EFFECT OF COVID-19 LOCKDOWN ON SPORTS PERFORMANCE PARAMETERS OF COMPETITIVE ATHLETES by Rupak Singh, Jayant Rastogi, Chandra Guru, Varad Apte, Karuna Datta, Atul Sharma

“…Conclusions: We found that the athletes exhibited generalized detraining features despite maintaining home-based physical activity. …”
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Numérique et autonomisation des élèves : quelle formation initiale des enseignants ? by Ghislaine Gueudet, Sophie Joffredo-Le Brun, Antoine Le Bouil, Carole Le Hénaff, Gwenaëlle Riou-Azou, Sabrina Srey

“…In this article we investigate how an initial training based on collective documentation work for designing classroom scenarios can contribute to achieving this objective for trainee teachers. …”
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A multi-model approach integrating whole-slide imaging and clinicopathologic features to predict breast cancer recurrence risk by Manu Goyal, Jonathan D. Marotti, Adrienne A. Workman, Graham M. Tooker, Seth K. Ramin, Elaine P. Kuhn, Mary D. Chamberlin, Roberta M. diFlorio-Alexander, Saeed Hassanpour

“…This method was trained and tested on 956 hematoxylin and eosin-stained whole-slide images of 950 ER+/HER2− breast cancer patients with corresponding clinicopathological features that had prior Oncotype DX testing. …”
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Machine Learning-Enhanced Discrimination of Gamma-Ray and Hadron Events Using Temporal Features: An ASTRI Mini-Array Analysis by Valentina La Parola, Giancarlo Cusumano, Saverio Lombardi, Antonio Alessio Compagnino, Antonino La Barbera, Antonio Tutone, Antonio Pagliaro

“…The model incorporates feature importance analysis to select the most discriminating temporal parameters from a comprehensive set of time-based features. …”
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A novel brain MRI classification framework integrating tuned single scale retinex and empirical wavelet entropy features by Paravathanani Rajendra Kumar, Krishna Prakash, Buraga Ram Sai Teja, Kota Akhil Manoj, Adi Bhuvaneswar, Divvela David Syam Kumar, Shonak Bansal, Sandeep Kumar, Mohammad Rashed Iqbal Faruque, K. S. Al-mugren

“…This feature set is used to train Support Vector Machine (SVM) and LPBoost classifiers. …”
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Ranking Assisted Unsupervised Morphological Disambiguation of Turkish by Hayri Volkan Agun, Ozkan Aslan

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Predictive analysis of clinical features for HPV status in oropharynx squamous cell carcinoma: A machine learning approach with explainability by Emily Diaz Badilla, Ignasi Cos, Claudio Sampieri, Berta Alegre, Isabel Vilaseca, Simone Balocco, Petia Radeva

“…This study aims to provide a comprehensive method based on pre-treatment clinical data for predicting the patient’s HPV status over a large OPSCC patient cohort and employing explainability techniques to interpret the significance and effects of the features. …”
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Physical transition from professional MMA to tactical firefighting: case study by Cole Smeester, Gabriel J. Sanders, Corey A. Peacock, Jose Antonio

“…Conditioning included one sprinting (100 m, 200 m, 400 m) and one long-distance (5 km) session on nonresistance days. Resistance training featured sled pushes/pulls, weighted carries, stair climbs, and weight room exercises to replicate firefighting demands and build strength. …”
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Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical featuresResearch in context by Shannon Doyle, Esther H. Lips, Eric Marcus, Lennart Mulder, Yat-Hee Liu, Francesco Dal Canton, Timo Kootstra, Maartje M. van Seijen, Ihssane Bouybayoune, Elinor J. Sawyer, Alastair M. Thompson, Sarah E. Pinder, Clara I. Sánchez, Jonas Teuwen, Jelle Wesseling, Jelle Wesseling, Jos Jonkers, Jacco van Rheenen, Esther H. Lips, Marjanka Schmidt, Lodewyk F.A. Wessels, Proteeti Bhattacharjee, Alastair Thompson, Serena Nik-Zainal, Helen Davies, Elinor J. Sawyer, Andrew Futreal, Nicholas Navin, E. Shelley Hwang, Fariba Behbod, Daniel Rea, Hilary Stobart, Deborah Collyar, Donna Pinto, Ellen Verschuur, Marja van Oirsouw

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AE-BoNet: A Deep Learning Method for Pediatric Bone Age Estimation using an Unsupervised Pre-Trained Model by Mojtaba Sirati-Amsheh, Elham Shabaninia, Ali Chaparian

“…While clinical bone age assessment techniques are time-consuming and prone to inter/intra-observer variability, deep learning-based methods are used for automated bone age estimation.Objective: The current study aimed to develop an unsupervised pre-training approach for automatic bone age estimation, addressing the challenge of limited labeled data and unique features of radiographic images of hand bones. …”
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Real-time earthquake magnitude prediction using designed machine learning ensemble trained on real and CTGAN generated synthetic data by Anushka Joshi, Balasubramanian Raman, C. Krishna Mohan

“…In this work, Conditional Tabular Generative Adversarial Networks (CTGAN), a deep machine learning tool, is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information. The result obtained using actual and mixed (synthetic and actual) datasets will be used for training the stacked ensemble magnitude prediction model, MagPred, designed specifically for this study. …”
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Combining Feature Dimensionality Reduction and NGO-CNN-BiLSTM′s Network Abnormal Traffic Detection Method in Recruitment Intelligence Platform by YUAN Xiaopeng, MIAO Sirui, ZHANG Qiaojun

“…In view of the challenges brought by computer viruses and network malicious attacks to the operation and maintenance of the recruitment intelligent platform system, this paper proposes a method to detect abnormal traffic of the recruitment smart platform based on feature dimensionality reduction combined with Northern Goshawk Optimization ( NGO) to optimize convolutional neural network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM). …”
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The Impact of Protein and Amino Acid Supplementation on Muscular Strength and Endurance in Recreational Gym-Goers During 8-Week Resistance Training by Sandor-Richard Nagy, Magdalena Mititelu, Ruxandra-Cristina Marin, Violeta Popovici, Annamaria Pallag, Tünde Jurca

“…<b>Conclusions:</b> Resistance training records of recreational athletes are significantly influenced by age, gender, body weight status, NS type, and daily diet features (<i>p</i> < 0.05). …”
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Estimating strawberry weight for grading by picking robot with point cloud completion and multimodal fusion network by Yiming Chen, Wei Wang, Junchao Chen, Jizhou Deng, Yuanping Xiang, Bo Qiao, Xinghui Zhu, Changyun Li

“…These features are then integrated at the feature level through gradient blending, realizing the combination of the strengths of both modalities. …”
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MEP-YOLOv5s: Small-Target Detection Model for Unmanned Aerial Vehicle-Captured Images by Shengbang Zhou, Song Zhang, Chuanqi Li, Shutian Liu, Dong Chen

“…This article introduces a drone detection model, MEP-YOLOv5s, which optimizes the Backbone, Neck layer, and C3 module based on YOLOv5s, and combines effective attention mechanisms to improve the training efficiency of the model by replacing the traditional CIoU loss (Complete Intersection over Union) with MPDIoU (Minimum Point Distance-based Intersection over Union) loss. …”
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Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm by Ruirui Zhong, Yixiong Feng, Puyan Li, Xuanyu Wu, Ao Guo, Ansi Zhang, Chuanjiang Li

“…Then, a feature extraction method integrating variational mode decomposition (VMD), L‐cliffs‐based effective mode selection, and sample entropy is devised to extract the latent features from the complex high‐dimensional feature space. …”
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DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing by Hao Qu, Lilian Zhang, Jun Mao, Junbo Tie, Xiaofeng He, Xiaoping Hu, Yifei Shi, Changhao Chen

“…Although deep learning-based local features excel at capturing high-level information and perform well on matching benchmarks, they struggle with generalization in continuous motion scenes, adversely affecting loop detection accuracy. …”
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Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance by Fauzi Adi Rafrastara, Wildanil Ghozi, Ramadhan Rakhmat Sani, Lekso Budi Handoko, Abdussalam Abdussalam, Elkaf Rahmawan Pramudya, Faizal M. Abdollah

“…As malware has progressed from its simpler, monomorphic variants to more sophisticated forms like oligomorphic, polymorphic, and metamorphic, a machine learning-based detection system is now required, surpassing the limitations of traditional signature-based methods. …”
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MF-YOLO: Mask Wearing Detection Algorithm for Dense Environments by Peng Wen, Zhengyi Yuan, Junhu Zhang, Haitao Li

“…To address the challenges of false positives and missed detections in face mask detection within dense environments, we propose the MF-YOLO face mask detection model. First, a feature map convolution approach is introduced to transform the feature map information into the initial weights of convolutional kernels, accelerating the convergence of model training. …”
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MDFT-GAN: A Multi-Domain Feature Transformer GAN for Bearing Fault Diagnosis Under Limited and Imbalanced Data Conditions by Chenxi Guo, Vyacheslav V. Potekhin, Peng Li, Elena A. Kovalchuk, Jing Lian

“…To address these challenges, this paper proposes a novel fault diagnosis framework based on a Multi-Domain Feature Transformer GAN (MDFT-GAN). …”
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