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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yan Qiao Benchu Zhang Zeyu Zhang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Faculty of Information Technology Macau University of Science and Technology Macau China

Anomaly detection based on Generative Adversarial Networks (GANs) has been demonstrated its superiority on in imaging area. However, most existing GAN-based methods require noise-free datasets to accurately train the network, whereas neither collecting an absolute clean dataset or labeling the anomalies in the dataset are quite difficult for Internet of Things (IoT) applications due to the harsh environment and the high dimensionality of the data. In this paper, we propose a robust GAN-based model for anomaly detection in IoT, which can accurately learn the data pattern unsupervisedly even with polluted training data. The new model adopts the framework of Bidirectional GAN (BiGAN) to enable an efficient representation mapping, and integrates the mechanism of Robust Principal Component Analysis (RPCA) to rule out the noises of data from the training set. To improve its performance, a new objective function and scoring function are elaborately designed, while a proximal method and Alternating Direction Method of Multiplier (ADMM) are incorporated into the training process. Comparing our method with four state-of-the-art anomaly detection methods, the experimental results strongly confirm the superiority of our method with polluted IoT datasets.

关键词： Training Smart cities Training data Generative adversarial networks Data models Internet of Things Anomaly detection

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Internet of Things: Perspective Path Based Tests in the Heterogeneity Environment

Internet of Things: Perspective Path Based Tests in the Hete...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Teik-Boon Tan Yiqi Tew Tong-Ming Lim Wai-Khuen Cheng Yen-Lin Chen Faculty of Computing and Information Technlogy Tunku Abdul Rahman University of Management and Technology Kuala Lumpur Malaysia Faculty of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology Kuala Lumpur Malaysia Centre for Business Incubation and Entrepreneurial Ventures Tunku Abdul Rahman University of Management and Technology Kuala Lumpur Malaysia Department of Computer Science Universiti Tunku Abdul Rahman Kampar Malaysia Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This paper provides an overview of the Internet of Things (IoT) environment and the challenges of conducting tests in such an environment. We provide explanations on challenges specifically on heterogeneous nature and the changes due to scaling that leads to the issue of complexity of tests with needs of combining different components and protocols of the IoT solution. We present the need for improvements as existing methods are only reducing minor irrelevant tests. Lastly, we propose using the perspective path-based tests on IoT architecture in the paper.

关键词： Protocols Logic gates Complexity theory Sensors Internet of Things Consumer electronics

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A Face Recognition Method In Machine Learning (ML) For Enhancing Security In Smart Home 2

A Face Recognition Method In Machine Learning (ML) For Enhan...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Alshar'e, Marwan Al Nasar, Mohammad Rustom Kumar, Rohit Sharma, Manish Dharamvir Tripathi, Vikas Gulf College Faculty of Computing Sciences Muscat Oman Aldar University College Garhoud School of Engineering & Technology Department of Information Technology Dubai United Arab Emirates Information Technology Institute of Management Studies Uttar Pradesh Noida India Jamia Millia Islamia Department of Civil Engineering New Delhi India The Oxford College of Engineering Department of Mca Bangalore India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

The study explores with Machine learning (ML), which is a type of neural network (AI) that empowers software programmers to start increasing prediction without being done with full to make this happen Exterior door security is of paramount importance and has become the cornerstone for the best and fastest security or sufficient to choose a feeling of protection to property owners, and together with technological innovations, especially in the Communication area, which tends to make emerging innovations in locking doors or windows have picked up a lot, including such locking doors and windows with faces and the others. Face - recognition technologies have also been developed and used for residential door locking mechanisms, and they are a basic and easy-to-use technology that detects the features of homeowners quite well. The advancement of facial recognition has resulted in one of the computer vision algorithms that is simple to build and has a high accuracy in recognising faces, and it has been widely utilised. When consumers review or exposure to new goods, does this have an instant influence on your suggestion system or does it take time, How do suggestions evolve as consumers interact more with the platform, many different of each sort of recommendation system in order to have a more detailed of their capabilities. © 2022 IEEE.

关键词： Data acquisition

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The Future of Crime Prevention: Police Case Analysis Using Machine Learning

The Future of Crime Prevention: Police Case Analysis Using M...

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Advancements in Computing (ICAC), International Conference on

作者： Dharsan. R Krishanthini M Traveena C Anubama L Mahaadikara M.D.J.T. Hansika Sanjeevi Shahikala Chandrasiri Department of Computer Science and Software Engineering (CSSE) Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka

Law enforcement agencies and police departments face challenges in analyzing and preventing accidents and crimes. This research presents a comprehensive machine learning system, “The Future of Crime Prevention: Police Case Analysis,” to enhance public safety. The system comprises components like accident case analysis, crime analysis, case document classification, and crimes against women analysis. It leverages various machine learning techniques to predict accident causes, analyze criminal cases, automate document classification, and cluster crimes against women. The objectives encompass bolstering public safety, enabling proactive decision-making, anticipating future crime rates, and optimizing document analysis. This research strives to provide effective tools for crime and accident prevention, addressing traditional challenges faced by police departments and crime divisions. In an era of advancing technology, the need for innovative solutions to ensure public safety has never been more crucial. Furthermore, it seeks to bridge the gap between law enforcement agencies and cutting-edge technology, revolutionizing the landscape of crime prevention and public safety.

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Edge Computing: Architecture, Implications and Future with Latest Trends

Edge Computing: Architecture, Implications and Future with L...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Krish Thakkar Akash Patel Aishwariya Budhrani Khushi Patel Sachin Patel Amit Nayak Department of Information Technology Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Changa Gujarat India Department of Computer Science & Engineering Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Changa Gujarat India Department of Computer Engineering Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Changa Gujarat India

As a result of the proliferation of Internet-connected smart devices brought about by the explosive growth of the Internet of Everything (IoE), lower bandwidth load, slower response, bad security, and poor privacy are just some of the issues that have arisen with data growth. Edge computing solutions have arisen as a response to the limitations of traditional cloud computing in meeting the data processing demands of modern societies. Edge computing, the new era of distributed computing, differs from cloud computing in that it places greater emphasis on being physically proximate to both the user and the data itself. The primary focus of this article is a critical examination of the associated literature and findings around edge computing. Edge computing is first briefly compared with cloud computing. Then, briefly discuss edge computing's architecture, followed by major challenges with edge computing and ending with latest trends with edge computing.

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Colon and lung cancer classification from multi-modal images using resilient and efficient neural network architectures

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Heliyon 2024年第9期10卷 e30625页

作者： Uddin, A. Hasib Chen, Yen-Lin Akter, Miss Rokeya Ku, Chin Soon Yang, Jing Por, Lip Yee Department of Computer Science and Engineering Khwaja Yunus Ali University Enayetpur Chouhali Sirajganj 6751 Bangladesh Department of Computer Science and Information Engineering National Taipei University of Technology Taipei 106344 Taiwan Department of Computer Science Universiti Tunku Abdul Rahman Kampar 31900 Malaysia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia

Automatic classification of colon and lung cancer images is crucial for early detection and accurate diagnostics. However, there is room for improvement to enhance accuracy, ensuring better diagnostic precision. This study introduces two novel dense architectures (D1 and D2) and emphasizes their effectiveness in classifying colon and lung cancer from diverse images. It also highlights their resilience, efficiency, and superior performance across multiple datasets. These architectures were tested on various types of datasets, including NCT-CRC-HE-100K (set of 100,000 non-overlapping image patches from hematoxylin and eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue), CRC-VAL-HE-7K (set of 7180 image patches from N = 50 patients with colorectal adenocarcinoma, no overlap with patients in NCT-CRC-HE-100K), LC25000 (Lung and Colon Cancer Histopathological Image), and IQ-OTHNCCD (Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases), showcasing their effectiveness in classifying colon and lung cancers from histopathological and Computed Tomography (CT) scan images. This underscores the multi-modal image classification capability of the proposed models. Moreover, the study addresses imbalanced datasets, particularly in CRC-VAL-HE-7K and IQ-OTHNCCD, with a specific focus on model resilience and robustness. To assess overall performance, the study conducted experiments in different scenarios. The D1 model achieved an impressive 99.80 % accuracy on the NCT-CRC-HE-100K dataset, with a Jaccard Index (J) of 0.8371, a Matthew's Correlation Coefficient (MCC) of 0.9073, a Cohen's Kappa (Kp) of 0.9057, and a Critical Success Index (CSI) of 0.8213. When subjected to 10-fold cross-validation on LC25000, the D1 model averaged (avg) 99.96 % accuracy (avg J, MCC, Kp, and CSI of 0.9993, 0.9987, 0.9853, and 0.9990), surpassing recent reported performances. Furthermore, the ensemble of D1 and D2 reached 93 % accuracy (J, MCC, Kp, and

关键词： Cancer image classification Colon cancer CT-Scan imaging Dense neural networks (DNN) Histopathological imaging Lung cancer Multi-modal network

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A BERT-Based Pre-Training Model for Solving Math Application Problems

A BERT-Based Pre-Training Model for Solving Math Application...

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2022 IEEE International Conference on Intelligent Education and Intelligent Research, IEIR 2022

作者： Jia, Yuhao Wang, Pingheng Zhang, Zhen Cheng, Chi Li, Zhifei Yu, Xinguo Central China Normal University Faculty of Artificial Intelligence in Education Wuhan China Wuhan Sports University School of Sports Engineering and Information Technology Wuhan China Wuhan University School of Information Management Wuhan China Hubei University School of Computer Science and Information Engineering Wuhan China Central China Normal University National Engineering Research Center for E-Learning Wuhan China

ISBN: (纸本)9798350345629

Solving the math application problem is hot research in intelligence education. An increasing number of research scholars are using pre-trained models to tackle machine solution problems. Noteworthily, the semantic relationships required in the machine solution task are for describing math problems, while those of the BERT model with pre-training weights are of general significance, which will cause a mismatched word vector representation. To solve this problem, we proposed a self-supervised pre-training method based on loss priority. We use the input data from the downstream task datasets to fine-tune the existing BERT model so that the dynamic word vector it obtained can better match the downstream tasks. And the size of the loss value of each data batch in each round of training will be recorded to decide which data should be trained in the next round, so that the model has a faster convergence speed. Furthermore, considering that in large-scale mathematics application problems, some problems have almost the same forms of solution. We proposed a machine solution model training algorithm based on the analogy of the same problem type. Extensive experiments on two well-known datasets show the superiority of our proposed algorithms compared to other state-of-the-art algorithms. © 2022 IEEE.

关键词： Semantics

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WildPose: HRNet-based Lightweight and Efficient Wildlife Pose Estimation

WildPose: HRNet-based Lightweight and Efficient Wildlife Pos...

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Asia-Pacific Signal and information Processing Association Annual Summit and Conference (APSIPA)

作者： Sibusiso Reuben Bakana Yongfei Zhang Bhekisipho Twala School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Faculty of Information and Communication Technology Tshwane University of Technology Tshwane South Africa

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Lightweight and efficient wildlife monitoring algorithms, including pose estimation, that can operate on edge devices with limited computational resources are urgently needed for wildlife studies and protections. To reduce computational costs while maintaining accuracy in animal pose estimation, this paper introduces WildPose, an HRNet-w32 based model, designed, which integrates Efficient Channel Attention (ECA) to enhance important feature representations without complex operations, Non-Local Blocks (NLB) to capture long-range contextual information and handle occlusions, and Depthwise Convolutions (DWConv) to reduce computational complexity and parameters. Additionally, the Online Hard Keypoint Mining with Mean Squared Error (OHKMSE) loss function is employed to improve accuracy for occluded keypoints detection by focusing on harder keypoints. When evaluated on the largest wildlife dataset of Animal Kingdom, WildPose demonstrated a nice trade-off between accuracy and efficiency, 75% reduction in parameters and 65% reduction in GFLOPs, while achieving an increased Percentage of Correct Keypoints (PCK) for occluded keypoints such as the hip, ankle, and tail. Therefore, WildPose can serve as a practical solution for real-time wildlife monitoring and artificial intelligence-based ecological studies, especially in resource-constrained environments.

关键词： Accuracy Biological system modeling Computational modeling Wildlife Pose estimation Tail Real-time systems Computational efficiency Monitoring Hip

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Residual and Attention Mechanism-Based Network for Aluminum-Silicon Alloy Microstructure Image Classification

Residual and Attention Mechanism-Based Network for Aluminum-...

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information Systems and Computing technology (ISCTech), International Conference on

作者： Xiao Yang Tao Shen Jiancheng Yin Yingli Liu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Faculty of Materials Science and Engineering Kunming University of Science and Technology Kunming China

To enhance the accuracy of aluminum-silicon alloy microstructure image classification, we propose an aluminum-silicon alloy microstructure image classification model based on an improved residual network, incorporating attention mechanisms and multi-scale thinking. The model is built upon the ResNet18 architecture, utilizing enhanced basic residual blocks to select informative features. Additionally, a multi-scale attention module is incorporated into the network to fuse multi-scale features and further extract effective features. Experimental results on an aluminum-silicon alloy microstructure image classification dataset demonstrate that compared to the best-performing model currently available, the proposed model achieves an increase in classification accuracy, precision, recall, and F1 score by 3.17%, 0.74%, 5.53%, and 3.26%, respectively, reaching 94.97%, 96.39%, 93.97%, and 95.17%. These results indicate that the proposed model exhibits strong capabilities in classifying aluminum-silicon alloy microstructure images and can facilitate further research on the properties of aluminum-silicon alloys.

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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