4-D ERT monitoring of oil spill contamination dynamics and remediation planning in southern Nigeria by Stanley Uchechukwu Eze, Difference Odeyovwi Ogagarue, Juyeon Jeong, Myung Jin Nam, Omafume M. Orji, Williams N. Ofuyah

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Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals by Shuting Xu, Ravinesh C. Deo, Oliver Faust, Prabal D. Barua, Jeffrey Soar, Rajendra Acharya

“…Model training and validation were performed using 5-fold cross-validation, wherein the dataset was randomly divided into five folds and the models were trained and tested iteratively to ensure robust performance. …”
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Analisis Sentimen Berbasis Aspek Pada Ulasan Pelanggan Restoran Menggunakan Algoritma Support Vector Machine (Studi Kasus: Depot Bamara) by Muhammad Fariz Firdaus, Dian Eka Ratnawati, Nanang Yudi Setiawan

“…Abstract Customer reviews play an important role in the evaluation of restaurant products and services. Customer reviews can help restaurants identify their strengths and weaknesses. …”
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Prognostic analysis of sepsis-induced myocardial injury patients using propensity score matching and doubly robust analysis with machine learning-based risk prediction model develo... by Pan Guo, Pan Guo, Li Xue, Fang Tao, Kuan Yang, YuXia Gao, Chongzhe Pei

“…This study aimed to evaluate the prognostic impact of SIMI and develop validated predictive models using advanced machine learning (ML) algorithms for identifying SIMI in critically ill sepsis patients.MethodsData were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v3.0) database. …”
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IMPLEMENTATION OF LEARNING MANAGEMENT SYSTEMS WITH GENERATIVE ARTIFICIAL INTELLIGENCE FUNCTIONS IN THE POST-PANDEMIC ENVIRONMENT by Denis-Cătălin Arghir

“…Emphasizing flexibility and adaptability, the proposed LMS caters to various training levels, being able to accommodate the needs of learners across different domains. …”
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Improved food recognition using a refined ResNet50 architecture with improved fully connected layers by Pouya Bohlol, Soleiman Hosseinpour, Mahmoud Soltani Firouz

“…This model with Adam optimizer, 10−3 initial learning rate, batch size 4, and image size 340 × 640 could recognize various foods with 97.25% accuracy and 0.2 loss. Response time and training time of this architecture compared to other algorithms were confidential; the training process and response time were 5.30 h and 1.2 s. …”
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A large-scale prospective nested case-control study: developing a comprehensive risk prediction model for early detection of pancreatic cancer in the community-based ESPRIT-AI coho... by Chaoliang Zhong, Penghao Li, Jia Zhao, Xue Han, Beilei Wang, Gang Jin

“…Multiple machine learning algorithms were evaluated, with the Random Forest demonstrating superior performance and selected as the final model. …”
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Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning by Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama

“…The performances of these models were evaluated in terms of Root Mean Squared Error (RMSE), coefficient of determination (R2), training time, and inference time. …”
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A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer by Tingting Ma, Tingting Ma, Tingting Ma, Tingting Ma, Tingting Ma, Mengran Zhao, Mengran Zhao, Mengran Zhao, Mengran Zhao, Mengran Zhao, Xiangli Li, Xiangchao Song, Xiangchao Song, Xiangchao Song, Xiangchao Song, Lingwei Wang, Lingwei Wang, Lingwei Wang, Lingwei Wang, Zhaoxiang Ye, Zhaoxiang Ye, Zhaoxiang Ye, Zhaoxiang Ye

“…The diagnostic ability of the signature and model were evaluated.ResultsLR algorithm was chosen for signature construction. …”
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Constructing a fall risk prediction model for hospitalized patients using machine learning by Cheng-Wei Kang, Zhao-Kui Yan, Jia-Liang Tian, Xiao-Bing Pu, Li-Xue Wu

“…Results The final model included the following variables: Adl_standing, Adl_evacuation, Age_group, Planned_surgery, Wheelchair, History_of_falls, Hypnotic_drugs, Psychotropic_drugs, and Remote_caring_system. Among the evaluated models, the Random Forest algorithm demonstrated superior performance, achieving an AUC of 0.814 (95% CI: 0.802–0.827) in the training set, 0.781 (95% CI: 0.740–0.821) in the validation set, and 0.795 (95% CI: 0.770–0.820) in the test set. …”
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MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer by Cici Zhang, Minzhi Zhong, Zhiping Liang, Jing Zhou, Kejian Wang, Jun Bu

“…Among the eight machine learning algorithms, the KNN model demonstrated superior performance to the other models in assessing the LVI status of patients with BC, with an accuracy of 0.696 and 0.642 in training and validation sets, respectively. …”
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Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network by Azizollah Khormali, Soroush Ahmadi, Aleksandr Nikolaevich Aleksandrov

“…In order to create a high-performing MLP-ANN model, various transfer functions and training algorithms were evaluated. Tansig was the most effective transfer function for the hidden layers. …”
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A 3D reconstruction platform for complex plants using OB-NeRF by Sixiao Wu, Changhao Hu, Boyuan Tian, Yuan Huang, Shuo Yang, Shanjun Li, Shengyong Xu

“…An exposure adjustment phase was integrated to improve the algorithm's robustness in uneven lighting conditions. …”
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Estimation Vehicular Waiting Time at Traffic Build-Up Queues by Mohamed Maher Ata, Mohamed El-Darieby, Baher Abdulhai, Emad Felemban, Saleh Basalamah, Basim Zafar

“…The performance of feedforward backpropagation algorithm artificial neural networks (ANNs) was evaluated and tested, based on a selected set of features. …”
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A rapid recognition method for radar active jamming based on the Hybrid 3_CNN‐Transformer model by Jingjing Wei, Lei Yu, Yinsheng Wei, Rongqing Xu

“…After concatenating these features and performing position encoding, the model utilises Transformers to capture global features, enhancing recognition accuracy and reducing training time. To enhance computational efficiency and reduce storage, we applied an L1 norm‐based unstructured pruning algorithm for model compression, achieving an 82% pruning rate and cutting inference time to 33 ms. …”
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Advanced Support Vector Machine- (ASVM-) Based Detection for Distributed Denial of Service (DDoS) Attack on Software Defined Networking (SDN) by Myo Myint Oo, Sinchai Kamolphiwong, Thossaporn Kamolphiwong, Sangsuree Vasupongayya

“…We evaluate the results by measuring a false alarm rate, a detection rate, and accuracy. …”
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Machine Learning Unveils the Impacts of Key Elements and Their Interaction on the Ambient-Temperature Tensile Properties of Cast Titanium Aluminides Employing SHAP Analysis by Shiqiu Liu, Li Liang

“…Comparative analysis of three algorithms within the training dataset proved the random forest regression (RFR) as the optimal modeling approach. …”
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Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging by Jorge Alberto Leal, Luis Hernan Ochoa, Carmen Cecilia Contreras

“…In this research algorithms of support vector machine (SVM) and a logic function were applied to identify automatically sections of carbonate rocks in wells located in the former Barco Concession, Catatumbo Basin - Colombia. …”
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Task shifting for non-communicable disease management in low and middle income countries--a systematic review. by Rohina Joshi, Mohammed Alim, Andre Pascal Kengne, Stephen Jan, Pallab K Maulik, David Peiris, Anushka A Patel

“…The majority of studies showed improved health outcomes when compared with usual healthcare, including reductions in blood pressure, increased uptake of medications and lower depression scores. Factors such as training of NPHWs, provision of algorithms and protocols for screening, treatment and drug titration were the main enablers of the task-shifting intervention. …”
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Ischemic Stroke Lesion Segmentation on Multiparametric CT Perfusion Maps Using Deep Neural Network by Ankit Kandpal, Rakesh Kumar Gupta, Anup Singh

“…The proposed network was evaluated on both training and test datasets. <b>Results:</b> The final optimized network takes three image sequences, namely CT, cerebral blood volume (CBV), and time to max (Tmax), as input to perform segmentation. …”
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