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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ango, Rithika Masih, Raj Kumar Reddy, C Kishor Kumar Shuaib, Mohammed Monika Singh, T. Alam, Shadab Stanley College of Engineering & Technology for Women Department of Computer Science and Engineering Hyderabad India Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798331518097

The threat posed by credit card fraud, and by extension, online banking, continues to grow with the convenience brought forth by online banking services. Many financial institutions and customers stand at great risk because of this reality. The purpose of the paper is to enhance the security of banking systems through advanced technologies - machine learning. The research employed the well-known dataset from Kaggle Credit Card, which contains numerous records of transactions labeled as either fraud or not, thus presenting an aspect similar to real life imbalanced data. The main focus in the present work is the XGBoost model which involves one of the ensemble learning methods, which is characterized by a high level of precision and fast execution times. As a result of hyperparameter optimization and feature engineering, the paper also works on the performance of the algorithm improving its ability to detect fraudulent transactions in a sea of normal transactions. The results obtained from the experiments show that the XGBoost model is not only proven to be more accurate than earlier approaches to fraud detection, but also greatly reduces the number of false positives. This study not only contributes to the existing literature on detecting financial fraud but also represents the ways in which finance can benefit from machine learning to design secure systems within banking operations. The metrics in which the model suggested outperformed the available approaches are, the accuracy is 98.60%, error rate is 2.10% while its precision reached 95.40, recall of 92.30, F1 score 93.20 and AUC-ROC 99.70. This study not only contributes to the existing literature on detecting financial fraud but also represents the ways in which finance can benefit from machine learning to design secure systems within banking operations. © 2024 IEEE.

关键词： Banking

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Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

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computer Systems science & engineering 2023年第4期45卷 483-495页

作者： B.Chellapraba D.Manohari K.Periyakaruppan M.S.Kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Owing to massive technological developments in Internet of Things(IoT)and cloud environment,cloud computing(CC)offers a highly flexible heterogeneous resource pool over the network,and clients could exploit various resources on *** IoT-enabled models are restricted to resources and require crisp response,minimum latency,and maximum bandwidth,which are outside the *** was handled as a resource-rich solution to aforementioned *** high delay reduces the performance of the IoT enabled cloud platform,efficient utilization of task scheduling(TS)reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user ***,this article concentration on the design of an oppositional red fox optimization based task scheduling scheme(ORFOTSS)for IoT enabled cloud *** presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud *** achieves the makespan by performing optimum TS procedures with various aspects of incoming *** designing of ORFO-TSS method includes the idea of oppositional based learning(OBL)as to traditional RFO approach in enhancing their efficiency.A wide-ranging experimental analysis was applied on the CloudSim *** experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.

关键词： Metaheuristics task scheduling cloud computing internet of things makespan red fox optimizer

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Adaptive pelican optimization with optimized mask RCNN for automatic lung cancer detection

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Multimedia Tools and Applications 2025年 1-21页

作者： Sudha, R. Maheswari, K.M. Uma Department of Computer Science and Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu India

Lung cancer is a prevalent and deadly disease worldwide, necessitating accurate and timely detection methods for effective treatment. Deep learning-based approaches have emerged as promising solutions for automated medical image analysis. This study proposes an enhanced Mask R-CNN framework tailored specifically for the automatic detection and severity analysis of lung cancer from CT images. The proposed approach consists of three stages namely, pre-processing, lung nodule detection, and segmentation using enhanced Mask R-CNN and severity analysis. Our framework employs a deep convolutional neural network architecture trained on a comprehensive dataset of annotated lung images. By incorporating a region-based convolutional neural network (R-CNN) with a mask prediction branch, our model accurately localizes lung tumors while providing precise pixel-level segmentation masks. To enhance the performance of mask RCNN, the parameter present in the classifier is optimally selected using the adaptive pelican optimization (APO) algorithm. The proposed framework detects lung tumors and provides a comprehensive severity analysis, enabling clinicians to assess cancer stage and progression accurately. Evaluation on a benchmark dataset demonstrates superior detection accuracy and robustness compared to existing methods. Our enhanced Mask R-CNN approach shows promise as a valuable tool for early diagnosis and severity assessment of lung cancer, potentially improving patient outcomes and healthcare efficiency. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Lung cancer

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Lung Cancer Detection by Employing Adaptive Entropy Variance Dropout Regularization in GAN Variants

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SN computer science 2024年第3期5卷 325页

作者： Thirumagal, E. Saruladha, K. Department of Computer Science and Engineering Puducherry Technological University Puducherry India School of Computer Science and Engineering REVA University Bengaluru India

Lung cancer segmentation using Deep Neural Networks (DNN) needs accurate pixel-level data which is typically small. This leads to overfitting issue, and in order to alleviate this, the research is been done on L2 regularization and dropout algorithms which are commonly used in DNNs. To solve this co-adaptation problem, this paper discusses the basics of existing L2 regularization and dropout algorithms and their limitations. Further, this paper proposes a novel entropy variance dropout regularization algorithm to alleviate the existing limitations and enhance the model accuracy. The proposed regularization technique drops the co-adaptation features using the information entropy and variance. The experiments were conducted on Generative Adversarial Networks (GAN) variants using L2 regularization, dropout regularization and proposed Adaptive Entropy Variance (AEV) dropout regularization. The experimental results prove that the GAN variants using the proposed method alleviate the overfitting problem and also yield high accuracy. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Dropout Entropy GAN Lung cancer Regularization Variance

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Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

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An Approach for Maintaining Structural Uniformity of Multiword Expressions 4

An Approach for Maintaining Structural Uniformity of Multiwo...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Barman, Abhijit Saha, Diganta Pal, Alok Ranjan Jadavpur University Department of Computer Science and Engineering Kolkata India Cem Department of Computer Science and Engineering Kolaghat India

ISBN: (纸本)9798350343670

In this work, a Minimum Edit Distance (MED)-based approach for lexical uniformity of a Multiword Expression (MWE) in Bengali text is described. MWE can take several different forms where there are blank spaces or hyphens ('-'), separating the constituent words. For Bengali MWEs, accuracy in recognition, classification, and sensory tagging tasks decreases by 5% because of the diverse architectures of MWEs. Ensuring structural consistency is a crucial preprocessing effort to construct a reliable system for the syntactic and semantic processing of Bengali MWEs. An F1-Score of 90.58% is measured as the algorithm's performance. © 2024 IEEE.

关键词： Bi-gram Minimum Edit Distance Multiword Expression (MWE) Natural Language Processing (NLP) Tokenization

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Explainable cost-sensitive deep neural networks for brain tumor detection from brain MRI images considering data imbalance

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Multimedia Tools and Applications 2025年 1-28页

作者： Shawon, Md Tanvir Rouf Shibli, G. M. Shahariar Ahmed, Farzad Joy, Sajib Kumar Saha Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

This paper presents a research study on the use of Convolutional Neural Network (CNN), ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile models to efficiently detect brain tumors in order to reduce the time required for manual review of the report and create an automated system for classifying brain tumors. An automated pipeline is proposed, which encompasses five models: CNN, ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile. The performance of the proposed architecture is evaluated on a balanced dataset and found to yield an accuracy of 99.33% for fine-tuned InceptionV3 model. Furthermore, Explainable AI approaches are incorporated to visualize the model’s latent behavior in order to understand its black box behavior. To further optimize the training process, a cost-sensitive neural network approach has been proposed in order to work with imbalanced datasets which has achieved almost 4% more accuracy than the conventional models used in our experiments. The cost-sensitive InceptionV3 (CS-InceptionV3) and CNN (CS-CNN) show a promising accuracy of 92.31% and a recall value of 1.00 respectively on an imbalanced dataset. The proposed models have shown great potential in improving tumor detection accuracy and must be further developed for application in practical solutions. We have provided the datasets and made our implementations publicly available at -https://***/shahariar-shibli/Explainable-Cost-Sensitive-DeepNeural-Networks-for-Brain-Tumor-Detection-from-Brain-MRI-Images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep neural networks

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Failure Prediction for Scientific Workflows Using Nature-Inspired Machine Learning Approach

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Intelligent Automation & Soft Computing 2023年第4期36卷 223-233页

作者： S.Sridevi Jeevaa Katiravan Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India

Scientiﬁc workﬂows have gained the emerging attention in sophisti-cated large-scale scientiﬁc problem-solving *** pay-per-use model of cloud,its scalability and dynamic deployment enables it suited for executing scientiﬁc workﬂow *** the cloud is not a utopian environment,failures are inevitable that may result in experiencingﬂuctuations in the delivered *** a single task failure occurs in workﬂow based applications,due to its task dependency nature,the reliability of the overall system will be affected *** rather than reactive fault-tolerant approaches,proactive measures are vital in scientiﬁc workﬂ*** work puts forth an attempt to con-centrate on the exploration issue of structuring a nature inspired metaheuristics-Intelligent Water Drops Algorithm(IWDA)combined with an efﬁcient machine learning approach-Support Vector Regression(SVR)for task failure prognostica-tion which facilitates proactive fault-tolerance in the scheduling of scientiﬁc workﬂow *** failure prediction models in this study have been implemented through SVR-based machine learning approaches and the precision accuracy of prediction is optimized by IWDA and several performance metrics were evaluated on various benchmark workﬂ*** experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques.

关键词： Failure prediction intelligent water drops support vector regression proactive fault-tolerance scientiﬁc workﬂows precision accuracy resource provisioning

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Drug Inventory and Supply Chain Tracking System 3

Drug Inventory and Supply Chain Tracking System

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Vinoparkavi, D. Mythily, V. Manojini, S. Thanusiya, C. Sandhya Sree, S. Department of Computer Science & Engineering Tamilnadu Erode India Nandha Engineering Department of Computer Science & Engineering India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523893

The Climate-Enhanced Drug Inventory and Supply Chain Monitoring System is a state-of-the-art platform designed to improve the oversight of pharmaceutical inventory and logistics. This system aims to refine the storage, transit, and restocking of medical necessities by weaving climate and meteorological insights into the strategic planning process. Weather elements are pivotal in the pharmaceutical supply network, particularly for temperature-sensitive medications such as vaccines, insulin, and biologics. This platform anticipates possible weather-induced interruptions and modifies logistics procedures accordingly. © 2025 IEEE.

关键词： Risk management

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Heart disease Prediction Using Data Mining Techniques 1

Heart disease Prediction Using Data Mining Techniques

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Sharma, Shubang Singh, Gurinder Negi, Poonam Chandigarh University Department of Computer Science and Engineering India Uttaranchal University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350373042

Heart sickness is known as one of the leading causes of loss of life in the globe. Medical tools and various hospital programs have a large amount of clinical information. Therefore, understanding heart data is critical to improving predictive accuracy. In wildcat analysis, 10 feature selection strategies, namely, ANOVA, Chi-square, aggregated data, Help, advanced characteristic selection, characteristic selection background, full feature choice, algorithmic characteristic removal, Lasso retreat, Ridge retreat, and 6 stages, namely, fence tree, random forest, vector support machine, K-neighbor, providing retrospect, and mathematician naive Bayes, using Cleveland database cardiopathy. 88.52%, 91.30% accuracy, 80.76% sensitivity, and 85-f-measure. 71% according to the selected tree category. © 2024 IEEE.

关键词： Diseases

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