A Review on Current Technologies and Future Direction of Water Leakage Detection in Water Distribution Network by Mohammed Rezwanul Islam, Sami Azam, Bharanidharan Shanmugam, Deepika Mathur

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Design and optimization of distributed energy management system based on edge computing and machine learning by Nan Feng, Conglin Ran

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Learning Analytics: A Data Mining and Machine Learning Perspective by Salam Ullah Khan, Kifayat Ullah, Mahvash Arsalan Lodhi, Sadaqat Ali Khan Bangash

“… Tremendous proliferation in data generation in the past few years has paved the way for new research and the development of new and improved techniques and algorithms in different fields of science and education. …”
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Improving pediatric care in Uganda with a digital platform and quality improvement initiative: A retrospective review of Smart Triage + QI. by Rebecca Goertzen, Yashodani Pillay, James Karugaba, Ivan Aine Aye Ishebukara, Ahmad Asdo, Dustin Dunsmuir, Justine Behan, Charly Huxford, Stefanie K Novakowski, Fredson Tusingwire, Ronald Kasyaba, Gloria Kakuru, John Khisa, Stephen Businge, Mike Kyewalyanga, Niranjan Kissoon, J Mark Ansermino

“…<h4>Methods</h4>Smart Triage is a risk prediction algorithm and digital platform that enables healthcare workers to triage patients and track treatments effectively. …”
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Location Design of Electrification Road in Transportation Networks for On-Way Charging by Yue Qiu, Yuchuan Du, Shanchuan Yu, Shengchuan Jiang

“…We develop a modified active set algorithm to solve the MPEC model. Numerical experiments are presented to demonstrate the performance of the model and the solution algorithm and analyze the impact of charging efficiency, battery size, and comfortable range.…”
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Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer by Longpeng Li, Longpeng Li, Jinfeng Zhao, Yaxin Wang, Zhibin Zhang, Wanquan Chen, Jirui Wang, Yue Cai

“…PCDRS, consisting of seven key genes, showed robust predictive ability over other signatures in different datasets. …”
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Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets by Weichuan Xiong, Yian Zhan, Rui Xiao, Fangpeng Liu

“…Prediction models were constructed and validated using six machine learning algorithms, achieving high accuracy (AUC > 0.75). …”
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Age- and Post-Based Complex Analyses of Heart Rate Variability in Young Male Handball Players for Potential Prevention of Overload-induced Injuries by Zita Petrovszki, Olivér Czimbalmos, Vera Gal, Gabor Korosi, Edit Nagy, Rita Mikulan, Gyongyi Horvath

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Performance analysis of advanced deep learning technics: Application to solar energy forecasting and management in several cities in Chad by Osée Mounkang, Claude Vidal Aloyem Kaze, Nzoko Tayo Dieudonné, Ghislain Junior Bangoup Ntegmi, Duclair Paul Edouard Pountounynyi, Hervice Roméo Fogno Fotso, Armel Zambou Kenfack, Germaine Kenmoe Djuidje, René Tchinda

“…This study proposes a hybrid artificial intelligence model that combines LLM and LSTM methods, utilizing the Adam optimization algorithm to make hourly solar radiation predictions over a seven-day period. …”
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Climatic suitability and potential distribution of earleaf acacia and its candidate biological control agent Trichilogaster sp by Sara S. MacDonald, Emily J. Le Falchier, Lindsay P. Campbell, Ryan Zonneveld, Carey R. Minteer

“…Both models exhibit high predictive accuracy, with AUC values above 0.9. This study emphasizes the need for continued monitoring and management of ELA and evaluates areas in Florida predicted to have suitable climatic conditions for Trichilogaster sp. nov. …”
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Evolution of the Swiss pork production systems and logistics: the impact on infectious disease resilience by Francesco Galli, Saskia Perret-Gentil, Antoine Champetier, Rita Lüchinger, Myriam Harisberger, Thibault Kuntzer, Stefan Rieder, Christina Nathues, Beatriz Vidondo, Hartmut Lentz, Vitaly Belik, Salome Dürr

“…For Swiss pig farms, we used prediction and clustering algorithms to classify 9’687 − 11’247 trading farms between 2014 and 2019 by one of eleven production types. …”
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Brugia malayi excreted/secreted proteins at the host/parasite interface: stage- and gender-specific proteomic profiling. by Sasisekhar Bennuru, Roshanak Semnani, Zhaojing Meng, Jose M C Ribeiro, Timothy D Veenstra, Thomas B Nutman

“…Moreover, this analysis was able to confirm the presence of 274 "hypothetical" proteins inferred from gene prediction algorithms applied to the B. malayi (Bm) genome. …”
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A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…The first section addresses process prediction, showcasing how AI techniques predict welding outcomes using historical data and process parameters, which enhances decision-making prior to actual implementation. …”
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Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods by Bilal Cemek, Yunus Kültürel, Emirhan Cemek, Erdem Küçüktopçu, Halis Simsek

“…This study compares two modeling approaches for predicting soil temperature at various depths: (i) fuzzy logic-based systems, including the Mamdani fuzzy inference system (MFIS) and the adaptive neuro-fuzzy inference system (ANFIS); (ii) supervised machine learning algorithms, such as multilayer perceptron (MLP), support vector regression (SVR), random forest (RF), extreme gradient boosting (XGB), and k-nearest neighbors (KNN), along with multiple Linear regression (MLR) as a statistical benchmark. …”
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Chaotic billiards optimized hybrid transformer and XGBoost model for robust and sustainable time series forecasting by Reham H. Mohammed, Asmaa Mohamed El-saieed

“…Abstract Accurate wind speed forecasting plays a key role in supporting renewable energy systems, improving flight safety, and enhancing weather prediction. However, the variability and non-stationary nature of wind patterns make reliable forecasting a difficult task. …”
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Study of machine learning techniques for outcome assessment of leptospirosis patients by Andreia Ferreira da Silva, Karla Figueiredo, Igor W. S. Falcão, Fernando A. R. Costa, Marcos César da Rocha Seruffo, Carla Cristina Guimarães de Moraes

“…In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. …”
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Identification and validation of biomarkers associated with glycolysis in polycystic ovarian syndrome by Rongyan Zhu, Xiao Yu, Yulan Li

“…Utilizing publicly available datasets, biomarkers were identified via differential analysis, various PPI algorithms, and validation of expression patterns. Subsequent analyses included functional enrichment, tissue and cell-specific expression profiling, m6A modification site prediction, compound screening, molecular network construction, and molecular docking. …”
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Behavior Modeling and Bio-Hybrid Systems: Using Reinforcement Learning to Enhance Cyborg Cockroach in Bio-Inspired Swarm Robotics by Le Minh Triet, Nguyen Truong Thinh

“…The RL framework achieves high prediction accuracy and control fidelity, significantly enhancing the operational capabilities of the bio-hybrid system. …”
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The interpretable machine learning model for depression associated with heavy metals via EMR mining method by Site Xu, Mu Sun

“…The optimal model was selected after parameter tuning with a Genetic Algorithm (GA). To enhance the interpretability of the model’s predictions, we applied SHapley Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithms. …”
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Target identification of natural products in cancer with chemical proteomics and artificial intelligence approaches by Guohua Li, Qian Shi, Qibiao Wu, Xinbing Sui

“…Recent advances in artificial intelligence (AI) have further enhanced the field by improving target prediction and streamlining data analysis. AI-driven models, especially machine learning algorithms, have proven effective in processing complex proteomic data and predicting potential NP-protein interactions. …”
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