Utilization of Deep Learning YOLO V9 for Identification and Classification of Toraja Buffalo Breeds by Abdul Rachman Manga', Herawati Herawati, Purnawansyah Purnawansyah

“…This study aims to develop and evaluate a buffalo breed detection system that supports the cultural practices of the Toraja community, particularly in the context of the Rambu Solo’ ceremony. …”
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A Method of Intelligent Driving-Style Recognition Using Natural Driving Data by Siyang Zhang, Zherui Zhang, Chi Zhao

“…Based on the clustering results, a comprehensive evaluation of the driving styles is conducted. Finally, a comparative evaluation of SVM, Random Forest, and KNN recognition indicates the superiority of the SVM algorithm and highlights the effectiveness of dimensionality reduction in optimizing characteristic parameters. …”
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Prediction of the remaining useful life of a milling machine using machine learning by Abbas Al-Refaie, Majd Al-atrash, Natalija Lepkova

“…The ML models were developed using a four-stage process including data pre-processing, training, evaluation, and deployment. Several ML algorithms were applied and the results were evaluated using five measures involving Accuracy, Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared, and R-squared adjusted. …”
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Deteksi Dan Klasifikasi Hama Potato Beetle Pada Tanaman Kentang Menggunakan YOLOV8 by Daniel Geoffrey Manurung, Mohammad Ryan Pinasthika, Muhammad Azka Obila Vasya, Rania Aprilia Dwi Setya Putri, Agustinus Parasian Tampubolon, Rakan Fadhil Prayata, Septia Khoirin Nisa, Novanto Yudistira

“…The YOLOv8 (You Only Look Once) model is implemented to detect objects in images, identifying the position and class of potato beetles. The data used for training is divided into training, validation, and testing datasets using the PyTorch framework. …”
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Klasifikasi Ulasan Palsu Menggunakan Borderline Over Sampling (BOS) dan Support Vector Machine (SVM) (Studi Kasus : Ulasan Tempat Makan) by Aisyah Awalina, Fitra Abdurrachman Bachtiar, Indriati Indriati

“…Step in testing BOS and SVM are split data of training and test data with 80%:20%, after that the search for the best parameters in the training data with 5-fold cross-validation, and evaluated with test data. …”
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QuantiFly: Robust Trainable Software for Automated Drosophila Egg Counting. by Dominic Waithe, Peter Rennert, Gabriel Brostow, Matthew D W Piper

“…Initial training typically requires approximately 10 minutes, while subsequent image evaluation by the software is performed in just a few seconds. …”
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Classification Prediction of Rockburst in Railway Tunnel Based on Hybrid PSO-BP Neural Network by Min Zhang

“…Then, the BP neural network is improved by using particle swarm optimization (PSO) combined with the simulated annealing algorithm. The results are obtained from the training data. …”
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Research on the application of deep learning based video recognition for power plant leakage and dripping by Zhang Yuan, Si Yuan

“…Then, by combining semantic segmentation, data augmentation, attention mechanisms, and changing activation functions with convolutional neural networks, the YOLOv5 algorithm is deeply optimized, including improvements in training strategies and model evaluation adjustments. …”
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AIpollen: An Analytic Website for Pollen Identification Through Convolutional Neural Networks by Xingchen Yu, Jiawen Zhao, Zhenxiu Xu, Junrong Wei, Qi Wang, Feng Shen, Xiaozeng Yang, Zhonglong Guo

“…After training for 203 epochs, our model achieved an accuracy of 97.01% on the test set and 99.89% on the training set. …”
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A cosine similarity-based method for improving the accuracy of GIS discharge spectrum recognition by CHEN Xiaoxin, ZHOU Tonghao, LIU Jiangming, DAI Pengfei, LIU Yanqi, LI Wendong, ZHANG Guanjun

“…By analyzing the spectra of various discharge types， phase features are summarized， and the features of each discharge type are compared with the spectrum of the discharge under evaluation. The resulting phase reference values are incorporated into the network structure for training. …”
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Thirty-day mortality risk prediction for geriatric patients undergoing non-cardiac surgery in the surgical intensive care unit by Mengke Ma, Jiatong Liu, Caiyun Li, Yingxue Chen, Huishu Jia, Aijie Hou, Hongzeng Xu

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Adding Data Quality to Federated Learning Performance Improvement by Ernesto Gurgel Valente Neto, Solon Alves Peixoto, Valderi Reis Quietinho Leithardt, Juan Francisco de Paz Santana, Julio C. S. Dos Anjos

“…As a result, Federated Learning (FL) allows IoT devices to collaborate in Artificial Intelligence (AI) training models while preserving data privacy. However, selecting high-quality data for training remains a critical challenge in FL environments with non-independent and identically distributed (non-iid) data. …”
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Optimization of Traffic Congestion Management in Smart Cities under Bidirectional Long and Short-Term Memory Model by Yujia Zhai, Yan Wan, Xiaoxiao Wang

“…Then, the experimental simulation verification and prediction performance evaluation are performed. Finally, the data predicted by the BiLSTM algorithm model are compared with the actual data and the data predicted by the long short-term memory (LSTM) algorithm model. …”
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A recurrent sigma pi sigma neural network by Fei Deng, Shibin Liang, Kaiguo Qian, Jing Yu, Xuanxuan Li

“…Abstract In this paper, a novel recurrent sigma‒sigma neural network (RSPSNN) that contains the same advantages as the higher-order and recurrent neural networks is proposed. The batch gradient algorithm is used to train the RSPSNN to search for the optimal weights based on the minimal mean squared error (MSE). …”
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Komparasi Data Mining Naive Bayes dan Neural Network memprediksi Masa Studi Mahasiswa S1 by Azahari Azahari, Yulindawati Yulindawati, Dewi Rosita, Syamsuddin Mallala

“…According to the data mining algorithm Naive bayes, there are 3229 students; 1769 as training data, 321 as testing data, and 1139 as target data. …”
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Patch-Based Segmentation with Spatial Consistency: Application to MS Lesions in Brain MRI by Roey Mechrez, Jacob Goldberger, Hayit Greenspan

“…A patch database is built using training images for which the label maps are known. …”
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Analisis Sentimen Ulasan Pengguna Aplikasi Alfagift Menggunakan Random Forest by M. Bagus Prayogi, Gustina Masitoh

“…The research steps include data collection, data labeling, data preprocessing, word weighting, data division into training and testing sets, Random Forest algorithm implementation, and model evaluation. …”
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Research and analysis of differential gene expression in CD34 hematopoietic stem cells in myelodysplastic syndromes. by Min-Xiao Wang, Chang-Sheng Liao, Xue-Qin Wei, Yu-Qin Xie, Peng-Fei Han, Yan-Hui Yu

“…After comprehensive evaluation, we ultimately selected three algorithms-Lasso regression, random forest, and support vector machine (SVM)-as our core predictive models. …”
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Predicting Student Loyalty in Higher Education Using Machine Learning: A Random Forest Approach by Qoriani Widayati, Kusworo Adi, R Rizal Isnanto, Eka Puji Agustini, Dewa Rizki Rahmat Julianto, Fawwaz Bimo Prakasa

“…The resulting dataset was processed through preprocessing, model training, and performance evaluation, employing metrics such as accuracy, precision, recall, and F1-score. …”
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Discriminator guided visible-to-infrared image translation by Decao Ma, Juan Su, Yong Xian, Shaopeng Li

“…The experimental results show that compared to the existing typical infrared image generation algorithms, the proposed method can generate higher-quality infrared images and achieve better performance in both subjective visual description and objective metric evaluation, and that it has better performance in the downstream tasks of the template matching and image fusion tasks.…”
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