Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model by Lei Shao, Quanjie Guo, Chao Li, Ji Li, Huilong Yan

“…Compared with the prediction results of EEMD-ARMA model, RNN model, LSTM model, and WOA-LSTM model, the combined prediction model optimized by whale bionics has less prediction error and higher prediction accuracy.…”
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AI illuminates paths in oral cancer by Devesh U. Kapoor, Pushpendra Kumar Saini, Narendra Sharma, Ankul Singh, Bhupendra G. Prajapati, Gehan M. Elossaily, Summya Rashid

“…The role of artificial intelligence in prognosis of oral cancer including predictive modeling, identification of prognostic factors and risk stratification also discussed significantly in this review. …”
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RuleKit2: Faster and simpler rule learning by Adam Gudyś, Cezary Maszczyk, Joanna Badura, Adam Grzelak, Marek Sikora, Łukasz Wróbel

“…Rules offer an invaluable combination of predictive and descriptive capabilities. Our package for rule-based data analysis, RuleKit, has proven its effectiveness in classification, regression, and survival problems. …”
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Dynamic Monitoring and Precision Fertilization Decision System for Agricultural Soil Nutrients Using UAV Remote Sensing and GIS by Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong

“…Real-time data processing algorithms enable rapid updates of soil nutrient status, while a time-series dynamic model captures seasonal variations and crop growth stage influences, improving prediction accuracy (RMSE reductions of 43–70% for nitrogen, phosphorus, and potassium compared to conventional laboratory-based methods and satellite NDVI approaches). …”
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Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach by Sara Dalmonte, Maria Adriana Cocozza, Dajana Cuicchi, Daniel Remondini, Lorenzo Faggioni, Paolo Castellucci, Andrea Farolfi, Emilia Fortunati, Alberta Cappelli, Riccardo Biondi, Arrigo Cattabriga, Gilberto Poggioli, Stefano Fanti, Gastone Castellani, Francesca Coppola, Nico Curti

“…Feature selection was performed using a novel approach derived from gene expression analysis, based on the DNetPRO algorithm. The prediction was done using a Support Vector Classifier (SVC) with a 10-fold cross-validation. …”
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A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features by Xuhui ZHANG, Yunkai CHI, Yuyang DU, Junying JIANG, Wenjuan YANG, Youjun ZHAO, Jicheng WAN, Yanqun WANG, Chenhui TIAN

“…The proposed method yielded a maximum error of 163 mm, indicating a 23.5% reduction compared to the 213 mm obtained using the PL-VINS algorithm. …”
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Harnessing Machine Learning for Intelligent Networking in 5G Technology and Beyond: Advancements, Applications and Challenges by Kristi Dulaj, Abdulraqeb Alhammadi, Ibraheem Shayea, Ayman A. El-Saleh, Mohammad Alnakhli

“…This research investigates ML approaches in 5G networks for adaptive spectrum usage, quality of service (QoS) management, predictive maintenance, and network optimization. By leveraging ML algorithms, 5G networks can forecast user behavior, allocate resources optimally, and dynamically adjust to changing conditions, enhancing performance and dependability. …”
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Adopting TOGAF Framework for Sustainable and Scalable Robusta Coffee Leaf Rust Management by Thein Oak Kyaw Zaw, Kalaiarasi Sonai Muthu Anbananthen, Saravanan Muthaiyah, Baarathi Balasubramaniam, Suraya Mohammad, Yunus Yusoff, Khairul Shafee Kalid

“…The framework leverages enterprise architecture principles to integrate learning algorithms, image detection, and systematic plantation mapping within a structured approach that enhances data organization, rust severity visualization, and predictive analysis. …”
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Optimizing additive manufacturing workflows using model-based systems engineering by Mohamed Amine Daoud, Khadija Bekkay Haouari, Meriem Hayani Mechkouri, Amine Ennawaoui, Hicham El hadraoui, Ilias Naser, Mustapha Ouardouz, Kamal Reklaoui

“…Future iterations of ASAM will incorporate advanced algorithms for real-time parameter optimization and predictive analytics. …”
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Artificial intelligence and machine learning in modern cardiology: Advancements in diagnosis, treatment and patient monitoring by Szymon Kopciał, Dawid Piecuch, Edyta Hańczyk, Karolina Kornatowska, Natalia Pawelec, Weronika Mazur

“…Description of the state of knowledge: AI and ML, including advanced neural networks and predictive analytics, demonstrate exceptional sensitivity and specificity in interpreting electrocardiograms (ECGs), echocardiograms, CT scans, and cardiac MRIs. …”
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A Comprehensive Overview of the Development and Research of Energy Savings of Electric Wheel Loader by Xiaotao Fei, Zuo Cheng, Shaw Voon Wong, Muhammad Amin Azman, Dawei Wang, Xiuxian Zhang, Qiuchen Shao, Qingqiu Lin

“…The analysis reveals that integrating optimal control strategies with machine learning algorithms—such as model predictive control (MPC)—is a critical pathway to achieving energy-efficient and assisted driving in next-generation EWLs. …”
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Artificial intelligence-based automated breast ultrasound radiomics for breast tumor diagnosis and treatment: a narrative review by Yinglin Guo, Ning Li, Chonghui Song, Juan Yang, Yinglan Quan, Hongjiang Zhang

“…Radiomics, an emerging field, focuses on extracting high-dimensional quantitative features from medical imaging data and utilizing them to construct predictive models for disease diagnosis, prognosis, and treatment evaluation. …”
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Enhanced Detection of Intrusion Detection System in Cloud Networks Using Time-Aware and Deep Learning Techniques by Nima Terawi, Huthaifa I. Ashqar, Omar Darwish, Anas Alsobeh, Plamen Zahariev, Yahya Tashtoush

“…We generate real DoS traffic, including normal, Internet Control Message Protocol (ICMP), Smurf attack, and Transmission Control Protocol (TCP) classes, and develop nine predictive algorithms, combining traditional machine learning and advanced deep learning techniques with optimization methods, including the synthetic minority sampling technique (SMOTE) and grid search (GS). …”
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The Role of Artificial Intelligence in Sports Analytics: A Systematic Review and Meta-Analysis of Performance Trends by Przemysław Pietraszewski, Artur Terbalyan, Robert Roczniok, Adam Maszczyk, Kajetan Ornowski, Daria Manilewska, Szymon Kuliś, Adam Zając, Artur Gołaś

“…Methods applied encompassed Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, reinforcement learning, and predictive modeling architectures. The pooled average classification accuracy was 87.78% (95% CI: 82.66–92.90), although substantial heterogeneity was observed across studies (I² = 93.75%). …”
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The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach by Shiqi Zhang, Shiqi Zhang, Jianan Jin, Benfeng Xu, Qi Zheng, Haibo Mou

“…IntroductionEpigenetic biomarkers are molecular indicators of epigenetic changes, and some studies have suggested that these biomarkers have predictive power for disease risk. This study aims to analyze the relationship between 30 epigenetic biomarkers and the risk of diabetes and cancer using machine learning modeling.MethodsThe data for this study were sourced from the NHANES database, which includes DNA methylation arrays and epigenetic biomarker datasets. …”
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Graph Split Federated Learning for Distributed Large-Scale AIoT in Smart Cities by Hanyue Xu, Kah Phooi Seng, Li-Minn Ang, Wei Wang, Jeremy Smith

“…The framework’s efficacy is validated through predictive modeling of autonomous vehicle passenger demand using real-world IoT data. …”
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Applications of fault-tolerant control system in the design of wind turbine generation systems: A comprehensive review and future prospects by Arslan Ahmed Amin, Muhammad Irfan, Turki Alsuwian, Saifur Rahman, Ansa Mubarak, Saba Waseem

“…The review discusses passive, active, and hybrid FTCS strategies alongside new technology like Model Predictive Control (MPC) and Adaptive Sliding Mode Control (ASMC), with active fault-tolerant control techniques showing a high level of fault detection and tolerance, especially in offshore wind turbines. …”
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Designing an Intelligent Scoring System for Crediting Manufacturers and Importers of Goods in Industry 4.0 by Mohsin Ali, Abdul Razaque, Joon Yoo, Uskenbayeva Raissa Kabievna, Aiman Moldagulova, Satybaldiyeva Ryskhan, Kalpeyeva Zhuldyz, Aizhan Kassymova

“…<i>Results</i>: Several metrics, including predictive credit risk, fraud detection, credit assessment accuracy, default rate comparison, loan approval rate comparison, and other important metrics affecting the credit card system, have been investigated to determine the effectiveness of modern credit card systems when using Blockchain technology and AI. …”
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Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach by Yehoon Jo, Mi-Yeon Shin, Sungkyoon Kim

“…The ML models provided robust predictive performance and novel insights into chemical and metabolic interactions, advocating for regulatory measures to reduce harmful exposures and integrate environmental factors into MetS prevention strategies.…”
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Identification and validation of glucocorticoid receptor and programmed cell death-related genes in spinal cord injury using machine learning by Feng Lu, Yingying Liu, Zhen Chen, Shuning Chen, Weidong Liang, Fuzhou Hua, Maolin Zhong, Lifeng Wang

“…Validation through qRT-PCR confirmed the differential expression of four genes (Abca1, Glipr1, Il10ra, and Cdh1), which demonstrated strong predictive performance. Pathway enrichment of GRRDEGs was analyzed using GO, KEGG, and Bayesian network methods, and immune cell infiltration was assessed via CIBERSORT. …”
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