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3rd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Cui, Zhengyan Zhang, Junjun Ding, Huijuan Noh, Giseop Park, Hyun Jun Cheongju University Department of Computer Information Engineering Cheongju Korea Republic of Cheongju University Division of Software Convergence Cheongju Korea Republic of

ISBN: (纸本)9781665490788

Graph convolutional networks (GCNs) are widely used in traffic prediction because they can better represent the structure of traffic network. GCNs can obtain deeper feature representation by aggregating neighborhood features. However, the network is prone to over-smoothing, as the aggregation neighborhood deepens. GCNs used for traffic prediction usually aggregate neighborhood features of order 2 or 3. To obtain deeper features, we use an adaptive hidden layer connection method to deepen neighborhood aggregation in traffic graph network for the first time. It adaptively adjusts the weight of hidden layer to increase the initial connection and hidden layer connection, which can obtain deeper neighborhood features and alleviate over-smoothing. We evaluated the model using real data from the road network in Beijing, and it showed good performance, especially in the long-term prediction. © 2023 IEEE.

关键词： Convolution

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CardioNetFusion: A Deep Learning and Explainable AI Integrated System for Cardiovascular Disease Detection 24

CardioNetFusion: A Deep Learning and Explainable AI Integrat...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Raja, Gunasekaran Essaky, Selvam Muthu, Surya Raj Suresh, Babith Sarish Cho, Jaehyuk Yoo, Seohyun Easwaramoorthy, Sathishkumar Veerappampalayam Anna University NGNLab Department of Computer Technology Chennai India Jeonbuk National University Department of Software Engineering Division of Electronics and Information Engineering Jeonju-si Korea Republic of Sunway University School of Engineering and Technology Malaysia

ISBN: (纸本)9798331530631

Early detection of Cardiovascular Diseases (CVD) plays a vital role in effective treatment and management. However, traditional methods for analyzing ECG signals are often limited in both accuracy and interpretability. In this work, we introduce a novel model, CardioNetFusion, designed to address these limitations by incorporating advancements in Deep Learning (DL) and Explainable AI (XAI). The model leverages the strengths of Convolutional Neural Networks (CNN), MobileNetV2, and VGG16 to improve the robustness of ECG signal interpretation. To further enhance transparency and provide healthcare professionals with actionable insights, saliency maps are utilized to visualize model predictions. Additionally, by integrating an Application Programming Interface (API) within an IoT-based sensor fusion framework, the model supports real-time cardiovascular health monitoring. Achieving a classification accuracy of 98.7% in detecting arrhythmias, CardioNetFusion surpasses existing methodologies and offers a practical, reliable solution for early CVD diagnosis and management. Traditional methods of ECG analysis often have limitations in terms of diagnostic accuracy and interpretability. This paper proposes a CardioNetFusion model to address the challenges of conventional methods of ECG data analysis by leveraging advances in Deep Learning (DL) and Explainable AI (XAI). The proposed CardioNetFusion model integrates Convolutional Neural Networks (CNN), MobileNetV2, and VGG16 models to improve the robustness of ECG signal interpretation. Additionally, we utilized saliency maps to enhance model transparency and provide clear, actionable insights for healthcare professionals. Integrating Application Programming Interface (API) in the IoT-based sensor fusion approach to our proposed model facilitates seamless real-time monitoring of cardiovascular health. With an impressive 98.7% accuracy in arrhythmia classification, CardioNetFusion outperformed existing methods and ensured reliab

关键词： Convolutional neural networks

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Optimization Performance Analysis of Different Initialization Methods in Single-Objective and Multi-Objective Chaotic Evolution 13

Optimization Performance Analysis of Different Initializatio...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Zhang, Xiaoyi Pei, Yan Jiang, Huiyan Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu965-8580 Japan University of Aizu Computer Science Division Aizuwakamatsu965-8580 Japan Northeastern University College of Software Shenyang110819 China

ISBN: (纸本)9798350373332

Chaotic Evolution (CE) is a significantly faster and more robust method for solving single-objective and multi-objective optimization problems. However, there are various factors that can impact the performance of CE, with population initialization being one of them. A well-executed initialization method can contribute to the discovery of better solutions and enhance both convergence rate and accuracy. This paper conducts a comparative experiment on CE algorithms using six different initialization methods. The performance of the CE algorithms utilizing these various initialization methods is analyzed and discussed. To assess convergence rate and accuracy, eight single-objective optimization benchmark functions and five multi-objective optimization benchmark functions are employed in our analysis. The evaluation results reveal significant disparities among the six initialization methods, indicating that different approaches to initialization have a substantial influence on the optimization performance of the CE algorithm. © 2024 IEEE.

关键词： Optimization algorithms

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Deep Learning-Based Action Classification Using One-Shot Object Detection

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computers, Materials & Continua 2023年第8期76卷 1343-1359页

作者： Hyun Yoo Seo-El Lee Kyungyong Chung Contents Convergence Software Research Institute Kyonggi UniversitySuwon-si16227Korea Department of Public Safety Bigdata Kyonggi UniversitySuwon-si16227Korea Division of Computer Science and Engineering Kyonggi UniversitySuwon-si16227Korea

Deep learning-based action classification technology has been applied to various fields,such as social safety,medical services,and *** an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their *** are various related studies on the real-time classification of actions in an ***,existing deep learning-based action classification models have prolonged response speeds,so there is a limit to real-time *** addition,it has low accuracy of action of each object ifmultiple objects appear in the ***,it needs to be improved since it has a memory overhead in processing image *** learning-based action classification using one-shot object detection is proposed to overcome the limitations of multiframe-based analysis *** proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the ***,a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action *** to the existing studies,the constructed model shows higher accuracy of 74.95%,and in terms of speed,it offered better performance than the current studies at 0.234 s per *** proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural ***,it is expected to contribute significantly to commercializing realistic streaming data analysis technologies,such as CCTV.

关键词： Human action classification artificial intelligence deep neural network pattern analysis video analysis

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Classical approaches and new deep learning trends to assist in accurately and efficiently diagnosing ear disease from otoscopic images 11

Classical approaches and new deep learning trends to assist ...

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11th European Workshop on Visual Information Processing, EUVIP 2023

作者： Jobanputra, Dhruv Chetan Bennamoun, Mohammed Boussaid, Farid Xu, Lian Kuthubutheen, Jafri The University of Western Australia Computer Science and Software Engineering Perth Australia The University of Western Australia Electrical Electronic and Computer Engineering Perth Australia The University of Western Australia Division of Surgery Medical School Perth Australia

ISBN: (纸本)9798350342185

Rural communities in Australia have limited access to Ear, Nose and Throat (ENT) specialists, resulting in a lack of expertise to provide a diagnosis of complex and chronic ear diseases. This literature review examines previous attempts at creating a computer-aided tool to accurately diagnose ear disease and gaps in the literature. A systematic search was conducted to identify relevant papers and the latest best trends in technology. Four papers showed significant results in ear disease detection with deep learning models providing the best performance. Some studies using larger datasets consisting of endoscopic images obtained accuracies of over 90%. No adequate model was found that used otoscopic images with a sensitivity of over 90%. Endoscopic images provide better quality images, making it unclear how the models would perform on otoscopic images. Advanced techniques such as Transformers have not yet been tested in ear disease detection and could help improve model accuracy. © 2023 IEEE.

关键词： Image classification

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SlowFast Based Real-Time Human Motion Recognition with Action Localization

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computer Systems Science & engineering 2023年第11期47卷 2135-2152页

作者： Gyu-Il Kim Hyun Yoo Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon16227Korea Contents Convergence Software Research Institute Kyonggi UniversitySuwon16227Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon16227Korea

Artificial intelligence is increasingly being applied in the field of video analysis,particularly in the area of public safety where video surveillance equipment such as closed-circuit television(CCTV)is used and automated analysis of video information is ***,various issues such as data size limitations and low processing speeds make real-time extraction of video data *** analysis technology applies object classification,detection,and relationship analysis to continuous 2D frame data,and the various meanings within the video are thus analyzed based on the extracted basic *** recognition is key in this *** recognition is a challenging field that analyzes human body movements,requiring the interpretation of complex movements of human joints and the relationships between various *** deep learning-based human skeleton detection algorithm is a representative motion recognition ***,motion analysis models such as the SlowFast network algorithm,have also been developed with excellent ***,these models do not operate properly in most wide-angle video environments outdoors,displaying low response speed,as expected from motion classification extraction in environments associated with high-resolution *** proposed method achieves high level of extraction and accuracy by improving SlowFast’s input data preprocessing and data structure *** input data are preprocessed through object tracking and background removal using YOLO and DeepSORT.A higher performance than that of a single model is achieved by improving the existing SlowFast’s data structure into a frame unit *** on the confusion matrix,accuracies of 70.16%and 70.74%were obtained for the existing SlowFast and proposed model,respectively,indicating a 0.58%increase in *** detection,based on behavioral classification,the existing SlowFast detected 2,341,164 cases,whereas the proposed model detected 3,11

关键词： Artificial intelligence convolutional neural network video analysis human action recognition skeleton extraction

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Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

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Intelligent Automation & Soft Computing 2023年第8期37卷 2371-2386页

作者： Min-Jeong Kim Byeong-Uk Jeon Hyun Yoo Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea Contents Convergence Software Research Institute Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea

With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more ***,it is necessary to develop a method that achieves this task using object and behavior information within video *** methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a *** this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is *** and motions are first extracted from continuous images by combining existing DL-based image analysis *** weight of the continuous data pattern is then analyzed through data structuring to classify the overall *** performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action *** model achieved an accuracy of 0.9817,indicating excellent ***,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.

关键词： Deep learning object detection abnormal behavior recognition classification data structuring

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A Patient Specific Algorithm for Plasmodium Malaria Detection on Cell Images

A Patient Specific Algorithm for Plasmodium Malaria Detectio...

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2023 IEEE National Aerospace and Electronics Conference, NAECON 2023

作者： Narayanan, Barath Narayanan De Silva, Manawaduge Supun Hardie, Russell C. University of Dayton Research Institute Sensor and Software Systems Division Dayton United States University of Dayton Department of Electrical and Computer Engineering Dayton United States

ISBN: (纸本)9798350338782

computer-aided detection of plasmodium malaria on cell images from digital microscopy provides an invaluable second opinion to medical experts. Traditionally, well-established deep learning algorithms are widely used to detect plasmodium, but these techniques have not been deployed due to the uninterpretable nature of the decisions made by the network. To address this, we present an ensemble method that fuses the predictions based on the class activation mapping (CAM) results from multiple networks via a novel selector network. We study the performance on a publicly available dataset of cell images by using three convolutional neural network architectures, Xception, ResNet50, and InceptionV3,to produce CAM results for the selector network and performance comparison. Our proposed approach helps enhance doctors' trust and outperforms the traditional methods by 1%. © 2023 IEEE.

关键词： Diseases

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Adaptive Coordination with Graph Convolution Networks and Grey Wolf Optimization for Next-Gen 5G Network Slicing

Adaptive Coordination with Graph Convolution Networks and Gr...

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2024 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2024

作者： Kowsalya, M. Ra, In-Ho Naveenraj, K.S. Thruvenkadam, S. Park, Seok-Woo School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computer & Software Kunsan National University Gunsan Korea Republic of Jio Platform Limited Internet Protocol Division Mumbai India

ISBN: (纸本)9798350352788

The need for flexible and effective 5G communication networks has prompted studies into novel approaches for network slicing, a fundamental paradigm that permits network service personalization for a range of applications. In this research, we provide a unique technique that combines Graph Convolution Networks (GCN) and Grey Wolf Optimization (GWO) strategies to improve the adaptive configuration of 5G network slices. GCN, a software capable of recording detailed network interactions, and GWO, well-known for its metaheuristic optimization, combine to offer a strong foundation for tackling dynamic network slicing challenges. We optimize GWO settings via rigorous testing and demonstrate the effectiveness of the integrated strategy by achieving gains in important performance measures including latency, throughput, and resource use. The investigation not only advances the continuing development of 5G networks but also highlights how cutting-edge machine learning and optimization techniques can completely transform network slicing approaches. © 2024 IEEE.

关键词： 5G mobile communication systems

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A Feature Fusion-Based Approach for Mammographic Mass Classification Using Deep Learning 1

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5th International Conference on Innovative Computing, IC 2022

作者： ur Rehman, Khalil Li, Jianqiang Pei, Yan Yasin, Anaa Ali, Saqib Saeed, Yousaf School of Software Engineering Beijing University of Technology Beijing China Beijing Engineering Research Center for IoT Software and Systems Beijing China Computer Science Division University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

Breast cancer seems to be the most deadly cancer for females globally. Mammographic masse detection is essential for the early diagnosis of breast cancer. The manual detection of breast masses using texture analysis from digital mammograms is hard because of its diverse patterns. Automatic detection of breast masses from mammograms with computer algorithms at early phases could help physicians to avoid unnecessary biopsies. In the current study, we investigated the limitations of texture analysis for mass detection by creating an MR taxon filter bank using Laplacian of Gaussian. We created a deep taxon feature fusion bank for the EfficinetNet DCNN to classify benign and malignant masses. The experimental results were examined on the public CBIS-DDSM and the local PINUM dataset. Our method yielded 0.96 and 0.98 accuracies, respectively. The results findings illustrated that our method significantly outperformed than the MobileNet, AlexNet, DenseNet, and previous studies. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mammography

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