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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Hao Qi Yu Zhang Ligang He Kang Luo Jun Huang Haoyu Lu Jin Zhao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China and Zhejiang-HUST Joint Research Center for Graph Processing Zhejiang Lab China Department of Computer Science University of Warwick United Kingdom

ISBN: (纸本)9798350323481

Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.

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Named Entity Recognition for Identifying Entities Related to Illegal Migration in Libya: An Analysis of Twitter Textual Data

Named Entity Recognition for Identifying Entities Related to...

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Sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Hajar Badr Zamzam Awahida Mansour Essgaer Asma Ajaal Abbas Ahessin Infromation System Department Faculty of Information Technology Sebha University Sebha Libya Computer Science Department Faculty of Sciences Sebha University Sebha Libya Department of Computer Technology Colledge of Techniqal Sciences Sebha Libya

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

The issue of illegal migration dominates political and media discourse, particularly on social media, generating interest in collecting and analyzing textual data from platforms like Twitter, and Facebook. Named Entity Recognition (NER) is a natural language processing technique used to extract information from unstructured text, including identifying named entities such as people, organizations, and locations. This study aims to contribute to the identification of entities within collected textual data from Twitter related to illegal migration associated with Libya. The study will employ data mining tools, particularly pre-trained NER models to achieve its objective. The research is significant as there is currently no Libyan study that has addressed this specific subject using this technique. NER can help researchers, policymakers, law enforcement agencies, and humanitarian organizations to better understand the scope, dynamics, and impact of this complex issue.

关键词： Social networking (online) Law enforcement Conferences Blogs Organizations Named entity recognition Media

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Medical Image Analysis of Lung Tumor Diagnosis Based on Generalized Fuzzy C-Means Clustering Algorithm

Medical Image Analysis of Lung Tumor Diagnosis Based on Gene...

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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Zwaid, Jassim Mohammed Ahmed Murad, Masrah Azrifah Azmi Khalid, Fatimah Binti Manshor, Noridayu Al-Jumaily, Abdulmajeed Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Affiliated to University of Anbar Ramadi Iraq Department of Software Engineering and Information System Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Multimedia Department Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Department of Signal Theory and Communications Universidad Carlos III de Madrid Leganés Madrid28911 Spain

In recent years, the rapid development of medical imaging technology has brought medical image analysis into the era of big data. CT imaging technology is one of the most common imaging methods for disease screening. This paper introduces medical image analysis of lung tumor diagnosis based on Generalized fuzzy c - means clustering algorithm was proposed. Brief introduce lung CT imaging to diagnose lung tumors requires a huge workload, which is often accompanied by long-term image reading and subjective evaluation. The experimental data comes from 15 patient samples which include two groups of samples are selected as test objects. The result shows the positive rate is high, resulting in misdiagnosis. In addition, great success of deep learning technology in the field of computer vision has made it possible to realize computer-aided diagnosis and screening of lung cancer. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Estimating Person Location in Bathroom with Spatial Ultrasound and Variational autoencoder

Estimating Person Location in Bathroom with Spatial Ultrasou...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Sato, Shun Yasuda, Yuto Ohara, Ryotaro Hamabe, Riku Genda, Takayuki Imanaka, Shoya Izumi, Shintaro Kawaguchi, Hiroshi Kobe University Graduate School of System Informatics Kobe Japan Kobe University Department of Computer Science and Systems Engineering Kobe Japan Kobe University Graduate School of Science Technology and Innovation Kobe Japan

ISBN: (纸本)9798350346459

In bathrooms, a fall can lead to severe injuries or even drowning. While taking preventive measures against such accidents is crucial, it's equally important to swiftly detect and request rescue when they occur. In such scenarios, knowing the exact location of the person in the bathroom becomes critical. However, concerns related to privacy and costs make the use of cameras and lidar impractical. To address these issues, we propose a method for estimating the 3D location of a person within the bathroom using ultrasound correlation values as input for a deep neural network. To validate our approach, we conduct experiments using two sensors in this study. The results are promising, as we achieved a mean square error of just 10.48 cm. © 2023 IEEE.

关键词： Location

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Ionic liquid multistate resistive switching characteristics in two terminal soft and flexible discrete channels for neuromorphic computing

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Microsystems & Nanoengineering 2022年第3期8卷 153-161页

作者： Muhammad Umair Khan Jungmin Kim Mahesh Y.Chougale Chaudhry Muhammad Furqan Qazi Muhammad Saqib Rayyan Ali Shaukat Nobuhiko P.Kobayashi Baker Mohammad Jinho Bae Hoi-Sing Kwok Department of Ocean System Engineering Jeju National University102 JejudaehakroJeju 63243Republic of Korea Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788UAE System on Chip Center Khalifa UniversityAbu Dhabi 127788UAE Department of Electronics and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina State Key Laboratory on Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Baskin School of Engineering Univerity of California Santa Cruz1156 High StreetSanta CruzCA 95064USA

By exploiting ion transport phenomena in a soft and flexible discrete channel,liquid material conductance can be controlled by using an electrical input signal,which results in analog neuromorphic *** paper proposes an ionic liquid(IL)multistate resistive switching device capable of mimicking synapse analog behavior by using IL BMIM FeCL_(4) and H_(2)O into the two ends of a discrete polydimethylsiloxane(PDMs)*** spike rate-dependent plasticity(SRDP)and spike-timing-dependent plasticity(STDP)behavior are highly stable by modulating the input ***,the discrete channel device presents highly durable performance under mechanical bending and *** the obtained parameters from the proposed ionic liquid-based synaptic device,convolutional neural network simulation runs to an image recognition task,reaching an accuracy of 84%.The bending test of a device opens a new gateway for the future of soft and flexible brain-inspired neuromorphic computing systems for various shaped artificial intelligence applications.

关键词： computing gateway state

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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Q-learning Based Meta-Heuristics for Scheduling Bi-Objective Surgery Problems with Setup Time

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Complex system Modeling and Simulation 2024年第4期4卷 321-338页

作者： Ruixue Zhang Hui Yu Adam Slowik Kaizhou Gao School of Computer Science Liaocheng UniversityLiaocheng 252000China Macao Institute of System Engineering Macao University of Science and TechnologyMacao 999078China the Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin 75-453Poland

Since the increasing demand for surgeries in hospitals,the surgery scheduling problems have attracted extensive *** study focuses on solving a surgery scheduling problem with setup *** a mathematical model is created to minimize the maximum completion time(makespan)of all surgeres and patient waiting time,*** time by the fatigue effect is included in the surgery time,which is caused by doctors’long working ***,four mate-heuristics are optimized to address the relevant *** novel strategies are designed to improve the quality of the initial *** improve the convergence of the algorithms,seven local search operators are proposed based on the characteristics of the surgery scheduling ***,Q-learning is used to dynamically choose the optimal local search operator for the current state in each ***,by comparing the experimental results of 30 instances,the *** based local search strategy's effectiveness is *** all the compared algorithms,the improved artificial bee colony(ABC)with Q-leaming based local search has the best competiiveness.

关键词： surgery scheduling meta-heuristics Q-earning makespan

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Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

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computers, Materials & Continua 2022年第11期73卷 2541-2554页

作者： K.Vijaya Kumar E.Laxmi Lydia Ashit Kumar Dutta Velmurugan Subbiah Parvathy Gobi Ramasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Christ UniversityBangalore560029India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of ManagementMoscow109542Russia

Recently,big data becomes evitable due to massive increase in the generation of data in real time ***,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,*** paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data *** AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT *** AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast *** addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature ***,softmax layer is applied to perform classification *** effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.

关键词： Object detection tracking convolutional neural network inception v2 image annotation

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Bird Swarm Algorithm with Fuzzy Min-Max Neural Network for Financial Crisis Prediction

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computers, Materials & Continua 2022年第10期73卷 1541-1555页

作者： K.Pradeep Mohan Kumar S.Dhanasekaran I.S.Hephzi Punithavathi P.Duraipandy Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin Department of Computing Technologies Associate ProfessorSRM Institute of Science and TechnologyKattankulathur603203India Department of Information Technology Kalasalingam Academy of Research and Education626126India Department of Computer Science and Engineering Sphoorthy Engineering CollegeHyderabadTelangana501510India Department of Electrical and Electronics Engineering J B Institute of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial *** is considered a challenging issue in the financial *** and machine learning(ML)models can be employed for the design of accurate FCP *** numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different *** study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for *** major intention of the BSA-FMMNN model is to determine the financial status of a firm or *** presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity ***,k-medoid clustering approach is employed for the outlier removal ***,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of *** utilization of proficient parameter selection process using BSA demonstrate the novelty of the *** experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.

关键词： Financial crisis predictive model machine learning outlier removal clustering metaheuristics

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Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

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computer systems Science & Engineering 2023年第8期46卷 2619-2635页

作者： Manar Ahmed Hamza Hala J.Alshahrani Jaber S.Alzahrani Heba Mohsen Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent *** refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided *** of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as *** literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and *** sources were used for processing and analyzing the Arabic language content like ***,an important challenge in this domain is the unavailability of sufficient and reliable *** this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)*** aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic *** the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful ***,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of ***,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM *** scenario establishes the novelty of current *** performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

关键词： Arabic corpus aspect based sentiment analysis arabic language deep learning battle royale optimization natural language processing

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