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SSRN 2022年

作者： Shu, Xiu Yang, Yunyun Xie, Ruicheng Liu, Jun Chang, Xiaojun Wu, Boying School of Science Harbin Institute of Technology Shenzhen China Information Systems Technology and Design Singapore University of Technology and Design Singapore Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia School of Mathematics Harbin Institute of Technology Harbin China

Medical image segmentation requires experienced doctors to provide accurate segmentation results as ground truth, since it involves plentiful medical knowledge. The deep learning-based model requires a large number of medical images and ground truth, during the training stage, which brings a heavy workload to the doctor. Therefore, based on the differences between medical images, we designed an active learning model to select more representative images for doctors to obtain the ground truth, which saves their time in segmenting similar medical images. Eventually, these representative images will serve as training sets and test sets, training a more accurate segmentation model. In this paper, we propose the active learning-based segmentation (ALS) model for medical images, in which we use the mean structure similarity index measure to measure how similar the two images are. Then we select some highly similar images from the entire training set. To our knowledge, we are the first to apply the active learning strategy based on the mean structure similarity index measure to medical image segmentation. Finally, we reduce the amount of data to 75%, 50%, 25%, and 12.5% to validate our ALS model. We use the selected images after active learning training and the medical semantic segmentation model to segment medical images, and the active learning model can reduce the cost of labeling and obtain accurate segmentation results through experiments, and even better than the segmentation results with a large number of highly similar data. © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Noise multiplication and bandwidth of photodetectors employed with Taylor dispersion in micro optical fiber channel system

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Journal of Optical Communications 2024年 45卷 s1383-s1398页

作者： Nagendram, Sanam Babu, Gade Harish Ahammad, Shaik Hasane Kiran, Kasi Uday Anwer, Twana Mohammed Kak Hossain, Md. Amzad Rashed, Ahmed Nabih Zaki Amin, Marwa Hamed Department of Artificial Intelligence KKR & KSR Institute of Technology and Sciences Guntur India Department of ECE CVR College of Engineering Hyderabad India Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of ECE Koneru Lakshmaiah Education Foundation Guntur522502 India Department of Physics College of Education Salahaddin University-Erbil Kurdistan Region Erbil44002 Iraq Institute of Theoretical Electrical Engineering Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum44801 Germany Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of VLSI Microelectronics Institute of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai602105 India

This work has clarified the noise multiplication and bandwidth of photodetectors employed with Taylor dispersion in micro optical fiber channel system. The simulation performance evaluation of slide and squeeze film damping and fluidics mechanics. The damping ratio versus gap distance for a cantilever beam and a mass suspended from beams are determined. The damping ratio with gap size for a suspended mass with and without holes is reported. The damping ratio variations with the gap distance variations for a mass suspended from beams is clarified. The velocity field for fluid flow in a circular channel, a square channel, a rectangular channel, a parabolic channel and a triangular channel is estimated. Besides the velocity field for the fluid flow in a channel with and without slip is outlined. The spreading of a diffusion front in one dimension in a resting fluid and the dispersion coefficient in a square channel [Taylor dispersion in micro fluidic channels] are clarified. The 2D diffusion profile of a solute in a T mixer and normalized concentration versus the channel width for the solute at the outlet of H filter are determined. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Photodetectors

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A lightweight Federated Learning-based Intrusion detection model for Internet of Things

A lightweight Federated Learning-based Intrusion detection m...

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IEEE International Conference on Multi Topic

作者： Noshina Tariq Mamoona Humayun Ghadah Naif Alwakid Bushra Almas NZ Jhanjhi Muhammad Zakwan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad Pakistan School of Arts Humanities and Social Sciences University of Roehampton London United Kingdom Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Institute of Information Technology Quaid-i-Azam University Islamabad Pakistan School of Computer Science Taylor’s University Selangor Malaysia Department of Avionics Engineering Air University Islamabad Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

The advancement of sophistication in botnet intrusions on Internet of Things (IoT) systems and the resources required by traditional IDS make strong security solutions necessary, which reduce resource usage while enhancing detection capability. Therefore, this paper tests the performance and performance comparison of classification models based on lightweight Federated Learning (FL) for IoT botnet intrusion detection using Random Forest (RF), XGBoost, and LightGBM classifiers. Utilizing a large-scale dataset from IoT traffic, the three models were tested in metrics such as confusion matrices, ROC curves, and Precision-Recall curves. The results show that the RF and XGBoost classifiers achieve high AUCs of 1.00, thereby, there is no trade-off between sensitivity and specificity. On the other hand, LightGBM scored a low of 0.58 AUC. The results show that robust ensemble methods can be applied to handle complex and imbalanced datasets commonly presented in IoT traffic in FL environments.

关键词： Radio frequency Measurement Federated learning Botnet Intrusion detection Sensitivity and specificity Internet of Things Ensemble learning Security Random forests

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Toward a Secure Wireless Sensor Network in Intrusion Detection System Utilizing Central-Smoothing Hypergraph Neural Network Optimized With Clouded Leopard Optimization Algorithm

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International Journal of Communication Systems 2025年第11期38卷

作者： Geerthik S Ramachandran A Jegan J Ishwarya M. V Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Artificial Intelligence & Data Science Saveetha Engineering College Thandalam Chennai India Assistant Professor Department of Computer Science and Engineering School of Computing SRM Institute Of Science And Technology Tiruchirappalli (Deemed To Be University) Trichy Tamilnadu India Associate Professor Department of Artificial Intelligence & Data Science Agni College of Technology Chennai India

Wireless sensor networks (WSNs) is the essential component of wireless technology that provides effective solutions for various monitoring applications. WSN is vulnerable to several security risks as intrusions, attacks, and suspicious activities. Therefore, this paper proposes a Secure WSN in intrusion detection system using Central-Smoothing Hypergraph Neural Network Optimized with Clouded Leopard Optimization Algorithm (WSN-IDS-CSHGNN-CLOA). Here, the input data is taken from WSN-DS database. The gathered data is pre-processed by regularized bias-aware ensemble Kalman filter (RBAEKF) for data cleaning and normalization. The pre-processed data is given into feature selection using Memetic Salp Swarm Optimization Algorithm (MSSOA) to select optimal features. The selected features are given into CSHGNN for classifying the IDS as denial of service (DoS), black hole, gray hole, flooding, and scheduling attacks in WSN. The CLOA is implemented to optimize the hyperparameters of CSHGNN. The performance of the proposed WSN-IDS-CSHGNN-CLOA approach attains 24.39%, 35.71%, and 25.55% higher accuracy; 24.44%, 34.28%, and 14.44% higher precision when compared to the existing techniques.

关键词： Central-Smoothing Hypergraph Neural Network Clouded Leopard Optimization Algorithm Memetic Salp Swarm Optimization Algorithm regularized bias-aware ensemble Kalman filter wireless sensor network

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ODMGIS: An Outlier Detection Method Based on Multi-Granularity Information Sets

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IEEE Transactions on Fuzzy Systems 2025年

作者： Zhang, Pengfei He, Zhaoxuan Wang, Dexian Jiang, Tao Li, Baolin Liu, Jia Huang, Wei Li, Tianrui The School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu611137 China The Acupuncture and Tuina School Chengdu University of Traditional Chinese Medicine Chengdu611137 China The School of Logistics Chengdu University of Information Technology Chengdu60020 China The School of Computer and Software Engineering Xihua University Chengdu610039 China The College of Computer and Data Science Fuzhou University Fuzhou35010 China The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

In the realm of data mining, outlier detection has emerged as a pivotal research focus, aimed at uncovering anomalies within datasets to extract meaningful and valuable insights. The objective is to leverage data mining methodologies to pinpoint anomalies within datasets, thereby revealing crucial and enlightening information. Herein, we introduce a groundbreaking outlier detection methodology, ODMGIS, that seamlessly integrates multi-granularity representation and information set concepts to devise the multi-granularity information set (MGIS) model. This model adeptly characterizes the distribution patterns of data points. Firstly, we employ entropy function and their complementary function as measurement tools to accurately quantify the inherent uncertainty in data with different distributions, and considers the sum of the two as a comprehensive representation of the overall uncertainty of the information source. Subsequently, an outlier score model is constructed based on MGIS, which can deeply characterize the degree of outlierness of samples, thereby effectively identifying abnormal points in the dataset. During validation, ODMGIS was rigorously tested on practical datasets from medicine and bioinformatics, and its performance was benchmarked against both traditional and the state-of-the-art algorithms, showcasing substantial benefits. This research not only contributes a fresh perspective to outlier detection but also sparks innovative avenues for exploring and advancing the granular computing theory. © 1993-2012 I IEEE. All rights reserved.

关键词： Granular computing

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Deep Learning-Based Point Cloud Registration: A Comprehensive Survey and Taxonomy

arXiv

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arXiv 2024年

作者： Zhang, Yu-Xin Gui, Jie Yu, Baosheng Cong, Xiaofeng Gong, Xin Tao, Wenbing Tao, Dacheng School of Cyber Science and Engineering Southeast University Nanjing210000 China Purple Mountain Laboratories Nanjing210000 China Lee Kong Chian School of Medicine Nanyang Technological University 308232 Singapore School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Computing & Data Science Nanyang Technological University #32 Block N4 #02a-014 50 Nanyang Avenue Singapore639798 Singapore

Point cloud registration involves determining a rigid transformation to align a source point cloud with a target point cloud. This alignment is fundamental in applications such as autonomous driving, robotics, and medical imaging, where precise spatial correspondence is essential. Deep learning has greatly advanced point cloud registration by providing robust and efficient methods that address the limitations of traditional approaches, including sensitivity to noise, outliers, and initialization. However, a well-constructed taxonomy for these methods is still lacking, making it difficult to systematically classify and compare the various approaches. In this paper, we present a comprehensive survey and taxonomy on deep learning-based point cloud registration (DL-PCR). We begin with a formal description of the point cloud registration problem, followed by an overview of the datasets, evaluation metrics, and loss functions commonly used in DL-PCR. Next, we categorize existing DL-PCR methods into supervised and unsupervised approaches, as they focus on significantly different key aspects. For supervised DL-PCR methods, we organize the discussion based on key aspects, including the registration procedure, optimization strategy, learning paradigm, network enhancement, and integration with traditional methods;For unsupervised DL-PCR methods, we classify them into correspondence-based and correspondence-free approaches, depending on whether they require explicit identification of point-to-point correspondences. To facilitate a more comprehensive and fair comparison, we conduct quantitative evaluations of all recent state-of-the-art approaches, using a unified training setting and consistent data partitioning strategy. Lastly, we highlight the open challenges and discuss potential directions for future study. We hope this comprehensive survey will contribute to the advancement of DL-PCR by serving as a fundamental reference for researchers and practitioners in the field. A c

关键词： Taxonomies

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Evaluating Machine Learning Classifiers for Breast Cancer Diagnosis: A Comparative Study Using Accuracy Metrics

Evaluating Machine Learning Classifiers for Breast Cancer Di...

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International Carnahan Conference on Security technology

作者： Mohanaprakash T A R. Kalaiyarasi Ramya. M Vyshnavi K Madhura Deventhiran I. Jaichitra Department of CSE SOET CMR University Bangaluru India Department of Artificial intelligence and Data Science Rajalakshmi Institute of Technology Chennai India Department of CSE St.Joseph's Institute of Technology OMR Chennai Department of IT Dr.MG.R.Educational and Research Institute Chennai India Department of CSE Panimalar Engineering College Chennai India

ISBN: (数字)9798350315875

ISBN: (纸本)9798350315882

Globally, breast cancer ranks among the top causes of death for women, and the number of cases is increasing yearly. For better treatment outcomes and patient survival, accurate diagnosis and prediction are essential. Developments in the early detection and diagnosis of cancer are essential for encouraging a long and healthy life. Reaching a high level of accuracy in cancer prediction improves patient care by allowing for prompt interventions. Enhancing survival rates requires machine learning-based breast cancer prediction. On the Breast Cancer Wisconsin Diagnostic dataset, this study tested all machine learning algorithm. Random Forest outperformed other classifiers and obtained the greatest accuracy (98.4%). The study emphasizes how crucial machine learning is to the detection and prognosis of breast cancer. The Python programming language and Scikit-Iearn library were used in the work, which was carried out under the Anaconda environment.

关键词： Measurement Support vector machines Accuracy Machine learning algorithms Breast cancer Security Reliability Prognostics and health management Random forests Python

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Solving water scarcity challenges in arid regions: A novel approach employing human-based meta-heuristics and machine learning algorithm for groundwater potential mapping

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Chemosphere 2024年 363卷 142859页

作者： Razavi-Termeh, Seyed Vahid Sadeghi-Niaraki, Abolghasem Farhangi, Farbod Khiadani, Mehdi Pirasteh, Saied Choi, Soo-Mi Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea Republic of Geoinformation Tech. Center of Excellence Faculty of Geodesy and Geomatics Engineering K.N. Toosi University of Technology Tehran19697 Iran School of Engineering Edith Cowan University 270 Joondalup Drive JoondalupWA6027 Australia Institute of Artificial Intelligence School of Mechanical and Electrical Engineering Shaoxing University China Department of Geotechnics and Geomatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells. This study introduced an innovative approach to GPM by enhancing the Random Forest (RF) algorithm. This enhancement involved integrating three metaheuristic algorithms inspired by human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), and SBO (Student Psychology Based Optimization). The modeling process used 70% training data and 30% evaluation data. data preprocessing was performed using the multicollinearity test method and frequency ratio (FR) technique to refine the dataset. Subsequently, the GPM was generated using four distinct models, demonstrating the combined power of machine learning and human-inspired metaheuristic algorithms. The performance of the models was systematically assessed through extensive statistical analyses, including root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC) for the receiver operating characteristic curve (ROC), Friedman tests, chi-squared tests, and Wilcoxon signed-rank tests. RF-ICA and RF-SPBO emerged as frontrunners, displaying statistically comparable accuracy and significantly outperforming RF-TLBO and the non-optimized RF model. The results of the GPM revealed the exceptional accuracy of RF-ICA, which exhibited a commanding AUC score of 0.865, underscoring its superiority in discriminating between different groundwater potential classes. RF-SPBO also displayed strong performance with an AUC of 0.842, highlighting its effectiveness in inaccurate classification. RF-TLBO and the non-optimized RF model achieved AU

关键词： Geographic information systems

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Als: Active Learning-Based Image Segmentation Model for Skin Lesion

SSRN

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SSRN 2022年

关键词： Semantic Segmentation

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Revolutionizing Urban Drainage: A Smart IoT Approach to Stormwater Management Using AdaBoosting Algorithm

Revolutionizing Urban Drainage: A Smart IoT Approach to Stor...

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Advances in Modern Age Technologies for Health and engineering science (AMATHE), International Conference on

作者： P S Thanigaivelu A. Sairam P. Vijayan A. Suresh Kumar N. Mohankumar T. R. GaneshBabu Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu India Department of Management Studies Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The management of stormwater in urban areas across the globe is becoming an increasingly difficult problem because of the increased risk of floods caused by climate change. Typical drainage methods often turn out to be insufficient, which calls for the development of novel approaches. This study suggests a smart stormwater management system powered by the Internet of Things and aims to boost urban resilience and reduce floods. The system continually monitors critical drainage characteristics, such as water levels, flow rates, and water quality, thanks to a network of Internet of Things sensors. To accurately predict probable flooding occurrences, advanced predictive algorithms examine this data together with incoming meteorological information. The technology performs real-time dynamic management of the drainage network, improving the efficiency of pumps and valves to reduce the likelihood of flooding. When potential flood hazards are identified, those who need to be informed, such as local authorities and citizens, are notified through an early warning system. The findings of this study lead to a more effective allocation of resources, an improvement in water quality, and an increase in the resilience of metropolitan areas to severe weather events. This approach, powered by the Internet of Things, promises a route toward sustainable and flood-resilient communities by transforming urban drainage.

关键词： Ethics Urban areas Water quality Prediction algorithms Real-time systems Stormwater Floods

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