Penerapan SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Kepribadian MBTI Menggunakan Naive Bayes Classifier by Mutiara Persada Pulungan, Andi Purnomo, Aliyah Kurniasih

“…Evaluation using the Hold-Out-Validation method by dividing the data into 90% training data and 10% test data. …”
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Efficient automated detection of power quality disturbances using nonsubsampled contourlet transform & PCA-SVM by Pampa Sinha, Kaushik Paul, Asit Mohanty, IM Elzein, Chandra Sekhar Mishra, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Ahmed Althobaiti, Hany S Hussein, Thamer AH Alghamdi, Ahmed M Ewais

“…These optimized features are used for training a multi-class support vector machine, with its parameters further optimized for enhanced classification accuracy. …”
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EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model by Weikang He, Xizhe Li, Yujin Wan, Honming Zhan, Nan Wan, Sijie He, Yaoqiang Lin, Longyi Wang, Wenxuan Yu, Liqing Chen

“…The hyperparameters of the model were optimized using the Rabbit Optimization Algorithm (ROA), and 10-fold cross-validation was employed to improve the stability and reliability of model evaluation, mitigating overfitting and bias. …”
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Optimization of a Coupled Neuron Model Based on Deep Reinforcement Learning and Application of the Model in Bearing Fault Diagnosis by Shan Wang, Jiaxiang Li, Xinsheng Xu, Ruiqi Wu, Yuhang Qiu, Xuwen Chen, Zijian Qiao

“…Using the SNR as the evaluation metric, the algorithm performs data screening on the replay buffer parameters before training the deep network for predicting coupled neuron model performance. …”
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Predicting outcomes following endovascular aortoiliac revascularization using machine learning by Ben Li, Badr Aljabri, Derek Beaton, Leen Al-Omran, Mohamad A. Hussain, Douglas S. Lee, Duminda N. Wijeysundera, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Mohammed Al-Omran

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Establishment of prognostic risk model related to disulfidptosis and immune infiltration in hepatocellular carcinoma by Zhe Xu, Chong Pang, Xundi Xu

“…WGCNA, univariate Cox, and LASSO algorithm were employed to select hub genes for constructing the prognostic model. …”
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Neuro-evolutionary models for imbalanced classification problems by Israa Al-Badarneh, Maria Habib, Ibrahim Aljarah, Hossam Faris

“…Training an Artificial Neural Network (ANN) algorithm is not trivial, which requires optimizing a set of weights and biases that increase dramatically with the increasing capacity of the neural network resulting in such hard optimization problems. …”
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The value of machine learning based on spectral CT quantitative parameters in the distinguishing benign from malignant thyroid micro-nodules by Zuhua Song, Qian Liu, Jie Huang, Dan Zhang, Jiayi Yu, Bi Zhou, Jiang Ma, Ya Zou, Yuwei Chen, Zhuoyue Tang

“…Predictive performance was evaluated via receiver operating characteristic (ROC) curve analysis. …”
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Astronomaly Protege: Discovery through Human-machine Collaboration by Michelle Lochner, Lawrence Rudnick

“…Using an evaluation subset, we show that, with minimal training, PROTEGE provides excellent recommendations and find that it is even able to recommend sources that the authors missed. …”
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Feature Selection and Hyper-parameter Tuning Technique using Neural Network for Stock Market Prediction by Karanveer Singh, Rahul Tiwari, Prashant Johri, Ahmed A. Elngar

“…Every stock every investor needs to foresee the future evaluation of stocks, so a predicted forecast of a stock’s future cost could return enormous benefit. …”
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A review of deep learning in blink detection by Jianbin Xiong, Weikun Dai, Qi Wang, Xiangjun Dong, Baoyu Ye, Jianxiang Yang

“…By overcoming the challenges identified in this study, the application prospects of deep learning-based blink detection algorithms will be significantly enhanced.…”
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A novel hybrid deep learning approach for super-resolution and objects detection in remote sensing by Muhammad Asif, Mohammad Abrar, Faizan Ullah, Abdu Salam, Farhan Amin, Isabel de la Torre, Mónica Gracia Villar, Helena Garay, Gyu Sang Choi

“…Preprocessing techniques, including data augmentation, are incorporated to improve the diversity and accuracy of the training dataset. Evaluation on datasets such as VEDAI-VISIBLE and VEDAI-IR demonstrated exceptional performance, achieving an mAP@0.5 of 97.2%, mAP@0.5:0.95 of 72.8%, and F1-Score of 0.93, with an inference time of 42 ms. …”
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Bi-modal contrastive learning for crop classification using Sentinel-2 and Planetscope by Ankit Patnala, Scarlet Stadtler, Scarlet Stadtler, Martin G. Schultz, Martin G. Schultz, Juergen Gall, Juergen Gall

“…First, we adopt the uni-modal contrastive method (SCARF) and, second, we use a bi-modal approach based on Sentinel-2 and Planetscope data instead of standard transformations developed for natural images to accommodate the spectral characteristics of crop pixels. Evaluation in three regions of Germany and France shows that crop classification with the pre-trained multi-modal model is superior to the pre-trained uni-modal method as well as the supervised baseline models in the majority of test cases.…”
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Prediction on rock strength by mineral composition from machine learning of ECS logs by Dongwen Li, Xinlong Li, Li Liu, Wenhao He, Yongxin Li, Shuowen Li, Huaizhong Shi, Gaojian Fan

“…This study proposes the use of Random Forest and Transformer algorithms to predict rock strength from Elemental Capture Spectroscopy (ECS) logs. …”
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Development of a risk prediction model for secondary infection in severe/critical COVID-19 patients by Yinmei Zhang, Mingmei Lin, Zhenchao Wu, Zhongyu Han, Liyan Cui, Jiajia Zheng

“…The random forest model demonstrated the best performance, with further evaluation showing an average AUC of 0.981 (CI 0.965–0.998) on the training set and 0.836 (CI 0.761–0.912) on the test set. …”
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Review of Surface-Defect Detection Methods for Industrial Products Based on Machine Vision by Quan Wang, Mengnan Wang, Jiadong Sun, Deji Chen, Pei Shi

“…The paper organizes industrial defect datasets by type (multi-product and single-product), evaluates data quality and availability, and summarizes common evaluation metrics for accuracy, efficiency by task requirements. …”
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HouseGanDi: A Hybrid Approach to Strike a Balance of Sampling Time and Diversity in Floorplan Generation by Azmeraw Bekele Yenew, Beakal Gizachew Assefa, Elefelious Getachew Belay

“…Evaluation of diversity using FID demonstrates an average 15.5% improvement over the state-of-the-art houseDiffusion model, with a 41% reduction in generation time. …”
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APPLICATION OF THE SUPPORT VECTOR MACHINE, LIGHT GRADIENT BOOSTING MACHINE, ADAPTIVE BOOSTING, AND HYBRID ADABOOST-SVM MODEL ON CUSTOMERS CHURN DATA by Felice Elena, Robyn Irawan, Benny Yong

“…The usage of oversampling technique is required to balance the number of observations in both classes of training data. Furthermore, a model comparison will be conducted using the F1-Score and the AUC score as the evaluation metric. …”
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A review of deep-learning-based models for afaan oromo fake news detection on social media networks by Kedir Lemma Arega, Kula Kekeba Tune, Asrat Mulatu Beyene, Wegderes Tariku, Nurhussen Menza Bune

“…Key components include a literature review, dataset compilation, preprocessing, feature extraction, model selection, training and validation, evaluation metrics, results analysis, discussion, and future work recommendations. …”
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Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism by Adel Binbusayyis, Mohemmed Sha

“…Furthermore, this model demonstrates a training time of 692.12 s and a prediction time of just 1.87 s. …”
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