Climate change reduces and shifts suitable habitats of Uapaca kirkiana Müll. Arg. to higher altitudes in Malawi by Bruno Kokouvi Kokou, Msiska Ulemu, Wouyo Atakpama, Séverin Biaou, Soloum Clément Teteli, Kouagou Raoul Sambieni, Frank Mnthambala, Tembo Mavuto, Paul Munyenyembe, Florent Noulèkoun

“…The maximum entropy (MaxEnt) algorithm was used to model the current and future distribution of the species based on 21 environmental variables and 480 species occurrences. …”
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Evaluation of AI-Powered Routine Screening of Clinically Acquired cMRIs for Incidental Intracranial Aneurysms by Christina Carina Schmidt, Robert Stahl, Franziska Mueller, Thomas David Fischer, Robert Forbrig, Christian Brem, Hakan Isik, Klaus Seelos, Niklas Thon, Sophia Stoecklein, Thomas Liebig, Johannes Rueckel

“…<b>Results</b>: The algorithm demonstrates high sensitivities (100% for findings >4 mm in diameter), a 17.8% MRA alert rate and positive predictive values of 11.5–43.8% (depending on whether inconclusive findings are considered or not). …”
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A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): initial benchmark results and comprehensive analysis by Seref Sagiroglu, Ramazan Terzi, Emrah Celtikci, Alp Özgün Börcek, Yilmaz Atay, Bilgehan Arslan, Mustafa Caglar Sahin, Kerem Nernekli, Umut Demirezen, Okan Bilge Ozdemir, Kevser Özdem Karaca, Nuh Azgınoğlu

“…ROI and whole tumor segmentations were successfully performed and compared with seven algorithms with accuracies of 87.61% and 97.18%. The Grad-CAM model also demonstrated satisfactory accuracy across the tests that were conducted. …”
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Apple Watercore Grade Classification Method Based on ConvNeXt and Visible/Near-Infrared Spectroscopy by Chunlin Zhao, Zhipeng Yin, Yushuo Tan, Wenbin Zhang, Panpan Guo, Yaxing Ma, Haijian Wu, Ding Hu, Quan Lu

“…These images served as input for training and prediction using the ConvNeXt deep convolutional neural network. …”
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A Multi-Objective PSO-GWO Approach for Smart Grid Reconfiguration with Renewable Energy and Electric Vehicles by Tung Linh Nguyen, Quynh Anh Nguyen

“…Conversely, the Grey Wolf Optimization algorithm excels in global exploration, offering robust mechanisms to circumvent local optima traps. …”
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Information support for decision making in problem situation description by S. F. Lipnitsky

“…As an implementation of the proposed model, the following algorithms have been developed: an algorithm for creating a dictionary of communicative fragments; algorithms for creating fragment-slot templates for sentences, texts and subject areas; an algorithm of user information support. …”
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Electricity nowcasting and forecasting efficiency in blockchain-integrated energy trading platforms by Ameni Boumaiza

“…Our methodology integrates real-time nowcasting algorithms and machine learning-based forecasting models to optimize electricity distribution, battery storage utilization, and cost efficiency within the marketplace. …”
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Artificial intelligence survival models for identifying relevant risk factors for incident diabetes in Azar cohort population by Neda Gilani, Mohammadhossein Somi, Farzaneh Hamidi, Pasqualina Santaguida, Elnaz Faramarzi, Reza Arabi Belaghi

“…In contrast, the RF analysis identified 21 important variables predicting a higher probability of having diabetes. …”
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Research Status and Development Trends of Deep Reinforcement Learning in the Intelligent Transformation of Agricultural Machinery by Jiamuyang Zhao, Shuxiang Fan, Baohua Zhang, Aichen Wang, Liyuan Zhang, Qingzhen Zhu

“…From the perspective of algorithm–hardware synergy, the article provides an in-depth analysis of DRL’s specific applications in agricultural ground platform navigation, path planning for intelligent agricultural end-effectors, and autonomous operations of low-altitude unmanned aerial vehicles. …”
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Adaptive Differential Privacy Cox-MLP Model Based on Federated Learning by Jie Niu, Runqi He, Qiyao Zhou, Wenjing Li, Ruxian Jiang, Huimin Li, Dan Chen

“…The first, LS-ADP, utilizes layer sensitivity to assess the influence of individual layer weights on model performance and develops an adaptive differential privacy algorithm. The second, ROW-DP, comprehensively assesses weight variations and absolute values to propose a random one-layer weighted differential privacy algorithm. …”
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Exploration of shared pathogenic factors and causative genes in early-stage endometrial cancer and osteoarthritis by Yiyun Bai, Sang Luo, Ruzhen Shuai, Xiaomei Zhang, Liwei Yuan, Dan Liu

“…Genes with diagnostic value were identified using multiple machine learning algorithms to construct EC prediction models and evaluate their performance. …”
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Statistical modeling and application of machine learning for antibiotic degradation using UV/persulfate-peroxide based advanced oxidation process by Musfekur Rahman Dihan, Md. Ashraful Alam, Surya Akter, Md. Abdul Gafur, Md. Shahinoor Islam

“…Pearson correlation and statistical multivariate linear regression (MLR) were applied to model the removal% and pHfinal of both antibiotics, along with the three machine learning algorithms, Artificial neural network (ANN), support vector machine (SVM), and Random Forest (RF), to make the same predictions. …”
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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…Models leveraged the patients' characteristics, the severity of COVID-19 at diagnosis, and the calculated proportion of days on different treatment combinations after diagnosis as features to predict the outcome. Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model's final outcome prediction.…”
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The Growing Impact of Natural Language Processing in Healthcare and Public Health by Aadit Jerfy, Owen Selden, Rajesh Balkrishnan PhD

“…Automating labor intensive and tedious tasks with language processing algorithms, using text analytics systems and machine learning to analyze social media data and extracting insights from unstructured data allows for better public sentiment analysis, enhancement of risk prediction models, improved patient communication, and informed treatment decisions. …”
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Visual analysis of research trends in diabetes-associated dry eye via bibliometrics by Zhe Yang, Jia-Yi Jiang, Xiao-Hui Zhang

“…With high prevalence of dry eye in Asia, valuable resources like the Korea National Health and Nutrition Examination Survey (KNHANES) database offer crucial data for developing risk prediction models for DADE. Building risk prediction models using machine learning algorithms is a promising future research direction, enabling physicians to identify high-risk individuals and implement early interventions.…”
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Assessment of copeptin and obestatin levels in coronary artery disease patients by Saraa Ali, Fatma F. Abdel Hamid, Samia Hussein, Shaimaa Wageeh, Doaa M. Ibrahim

“…They are currently assessed clinically through myocardial enzymes, electrocardiography, risk score algorithms, and angiography. It is important to find a suitable marker to differentiate them from other causes of chest pain. …”
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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

“…This study investigates machine learning and deep learning algorithms for seizure prediction, comparing their effectiveness on a large EEG dataset of epileptic patients. …”
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Hybrid Machine Learning-Based Fault-Tolerant Sensor Data Fusion and Anomaly Detection for Fire Risk Mitigation in IIoT Environment by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Shashi Bhushan, Vinay Rishiwal, Manish Kumar

“…The proposed approach also deploys machine learning algorithms to dynamically adjust probabilistic models based on real-time sensor reliability, thereby improving prediction accuracy even in the presence of unreliable sensor data. …”
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Modeling forest canopy structure and developing a stand health index using satellite remote sensing by Pulakesh Das, Parinaz Rahimzadeh-Bajgiran, William Livingston, Cameron D. McIntire, Aaron Bergdahl

“…The plot-level data were used to develop regression models for LAI and LCR estimation using microwave (Sentinel-1) and optical (Sentinel-2) remote sensing data and applying the Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms. The RF model showed higher prediction accuracy than the SVM model at the site level. …”
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Machine learning based on patch antenna design and optimization for 5 G applications at 28GHz by Md․Sohel Rana, Sheikh Md․ Rabiul Islam, Sanjukta Sarker

“…The efficiency of the design-I was 89.66 %, and design-II was, 52.65 %. In this paper, a predictive model was developed using a polynomial regression algorithm to create a predictive model for the designed antenna. …”
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