AI Machine Learning–Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis by Hocheol Lee, Myung-Bae Park, Young-Joo Won

“…The dataset was split into 70% training and 30% testing sets. Model performance was evaluated using accuracy, precision, recall, F1 score, and area under the curve (AUC). …”
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Preference learning based deep reinforcement learning for flexible job shop scheduling problem by Xinning Liu, Li Han, Ling Kang, Jiannan Liu, Huadong Miao

“…Furthermore, the Pearson correlation coefficient (PCC) is used to evaluate the performance of the preference model. Finally, comparative experiments on FJSP benchmark instances of varying sizes demonstrate that PBMP outperforms traditional scheduling strategies such as dispatching rules, OR-Tools, and other deep reinforcement learning (DRL) algorithms, achieving superior scheduling policies and faster convergence. …”
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Automated classification of online reviews of otolaryngologists by Jake G. Stenzel, Nicholas R. Schultz, Michael J. Marino

“…Using scikit‐learn, an NLP algorithm was trained and validated on the subsets, with F1 scores evaluating text classification accuracy against manual categorization. …”
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A Swarm-Based Multi-Objective Framework for Lightweight and Real-Time IoT Intrusion Detection by Hessah A. Alsalamah, Walaa N. Ismail

“…MOOIDS-IoT combines a Genetic Algorithm (GA)-based feature selection technique with a multi-objective Particle Swarm Optimization (PSO) algorithm. …”
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Shale volume estimation using machine learning methods from the southwestern fields of Iran by Parirokh Ebrahimi, Ali Ranjbar, Yousef Kazemzadeh, Ali Akbari

“…Nine petrophysical log datasets, including SP, RHOZ, PEFZ, NPHI, HLLS, HLLD, HCAL, and DT, were utilized as input features for training the models. The models were evaluated based on performance metrics such as correlation coefficient (R2), average relative error (ARE), root mean square error (RMSE), and mean squared error (MSE). …”
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Interactive online learning method for students based on artificial intelligence by Cizhang Li, Wenfen Yin

“…A comprehensive literature review was conducted using databases such as IEEE Xplore, ACM, and Google Scholar, focusing on studies from the last decade. The model was evaluated through experimental analysis using key regression and classification metrics, including Mean Absolute Error, Root Mean Square Error, R2 Score, Accuracy, Precision, Recall, Sensitivity, Specificity, and F1-Score with training time. …”
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DLLabelsCT: Annotation tool using deep transfer learning to assist in creating new datasets from abdominal computed tomography scans, case study: Pancreas. by Henrik Mustonen, Antti Isosalo, Minna Nortunen, Mika Nevalainen, Miika T Nieminen, Heikki Huhta

“…Deep learning (DL) applications, which use large convolutional neural networks (CNN), hold considerable potential, especially in optimizing radiological evaluations. However, training DL algorithms to clinical standards requires extensive datasets, and their processing is labor-intensive. …”
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Diabetic Retinopathy Detection Using DL-Based Feature Extraction and a Hybrid Attention-Based Stacking Ensemble by Sanjana Rajeshwar, Shreya Thaplyal, Anbarasi M., Siva Shanmugam G.

“…This paper addresses this challenge by utilizing advanced deep learning (DL) algorithms with established image processing techniques to enhance accuracy and efficiency in detection. …”
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Unpacking Performance Variability in Deep Reinforcement Learning: The Role of Observation Space Divergence by Sooyoung Jang, Ahyun Lee

“…Deep Reinforcement Learning (DRL) algorithms often exhibit significant performance variability across different training runs, even with identical settings. …”
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Object Recognition and Positioning with Neural Networks: Single Ultrasonic Sensor Scanning Approach by Ahmet Karagoz, Gokhan Dindis

“…With the proposed solution, by training a reasonable amount of obtained data, 90% accuracy was achieved in the classification and position estimation of multiple objects with the CNN algorithm as a result of converting the signals obtained from ultrasonic sensors into images.…”
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Snoring Sound Recognition Using Multi-Channel Spectrograms by Ziqiang YE, Jianxin PENG, Xiaowen ZHANG, Lijuan SONG

“…Three methods of data set partitioning are used to evaluate the performance of feature maps. The proposed feature maps were compared through the training set and test set of independent subjects by using a CNN model. …”
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Comparison of Machine Learning Methods (Linear Regression, Random Forest, and XGBoost) for Predicting Poverty in Central Java in 2024 by Zahwa Bunga Putri Pratama, Yani Parti Astuti

“…The data were normalized using StandardScaler and split into training (80%) and testing (20%) sets. This study compares three regression algorithms—Linear Regression, Random Forest, and XGBoost—to evaluate their effectiveness in modeling the complexity of socio-economic data. …”
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Comparative Analysis of The Combined Model (Spatial and Temporal) and Regression Models for Predicting Murder Crime by Laith S. Ibrahim, Ghadeer Jasim Mohammed

“…The data was analyzed and then divided into two sets, a training and testing set, to perform these models in prediction. …”
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Development of Ai-Based Crop Quality Grading Systems using Image Recognition by Dusi Prerna, Sharma Pooja

“…Moreover, the Transfer Learning has the shortest training time, which shows the preference of this approach. …”
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Automatic text generation system for endangered languages based on conditional generative adversarial networks by Zhong Luo

“…We discuss integrating advanced models like GPT-2 and RoBERTa to address training instability and gradient explosion challenges. …”
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Utilizing UAV and orthophoto data with bathymetric LiDAR in google earth engine for coastal cliff degradation assessment by Paweł Tysiąc, Rafał Ossowski, Łukasz Janowski, Damian Moskalewicz

“…Next, by applying Random Forest classifier within Google Earth Engine, we evaluated the importance of features in detecting these degraded zones. …”
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Thermal field reconstruction based on weighted dictionary learning by Tianyi Zhang, Wenchang Li, Jinyu Xiao, Jian Liu

“…The dictionary learning technology is exploited to train the model and the minimum weighted mean square error evaluation method is incorporated to improve the reconstruction accuracy near the temperature triggering threshold. …”
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Exhaustive search for novel multicomponent alloys with brute force and machine learning by Viktoriia Zinkovich, Vadim Sotskov, Alexander Shapeev, Evgeny Podryabinkin

“…The candidate structures are evaluated using the low-rank potential (LRP), trained to reproduce energies of structures equilibrated with density functional theory (DFT). …”
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A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems by Guillaume Canat, Antonin Duval, Nina Gidel-Dissler, Alexandra Boussommier-Calleja

“…Abstract The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based computer vision algorithms to assist embryologists in their morphokinetic evaluation. …”
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Development and Optimization of a Novel Deep Learning Model for Diagnosis of Quince Leaf Diseases by A. Naderi Beni, H. Bagherpour, J. Amiri Parian

“…This study employs a novel CNN algorithm alongside two pre-trained models to effectively identify and classify various types of quince diseases.Materials and MethodsImages of healthy and diseased leaves were acquired from several databases. …”
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