Optimized predictive maintenance for streaming data in industrial IoT networks using deep reinforcement learning and ensemble techniques by K. Varalakshmi, J. Kumar

“…This paper introduces an ensemble-based framework combining Deep Reinforcement Learning (DRL), Random Forest (RF), and Gradient Boosting Machines (GBM) to improve fault prediction and maintenance efficiency. …”
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Research progress in globular fruit picking recognition algorithm based on deep learning by LI Hui, ZHANG Jun, YU Shuochen, LI Zhixin

“…Automatic picking equipment combined with computer vision often uses object detection algorithms to identify objects, and object detection algorithms can be divided into both traditional algorithms and deep learning-based object detection algorithms. Traditional algorithms identify the position and bounding box of a specific object in an image or a video, usually by preprocessing the image (Scaling, grayscale or normalization), feature extraction (using traditional hand-designed features or automatic learning based on machine learning), classification or regression (confirming object class and location), and non-maximum suppression to further optimize and filter detected objects. …”
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Intelligent Deep Learning and Keypoint Tracking-Based Detection of Lameness in Dairy Cows by Zongwei Jia, Yingjie Zhao, Xuanyu Mu, Dongjie Liu, Zhen Wang, Jiangtan Yao, Xuhui Yang

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Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion by Jingru Wang, Shipeng Wen, Wenjie Liu, Xianglian Meng, Zhuqing Jiao

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Deep-learning-based detection of underwater fluids in multiple multibeam echosounder data by Tyméa Perret, Gilles Le Chenadec, Arnaud Gaillot, Yoann Ladroit, Stéphanie Dupré

“…Our study investigated the use of a YOLO-based deep learning supervised approach to automate the detection of fluids emitted from cold seeps (gaseous methane) and volcanic sites (liquid carbon dioxide). …”
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Cutting Force Estimation Using Milling Spindle Vibration-Based Machine Learning by Je-Doo Ryu, Hoon-Hee Lee, Kyoung-Nam Ha, Sung-Ryul Kim, Min Cheol Lee

“…This study proposes a cost-effective alternative by estimating cutting forces using spindle vibration data through a long short-term memory (LSTM)-based machine learning model. First, the correlation between cutting force and tool wear is analyzed to emphasize the need for accurate force estimation. …”
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Using concept maps to structure a small-scale literature review: an approach to research-based learning in pre-service teacher education by Victoria I. Marín

“…One popular (meta)methodology considered in educational research (and that is well suited to school research) is design-based research (DBR). Incorporating this approach into RBL as a method for developing teaching innovation in schools could be one way to include RBL in teacher education and place pre-service teachers in the position of teacher as researcher. …”
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An Ensemble Learning-Based Predictive Parameterization Approach for Permanent Magnet Synchronous Machines by Sema Nur Ipek, Nur Bekiroglu, Murat Taskiran

“…Permanent Magnet Synchronous Machines (PMSMs) are extensively utilized for their ability to deliver accurate position control, and the equivalent circuit characteristics of these machines are essential in several applications, particularly in formulating the control strategy. …”
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PENGARUH MODEL PROBLEM BASED LEARNING BERBANTUAN GEOGEBRA DAN TEKA TEKI SILANG (TTS) TERHADAP HASIL BELAJAR SISWA KELAS XI-4 SMA NEGERI 3 SALATIGA by Kamelia Nahar, Anna Royyana Nikmah, Kriswandani Kriswandani

“…This means that the average score after being taught using the Problem Based Learning model assisted by GeoGebra and Crossword Puzzles is better or has a positive effect compared to the average score before the model was applied. …”
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Resilience and online learning emotional engagement among college students in the digital age: a perspective based on self-regulated learning theory by Dandan Ge

“…Abstract Background Self-efficacy perception and strategy use are two key processes for achieving self-regulated learning. Based on the perspective of self-regulated learning theory, this study explores the mediating mechanism of self-regulated learning efficacy, and strategy use (self-control and emotion regulation strategies) between resilience and online learning emotional engagement. …”
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An efficient non-parametric feature calibration method for few-shot plant disease classification by Jiqing Li, Zhendong Yin, Dasen Li, Hongjun Zhang, Mingdong Xu

“…The temporal and spatial irregularity of plant diseases results in insufficient image data for certain diseases, challenging traditional deep learning methods that rely on large amounts of manually annotated data for training. …”
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FDIA Detection in Power Grid Based on Opposition-Based Whale Optimization Algorithm and Multi-layer Extreme Learning Machine by Lei XI, Yixiao WANG, Miao HE, Chen CHENG, Xilong TIAN

“…Therefore, this paper proposes a FDIA location detection method based on opposition-based learning whale optimization algorithm and multi-layer extreme learning machine (OWOA-ELMML). …”
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An object detection method in foggy weather based on novel feature enhancement and fusion by ZHU Lei, ZHAO Han, WANG Weili

“…To further improve the detection accuracy of objects in foggy scenes for deep learning networks, a foggy object detection method called NFF-YOLOX is proposed based on the YOLOX network. …”
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A Delphi-based needs assessment to identify and prioritise procedural skills through consensus for simulation-based learning in neurosurgery by Adam F. Roche, Dara O. Kavanagh, Darach Crimmins, Vincent Healy, Gulam Zilani, Lars Konge, Leizl Joy Nayahangan, Danyal Z. Khan, Daniel Murray, Javier Francisco Cuello, Caitriona Cahir, Niamh Murphy, Claire M. Condron

“…This approach aimed to enhance understanding of the essential procedures that should shape a technical skills framework for neurosurgical simulation-based learning curricula. Methods We utilised a modified Delphi process and Copenhagen Academy for Medical Education and Simulation (CAMES) Needs Assessment Formula (NAF) to reach consensus. …”
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Pre-class learning assessment on integrating artificial intelligence into evidence-based medicine curriculum by ZUHAER·Yisha, CHEN Xiaowei, CAO Wangnan, WU Shanshan, LIU Junchang, SUN Yumei, WU Tao, ZHAN Siyan, SUN Feng

“…Objective To investigate the knowledge base, learning attitudes, and practical abilities of students enrolling in an evidence-based medicine (EBM) course integrated with artificial intelligence (AI).Methods The study targeted students enrolled in the 2024–2025 academic year course “Evidence Synthesis and Application: Network Meta-Analysis” at Peking University Health Science Center. …”
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Enhancing medical education in Nepal through problem-based learning (PBL) and collaborative action research strategies by Alok Atreya, Laxman Gnawali, Ritesh G. Menezes, Samata Nepal

“…Abstract Background Teamwork and collaboration among students are essential for successful problem-based learning (PBL) implementation; however, many medical colleges in Nepal face obstacles to implementing successful PBL sessions. …”
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Students’ Problem-solving Skills and Perceptions through Problem-based Learning Assisted by Google Classroom by Nur Iva Wulansari, Febi Putri, Lia Yuliati, Nasikhudin Nasikhudin

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Quality improvement of a community-engaged authorship system: lessons learned from the RECOVER initiative by Shari Esquenazi-Karonika, Patenne D. Mathews, Marion J. Wood, Praveen M. Mudumbi, Janelle Linton, Jasmine Briscoe, Elle Seibert, K. Coombs, Gregory Laynor, Stuart D. Katz, Alicia Chung

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A Semantic Segmentation-Based GNSS Signal Occlusion Detection and Optimization Method by Zhe Yue, Chenchen Sun, Xuerong Zhang, Chengkai Tang, Yuting Gao, Kezhao Li

“…To address this issue, we dig into the environmental perception perspective to propose a semantic segmentation-based GNSS signal occlusion detection and optimization method. …”
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INRNet: Neighborhood Re-Ranking-Based Method for Pedestrian Text-Image Retrieval by Kehao Wang, Yuhui Wang, Lian Xue, Qifeng Li

“…In order to address the issues arising from the above methods, we introduce Implicit Neighbourhood Reranking Network (INRNet) which utilizes a bilateral feature extractor to learn global image-text matching knowledge and leverages nearest neighbors as prior knowledge to mine positive samples. …”
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