Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions in North Aceh by Novia Hasdyna, Rozzi Kesuma Dinata, Balqis Yafis

“… The K-Means algorithm is a fundamental tool in machine learning, widely utilized for data clustering tasks. …”
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Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader by Qiang Liu, Songyong Liu, Qianjin Dai, Xiao Yu, Daoxiang Teng, Ming Wei

“…In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. …”
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Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands by Yuan Feng, Jongwan Eun, Seunghee Kim, Yong-Rak Kim

“…Recently, physics-informed neural networks (PINNs), which incorporate partial differential equations (PDEs) to solve forward and inverse problems, have attracted increasing attention in machine learning research. In this study, we applied PINNs to tackle hydraulic and thermal transport coupling forward problems in silty sands. …”
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Mitigating GNSS Multipath Effects Using XGBoost Integrated Classifier Based on Consistency Checks by Dengao Li, Xiaoli Ma, Jumin Zhao, Fanming Wu

“…Then, the remaining available measurements are used as the second-layer input, and the measurements are used as learning data using an integrated machine learning method, XGBoost, to efficiently detect and identify non-line-of-sight (NLOS), LOS, and other reflective multipath signals. …”
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A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems by Li Duan, Jingxian Zhou, You Wu, Wenyao Xu

“…The traditional methods of detecting botnets commonly used machine learning algorithms, and it is difficult to detect and control botnets in a network because of unbalanced traffic data. …”
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Early identification of dropouts during the special forces selection program by Ruud J. R. den Hartigh, Rik Huijzer, Frank J. Blaauw, Age de Wit, Peter de Jonge

“…Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable. …”
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Data-driven model discovery and model selection for noisy biological systems. by Xiaojun Wu, MeiLu McDermott, Adam L MacLean

“…Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of the system. Machine learning methods offer alternative means of model construction: differential equation models can be learnt from data via model discovery using sparse identification of nonlinear dynamics (SINDy). …”
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Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid by N. A. Serebryakov

“…The article is devoted to the problem of improving the accuracy of short-term load forecasting of electrical engineering complex of regional electric grid with the use deep machine learning tools. The effectiveness of the application of the adaptive learning algorithm for deep neural networks for short-term load forecasting of this electrical complex has been investigated. …”
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The use of drones and Artificial Intelligence for dugong sighting detection in a limited resource scenario by Ario Digdo Akbar, Astari Elisabeth, Riskyta Arinda Bella, Cahyono Topan

“…Therefore, more flights are needed to enhance the sample size for machine learning.…”
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Exploring Bare Ownership Supply of Housing in Urban Environments by Maria Rosaria Guarini, Alejandro Segura-de-la-Cal, Francesco Sica, Yilsy Núñez-Guerrero

“…This paper examines the status of bare ownership in the city of Rome by web scraping the house offers published on web portals and segmenting those offered as bare ownership. Machine learning analysis based on neural networks and binary logit regression allows for the observation of the particular behavior of the housing supply in bare ownership; it shows the different intrinsic and extrinsic characteristics that determine this Real Estate segment. …”
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Improvising Personalized Travel Recommendation System with Recency Effects by Paromita Nitu, Joseph Coelho, Praveen Madiraju

“…In this project, we have analyzed users’ twitter data, as well as their friends and followers in a timely fashion to understand recent travel interest. A machine learning classifier identifies tweets relevant to travel. …”
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Online Self-Supervised Learning for Accurate Pick Assembly Operation Optimization by Sergio Valdés, Marco Ojer, Xiao Lin

“…It then builds and evaluates multiple regression models through an auto machine learning implementation. The system selects the best-performing model to correct the misalignment and dynamically chooses between corrective strategies and the learned model, optimizing the cycle times and improving the performance during the cycle, without halting the production process. …”
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Bioinformatic analysis of ferroptosis related biomarkers and potential therapeutic targets in vitiligo by Linli Liu, Lingli Deng, Li Guan, Yuan Hu, Qianying Li, Chunshui Yu

“…Functional enrichment analysis revealed that these DE-FRGs are significantly involved in oxidative stress, immune regulation, and vitiligo-associated signaling pathways. Utilizing machine learning approaches, including LASSO and SVM-RFE, we identified four key marker genes (ALOX5, SNCA, SLC1A4, and IL33) with strong diagnostic potential. …”
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Digital technology and artificial intelligence issues in scientific works by A. N. Timokhovich, E. G. Samokhodkina, E. V. Samokhodkin, A. A. Elzon

“…The main semantic units, reflecting different aspects of the research field are digitalization; artificial intelligence (additional semantic units: knowledge representation, theorem proving, computer vision, robotics, machine learning, multi-agent systems, artificial intelligence tools); neural networks (additional semantic units: learning with a teacher, learning without a teacher, input data); strong or general artificial intelligence, weak or applied artificial intelligence; Marusya voice assistant, Alisa voice assistant, Siri voice assistant, Bixby voice assistant, Google Assistant; speech recognition, fingerprint recognition, human face identification. …”
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Air Transportation Direct Share Analysis and Forecast by Xufang Zheng, Chia-Mei Liu, Peng Wei

“…To find factors which have significant impacts on O&D direct share and to build an accurate model for O&D direct share forecasting, both parametric and nonparametric machine learning models are investigated in this research. …”
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A scientometric review of the relationship between learning agility and work engagement in modern management context by Farira Nareswari, Rini Juni Astuti

“…The results showed that learning agility, the ability to quickly adapt to new experiences, work commitment, focus on completing tasks and achieving goals are closely related. Machine learning, artificial neural networks, and predictive analytics can improve learning agility and work engagement. …”
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A Review Article: Mitigation Strategies for Seismic Damage in Structures by Raihana M. Ejam, Hatem Hadi Obeid

“…Additionally, the review discusses the potential of machine learning techniques in predicting seismicity rates and improving risk management in seismic-prone areas. …”
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Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model by Yongyi Li, Ming Zhang, Yixing Ding, Zhenghua Zhou, Lingyu Xu

“…We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. …”
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An Improved YOLOv7-Tiny-Based Algorithm for Wafer Surface Defect Detection by Mengyun Li, Xueying Wang, Hongtao Zhang, Xiaofeng Hu

“…To address the shortcomings of manual inspection and the limitations of existing machine learning methods, this paper proposes a wafer defect detection algorithm based on an improved YOLOv7-tiny. …”
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Investigation of ANN Architecture for Predicting Load-Carrying Capacity of Castellated Steel Beams by Thuy-Anh Nguyen, Hai-Bang Ly, Van Quan Tran

“…The ANN model seems to be the best algorithm of machine learning for predicting the CSB load-carrying capacity.…”
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