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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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Review of on-board conductive charger topologies for electric transportation

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International Journal of Power Electronics 2023年第4期17卷 406-442页

作者： Vijaya Anand, N. Jaya Sai Praneeth, A.V. Yalla, Naveen Sood, Vijay K. Department of EEE P.V.P. Siddhartha Institute of Technology Kanuru Vijayawada520007 India Advance Engineering Group Borg Warner Technical Center Bascharage Luxembourg Varanasi221005 India Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University 2000 Simcoe Street North OshawaONL1H 7K4 Canada

This paper presents an overview of on-board conductive charger topologies for electric vehicles (EVs). Battery packs in electric and plug-in hybrid electric vehicles (EVs/PHEVs) need frequent energy refills to fulfil their duty cycles and overcome the range anxiety of vehicle owners. Due to the limited availability of fast charging infrastructures, all automotive manufacturers prefer an on-board battery charger in their vehicles. However, there are power level limitations for these chargers because of the weight, space and cost constraints. On-board charging systems can be either conductive or inductive. This paper reviews the topologies that are involved in conductive charging only. Globally, all automotive companies provide levels 1 and 2 for residential charging of vehicles. Single- and two-stage power converter topologies are summarised with their limitations. Furthermore, the current trends in the DC-DC converters for the two-stage conversion battery charger topologies are also outlined. This paper provides an overview of various topologies and configurations involved in EV charging with their limitations. © 2023 Inderscience Enterprises Ltd.

关键词： Topology

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Heart Disease Classification Using Supervised Machine Learning Algorithms 2

Heart Disease Classification Using Supervised Machine Learni...

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2nd International Conference on computer science, engineering and Applications, ICCSEA 2022

作者： Saraswathi, R. Shobarani, G. Veerma Devi, S. Sreenivasa Institute of Technology and Management Studies Computer Science Engineering Andhra Pradesh Chittoor India K.M.G College of Arts&Science Pg & Research Department of Computer Science Tamil Nadu Gudiyatham Vellore India Department of Computer Science Tamil Nadu Palacode India

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Heart disease cases are increasing at a rapid rate, and it's critical and concerning to predict any such infections ahead of time. This is a difficult job since it must be completed accurately and efficiently. This researchpaper focuses on which patients are more likely to develop a coronary artery disease based on several clinical criteria. Anaccurateforecast of coronary artery disease can stop life-threatening complications, whereas an inappropriateforecast can be fatal at the similar time. The outcomes and examination of the UCI Machine Learning Heart Disease dataset are considered and examined in this work using various AI computations and profound learning. In this paper we use the dataset covers 14 main features that remained used to carry out the research. Using the exactness and disorder framework, several promising outcomes are achieved and authorised. This system forecasts the future of cardiovascular illness. The consequences of this framework indicate the likelihood of coronary illness happening at a certain rate. Clinical borders are defined in the datasets that were used. Using an information mining grouping strategy, this framework evaluates such boundaries. Two main Machine Learning Algorithms are used to manage the datasets in Python programming. © 2022 IEEE.

关键词： Heart

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Semi-Supervised Clustering Framework for Fine-grained Scene Graph Generation 39

Semi-Supervised Clustering Framework for Fine-grained Scene ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Jiarui Wang, Chuan Zhang, Jun Wu, Shuyi Jinjing, Zhao Liu, Zeming Yang, Liang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Institute of Information Engineering CAS China School of Computer Science and Technology Beijing JiaoTong University China Guangdong Provincial Key Lab of Intell. Info. Processing & Shenzhen Key Lab of Media Security Shenzhen University China Information Research Center of Military Science PLA Academy of Military Science China National Key Laboratory of Science and Technology on Information System Security China School of Computer Science and Engineering Beihang University China School of Artificial Intelligence Hebei University of Technology China

ISBN: (纸本)157735897X

Scene Graph Generation (SGG) aims to detect all objects and identify their pairwise relationships existing in the scene. Considering the substantial human labor costs, existing scene graph annotations are often sparse and biased, which result in confusion training with low-frequency predicates. In this work, we design a Semi-Supervised Clustering framework for Scene Graph Generation (SSC-SGG) that uses the sparse labeled data to guide the generation of effective pseudo-labels from unlabeled object pairs, thus enriching the labeled sample space, especially for low-frequency interaction samples. We approach from the perspective of clustering, reducing the problem of confirmation bias in a self-training manner. Specifically, we first enhance the model’s robustness to feature extraction via prototype-based clustering, aggregating different relationship augmented features onto the same prototype. Secondly, we design a dynamic pseudo-label assignment algorithm based on a mini-batch, which adjusts the detection sensitivity to different frequency samples from the historical assignment. Finally, we conduct joint training on the pseudo-labels and the labeled data. We conduct experiments on various SGG models and achieve substantial overall performance improvements, demonstrating the effectiveness of SSC-SGG. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Factoring 3D Convolutions for Medical Images by Depth-wise Dependencies-induced Adaptive Attention

Factoring 3D Convolutions for Medical Images by Depth-wise D...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zeng, Na Fang, Jiansheng Wang, Xingyue Lu, Xiaoxi Huang, Jingqi Miao, Hanpei Liu, Jiang Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Shenzhen518055 China CVTE Research Guangzhou510530 China

ISBN: (纸本)9781665468190

It turns out that convolutional neural networks (CNNs) have excellent medical image processing capabilities. Hence, effectively and efficiently deploying CNNs on devices with varying computing power to make computer-aided diagnosis puts on the agenda. However, it is a dilemma to balance the limited computing resources and model complexity. Previously, we proposed factorized convolution with spectral normalization (FConvSN) to mitigate the bottleneck of deploying CNNs for 2D medical images. But due to the cube structure of 3D convolutional kernels, it does not work well for 3D medical images. Directly flattening 3D kernels to 2D weights for matrix factorization may undermine the learning ability along depth-wise, resulting in the loss of depth information and the decline of model performance. To this end, we factorize a 3D convolutional kernel to 2D weight matrices with depth-wise dimensions, then assign an attentive score for each 2D weight matrix by a depth-wise dependencies-induced adaptive attention block (AA). AA with a temperature hyper-parameter helps convolution kernel to better capture depth-wise dependencies in 3D medical images, improving its learning ability along the depth direction. We term this novel factorized convolution as FConvAA used for compressing model complexity without impairing the depth-wise expressivity. We also impose spectral normalization (SN) for FConvAA to constrain spectral norm-wise weights. We conduct extensive experiments on the public lung CT dataset LUNA16 and the private retina OCT dataset to demonstrate the effectiveness and feasibility of our FConvAA. © 2022 IEEE.

关键词： Convolution

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Decentralized Framework for Collection and Secure Storage of Google Street View Data: Case Study

Decentralized Framework for Collection and Secure Storage of...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Mallikarachchi, Sanjaya Ho, Bonnie Kanj, Iyad Seneviratne, Oshani Godage, Isuru S. DePaul University Jarvis College of Computing & Digital Media School of Computing ChicagoIL United States Rensselaer Polytechnic Institute Department of Computer Science TroyNY United States Texas A&M University Department of Engineering Technology & Industrial Distribution College StationTX United States

ISBN: (纸本)9798350331790

Image data plays a pivotal role in the current data-driven era, particularly in applications such as computer vision, object recognition, and facial identification. Google Maps® stands out as a widely used platform that heavily relies on street view images. To fulfill the pressing need for an effective and distributed mechanism for image data collection, we present a framework that utilizes smart contract technology and open-source robots to gather street-view image sequences. The proposed framework also includes a protocol for maintaining these sequences using a private blockchain capable of retaining different versions of street views while ensuring the integrity of collected data. With this framework, Google Maps® data can be securely collected, stored, and published on a private blockchain. By conducting tests with actual robots, we demonstrate the feasibility of the framework and its capability to seamlessly upload privately maintained blockchain image sequences to Google Maps® using the Google Street View® Publish API. © 2023 IEEE.

关键词： Smart contract

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Depth-Threshold Circuits vs. Depth-(+1) AND-OR Trees 55

Depth-Threshold Circuits vs. Depth-(+1) AND-OR Trees

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55th Annual ACM Symposium on Theory of computing, STOC 2023

作者： Hatami, Pooya Hoza, William M. Tal, Avishay Tell, Roei Ohio State University Department of Computer Science and Engineering ColumbusOH United States University of California Simons Institute for the Theory of Computing BerkeleyCA United States University of California Department of Electrical Engineering and Computer Sciences BerkeleyCA United States Institute for Advanced Study School of Mathematics PrincetonNJ United States Rutgers University DIMACS PiscatawayNJ United States

ISBN: (纸本)9781450399135

For any n ,• and d = o(loglog(n)), we prove that there is a Boolean function F on n bits and a value γ= 2-(d) such that F can be computed by a uniform depth-(d + 1) AC0 circuit with O(n) wires, but F cannot be computed by any depth-d TC0 circuit with n1 +-3 wires. This bound matches the current state-of-the-art lower bounds for computing explicit functions by threshold circuits of depth d > 2, which were previously known only for functions outside AC0 such as the parity function. Furthermore, in our result, the AC0 circuit computing F is a monotone∗read-once formula∗(i.e., an AND-OR tree), and the lower bound holds even in the average-case setting with respect to advantage n-3. At a high level, our proof strategy combines two prominent approaches in circuit complexity from the last decade: The celebrated∗random projections∗method of Håstad, Rossman, Servedio, and Tan (J. ACM 2017), which was previously used to show a tight average-case depth hierarchy for AC0;and the line of works analyzing the effect of∗random restrictions∗on threshold circuits. We show that under a modified version of Håstad, Rossman, Servedio, and Tan's projection procedure, any depth-d threshold circuit with n1 +-3 wires simplifies to a near-trivial function, whereas an appropriately parameterized AND-OR tree of depth d + 1 maintains structure. © 2023 Owner/Author.

关键词： Wire

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Guest Editorial Special Section on Generative AI and Large Language Models Enhanced 6G Wireless Communication and Sensing

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 0-2页

作者： Jiacheng Wang Geng Sun Dusit Niyato Hina Tabassum Gabriel-Miro Muntean Nelson Fonseca College of Computing and Data Science Nanyang Technological University Jurong West Singapore College of Computer Science and Technology Jilin University Changchun China Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University Toronto ON Canada School of Electronic Engineering Dublin City University Dublin 9 Ireland Institute of Computing State University of Campinas Campinas Brazil

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Generic Sentimental Analysis in Web Data Recommendation Based on Social Media Scalable Data Analytics Using Machine Learning Architecture

Generic Sentimental Analysis in Web Data Recommendation Base...

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Sekaran, Ramesh Rajeyyagari, Sivaram Munnangi, Ashok Kumar Parasuraman, Manikandan Ramachandran, Manikandan Kumar, Anil Karnataka Bengaluru562112 India Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India School of Computing SASTRA Deemed University Tamil Nadu Thanjavur613401 India Tula’s Institute Dehradun248197 India

ISBN: (纸本)9789819965434

Sentimental analysis is a method for distinguishing proof of articulation, mentality, or sensations of clients. It characterized as bad, positive, good, ominous, and so on from a piece of text in the record. Recommendation systems are important intelligent systems that assume a crucial part in giving specific data to users. Deep learning emerged as an important approach to settling opinion order issues in the late days. This research proposes a novel technique in generic sentimental analysis for web data classification with a recommendation system in social media analytics using machine learning techniques. Here, the web data input is processed, and remove missing values and normalization. Then the processed data is classified using convolutional discriminant kernel component analysis and their data recommendation in social media using reinforcement multilayer neural networks. The experimental analysis is carried out for various social media datasets regarding the accuracy, average precision, recall, actual positive rate, and F-measure. The proposed technique attained an accuracy of 98%, average precision of 79%, recall of 72%, real positive rate of 63%, and F-measure of 68%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intelligent systems

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