A Systematic Literature Review of Concept Drift Mitigation in Time-Series Applications by Mujaheed Abdullahi, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Yahia Baashar, Abdussalam Ahmed Alashhab, Afroza Afrin

“…Machine Learning (ML) plays a key role in time-series applications because it analyzes observed data and predicts future values. The effectiveness of ML models in time-series forecasting is reduced by the occurrence of Concept Drift (CD). …”
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A Combined Approach Using T2*-Weighted Dynamic Susceptibility Contrast MRI Perfusion Parameters and Radiomics to Differentiate Between Radionecrosis and Glioma Progression: A Proof... by José Pablo Martínez Barbero, Francisco Javier Pérez García, David López Cornejo, Marta García Cerezo, Paula María Jiménez Gutiérrez, Luis Balderas, Miguel Lastra, Antonio Arauzo-Azofra, José M. Benítez, Antonio Jesús Láinez Ramos-Bossini

“…These features were combined into a single dataset and 14 classification algorithms were evaluated with GroupKFold cross-validation (k = 4). …”
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Diagnostic Utility of ¹⁸F-FDG PET/CT in Infective Endocarditis by Corina-Ioana Anton, Alice-Elena Munteanu, Mihaela Raluca Mititelu, Militaru Alexandru Ștefan, Cosmin-Alexandru Buzilă, Adrian Streinu-Cercel

“…PET/CT reclassified 13 patients from possible to definite IE, demonstrating an overall sensitivity of 83.3%, specificity of 93.7%, positive predictive value (PPV) of 83.3%, and negative predictive value (NPV) of 93.7%. …”
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Internet of Things Applications for Energy Management in Buildings Using Artificial Intelligence—A Case Study by Izabela Rojek, Dariusz Mikołajewski, Adam Mroziński, Marek Macko, Tomasz Bednarek, Krzysztof Tyburek

“…By using IoT sensors and smart meters, buildings can collect real-time data on energy usage patterns, occupancy, temperature, and lighting conditions.AI algorithms then analyze this data to identify inefficiencies, predict energy demand, and suggest or automate adjustments to optimize energy use. …”
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Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning by Junyang Ma, Junyang Ma, Shufu Hou, Xinxin Gu, Peng Guo, Jiankang Zhu

“…The random forest and Lasso algorithms were used to identify genes with diagnostic value, leading to nomogram construction. …”
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Comprehensive Review on the Control of Heat Pumps for Energy Flexibility in Distribution Networks by Gustavo L. Aschidamini, Mina Pavlovic, Bradley A. Reinholz, Malcolm S. Metcalfe, Taco Niet, Mariana Resener

“…Real-time optimization is achieved through model predictive control (MPC), which relies on a predictive model to optimize decisions over a time horizon, and reinforcement learning (RL), which takes a model-free approach by learning optimal strategies through direct interaction with the environment. …”
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Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach by Sakhr Alshwayyat, Mahmoud Bashar Abu Al Hawa, Mustafa Alshwayyat, Tala Abdulsalam Alshwayyat, Siya sawan, Ghaith Heilat, Hanan M. Hammouri, Sara Mheid, Batool Al Shweiat, Hamdah Hanifa

“…To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five Machine Learning (ML) algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of ML models. …”
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Prognostic significance of calcium-related genes in lung adenocarcinoma and the role of TNNC1 in macrophage polarization and erlotinib resistance by Jian Feng, Zhe Chen, Gaoming Wang, Yu Yao, Xuewen Min, Jing Luo, Kai Xie

“…Differences in immune infiltration and potential mechanisms in LUAD were explored using seven algorithms. The relationship between signature genes, chemotherapy sensitivity, and potential targeted therapies was evaluated. …”
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Leveraging computer-aided drug design for the discovery of phytohormone analogs: A review by Yuling Guo, Ghulam Qanmber, Zhao Liu, Han Yang, Jun Li, Zhikun Yang, Zhongxian Li, Linlin Yang, Zuoren Yang

“…These include the need for more accurate models and advanced algorithms to predict hormone-receptor interactions and metabolic pathways. …”
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Unmanned Aerial Vehicle Remote Sensing for Monitoring Fractional Vegetation Cover in Creeping Plants: A Case Study of <i>Thymus mongolicus</i> Ronniger by Hao Zheng, Wentao Mi, Kaiyan Cao, Weibo Ren, Yuan Chi, Feng Yuan, Yaling Liu

“…FVC estimation models were developed using four algorithms: multiple linear regression (MLR), random forest (RF), support vector regression (SVR), and artificial neural network (ANN). …”
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Selection of a Complex of Informative Features for Assessing the Internal Characteristics of Eggs for Consumption by Toncho Kolev, Mariya Georgieva-Nikolova, Miglena Kazakova, Danail Bonchev, Hristo Lukanov, Zlatin Zlatev

“…This paper concerns the methods and algorithms developed for tracking changes in the internal characteristics of eggs during storage that emphasize rapid and simple classification and satisfactory regression accuracy, considering technological requirements. …”
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Two-step consensus clustering approach to immune cell infiltration: An integrated exploration and validation of prognostic and immune implications in sarcomas by Ao-Yu Li, Jie Bu, Hui-Ni Xiao, Zi-Yue Zhao, Jia-Lin Zhang, Bin Yu, Hui Li, Jin-Ping Li, Tao Xiao

“…Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms were applied to decipher the immune cell infiltration landscape and TME profiles of sarcomas. …”
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Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs by Jinjun Li, Kai Zhao, Guotai Yang, Haohao Lv, Renxin Zhang, Shuhan Li, Zhiyuan Chen, Min Xu, Naixue Yang, Shaoxing Dai

“…For TCMbank, 714 potential senolytic compounds were predicted and 81 medicinal herbs with possible senolytic properties were identified. …”
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Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer by Minghan Jiang, Minghan Jiang, Zeyang Miao, Run Xu, Mengyao Guo, Xuefeng Li, Guanwu Li, Peng Luo, Su Hu, Su Hu

“…LASSO regression selected optimal features, followed by model construction via five algorithms (logistic regression, decision tree, random forest, SVM, AdaBoost). …”
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Differing Effects of Alcohol Use on Epigenetic and Brain Age in Adult Children of Parents with Alcohol Use Disorder by Jamie L. Scholl, Kami Pearson, Kelene A. Fercho, Austin J. Van Asselt, Noah A. Kallsen, Erik. A. Ehli, Kari N. Potter, Kathleen A. Brown-Rice, Gina L. Forster, Lee A. Baugh

“…We examined structural brain differences and applied machine learning algorithms to predict biological brain and DNA methylation ages to investigate differences and determine any accelerated aging between these groups. …”
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Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning by Fengmao Zhao, Xiangjun Liu, Jingang Gui, Hailang Sun, Nan Zhang, Yun Peng, Ming Ge, Wei Wang

“…Methods ML models were constructed and validated to predict prognosis and metastasis in MB patients. Eight algorithms were evaluated, and the optimal model was selected. …”
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Establishment of a prognostic model based on ER stress-related cell death genes and proposing a novel combination therapy in acute myeloid leukemia by Minghui Wang, Huajian Xian, Xiaoli Xia, Wenjie Zhang, Zixuan Huang, Chaoqun Lu, Yuling Zheng, Yixin Wang, Shufeng Xie, Renyao Pan, YaoYifu Yu, Ruiheng Wang, Huijian Zheng, Guorui Huang, Han Liu

“…Results The ERCDI demonstrated strong prognostic and predictive performance for prognosis in AML patients. …”
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Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning by Zuhui Pu, Zuhui Pu, Tony Bowei Wang, Ying Lu, Ying Lu, Zijing Wu, Zijing Wu, Yuxian Chen, Ziqi Luo, Xinyu Wang, Lisha Mou, Lisha Mou

“…We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). …”
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Emotional intelligence in management activities аnd artificial intelligence technologies by M. S. Laschenov, R. A. Bondarenko, I. V. Slomova

“…The article examines the role of emotional intelligence (emotional quotient) in the process of management activities and the competing position of artificial intelligence (hereinafter referred to as AI), which is trying to master it through learning algorithms and implementation in management practice. …”
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Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

“…Abstract This research shows the utilization of various tree-based machine learning algorithms with a specific focus on predicting Salicylic acid solubility values in 13 solvents. …”
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