Category Archives: Uncategorized

Authors: Dr.S.Dhanabal,M.E.,Ph.D., Amittha K, Deeksha T, Deepshika M

Abstract: Women’s safety has become a significant social and technological concern, particularly in remote and rural regions where immediate communication during emergencies is often unreliable due to poor network connectivity. Most existing safety solutions depend on cellular networks, which may fail in such environments, creating a critical need for an alternative approach. This project proposes ResQHer, a smart women’s safety device that employs LoRa-based hybrid communication to ensure long-range, low-power, and reliable transmission of distress alerts even in areas with limited or no internet access. The system integrates an ESP32 microcontroller, a LoRa SX1278 module for long-distance communication, GPS for real-time location tracking, and GSM/Wi-Fi as backup channels. When the emergency trigger is activated, the device instantly sends alert messages along with precise location details to predefined contacts and a centralized monitoring gateway. Designed to be compact, wearable, and energy-efficient, the device is suitable for everyday use and supports future enhancements such as continuous tracking, data logging, and integration with emergency response services. Overall, ResQHer aims to improve women’s personal safety by enabling faster assistance, dependable communication, and enhanced situational awareness across both urban and rural environments.

DOI: https://doi.org/10.5281/zenodo.19475804

 

Published by: vikaspatanker

Authors: Mrs.S.Subha, Kiruthika M, Harrshinee L, Kanika V

Abstract: Vision-based navigation has become increasingly important in fields such as assistive technology, robotics, and autonomous driving, as it enables systems to understand and interact with complex environments. This study introduces a Vision-Based Navigation Assistant that combines object detection with depth estimation to improve real-time awareness and navigation safety. The system utilizes deep learning techniques to detect and categorize surrounding objects while estimating their distances through monocular or stereo vision approaches. This integrated method allows the system to deliver relevant information about obstacles, pathways, and potential risks. Designed for efficiency, the framework can run on embedded devices, ensuring portability and minimal processing delay. Furthermore, it provides feedback through audio or haptic signals, making it especially useful for visually impaired individuals and autonomous systems. Experimental evaluations indicate enhanced accuracy in identifying objects and estimating distances, resulting in dependable performance across both indoor and outdoor settings. Overall, the proposed solution demonstrates the effectiveness of computer vision in developing intelligent navigation aids that enhance mobility, safety, and user independence.

DOI: https://doi.org/10.5281/zenodo.19471270

 

Published by: vikaspatanker

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

 

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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

 

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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.

Published by: vikaspatanker

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

Published by: vikaspatanker