Authors: Aashutosh Kushwaha, Tapas Kumar Nandi

Abstract: The advancement ofi human civilization into the solar system is fiundamentally constrained by the energy density limitations ofi chemical propulsion. Nuclear propulsion, encompassing thermal, electric, and pulse architectures, ofifiers a transfiormative leap in specifiic impulse and payload capacity by leveraging the high energy density ofi nuclear fiission. This report provides a technically rigorous examination ofi the evolution, physics, and design ofi nuclear rocket systems. It begins with a detailed historical reconstruction ofi the United States' Project Rover and NERVA programs, alongsidg thg Sovigt Union’s RD-0410 development, highlighting the achievement ofi specifiic impulses exceeding 840 seconds. The fiundamental physics ofi neutron kinetics and heat transfier in extreme environments are derived, fiocusing on the McCarthy- Wolfi and Taylor correlations fior supercritical hydrogen. A comparative analysis ofi propellants—liquid hydrogen, ammonia, and methane—reveals the critical trade-ofifis between mass efifiiciency and storage density. Advanced concepts, including gas-core reactors, nuclear light bulbs, and the pulse propulsion ofi Project Orion, are evaluated fior their potential to achieve interstellar velocities. The report concludes with an analysis ofi the current DARPA/NASA DRACO mission and the shifit toward High-Assay Low-Enriched Uranium (HALEU) fiuels, outlining a path fior the next generation ofi deep-space transportation.

DOI: https://doi.org/10.5281/zenodo.19553037

Published by: vikaspatanker

Authors: Gayathri Kodipaka, Kompalli Sri Divya Muktha, Sowmya Manukonda

Abstract: Polycystic Ovary Syndrome (PCOS) and Anemia are among the most prevalent yet underdiagnosed health conditions affecting women in India, largely due to delayed symptom recognition, lack of awareness, and limited access to preventive healthcare. This project presents an AI-based early risk detection system designed to provide non-diagnostic risk assessment and health awareness support. The system analyzes user-provided inputs such as lifestyle habits, menstrual irregularities, fatigue levels, dietary patterns, and basic lab values like hemoglobin range to estimate a personalized risk probability for PCOS and Anemia. Machine learning models including Logistic Regression and XGBoost are employed to identify patterns associated with elevated risk levels. The application is developed using Python for model implementation, Streamlit for an interactive and accessible user interface, and SQLite for lightweight data storage. Unlike conventional period-tracking applications, this solution focuses on preventive risk scoring tailored to Indian women, aiming to encourage early medical consultation and improve health outcomes across both rural and urban populations.

DOI: https://doi.org/10.5281/zenodo.19552421

 

Published by: vikaspatanker

Authors: Md Ali Bashar Alam, Uzma Fathima, Dr.A. Kannagi

Abstract: The rapid adoption of artificial intelligence (AI) has created significant opportunities for innovation, efficiency, and competitive growth among small-scale businesses. However, limited resources, lack of technical expertise, and growing ethical concerns make it challenging for small enterprises to implement AI responsibly. This research paper explores the design and development of responsible AI tools tailored specifically for small-scale business environments, focusing on transparency, fairness, accountability, data privacy, and regulatory compliance. By analysing existing global AI ethics frameworks and governance principles, the study proposes a practical model that integrates ethical guidelines into scalable and cost-effective AI solutions. The research highlights key challenges such as algorithmic bias, data protection risks, limited infrastructure, and information asymmetry faced by small businesses, while presenting strategies to mitigate these issues through explainable AI, lightweight governance mechanisms, and certification-based approaches. Furthermore, the paper discusses how responsible AI adoption can enhance customer trust, reduce reputational risk, and support sustainable digital transformation. The findings aim to bridge the gap between high-level ethical principles and real-world implementation by offering a structured framework that enables small-scale enterprises to deploy AI systems safely, ethically, and efficiently. Ultimately, this study contributes to the advancement of inclusive and trustworthy AI ecosystems by empowering small businesses to adopt responsible innovation practices without compromising operational feasibility or economic growth.

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Authors: Ranjith Durgunala, Harshith Manchikkanti, Rahul Perugu, Sunadh Rithvik Ponnuru

Abstract: Therapid increase in sedentary lifestyles and unhealthy dietary habits has raised serious concerns regarding physical fitness and overall well-being. This project presents an AI Framework for Personalized Fitness & Diet Recommendation System designed to provide intelligent and customized health guidance. The system gathers essential user information including age, gender, height, weight, activity level, medical conditions, dietary preference, and fitness goals. Using this data, Body Mass Index (BMI) is calculated to assess the user’s health status. Machine learning algorithms analyze user profiles to generate personalized workout routines and diet plans tailored for fat loss, muscle gain, weight gain, or general fitness. A progress tracking module records daily weight, workout completion, and calorie intake to evaluate improvement. In addition, predictive models estimate expected fitness outcomes over 30, 60, and 90 days. The proposed framework enhances decision-making through data-driven insights, improves user engagement, and promotes sustainable lifestyle changes using artificial intelligence and machine learning techniques.

Published by: vikaspatanker

Authors: Gudimella Akhilesh, Peruri Karthik Sai, Kunburu Manikanta Reddy, Dr. Atul Kumar Ramotra

Abstract: Career decision-making among engineering students is often influenced by trends rather than a proper evaluation of individual skill sets, leading to skill–career mismatch. This project presents CareerLens, an explainable skill-based career recommendation system designed to guide students in selecting suitable academic streams and job roles. The system analyzes user-provided technical skills along with proficiency levels, maps them to predefined career requirements, and computes readiness scores to generate personalized recommendations. Additionally, it identifies skill gaps and suggests improvements to enhance career readiness. By emphasizing transparency, interpretability, and skill-driven guidance, CareerLens aims to bridge the gap between student capabilities and evolving industry demands.

DOI: https://doi.org/10.5281/zenodo.19550142

 

Published by: vikaspatanker

Authors: Mr.MD. Abdul kala, V.Krupa, M.pavani M. Hemanth sai

Abstract: Diabetic Retinopathy (DR) is a serious eye disease caused by long-term diabetes. It is one of the main causes of blindness around the globe. Early detection and prompt treatment are crucial to prevent permanent vision loss. Unfortunately, traditional diagnostic methods depend on the manual inspection of retinal fundus images by ophthalmologists. This process is time-consuming, subjective, and requires specialized skills. This project presents a Hybrid CNN-MLP Model for automated detection and classification of diabetic retinopathy using retinal images. The system combines Convolutional Neural Networks (CNN) for feature extraction and Multilayer Perceptron (MLP) for classification. The CNN component effectively captures spatial features like microaneurysms, hemorrhages, and exudates. Meanwhile, the MLP classifies these features into different levels of DR severity. The system is created using Python, TensorFlow/Keras, and Flask for online interaction. Users can upload retinal images, enter patient information, and receive real-time predictions with confidence scores, medical suggestions, and downloadable PDF reports. The system also keeps a record of patient history and provides visual analytics through graphs. This proposed model shows better accuracy, efficiency, and usability. It serves as a valuable tool for early screening and supports healthcare professionals in making decisions.

DOI: https://doi.org/10.5281/zenodo.19549841

 

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Authors: Mrs. G. Monika, B. Bindu, U. Edukondalu, K.Varshith

Abstract: Insider threat is one of the biggest problems facing organizational security since insiders are individuals with authorized access to an organization’s information assets. Organizational security solutions can only detect outsider attacks and do not perform effectively when faced with malicious behaviors or accidental acts carried out by insiders. In this research paper, a method of detecting insider threat using behavioral anomaly is outlined. This solution aims at continuous observation of user behavior such as logging on, file access and general interaction with the system resources. Machine learning algorithms are employed in modeling user behavior and alerting any deviation that can imply an act of malice.

DOI: https://doi.org/10.5281/zenodo.19549305

 

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Authors: Rajat Vijaybahadur Singh, Prasad Balasaheb Varpe, Pranav Navnath Lokhande, Krushna Suresh Awate

 

Abstract: Agriculture plays a vital role in the economy, and efficient farming techniques are essential for increasing crop productivity. Fertilizer application is one of the most important processes in agriculture, but traditional methods of applying fertilizers are time-consuming, labor-intensive, and often result in uneven distribution. To overcome these problems, a solar-powered fertilizer spraying machine is developed in this project. The main objective of this project is to design and fabricate a cost-effective, eco-friendly, and efficient fertilizer spraying system that reduces manual effort and ensures uniform spraying. The machine consists of a solar panel, battery, solar charge controller, water motor pump, storage tank, nozzle, flow pipes, and a four-wheel frame. The solar panel converts sunlight into electrical energy, which is stored in the battery and used to operate the motor pump. The pump creates pressure to spray the fertilizer solution through the nozzle in the form of fine droplets. The system provides several advantages such as reduced labor, time saving, uniform distribution of fertilizers, and low operating cost due to the use of solar energy. The four-wheel structure makes the machine portable and easy to operate in agricultural fields. It is especially useful for small and medium-scale farmers. This project demonstrates the effective use of renewable energy in agriculture and contributes to sustainable farming practices. The developed machine is simple in design, economical, and capable of improving overall agricultural efficiency and productivity.

DOI: https://doi.org/10.5281/zenodo.19548929

 

Published by: vikaspatanker

Authors: P. Dr. K. Rama Linga Reddy, Penumarty Srilakshmi Bhanupriya, Nikitha Mora, B. Suchitra, Sruthi Gujjula

Abstract: The paper describes the design of an Autonomous Signal Deception and Offensive System to be used at the Battlefield – a Simulink-based RF electronic warfare (EW) simulation system based on an earlier created hardware prototype, the Ultrasonic Deception System. In the previous system, four deception methods, including range deception, angle deception, stealth, and noise injection are shown with Arduino Mega and ultrasonic sensors, and the choice of the technique is done manually by an operator. The proposed system completely removes any manual involvement and adds five important extensions: autonomous selection of deception techniques with a randomized decision engine, an offensive electromagnetic pulse (EMP) generation subsystem, a cryptographic Identification Friend or Foe (IFF) protocol based on challenge-response authentication via XOR operations and pre-shared secret keys, an accurate RF channel model including path loss, propagation delay, and additive white Gaussian noise (AWGN). This system is implemerandi-based technique selection, and a Countermeasure Generation block that generates high-amplitude EMP pulses. The results of simulations show that autonomous threat classification is successful, the deployment of unpredictable deception techniques, and the possibility to quantify the degradation of the enemy system. Performance is analyzed based on six metrics such as IFF classification accuracy, deception effectiveness, SNR degradation, Shannon channel capacity, deception unpredictability entropy and system health degradation rate.

DOI: http://doi.org/

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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.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.179

Published by: vikaspatanker