Compiling a Corpus of Audiovisual Materials for EFL Learning: Selection, Analysis, and Exploitation by Paula Wood-Borque

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Using active learning selection approach for cross-project software defect prediction by Wenbo Mi, Yong Li, Ming Wen, Youren Chen

“…In this paper, we design an active learning selection algorithm for cross-project defect prediction to alleviate the above problems. …”
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Learning selection-based augmented reality interactions across different training modalities: uncovering sex-specific neural strategies by John Hayes, Joseph L. Gabbard, Ranjana K. Mehta

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Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions for Staphylococcus aureus via metagenomic sequencing by Xuefeng Jia, Yongfen Xiong, Yanping Xu, Fangyuan Chen, Peng Han, Jieming Qu, Quanli He, Guanhua Rao

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Multi-Feature Lightweight DeeplabV3+ Network for Polarimetric SAR Image Classification with Attention Mechanism by Junfei Shi, Shanshan Ji, Haiyan Jin, Yuanlin Zhang, Maoguo Gong, Weisi Lin

“…The proposed method integrates feature extraction, learning, selection, and classification into an end-to-end network framework. …”
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Analysis of diagnostic genes and molecular mechanisms of Crohn’s disease and colon cancer based on machine learning algorithms by Jie Xiao, Junyao Liang, Tao Zhou, Man Zhou, Dexu Zhang, Hui Feng, Chusen Tang, Qian Zhou, Weiqing Yang, Xiaoqin Tan, Wanjia Zhang, Yin Xu

“…This study identified three genes through machine learning selection: DPEP1, MMP3, and MMP13. The ROC curves demonstrated that the machine learning model constructed with these three genes has a high level of accuracy, confirming their potential as biomarkers. …”
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STUDENTS’ RESPONSES ON ENGLISH DEPARTMENT LECTURERS’ COMPETENCE by Nani Hizriani, Fitri Ramadhanti

“…They also expect that the lecturers use interesting media in teaching and learning, select appropriate method and technique in teaching, and give more feedbacks to the students. …”
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Enhanced Reward Function Design for Source Term Estimation Based on Deep Reinforcement Learning by Junhee Lee, Hongro Jang, Minkyu Park, Hyondong Oh

“…In deep reinforcement learning, selecting an appropriate reward function is crucial as it directly impacts the learning performance. …”
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Short-term residential load forecasting via transfer learning and multi-attention fusion for EVs’ coordinated charging by Shuhua Gao, Yuanbin Liu, Jing Wang, Zhengfang Wang, Xu Wenjun, Renfeng Yue, Ruipeng Cui, Yong Liu, Xuezhong Fan

“…To address data scarcity issues, we introduce transfer learning, selecting the best-performing model on the target task as the source model to avoid negative transfer effects. …”
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Advancing Women into Leadership Positions in South African Educational Institutions: The Role of Education and Mentorship by Thizwilondi J. Mudau, Matodzi G Sikhwari

“…Using a qualitative case study design, data was collected through semi-structured interviews and focus groups with 20 female leaders, educators, and policymakers from South African institutions of higher learning, selected through purposive sampling. Thematic analysis revealed that access to education and mentorship programmes significantly enhance women’s leadership abilities, while societal norms and limited institutional support remain barriers. …”
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Federated Learning for All: A Reinforcement Learning-Based Approach for Ensuring Fairness in Client Selection by Saeedeh Ghazi, Saeed Farzi, Amirhossein Nikoofard

“…In federated learning, selecting participating devices (clients) is critical due to their inherent diversity. …”
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Risk factors associated with children learning disorders at school: a socio-medical problem. by Beatriz Sabina Roméu, Zenaida María Saéz, Margarita Roméu Escobar

“…Control group: 40 children with normal learning selected by simple random sampling. Interviews were conducted with parents, teachers and children as well as observations of school activities. …”
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Selective opposition based constrained barnacle mating optimization: Theory and applications by Marzia Ahmed, Mohd Herwan Sulaiman, Md. Maruf Hassan, Md. Atikur Rahaman, Masuk Abdullah

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Research on Inspection Method of Intelligent Factory Inspection Robot for Personnel Safety Protection by Ruohuai Sun, Bin Zhao, Chengdong Wu, Xiaohong Qin

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Strains and stressors: an analysis of touchscreen learning in genetically diverse mouse strains. by Carolyn Graybeal, Munisa Bachu, Khyobeni Mozhui, Lisa M Saksida, Timothy J Bussey, Erica Sagalyn, Robert W Williams, Andrew Holmes

“…Stress facilitated reversal learning (selectively during the late stage of reversal) in C57BL/6J, but did not affect learning in DBA/2J. …”
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Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning by Shuxian Pan, Zibing Wang

“…Based on machine learning-selected indicators, antiviral therapy and The HBV DNA copy number showed a significant correlation with both the occurrence and severity of irAEs. …”
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Pedestrian Re-Recognition Algorithm Based on Optimization Deep Learning-Sequence Memory Model by Feng-Ping An

“…At the same time, this paper proposes a selective dropout method for shallow learning. Selective dropout uses the classifier obtained through shallow learning to modify the probability that a node weight in the hidden layer is set to 0, thereby eliminating the overfitting phenomenon of the deep learning model. …”
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Selective state models are what you need for animal action recognition by Edoardo Fazzari, Donato Romano, Fabrizio Falchi, Cesare Stefanini

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Individualized functional brain mapping machine learning prediction of symptom-change resulting from selective kappa-opioid antagonism in an anhedonic sample from a Fast-Fail trial by Matthew D. Sacchet, Joseph L. Valenti, Poorvi Keshava, Shane W. Walsh, Moria J. Smoski, Andrew D. Krystal, Diego A. Pizzagalli

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Unveiling new therapeutic horizons in rheumatoid arthritis: an In-depth exploration of circular RNAs derived from plasma exosomes by Guoqing Li, Hongyi Chen, Jiacheng Shen, Yimin Ding, Jingqiong Chen, Yongbin Zhang, Mingrui Tang, Nan Xu, Yuxuan Fang

“…A diagnostic xgboost model was developed using common hub genes identified by random forest and least absolute shrinkage and selection operator (LASSO), with intersection genes derived from overlapping machine learning-selected genes. Diagnostic performance evaluated via receiver operating characteristic (ROC) curves using pROC for area under the curve (AUC). …”
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