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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Kalpana, Ponugoti Malleboina, Kavya Nikhitha, Musku Saikiran, Patanm Kumar, Satri Nithish Avn Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350373110

Before the approach of Information Communication Innovations (ICT), social intelligent essentially happened inside restricted social limits, such as neighborhood geological regions. The later headways in communication innovations have essentially outperformed the imperatives of time and space inborn in conventional communication. These innovative developments have started a insurgency in user-generated substance, online social systems, and the aggregation of broad information related to human behavior. In any case, the abuse of these social innovations, especially social media (SM) stages, has presented a unused measurement of hostility and savagery that shows only in the advanced domain. This work sheds light on a novel frame of forceful behavior illustrated on SM stages. It moreover talks about the inspirations behind creating prescient models to combat such behavior. We conduct a comprehensive survey of forecast models for cyberbullying and distinguish key challenges related with building these models on SM stages. This paper offers experiences into the generally handle of cyberbullying location, with a specific center on strategy. Whereas we expound on information collection and highlight designing forms, critical consideration is given to include choice calculations and the utilization of different machine learning calculations for anticipating cyberbullying behaviors. At last, we highlight issues and challenges that point towards unused investigate roads for researchers to investigate. © 2024 IEEE.

关键词： Adversarial machine learning

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Anomaly Detection in Sensor Data using Machine Learning 15

Anomaly Detection in Sensor Data using Machine Learning

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Das, Kaushik Neog, Sankhapani Bora, Priyanuj Chettry, Digbijoy Bora, Rishab Dibrugarh University Institute of Engineering and Technology Department of Computer Science and Engineering Dibrugarh India

ISBN: (纸本)9798350370249

Our research investigates the application of single-class Support Vector Machine (SVM) for anomaly detection, which is critical for predictive maintenance. The study utilizes real-world datasets to validate the effectiveness of these methodologies in accurately distinguishing between normal and anomalous data points that indicate potential machine health issues. It examines the effects of varying anomaly proportions and noise levels, one-class classifiers, and anomaly scoring thresholds can significantly enhance predictive maintenance capabilities. The results demonstrate that these approaches facilitate early fault detection, allowing for timely interventions and reducing unplanned downtime, ultimately improving machine reliability and maintenance efficiency. © 2024 IEEE.

关键词： Predictive maintenance

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Enhancing Vendor Selection in Supply Chains Using Machine Learning: A Comparative Study of Optimization Algorithm

Enhancing Vendor Selection in Supply Chains Using Machine Le...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Yadav, Nisha Kumar, Vivek Gupta, Megha Singh, Hitesh Noida Institute Of Engineering & Technology Computer Science & Engineering Department Greater Noida India

ISBN: (纸本)9798350366846

Optimal supplier selection step is a vital feature of the supply chain, having a great effect on the company's financial and operating results. The standard methodologies that relied on the lowest rate acceptances have been changed for this purpose as they should be replaced with a more in-depth one that uses multidisciplinary sourcing teams and the most sophisticated analytics. This paper suggests a predictive model for building an appropriate tendering system with machine learning methods. The evaluation model is developed with pre-established criteria that measure the vendor performance on such aspects as the quality, cost, and availability. This generates a combined score useful for successful choices. Data of previous years and optimization algorithms like Nadam, Adam and RMSprop are used and their efficacy is assessed in terms of predicting the model. Neural network architecture developed is already showing the strength of data-driven methods and this is how the management can be better optimised and vendor selection practice improved. The results should have widened the scope of the optimization methods and should emphasized an appropriate selection of optimization algorithms for the high performance. © 2024 IEEE.

关键词： Supply chain management

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Wrapper Based Linear Discriminant Analysis(LDA)for Intrusion Detection in IIoT

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computer Systems Science & engineering 2023年第5期45卷 1625-1640页

作者： B.Yasotha T.Sasikala M.Krishnamurthy Department of Computer Science and Engineering MNM Jain Engineering CollegeChennai600097India Department of School of Computing Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering KCG College of TechnologyChennai600097India

The internet has become a part of every human ***,various devices that are connected through the internet are ***,the Industrial Internet of things(IIoT)is an evolutionary technology interconnecting various industries in digital platforms to facilitate their ***,IIoT is being used in various industrial fields such as logistics,manufacturing,metals and mining,gas and oil,transportation,aviation,and energy *** is mandatory that various industrial fields require highly reliable security and preventive measures against *** detection is defined as the detection in the network of security threats targeting privacy information and sensitive *** Detection Systems(IDS)have taken an important role in providing security in the field of computer *** of intrusion is completely based on the detection functions of the *** an IIoT network expands,it generates a huge volume of data that needs an IDS to detect intrusions and prevent network *** research works have been done for preventing network *** day,the challenges and risks associated with intrusion prevention are increasing while their solutions are not properly *** this regard,this paper proposes a training process and a wrapper-based feature selection With Direct Linear Discriminant Analysis LDA(WDLDA).The implemented WDLDA results in a rate of detection accuracy(DRA)of 97%and a false positive rate(FPR)of 11%using the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)dataset.

关键词： Intrusion detection IIoT wrapper support vector machine(SVM) LDA random forest(RF) feature selection

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Classifying chest x-rays for COVID-19 through transfer learning: a systematic review

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Multimedia Tools and Applications 2025年第2期84卷 689-748页

作者： Mallick, Devanshi Singh, Arshdeep Ng, Eddie Yin-Kwee Arora, Vinay Thapar Institute of Engineering & Technology Punjab Patiala India Wipro Limited Gurugram India School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore Computer Science Engineering Department Thapar Institute of Engineering & Technology Punjab Patiala India

This study makes a comprehensive assessment of the predominant Transfer Learning (TL) techniques employed for the classification of COVID-19 cases in Chest X-rays (CXR) images. The methodologies have been selected on the basis of their merits and demerits, suitability, and possible impact on the development of the region being studied. The study examines the various methods of TL employed in the classification of COVID-19 cases with the objective to gain a deeper understanding about all the aspects of these methodologies. It can be of great significance for the researchers and medical professionals in making well-informed decisions about the implementation of these techniques to improve the precision and effectiveness of COVID-19 diagnosis. The practical consequences of these techniques help in early identification of such cases for having a suitable intervention. As many as 48 studies conducted during the period 2020–2023 have been included in the current research work for having an assessment about the problem under investigation. The study has specifically focused on transfer learning-based models utilized for the identification of COVID-19 through CXR pictures. It highlights the challenges posed by dataset dynamics, methodological variations, and performance metrics of different models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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Compete to Win: Enhancing Pseudo Labels for Barely-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2023年第11期42卷 3244-3255页

作者： Wu, Huimin Li, Xiaomeng Lin, Yiqun Cheng, Kwang-Ting The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Shenzhen Research Institute Shenzhen518057 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering The Department of Computer Science and Engineering Hong Kong Hong Kong

This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the existing state-of-the-art semi-supervised solution cross pseudo supervision is the unsatisfactory precision of foreground classes, leading to a degenerated result under barely-supervised learning. In this paper, we propose a novel Compete-to-Win method (ComWin) to enhance the pseudo label quality. In contrast to directly using one model's predictions as pseudo labels, our key idea is that high-quality pseudo labels should be generated by comparing multiple confidence maps produced by different networks to select the most confident one (a compete-to-win strategy). To further refine pseudo labels at near-boundary areas, an enhanced version of ComWin, namely, ComWin + , is proposed by integrating a boundary-aware enhancement module. Experiments show that our method can achieve the best performance on three public medical image datasets for cardiac structure segmentation, pancreas segmentation and colon tumor segmentation, respectively. The source code is now available at https://***/Huiimin5/comwin. © 1982-2012 IEEE.

关键词： Image segmentation

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Comparative Analysis of Deep Learning Approaches for Harmful Brain Activity Detection Using EEG 8

Comparative Analysis of Deep Learning Approaches for Harmful...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Bhatti, Shivraj Singh Yadav, Aryan Monga, Mitali Kumar, Neeraj Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Patiala India

ISBN: (纸本)9798331531539

The classification of harmful brain activities, such as seizures and periodic discharges, play a vital role in neurocritical care, enabling timely diagnosis and intervention. Electroencephalography (EEG) provides a non-invasive method for monitoring brain activity, but the manual interpretation of EEG signals are time-consuming and rely heavily on expert judgment. This study presents a comparative analysis of deep learning architectures, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and EEGNet, applied to the classification of harmful brain activities using both raw EEG data and time-frequency representations generated through Continuous Wavelet Transform (CWT). We evaluate the performance of these models use multimodal data representations, including high-resolution spectrograms and waveform data, and introduce a multi-stage training strategy to improve model robustness. Our results show that training strategies, data preprocessing, and augmentation techniques are as critical to model success as architecture choice, with multi-stage TinyViT and EfficientNet demonstrating superior performance. The findings underscore the importance of robust training regimes in achieving accurate and efficient EEG classification, providing valuable insights for deploying AI models in clinical practice. © 2024 IEEE.

关键词： Electroencephalography

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Optimizing task scheduling in cloud environments: a hybrid golden search whale optimization algorithm approach

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Neural Computing and Applications 2025年第17期37卷 10851-10873页

作者： Acharya, Biswaranjan Panda, Sucheta Das, Satyabrata Majhi, Santosh Kumar Gerogiannis, Vassilis C. Kanavos, Andreas Department of Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Burla India Bilaspur India Department of Digital Systems University of Thessaly Larissa Greece Department of Informatics Ionian University Corfu Greece

Managing fluctuating workloads and optimizing resource utilization in cloud environments pose significant challenges, particularly in fields requiring real-time data processing, such as healthcare. This paper introduces a novel hybrid metaheuristic technique, the Golden Search Whale Optimization Algorithm (GSWOA), specifically designed for scheduling independent dynamic biomedical data. GSWOA merges the strengths of Golden Search Optimization (GSO) and Whale Optimization Algorithm (WOA), optimizing numerical function optimization and achieving a balance between exploration and exploitation. The algorithm’s effectiveness was assessed using MATLAB by applying standard benchmark functions and further evaluated on a real-world biomedical dataset within the CloudSim environment. The performance evaluations demonstrate that GSWOA significantly outperforms existing metaheuristic and traditional scheduling techniques, achieving a 42.71% increase in resource utilization and a 14.17% reduction in makespan compared to conventional methods. These results highlight GSWOA’s potential to enhance scheduling efficiency substantially in cloud computing infrastructures, suggesting it is a powerful tool for complex task allocations. Future research will explore the scalability of GSWOA and its applicability across other data-intensive sectors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Scheduling algorithms

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Quantitative Assessment of XAI Methods for COVID-19 Detection: A Comparative Approach

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SN computer Science 2025年第2期6卷 1-16页

作者： Rajpoot, Reenu Jain, Sweta Semwal, Vijay Bhaskar Singh, Deepankar Department of Computer Science and Engineering Maulana Azad National Institute of Technology MP Bhopal 462003 India

With the growing use of deep learning models in medical imaging, it is critical to interpret these models to ensure precise diagnoses. A recent study using the SARS-COV2 dataset evaluated the efficiency of several Explainable Artificial Intelligence (XAI) techniques—LIME, GradCAM, SHAP, and GradCAM++—on deep learning models for detecting anomalies in COVID-19 CT scans. The study assessed five models based on accuracy, precision, recall, and F1-score. ResNet50 outperformed other models, achieving the highest accuracy of 95.26%, along with the best precision (96.24%), recall (94.14%), and F1-score (96.17%). The study further compared the effectiveness of different XAI techniques applied to ResNet50. Among the techniques, GradCAM++ provided the best interpretability, achieving a confidence score of 99.41% and the lowest prediction entropy of 5.21, outperforming GradCAM, LIME, and SHAP. GradCAM achieved a confidence score of 98.82% with a prediction entropy of 9.25, while LIME and SHAP showed lower confidence scores of 95.23% and 78.91% respectively, with higher prediction entropy values. The attribution maps revealed that the models focused on different regions of the CT scans. The quantitative analysis highlighted significant differences in model performance and XAI technique efficiency. These findings can guide the selection of suitable XAI techniques and deep learning models for similar applications in the future. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Black Box Explainable artificial intelligence GradCAM GradCAM++ LIME SHAP

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Contextual Flow of Information in Tourism using BLE Proximity Detection to Enhance the Tourism Experience 5

Contextual Flow of Information in Tourism using BLE Proximit...

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5th International Conference on Emerging Smart Computing and informatics, ESCI 2023

作者： Gala, Ayush Borgaonkar, Chinmay Kulkarni, Vaishnavi Wakode, Madhuri Kale, Geetanjali Pune Institute of Computer Technology Department of Computer Engineering Pune411043 India

ISBN: (纸本)9781665475242

The IoT represents a great opportunity for tourism and hospitality to increase customer satisfaction while simulta-neously reducing operational costs. Smart tourism involves using smart technology and practices to boost resource management and the sustainability of tourism, while growing the overall competitiveness. Travel and tourism companies will depend on IoT technology to create an array of benefits both in internal and external environments. Through our study, we explore BLE beacon based proximity detection and information delivery, and its various use cases to overcome the complications of the travel and tourism industry. As the key technology, Bluetooth Low Energy (BLE) beacons are deployed at the core of our model along with a front-end application that interacts with the beacons to personalize and satisfy the tourists' demands. By combining smartphone capabilities with beacon technology, messages can be sent to tourists at the point they are most relevant, based on their proximity to the beacon. This would be especially effective on walking tours around a city. When tourists walk past a site of historical or ideological importance, an ancient ruin for example, they can be sent prompts or messages which describe what they are seeing and what it means in terms of the destination's history or culture. Our implementation emulates a tourist scenario enabled by BLE beacons and cloud resources using our experimental model. Moreover, we examine the practical implications about the role and use of IoT in tourism which should enable the industry to keep up with global tourism trends and put them on an equal footing with other participants in the online tourism and travel market. © 2023 IEEE.

关键词： Internet of things

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