated current, allowing evaluation of winding resistance, leakage reactance, and copper losses. The simulation accurately captures current density and I²R losses in both primary and secondary windings. The star–delta connection is properly modeled to obtain correct phase relationships and loss values. Results from Altair Flux demonstrate realistic loss estimation consistent with transformer theory. The study confirms that core losses remain nearly constant with load, while copper losses vary with the square of current. Overall, the work validates Altair Flux as an effective tool for detailed electromagnetic analysis of transformer performance using standard no-load and short-circuit test procedures.

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Authors: Mr. Shashank Tiwari, Kommunuri Ashok Kumar, Valigonda Laxmiprasanna, Kayitha Sai Rachana

Abstract: The Conversational Product Recommendation System using LLM is an AI-driven application designed to enhance product recommendation by enabling natural language interaction between users and the system. In modern e-commerce environments, users often face information overload due to the vast number of available products. Traditional recommendation systems rely on static filtering methods and fail to understand complex user queries expressed in natural language. To address these limitations, the proposed system integrates Large Language Models (LLMs) with Natural Language Processing (NLP) techniques to interpret user intent, preferences, and constraints. The system provides a chatbot-based interface where users can interact conversationally, refine their queries, and receive personalized product recommendations in real time. A recommendation engine processes extracted features and ranks products based on relevance, while a backend database manages product data and user interactions.

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Authors: M. Sujana Priyadarshini, Akula Swathi

Abstract: The exponential growth of email communication has led to an increase in unsolicited and potentially harmful spam messages, posing significant challenges to both users and organizations. Traditional spam detection techniques primarily focus on classification accuracy while often neglecting data security and privacy concerns. This paper presents a secure and efficient email spam detection system that integrates machine learning with cryptographic techniques. The proposed approach utilizes Support Vector Machine (SVM) for effective classification of emails based on textual features. To ensure data confidentiality, Advanced Encryption Standard (AES) is employed for encrypting email content, while Elliptic Curve Cryptography (ECC) is used for secure key exchange. The integration of classification and encryption mechanisms enables the system to provide reliable spam detection while preserving sensitive information. The proposed framework is suitable for real-world applications where both accuracy and data privacy are essential.

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Authors: M. Sujana Priyadarshini, Gunduprolu vijayakumar

Abstract: The study proposes an intelligent and reliable hybrid framework for enhancing fishermen safety and communication in marine environments using machine learning and electromagnetic water networks. Fishing activities in deep-sea regions involve significant risks due to unpredictable weather conditions, accidental border crossings, and limited communication facilities. Traditional monitoring systems rely heavily on manual observation and basic GPS tracking, which are often inefficient in handling real-time emergencies and dynamic ocean conditions. Additionally, the lack of continuous monitoring and predictive capabilities increases the vulnerability of fishermen to accidents and environmental hazards.To address these challenges, the proposed system integrates real-time data acquisition from multiple sources, including GPS tracking, environmental sensors, and electromagnetic sensors, to ensure continuous monitoring of marine conditions. The system employs machine learning techniques such as anomaly detection algorithms to identify abnormal vessel behavior, including sudden stops, unusual movements, and route deviations that may indicate distress situations. Furthermore, time-series data collected from sensors is analyzed using advanced deep learning techniques to predict environmental changes such as weather fluctuations and sea conditions.The model is trained and evaluated to accurately detect potential risks and provide early warning alerts, thereby enabling proactive decision-making. The proposed multi-layer framework enhances system performance by combining real-time monitoring, anomaly detection, and predictive analysis. This integrated approach improves communication between fishermen and coastal authorities through wireless technologies, ensuring timely response during emergencies.The system significantly enhances maritime safety, reduces the risk of accidents, and improves operational efficiency. By leveraging machine learning and real-time data processing, the proposed solution provides a scalable, efficient, and intelligent framework for ensuring the safety and security of fishermen in modern maritime environments.

 

 

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Authors: P. Chakradhar Rao, Vakadi venkata krishna

Abstract: The study proposes an efficient and secure hybrid framework for detecting Android malware in modern mobile environments. The widespread adoption of Android smartphones has led to increased security risks, as these devices are frequently targeted by sophisticated malware attacks. Furthermore, the growing integration of Android applications with Internet of Things (IoT) systems amplifies the potential impact of such threats. Detecting malware manually in large-scale and continuously evolving datasets is both time-consuming and ineffective. To address these challenges, our approach integrates real-time data acquisition and deep learning techniques. Malware hash values are dynamically updated using data extracted from Twitter at regular intervals of 48 hours, ensuring the system remains up-to-date with emerging threats. In addition, application features, particularly permissions, are analyzed using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) architecture for accurate classification. The model is trained and evaluated to distinguish between benign and malicious applications, achieving a detection accuracy of approximately 94%. The proposed multi-layer framework enhances detection efficiency by combining traditional signature-based methods with intelligent learning mechanisms. This integrated system improves reliability, strengthens mobile security, and provides an effective solution for real-time Android malware detection and prevention.

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Authors: Mr. Akula Sri Naga Sai Veera Pawan Anirudh, Mrs. G Prameela

Abstract: The rapid rise of e-commerce platforms has created a need for complex pricing systems that react to market conditions in real-time to improve market share and customer satisfaction. In this paper, we present a new Edge-AI powered situational pricing optimization framework based on a Deep Reinforcement Learning (DRL) model, leveraging the low latency pricing decision-making capability of a distributed edge computing network. In our model, we use federated learning processes with multi-agent deep reinforcement learning to create hybrid pricing intelligence based on the ongoing analysis of patterns of customer behaviour, competitors and market volatility signals. Our framework offers a solution to the fundamental limitations of cloud-based traditional pricing systems (and understandings) in shipping complex processes to ultra-sophisticated AI pricing engines that function on lightweight AI models located at edge nodes in the network, improving latency from seconds to milliseconds. Our experimental validation based on real e-commerce data shows a 23.4% im-provement in revenue optimizations, 18.7% improvements in reduction for de-cision latency of price adjustments and a remarkable 31.2% increase in customer satisfaction metrics relative to the previous centralized mode (cloud-based). This system offers a decentralized framework that can scale globally to support multi-market e-commerce operations, while also improving data privacy and confidential processing in compliance with regulatory demands.

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Authors: Amitesh Sarkar

Abstract: Peace education has become an important element in promoting peace, morality, and social unity in the modern societies. This paper examines how spiritual pedagogy, especially those applied by the Ramakrishna Mission can be used to enhance peace education. The study is descriptive and analytical and incorporates both philosophical and empirical information. This research points out the importance and impact of value based education based on spirituality on increasing emotional intelligence, ethical reasoning, and conflict management in learners. The main dimensions of peace education such as ethical awareness, emotional stability, social harmony, and conflict resolution are assessed with the help of a structured dataset. The results indicate that spiritual pedagogy plays a very important role in holistic growth and harmonious coexistence. It is concluded that the concept of incorporating spiritual values into the contemporary education systems can reinforce the peace-building processes at the international level.

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Authors: Adi Gowri Tejaswini, DJ Rishika, Rumaan Tamheen, Vasa Sravya

Abstract: Monitoring crowd density is a crucial task for ensuring safety and preventing overcrowding-related issues. The traditional methods for monitoring crowds involve manual observation and camera surveillance, which are time-consuming and require continuous monitoring. This paper proposes a hybrid approach for crowd detection using Raspberry Pi, incorporating wireless device detection, Bluetooth scanning, infrared sensing, and computer vision. The system estimates the crowd density based on wireless device detection and verifies the presence of people through OpenCV-based human detection. The infrared sensor is used to improve the accuracy of the system by tracking entry and exit movements. The hybrid approach is an improvement over traditional methods, reducing the limitations associated with each method. The paper also discusses different approaches to crowd detection, highlighting the advantages and limitations of these methods, and the benefits of a hybrid approach for real-time applications.

DOI: https://doi.org/10.5281/zenodo.19510228

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Authors: Dr. CH. Kishore Kumar, Vovaldas Tejaswini, Beeram Pranaya, Diya Shaik, Sana Shaik

Abstract: Fit Fuel is an integrated web-based fitness and nutrition platform designed to help users exercise correctly and maintain healthy eating habits in one place. Unlike fragmented solutions that separate workout guidance and diet planning across multiple platforms, Fit Fuel unifies both services within a single website for better convenience, consistency, and personalization. The system provides muscle- specific exercise guidance using clear posture images that help users understand correct workout techniques without relying on video streaming. In addition to exercise guidance, Fit Fuel generates personalized Indian meal plans based on the user’s daily calorie requirements, dietary preferences, and allergies. The platform also includes features such as streak tracking, daily journaling, and profile management to encourage regular engagement and long- term habit formation. By combining fitness instruction, nutrition planning, and motivational tools into a single web interface, Fit Fuel promotes a holistic and user-friendly approach to health management.

DOI: https://doi.org/10.5281/zenodo.19509880

 

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