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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Naveen Kumaran, R. Sanjay Abhishek, J. Bhuvana, S. Karthick, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering SRM Institute of Science and Technology Uttar Pradesh Delhi–NCR Campus Ghaziabad India

ISBN: (纸本)9789819788354

Recent advancements in Smart Assistants (SAs) as well as home automation have captured the attention of both researchers and consumers. Virtual Assistants (VAs) that are speech-enabled are commonly referred to as smart speakers. They offer a wide range of network-based services. In some cases, they seamlessly link to smart environments, introducing innovative and efficient user interfaces. Nevertheless, these devices present their own set of challenges and limitations. The distinctive features of this technology, particularly its hand-free operation handled by voice and implementation of voice-user interface create a unique landscape. Existing technology adoption models, however, fall short in offering a comprehensive explanation for the adoption of this innovative technology. To gain deeper insights into the motivation behind approving and using in-home voice assistants, this work shifts its focus towards examining voice interactions. Utilization of speech recognition for converting speech into text enables constructing an attractive personalized assistant. This development facilitates seamless tasks such as sending emails without manual typing, conducting Google searches without opening the browser, and accomplishing various daily activities including playing music or launching a preferred IDE, all with a single voice command. To assess its performance, the system is seamlessly integrated with home automation (open-source) environment and operated continuously for some days. During this period, users are encouraged to interact with it, and the results demonstrate increased accuracy, reliability and appeal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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Deepfake Classification for Human Faces using Custom CNN 7

Deepfake Classification for Human Faces using Custom CNN

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Kalemullah, Amaan M. Prakash, P. Sakthivel, V. Vellore Institute of Technology Chennai School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

This paper emphasizes the urgent need for effective deepfake classification methods, particularly for human faces, due to the escalating threat of this technology. The research proposes a comprehensive approach using a Convolutional Neural Network (CNN) model and two Transfer Learning models (ResNet-50 and EfficientNet B7) to address this challenge. It investigates the synthesis of realistic-looking facial manipulations and their societal impacts, highlighting the importance of accurate classification in mitigating these effects. The study evaluates and compares the proposed models' accuracy in detecting manipulated facial content, analyzing their strengths and limitations. Overall, the paper provides a timely exploration of deepfake classification, offering practical solutions to enhance digital security and trustworthiness. © 2024 IEEE.

关键词： Convolutional neural networks

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Intelligent Vehicle Authentication System

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Materials Today: Proceedings 2022年 62卷 4599-4604页

作者： Prabhakar Rao, B.V.A.N.S.S. Kumar Singh, Rabindra School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

Today we can no longer imagine our lives without a vehicle for any means of transportation. They play a very important role in everyone's life. But as their importance in our lives grows, so does the fraud and antisocial activity associated with these vehicles. License plate forgeries, car theft, antisocial activity in stolen vehicles are times to improve detection skills and tracking capabilities. This framework will help how to trace a suspicious and fraudulent vehicle. And with the COVID pandemic, the world is moving towards an autonomous system that requires minimal human intervention. Our goal in this document is to use computer vision technology to create a reliable, autonomous and affordable system. This allows you to identify or track a vehicle that has been fraudulent or stolen. © 2022

关键词： Intelligent systems

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Combat with Class Overlapping in Software Defect Prediction Using Neighbourhood Metric

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SN computer science 2023年第5期4卷 695页

作者： Gupta, Shivani Richa Kumar, Ranjeet Jain, Kusum Lata School of Computer Science Engineering Vellore Institute of Technology Chennai India Department of Computer science and Engineering Birla Institute of Technology Mesra Ranchi India School of Electronics Engineering Vellore Institute of Technology Chennai India School of Computer & Communication Engineering Manipal University Jaipur Jaipur India

The characteristics of data is a open problem which has been tended perceived in data analysis in machine learning research from last decades. The researcher defined some measures to identify the characteristics of the dataset by applying data complexity measures to find the fitness for purpose. The presence of class overlapping in data-sets, significantly affect performance of the classifiers. Data complexity measures provide quantitative insight in quality of the data set and overlapping existent in it. Machine learning techniques are also utilized by several researchers on healthcare datasets in software defect prediction. In this paper, our aim is to evaluates the effectiveness of new overlap measure: Near Enemy Ratio, and its effect on complexity measures and performance of the classifier. The new ration is based on nearest instances to the target instance. The experimental result offers insights in usefulness of the method and help us decide whether this solution should be applied on a particular data-set or not. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AUC Class imbalance Class overlap G-mean Recall Software defect prediction

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Biomedical Data Management and Analytics in IOMT 8th

Biomedical Data Management and Analytics in IOMT

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8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Niha, K. Surendiran, B. Amutha, S. School of Computer Science and Engineering VIT Vellore Campus Tamil Nadu Vellore India Department of Computer Science and Engineering National Institute of Technology Puducherry India School of Computer Science and Engineering VIT Chennai Campus Chennai India

ISBN: (纸本)9789819732449

The IoMT is a device that works with different healthcare systems to help develop networking technologies. It reduces the unnecessary burden of visiting the hospital. Instead, it enables the transfer of medical related data with physicians through a secured network. Smart devices, such as wearable and medical monitors are used for healthcare use in the IoMT network. Telehealth and other services are adapted in homes and hospitals in other real-time locations. Approximately in the globe, 80% of healthcare have implemented IoT (Internet of Things) technologies and 20% are expected to do the same in the future. This paper focuses on four topics: (1) recent advances on Internet of medical things, (2) highlights the emerging data fusion process for data preparation, (3) discusses the life cycle of sensor devices and their protocols, and (4) processing data with different techniques and theories. In a nutshell, this paper gives a comprehensive overview of IoMT research advancement, application, data management, and analytics techniques along with the current research challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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Automatic Person Detection in Search and Rescue Operations Using Deep Based MultiModel CNN Detectors

Automatic Person Detection in Search and Rescue Operations U...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Vasudevan, I. Nithva, N.S. Ksr College of Engineering Computer Science and Engineering Tamilnadu Namakkal India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

ISBN: (纸本)9798350310061

Search and rescue operations are critical tasks where quick and accurate detection of humans can save lives. Due to growing number of people doing adventure tourism and going to other inaccessible places it is very important to organize a search and rescue operations to aid and health care to the needy. It's very difficult to find people who are relatively very small considering the area where they are. The detection of persons and objects in images and videos captured by drones during operations is of great importance, and thus, the ability to do so automatically is highly significant. Due to their ability to quickly capture a large, controlled area in a short amount of time, UAVs or drones are becoming increasingly involved in search operations. Despite the high success rate in detecting objects in normal ground captured images, detecting humans or other objects from UAVs (Unmanned Aerial Vehicles) still remains a challenging task in captured images or aerial images due to a few challenges like weather conditions, varying attitude, pose and scale variations, artefacts like people wearing hats and camouflaged environment. This project use CNN detectors for human detection in search and rescue operations. © 2023 IEEE.

关键词： Drones

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Implementation of Multi-Label Fuzzy Classification System using Topic Detection Data set 4

Implementation of Multi-Label Fuzzy Classification System us...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kanagaraj, R. Krishnaraj, N. Selvakumar, J. Ramprasath, J. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore641022 India Department of Information Technology Dr.Mahalingam College of Engineering and Technology Pollachi642003 India

Multiclass Classification can be implemented by using consequent approaches to translate the multiclass problem into binary class classification problems and fuzzy classification methods. This work proposes a predictive analysis of the multiclass fuzzy Classification integrated with time series historical data and topic detection. The fuzzy classification techniques can be successfully applied to Topic detection and sub-topic detection. Text databases' manual topic detection method must be more feasible, uncontrollable and effective. Thus, initiating the huge amount of data implemented by manual methods is idealistic. Fuzzy historical data is more significant for data analysis in different models to make predictions. Innumerable fuzzy logic on time series methods has been implemented for data prediction. A Multiclass Fuzzy Time Series Classification Algorithm has been implemented to analyze and predict the topic detection database. The outcomes of the Fuzzy classification technique have been implemented for the need for an extensive pattern of topic detection. An enhanced Multiclass Fuzzy Time Series Classification Algorithm has been applied to achieve the efficient de-fuzzification operation of the topic detection data set. To illuminate the forecasting method, the historical data of multi-labeled has been used for the predictive model. The investigation result illustrates that the MHTSC algorithm generates mode fuzzy classification and irregular rules, efficiently reducing the error rate from multi-labeled data. © 2025 The Authors. Published by Elsevier B.V.

关键词： Fuzzy Classification Fuzzy Time Series MHTSC Topic Detection

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A model for screening eye diseases using optical coherence tomography images

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International Journal of computers and Applications 2022年第4期44卷 396-400页

作者： Gupta, Sanchay Chandra, Siddharth Maheswari, N. Sivagami, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

Age-Related Macular Degeneration (AMD) is one of the most common type of eye ailments which generally affects elderly people of age above 60 years and which damages their central vision. Diabetic Macular Edema (DME) is an another form of irreversible vision loss that occurs in diabetic people because of the accumulation of fat in retinal nerves. Therefore it is necessary to screen AMD and DME at an early stage, as it would help in the treatment and prevention of complete blindness. This paper aims to create a system for screening AMD and DME on Optical Coherence Tomography images using Image processing techniques in order to improve the feature to be extracted from the images using Convolutional Neural Network. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Support vector machines

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Qiskit Simulation of CNOT Equivalent Circuits

Qiskit Simulation of CNOT Equivalent Circuits

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Vishwakarma, Soumya Premjit, Jerelyn P. Balakrishnan, S. Manoov, R. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Advanced Sciences Department of Physics Vellore India

ISBN: (纸本)9798350314946

It is known that Controlled NOT (CNOT) operation can generate maximally entangled states which are necessary for quantum communication processes. Further, CNOT is one of the universal two-qubit operators. In this work, we study the various equivalent circuits of CNOT available in the literature. We show the possibility of implementing equivalent circuits in Qiskit. © 2023 IEEE.

关键词： Equivalent circuits

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A Novel Edge-Aware Image Fusion Approach for Thermal and Visible Images 3

A Novel Edge-Aware Image Fusion Approach for Thermal and Vis...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Thanaraj, Palani K. Kumar, Suvidha Rupesh School of Computer Science and Engineering Vellore Institute of Technology Chennai India

In this work, we present a novel image fusion approach aimed at enhancing the quality of fused images generated from thermal (infrared - IR) and visible image pairs. We compare our proposed method with the conventional Discrete Wavelet Transform (DWT) approach, using a comprehensive set of image quality metrics, including Peak Signal-to-Noise Ratio (PSNR), Spatial Frequency (SF), and Human Visual System (HVS)-based metrics. Our experimental results unequivocally demonstrate the superior performance of our proposed fusion method. Notably, our approach excels in preserving and enhancing edge information, a vital aspect in various image fusion applications such as surveillance and medical imaging. The consistently higher PSNR values across all sample images reflect our method's exceptional edge enhancement capabilities. Its remarkable edge enhancement capabilities, computational efficiency, and adaptability position it as an efficient and versatile alternative to conventional DWT-based approaches and deep learning-based methods. © 2023 Elsevier B.V.. All rights reserved.

关键词： Discrete wavelet transforms

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