Implementation of the Drill Method in Learning Arabic with a Communicative Approach by Muhammad Zikran Adam, Laily Fitriani

“…The result indicates that the drill method with a communicative approach is implemented through three steps. Firstly, the planning stage involves preparing a learning program plan. …”
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The Impact of Debate Lessons, Powered by Generative AI, on Student Learning by Seow Yongzhi

“…Inquiry-based learning is structured by a Humanities inquiry cycle involving four steps and can take various forms in the classroom. …”
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Sparse Learning of the Disease Severity Score for High-Dimensional Data by Ivan Stojkovic, Zoran Obradovic

“…Some steps in that direction have been taken and machine learning algorithms for extracting scoring functions from data have been proposed. …”
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Causal structure learning in directed, possibly cyclic, graphical models by Semnani Pardis, Robeva Elina

“…We consider the problem of learning a directed graph G⋆{G}^{\star } from observational data. …”
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Deep learning to promote health through sports and physical training by Xinyue Li

“…Recent advancements in deep learning and time-series analysis offer an opportunity to develop more personalized and accurate predictive models for assessing health improvement trends.MethodsThis study proposes a Health Improvement Score (HIS) prediction model based on a sequence-to-sequence deep learning architecture with Long Short-Term Memory (LSTM) networks and an attention mechanism. …”
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Medical image classification by incorporating clinical variables and learned features by Jiahui Liu, Xiaohao Cai, Mahesan Niranjan

“…Our method contains two main steps and is effective in tackling the extra challenge raised by the scarcity of medical data. …”
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Enhancing temporal learning in recurrent spiking networks for neuromorphic applications by Ismael Balafrej, Soufiyan Bahadi, Jean Rouat, Fabien Alibart

“…Firstly, we incorporate neuron-level synaptic delays, which not only allow the gradient to skip time steps but also reduce the overall neuron population’s firing rate. …”
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Student Engagement Dataset (SED): An Online Learning Activity Dataset by M. S. S. Kassim, Z. H. Azizul, A. A. H. Ahmad Fuaad

“…Distance learning has become a popular educational medium, and the Internet has spread since the early 2000s. …”
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Deep learning in defects detection of PV modules: A review by Katleho Masita, Ali Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…The importance of preprocessing steps such as image normalization, registration, and segmentation is emphasized to enhance detection accuracy. …”
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The Baganduang Boat Value-Based Learning Model in Elementary Schools by Elpri Darta Putra, Bunyamin Maftuh, Mamat Supriatna

“… This study aims to develop a learning model based on the value of baganduang boats to foster the character of eloklah budi elementary school students. …”
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Spatio-Temporal Meta-Graph Learning for Recommendation on Heterogeneous Graphs by Xiaofei Yang, Wenjing Yang, Xiaoshuang Wang, Mei Gao, Lu Zhang

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An IoT and Machine Learning-based Neonatal Sleep Stage Classification by Awais Abbas, Hafiz Sheraz Sheikh, SaadUllah Farooq Abbasi

“…For this reason, we proposed an algorithm for neonatal sleep-wake classification using machine learning. The proposed research is divided into three steps. …”
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Intelligent System for Student Performance Prediction Using Machine Learning by Mustafa S. Ibrahim Alsumaidaie, Ahmed Adil Nafea, Abdulrahman Abbas Mukhlif, Ruqaiya D. Jalal, Mohammed M AL-Ani

“…The steps of research methodology contained (data collection, preprocessing, feature identification, model construction, and evaluation). …”
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Hyperspectral Identification of Milk Adulteration Using Advance Deep Learning by Muhammad Aqeel, Ahmed Sohaib, Muhammad Iqbal, Syed Sajid Ullah

“…The non-destructive method utilizes hyperspectral imaging (HSI) with the Specim Fx-10 (397–1003 nm) for image-based analysis, involving preprocessing steps like image scaling, ROI selection, radiometric correction, and spectral reflectance extraction using the empirical line method (ELM). …”
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Weakly supervised learning in thymoma histopathology classification: an interpretable approach by Chunbao Wang, Chunbao Wang, Xianglong Du, Xiaoyu Yan, Xiali Teng, Xiaolin Wang, Zhe Yang, Hongyun Chang, Yangyang Fan, Caihong Ran, Jie Lian, Chen Li, Hansheng Li, Lei Cui, Yina Jiang

“…This study presents an AI-assisted diagnostic model that combines weakly supervised learning with a divide-and-conquer multi-instance learning (MIL) approach to improve classification accuracy and interpretability.MethodsWe applied the model to 222 thymoma slides, simplifying the five-class classification into binary and ternary steps. …”
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Deep learning in next-generation vaccine development for infectious diseases by Manojit Bhattacharya, Yi-Hao Lo, Srijan Chatterjee, Arpita Das, Zhi-Hong Wen, Chiranjib Chakraborty

“…Similarly, artificial intelligence (AI) is quickly transforming the vaccine development landscape. Deep learning (DL), a subset of AI, is used in the landscape of vaccine development in terms of its algorithms, tools, and technologies. …”
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Analyzing Financial Stability by Predicting Bankruptcy Situations with Machine Learning by Mohd Naved, Ravi Kumar, Shaiku Saheb

“…These models can use factors including liquidity ratios, debt levels, market circumstances, and economic indicators to offer early warnings of possible financial instabilities, allowing organizations to take proactive steps to reduce risk and avert bankruptcy. This paper endeavors to forecast bankruptcies by leveraging ML models. …”
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Machine learning applications in river research: Trends, opportunities and challenges by Long Ho, Peter Goethals

“…Unsupervised and supervised learning have dominated river research while neural networks and deep learning have also gradually gained popularity. …”
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Development of time to event prediction models using federated learning by Rasmus Rask Kragh Jørgensen, Jonas Faartoft Jensen, Tarec El-Galaly, Martin Bøgsted, Rasmus Froberg Brøndum, Mikkel Runason Simonsen, Lasse Hjort Jakobsen

“…Alternatively, federated learning (FL) can be utilized to train models based on data located at multiple sites. …”
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Smart deep learning model for enhanced IoT intrusion detection by Faisal S. Alsubaei

“…Existing approaches, however, are usually hampered by the inability to effectively counter the sophisticated and evolving nature of such threats, especially in preprocessing optimization and hyperparameter tuning, which typically adopt conventional machine learning and deep learning models. This paper addresses these limitations with large preprocessing steps followed by hyperparameter tuning of machine learning XGBoost and deep learning Sequential Neural Network (OSNN) algorithms through Grid Search for their best values to improve multiclass intrusion detection across varied datasets. …”
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