A New Lightweight Hybrid Model for Pistachio Classification Using Transformers and EfficientNet by Muhammet Cakmak

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A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review by Ivan Maleš, Marko Kumrić, Andrea Huić Maleš, Ivan Cvitković, Roko Šantić, Zenon Pogorelić, Joško Božić

“…Artificial intelligence (AI) and machine learning (ML) are transforming the management of acute appendicitis by enhancing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. …”
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Interpretable model based on MRI radiomics to predict the expression of Ki-67 in breast cancer by Li Zhang, Qinglin Du, Mengyi Shen, Xin He, Dingyi Zhang, Xiaohua Huang

“…The Shapley Additive Explanation (SHAP) algorithm was employed to explain the optimal model, and the AUC was used to assess the model’s performance. …”
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Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery by Maria Zavadskaya, Anastasia Orlova, Andrei Dmitrenko, Vladimir Vinogradov

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Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study by Changxiao Han, Guangyi Yang, Haibao Wen, Minrui Fu, Bochen Peng, Bo Xu, Xunlu Yin, Ping Wang, Liguo Zhu, Minshan Feng

“…Conclusion Our machine learning model demonstrates robust performance in identifying suitable candidates for spinal manipulation among neck pain patients, offering clinicians an evidence-based practical tool to optimize patient selection and potentially improve treatment outcomes.…”
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ATHEMATICAL MODEL FOR THE OPTIMISATION OF HIERARCHICAL MULTI-LEVEL PRODUCTION SYSTEMS by Yu. V. Minaeva

“…Objectives The aim of the study is to develop a mathematical model for the complex solution of various problems in designing and reconstructing the technological system of a production workshop of a machine-building enterprise.Methods Complex system theory and an aggregative decomposition approach are used as the methodological basis for modelling complex hierarchical productions, making it possible to represent a complex system in the form of a set of interconnected subsystems.Results A mathematical model designed for a complex solution of problems associated with the formation of an optimal production programme and selection ofequipment was developed. …”
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Emo-SL Framework: Emoji Sentiment Lexicon Using Text-Based Features and Machine Learning for Sentiment Analysis by Manar Alfreihat, Omar Saad Almousa, Yahya Tashtoush, Anas AlSobeh, Khalid Mansour, Hazem Migdady

“…Emoji weighting is integrated with text-based feature extraction using lexicons to train classifiers on an Arabic tweet dataset. ML models, including Support Vector Machines (SVM), Naive Bayes, Random Forests, and K-Nearest Neighbors (KNN) are evaluated after optimal preprocessing and normalization. …”
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An Explainable LSTM-Based Intrusion Detection System Optimized by Firefly Algorithm for IoT Networks by Taiwo Blessing Ogunseyi, Gogulakrishan Thiyagarajan

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Classification of Fritillaria thunbergii appearance quality based on machine vision and machine learning technology by DONG Chengye, LI Dongfang, FENG Huaiqu, LONG Sifang, XI Te, ZHOU Qin’an, WANG Jun

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MILP Modeling and Optimization of Multi-Objective Three-Stage Flexible Job Shop Scheduling Problem With Assembly and AGV Transportation by Shiming Yang, Leilei Meng, Saif Ullah, Biao Zhang, Hongyan Sang, Peng Duan

“…To solve this problem, a mixed-integer linear programming model (MILP) is developed and the optimal Pareto front for small-scale instances are solved by using the <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-method. …”
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A Risk-Optimized Framework for Data-Driven IPO Underperformance Prediction in Complex Financial Systems by Mazin Alahmadi

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Data-Driven Energy Efficiency Modeling in Large-Scale Networks: An Expert Knowledge and ML-Based Approach by David Lopez-Perez, Antonio De Domenico, Nicola Piovesan, Merouane Debbah

“…This paper introduces the simulated reality of communication networks (SRCON) framework, a novel, data-driven modeling paradigm that harnesses live network data and employs a blend of machine learning (ML)- and expert-based models. …”
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Maximum Power Point Determination of Bifacial PV Using Multi-Verse Optimization Algorithm Applied on Different Cell Models by Najdoska Angela, Cvetkovski Goga

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Biogas production using zirconium and zinc-based nanocatalysts and evaluation using a predictive modeling approach by Abbas A. Abdullahi, Mustapha D. Garba, Tawfik A. Saleh

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A Robust-Based Home Energy Management Model for Optimal Participation of Prosumers in Competitive P2P Platforms by Alaa Al Zetawi, Marcos Tostado-Véliz, Hany M. Hasanien, Francisco Jurado

“…This paper addresses this issue by developing a home energy management model for optimal participation of prosumers in competitive P2P platforms. …”
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Advanced explainable models for strength evaluation of self-compacting concrete modified with supplementary glass and marble powders by Khan Kaffayatullah, Khan Muhammad Ehsan Ullah, Al-Naghi Ahmed A. Alawi, Amin Muhammad Nasir, Iftikhar Bawar, Qadir Muhammad Tahir

“…However, predicting the compressive strength (CS) of such mixes through traditional testing methods is time-consuming, costly, and limits rapid mix optimization. This motivates the adoption of machine learning (ML) techniques, which can efficiently analyze complex datasets and identify patterns that influence concrete performance. …”
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Acoustic signatures of bulbar ALS: Predictive modeling with sustained vowels and LightGBM by Zahra Farrokhi, Seyed Amirali Zakavi, Arian Sarafraz, Maryam Valifard, Salar Yousefzadeh, Zahra Mashhadi Tafreshi, Omid Anbiyaee, Navid Rostami, Mahsa Asadi Anar, Niloofar Deravi

“…A LightGBM (Light Gradient Boosting Machine)-based model was built and optimized using 5-fold cross-validation to separate ALS cases. …”
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Collision Risk Perception Models Using Physiological and Eye-Tracking Signals by Hyowon Lee, Ocktaeck Lim, Amandeep Singh, Siby Samuel

“…Accurate risk perception is essential for safe driving, particularly in dynamic and high-risk traffic environments. This study develops machine learning (ML)-based user risk perception models using physiological recording systems to assess driving risks across various scenarios. …”
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A User-Centric Smart Library System: IoT-Driven Environmental Monitoring and ML-Based Optimization with Future Fog–Cloud Architecture by Sarkan Mammadov, Enver Kucukkulahli

“…User feedback was collected via surveys and incorporated into models trained using Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Extreme Gradient Boosting (XGBoost), and Naive Bayes. …”
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The clinical prediction model to distinguish between colonization and infection by Klebsiella pneumoniae by Xiaoyu Zhang, Xifan Zhang, Deng Zhang, Jing Xu, Jingping Zhang, Xin Zhang

“…Six predictive models were constructed using 15 key influencing factors, including Classification and Regression Trees (CART), C5.0, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), Random Forest (RF), and Nomogram. …”
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