A Triple-Optimized Extreme Learning Machine Model for Power Load Forecasting by Haoxiang Gao, Weixin Kang, Miao Fan

“…The ELM model, characterized by its high efficiency and expeditious training, has become a prevalent approach in the domain of electricity load forecasting. …”
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Continuous and Unconstrained Tremor Monitoring in Parkinson’s Disease Using Supervised Machine Learning and Wearable Sensors by Fernando Rodriguez, Philipp Krauss, Jonas Kluckert, Franziska Ryser, Lennart Stieglitz, Christian Baumann, Roger Gassert, Lukas Imbach, Oliver Bichsel

“…We developed and implemented a supervised machine learning algorithm, trained and tested on tremor scores. We evaluated the algorithm on a 67-hour database comprising sensor data and clinical tremor scores for 24 Parkinson patients at four extremities for periods of about 3 hours. …”
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Predicting filtration coefficient and formation damage coefficient for particle flow in porous media using machine learning by Xuejia Du, George K. Wong

“…Collected data were randomly partitioned into training (80 %) and testing (20 %) subsets. Four regression algorithms were employed, treating λ or γ as the target variable, with injection velocity (um), particle concentration (Cin), and ratio of mean pore size (Dpore) to mean particle size (Dp) as features. …”
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Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy by Aykut Eser, Elmas Aşkar Ayyıldız, Mustafa Ayyıldız, Fuat Kara

“…For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. …”
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CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines by Shubham Choudhury, Naman Kumar Mehta, Gajendra P. S. Raghava

“…Subsequently, we also fine-tuned DNABERT-2 on our training dataset and evaluated the fine-tuned DNABERT-2 model on the validation dataset. …”
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Identification of ferroptosis-related gene signatures in temporal lobe epilepsy with hippocampal sclerosis by Fan Gao, Jinzi Li

“…We used weighed gene co-expression network analysis (WGCNA) algorithm, single-factor logistic regression analysis, and LASSO algorithm to screen characteristic FR-DEGs. …”
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A Comparative Study of YOLO, SSD, Faster R-CNN, and More for Optimized Eye-Gaze Writing by Walid Abdallah Shobaki, Mariofanna Milanova

“…Using this dataset, we evaluated the performance of several computer vision algorithms across three key areas. …”
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A novel hybrid model for predicting the bearing capacity of piles by Li Tao, Xinhua Xue

“…Six statistical indices (e.g., coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), relative root mean squared error (RRMSE), BIAS and discrepancy ratio (DR)) were used to evaluate the performance of the models. The R2, MAE, RMSE, RRMSE and BIAS values of the IPSO-LSSVM model were 1, 4.27 kN, 6.164 kN, 0.005 and 0, respectively, for the training datasets and 0.9977, 22 kN, 36.03 kN, 0.0275 and –11, respectively, for the testing datasets. …”
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Massive discovery of crystal structures across dimensionalities by leveraging vector quantization by ZiJie Qiu, Luozhijie Jin, Zijian Du, Hongyu Chen, Guanyao Mao, Yan Cen, Siqi Sun, Yongfeng Mei, Hao Zhang

“…Benchmark evaluations on diverse datasets demonstrate VQCrystal’s capabilities in representation learning and crystal discovery. …”
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Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements by Shuangling Ma, Zijie Situ, Xiaobo Peng, Zhangyang Li, Ying Huang

“…This study focuses on rehabilitation training scenarios, aiming to capture the motor intentions of patients with partial or complete motor impairments (such as stroke survivors) and provide feedforward control commands for exoskeletons. …”
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Improving Earth surface temperature forecasting through the optimization of deep learning hyper-parameters using Barnacles Mating Optimizer by Zuriani Mustaffa, Mohd Herwan Sulaiman, Muhammad ‘Arif Mohamad

“…The forecasting model is trained on a global temperature dataset with seven inputs and compared with DL models optimized by Particle Swarm Optimization (PSO), Harmony Search Algorithm (HSA), and Ant Colony Optimization (ACO). …”
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Enhancing game outcome prediction in the Chinese basketball league through a machine learning framework based on performance data by Yuhua Zhong

“…To evaluate model performance, a diverse set of machine learning algorithms, including support vector machines, Naive Bayes, k-nearest neighbors, logistic regression, multi-layer perceptron with contrastive loss, and XGBoost are employed, with metrics such as Accuracy, F1 Score, Recall, Precision, and AUROC used for comparison. …”
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Predicting the performance of ORB-SLAM3 on embedded platforms by Jacques Matthee, Kenneth Uren, George van Schoor, Corne van Daalen

“…Therefore, a need exists to  evaluate the performance of SLAM algorithms in practical embedded environments – this paper addresses this need by creating  prediction models to estimate the performance that ORB-SLAM3 can achieve on embedded platforms. …”
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Research on load labeling method for big data in novel power system by ZHANG Ximing, XU Huan, YANG Qiuyong

“…The synthetic dataset is compared with the real dataset, and the smart grid machine learning algorithm is trained and tested on this basis. The simulation results verify the effectiveness of the proposed method.…”
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AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease. by Jiansong Fang, Ling Wang, Yecheng Li, Wenwen Lian, Xiaocong Pang, Hong Wang, Dongsheng Yuan, Qi Wang, Ai-Lin Liu, Guan-Hua Du

“…Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB) and recursive partitioning (RP) algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. …”
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ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection by Bin Li, Jie Li, Mingyu Jia

“…In addition, ADFCNN-BiLSTM addresses the issue of class imbalance during the training process at both the data level and algorithm level. …”
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A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection by Tomasz Kocejko

“…The dataset was then split into training and test sets with an 80/20 ratio. Machine-learning models were trained, with hyperparameters optimized via grid search. …”
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An Efficient Deep Learning-Based Framework for Predicting Cyber Violence in Social Networks by Younes Fayand Fathabad, Mohammad Ali Balafar, Amin Golzari Oskouei, Kamal Koohi

“…Deep learning-based algorithms have proven effective in identifying violent content, yet existing models often struggle with understanding contextual nuances and implicit forms of cyber violence. …”
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T_SRNET: A multimodal model based on convolutional neural network for emotional speech enhancement by Shaoqiang Wang, Lei Feng, Li Zhang

“…However, the lack of high-quality speech annotation datasets makes it difficult for many models to provide sufficient data for training, resulting in poor model generalization performance. …”
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A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference by Sadhana Selvakumar, B. Senthilkumar

“…Abstract Medical image analysis using deep learning algorithms has become a basis of modern healthcare, enabling early detection, diagnosis, treatment planning, and disease monitoring. …”
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