Alloys innovation through machine learning: a statistical literature review by Alireza Valizadeh, Ryoji Sahara, Maaouia Souissi

“…This review systematically analyzes over 200 publications to explore the growing role of data-driven methods and their potential benefits in accelerating alloy development. The review presents a comprehensive overview of different aspects of alloy innovation by machine learning and other computational approaches used in recent years. …”
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Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT by Rana Albelaihi

“…This paper presents the Mobility-Aware Client Selection (MACS) strategy, developed to address the challenges associated with client mobility in Federated Learning (FL). …”
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The sound of science: a sonification learning experience in an Italian secondary school by Giacomo Eramo, Serafina Pastore, Mario De Tullio, Valeria Rossini, Alessandro Monno, Ernesto Mesto

“…IntroductionThe present article reports on a case study aimed at improving STEAM education in secondary schools. …”
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Predictive model of ulcerative colitis syndrome with ensemble learning and interpretability methods by Ling Zhu, Shan He, Wanting Zheng, Yuanyuan Tong, Feng Yang

“…But syndrome differentiation of UC presents a longstanding challenge in TCM due to its chronic nature and varied manifestations. …”
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Plucking Point and Posture Determination of Tea Buds Based on Deep Learning by Chengju Dong, Weibin Wu, Chongyang Han, Zhiheng Zeng, Ting Tang, Wenwei Liu

“…In order to improve the accuracy and efficiency of machine plucking tea leaves, a method is presented in this paper to determine the plucking point and plucking posture based on the instance segmentation deep learning network. …”
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Detection of cotton crops diseases using customized deep learning model by Hafiz Muhammad Faisal, Muhammad Aqib, Saif Ur Rehman, Khalid Mahmood, Silvia Aparicio Obregon, Rubén Calderón Iglesias, Imran Ashraf

“…This study presents dissimilar state-of-the-art deep learning models for disease recognition including VGG16, DenseNet, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet models. …”
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A Multitask Deep Learning Model for Predicting Myocardial Infarction Complications by Fazliddin Makhmudov, Normakhmad Ravshanov, Dilshot Akhmedov, Oleg Pekos, Dilmurod Turimov, Young-Im Cho

“…This study presents a multitask deep learning model designed to simultaneously address two related tasks: multidimensional binary classification of myocardial infarction complications and multiclass classification of mortality causes. …”
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Deconstructing ‘Learning Spaces’: A Narrative Inquiry on the Right to Education in Indonesia by Raymond Andaya, Ai Kihara-Hunt, Kenya Winanti, Fathia Fairuza

“…The article presents three key findings based on a spatial analysis of non-state learning spaces in Indonesia. …”
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Distanced Large Group Simulations as a Learning Method for Interprofessional Collaboration by Marja Silén-Lipponen, Eija Piippo-Savolainen, Mina Azimirad, Terhi Saaranen

“…Digitalization in healthcare education has shifted simulation learning methods to distanced implementations. Successful transition to distance education requires effective communication and the teacher’s good ability to use digital learning methods, as well as students’ active interaction and motivation throughout the entire educational process. …”
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Dual-Stage Clean-Sample Selection for Incremental Noisy Label Learning by Jianyang Li, Xin Ma, Yonghong Shi

“…To address this critical gap, this paper presents a dual-stage clean-sample selection method for Incremental Noisy Label Learning (DSCNL). …”
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Abortion learning mechanisms for nurses and midwives: a scoping review of evidence by Martha Nicholson, Lesley Hoggart

“…The authors included 43 studies and identified five learning mechanisms. The evidence is presented under three themes: (1) the adequacy of abortion learning mechanisms for nurses and midwives, (2) listening to nurses and midwives’ experiences, and (3) barriers to abortion training. …”
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The Profile of Students’ Analytical Thinking Skills on Chemistry Systemic Learning Approach by Nur Fitriyana, Marfuatun Marfuatun, Erfan Priyambodo

“…Meanwhile, the CSLA was integrated in the teaching-learning process and in the STAT, the CSLA was presented in the form of cyclic diagrams. …”
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Modeling Local Demand for Mobile Spectrum: An Interpretable Machine Learning Approach by Janaki Parekh, Elizabeth Yackoboski, Amir Ghasemi, Halim Yanikomeroglu

“…To address this gap, this paper presents a data-driven approach to estimate localized mobile spectrum demand within the context of spectrum regulation. …”
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Machine Learning Approaches for Real-Time Mineral Classification and Educational Applications by Paraskevas Tsangaratos, Ioanna Ilia, Nikolaos Spanoudakis, Georgios Karageorgiou, Maria Perraki

“…The main objective of the present study was to develop a real-time mineral classification system designed for multiple detection, which integrates classical computer vision techniques with advanced deep learning algorithms. …”
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Computational design exploration of rocket nozzle using deep reinforcement learning by Aagashram Neelakandan, Arockia Selvakumar Arockia Doss, Natrayan Lakshmaiya

“…Deep Reinforcement Learning (DRL) has emerged as a powerful tool for solving high-dimensional optimization problems in complex, unexplored domains. …”
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Deep Learning in Power Systems: A Bibliometric Analysis and Future Trends by Seyed Mahdi Miraftabzadeh, Andrea Di Martino, Michela Longo, Dario Zaninelli

“…This paper presents a bibliometric analysis and future trends of deep learning in power systems, aiming to identify its fundamental characteristics and summarize the research hot topics and future trends. …”
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A survey on underwater coral image segmentation based on deep learning by Ming Li, Hanqi Zhang, Armin Gruen, Deren Li

“…Understanding recent achievements and their relevance to coral ecology monitoring needs is crucial for future planning. This paper presents a literature review on underwater coral image segmentation, focusing on the deep learning implementation pipeline. …”
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Fast and accurate obstacle detection based on stereo vision and deep learning by S. Badrloo, S. Badrloo

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Analysis of goal, feedback and rewards on sustained attention via machine learning by Nethali Fernando, Nethali Fernando, Matthew Robison, Matthew Robison, Pedro D. Maia, Pedro D. Maia

“…IntroductionSustaining attention is a notoriously difficult task as shown in a recent experiment where reaction times (RTs) and pupillometry data were recorded from 350 subjects in a 30-min vigilance task. Subjects were also presented with different types of goal, feedback, and reward.MethodsIn this study, we revisit this experimental data and solve three families of machine learning problems: (i) RT-regression problems, to predict subjects' RTs using all available data, (ii) RT-classification problems, to classify responses more broadly as attentive, semi-attentive, and inattentive, and (iii) to predict the subjects' experimental conditions from physiological data.ResultsAfter establishing that regressing RTs is in general a difficult task, we achieve better results classifying them in broader categories. …”
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Deep Learning Based Mobile Application for Automated Plant Disease Detection by B. Ramana Reddy, Gauri Kalnoor, Mudigonda Devashish, Palagiri Sai Karthik Reddy

“…Plant diseases remain a significant threat to global agriculture, necessitating rapid and accurate detection to minimize crop loss. This paper presents a lightweight, end-to-end system for plant leaf disease detection and severity estimation, optimized for real-time field deployment. …”
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