Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data by Ali Asghar Zolfaghari, Maryam Raeesi, Giuseppe Longo-Minnolo, Simona Consoli, Miles Dyck

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Quantitatively Detecting Camellia Oil Products Adulterated by Rice Bran Oil and Corn Oil Using Raman Spectroscopy: A Comparative Study Between Models Utilizing Machine Learning Alg... by Henan Liu, Sijia Ma, Ni Liang, Xin Wang

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Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain by Manuel J. García Rodríguez, Vicente Rodríguez Montequín, Francisco Ortega Fernández, Joaquín M. Villanueva Balsera

“…This paper develops a pioneering algorithm to recommend potential bidders using a machine learning method, particularly a random forest classifier. …”
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Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease by Tiantian Bai, Mengru Xu, Taotao Zhang, Xianjie Jia, Fuzhi Wang, Xiuling Jiang, Xing Wei

“…This study focuses on integrating swarm intelligence feature selection algorithms (including whale optimization algorithm, cuckoo search algorithm, flower pollination algorithm, Harris hawk optimization algorithm, particle swarm optimization algorithm, and genetic algorithm) with machine learning technology to improve the early diagnosis of cardiovascular disease. …”
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Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection by Ruo-Fei Xu, Zhen-Jing Liu, Shunan Ouyang, Qin Dong, Wen-Jing Yan, Dong-Wu Xu

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Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators by Hoa Thi Nguyen, Roland Olsson, Oystein Haugen

“…We also show that our evolutionary algorithm is capable of creating skip-connections and gating mechanisms adapted to the specific characteristics of our dataset.…”
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Predicting the Bitcoin’s price using AI by Gil Cohen, Avishay Aiche

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PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm by Tianyi Zhang, Jing Yang, Ru Liu, Qiyuan Feng

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Adaptive system of identification of parameters of asynchronous three-phase motor by A. J. Afanasiev, V. G. Makarov, Y. A. Yakovlev, V. N. Khannanova

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Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiency by Leszek Sliwko

“…This research investigates how Machine Learning (ML) algorithms can assist in workload allocation strategies by detecting tasks with node affinity operators (referred to as constraint operators), which constrain their execution to a limited number of nodes. …”
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Search Guidance Network Assisted Dynamic Particle Swarm Optimization Algorithm by LIU Zhi, SONG Wei

“…In dynamic optimization problems (DOPs), environmental changes can be characterized as different dynamics, and adaption of dynamic optimization algorithms (DOAs) in different dynamic environments is vital. …”
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Multi-head surface mounting placement optimisation based on adaptive multi-point crossover operator by Song Zhiqiang, Zhang Chunfu

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Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic Factors by Masmur Tarigan, Ford Lumban Gaol, Tuga Mauritsius, Widodo Budiharto

“…GASS addresses these challenges by incorporating real-time, dynamic data, enabling the system to adapt to machine downtimes, fluctuating job priorities, and process variability. …”
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ARTIFICIAL INTELLIGENCE AND ITS ROLE IN INTERNATIONAL MANAGEMENT by Alina GUȘE (FRĂTICĂ-DRAGOMIR)

“…Artificial Intelligence (AI) represents the ability that technologies or machines have to copy human intelligence as close as possible in order to solve problems and achieve goals. …”
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Multi-objective flexible job-shop scheduling in hospital using discrete particle swarm optimization algorithm with adaptive inertia weight (DPSO-AIW) by Md. Limonur Rahman Lingkon, Adri Dash

“… A multi-objective Flexible Job-shop Scheduling technique for hospitals is proposed using DPSO-AIW i.e. discrete particle swarm optimization with adaptive inertia weight method. The approach encodes the layer of the chromosomes using an operation sequence (OS) and machine assignment (MA) which is a two-layer coding structure. …”
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A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning by Junyao Ding, Jianchao Du, Hejie Wang, Song Xiao

“…Then, the improved genetic algorithm is used to search for the global optimal feature subset further. …”
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Research on optimal selection of runoff prediction models based on coupled machine learning methods by Xing Wei, Mengen Chen, Yulin Zhou, Jianhua Zou, Libo Ran, Ruibo Shi

“…Employing a “decomposition-reconstruction” strategy combined with robust optimization algorithms enhances the performance of machine learning prediction models, thereby significantly improving the runoff prediction capabilities in watershed hydrological models.…”
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Rapid classification of rice according to storage duration via near-infrared spectroscopy and machine learning by Chen Zhai, Wenxiu Wang, Man Gao, Xiaohui Feng, Shengjie Zhang, Chengjing Qian

“…Therefore, we investigated the ability of near-infrared spectroscopy combined with machine learning algorithms to distinguish rice storage duration. …”
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Bias Adaptive Statistical Inference Learning Agents for Learning from Human Feedback by Jonathan I Watson

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Adaptive hierarchical smoothing strategy and fuzzy logic-based torque vectoring algorithm for comfort and propulsion optimization in over-actuated hybrid electric vehicles by Adel Oubelaid, Nima Khosravi, Youcef Belkhier, Toufik Rekioua, Djamila Rekioua, Mohamed Benbouzid

“…The strategy is built upon two key innovations: an Adaptive Hierarchical Smoothing Strategy (AHSS) to mitigate transient disturbances during power source and drive mode transitions, and a fuzzy logic-based torque vectoring algorithm to maximize propulsion efficiency by dynamically distributing torque across one or two permanent magnet synchronous machines (PMSMs). …”
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