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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Peng, Zhonghua Hu, Rong Wang, Fuen Fan, Haoyi Eng, Yee Wei Li, Zuoyong Zhou, Liwei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fujian Fuzhou350121 China Department of Nutrition The First Affiliated Hospital of Zhengzhou University Henan Zhengzhou450052 China

ISBN: (纸本)9783031200984

Cervical cancer is one of the most widespread malignancies affecting women’s health worldwide today. However, the task of detection is particularly difficult due to the complex background of the cervical smear, where cells are previously stacked in clusters. To address this problem, we utilize the YOLOv5 as the baseline and build on YOLOv5 by using the simple Transformer Block which only combines with multi-head self attention layers and MLP to better extract cell features as well as to obtain global information. In addition, we allow the model to refine features adaptively to assist with detection by using Convolutional Block Attention Module (CBAM), an attention module being simple and effective for feed-forward convolutional neural networks, in the complex background information. Finally, we compare the model with YOLOv5 as baseline. In CDetector dataset, our model obtains 52.5% mAP@.5, which is 6% better than baseline. In transfer learning, it is 62.2%, which outperforms baseline by 3.2%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cells

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Wireless Body Area Network (WBAN) Centric Healthcare System for Continuous Monitoring

Wireless Body Area Network (WBAN) Centric Healthcare System ...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： M. Anand Kumar V. Valli Mayil T. Ratha Jeyalakshmi Anshul Ghanshala Anshika Gupta Dept of Computer Applications Graphic Era University Dehradun India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Data Science Jain University School of Sciences Banglore India Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Stroke is one the most dangerous disease all across the world which would lead to serious disability or death among all aged people due to changes in environmental factors. Recent reports indicate that a mere 75 million people will suffer from stroke-related disabilities by the year 2030. It was also identified that a stroke occurs every 40 seconds on average across the globe. One out of every five stroke-related patients would die in a year. A Recent health survey indicates that most developed countries face a high number of stroke patients in very recent years. There is also a rapid growth in the number of stroke cases in developing countries. To prevent the high-risk factors related to stroke diseases, there is much need for continuous observation of physiological signals in real-time and to analyze the data. One of the main strategies is wearable technology that employs wireless sensor networks to link numerous medical devices and associated systems. They have to ability to function independently across several platforms. Wearable devices are used in most medical applications because of their noteworthy qualities, including adaptability, versatility, inexpensiveness, and efficiency in various platforms. Integration of the Internet of Things (IoT) with wearable devices and Fog Computing provides a more flexible mechanism to deploy a continuous healthcare monitoring system to monitor vital symptoms and to detect the early phase of stroke factors instantly. To predict stroke-related disorders, this research proposes a Wireless Body Area Network (WBAN)-centric healthcare system for continuous monitoring and examination of physiological signals. Initially, the proposed model predicts the nature of the symptoms based on the parameters collected by the IoT sensors. Deep learning techniques are then applied to predict the risk factors that are critical to the stroke patient during the emergency condition. The key contribution of this research work is to provide an emerg

关键词： Wireless communication Wireless sensor networks Wearable computers Medical services Predictive models Stroke (medical condition) Body area networks

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Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease

Informatics and Health

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Informatics and Health 2024年第2期1卷 70-81页

作者： Temidayo Oluwatosin Omotehinwa David Opeoluwa Oyewola Ervin Gubin Moung Department of Mathematics and Computer Science Federal University of Health SciencesP.M.B.145OtukpoNigeria Department of Mathematics and Statistics Federal University KashereP.M.B.0182GombeNigeria Data Technologies and Applications(DaTA)Research Group Faculty of Computing and InformaticsUniversiti Malaysia SabahKota Kinabalu 88400SabahMalaysia

Background:Coronary heart disease(CHD)remains a prominent cause of mortality globally,necessitating early and accurate detection *** diagnostic approaches can be invasive,costly,and time-consuming,necessitating the need for more efficient *** aimed to optimize the Light Gradient-Boosting Machine(LightGBM)algorithm to enhance its performance and accuracy in the early detection of CHD,providing a reliable,cost-effective,and non-invasive diagnostic ***:The Framingham Heart Study(FHS)dataset publicly available on Kaggle was used in this *** Imputations by Chained Equations(MICE)were applied separately to the training and testing sets to handle missing ***-SMOTE(Synthetic Minority Over-sampling Technique)was used on the training set to balance the *** LightGBM algorithm was selected for its efficiency in classification tasks,and Bayesian Optimization with Tree-structured Parzen Estimator(TPE)was employed to fine-tune its *** optimized LightGBM model was trained and evaluated using metrics such as accuracy,precision,and AUC-ROC on the test set,with cross-validation to ensure robustness and ***:The optimized LightGBM model showed significant improvement in early CHD *** baseline LightGBM model with dropped missing values had an accuracy of 0.8333,sensitivity of 0.1081,precision of 0.3429,F1 score of 0.1644,and AUC of *** MICE imputation,performance improved to an accuracy of 0.9399,sensitivity of 0.6693,precision of 0.9043,F1 score of 0.7692,and AUC of *** combined approach of Borderline-SMOTE,MICE imputation,and TPE for LightGBM achieved an accuracy of 0.9882,sensitivity of 0.9370,precision of 0.9835,F1 score of 0.9597,and AUC of 0.9963,indicating a highly effective and robust ***:The optimized model demonstrated outstanding performance in early CHD *** study’s strengths include its comprehensive approach to addres

关键词： Clinical decision making Coronary heart disease Light gradient-boosting machine Machine learning MICE Tree-structured Parzen estimator

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Decision Making and Recognition of String Characters using NN-Fuzzy Implication System

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Procedia computer science 2025年 258卷 893-902页

作者： Santosh Kumar Henge Avinash Bhagat Sanjeev Kumar Mandal Nikhil Reddy Viraati Ravleen Singh K. Rushi Kedhar Associate Professor Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Warangal 506371 India Associate Professor School of Computer Applications Lovely Professional University Phagwara Punjab India Assistant Professor Department of CS and IT Jain (Deemed-to-be University) Bangalore India Department of Data Insights Crisp Shared Services 7160 Columbia Gateway Dr Columbia United States of America Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddewaram AP India BE-CSE RV Institute of Technology and Management Bengaluru India

This research proposes the neural network (NN)-fuzzy logic control (FLC)-based methodology, which is designed with two stages of execution. Stage-1 is composed with the NN approach; it takes the input from the scanned image, the input from the classified clustered group, processes it with hidden layers, and generates the final output, which is input to the FLC in stage 2. The input of stage 2 converts the input into fuzzified data (0s and 1s) and trains the FLC system with fuzzified set values. The trained data has been simulated for further stages in the FLC-based decision-making approach and integrated to identify the character patterns based on the trained dataset. FLC-based planting and normalization techniques are integrated for re-training the data. The dataset has been framed with more than 6200 handwritten image characters, which were written by 235 writers. It aligned with the pre-processing, classification, and filtering stages and achieved a 99.8% recognition rate.

关键词： Fuzzy Logic Control (FLC) Artificial Neural Network (ANN) Multiple Layered Perceptron (MLP) Convolutional Neural Network (CNN) Feed Forward Neural Network (FNN) Optical Character Recognition (OCR) Handwritten (HW)

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A method of fundamental matrix estimation based on nsga-ii and improved quasi-affine transform

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第2期6卷 313-327页

作者： Fan, Yi-Kai Liu, Shi-Jian Kong, Ling-Ping Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China VSB-Technical University of Ostrava Czech Republic Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Information Management Chaoyang University of Technology Taiwan

The fundamental matrix is the mainstream solution to computer vision problems such as 3D reconstruction, real-time location and map building. Accuracy and efficiency are two main measurement indexes in fundamental matrix estimation. When the accuracy is not enough, it often needs to be corrected through back-end optimization and other costly ways, and low efficiency will affect the real-time performance of the system. In order to solve this problem, this paper proposes a new estimation method of fundamental matrix based on improved quasi affine transformation. Specifically, based on the QUATRE algorithm, this method first proposes a population cooperation method based on a specific "gene-chromosome" pattern. Secondly, combining the advantages of NSGA-II in solving multi-objective problems, the Pareto dominance relationship of the population chromosome was firstly calculated according to the objective function of mean polarity distance and internal points in the way of NSGA-II, and the crowding degree was also calculated. The operations of population initialization, mutation and crossover in the discrete solution space represented by the homogeneous coordinate system are redefined. The selection operation is then performed according to the elite policy of NSGA-II. In addition, a confidence-based method to determine the number of iterations is proposed to accelerate the algorithm. Experimental results show that the proposed method can effectively eliminate noise and mismatching, and is superior to the current mainstream methods in accuracy and efficiency, and can effectively solve the problem of fundamental matrix estimation. © 2021 Global Research Online. All rights reserved.

关键词： Efficiency

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RailPC: A large-scale railway point cloud semantic segmentation dataset

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1548-1560页

作者： Tengping Jiang Shiwei Li Qinyu Zhang Guangshuai Wang Zequn Zhang Fankun Zeng Peng An Xin Jin Shan Liu Yongjun Wang Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Normal UniversityNanjingChina Technology Innovation Center for Integrated Applications in Remote Sensing and Navigation Ministry of Natural ResourcesNanjingChina Eastern Institute of Technology(EIT) NingboChina Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology TianjinChina College of Computer Science and Engineering Northwest Normal UniversityLanzhouChina Mckelvey School of Engineering Washington University in St.LouisSt.LouisMissouriUSA School of Electronic and Information Engineering Ningbo University of TechnologyNingboChina

Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value,but its development is severely hindered by the lack of suitable and specific ***,the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non-overlapping special/rare categories,for example,rail track,track bed *** harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation,we introduce RailPC,a new point cloud *** provides a large-scale dataset with rich annotations for semantic segmentation in the railway ***,RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning(MLS)point cloud dataset and is the first railway-specific 3D dataset for semantic *** covers a total of nearly 25 km railway in two different scenes(urban and mountain),with 3 billion points that are finely labelled as 16 most typical classes with respect to railway,and the data acquisition process is completed in China by MLS *** extensive experimentation,we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation *** on our findings,we establish some critical challenges towards railway-scale point cloud semantic *** dataset is available at https://***/NNU-GISA/GISA-RailPC,and we will continuously update it based on community feedback.

关键词： data benchmark MLS point clouds railway scene semantic segmentation

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KAC-Unet: A Medical Image Segmentation With the Adaptive Group Strategy and Kolmogorov-Arnold Network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Lin, Shiying Hu, Rong Li, Zuoyong Lin, Qinghua Zeng, Kun Wu, Xiang Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Fuzhou University Affiliated Provincial Hospital Provincial Clinical Medical College Fujian Medical University Department of Urology Fuzhou350001 China

In the field of deep learning-based medical image segmentation, convolutional neural networks (CNNs) extract image features by combining linear convolutional layers with nonlinear activation functions. However, excessive stacking of linear layers in the network limits the model’s ability to capture fine-grained details. In addition, the feature distribution imbalance caused by the traditional fixed grouping strategy (FGS) can affect the deep model’s capacity to perceive the overall structure of the image. To address these challenges, we propose a medical image segmentation framework, called Kolmogorov-Arnold Network with the adaptive group strategy and contextual Transformer based on Unet (KAC-Unet). First, we propose the adaptive group strategy (AGS) to balance the grouping of different input channels, alleviating the performance degradation caused by differences in group information. Then, we propose the Shift Tokenized Kolmogorov-Arnold Network (KAN) Block to capture complex features in medical images through flexible nonlinear transformations and shift operations. Extensive experiments are conducted on three medical image segmentation datasets. The results demonstrate the effectiveness and superiority of our proposed method compared with state-of-the-art algorithms. © 1963-2012 IEEE.

关键词： Convolutional neural networks

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A Survey DDoS Attack in IoT Cloud Platform

A Survey DDoS Attack in IoT Cloud Platform

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Josy Elsa Varghese Vidya Rao Dept. of CSE (Cyber Security and CSE( AI and ML) Ramaiah Institute of Technology Bangalore India Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The Internet of Things (IoT) and cloud computing are the key components in the industrial revolution. The IoT platform provides provisioning, administration, and automation of smart items inside a specific IoT architecture, enabling the goal of connecting physical objects to the Internet so they may collaborate, communicate, and act intelligently without the need for human involvement. On the other hand, cloud computing has brought consumer and business applications online, revitalizing IT services and saving businesses money by eliminating the need to build and maintain on-site IT infrastructure for data storage and application operations. The long-term cost-effectiveness of cloud-based solutions includes enhanced security, corporate data mobility, more employee cooperation, and more sophisticated disaster recovery plans. The majority of cyber physical systems are currently hosted on IoT cloud platforms due to their advantages. However, security issues of the platform have to be addressed. Distributed Denial of Service attacks (DDoS) are among the most common. This paper studies the importance of IoT cloud architecture, discusses the defensive measures against DDoS attacks, and suggests a high-level conceptual IoT architecture to defend DDoS attacks while addressing future research challenges.

关键词： Surveys Cloud computing System performance computer architecture Denial-of-service attack Internet of Things Security computer crime Business Self-service

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EUDRL: Explainable Uncertainty-Based Deep Reinforcement Learning for Portfolio Management

EUDRL: Explainable Uncertainty-Based Deep Reinforcement Lear...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Jia Lina Shahab S. Banda Hao-Ting Paib Bharat S. Rawal International Graduate School of Artificial Intelligence Douliu City Yunlin Taiwan Bachelor Program of Big Data Applications National Pingtung University Pingtung Taiwan Department of Computer Science Digital Technologies University Ggrambling La USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Supervised deep learning (SDL) has shown remarkable success in various financial applications, such as stock prediction and fraud detection. However, SDL’s reliance on class labels renders it unsuitable for portfolio management (PM) tasks, where such labels are often unavailable. To address this limitation, we propose a novel two-level architecture based on deep reinforcement learning (DRL) for PM, which does not require class labels. Our approach comprises several local agents that provide trading decisions and uncertainty assessments for individual stocks, and a global agent that makes portfolio management decisions based on the outputs of the local agents. Additionally, we incorporate the concept of explainable AI (XAI) into our framework using the SHAP (Shapley additive explanations) method, enhancing the transparency and interpretability of the global agent’s decisions. Our experimental results demonstrate that the proposed architecture consistently yields profitable outcomes in the market.

关键词： Uncertainty Correlation Additives Explainable AI Computational modeling computer architecture Deep reinforcement learning Fraud Portfolios

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An Orchestrator Architecture for Multi-tier Edge/Cloud Video Streaming Services

An Orchestrator Architecture for Multi-tier Edge/Cloud Video...

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IEEE International Conference on Edge Computing (EDGE)

作者： Eduardo S. Gama Natesha B V Roger Immich Luiz F. Bittencourt Institute of Computing State University of Campinas (UNICAMP) Brazil Data Science and Computer Applications MIT MAHE Manipal India Federal University of Rio Grande do Norte (UFRN) Brazil

Video streaming has become a prevalent form of entertainment and a vital means of communication, but the challenges of delivering high quality video content over the internet are numerous. One of the key challenges is the varying network conditions that can significantly impact video streaming quality, such as bandwidth fluctuations and packet loss. To overcome these challenges, an adaptive video streaming architecture is needed to adjust the video streaming in real-time to match the changing network conditions and ensure a high Quality of Experience (QoE) for the end-user. This article presents MIGRATE, an orchestrator architecture for video streaming services capable of adapting to user demand in real-time. The study considers an edge/cloud multi-tier network infrastructure. In addition, an Integer Linear Programming (ILP) model and a Greedy solution are proposed to decide the distribution of connections between users and services. Experimental results show that based on the optimization strategy used, it is observed that there is a trade-off between the resources used and the QoE provided to users. Further, we discuss the importance of considering QoE metrics and user engagement in designing video streaming systems.

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