Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm by El Assri Nasima, Ennejjar Mohammed, Jallal Mohammed Ali, Chabaa Samira, Zeroual Abdelouhab

“…The primary focus is on evaluating the performance of two prominent and widely-used machine learning algorithms: Artificial Neural Networks (ANN) and Random Forest (RF). …”
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Synergistic use of satellite, legacy, and in situ data to predict spatio-temporal patterns of the invasive Lantana camara in a savannah ecosystem by Lilly Theresa Schell, Emma Evers, Sarah Schönbrodt-Stitt, Konstantin Müller, Maximilian Merzdorf, Drew Arthur Bantlin, Insa Otte

“…In this study, we modeled the suitable habitat and potential distribution of the notorious invader Lantana camara in the Akagera National Park (1,122 km²), a savannah ecosystem in Rwanda. Spatiotemporal patterns of Lantana camara from 2015 to 2023 were predicted at a 30-m spatial resolution using a presence-only species distribution model, implementing a Random Forest classification algorithm and set up in the Google Earth Engine. …”
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Evaluation of K-Means Algorithm for Faulted Landforms Extraction and Offset Measurement With an Example From the Eastern Kunlun Fault by Shengchao Zhou, Zhou Lin

“…Although supervised deep learning methods have great potential for image recognition and segmentation, due to the absence of data sets, we apply the K-means algorithm, an easy and practical unsupervised machine learning method with minimal parameters, to extract displaced geomorphic markers. …”
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Optimizing Cancer Detection: Swarm Algorithms Combined with Deep Learning in Colon and Lung Cancer using Biomedical Images by HariKrishna Pathipati, Lova Naga Babu Ramisetti, Desidi Narsimha Reddy, Swetha Pesaru, Mashetty Balakrishna, Thota Anitha

“…Eventually, the whale optimization algorithm (WOA) is used to optimally choose the hyperparameters of the CNN‐BiGRU model. …”
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Identification of biomarkers related to Escherichia coli infection for the diagnosis of gastrointestinal tumors applying machine learning methods by Tingting Ge, Wei Wang, Dandan Zhang, Xubo Le, Lumei Shi

“…Conclusions: Overall, we identified and validated 8 robust genes related to E. coli applying bioinformatics and machine learning algorithms, providing theoretical foundations for the relationship between E. coli-related dysbiosis and gastrointestinal tumors.…”
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A Novel Local Binary Patterns-Based Approach and Proposed CNN Model to Diagnose Breast Cancer by Analyzing Histopathology Images by Mehmet Gul

“…The histopathology images improved with the QS-LBP method were then analyzed with the most commonly used Random Forest and Optimized Forest algorithms among machine learning algorithms. The BreaKHis dataset contains images with 40X, 100X, 200X, and 400X magnification resolutions and contains approximately 7924 images. …”
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Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm by Sandeep Samantaray, Abinash Sahoo, Falguni Baliarsingh

“…In order to simulate GWL, five data-driven (DD) models, including the hybridization of support vector regression (SVR) with two optimisation algorithms i.e., firefly algorithm and particle swarm optimisation (FFAPSO), SVR-FFA, SVR-PSO, SVR and Multilayer perception (MLP), have been examined in the present study. …”
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Machine learning based screening of biomarkers associated with cell death and immunosuppression of multiple life stages sepsis populations by Jie Yang, Fanyan Ou, Binbin Li, Lixiong Zeng, Qiuli Chen, Houyu Gan, Jianing Yu, Qian Guo, Jihua Feng, Jianfeng Zhang

“…This study, through the integrated application of computational biology and machine learning algorithms, discovered biomarkers of PCD patterns that affect cytokine storm-mediated inflammation and immunosuppressive effects in sepsis populations across different age groups (neonates, children, and adults). …”
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Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging by Damrongvudhi Onwimol, Pongsan Chakranon, Kris Wonggasem, Papis Wongchaisuwat

“…To simplify the analysis and reduce computational complexity, a subset of key spectral wavelengths was selected using a successive projection algorithm. Deep learning models were trained on these selected wavelengths to directly learn patterns from the raw spectral data. …”
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Clinical Characterization of Patients with Syncope of Unclear Cause Using Unsupervised Machine-Learning Tools: A Pilot Study by María-José Muñoz-Martínez, Manuel Casal-Guisande, María Torres-Durán, Bernardo Sopeña, Alberto Fernández-Villar

“…This study aims to explore the potential of unsupervised machine learning (ML), specifically clustering algorithms, to identify clinically meaningful subgroups within a cohort of 123 patients with SUC. …”
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Machine Learning-Based Differential Diagnosis of Parkinson’s Disease Using Kinematic Feature Extraction and Selection by Masahiro Matsumoto, Abu Saleh Musa Miah, Nobuyoshi Asai, Jungpil Shin

“…The final feature set is used for classification, achieving a classification accuracy of 66.67% for each dataset and 88.89% for each patient, with particularly high performance for the MSA and HC groups using the SVM algorithm. This system shows potential as a rapid and accurate diagnostic tool in clinical practice, though further data collection and refinement are needed to enhance its reliability.…”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…The clinical community has a lot of diabetes diagnostic data. Machine learning algorithms may simplify finding hidden patterns, retrieving data from databases, and predicting outcomes. …”
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Incorporating soil moisture data into a machine learning framework improved the predictive accuracy of corn yields in the U.S. by Bishwoyog Bhattarai, Zachary Leasor, André Fróes de Borja Reis

“…Understanding environmental factors that influence corn yield is crucial for improving crop management and designing more resilient cropping systems. Leveraging machine learning (ML) techniques capable of handling large-scale datasets offers a promising alternative for uncovering hidden patterns and generating actionable insights to improve crop yield. …”
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Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands Based on In Situ Internet of Things Monitoring by Jie Song, Yujun Yi

“…The elucidation of transport pattern and prediction of water and salt in estuarine wetland soils remain significant challenges. …”
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Development of a Diagnostic Model for Focal Segmental Glomerulosclerosis: Integrating Machine Learning on Activated Pathways and Clinical Validation by Ge Y, Liu X, Shu J, Jiang X, Wu Y

“…We then developed a highly accurate diagnostic model by integrating nine machine learning algorithms into 101 combinations, achieving near-perfect AUC values across training, validation, and external cohorts. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

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Integrating bioinformatics and machine learning to elucidate the role of protein glycosylation-related genes in the pathogenesis of diabetic kidney disease. by Ziyang Liu, Zengyuan Qin, Wenxin Bai, Shasha Wang, Chunling Huang, Na Li, Lei Yan, Yue Gu, Fengmin Shao

“…Functional enrichment, immune cell infiltration analysis, and machine learning algorithms (including feature selection for hub genes) were employed. qPCR validation was performed on clinical DKD and normal kidney tissues, and ROC curves were generated to assess diagnostic potential.…”
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Advancing Hydrogel-Based 3D Cell Culture Systems: Histological Image Analysis and AI-Driven Filament Characterization by Lucio Assis Araujo Neto, Alessandra Maia Freire, Luciano Paulino Silva

“…<b>Background:</b> Machine learning is used to analyze images by training algorithms on data to recognize patterns and identify objects, with applications in various fields, such as medicine, security, and automation. …”
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Predictive Modeling of Yoga's Impact on Venous Clinical Severity Scoring Using Gaussian Process Classification and Advanced Optimization Algorithms by Yazdan Ashgevari, Faranak Kazemi

“…The study focuses on individuals diagnosed with VCSS, using machine learning to analyze complex patterns in their clinical severity ratings before and during yoga practice. …”
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Fault Diagnosis of Train Bogie Bearing Based on Multi-scale Sample Entropy Improved Extreme Learning Machine by JIN Zhenzhen, HE Deqiang, MIAO Jian, XU Weichang

“…Finally, the feature vector set is divided into test set and training set, and the improved extreme learning machine is used as a pattern recognition algorithm for fault pattern recognition. …”
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