Predictive optimization in automotive supply chains: a BiLSTM-Attention and reinforcement learning approach by Asmae Amellal, Issam Amellal, Mohammed Rida Ech-charrat, Hamid Seghiouer

“…To further our analysis, we incorporated reinforcement learning, evaluating three algorithms: Q-learning, Deep Q-Networks (DQN), and SARSA. …”
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Drug discovery and mechanism prediction with explainable graph neural networks by Conghao Wang, Gaurav Asok Kumar, Jagath C. Rajapakse

“…Abstract Apprehension of drug action mechanism is paramount for drug response prediction and precision medicine. The unprecedented development of machine learning and deep learning algorithms has expedited the drug response prediction research. …”
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The role of the SOX2 gene in cervical cancer: focus on ferroptosis and construction of a predictive model by Shenping Liu, Zhi Wei, Huiqing Ding

“…Objective To delineate the association between SOX2 expression and ferroptosis in cervical cancer and develop a robust, SOX2-centric model for predicting prognosis and enhancing personalized treatment. …”
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Machine learning-based predictive analysis of energy efficiency factors necessary for the HIFU treatment of adenomyosis by Ziyan Liu, Ziyi Liu, Yuan Wang, Xiyao Wan, Xiaohua Huang

“…EEF values were calculated based on T2WI fat suppression (T2WI-FS) sequences, and radiomics features were extracted. Predictive features were selected using minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) methods, and two joint—based on decision tree and random forest algorithms—models were developed for EEF prediction.ResultsThe decision tree model achieved a mean absolute error (MAE) of 8.095 on the test set, while the random forest model exhibited an MAE of 8.231. …”
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PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS by Raghda Azad Hasan, Ibrahim Ahmed Saleh

“…The model applies the basic algorithms (Random Forest (RF), Decision Tree (DT), Extra Tree) and the learning model ensemble (Adaboost, xgboost ,Stack, Voting, bagging) and metrics (accuracy, recall, F1 score, accuracy) to measure the prediction performance of the models and a comparison was made between the proposed model algorithms. …”
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Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food by Zhenlong Wang, Wei An, Jiaxue Wang, Hui Tao, Xiumin Wang, Bing Han, Jinquan Wang

“…This study aims to develop a rapid and cost-effective method using an electronic nose (E-nose) and machine learning algorithms to predict whether ZEN levels in pet food exceed the regulatory limits (250 µg/kg), as set by Chinese pet food legislation. …”
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Predictive Performance of Machine Learning with Evoked Potentials for SCI and MS Prognosis: A Meta-Analysis by Constantinos Koutsojannis, Dionysia Chrysanthakopoulou

“…Machine learning (ML), using data-driven algorithms, enhances EPs’ prognostic utility, but evidence synthesis is limited. …”
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Machine Learning Analysis to Identify Predictive Factors of Caudal Epidural Pulse Radiofrequency in the Treatment of Coccygodynia by Sir E, Aydogan S, Batur Sir GD, Celenlioglu AE

“…Ender Sir,1 Sena Aydogan,2 Gul Didem Batur Sir,2 Alp Eren Celenlioglu1 1Department of Algology and Pain Medicine, University of Health Sciences Gulhane School of Medicine, Ankara, Turkey; 2Department of Industrial Engineering, Gazi University, Ankara, TurkeyCorrespondence: Ender Sir, Email endersir@gmail.comBackground: This study aims to use machine learning (ML) to explore predictive parameters related to the efficacy of caudal epidural pulsed radiofrequency (CEPRF) treatment for coccygodynia.Methods: Five different ML methods were used to predict treatment success at 6 months after CEPRF. …”
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Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption by Retno Wahyusari, Sunardi Sunardi, Abdul Fadlil

“…This research investigates how to accurately predict electrical energy consumption to address growing global energy demands. …”
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A Crime Data Analysis of Prediction Based on Classification Approaches by Fatima Shaker Hussain, Abbas Fadhil Aljuboori

“…Various machine learning algorithms on the dataset of Boston city crime are Decision Tree, Naïve Bayes and Logistic Regression classifiers have been used here to predict the type of crime that happens in the area. …”
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Seismic Events Prediction Using Deep Temporal Convolution Networks by Yue Geng, Lingling Su, Yunhong Jia, Ce Han

“…Results show that DCTCNN and CNN-LSTM are superior than the other five algorithms, and they successfully complete the seismic prediction task.…”
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Balancing the Parameters of Perforated Solar Screens to Optimize Daylight and Glare Performance in Office Buildings by Aya M.F. El-Bahrawy

“…The results showed a noticeable reduction in the overlit areas in the balanced solutions by 21-61% compared to the base case, outperforming independent parameter optimizations by up to 36%. …”
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Predictive model for initial response to first-line treatment in children with infantile epileptic spasms syndrome by Wenrong Ge, Lin Wan, Zong Wang, Lijun Fu, Guang Yang

“…Methods Using a dataset from our previously published research, we constructed and tested a predictive model for the initial response to first-line treatment in children with IESS. …”
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Immunological biomarkers and gene signatures predictive of radiotherapy resistance in non-small cell lung cancer by Jie Lv, Chun-yang Yu, Yao-zu Xiong, Ting-ting Dai, Xiao-chu Hu, Peng Pan, Shun Yue, Chang-hua Yu

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Predictive value of machine learning model based on CT values for urinary tract infection stones by Jiaxin Li, Yao Du, Gaoming Huang, Chiyu Zhang, Zhenfeng Ye, Jinghui Zhong, Xiaoqing Xi, Yawei Huang

“…Seven machine learning algorithms along with eleven preoperative variables were used to construct the prediction model. …”
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Strategies and Challenges for Unmanned Aerial Vehicle-Based Continuous Inspection and Predictive Maintenance of Solar Modules by Ghulam E. Mustafa Abro, Amjad Ali, Sufyan Ali Memon, Tayab Din Memon, Faheem Khan

“…Our review paper emphasizes instant functionality and practical insights for predictive maintenance, without necessitating complex computing resources or optimization tools. …”
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Artificial intelligence in clinical decision support and the prediction of adverse events by S. P. Oei, T. H. G. F. Bakkes, M. Mischi, R. A. Bouwman, R. A. Bouwman, R. J. G. van Sloun, S. Turco

“…This review focuses on integrating artificial intelligence (AI) into healthcare, particularly for predicting adverse events, which holds potential in clinical decision support (CDS) but also presents significant challenges. …”
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A Performance Analysis of Business Intelligence Techniques on Crime Prediction by Ivan, Niyonzima, Emmanuel Ahishakiye, Elisha Opiyo Omulo, Ruth Wario

“…There is a need to identify the most efficient algorithm that can be used in crime prediction given the past crime data. …”
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Optimizing Cloud Computing Performance With an Enhanced Dynamic Load Balancing Algorithm for Superior Task Allocation by Raiymbek Zhanuzak, Mohammed Alaa Ala'Anzy, Mohamed Othman, Abdulmohsen Algarni

“…Evaluations using CloudSim simulations demonstrate that the EDLB algorithm achieves substantial average improvements over benchmark algorithm and the-state-of-the-art algorithm, including a 59.46% reduction in total makespan, a 12.70% reduction in average makespan, a 22.46% reduction in execution time, and a 3.10% increase in resource utilisation. …”
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Enhancing Healthcare With WBAN and Digital Twins: A Machine Learning Approach for Predictive Health Monitoring by Rishit Mahapatra, Deepak Sethi, Kaushik Mishra

“…These parameters feed into the digital twins, further refining the predictive and diagnostic capabilities of the models. …”
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