Category Archives: Uncategorized

Authors: Dr. J. Yogapriya, Sangsai ST, Praveen R, Naresh P

Abstract: Reliable navigation in environments where Global Positioning System (GPS) signals are unavailable or degraded remains a critical challenge for autonomous systems, defense operations, and underground or indoor applications. This research proposes a robust navigation framework that leverages Doppler-based velocity estimation to enhance positioning accuracy in GPS-denied environments. The system integrates inertial measurement units (IMUs) with Doppler shift observations derived from radio frequency or acoustic signals to provide continuous and drift-reduced localization. A sensor fusion approach, combining Extended Kalman Filtering and machine learning-based error correction, is employed to mitigate accumulated drift and measurement noise. The proposed model is evaluated in complex scenarios such as urban canyons, tunnels, and indoor settings, demonstrating improved trajectory estimation and resilience compared to conventional inertial-only methods. Experimental results indicate that Doppler-assisted navigation significantly enhances reliability, reduces positional error, and ensures continuous operation in challenging conditions. This approach offers a scalable and efficient solution for next-generation navigation systems in autonomous vehicles and robotics.

DOI: https://doi.org/10.5281/zenodo.19471216

 

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Authors: Mrs.L.Nivetha, M.Tharunsuriya, R.Sharugas, S.Vaitheesh

Abstract: The primary objective of this proposed research is to develop a new deep learning algorithm that can analyze neuroimaging data for early detection and diagnosis of brain diseases such as epilepsy, Parkinson's disease, Alzheimer's disease, and brain tumors. The algorithm will be developed using a combination of supervised and unsupervised learning techniques. The dataset will include a large number of neuroimaging scans, including MRI, CT, and PET scans, from patients with different brain diseases as well as healthy controls. The algorithm will be trained to differentiate between healthy and diseased brain scans and to classify different types of brain diseases based on the patterns observed in the neuroimaging data. The proposed algorithm will incorporate advanced deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, which are specifically designed for processing large and complex datasets. The algorithm will also use transfer learning, which involves transferring knowledge learned from one task to another, to enhance the accuracy of the classification model. The proposed algorithm will be able to detect subtle changes in brain structure and function that may not be visible to the naked eye, enabling earlier detection and diagnosis of brain diseases. The proposed algorithm has the potential to significantly improve the accuracy and speed of diagnosis of brain diseases, leading to earlier and more effective treatment. It could also help identify new biomarkers for brain diseases, leading to a better understanding of the underlying mechanisms and potential new targets for therapy. Ultimately, the proposed algorithm could improve the quality of life for millions of people around the world who suffer from brain diseases such as epilepsy, Parkinson's disease, Alzheimer's disease, and brain tumors.

DOI: https://doi.org/10.5281/zenodo.19471133

 

Published by: vikaspatanker

Authors: Dr. J. Yogapriya, Johithasri A K, Keerthika A

Abstract: Blood centrifugation is a crucial step in diagnostic testing; however, many rural and low-resource healthcare centers lack access to standard centrifuge machines due to high cost and maintenance requirements. This project proposes an ultra-affordable, portable smart blood centrifuge integrated with Artificial Intelligence and Internet of Things technologies. The device employs a high-speed motor to separate blood components, while an embedded camera continuously monitors the separation process. AI algorithms analyze the captured images to accurately determine the completion of plasma separation, preventing over- or under-centrifugation. An IoT module enables real-time monitoring and status notifications through a mobile application, allowing safe and remote operation. By reducing dependence on skilled manpower and conventional laboratory infrastructure, this intelligent system enhances diagnostic reliability and supports timely clinical decision-making, making it highly suitable for primary healthcare centers, mobile medical units, and underserved regions.

DOI: https://doi.org/10.5281/zenodo.19471072

 

Published by: vikaspatanker

Authors: Mr.M.Thangadurai, Lathishna R, Jamuna N, Madhunisha S

Abstract: The Smart Blood Donor Finder System is an efficient and technology-driven solution designed to connect blood donors with recipients in real time. The system aims to address the critical challenge of blood shortages by creating a centralized digital platform where donors can register their details, including blood group, location, and availability. When a request is made, the system quickly identifies suitable donors based on compatibility and proximity, ensuring faster response during emergencies. It utilizes database management, location-based services, and communication technologies such as SMS or notifications to alert potential donors instantly. The system also maintains donor history, eligibility status, and previous donation records to ensure safety and reliability. By reducing manual effort and delays in searching for donors, this system improves efficiency in healthcare services. Overall, the Smart Blood Donor Finder System enhances accessibility, saves time, and increases the chances of timely blood availability, ultimately contributing to saving lives.

DOI: https://doi.org/10.5281/zenodo.19470987

 

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Authors: Manoj, Anmol Dobriyal, Suneet Bhalla, Utsav

Abstract: Agriculture in India faces significant challenges due to climate variability, inefficient resource utilization, and lack of real-time field monitoring. Traditional methods rely on manual observation, which often results in delayed detection of critical environmental changes affecting crop health. This paper presents an Internet of Things (IoT) based crop monitoring system designed to provide continuous real-time monitoring of key agricultural parameters. The system integrates soil moisture, temperature, humidity, and gas sensors with an Arduino Nano and NodeMCU for data acquisition and processing. A Global System for Mobile Communications (GSM) SIM800A module is utilized to transmit alert messages when any parameter exceeds predefined threshold values, ensuring remote accessibility without dependence on internet connectivity. A 16×2 Liquid Crystal Display (LCD) provides local visualization of sensor data. The proposed system focuses on simplicity, low cost, and reliability, making it suitable for deployment in rural areas with limited infrastructure. Experimental observations demonstrate that the system accurately monitors environmental conditions and generates timely alerts, enabling improved decision-making and reducing potential crop losses. The solution provides a practical approach toward enhancing agricultural monitoring using IoT technologies in resource-constrained environments.

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Published by: vikaspatanker

Authors: Mahbub Alam, Rakesh Kumar Paswan, Nitish Ravidas, MD Sohail Ahmad, Mr. S.B. Patil

Abstract: Pipeinspectionrobotsare designedto monitorandassess the conditionof pipelinesin industriessuch as oil and gas, watersupply,andsewage systems. Theserobotscan travel inside pipes to detectcracks,blockages,corrosion,andotherstructuraldefectsthatare difficult or dangerousfor humansto inspect.Equippedwith sensors,cameras,andwireless communicationsystems,the robotcapturesreal-timedata and images of thepipeline’ s internalcondition.This technologyimprovesinspectionaccuracy,reducesmaintenancecosts, and enhancessafety by minimizingthe needfor manualinspection in confinedspaces.

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Published by: vikaspatanker

Authors: Mr.Y.H.S.S. Phaneedra, Polisetty Nikhitha Sowmya, Kolla Triveni, Garaga Naveen Kumar, Kadali Nikitha Sri Satya Gayatri

Abstract: Network Intrusion Detection Systems (NIDS) play a critical role in modern cybersecurity infrastructures by monitoring network traffic and identifying suspicious or malicious activities. In recent years, machine learning techniques have significantly improved the performance of intrusion detection systems by enabling automated traffic analysis and anomaly detection. However, the integration of machine learning into security systems also introduces new vulnerabilities that can be exploited by attackers. One such threat is adversarial machine learning, where malicious actors manipulate training or testing data to deceive machine learning models and degrade their performance. This study presents a comprehensive analysis of adversarial machine learning attacks targeting network intrusion detection systems. The work explores how adversarial samples are generated by introducing small perturbations into original datasets, which results in incorrect predictions by the intrusion detection model. Furthermore, the paper classifies adversarial attacks based on several criteria, including attacker knowledge level, misclassification objectives, affected learning phase, and the intended security violation. Understanding these attack strategies is essential for designing more robust and secure intrusion detection systems capable of defending against adversarial manipulation.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.145

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Authors: Mr.Y.Manas Kumar, Sathi Chaitanya Sai Durga, Kollu Ruby Sophia, Gaduthuri Alekhya, Nalluri Lishitha Devi, Pallala Sasi Kiran Reddy

Abstract: The rapid growth of renewable energy sources has increased the importance of accurate wind power forecasting for reliable power system operation. Wind power generation is inherently variable due to changing weather conditions, making prediction a challenging task. This paper presents an intelligent wind power forecasting framework based on Bayesian Feature Selection combined with machine learning models. The proposed approach processes numerical weather prediction data and removes irrelevant spatial features to improve prediction accuracy. A dimensionality reduction technique is applied to select the most informative sub-areas of weather data, thereby reducing computational complexity while maintaining important predictive information. Various machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, and Convolutional Neural Networks are employed for forecasting regional wind power output. The proposed model enhances prediction performance by optimizing feature selection and improving model efficiency. Experimental evaluation demonstrates that the system significantly improves forecasting accuracy while reducing the dimensionality of input data. The framework can assist energy providers and power grid operators in planning and managing renewable energy resources more effectively.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.144

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Authors: Dr Manjula Devarakonda Venkata, Vasa Neeharikasri, Vudatha Rama Subrahmanyam, Suravarapu Venkatesh, Malagala Pavan, Mattaparthi Jaya Praneeth

Abstract: Unmanned Aerial Vehicles (UAVs), commonly known as drones, are increasingly used in various applications such as surveillance, logistics, environmental monitoring, and disaster management. Despite their numerous benefits, the rapid adoption of UAV systems has introduced significant cybersecurity challenges. UAV communication networks are vulnerable to different types of cyber threats including GPS spoofing, data injection attacks, and network intrusions, which can compromise system functionality, mission objectives, and data security. To address these challenges, this study proposes a machine learning-based framework for detecting cyber attacks in UAV systems. The proposed approach combines supervised and unsupervised learning techniques to analyse UAV telemetry data, communication signals, and operational parameters in real time. By performing behavioural analysis and anomaly detection, the system can identify abnormal patterns and isolate potential cyber threats with high accuracy and minimal false positives. Experimental evaluation demonstrates that the proposed framework can effectively detect various attack scenarios while maintaining efficient response time and reliable performance. The integration of machine learning techniques into UAV cybersecurity systems provides a robust solution for enhancing the safety and reliability of drone communication networks.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.143

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