Suchergebnisse - parallel string-machine algorithm*

An adaptive shuffled frog-leaping algorithm for parallel batch processing machines scheduling with machine eligibility in fabric dyeing process.

Autoren: Lei, Deming Dai, Tao

Quelle: International Journal of Production Research; Nov2024, Vol. 62 Issue 21, p7704-7721, 18p

Schlagwörter: BATCH processing, DYEING machines, MANUFACTURING processes, PARALLEL algorithms, CLOTHING industry

A Shuffled Frog-Leaping Algorithm with Competition for Parallel Batch Processing Machines Scheduling in Fabric Dyeing Process.

Autoren: Li, Mingbo Lei, Deming

Quelle: Computer Modeling in Engineering & Sciences (CMES); 2025, Vol. 143 Issue 2, p1789-1808, 20p

Schlagwörter: BATCH processing, PARALLEL processing, PARALLEL algorithms, MANUFACTURING industries, PRODUCTION scheduling

Machine learning in supply chain management: systematic literature review and future research agenda.

Autoren: Vlachos, Ilias Reddy, Pulagam Gautam

Quelle: International Journal of Production Research; Aug2025, Vol. 63 Issue 16, p5987-6016, 30p

Schlagwörter: MACHINE learning, SUPPLY chain management, SUPPLY chain disruptions, ARTIFICIAL intelligence, DEMAND forecasting

A systematic literature study of machine learning techniques based intrusion detection: datasets, models, challenges, and future directions.

Autoren: Rehman, Hafiz Muhammad Raza Ur Liaquat, Saira Gul, Muhammad Junaid et al.

Quelle: Journal of Big Data; 11/28/2025, Vol. 12 Issue 1, p1-36, 36p

Schlagwörter: MACHINE learning, INTRUSION detection systems (Computer security), DETECTION algorithms, DATA libraries, INTERNET security

Machine Learning Approaches for Microorganism Identification, Virulence Assessment, and Antimicrobial Susceptibility Evaluation Using DNA Sequencing Methods: A Systematic Review.

Autoren: Abhadionmhen, Abel Onolunosen Asogwa, Caroline Ngozi Ezema, Modesta Ero et al.

Quelle: Molecular Biotechnology; Nov2025, Vol. 67 Issue 11, p4038-4066, 29p

A Population Diversity-Based Artificial Bee Colony Algorithm for Assembly Hybrid Flow Shop Scheduling with Energy Consumption.

Autoren: Zuo, Yandi Wang, Pan Li, Ming

Quelle: Applied Sciences (2076-3417); Oct2023, Vol. 13 Issue 19, p10903, 22p

Schlagwörter: FLOW shop scheduling, BEES algorithm, ENERGY consumption, FLOW shops, HONEYBEES, SUSTAINABILITY, PRODUCTION scheduling

Concurrent workload mapping for multicore security systems.

Autoren: Soewito, Benfano Ning Weng

Quelle: Concurrency & Computation: Practice & Experience; Jul2009, Vol. 21 Issue 10, p1281-1306, 26p, 3 Charts, 10 Graphs

Schlagwörter: SECURITY systems, COMPUTER network security, PARALLEL programming, AUTOMATIC programming (Computer science), COMPUTER programming, ALGORITHMS

A Bi-Population Evolutionary Algorithm With Feedback for Energy-Efficient Fuzzy Flexible Job Shop Scheduling.

Autoren: Pan, Zixiao Lei, Deming Wang, Ling

Quelle: IEEE Transactions on Systems, Man & Cybernetics. Systems; Aug2022, Vol. 52 Issue 8, p5295-5307, 13p

Schlagwörter: PRODUCTION scheduling, EVOLUTIONARY algorithms, ENERGY consumption, TABU search algorithm

Is the Corpus Ready for Machine Translation? A Case Study with Python to Pseudo-Code Corpus.

Autoren: Rai, Sawan Belwal, Ramesh Chandra Gupta, Atul

Quelle: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Feb2023, Vol. 48 Issue 2, p1845-1858, 14p

Schlagwörter: MACHINE translating, MACHINE learning, PYTHON programming language, CORPORA

Computational analysis of DEHP's oncogenic role in colorectal cancer.

Autoren: Zhu, Zhou Qin, Jian He, Chungang et al.

Quelle: Discover Oncology; 5/21/2025, Vol. 16 Issue 1, p1-17, 17p

Schlagwörter: COLORECTAL cancer, DIETHYLHEXYL phthalate, MACHINE learning, GENE expression, PROTEIN-protein interactions, CARCINOGENS, DRUG target, IMMUNOREGULATION

Asthma exacerbation prediction using shallow and deep learning approaches: A systematic review.

Autoren: Abhadiomhen, Stanley Ebhohimhen Nzeh, Royransom Chiemela Ezema, Modesta Ero et al.

Quelle: Network Modeling & Analysis in Health Informatics & Bioinformatics; 9/8/2025, Vol. 14 Issue 1, p1-26, 26p

Machine learning as a teaching strategy education: A review.

Autoren: Ramos Rivadeneira, Deixy Ximena Jiménez Toledo, Javier Alejandro

Quelle: EAI Endorsed Transactions on Scalable Information Systems; 2024, Vol. 11 Issue 6, p1-11, 11p

Schlagwörter: ARTIFICIAL intelligence, DIGITAL technology, EDUCATIONAL planning, LEARNING strategies, EFFECTIVE teaching

Fault diagnosis of photovoltaic strings by using machine learning‐based stacking classifier.

Autoren: Liu, Bo Sun, Kai Wang, Xiaoyu et al.

Quelle: IET Renewable Power Generation (Wiley-Blackwell); 2/24/2024, Vol. 18 Issue 3, p384-397, 14p

Schlagwörter: STACKING machines, MACHINE learning, SHORT circuits, NAIVE Bayes classification, FAULT diagnosis

Artificial intelligence in entrepreneurship education: a scoping review.

Autoren: Chen, Li Ifenthaler, Dirk Yau, Jane Yin-Kim et al.

Quelle: Education + Training; 2024, Vol. 66 Issue 6, p589-608, 20p

Schlagwörter: MACHINE learning, ENTREPRENEURSHIP education, ARTIFICIAL intelligence, EVIDENCE gaps, DATA analytics

Australian perspectives on artificial intelligence in veterinary practice.

Autoren: Currie, Geoff Hespel, Adrien‐Maxence Carstens, Ann

Quelle: Veterinary Radiology & Ultrasound. May2023, Vol. 64 Issue 3, p473-483. 11p.

Transportation carbon reduction technologies: A review of fundamentals, application, and performance.

Autoren: Xin Wang Xiuping Dong Zihe Zhang et al.

Quelle: Journal of Traffic & Transportation Engineering (English Edition); Dec2024, Vol. 11 Issue 6, p1340-1377, 38p

Schlagwörter: CARBON emissions, CLIMATE change, ARTIFICIAL intelligence, RENEWABLE energy sources, PRIVATE sector

Machine Learning for Labor Optimization: A Systematic Review of Strategies in Healthcare and Logistics.

Autoren: Kumar, Sooraj Kumar, Samesh Shiwlani, Ashish

Quelle: Pakistan Social Sciences Review; Jan-Mar2025, Vol. 9 Issue 1, p631-651, 21p

Schlagwörter: MACHINE learning, LOGISTICS, SUPERVISED learning, REINFORCEMENT learning, COST effectiveness

A Hybrid Imperialist Competitive Algorithm for the Flexible Job Shop Problem.

Autoren: Ghasemishabankareh, Behrooz Shahsavari-Pour, Nasser Basiri, Mohammad-Ali et al.

Quelle: Artificial Life & Computational Intelligence: Second Australasian Conference, ACALCI 2016, Canberra, ACT, Australia, February 2-5, 2016, Proceedings; 2016, p221-233, 13p

A systematic review of the research trends of machine learning in supply chain management.

Autoren: Ni, Du Xiao, Zhi Lim, Ming K.

Quelle: International Journal of Machine Learning & Cybernetics; Jul2020, Vol. 11 Issue 7, p1463-1482, 20p

Application of Hybrid Firefly Algorithm-Tabu Search Technique to Minimize the Makespan in Job Shop Scheduling problem.

Autoren: Kharat, Manoj Govind Khadke, Siddhant Sanjeev Raut, Rakesh D. et al.

Quelle: International Journal of Applied Industrial Engineering; Jul-Dec2016, Vol. 3 Issue 2, p1-21, 21p