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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Pavithra, V. Danisha, B.R. Divyadharshini, V. Koresh, H. James Deva Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331523923

Snow accumulation on solar panels reduces their performance in energy generation regions. Hence certain image processing methods were developed for segmenting snow from the normal areas of solar panels. Graph Cut and Flood Fill are the popular semi-automatic method used for this application where it relies on detecting an efficient region for marking the segmentation process. Similarly, the manual intervention fails to provide consistent results in such methods. Therefore, a customized graph cut methodology is proposed in this work by combining super pixel-based feature extraction and k-means clustering. At first, the input images are preprocessed by dividing the pixels to form super pixels as a group to reduce the computational complexity without losing the meaningful information. Features like intensity, edge density, texture, and color are extracted from these super pixel groups for segmenting the snow-covered regions on a solar panel. Further, the extracted features are normalized by a k-means clustering algorithm for identifying the highest intensity regions to observe the snow-covered regions. A graph cut-based segmentation technique is applied at the end to ensure the precise boundary delineation. The work is also incorporated with several post-processing techniques for improving the segmentation mask quality. This proposed method is evaluated against Graph cut and flood fill and satisfied with an average dice similarity coefficient of 93.09% and 95.11%. © 2025 IEEE.

关键词： Image segmentation

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IoT based Water Management and Monitoring System 3

IoT based Water Management and Monitoring System

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Muthulakshimi, S. Sathyamoorthy, Malathy Akash, S. Charumathi, P. Sri Suvathi, N.M. Department of Information Technology KPR Institute Of Engineering And Technology Coimbatore India

ISBN: (纸本)9798331527549

IoT (Internet of Things) has revolutionized various industries, and water management is no exception. This abstract introduces an IoT-based water management system designed to address the challenges of water scarcity, pollution, and inefficient distribution. Leveraging IoT devices like sensors, actuators, and communication modules, the system creates a networked infrastructure for real-time monitoring and management of water resources. Sensors deployed throughout the water distribution network collect data on flow, pressure, quality, and consumption patterns, which is then transmitted to a central server or cloud platform. The system instantly detects pipe leakage and promptly halts the water flow, even in the absence of human presence. This automated response helps prevent water scarcity. The system supports remote monitoring and control via web-based dashboards or mobile applications, facilitating proactive maintenance and troubleshooting. Additionally, it provides data analytics and reporting functionalities, empowering stakeholders to make informed decisions and promote water conservation initiatives. Overall, this IoT-based water management system offers a comprehensive solution to enhance sustainability, efficiency, and resilience in water distribution networks. © 2025 IEEE.

关键词： Solenoid valves

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Face Recognition Based Vehicle Theft Detection

Face Recognition Based Vehicle Theft Detection

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Prasad, J. Chakravarthi, C.T.R. Daniel, K. Deepak, R. Dheshnamoorthi, M. Kpr Institute of Engineering & Technology Department of Ece Coimbatore India

ISBN: (纸本)9798331509828

Adding a vehicle theft security system and improving vehicle technology systems are becoming more and more popular study topics in an effort to prevent car theft in parking lots and occasionally when driving in unsafe locations. This work objective is to develop a prototype model to detect vehicle theft using a facial recognition algorithm. The image is initially captured using a camera that is angled differently, and it is subsequently trained using the cascade Haar classifier technique. Furthermore, a linear binary pattern algorithm in a Python environment is used to extract the captured images. If the extracted image and the training image differ, theft is detected, and the vehicle owner is contacted via the mobile app. The developed prototype model offers a superior security alarm for vehicle theft and is tested with a real-time application. © 2025 IEEE.

关键词： Crime

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Monitoring COVID-19 environment: a real-time facial mask detection using YOLO models

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Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Pandiyan, P. Thangaraj, Rajasekaran V, Balasubramanian V. K, Manavalasundaram S, Balakrishnan Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective measure for themselves and others. Coronavirus protection guidelines have been published by the World Health Organization (WHO). According to WHO standards, COVID-19 can be prevented by wearing a mask in public places and congested regions. In these places, it is very difficult to personally check to see if people are wearing face masks or not. Methods: The objective of this research work is to build a powerful, efficient, and real-time approach for detecting people not wearing masks. Three cutting-edge object identification models, namely YOLOv4, Tiny-YOLOv4, and YOLOv5, are employed in this study for the identification of masked faces. Result: The proposed YOLOv5 model is evaluated using real-time images captured using a smartphone or tablet. The test images include both single and multiple people with and without masks. The YOLOv5 model achieved recognition accuracy of 88.90% with an average detection speed of 0.0316 s per image, whereas the YOLOv4 and Tiny-YOLOv4 produced recognition accuracy of 82.24% and 74.80% with an average detection speed of 0.0530 s and 0.0541 s per image, respectively. Conclusion: The comparative performance suggests that the YOLOv5 model has a maximum recognition accuracy of 88.90% in face mask identification tasks compared to other models such as the YOLOv4 and Tiny-YOLOv4. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： COVID-19

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Deep Learning-Based Anomaly Detection for Early Cancer Detection in CT Scans 8th

Deep Learning-Based Anomaly Detection for Early Cancer Detec...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Azariya, S. David Samuel Mohanraj, V. Soms, Nisha Emilyn, J. Jeba Sona College of Technology Salem India KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9789819784219

Early cancer identification is essential for patients’ prognosis and survival rates to be improved. Due to their capacity to record precise anatomical details, computed tomography (CT) scans are often utilized for cancer screening and diagnosis. However, detecting subtle anomalies indicative of early-stage cancer in CT scans can be challenging for radiologists, often leading to delayed diagnoses. This study suggests a Deep Learning-Based Anomaly diagnosis method for CT scan-based early cancer diagnosis. We use deep convolutional neural networks’ strength to automatically learn and extract beneficial characteristics from CT scans, enabling the detection of slight abnormalities that might not be visible to human observers. The proposed model is trained on a carefully curated dataset extracted from the Cancer Imaging Archive, comprising middle slices from 475 CT series obtained from 69 patients. We assess the deep learning model's performance by utilizing various metrics, such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Our findings show that the suggested approach outperforms conventional computer-aided diagnosis methods in detecting early cancer abnormalities with high accuracy and sensitivity. The results of this study have the potential to substantially influence clinical practice by assisting radiologists in quickly identifying cases of early-stage cancer and enabling timely and focused therapies, eventually improving patient outcomes and survival rates. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Influence of cryogenic treatment on mechanical and ballistic properties of AA5754 alloy friction stir welded joints

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Defence technology(防务技术) 2025年第4期46卷 184-198页

作者： V.Manoj Mohan Prasath S.Dharani Kumar Saurabh S.Kumar Department of Mechanical Engineering KPR Institute of Engineering and TechnologyCoimbatore 641407India Scientific Officer Ballistic Research Centre&Testing RangeNational Forensic Sciences UniversityGandhi Nagar 382007India

In the present study,the mechanical and ballistic properties of friction stir welded(FSW)aluminum alloy(AA5754)samples were investigated,both untreated and cryogenically treated,when impacted by a 7.62 mm armour-piercing(AP)bullet at an impact velocity of 682±20 m/*** FSW technique was used to prepare the welded samples for AA5754,with an axial force of 7 kN,a feed rate of 20 mm/min,and a speed of 1200 *** cryogenic treatments performed after welding,including deep cryogenic treatment(DCT)at196℃ and shallow cryogenic treatment(SCT)at80℃,for 6 and 72 h,*** microstructure and mechanical characteristics of cryogenically treated and untreated joints were *** cryogenic treatment refined the grain size(1.05 μm)and enhanced the microhardness(93 Hv).Moreover,DCT-FSW significantly improved the tensile strength(13.93%)and impact strength(8.45%)compared to untreated FSW ***,in untreated FSW samples,the fracture behaviour varied:the impact fracture mode primarily exhibited ductile failure,while the tensile fracture exhibited a mixed fracture *** contrast,the tensile and impact fracture modes of the DCT-FSWwere dominated by a ductile failure *** DCT-FSW target demonstrated a lower depth of penetration(DOP)of 31 mm compared to the SCT-FSWand untreated FSW ***-ballistic SEM analysis in the crater region of all three zones revealed the formation of frictional grooves,small cracks,and adiabatic shear bands(ASBs).

关键词： AA5754 alloy Ballistic and mechanical properties Cryogenic treatment Depth of penetration

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Stir Casting Synthesis and Evaluation of SiC Nanoparticle-Reinforced AZ31 Magnesium Alloy Nanocomposites

Stir Casting Synthesis and Evaluation of SiC Nanoparticle-Re...

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2025 Automotive Technical Papers, WONLYAUTO 2025

作者： Thillikkani, S. Kumar, N. Mathan Francis Luther King, M. Soundararajan, R. Kannan, S. Akshaya College of Engineering and Technology Department of India KPR Institute of Engineering and Technology India Swarnandhra College of Engineering and Technology India Sri Krishna College of Engineering Department of Mechanical India RVS School of Engineering and Technology India

In an attempt to improve its mechanical characteristics in the as-fasted conditions, the AZ31 Mg alloy was investigated herein from being reinforced with diverse SiC weight percentages (3, 6, and 9 wt.%). To develop lightweight AZ31-SiC composites, a simple and inexpensive technique, the stir casting process, was used. Microstructural analysis of the as-cast samples showed that the SiC particles were distributed rather uniformly, were firmly bonded to the matrix, and had very little porosity. The substantial improvement in tensile, compressive, and hardness characteristics was caused by fragmentation and spreading of the Mg17Al12 phase, while the addition of SiC had only a slight effect on the microstructure in the as-cast state. Surfaces of AZ31-SiC composites were analyzed using scanning electron microscopy. A study identified the AZ31-SiC composite as a unique material for applications involving a high compressive strength, such as those found in the aviation and automobile engineering fields. © 2025 SAE International.

关键词： Magnesium alloys

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Behavioural Aspects of 2.5 Ghz in an Indoor Environment 8th

Behavioural Aspects of 2.5 Ghz in an Indoor Environment

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8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Mythili, S. Chaulagain, Aditi Kalamani, M. Prabhakaran, V. Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Sathyamangalam India Department of Civil Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India

ISBN: (纸本)9789819757855

In the current era, the world is propelled by the rapid advancements of next-generation technologies, which play a pivotal role in shaping various aspects of our lives. This research paper aims to delve into the intricate nature of indoor wireless communication channels operating at 2.5 GHz for wireless local area networks. The central objective of this study is to meticulously examine the prevailing elements that wield influence over signal transmission among sensor nodes strategically positioned within indoor environments. By comprehensively analysing these factors, we aim to gain a deeper understanding of the dynamics at play within indoor wireless networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor nodes

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Effect of α-Fe2O3 secondary phase on structural, vibrational, elastic and electromagnetic properties of Mg2+ ion doped ZnFe2O4 nanoferrite: Electromagnetic interference (EMI) suppression in X-band frequency

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Ceramics International 2025年第13期51卷 17246-17258页

作者： Harshini, N. Bhavani, I. Durga Dileep, N. Babu, Ch. Ajay Prasad, S.A.V. Srinivas, Ch. Hanuman, C.R.S. Ismail, Mohammad Kumar, E. Ranjith Rout, S.K. Meena, Sher Singh JNTU-K Research Centre-Nanomaterials and Nanomagnetism Research Laboratory Department of Physics Sasi Institute of Technology & Engineering Andhra Pradesh Tadepalligudem534101 India Department of Electronics & Communication Engineering Sasi Institute of Technology & Engineering Andhra Pradesh Tadepalligudem534101 India Department of Physics KPR Institute of Technology & Engineering Tamil Nadu Coimbatore641 407 India Department of Physics Birla Institute of Technology Mesra Jharkhand Ranchi835215 India Solid State Physics Division Bhabha Atomic Research Centre Maharashtra Mumbai400 085 India

Shielding performance of MgxZn1–xFe2O4 (x = 0.0, 0.5, 1.0) nanoferrites for electromagnetic interference in X-band frequency have been reported. Spinel phase confirmation is identified by X-ray diffraction patterns and Fourier Transform Infrared spectra. Secondary phase of α-Fe2O3 is identified in Mg- and Mg-Zn ferrites. It is a clear indication for the redistribution of cations owing to the variation of intensity of (222) reflection peak. The experimental lattice parameter (a) and average crystallite size (D) were found to be within the range of 8.3793–8.4395 Å and 43.2–75.2 nm respectively. The average particle size from FE-SEM is in the range of 136.1–225.9 nm. The elemental analysis from energy dispersive spectra revealed the stoichiometry of ferrite compositions. The Poisson ratio indicates that the present ferrites are compressible and elastic nature. The variation of dielectric permittivity and magnetic permeability with frequency is non-linear. The Cole-Cole analysis revealed the distinct di-electric relaxations in the present ferrite compositions. The present ferrite compositions are effectively shielding the electromagnetic interference in X-band frequency. The total shielding SET = 19.6 dB at 8.2 GHz and 18.9 dB at 12.4 GHz are observed for ZnFe2O4 ferrite composition. Zn-ferrite is showing good shielding performance compared to other two compositions. © 2025

关键词： Crystallite size

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Implementation of AI-Powered Robotic Chatbot for Intelligent Customer Interaction

Implementation of AI-Powered Robotic Chatbot for Intelligent...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Premkumar Duraisamy Gokul C Jeevanandhan A Shirthik Ram S Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electrical Communication and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

This work presents the implementation of an AI-powered robotic chatbot designed to transform customer service in the online shopping sector. The chatbot is engineered to manage key customer interactions, including order placement, shipment tracking, order modifications, and cancellations, through a seamless conversational interface. Utilizing advanced natural language processing (NLP) models from Hugging Face Transformers and intent recognition via Dialogflow, the system ensures context-aware and precise responses. The integration reduces response times and enhances customer satisfaction. Experimental evaluation demonstrates high accuracy in intent recognition and response generation, validating the chatbot's efficacy as a reliable solution for automating customer service tasks. The paper details the system's architecture, implementation, and performance, highlighting its capability to optimize customer support and boost e-commerce operational efficiency.

关键词： Accuracy Intent recognition Customer services Face recognition Chatbots Transformers Electronic commerce Time factors Usability Robots

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