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Hybrid Flow Shop with Setup Times Scheduling Problem

computer Systems science & engineering 2023年第1期44卷 563-577页

作者： Mahdi Jemmali Lotfi Hidri Department of Computer Science and Information College of Science at ZulfiMajmaah UniversityMajmaah11952Saudi Arabia Mars Laboratory University of SousseSousseTunisia Department of Computer Science Higher Institute of Computer Science and Mathematics of MonastirMonastir UniversityMonastir5000Tunisia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The two-stage hybridflow shop problem under setup times is addressed in this *** problem is *** the other hand,the studied problem is modeling different real-life applications especially in manufacturing and high *** this kind of problem requires the development of adapted *** this context,a metaheuristic using the genetic algorithm and three heuristics are proposed in this *** approximate solutions are using the optimal solution of the parallel machines under release and delivery ***,these solutions are iterative procedures focusing each time on a particular stage where a parallel machines problem is called to be *** general solution is then a concatenation of all the solutions in each *** addition,three lower bounds based on the relaxation method are *** lower bounds present a means to evaluate the efficiency of the developed algorithms throughout the measurement of the relative *** experimental result is discussed to evaluate the performance of the developed *** total,8960 instances are implemented and tested to show the results given by the proposed lower bounds and *** indicators are given to compare between *** results illustrated in this paper show the performance of the developed algorithms in terms of gap and running time.

关键词： Hybridﬂow shop genetic algorithm setup times heuristics lower bound

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Sri Potha: A Voice-Based Sinhala Document Maker

Sri Potha: A Voice-Based Sinhala Document Maker

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： De Silva, D.I. Piyadali, D.S.P. Dilrukshi, M.G.S. Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9798350319200

This study discusses the development of a web application aimed at facilitating Sinhala document creation, with a specific emphasis on Sinhala voice-to-text conversion and the handling of Sinhala commands through vocal input. Leveraging machine learning techniques, including convolutional neural networks and Natural Language Processing, the application's core features were established. Extensive research was conducted to tailor the application's content to the needs of its primary users, ensuring maximum effectiveness. The user-friendly interfaces of the web application are designed for clarity, simplicity, and consistency. The primary objective of this research is to comprehensively analyze the implementation of Sinhala voice-to-text conversion and Sinhala command handling systems. These systems are primarily designed to benefit diverse users, including journalists, content writers, and differently abled individuals with verbal abilities, by enhancing the efficiency of creating Sinhala documents. Through a detailed exploration of the research methodology, this study offers insight into the development process of the web-based system. The outcomes of the linguistic model training, presented within the study, reveal achievements and advancements that address the limitations inherent in existing solutions. Key findings from this research demonstrate the successful functionality of the Sinhala voice-to-text converter and the efficacy of the Sinhala command handler. The voice-to-text conversion system achieved an impressive accuracy rate of over 80%, while the Sinhala command handler exhibited an accuracy of approximately 80%. Moreover, this research envisions potential applications that extend beyond document creation. The technology showcased in the web application holds promise for broader language-based applications, impacting education, accessibility, and communication across the native Sinhala-speaking community in Sri Lanka. In summary, this research showcases th

关键词： convolutional neural network machine learning natural language processing

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Ensemble based unsupervised anomaly detection with concept drift adaptation for time series data 10

Ensemble based unsupervised anomaly detection with concept d...

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10th IEEE Smart World Congress, SWC 2024

作者： Li, Danlei Nair, Nirmal Wang, Kevin I-Kai Sakurai, Kouichi University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Department of Informatics Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798331520861

Anomaly detection is the identification of instances that substantially deviate from the majority of the data and do not conform to a well-defined normal behavior. Investigating time series anomalies has become increasingly popular in recent years due to widely deployed IoT devices and sensors. Although most existing methods apply image anomaly detection algorithms directly on time series and commonly assume the data is IID (independent and identically distributed). However, in most realworld applications, the underlying data distribution alters over time. Changes in data over time are referred to as concept drift and can significantly degrade the efficacy of an anomaly detection model. Existing research also indicates that the majority of anomaly detection frameworks ignored concept drift, resulting in poor long-term model performance. This paper presents an ensemble-based, unsupervised anomaly detection framework that is adaptive to the concept drift in time series data. Both distribution and performance-based techniques are used to detect concept drift and update the model to ensure the framework's longterm performance reliability. The proposed method is evaluated using a real-world univariate time series data that captures sea surface temperature data over a 2 -year period. By comparing our proposed framework to other state-of-the-art algorithms, we demonstrate our proposed method has obvious benefits in accuracy and long-term stability. © 2024 IEEE.

关键词： Anomaly detection Concept drift adaptation Ensemble method Time series Unsupervised method

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Parallel software-Based Self-Testing with Bounded Model Checking for Kilo-Core Networks-on-Chip

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Journal of computer science & Technology 2023年第2期38卷 405-421页

作者：张颖季鹏飞朱潘玮 Zebo Peng 李华伟江建慧 School of Software Engineering Tongji UniversityShanghai 200092China Department of Computer and Information Science Linkoping UniversityLinkoping 58183Sweden Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Online testing is critical to ensuring reliable operations of the next generation of supercomputers based on a kilo-core network-on-chip(NoC)interconnection *** present a parallel software-based self-testing(SBST)solution that makes use of the bounded model checking(BMC)technique to generate test sequences and parallel *** this method,the parallel SBST with BMC derives the leading sequence for each router’s internal function and detects all functionally-testable faults related to the function.A Monte-Carlo simulation algorithm is then used to search for the approximately optimum configuration of the parallel packets,which guarantees the test quality and minimizes the test ***,a multi-threading technology is used to ensure that the Monte-Carlo simulation can reach the approximately optimum configuration in a large random space and reduce the generating time of the parallel *** results show that the proposed method achieves a high fault coverage with a reduced test ***,by performing online testing in the functional mode with SBST,it effectively avoids the over-testing problem caused by functionally untestable turns in kilo-core NoCs.

关键词： software-based self-testing(SBST) parallel test kilo-core networks-on-chip(NoCs) online testing

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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

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Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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Fusion of speech and handwritten signatures biometrics for person identification

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International Journal of Speech Technology 2023年第4期26卷 833-850页

作者： Abushariah, Ahmad A. M. Abushariah, Mohammad A. M. Gunawan, Teddy Surya Chebil, J. Alqudah, Assal A. M. Ting, Hua-Nong Mustafa, Mumtaz Begum Peer Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan ECE Department Faculty of Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics including speech, fingerprint, iris, handwritten signatures, and others. The fusion of more than one human biometric produces bimodal and multimodal API systems that normally outperform single modality systems. This paper presents our work towards fusing speech and handwritten signatures for developing a bimodal API system, where fusion was conducted at the decision level due to the differences in the type and format of the features extracted. A data set is created that contains recordings of usernames and handwritten signatures of 100 persons (50 males and 50 females), where each person recorded his/her username 30 times and provided his/her handwritten signature 30 times. Consequently, a total of 3000 utterances and 3000 handwritten signatures were collected. The speech API used Mel-Frequency Cepstral Coefficients (MFCC) technique for features extraction and Vector Quantization (VQ) for features training and classification. On the other hand, the handwritten signatures API used global features for reflecting the structure of the hand signature image such as image area, pure height, pure width and signature height and the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network for features training and classification. Once the best matches for both the speech and the handwritten signatures API are produced, the fusion process takes place at decision level. It computes the difference between the two best matches for each modality and selects the modality of the maximum difference. Based on our experimental results, the bimodal API obtained an average recognition rate of 96.40%, whereas the speech API and the handwritten signatures API obtained average recognition rates of 92.60% and 75.20%, respectively. Therefore, the bimodal API system is a

关键词： Biometrics

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DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

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computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin Salman A.Alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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ICCG:low-cost and efficient consistency with adaptive synchronization for metadata replication

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Frontiers of computer science 2025年第1期19卷 53-70页

作者： Chenhao ZHANG Liang WANG Jing SHANG Zhiwen XIAO Limin XIAO Meng HAN Bing WEI Runnan SHEN Jinquan WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyberspace Security Hainan UniversityHaikou 570228China China Mobile Information Technology Center Beijing 100033China

The rapid growth in the storage scale of wide-area distributed file systems (DFS) calls for fast and scalable metadata management. Metadata replication is the widely used technique for improving the performance and scalability of metadata management. Because of the POSIX requirement of file systems, many existing metadata management techniques utilize a costly design for the sake of metadata consistency, leading to unacceptable performance overhead. We propose a new metadata consistency maintenance method (ICCG), which includes an incremental consistency guaranteed directory tree synchronization (ICGDT) and a causal consistency guaranteed replica index synchronization (CCGRI), to ensure system performance without sacrificing metadata consistency. ICGDT uses a flexible consistency scheme based on the state of files and directories maintained through the conflict state tree to provide an incremental consistency for metadata, which satisfies both metadata consistency and performance requirements. CCGRI ensures low latency and consistent access to data by establishing a causal consistency for replica indexes through multi-version extent trees and logical time. Experimental results demonstrate the effectiveness of our methods. Compared with the strong consistency policies widely used in modern DFSes, our methods significantly improve the system performance. For example, in file creation, ICCG can improve the performance of directory tree operations by at least 36.4 times.

关键词： metadata management metadata replication consistency directory tree replica index

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Density estimation-based method to determine sample size for random sample partition of big data

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Frontiers of computer science 2024年第5期18卷 57-70页

作者： Yulin HE Jiaqi CHEN Jiaxing SHEN Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Computing and Decision Sciences Lingnan UniversityHong Kong 999077China

Random sample partition(RSP)is a newly developed big data representation and management model to deal with big data approximate computation *** research and practical applications have confirmed that RSP is an efficient solution for big data processing and ***,a challenge for implementing RSP is determining an appropriate sample size for RSP data *** a large sample size increases the burden of big data computation,a small size will lead to insufficient distribution information for RSP data *** address this problem,this paper presents a novel density estimation-based method(DEM)to determine the optimal sample size for RSP data ***,a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz(DKW)inequality by using the fixed-point iteration(FPI)***,a practical sample size is determined by minimizing the validation error of a kernel density estimator(KDE)constructed on RSP data blocks for an increasing sample ***,a series of persuasive experiments are conducted to validate the feasibility,rationality,and effectiveness of *** results show that(1)the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality;(2)the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function(p.d.f);and(3)DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of *** demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation.

关键词： random sample partition big data sample size Dvoretzky-Kiefer-Wolfowitz inequality kerneldensity estimator probability density function

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