Comparison of machine learning and validation methods for high-dimensional accelerometer data to detect foot lesions in dairy cattle. by Muhammad Usman Riaz, Luke O'Grady, Conor G McAloon, Finnian Logan, Isobel Claire Gormley

“…To handle this, researchers often summarize accelerometer data into indices like step counts, which simplifies analysis but may sacrifice important details needed for accurate prediction of lameness. …”
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Injecting structure-aware insights for the learning of RNA sequence representations to identify m6A modification sites by Yue Yu, Shuang Xiang, Minghao Wu

“…The core innovation of M6A-SAI lies in its ability to incorporate structural information through a multi-step process: initially, the model utilizes a Transformer encoder to learn RNA sequence representations. …”
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Enhancing high pressure pulsation test bench performance: a machine learning approach to failure condition tracking by Aslı Aksoy, Ömer Haki

“…The results of this study are described as a fundamental step in the company’s smart maintenance programme. …”
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Intelligent Energy-Efficient Train Trajectory Optimization Approach Based on Supervised Reinforcement Learning for Urban Rail Transits by Guannan Li, Siu Wing Or, Ka Wing Chan

“…This paper proposes a three-step supervised reinforcement learning-based intelligent energy-efficient train trajectory optimization (SRL-IETTO) approach for iATO by hybrid-integrating deep reinforcement learning (DRL) and supervised learning. …”
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Optimizing Kernel Extreme Learning Machine based on a Enhanced Adaptive Whale Optimization Algorithm for classification task. by ZeSheng Lin

“…Data classification is an important research direction in machine learning. In order to effectively handle extensive datasets, researchers have introduced diverse classification algorithms. …”
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A hybrid deep learning framework for short-term load forecasting with improved data cleansing and preprocessing techniques by Muhammad Sajid Iqbal, Muhammad Adnan, Salah Eldeen Gasim Mohamed, Muhammad Tariq

“…In this study, a comprehensive four-step approach is introduced for short-term load forecasting (STLF) aimed at precisely estimating power demand and generation. …”
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Nurse educators' challenges of problem-based learning implementation at Ethiopian public universities: A phenomenological qualitative study. by Getachew Nigussie Bolado, Bizuayehu Atinafu Ataro, Tamirat Ersino Kebamo, Agumas Shibabaw Ayana, Worku Mimani Minuta

“…The data analysis was done by OpenCode software and conventional content analysis was carried out following Colaizzi's 7-step approach.<h4>Results</h4>In the study, four major themes and eighteen subthemes emerged, covering challenges encountered by nurse educators in Ethiopian public universities: challenges about educators themselves, nursing students, institutions/universities, problem-based learning strategies. …”
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LandNet: Combine CNN and Transformer to Learn Absolute Camera Pose for the Fixed-Wing Aircraft Approach and Landing by Siyuan Shen, Guanfeng Yu, Lei Zhang, Youyu Yan, Zhengjun Zhai

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A comparative study of query strategies in Pareto active learning for heat treatment optimization of medium-Mn steel by Seung-Hyeon Kim, Jin-Young Lee, Young-Kook Lee

“…This study employs a Pareto Active Learning (PAL) framework to optimize multi-step heat treatment conditions for medium-Mn steels, aiming to maximize both ultimate tensile strength (UTS) and total elongation (TE) while minimizing the number of experimental iterations. …”
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Improving Vertebral Fracture Detection in C-Spine CT Images Using Bayesian Probability-Based Ensemble Learning by Abhishek Kumar Pandey, Kedarnath Senapati, Ioannis K. Argyros, G. P. Pateel

“…In this study, Wide ResNet-40, DenseNet-121, and EfficientNet-B7 are chosen, fine-tuned, and used as base models, and a Bayesian-based probabilistic ensemble learning method is proposed for fracture detection in cervical spine CT images. …”
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DECISION SUPPORT SYSTEM STATISTICAL SYNTHESIS, BASED ON AN ASSESSMENT CARRIED OUT WITH THE INTENSITY OF INTERACTION TEACH AND THE LEARNING ENVIRONMENT by E. V. Kondratyonok, V. A. Kondratyonok

“…Decision Support System should take into account the strength of the interaction with the student learning environment. This ensures a high level of knowledge and ability of their application by alumni.The method of learning process modeling of this system in terms of statistics is examined. …”
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Active learning based surrogate ensemble assisted multi-objective optimization framework for reservoir water-flooding optimization by Lian Wang, Liang Zhang, Rui Deng, Jianhua Qu, Hehua Wang, Liehui Zhang, Xing Zhao, Bing Xu, Xindong Lv, Caspar Daniel Adenutsi

“…Abstract Surrogate-assisted management of water-flooding, as a reservoir production optimization strategy, dynamically adjusts development schemes at each production step, leading to improved economic benefits and enhanced recovery. …”
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Deep Learning Integrating Scale Conversion and Pedo‐Transfer Function to Avoid Potential Errors in Cross‐Scale Transfer by Peijun Li, Yuanyuan Zha, Yonggen Zhang, Chak‐Hau Michael Tso, Sabine Attinger, Luis Samaniego, Jian Peng

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An ensemble of deep representation learning with metaheuristic optimisation algorithm for critical health monitoring using internet of medical things by Mai Alduailij

“…Thus, at present, deep learning (DL) is considered a safe IoMT system, as it can enhance the system’s performance. …”
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Electric Bus Charging Schedules Relying on Real Data-Driven Targets Based on Hierarchical Deep Reinforcement Learning by Jiaju Qi, Lei Lei, Thorsteinn Jonsson, Lajos Hanzo

“…The charging scheduling problem of Electric Buses (EBs) is investigated based on Deep Reinforcement Learning (DRL). A Markov Decision Process (MDP) is conceived, where the time horizon includes multiple charging and operating periods in a day, while each period is further divided into multiple time steps. …”
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Machine-Learning-Based Activity Tracking for Individual Pig Monitoring in Experimental Facilities for Improved Animal Welfare in Research by Frederik Deutch, Marc Gjern Weiss, Stefan Rahr Wagner, Lars Schmidt Hansen, Frederik Larsen, Constanca Figueiredo, Cyril Moers, Anna Krarup Keller

“…Video-based machine learning models for monitoring experimental pigs provide a continuous and objective observation method for animal misthriving detection. …”
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COVID-19 and e-Learning: The skills and abilities required for e-learners in higher education during the coronavirus era by Fatemeh Narenji Thani, Fatemeh Tizhoosh Jalali, Zeinab sadat Mostafavi

“…Finally, the results indicated that the most critical components from e-learners’ view which more critical in digital learning were effective communication, use of the computer, active learning, learning skills, resilience, motivation toward e-learning, emotional intelligence, interaction with e-learning system, self-direct learning, time management, flexibility and adaptability, critical thinking, creativity, asking someone for help, leadership skills, stress management, self-organized, cognitive skills and self-assessment…”
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Machine learning model design for high performance cloud computing &amp; load balancing resiliency: An innovative approach by Nilayam Kumar Kamila, Jaroslav Frnda, Subhendu Kumar Pani, Rashmi Das, Sardar M.N. Islam, P.K. Bharti, Kamalakanta Muduli

“…The proposed integrated design approach has been analyzed on different step actions and decisions based on machine learning regression and classification models, which auto-correct the performance of the system at real run time instances. …”
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Debris-covered glaciers mapping based on machine learning and multi-source satellite images over Eastern Pamir by Aamir Feroz, Shuang Liang, Gunayu Li, Xinwu Li, Zahid Ur Rahman, Bojin Yang

“…This was a significant step forward from previous work in the area.…”
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A Task Offloading and Resource Allocation Strategy Based on Multi-Agent Reinforcement Learning in Mobile Edge Computing by Guiwen Jiang, Rongxi Huang, Zhiming Bao, Gaocai Wang

“…Many existing pieces of research adopted single-agent reinforcement learning to solve this problem, which has some defects such as low robustness, large decision space, and ignoring delayed rewards. …”
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