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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Bharanidharan, N. Aggarwal, Pratham Batra, Aryan Shah, Deep Sannasi Chakravarthy, S.R. Rajaguru, Harikumar Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Sathyamangalam India

ISBN: (纸本)9798350370867

When compared to different types of malignant tumors, pancreatic malignancy is the 12th widespread tumor disease among humans globally. Pancreatic cancer can be identified at the earlier stages using microarray gene analysis. The objective of this work is to classify the gene samples as normal and tumoral through the usage of Particle Swarm Optimization technique along with the supervised machine learning classifiers. A variance-based feature selection is employed to select the most appropriate features and the selected features are transformed using particle swarm optimization to improve the classification performance. Four different machine learning supervised algorithms are realized as classification techniques. When these four vanilla classifiers are considered, the random forest algorithm performs relatively well with balanced accuracy score of 87.5%. This score is improved to 94.4% through the usage of the proposed technique. In addition, the proposed technique enhances the prediction of the other two classifiers as well. © 2023 IEEE.

关键词： Gene expression

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Effective Mobility Identification in Mobile Fog Environment with the Internet of Things

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第15期16卷 157-171页

作者： Deepa, D. Jothi, K.R. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Fog extends the cloud to be closer to the end devices so that it acts on IoT data within a millisecond. Almost 60% of data can be analyzed that is physically near to the IoT data. This proximity has various advantages, including reduced latency, which improves the user experience. However, because the distance to a fog service may vary as a user moves from one location to another, user mobility may restrict such benefits in practice. A fog service migration is based on a mitigation approach that allows the service to always be close enough to a user. Quality of Service is decreased because of the mobility of the user’s location. Predicting the future location in advance improves the efficiency of service provisioning. In this work, a dynamic mobility model is proposed to find the user location in advance. This experiment was carried out by LuST mobility data set collected by Luxembourg Simulation of Urban Mobility (SUMO) Traffic (LuST). This result is give better accuracy of location prediction up to 98.87% when compared with existing methods. © 2022. International Journal of Interactive Mobile *** Rights Reserved

关键词： Efficiency

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ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED CRIMINAL DETECTION USING A DEEP LEARNING APPROACH

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Jagadeesan, S. Sujigarasharma, K. Manjula, R. Vetriselvi, T. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305708

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases. © 2023 IEEE.

关键词： CNN Criminal detection Face detection Machine learning

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Performance Analysis of CNN Models for Evaluating Biglycan as a Biomarker in Predicting Breast Cancer

Performance Analysis of CNN Models for Evaluating Biglycan a...

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International Conference on computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Krishnan, Sushil Sundaresan, Yokesh Babu Bachelor of Technology in Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781839539848

Breast cancer is found in the present generation at a higher level. Since the middle of the 20th century numerous unfavorable changes have been made to food, our way of life, obesity, the situation, and our microbiomes. Because of these variations, people have become more susceptible to cancer at an earlier age. The considered dataset is images of breast cancer-positive and cancer-negative histological sections utilizing the Biglycan biomarker. The dataset and models created from it can be used by pathologists and other healthcare practitioners to address probable disorders. Three widely used CNN models were examined for performance, and we discovered that MobileNet-V2 outperformed them all. Transfer learning-based breast cancer classification with the Biglycan biomarker dataset helps in tracking at the earlier stages. Additionally, our model outperformed earlier research using the same dataset in terms of accuracy. Our ability to create high-performance devices with lower power consumption and cheaper costs is made possible by the MobileNet-V2's ability to achieve greater precision while using a lighter model. In comparison with the existing work using LeNet, MobileNet-V2 outperforms. © The Institution of engineering & technology 2023.

关键词： Biomarkers

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An with effective ensemble classifier for network intrusion detection system

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Peer-to-Peer Networking and Applications 2025年第2期18卷

作者： Nandhini, U. Santhosh Kumar, S.V.N. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

The increasing frequency of network attacks poses a significant challenge in maintaining network security. In response to security vulnerabilities, the employment of Intrusion Detection systems (IDS) has become crucial. An IDS is a software application, which monitors network traffic to monitor and identify various malicious actions in the network. Network Intrusion Detection systems (NIDS) specifically examine network traffic to identify and monitor the various malicious activities identified by the network nodes. However, many existing IDS solutions encounter difficulties in achieving optimal feature selection, aiming for high classification accuracy while minimizing false alarm rates. In the proposed system, the Linear Discriminant Analysis (LDA) technique is employed to reduce the dataset dimensionality. Further, an Enhanced Whale Optimization Algorithm (EWOA) is utilized for efficient feature selection for global optimization tasks. To perform classification, an ensemble classifier is proposed which employs Random Forest (RF) and XGBoost. The main objective of the proposed approach is to improve classification accuracy while minimizing the false alarm rate by combining the benefits of RF and XGBoost. The proposed work is implemented by using the python jupyter framework. The experimental results show that the proposed system has an overall classification accuracy of 96.08%, a false alarm rate of 0.01%, and an overall detection rate of 94.03%, by using the NSL-KDD dataset when it is compared with their counterparts. © The Author(s).

关键词： Application programs

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Conditional adversarial segmentation and deep learning approach for skin lesion sub-typing from dermoscopic images

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Neural Computing and Applications 2024年第26期36卷 16445-16463页

作者： Mirunalini, P. Desingu, Karthik Aswatha, S. Deepika, R. Deepika, V. Jaisakthi, S.M. Department of Computer Science Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai603110 India School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai600127 India

Automatic skin lesion subtyping is a crucial step for diagnosing and treating skin cancer and acts as a first level diagnostic aid for medical experts. Although, in general, deep learning is very effective in image processing tasks, there are notable areas of the processing pipeline in the dermoscopic image regime that can benefit from refinement. Our work identifies two such areas for improvement. First, most benchmark dermoscopic datasets for skin cancers and lesions are highly imbalanced due to the relative rarity and commonality in the occurrence of specific lesion types. Deep learning methods tend to exhibit biased performance in favor of the majority classes with such datasets, leading to poor generalization. Second, dermoscopic images can be associated with irrelevant information in the form of skin color, hair, veins, etc.;hence, limiting the information available to a neural network by retaining only relevant portions of an input image has been successful in prompting the network towards learning task-relevant features and thereby improving its performance. Hence, this research work augments the skin lesion characterization pipeline in the following ways. First, it balances the dataset to overcome sample size biases. Two balancing methods, synthetic minority oversampling TEchnique (SMOTE) and Reweighting, are applied, compared, and analyzed. Second, a lesion segmentation stage is introduced before classification, in addition to a preprocessing stage, to retain only the region of interest. A baseline segmentation approach based on Bi-Directional ConvLSTM U-Net is improved using conditional adversarial training for enhanced segmentation performance. Finally, the classification stage is implemented using EfficientNets, where the B2 variant is used to benchmark and choose between the balancing and segmentation techniques, and the architecture is then scaled through to B7 to analyze the performance boost in lesion classification. From these experiments, we find

关键词： Pipelines

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Utilizing Machine Learning to foster employee mental health in modern workplace environment

Utilizing Machine Learning to foster employee mental health ...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Chauhan, Tania Renjith, P.N. Vellore Institute of Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348460

This study offers a novel approach to tackling the increasing problem of workplace stress by utilizing machine learning techniques. Professionals in modern work contexts experience heightened levels of stress due to demanding job requirements, technical challenges, and the common expectation of perpetual availability. This work presents a comprehensive stress detection and mitigation method that utilizes advanced machine learning techniques, including image processing and real-time monitoring, to identify indicators of stress and offer personalized treatments. The system comprises several components, encompassing data collection and user interfaces, with the objective of promoting a more salubrious work environment and enhancing employee welfare and efficiency. © 2024 IEEE.

关键词： image processing machine learning Mental stress real-time monitoring stress recognition

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Efficient Authentication System Using Wavelet Embeddings of Otoacoustic Emission Signals

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computer systems science & engineering 2023年第5期45卷 1851-1867页

作者： V.Harshini T.Dhanwin A.Shahina N.Safiyyah A.Nayeemulla Khan Department of Information Technology Sri Sivasubramaniya Nadar College of EngineeringChennai603110India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Biometrics,which has become integrated with our daily lives,could fall prey to falsification attacks,leading to security *** our paper,we use Transient Evoked Otoacoustic Emissions(TEOAE)that are generated by the human cochlea in response to an external sound stimulus,as a biometric *** are robust to falsification attacks,as the uniqueness of an individual’s inner ear cannot be *** this study,we use both the raw 1D TEOAE signals,as well as the 2D time-frequency representation of the signal using Continuous Wavelet Transform(CWT).We use 1D and 2D Convolutional Neural Networks(CNN)for the former and latter,respectively,to derive the feature *** corresponding lower-dimensional feature maps are obtained using principal component analysis,which is then used as features to build classifiers using machine learning techniques for the task of person identification.T-SNE plots of these feature maps show that they discriminate well among the *** the various architectures explored,we achieve a best-performing accuracy of 98.95%and 100%using the feature maps of the 1D-CNN and 2D-CNN,respectively,with the latter performance being an improvement over all the earlier *** performance makes the TEOAE based person identification systems deployable in real-world situations,along with the added advantage of robustness to falsification attacks.

关键词： Person identification system cochlea:transient evoked otoacoustic emission wavelet transform convolutional neural network

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Uncovering the Hidden Patterns of the COVID-19 Global Pandemic: An in-Depth Data Analytics Approach

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SN computer science 2024年第8期5卷 1-15页

作者： Sarobin, M. Vergin Raja Rathore, Jinen Mishra, Rajat Moolya, Vinay Vittal Seth, Yash School of Computer Science and Engineering Vellore Institute of Technology TN Chennai 600127 India

COVID-19 is a highly infectious respiratory illness caused by the novel coronavirus. It was first identified in Wuhan, China in December 2019 and has since spread globally, infecting and killing a vast number of people, leading to a worldwide pandemic. The pandemic has left the world in disarray. We wished to apply data analytics and regression models to understand and study the data – OwiD (Our World in Data) real time covid dataset - to analyse and draw trends and factors that led to the widespread of the virus. Doing so, allows us to identify key factors and trends that played a vital role in the rapid spread of the virus. We can thus determine the underlying hidden patterns of key factors. This will help provide a better understanding and determine the potential reasons COVID-19 took the world by storm with its fast-paced spread. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Data analytics OWiD (Our World in Data) Pandemic Regression Virus

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A Review of Intelligent Configuration and Its Security for Complex Networks

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Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security Software defined network Lifecycle management

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