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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Balaraman, Kalaiselvi Claret Angelin, S.P. Department of Computer Science College of Science and Humanities SRM Institute of Science and Technology India Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology India

ISBN: (纸本)9798400709418

Healthcare is a crucial part of human beings;thus, keeping a check by recording and monitoring certain essential functions of the body is important. Using sensors to record multiple physiological processes can be discomforting for the body. This is especially true if the recording is done for a prolonged period, due to this reason that methods for remote physiological monitoring are becoming popular. These methods do not involve the physical interaction of any sensors with the human body or skin. Several studies have proposed the detection of Blood Volume Pulse, or BVP, to calculate Heart Rate and Heart Rate Variability. In this particular study, the main focus is on measuring the Heart Rate (HR). The algorithm utilized in this study is Independent Component Analysis (ICA). The primary function of this algorithm is to separate the main source signal essential for measurement from the noise in the RGB channels of the facial video. In this paper, the generalization of existential methods is addressed. In addition, the process considered in this approach is much more accurate. It is possible due to the implementation of a light equalization scheme. This scheme helps reduce issues of unequal facial light and minimize the effect of shadows - the selection of the best and most accurate signal as outputted by the ICA module. © 2023 ACM.

关键词： Independent component analysis

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Software Design Specification Proposal of a Diagnostic Decision Support System for Clinical Low Back Pain 14

Software Design Specification Proposal of a Diagnostic Decis...

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14th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 13th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2023

作者： Gambo, Ishaya Agbonkhese, Christopher Department of Computer Science and Engineering Obafemi Awolowo University Ile-Ife Nigeria Department of Digital and Computational Studies Bates College LewistonME04240 United States

Low Back Pain (LBP) is a prevalent health issue that affects many people's daily functioning and well-being. However, diagnosing LBP is challenging because it requires expert knowledge of the spine's structure and function. Therapists usually rely on asking patients questions to guide their tests, but this method is not very efficient. Moreover, existing systems for LBP diagnosis and information exchange are narrow in their coverage. To address these limitations, this paper proposes a design specification for a web-based decision support system that offers a preliminary diagnosis and suggests a comprehensive diagnosis that may have multiple components. The system design incorporates an intuitive and user-friendly framework that can handle the complex features of LBP diagnosis and uses verbal probability estimation to cope with the uncertainty involved. The design specification can facilitate communication among the relevant stakeholders. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

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Enhancing Medical Image Diagnosis with Convolutional Neural Networks 2

Enhancing Medical Image Diagnosis with Convolutional Neural ...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Mahadevan, K. Pushparani, S. SashiRekha, K. Sivagami, V.M. Usharani, R. Department of Computer Science and Engineering CARE College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Chennai India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350383867

This paper is a comparative analysis of medical image diagnosis algorithms with Convolutional Neural Networks (CNNs) and other methods;such as Support Vector Machine (SVM), Random Forest, and k-nearest Neighbors (k-NN) The study performs evaluations on datasets with multimodalities like medical images and diagnostic and various imaging modalities. Results shows that CNNs have the upper hand over traditional, static algorithms across several metrics that assesses the performance of the system such as accuracy, precision, recall, and F1. In particular, CNN precision was 90.5% while the recall was 93.7%, and the accuracy was 92.1% as well as F1-score 92.0%. In addition, the high area under the Receiver Operating Characteristic curve (AUC-ROC) of over 0.95 for the CNN also signed for its excellent discriminatory power. These outcomes only exemplify CNN usefulness in the diagnostic area because it is an exact and accurate method for analyzing medical images. Although the nets CNNs are deployed in clinical cases may help address concerns regarding interpretability, compliance with regulations, and ethical opinions, such issues still exist and require further exploration. Overall, this research takes the first steps in directing algorithmic approach towards medical diagnosis and show the feasibility in deployment of CNNs as inseparable pieces of equipment in modern healthcare. © 2024 IEEE.

关键词： Random forests

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IOT and Machine Learning Based Real-Time Protection Against Cyber-Physical Threat Mitigation 4

IOT and Machine Learning Based Real-Time Protection Against ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Saini, Harnit Mohammad, Faz Singh, Manoj Kumar Sharma, Kapil Kumar Shukla, Meenu Singh, Ramendra Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Ims Engineering College Department of Computer Science & Engineering Ghaziabad India Ims Engineering College Department of Mca Ghaziabad India Maharishi University of Information Technology School of Engineering and Technology Noida India Ghaziabad India

ISBN: (纸本)9798350360165

An investigation is carried out into the dynamic relationship between machine learning algorithms used in IoT security, with detailed scrutiny of performance indicators to give a fuller view of what happens. The study pays particular attention to metrics like accuracy, precision, recall, and F1 score when ten varied samples are being examined. As a result, all the algorithms exhibit excellent performance with 95.5 %-98.2 % accuracy values, inspiring high reliability under diverse scenarios. Precision and recall metrics also depict the models' capabilities. Balancing precision and recall can be observed in F1 scores that are between 95.2% and 98.0%, and these numbers show that the approach based on this understanding of trade-offs is characterized by its operationalization. At the other end, results obtained from performance metrics go beyond numerical benchmarks to give a holistic appreciation of the algorithms' strength and areas for possible enhancement. The paper also does not provide only a theoretical approach to machine learning related to security in the IoT environment but also provides information about security considerations for the successful deployment of powerful, adaptive models into real-life scenarios. In this case, findings of the research help in understanding how to make sure that smart and effective measures are implemented during the process of developing security solutions considering all related issues in connection with the IoT technology landscape. © 2024 IEEE.

关键词： Adversarial machine learning

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Design of Approximate Multiplier using Highly Compressed 5_2 Counter 6

Design of Approximate Multiplier using Highly Compressed 5_2...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Nelmin, Bathlin N. Shaji, R.S. Hariprasad, S. Shettygari, Sudeshna Vasan, J.K. Vijayan, V. Department of Electronics and Communication Engineering University College of Engineering Nagercoil India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331522667

Approximate computing effectively impacts low-power VLSI circuits by reducing circuit overhead. It introduces minimal error in the output, which does not significantly affect the application. This paper proposes an approximate multiplier constructed using 5_3 and 5_2 counters. The proposed 5_2 counter delivers two outputs instead of three, thereby achieving 60% compression. The overhead of the 5_2 counter circuit is reduced, achieving a power delay product (PDP) improvement of 18%. This reduction comes with an addition of an 6% error rate. The newly designed multiplier has less delay, where the Mean error distance (MED) is kept as low as possible. The complexity of the 5_2 counter is also reduced since the counter uses only a full adder and three gates. This is achieved by applying various error optimization mechanisms. The error parameters, (i) Error Distance (ED), and (ii) Mean Error Distance (MED) are analyzed and compared with existing works. The proposed multipliers are involved in image contrast application with better Peak Signal to Noise Ratio (PSNR) value. © 2025 IEEE.

关键词： Delay circuits

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Energy Efficient Mathematical Model for Cyber-Physical Systems: A Model for Industrial Internet of Things

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SN computer science 2023年第5期4卷 1-7页

作者： Veerachamy, Ramachandran Rao, Ganga Rama Koteswara Arivanantham, Vishnu Priya Kuppusamy, Sangeetha Velayutham, Priya Govindaraj, Rajeshkumar Department of Computer Science and Engineering GITAM School of Computing GITAM Deemed to be University Bengaluru 561203 India Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 520002 India Department of Computational Intelligence School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal 637018 India Department of Information Science and Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamagalam 638401 India

Proliferated usage of computer devices, smart objects, and electronics significantly impacts the atmosphere, which is inevitable. The existing context has engineered schemes, or Cyber-Physical Systems (CPS), that integrate physical and computation components. The extensive usage of devices has utilized more energy, creating complexity in environmental handling. The problem is improving an automotive function’s reliability, application, or functionality with time and energy constraints. By applying smart technologies, namely Deep Learning (DL) and Machine Learning (ML) in CPS and the Internet of Things (IoT), enduring the utilization of these intensive DL-based approaches has a negative effect on resource utilization. The proposed Energy Efficient Mathematical Model (EEMM) is designed with a mathematical model for IIoT, which guides in solving the drawback of the existing system. The mathematical model integrated CPS-based proposed EEMM has outperformed the existing state-of-art technique. The proposed EEMM attains minimal Energy Consumption (EC) of 521 Jules and the highest reliability rate of 0.987. The proposed EEMM in this research has attained minimal energy usage of 0.987 i for 100 tasks, which is minimal than other existing techniques. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cyber-attacks Energy IIoT IoT Mathematical model Network Smart objects

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Multiplicative resonant enhancement of chemical detection

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Physical Review Applied 2024年第5期22卷 054006页

作者： Wenchao Ma Raphaël Pestourie Zin Lin Alan Aguirre-Soto Hadley D. Sikes Steven G. Johnson Department of Chemistry School of Computational Science and Engineering Bradley Department of Electrical and Computer Engineering School of Engineering and Sciences Department of Chemical Engineering Department of Mathematics

Optical resonances can increase the sensitivity of measurements to material perturbations and also accelerate photochemical reactions. Here, we show that these two effects can be combined multiplicatively, to enhance the detection via weak or low-concentration photochemical reactions far beyond what could previously be attained. For an optical resonance with quality factor Q, the sensitivity of our detection scheme is enhanced by ∼Q2 (where ∼ denotes approximate proportionality), as demonstrated by both theoretical arguments and numerical simulations of a simple optical-grating resonance coupled with reaction-diffusion equations. Such an approach opens a door to further improvements by careful design of the resonance: even a three-parameter optimization of the grating resonance yields an additional ≈7 times improvement.

关键词： Analytical chemistry Fano resonance Metrology Nanophotonics Optimization problems Photochemistry Devices Reaction diffusion systems

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Guardian of the Ensembles: Introducing Pairwise Adversarially Robust Loss for Resisting Adversarial Attacks in DNN Ensembles

Guardian of the Ensembles: Introducing Pairwise Adversariall...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Shukla, Shubhi Dalui, Subhadeep Alam, Manaar Datta, Shubhajit Mondal, Arijit Mukhopadhyay, Debdeep Chakrabarti, Partha Pratim Centre for Computational and Data Sciences Iit Kharagpur India Iit Kharagpur Computer Science and Engineering Department India Center for Cyber Security New York University Abu Dhabi United Arab Emirates Iit Kharagpur Department of Artificial Intelligence India Iit Patna Computer Science and Engineering Department India

ISBN: (纸本)9798331510831

Adversarial attacks rely on transferability, where an adversarial example (AE) crafted on a surrogate classifier tends to mislead a target classifier. Recent ensemble methods demonstrate that AEs are less likely to mislead multiple classifiers in an ensemble. This paper proposes a new ensemble training using a Pairwise Adversarially Robust Loss (PARL) that by construction produces an ensemble of classifiers with diverse decision boundaries. PARL utilizes outputs and gradients of each layer with respect to network parameters in every classifier within the ensemble simultaneously. PARL is demonstrated to achieve higher robustness against black-box transfer attacks than previous ensemble methods as well as adversarial training without adversely affecting clean example accuracy. Extensive experiments using standard Resnet20, WideResnet28-10 classifiers demonstrate the robustness of PARL against state-of-the-art adversarial attacks. While maintaining similar clean accuracy and lesser training time, the proposed architecture has a 24.8% increase in robust accuracy (= 0.07) from the state-of-the art method. Code is available at: https://***/shubhishukla10/PARL © 2025 IEEE.

关键词： adversarial attack decision-boundary diversity defense ensemble models

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Machine Learning Approach for Handling Imbalanced Students' Performance Data 15

Machine Learning Approach for Handling Imbalanced Students' ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sujatha, E. Divya, S. Sakthivelan, R.G. Gopirajan, P.V. Thandalam Chennai India Chennai India Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur campus Chennai India

ISBN: (纸本)9798331300579

Imbalanced student performance data in educational institutions is crucial for any machine learning prediction model. It affects the efficiency of classifiers and challenges the sampling methods for having a more significant number of features. The proposed model was designed to predict the student's performance by comparing the results with popular similar models. The Kaggle dataset having 386 rows and 33 columns of student data, was used in this study. In addition, the Portugal database was considered for experimental analysis, containing 394 students and 19 attributes. It proves that the Random Forest classifier yields the highest accuracy at 84.15% in handling imbalanced datasets. The results show that the SVM-SMOTE is higher accuracy as, 94.76%, than the other sampling methods in predicting the student's performance with various features. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Twitter sentiment analysis using conditional generative adversarial network

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

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