Modified Whale Optimization Algorithm for Multiclass Skin Cancer Classification by Abdul Majid, Masad A. Alrasheedi, Abdulmajeed Atiah Alharbi, Jeza Allohibi, Seung-Won Lee

“…To address these challenges, this paper proposes an innovative deep learning-based framework that integrates an ensemble of two pre-trained convolutional neural networks (CNNs), SqueezeNet and InceptionResNet-V2, combined with an improved Whale Optimization Algorithm (WOA) for feature selection. …”
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Development of recognition algorithms in the control system of robot drives for plucking tea by Yan Yang, Chicherin I.V., Lijun Zhao, Chanjuan Long, Ignatieva E.A.

“…The reliability of the trained model is evaluated using four training indicators: precision, recall, F1 score (harmonic mean of precision and recall), and mean average precision (mAP). …”
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A Study on the Impact of Obstacle Size on Training Models Based on DQN and DDQN by Lu Siyu, Tao Ye, Zeng Junwei, Zuo Qihuan

“…While DDQN introduces another network for evaluation, which could better decrease overestimation and explore more possible actions to improve training performance. …”
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Method of training neural networks for use in exchange robots based on intuitive analysis by S. S. Khromov

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Neural Network VS Genetic and Particle Swarm Optimization Algorithms in Bankruptcy by Alireza Azarberahman

“…Neural networks (NNs) choose the optimal network with the least error in training and evaluating patterns in the second phase. …”
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Performance of Machine Learning Algorithms on Automatic Summarization of Indonesian Language Texts by Galih Wiratmoko, Husni Thamrin, Endang Wahyu Pamungkas

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Contrastive Learning Algorithm for Low-Resource Cryptographic Attack Event Detection by Peng Luo, Rangjia Cai, Yuanbo Guo

“…Thus, we propose a method CLAD: Contrastive Learning Algorithm for Detecting Low-resource Cryptographic Attack Event. …”
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Deep Reinforcement Learning for MU-MIMO Beamforming Training in mmWave WLAN by Buseong Jo, Mun-Suk Kim, Sukyoung Lee

“…In IEEE 802.11ay wireless local area network (WLAN), a single access point (AP) performs multi-user multiple-input-multiple-output (MU-MIMO) beamforming training (BFT) to enable simultaneous directional communications with multiple stations (STAs). …”
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Sensitivity analysis of factors influencing the curve negotiation performance of hot metal train by TIAN Mingjie, HUANG Zhihui, CHEN Xuejing, YANG Quan, YU Hongda

“…Based on the response surface method, relationships were mapped between operational safety evaluation indexes and each parameter. A sensitivity analysis of factors affecting the performance of hot metal trains negotiating curves was carried out by using the gradient algorithm. …”
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Meta-transformer: leveraging metaheuristic algorithms for agricultural commodity price forecasting by G. H. Harish Nayak, Md. Wasi Alam, B. Samuel Naik, B. S. Varshini, G. Avinash, Rajeev Ranjan Kumar, Mrinmoy Ray, K. N. Singh

“…To address these challenges, this study proposes a novel framework that combines Transformer models with Metaheuristic Algorithms (MHAs), including the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) to enhance agricultural price forecasting accuracy. …”
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Neighboring Algorithm for Visual Semantic Analysis toward GAN-Generated Pictures by Lu-Ming Zhang, Yichuan Sheng

“…Finally, the property of generative similarity that produced by the GAN models are trained on a variety of classical datasets. Comprehensive experimental results have shown that our algorithm substantially improves the efficiency and accuracy of the natural evaluation of pictures generated by GAN. …”
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Belief in building a full-fledged distance learning course in athletic training by Andrii Yefremenko, Illia Shutieiev

“…An algorithm for organising practical training in athletic training has been formed. …”
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Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms by Mintae Kim, Muharrem A. Senturk, Liang Li

“…The dataset includes LL, PL, <i>W</i>, PI, <i>G_s</i>, and <i>e</i>₀ as the inputs, with <i>C_c</i> as the output parameter. The algorithms are trained and evaluated using metrics such as the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). …”
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Predicting agricultural drought in central Europe by using machine learning algorithms by Endre Harsányi

“…Like the training stage, RF outperformed among other algorithms achieving the highest accuracy. …”
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Biometric monitoring system based on K-means ＆MTLS-SVM algorithm by Jingming XIA, Lingling TANG, Ling TAN, Han ZHENG

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Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms by Seon Ui Lee, Sae Kyung Choi, Yun Sung Jo, Jeong Ha Wie, Jae Eun Shin, Yeon Hee Kim, Kicheol Kil, Hyun Sun Ko

“…Two sets of variables from the first trimester until 13 weeks (E1) and the early third trimester until 28 weeks (T1) were used to develop the FGR prediction models using a machine learning algorithm. The dataset was randomly divided into training and test sets (7:3 ratio). …”
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Biometric monitoring system based on K-means ＆MTLS-SVM algorithm by Jingming XIA, Lingling TANG, Ling TAN, Han ZHENG

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Evolutionary search algorithm for learning activation function of an artificial neural network by Yurshin Viacheslav

“…The proposed method aims to enhance the efficiency of the search process for optimal activation functions. Our algorithm employs genetic programming to evolve the general form of activation functions, while gradient descent optimizes their parameters during network training. …”
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Prioritized Experience Replay–Based Path Planning Algorithm for Multiple UAVs by Chongde Ren, Jinchao Chen, Chenglie Du

“…Finally, we propose the PERDE-MADDPG algorithm based on PER and delayed update skills, which is evaluated against the MATD3, MADDPG, and SAC methods in simulation scenarios to confirm its efficacy.…”
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YOLOGX: an improved forest fire detection algorithm based on YOLOv8 by Caixiong Li, Yue Du, Xing Zhang, Peng Wu

“…Finally, the proposed Focal-SIoU loss function replaces the original loss function, effectively reducing directional errors by combining angle, distance, shape, and IoU losses, thus optimizing the model training process. YOLOGX was evaluated on the D-Fire dataset, achieving a mAP@0.5 of 80.92% and a detection speed of 115 FPS, surpassing most existing classical detection algorithms and specialized fire detection models. …”
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