Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state by Aydin Larestani, Sara Sahebalzamani, Abdolhossein Hemmati-Sarapardeh, Ali Naseri

“…Among the tested models, the extra trees (ET) algorithm demonstrated superior performance, achieving an average absolute percent relative error (AAPRE) of approximately 3%, indicating its high reliability for Rs prediction. …”
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Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory by Jiang Bian, Yang Wang, Zhaoshuai Dang, Tianchun Xiang, Zhiyong Gan, Ting Yang

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Constructing a predictive model of negative academic emotions in high school students based on machine learning methods by Shumeng Ma, Ning Jia, Xiuchao Wei, Wanyi Zhang

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Are Natural Language Processing methods applicable to EPS forecasting in Poland? by Wojciech Kurylek

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Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications by Asghar Khan, Saeed Islam, Muhammad Ismail, Abdulaziz Alotaibi

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Development and validation of the Immune Profile Score (IPS), a novel multiomic algorithmic assay for stratifying outcomes in a real-world cohort of patients with advanced solid ca... by Yan Liu, Matthew Zibelman, Timothy Chan, Justin Guinney, Douglas Adkins, Ezra E W Cohen, Michelle M Stein, Halla Nimeiri, Sandip P Patel, Alia D Zander, Rossin Erbe, Ailin Jin, Sayantoni Mukhopadhyay, Ben Terdich, Mario G Rosasco, Nirali Patel, Chithra Sangli, Timothy Taxter, Seung Won Hyun, Brett M Mahon, A Kate Sasser, Michelle A Ting-Lin, Kyle A Beauchamp

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A new method for determining factors Influencing productivity of deep coalbed methane vertical cluster wells by HUANG Li, XIONG Xianyue, WANG Feng, SUN Xiongwei, ZHANG Yixin, ZHAO Longmei, SHI Shi, ZHANG Wen, ZHAO Haoyang, JI Liang, DENG Lin

“…Predictions using the neural network method were more accurate, with a relative error of less than 10% compared to measured values. 2） Using Kendall's tau-b correlation analysis, the discrete dominant factor was identified as the microstructural position, primarily located in uplifted positive structural zones, with the secondary factor being fracture development, categorized mainly as “well-developed” or “developed.” 3） By combining lasso regression-random forest- decision tree algorithm to iteratively eliminate irrelevant factors, the continuous dominant factors influencing productivity were ranked in descending order as: ash content, average construction discharge rate, total sand volume pumped, flowback rate at gas breakthrough, net pay thickness, acoustic travel time, gamma ray log value, average construction pressure, percentage of 100-mesh sand, and average gas measurement value. …”
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DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment by Goda Srinivasa Rao, P. Santosh Kumar Patra, V.A. Narayana, Avala Raji Reddy, G.N.V. Vibhav Reddy, D. Eshwar

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Research on short-term power load forecasting based on deep reinforcement learning with multiple intelligences by Tianyun Luo, Dunlin Zhu, Jinming Liu, Sheng Yang, Jinglong He, Yuan Fu

“…In this paper, we analyze the multi-intelligence application architecture in power load forecasting, and analyze the function of each intelligent unit applied to short-term power load forecasting; based on clarifying the interaction relationship of each intelligent unit in short-term power load forecasting, we model short-term power load forecasting as a distributed and partially observable Markov decision-making process, which is suitable for multi-intelligence deep reinforcement learning; based on the MATD3 algorithm, a centralized training-distributed execution framework is used to train multiple intelligences within the model to achieve short-term power load forecasting. …”
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Traffic Classification in Software-Defined Networking Using Genetic Programming Tools by Spiridoula V. Margariti, Ioannis G. Tsoulos, Evangelia Kiousi, Eleftherios Stergiou

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Association of sarcopenia with all-cause and cause-specific mortality in cancer patients: development and validation of a 3-year and 5-year survival prediction model by Feng Cui, Xiangji Dang, Daiyun Peng, Yuanhua She, Yubin Wang, Ruifeng Yang, Zhiyao Han, Yan Liu, Hanteng Yang

“…Furthermore, we plan to develop risk prediction models using machine learning algorithms to predict 3-year and 5-year survival rates in cancer patients. …”
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Realizing the promise of machine learning in precision oncology: expert perspectives on opportunities and challenges by Vasileios Nittas, Kelly E. Ormond, Effy Vayena, Alessandro Blasimme

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A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system by Amna Ikram, Sunnia Ikram, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Adil Hussain, Amal H. Alharbi, Marwa M. Eid, Marwa M. Eid

“…The dataset includes four intercropping treatments: SM (sole maize), SS (sole soybean), 2M2S (two rows of maize with alternating two rows of soybean), and 2M3S (two rows of maize with alternating three rows of soybean). …”
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Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques by Raghavendra C. Kamath, G. S. Vijay, Ganesha Prasad, P. Krishnananda Rao, Uday Kumar Shetty, Gautham Parameshwaran, Aniket Shenoy, Prithvi Shetty

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A Macro-Control and Micro-Autonomy Pathfinding Strategy for Multi-Automated Guided Vehicles in Complex Manufacturing Scenarios by Jiahui Le, Lili He, Junhong Zheng

“…At the macro level, a central system employs a modified A* algorithm for preliminary pathfinding, guiding the AGVs toward their targets. …”
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Many-Objective Truss Structural Optimization Considering Dynamic and Stability Behaviors by João Marcos P. Vieira, José Pedro G. Carvalho, Dênis E. C. Vargas, Érica C. R. Carvalho, Patrícia H. Hallak, Afonso C. C. Lemonge

“…Such new MOSOPs have more than three objective functions and are called many-objective structural optimization problems. …”
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DQN Simulation Model of Green Cooperative Market Behavior of Multi-Load Users for Optimal Evolution of New-Type Power System by ZHANG Shuo, YUAN Chunhui, LI Yingzi, XIAO Yangming, WEI Ming, HE Yunzheng

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Wasserstein Distributionally Robust Optimization for Chance Constrained Facility Location Under Uncertain Demand by Iman Seyedi, Antonio Candelieri, Enza Messina, Francesco Archetti

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A hybrid deep learning-based intrusion detection system for EV and UAV charging stations by Rosebell Paul, Mercy Paul Selvan

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A real-time mapping method for knowledge graph-driven large language models: a focus on indoor fire evacuations by Tianxin Wang, Ping Du, Pei Dang, Tao Liu, Pengpeng Li

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