Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT)
Chaithanya, Aravalli Sainath
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Computer Science & IT Electrical & Electronics Engineering

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Fault detection in single-hop and multi-hop wireless sensor networks using a deep learning algorithm Padmasree, Ramineni; Chaithanya, Aravalli Sainath
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp453-461

Abstract

The wireless sensor network (WSN) has received significant recognition for its positive impact on environmental monitoring, yet its reliability remains prone to faults. Common factors contributing to faults include connectivity loss from malfunctioning node interfaces, disruptions caused by obstacles, and increased packet loss due to noise or congestion. This research employs a variety of machine learning and deep learning techniques to identify and address these faults, aiming to enhance the overall lifespan and scalability of the WSN. Classification models such as support vector machine (SVM), gradient boosting clasifer (GBC), K-nearest neighbours (KNN), random forest, and decision tree were employed in model training, with the decision tree emerging as the most accurate at 90.23%. Additionally, a deep learning approach, the recurrent neural network (RNN), effectively identified faults in sensor nodes, achieving an accuracy of 93.19%.
Chatbot for virtual medical assistance Chaithanya, Aravalli Sainath; Vishista, Sampangi Lahari; MadhuSri, Adepu
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp914-922

Abstract

A healthy population is vital for societal prosperity and happiness. Amidst busy lifestyles and the challenges posed by the COVID-19 pandemic, individuals often neglect their health needs. To address this, we introduce a novel approach utilizing a chatbot for virtual medical assistance. Tailored for individuals confined indoors or hesitant to visit hospitals for minor ailments, our chatbot offers personalized medical support by diagnosing ailments based on user-reported symptoms and engaging in interactive conversations. Leveraging a robust dataset containing 132 symptoms, 41 diseases, and corresponding medications, our chatbot employs a systematic approach for symptom refinement, enhancing diagnostic precision. Upon identifying a disease, the chatbot promptly suggests basic medications tailored to the specific ailment. Furthermore, our system integrates user demographics to evaluate medication history and current state, allowing for personalized medication recommendations based on individual needs. Through extensive testing and validation, we demonstrate the effectiveness of our chatbot in accurately predicting ailments and providing timely treatment advice. Our study introduces a novel paradigm for medicine recommendation and disease prediction, with the potential to enhance healthcare accessibility and effectiveness.
Instance segmentation for PCB defect detection with Detectron2 Chaithanya, Aravalli Sainath; Devi, Lavadya Nirmala; Srividya, Putty
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4172-4180

Abstract

Printed circuit boards (PCBs) are essential in modern electronics, where even minor defects can lead to failures. Traditional inspection methods struggle with complex PCB designs, necessitating automated deep learning techniques. Object detection models like Faster R-CNN and YOLO rely on bounding boxes for defect localization but face overlap issues, limiting precise defect isolation. This paper presents a segmentation-based PCB defect detection model using Detectron2’s Mask R-CNN. By leveraging instance segmentation, the model enables pixel-level defect localization and classification, addressing challenges such as shape variations, complex structures, and occlusions. Trained on a dataset of 690 COCO-annotated images, the model underwent rigorous experimentation and parameter tuning. Evaluation metrics, including loss functions and mean average precision (mAP), assessed performance. Results showed a steady decline in loss values and high precision for defects like mouse bites and missing holes. However, performance was lower for complex defects like spurs and spurious copper. This study highlights the effectiveness of instance segmentation in PCB defect detection, contributing to improved quality control and manufacturing automation.
Co-Authors Devi, Lavadya Nirmala MadhuSri, Adepu Padmasree, Ramineni Srividya, Putty Vishista, Sampangi Lahari