Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning by Nehir Uyar, Azize Uyar

“…Specifically, GBT and RF achieved the highest R² value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. …”
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Analyzing the Impact of Geoenvironmental Factors on the Spatiotemporal Dynamics of Forest Cover via Random Forest by Hendaf N. Habeeb, Yaseen T. Mustafa

“…This study investigates the spatiotemporal dynamics of forest cover in the Duhok District in the Kurdistan Region of Iraq over a decade (2013–2023), emphasizing the impact of geoenvironmental factors via Random Forest algorithms and Landsat data. This research integrates datasets including fractional vegetation cover (FVC), groundwater levels, climate data, topography, and soil moisture data, offering a comprehensive analysis of the factors influencing forest cover. …”
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Exploring Brain Activity in Different Mental Cognitive Workloads by Sahar Oftadeh Balani, Ali Fawzi Al-Hussainy, Alhan Abd Al-Hassan Shalal, Mohammed Ubaid, Zinab Aluquaily, Jaafar Alamoori, Saeid Motevalli

“…These changes in connectivity and complexity facilitate the integration of multiple cognitive processes essential for effective arithmetic problem-solving. …”
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Development of a host-signature-based machine learning model to diagnose bacterial and viral infections in febrile children by Fang Bai, Zelong Gong, Dong Cui, Xiaomei Zhang, Wenteng Hong, Yi Gao, Kai Lin, Weijie Chen, Lu Li, Juan Huang, Biying Zheng, Junfa Xu, Na Xiao

“…By utilizing the transformed value RefValue (i) of these five genes, the RF model achieved an AUC of 0.9917 in training and 0.9517 in testing for diagnosing B/V infection in children. …”
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Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis by Yi Xu, Ying Mao, Xiaoting Hua, Yan Jiang, Yi Zou, Zhichao Wang, Zubi Liu, Hongrui Zhang, Lingling Lu, Yunsong Yu

“…By constructing training and test datasets on all SNPs, intersected SNPs, and randomly generated SNPs, we developed a Machine learning (ML) framework using twelve different algorithms. Then, we compared the performances of the various ML models and used the SHapley Additive exPlanations (SHAP) framework to decipher why and how decisions are made within the optimal algorithm. …”
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A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data by Jordi Mahardika Puntu, Ping-Yu Chang, Haiyina Hasbia Amania, Ding-Jiun Lin, M. Syahdan Akbar Suryantara, Jui-Pin Tsai, Hwa-Lung Yu, Liang-Cheng Chang, Jun-Ru Zeng, Lingerew Nebere Kassie

“…A key contribution of this work is the rigorous harmonization of these data sets, ensuring consistent resistivity values across different methods before constructing the 3D resistivity model, addressing a gap in previous studies that typically handled these data sets separately, either building models individually or comparing results side-by-side without fully integrating the data. …”
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An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph by Jian He, Yanling Wu, Linxi Yuan, Jiangguo Qiu, Menglong Li, Xuemei Pu, Yanzhi Guo

“…Following the extraction of graph features by utilizing graph embedding algorithms, our next step was the retrieval of the attributes of individual gene and drug nodes. …”
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Improving agricultural commodity allocation and market regulation: a novel hybrid model based on dual decomposition and enhanced BiLSTM for price prediction by Lihua Zhang, Fushun Wang, Fushun Wang, Kejian Wang, Kejian Wang, Zhenxue He, Zhenxue He, Chen Chen, Chen Chen, Jiahao Liu, Jiahao Liu, Chao Wang, Chao Wang, Zhe Wang

“…This paper proposes a sustainable hybrid model SV-PSO-BiLSTM which integrates Seasonal-Trend decomposition procedure based on Loess (STL), Variational Mode Decomposition (VMD), Particle Swarm Optimization (PSO), and Bidirectional Long Short-Term Memory (BiLSTM) neural networks. …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

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Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach by Fetlework Gubena Arage, Zinabu Bekele Tadese, Eliyas Addisu Taye, Tigist Kifle Tsegaw, Tsegasilassie Gebremariam Abate, Eyob Akalewold Alemu

“…These algorithms were employed to predict cervical cancer screening uptake. …”
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Leveraging machine learning in nursing: innovations, challenges, and ethical insights by Sophie So Wan Yip, Sheng Ning, Niki Yan Ki Wong, Jeffrey Chan, Kei Shing Ng, Bernadette Oi Ting Kwok, Robert L. Anders, Simon Ching Lam

“…Aim/objectiveThis review aims to provide a comprehensive analysis of the integration of machine learning (ML) (1) in nursing by exploring its implications on patient care, nursing practices, and healthcare delivery. …”
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UAVs’ Flight Dynamics Is All You Need for Wind Speed and Direction Measurement in Air by Sihong Zhu, Tonghui Zhao, Huanji Zhang, Yichao Chen, Dongxu Yang, Yi Liu, Junji Cao

“…The proposed method leverages data from sensors onboard UAV platforms, combined with advanced ML algorithms trained on ground-truth measurements obtained through high-resolution LiDAR systems. …”
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Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud by Chiranjit Singha, Kishore Chandra Swain, Satiprasad Sahoo, Ayad M. Fadhil Al-Quraishi, Joseph Omeiza Alao, Hussein Almohamad, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…As per the Boruta algorithm, 16 attributes were deemed important, while 14 attributes had less influence on AGB. …”
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Optimizing XGBoost Hyperparameters for Credit Scoring Classification Using Weighted Cognitive Avoidance Particle Swarm by Atul Vikas Lakra, Sudarson Jena, Kaushik Mishra

“…The optimal hyperparameter values for the XGBoost model can vary significantly depending on the specific problem at hand. …”
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Neural network prediction model based on Levy flight and natural biomimetic technology for its application in cancer prediction. by Ruiyu Zhan

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”
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Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy by Junlang Yuan, Ke Yang, Taiwei Yang, Haoran Xu, Ting Xiong, Shidong Fan

“…Subsequently, five machine learning algorithms, such as RF and XGBoost, are used in combination with a grid search to find the optimal hyperparameters, and Lasso is used as the meta-learner to integrate the prediction results. …”
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An explainable predictive machine learning model for axillary lymph node metastasis in breast cancer based on multimodal data: A retrospective single-center study by Yuxi liu, Yunfeng Wu, Qing Xia, Hao He, Haining Yu, Ying Che

“…Ten machine learning algorithms—including Naïve Bayes, Random Forest, Logistic Regression, and Support Vector Machines—were implemented to construct predictive models. …”
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Diet Engine: A real-time food nutrition assistant system for personalized dietary guidance by Asim Moin Saad, Md. Raihanul Haque Rahi, Md. Manirul Islam, Gulam Rabbani

“…Moreover, a personalized chatbot provides diet advice, meal recommendations, and fitness suggestions. By seamlessly integrating advanced deep learning algorithms with user-centric features, this study underscores the transformative potential of Diet Engine in fostering healthier eating habits, raising nutritional awareness, and contributing to a global shift toward more informed and sustainable lifestyle choices.…”
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Infrared Small Target Detection Based on Compound Eye Structural Feature Weighting and Regularized Tensor by Linhan Li, Xiaoyu Wang, Shijing Hao, Yang Yu, Sili Gao, Juan Yue

“…Current single-aperture small target detection algorithms fail to exploit the spatial relationships among compound eye apertures, thereby underutilizing the inherent advantages of compound eye imaging systems. …”
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Recent Trends in Proxy Model Development for Well Placement Optimization Employing Machine Learning Techniques by Sameer Salasakar, Sabyasachi Prakash, Ganesh Thakur

“…Well placement optimization refers to the identification of optimal locations for wells (producers and injectors) to maximize net present value (NPV) and oil recovery. It is a complex challenge in all phases of production (primary, secondary and tertiary) of a reservoir. …”
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