Solar Agro Savior: Smart Agricultural Monitoring Using Drones and Deep Learning Techniques by Manu Mundappat Ramachandran, Bisni Fahad Mon, Mohammad Hayajneh, Najah Abu Ali, Elarbi Badidi

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Evaluation of hydraulic fracturing using machine learning by Ali Akbari, Ali Karami, Yousef Kazemzadeh, Ali Ranjbar

“…These results demonstrate RF’s capability in handling complex subsurface data with high accuracy and low computational cost. The proposed framework not only enhances predictive accuracy in HF evaluation but also serves as a practical tool for optimizing fracturing design and decision-making in field operations. …”
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Graph-based method for constructing consensus trees by Torquet Elio, Jansson Jesper, Tahiri Nadia

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Fuzzy Decision-Making Analysis of Quantitative Stock Selection in VR Industry Based on Random Forest Model by Jia-Ming Zhu, Yu-Gan Geng, Wen-Bo Li, Xia Li, Qi-Zhi He

“…Secondly, different from the single effective frontier algorithm, the research is based on the random forest algorithm, calculates the average AUC of the index, and continuously checks and tests the results to obtain the optimal investment portfolio. …”
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An Adaptive Large Neighborhood Search for the Larger-Scale Instances of Green Vehicle Routing Problem with Time Windows by Zixuan Yu, Ping Zhang, Yang Yu, Wei Sun, Min Huang

“…In the fast insertion method, the feasibility of a new route is judged by checking the constraints of partial customers after the inserted customer, instead of checking the constraints of all customers. Thus, the computational time of the ALNS algorithm is greatly saved. …”
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Unregistered Biological Words Recognition by Q-Learning with Transfer Learning by Fei Zhu, Quan Liu, Hui Wang, Xiaoke Zhou, Yuchen Fu

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Multi-Scenario Robust Distributed Permutation Flow Shop Scheduling Based on DDQN by Shilong Guo, Ming Chen

“…After 2000 iterations, the DDQN algorithm achieves a 15% reduction in makespan with only a 2.5% increase in computational time compared to random search, this joint simulation system offers an efficient and stable solution for the modeling and optimization of the MSRDPFSP issue.…”
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Use of machine learning to predict creativity among nurses: a multidisciplinary approach by Rola H. Mudallal, Majd T. Mrayyan, Mohammad Kharabsheh

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Increasing Minority Recall Support Vector Machine Model for Imbalanced Data Classification by Chunye Wu, Nan Wang, Yu Wang

“…In the experiments, the effects of different parameters on the performance of the algorithm were analyzed, and the optimal parameters for a recall rate of 1 were determined. …”
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Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction by Licheng Qu, Minghao Zhang, Zhaolu Li, Wei Li

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Control and management of energy in a PV system equipped with batteries storage by Kamal Hirech, Mustapha Melhaoui, Rachid Malek, Khalil Kassmi

“…The objective is to exploit the maximum of power using Hill climbing improved algorithm that considers optimal electrical characteristics of PV panels regardless of the system perturbation, to manage the energy between blocs of PV system in order to control the charge/discharge process and inject the energy surplus into the grid and also to estimate the state of charge with precision. …”
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A Hybrid Learning Framework for Enhancing Bridge Damage Prediction by Amal Abdulbaqi Maryoosh, Saeid Pashazadeh, Pedram Salehpour

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Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams by Mohammed Majeed Hameed, Faidhalrahman Khaleel, Mohamed Khalid AlOmar, Siti Fatin Mohd Razali, Mohammed Abdulhakim AlSaadi

“…Therefore, developing an efficient algorithm to select the optimal input parameters that have the highest information content to represent the target and minimise redundant data is very important. …”
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An improved permeability estimation model using integrated approach of hybrid machine learning technique and Shapley additive explanation by Christopher N. Mkono, Chuanbo Shen, Alvin K. Mulashani, Patrice Nyangi

“…The group method of data handling with differential evolution (GMDH-DE) algorithm was used to predict permeability due to its capability to manage complex, nonlinear relationships between variables, reduced computation time, and parameter optimization through evolutionary algorithms. …”
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Efficient RANSAC in 4D Plane Space for Point Cloud Registration by Chang Liu, Chao Liu, Yuming Zhang, Zhongqi Wu, Jianwei Guo

“…Based on those new descriptors, we randomly select corresponding plane pairs to compute candidate transformations, followed by a hypotheses verification step to identify the optimal registration. …”
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MDEALNS for Solving the Tapioca Starch Logistics Network Problem for the Land Port of Nakhon Ratchasima Province, Thailand by Chakat Chueadee, Preecha Kriengkorakot, Nuchsara Kriengkorakot

“…The MDEALNS algorithm designed a logistics network linking the optimal route and a suitable open/close factory allocation with the lowest transport cost for tapioca starch. …”
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Research Progress on Atmospheric Turbulence Perception and Correction Based on Adaptive Optics and Deep Learning by Qinghui Liu, Yihang Di, Mengmeng Zhang, Zhenbo Ren, Jianglei Di, Jianlin Zhao

“…Recent years have witnessed substantial advancements in AO technology, driven by synergistic progress in fundamental theories, optoelectronic devices, and computational algorithms. Furthermore, artificial intelligence‐driven turbulence processing frameworks leveraging deep neural networks have emerged as a prominent research frontier, demonstrating remarkable potential in intelligent wavefront sensing and nonlinear compensation. …”
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Portable Impedance Analyzer for FET-Based Biosensors with Embedded Analysis of Randles Circuits’ Spectra by Norman Pfeiffer, Martin Bach, Alice Steiner, Anna-Elisabeth Gerhardt, Joan Bausells, Abdelhamid Errachid, Albert Heuberger

“…The introduced algorithm was evaluated on 100 simulated spectra of Randles circuits. …”
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Review of machine learning applications for predicting the quality of biomass briquettes for sustainable and low-carbon energy solutions by Constance Nakato Nakimuli, Fred Kaggwa, Johan De Greef, David Kilama Okot, Julien Blondeau, Simon Kawuma

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Deep learning-based technique for investigating the behavior of MEMS systems with multiwalled carbon nanotubes and electrically actuated microbeams by Muhammad Amir, Jamshaid Ul Rahman, Ali Hasan Ali, Ali Raza, Zaid Ameen Abduljabbar, Husam A. Neamah

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