Inverse Kinematics of a 7-Degree-of-Freedom Robotic Arm Based on Deep Reinforcement Learning and Damped Least Squares by Shusheng Yu, Gongquan Tan

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Meta-learning based softmax average of convolutional neural networks using multi-layer perceptron for brain tumour classification by Irwan Budi Santoso, Shoffin Nahwa Utama, Supriyono

“…The variability in tumour shape, size, and position poses challenges to classification methods. …”
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Chess Position Evaluation Using Radial Basis Function Neural Networks by Dimitrios Kagkas, Despina Karamichailidou, Alex Alexandridis

“…The game of chess is the most widely examined game in the field of artificial intelligence and machine learning. In this work, we propose a new method for obtaining the evaluation of a chess position without using tree search and examining each candidate move separately, like a chess engine does. …”
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Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery by Hongchao Wang, Wenliao Du

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Majority Voting Ensemble of Deep CNNs for Robust MRI-Based Brain Tumor Classification by Kuo-Ying Liu, Nan-Han Lu, Yung-Hui Huang, Akari Matsushima, Koharu Kimura, Takahide Okamoto, Tai-Been Chen

“…Performance was assessed using accuracy, Kappa coefficient, true positive rate, precision, confusion matrix, and ROC curves. …”
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Modern Evaluation Methods at Various Levels of Education by I. N. Emelyanova, O. A. Teplyakova, G. Z. Efimova

“…However, along with positive dynamics of active methods application for learning assessment, non-systemic use of such methods is noted as well. …”
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Research on Trajectory Tracking Control Method for Crawler Robot Based on Improved PSO Sliding Mode Disturbance Rejection Control by Zhiyong Yang, Qing Lang, Yuhong Xiong, Shengze Yang, Changjin Zhang, Lielei Deng, Daode Zhang

“…The standard deviations of the position errors were 3.19 and 4.28, respectively. Compared with conventional PSO-based SMADRC and standard SMADRC methods, the proposed approach improved the navigation tracking accuracy. …”
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Comprehensive Evaluation of Phenotypic Traits in Vegetable Faba Bean (Vicia faba L.) Germplasms Based on Multivariate Statistical Methods by Linlin Wang, Yangmin Zhong, Ruifang Ma, Hanmei Li, Tingfu Liu

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Mixed-methods feasibility outcomes for a novel ACT-based video game ‘ACTing Minds’ to support mental health by Darren J Edwards, Andrew H Kemp, Tom C Gordon

“…Objectives To determine the feasibility and acceptability of ‘ACTing Minds’, a novel single-player adventure video game based on acceptance and commitment therapy (ACT).Design A single-arm, mixed-methods repeated measures feasibility study.Setting Intervention and questionnaires were completed at home by participants. …”
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An AI-based automatic leukemia classification system utilizing dimensional Archimedes optimization by Warda M. Shaban

“…DAOA is based on the Archimedes Optimization Algorithm (AOA) and Dimensional Learning Strategy (DLS). …”
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Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact by A. Canberk Ulusoy, Tuğçe Toprak, M. Alper Selver, Pelin Güneri, Betül İlhan

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Satisfaction and Self-Confidence of Moroccan Nursing Students in Simulation-Based Learning and Their Associations with Simulation Design Characteristics and Educational Practices by Hicham Blaak, Abdelmajid Lkoul, Hayat Iziki, Abdelhadi El Haddaouy, Ahmed Kharabch, Rachid Razine, Lahcen Belyamani, Majdouline Obtel

“…Furthermore, various learning methods (B = 0.112, <i>p</i> = 0.037, 95% CI [0.007; 0.217]) and objectives/information clarity (B = 0.175, <i>p</i> = 0.040, 95% CI [0.008; 0.342]) had a significant positive effect on satisfaction. …”
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Meta-analysis of machine learning models for the diagnosis of central precocious puberty based on clinical, hormonal (laboratory) and imaging data by Yilin Chen, Xueqin Huang, Lu Tian

“…With the widespread application of artificial intelligence in medicine, some studies have utilized clinical, hormonal (laboratory) and imaging data-based machine learning (ML) models to identify CPP. …”
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LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning by Róbert Stollmayer, Selda Güven, Christian Marcel Heidt, Kai Schlamp, Pál Novák Kaposi, Oyunbileg von Stackelberg, Hans-Ulrich Kauczor, Miriam Klauss, Philipp Mayer

“…These limitations could potentially be alleviated using recent deep learning-based segmentation and classification methods such as nnU-Net. …”
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Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning–based histomic clusters by Byungsoo Ahn, Eunhyang Park

“…Conclusions Deep learning-based histologic analysis effectively stratifies HGSC into clinically relevant prognostic groups, highlighting the role of mitochondrial dynamics and energy metabolism in disease progression. …”
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Research on the application of network security defence in database security services based on deep learning integrated with big data analytics by Feilu Hang, Linjiang Xie, Zhenhong Zhang, Wei Guo, Hanruo Li

“…Additionally, an Artificial Neural Network (ANN)-based Deep Learning (DL) method for Anomaly Detection (AD) is presented in this work. …”
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Protocol-Agnostic and Packet-Based Intrusion Detection Using a Multi-Layer Deep-Learning Architecture at the Network Edge by Rodolphe Picot, Felipe Gohring de Magalhaes, Ahmad Shahnejat Bushehri, Maroua Ben Atti, Gabriela Nicolescu, Alejandro Quintero

“…Unlike existing approaches that transform packets into alternative representations such as images or NLP-based techniques, which introduce additional overhead, our method processes packets directly, eliminating the need for complex components like Recurrent Neural Networks (RNNs) or convolutional layers. …”
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Application of Machine Learning Models for the Early Detection of Metritis in Dairy Cows Based on Physiological, Behavioural and Milk Quality Indicators by Karina Džermeikaitė, Justina Krištolaitytė, Ramūnas Antanaitis

“…Models were evaluated based on accuracy, sensitivity, specificity, positive and negative predictive values (PPV, NPV), area under the receiver operating characteristic (ROC) area under the curve (AUC), and Matthews correlation coefficient (MCC). …”
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Screening of CAD-related secretory genes associated with type II diabetes based on comprehensive bioinformatics analysis and machine learning by Li Xie, Han Xiao, Maoyu Zhao, Li Xu, Si Tang, Youzhu Qiu

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Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis by Ibrahim Mohammadzadeh, Bardia Hajikarimloo, Behnaz Niroomand, Nasira Faizi, Pooya Eini, Mohammad Amin Habibi, Alireza Mohseni, Mohammadmahdi Sabahi, Abdulrahman Albakr, Michael Karsy, Hamid Borghei-Razavi

“…Abstract Background Predicting pituitary adenoma (PA) recurrence after surgical resection is critical for guiding clinical decision-making, and machine learning (ML) based models show great promise in improving the accuracy of these predictions. …”
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