Quinary Classification of Human Gait Phases Using Machine Learning: Investigating the Potential of Different Training Methods and Scaling Techniques by Amal Mekni, Jyotindra Narayan, Hassène Gritli

“…The study utilizes data from 100 individuals obtained from an open-access platform and employs two distinct training methodologies. The first approach adopts stratified random sampling, where 80% of the data from each subphase are allocated for training and 20% for testing. …”
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Benchmarking Machine Learning Algorithms for Bearing Fault Classification Using Vibration Data: A Deployment-Oriented Study by Prasanta Kumar Samal, R. Srinidhi, Pramod Kumar Malik, H. J. Manjunatha, Imran M. Jamadar

“…The classifiers were evaluated across multiple metrics including validation accuracy, test accuracy, misclassification cost, training time, and area under the receiver operating characteristic (ROC) curve (AUC). …”
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Integration of MRMR algorithm with advanced neural networks for modeling long-term crop water demand in agricultural basins by Ahmed Elbeltagi, Abdullah A. Alsumaiei, Ali Raza, Mustafa Al-Mukhtar, Salim Heddam

“…Therefore, this study aims to achieve more accurate AET predictions through i) evaluating the performance of five artificial neural network (ANN) models optimized with the minimum redundancy maximum relevance (MRMR) algorithm to estimate monthly AET across diverse agroclimatic zones in China and ii) selecting the model with the highest accuracy based on performance metrics and minimal error between estimated and actual AET values. …”
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Performance Comparison of 10 State-of-the-Art Machine Learning Algorithms for Outcome Prediction Modeling of Radiation-Induced Toxicity by Ramon M. Salazar, PhD, Saurabh S. Nair, MS, Alexandra O. Leone, MBS, Ting Xu, PhD, Raymond P. Mumme, BS, Jack D. Duryea, BA, Brian De, MD, Kelsey L. Corrigan, MD, Michael K. Rooney, MD, Matthew S. Ning, MD, Prajnan Das, MD, Emma B. Holliday, MD, Zhongxing Liao, MD, Laurence E. Court, PhD, Joshua S. Niedzielski, PhD

“…Purpose: To evaluate the efficacy of prominent machine learning algorithms in predicting normal tissue complication probability using clinical data obtained from 2 distinct disease sites and to create a software tool that facilitates the automatic determination of the optimal algorithm to model any given labeled data set. …”
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Flood-drought shifts monitoring on arid Xinjiang, China using a novel machine learning based algorithm by Sulei Naibi, Anming Bao, Ye Yuan, Jiayu Bao, Rafiq Hamdi, Tao Yu, Xiaoran Huang, Ting Wang, Tao Li, Jingyu Jin, Gang Long, Piet Termonia

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Improvement of Wild Horse Optimizer Algorithm with Random Walk Strategy (IWHO), and Appointment as MLP Supervisor for Solving Energy Efficiency Problem by Şahiner Güler, Erdal Eker, Nejat Yumuşak

“…The IWHO algorithm was applied to MLP training, addressing a real-world problem. …”
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CAD-Assisted 3D Assembly of Automobile Electrical Switch considering PSO-BP Neural Network Algorithm by Hanting Zhang

“…Moreover, this article combines simulation research to evaluate the effect of this algorithm. After confirming the performance of the effect, this paper uses a case study to study the effect of the application of the PSO-BP neural network algorithm to the automotive electrical switch. …”
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Urban growth simulation using cellular automata model and machine learning algorithms (case study: Tabriz metropolis) by Omid Ashkriz, Babak Mirbagheri, Ali Akbar Matkan, Alireza Shakiba

“…To prevent over-fitting of algorithms to training samples and to obtain optimistic results, in the process of extracting optimal parameters of machine learning algorithms, the spatial cross-validation method was used to reduce the spatial correlation between training and test data.Results and discussion: The results showed that the random forest algorithm with the area under the ROC curve of 0.9228 compared to the support vector machine and multilayer perceptron neural network algorithms with 0.8951 and 0.8726, respectively, had a better performance in estimating the change potential of non-urban to urban areas. …”
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Efficient deep learning algorithms for lower grade gliomas cancer MRI image segmentation: A case study by AmirReza BabaAhmadi, Zahra FallahPour

“…It evaluates various pre-trained atrous-convolutional architectures and U-Nets, proposing a novel transformer-based approach that surpasses traditional methods. …”
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Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm by Xuehui Fan, Ruixue Ye, Yan Gao, Kaiwen Xue, Zeyu Zhang, Jing Xu, Jingpu Zhao, Jun Feng, Yulong Wang

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Novel Algorithm to Estimate Fat‐Free Muscle Volumes in Women Using the Urinary Deuterated‐Creatine Dilution Method by Darren Yuen Zhang Tan, Wei Fun Cheong, Shanshan Ji, Amaury Cazenave‐Gassiot, Jane Cauley, Liang Shen, Eu‐Leong Yong

“…The deuterated creatine (D3Cr) dilution method provides a direct assessment of muscle mass, but its accuracy in Asian women has not been evaluated. Our aim was to develop a new D3Cr algorithm incorporating anthropomorphic variables that can estimate fat‐free muscle mass, using magnetic resonance imaging (MRI) as the reference standard. …”
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Application of Convolutional Neural Network (CNN) Algorithm with ResNet-101 Architecture for Monkey Pox Detection in Human by Al Fathir Rizal Januar, Jamaludin Indra, Dwi Sulistya Kusumaningrum, Sutan Faisal

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Comparative Model Efficiency Analysis Based on Dissimilar Algorithms for Image Learning and Correction as a Means of Fault-Finding by Joe Benganga, Tshepo Kukuni, Ben Kotze, Lepekola Lenkoe

“…Additionally, two data transfer models were evaluated against “our model”. The results demonstrate that VGG16 performed better in terms of data accuracy than both the testing and training models, while the Resnet_50 algorithm performed poorly in terms of the loss encountered compared to the other three algorithms.…”
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Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python by Polina Lemenkova

“…The images were collectedon 2013 and 2023 to evaluate land cover categories in each of the year. …”
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RPEE-Heads Benchmark: A Dataset and Empirical Comparison of Deep Learning Algorithms for Pedestrian Head Detection in Crowds by Mohamad Abubaker, Zubayda Alsadder, Hamed Abdelhaq, Maik Boltes, Ahmed Alia

“…In addition to introducing the RPEE-Heads dataset, this paper evaluates eight state-of-the-art object detection algorithms using the dataset and analyzes the impact of head size on detection accuracy. …”
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Comparing supervised classification algorithm–feature combinations for Spartina alterniflora extraction: a case study in Zhanjiang, China by Qiujie Chen, Qiujie Chen, Chunyan Shen, Hong Du, Danling Tang

“…The sample dataset was divided into training and validation sets at a ratio of 7:3, yielding a sub-dataset with Jeffries–Matusita distances of 1.893–2.000, which satisfied classification requirements. …”
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High-fidelity learning-based motion cueing algorithm by bypassing worst-case scenario-based tuning technique by Mohammad Reza Chalak Qazani, Houshyar Asadi, Zoran Najdovski, Shehab Alsanwy, Muhammad Zakarya, Furqan Alam, Hassen M. Ouakad, Chee Peng Lim, Saeid Nahavandi

“…The motion cueing algorithm (MCA) enhances the realism of simulator driving experiences by generating vehicle motions within platform limitations. …”
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Deep learning algorithms enable MRI-based scapular morphology analysis with values comparable to CT-based assessments by Hanspeter Hess, Alexandra Oswald, J. Tomás Rojas, Alexandre Lädermann, Matthias A. Zumstein, Kate Gerber

“…A deep learning-based segmentation network was trained with paired CT derived scapula segmentations. …”
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Prediction of barite scale formation and inhibition in hydrocarbon reservoirs using AI modeling: Focus on different optimization algorithms by Ouafa Belkacem, Ahmed Rezrazi, Kamel Aizi, Lokmane Abdelouahed, Maamar Laidi, Abdelhafid Touil, Leila Cherifi, Salah Hanini

“…Notably, the MLP and SVR models were enhanced through training with the particle swarm optimization algorithm (PSO) and data augmentation techniques. …”
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Pipeline corrosion rate prediction model using BP neural network based on improved sparrow search algorithm by Shuhui XIAO, Chuanjia DU, Chengjun WANG

“…Additionally, a hybrid sine-cosine algorithm was introduced to update the location of discoverers, and a Levy flight strategy was incorporated to update the location of followers within the Sparrow Search Algorithm. …”
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