Moisture prediction in chicken litter using hyperspectral data and machine learning by Ahmad Tulsi, Abdul Momin, Victoria Ayres

“…These results reveal a key insight: preprocessing enhances linear model accuracy but may suppress critical non-linear patterns needed by tree-based models like XGBoost. …”
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Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

“…These findings underscore the effectiveness of ML in Urban Building Energy Modelling (UBEM), particularly in forecasting energy consumption patterns and identifying high-energy-use buildings. …”
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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…This study aims to identify the best deep learning and machine learning models to identify the unethical behavior patterns of learners using distance education exam data of an educational institution. …”
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Design and Simulation of a Semi–Automatic Paver Block Making Machine. by Wamani, Samuel

“…The pedestrian streets and squares look more attractive when pavers of various patterns and hues are used. The goal of this project is to develop and build an automatic paver block production machine that can effectively and economically create high-quality paver blocks. …”
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Analysis of Partial Discharge Measurements using Coupling Capacitor in Rotating Machine by Ahmad Syukri Abd Rahman, Mohamad Nur Khairul Hafizi Rohani, Nur Dini Athirah Gazata, Afifah Shuhada Rosmi, Ayob Nazmi Nanyan, Aiman Ismail Mohamed Jamil, Mohd Helmy Halim Abdul Majid, Normiza Masturina Samsuddin

“…Additionally, this paper provides a detailed study of PD signal characteristics, specifically focusing on phase-resolved partial discharge (PRPD) patterns, to understand the behavior of PD in HV rotating machines, enhancing fault diagnosis and preventive maintenance strategies.…”
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Advancing agriculture with machine learning: a new frontier in weed management by Mohammad MEHDIZADEH, Duraid K. A. AL-TAEY, Anahita OMIDI, Aljanabi Hadi Yasir ABBOOD, Shavan ASKAR, Soxibjon TOPILDIYEV, Harikumar PALLATHADKA, Renas Rajab ASAAD

“…This review examines the potential of machine learning in chemical weed management. Machine learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate predictions. …”
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Interpretable Machine Learning reveals the Role of PANoptosis in the Diagnosis and Subtyping of NAFLD by Feng Tian, Yuqi Zhao, Xinyang He, Yu Zhang, Minxuan Hu, Yiwei Liang, Ziyou Tian, Yaxian Gao, Yongwei Wang

“…Unsupervised consensus clustering stratified NAFLD patients into two PANoptosis-driven subtypes (C1/C2 and CA/CB), revealing distinct immune cell infiltration patterns and pathway activation. Single-cell sequencing further localized hub genes to immune cells and revealed their cell communication, implicating their roles in the progression of NAFLD. …”
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Practical Recommendations for Artificial Intelligence and Machine Learning in Antimicrobial Stewardship for Africa by Tafadzwa Dzinamarira, Elliot Mbunge, Claire Steiner, Enos Moyo, Adewale Akinjeji, Kaunda Yamba, Loveday Mwila, Claude Mambo Muvunyi

“…The deployment of AI‐driven solutions presents unprecedented opportunities for optimizing treatment regimens, predicting resistance patterns, and improving clinical workflows. However, successfully integrating these technologies into Africa's health systems faces considerable obstacles, including limited human capacity and expertise, widespread public distrust, insufficient funding, inadequate infrastructure, fragmented data sources, and weak regulatory and policy enforcement. …”
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Live Weight Prediction in Norduz Sheep Using Machine Learning Algorithms by Cihan Çakmakçı

“…There were no differences between the means of actual and predicted LWs by machine learning models. The fact that the models generalized well on the testing data sets indicates that machine learning algorithms have valid predictive patterns and are effective methods in LW weight of Norduz sheep. …”
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Compositional and Machine Learning Tools to Model Plant Nutrition: Overview and Perspectives by Léon Etienne Parent

“…Using a Brazilian onion (<i>Allium cepa</i>) database, the combined CoDa and ML methods returned crop response patterns, allowing feature-specific fertilizer recommendations to be made. …”
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Understanding EMS response times: a machine learning-based analysis by Peter Hill, Jakob Lederman, Daniel Jonsson, Peter Bolin, Veronica Vicente

“…Advanced ML techniques, including Gradient Boosting models, were applied to evaluate the influence of diverse variables such as call handling times, travel times, weather patterns, and resource availability. Feature engineering was employed to extract meaningful insights, and statistical models were used to validate the relationships between key predictors and response times. …”
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Design and Performance Testing of Seed Potato Cutting Machine with Posture Adjustment by Yingsi Wu, Xiangming La, Xuan Zhao, Fei Liu, Jianguo Yan

“…Three types of seed potato cutters were developed based on the distribution patterns of bud eyes. The movement mechanism of the posture adjustment process was analyzed, and a mathematical model was established. …”
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Enhancing Electricity Consumption Forecasting in The Republic of Kazakhstan Using Machine Learning by Vladimir Madin, Olga Salykova, Irina Ivanova, Olga Bizhanova, Dinara Aldasheva

“…The results indicate that while traditional statistical models effectively capture seasonal patterns, machine learning-based approaches, particularly LSTM, demonstrate superior performance in identifying complex nonlinear trends. …”
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Application of machine learning in acute upper gastrointestinal bleeding: bibliometric analysis by Qun Li, Guolin Chen, Qiongjie Li, Dongna Guo

“…BackgroundIn the past decade, the application of machine learning (ML) in the clinical management of acute upper gastrointestinal bleeding (AUGIB) has received much attention and has become a hot research topic. …”
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A machine learning model for early detection of sexually transmitted infections by Juma Shija, Judith Leo, Elizabeth Mkoba

“…Also, it can provide insights into infection patterns, allowing practitioners to adapt their responses. …”
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Machine learning assisted estimation of total solids content of drilling fluids by B.T. Gunel, Y.D. Pak, A.Ö. Herekeli, S. Gül, B. Kulga, E. Artun

“…In contrast to human-intensive methods, machine learning may offer a viable alternative for solids content estimation due to its pattern-recognition capability. …”
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EMG-based body–machine interface for targeted trunk muscle activation by Carolina Correia, Andrea Bandini, Silvestro Micera, Sara Moccia

“…We hypothesize that by visually guiding users to track these generated trajectories, the BoMI could evoke the EMG patterns implicitly encoded within them. Tested with neurotypical individuals, the BoMI effectively elicited the desired trunk EMG profiles, achieving a mean similarity index of 0.82 ± 0.13, a correlation coefficient of 0.95 ± 0.03, and minimal timing mismatches. …”
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Machine learning-based predictive modelling of mental health in Rwandan Youth by Fauste Ndikumana, Josias Izabayo, Joseph Kalisa, Mathieu Nemerimana, Emmanuel Christian Nyabyenda, Sylivain Hirwa Muzungu, Isaac Komezusenge, Melissa Uwase, Similien Ndagijimana, Celestin Twizere, Vincent Sezibera

“…Abstract Globally, mental disorders are a significant burden, particularly in low- and middle-income countries, with high prevalence in Rwanda, especially among survivors of the 1994 genocide against Tutsi. Machine learning offers promise in predicting mental health outcomes by identifying patterns missed by traditional methods. …”
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Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning by Renata Retkute, Kathleen S. Crew, John E. Thomas, Christopher A. Gilligan

“…For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. …”
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Machine Learning Reveals Aneuploidy Characteristics in Cancers: The Impact of BEX4 by Aizhong Xu, Jianjun Liu, Li Tong, Tingting Shen, Songlin Xing, Yujie Xia, Bosen Zhang, Zihao Wu, Wenkang Yuan, Anhai Yu, Zijie Kan, Wenqi Yang, Chao Zhang, Chong Zhang

“…Methods: Patients from The Cancer Genome Atlas (TCGA) were stratified into high-score (training) and low-score (control) groups based on the aneuploidy score. Mfuzz expression pattern clustering and functional enrichment were applied to genes with BEX4 as the core to explore their regulatory mechanisms. …”
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