Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review by Habiba Njeri Ngugi, Andronicus A. Akinyelu, Absalom E. Ezugwu

“…This paper presents a review of machine learning (ML) and deep learning (DL) techniques for crop disease diagnosis, focusing on Support Vector Machines (SVMs), Random Forest (RF), k-Nearest Neighbors (KNNs), and deep models like VGG16, ResNet50, and DenseNet121. …”
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Philosophical analysis of the Recovery College learning model: characterization and connections to learning theories by Galaad Lefay, Galaad Lefay, Catherine Briand, Catherine Briand, Anick Sauvageau, Anick Sauvageau, Marie-Josée Drolet, Marie-Josée Drolet, Marie-Josée Drolet, Brigitte Vachon, Brigitte Vachon, Francesca Luconi, Aliki Thomas, Aliki Thomas, Juliette Nadeau-Tremblay

“…A theoretical and philosophical analysis of this learning model would enhance our understanding of its mechanisms of action and enrich the pedagogical practices of RCs while considering adaptations for other contexts.ObjectivesThis study aims to define and characterize the Recovery College learning model and identify its connections with the key learning theories through a theoretical and philosophical analysis.MethodologyThe study employs a hermeneutic philosophical approach consisting of six steps: 1. define and characterize the RC learning model, 2. identify, define, and describe the key learning theories, 3. select the perspectives and questions for philosophical analysis, 4. analyze the RC learning model through the chosen philosophical perspectives and questions, 5. identify the philosophical connections with the key learning theories, and 6. validate the analysis process.ResultsThe analysis identified five mechanisms of action, nine key principles of RC and four operations. …”
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SToP (See, Treat, and Prevent) activities for impetigo control in remote Western Australia: a cluster randomised, stepped-wedge Trial by Prof Asha Bowen

“…The SToP Trial was a large stepped-wedge cluster randomised control trial of ‘See’, ‘Treat’, and ‘Prevent’ skin health activities with data collection occurring between 2019-2022 in the Kimberley region of Western Australia. …”
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Accelerating the inference of string generation-based chemical reaction models for industrial applications by Mikhail Andronov, Natalia Andronova, Michael Wand, Jürgen Schmidhuber, Djork-Arné Clevert

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The MFM program: a successful model in the field of food addiction recovery by Esther Helga Gudmundsdottir, Veronique Rynn

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Segment, Compare, and Learn: Creating Movement Libraries of Complex Task for Learning from Demonstration by Adrian Prados, Gonzalo Espinoza, Luis Moreno, Ramon Barber

“…The algorithm divides tasks into simpler subtasks and generates motion primitive libraries that group common subtasks for use in subsequent learning processes. Our algorithm is based on an initial segmentation step using a heuristic method, followed by probabilistic clustering with Gaussian Mixture Models. …”
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A comprehensive analysis of deep learning and transfer learning techniques for skin cancer classification by Manishi Shakya, Ravindra Patel, Sunil Joshi

“…Prior to segmentation, preprocessing step is performed which involves scaling, denoising, and enhancing the image. …”
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Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops by Tariq Ali, Saif Ur Rehman, Shamshair Ali, Khalid Mahmood, Silvia Aparicio Obregon, Rubén Calderón Iglesias, Tahir Khurshaid, Imran Ashraf

“…Abstract Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. …”
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Multimodal Digital Learning and Local Indigenous Frameworks to Enhance Critical Thinking in History Learning by Suci Rahayu, Sariyatun Sariyatun, Leo Agung Sutimin, Deny Tri Ardianto

“…Conclusion: The incorporation of multimodal digital teaching materials in history lessons empowers students with good critical thinking skills while providing an engaging and reflective space to learn. It encourages learners to actively participate, consider learning from different perspectives, and step outside the confines of the classroom. …”
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Improving K-12 Schooling in Response to the COVID-19 Pandemic Through Tutoring: One Step Forward in Addressing an Ongoing Public Health Concern by Timothy E. Morse, Giang-Ngyuen Thi Ngyuen

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AI Literacy and Adaptive Learning in Moroccan Education: Advancing Critical Thinking and Personalized Learning by Ismaili Yassine

“…It also offers practical ideas for improving teaching and learning with technology.…”
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DEVELOPMENT OF HYPERLINK-BASED LEARNING MEDIA TO IMPROVE STUDENT LEARNING OUTCOMES IN ATOM MATERIALS by Vina Anastasya Hara Tambun, Marini Damanik

“…So that the world of education must be able to utilize technology to create more interesting, comprehensive, and interactive multimedia-based learning media. The purpose of this research and development is to describe the preparation steps and feasibility (validity) of Hyperlink-based PowerPoint learning media to improve student learning outcomes through learning media using the Hyperlink-based PowerPoint learning model which was developed in chemistry learning the development of atomic theory. …”
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Does problem-based learning increase accounting students' critical thinking and learning outcomes? by Nurhasanah Nurhasanah, Eeng Ahman, Dadang Dahlan, Hari Mulyadi

“…As a result, we found that the PBL model improved critical thinking and student learning outcomes. In implementing the PBL model, we employed 10 learning steps, from identifying learning objectives, designing problems, encouraging students to research and find solutions to problems, and contextualizing learning outcomes. …”
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Wavelet Multiresolution Analysis-Based Takagi–Sugeno–Kang Model, with a Projection Step and Surrogate Feature Selection for Spectral Wave Height Prediction by Panagiotis Korkidis, Anastasios Dounis

“…In addition, we introduce a projection step, to further refine the approximation capabilities of the resulting predictive system. …”
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Developing standards for the implementation of stepped care in child and youth mental health service settings: protocol for a multi-method, delphi-based study by Amy Salmon, Jai Shah, Joshua Rash, Karen Tee, Bryan Young, Sarah Mughal, AnnMarie Churchill

“…Introduction Canadian youth mental health (YMH) systems have the potential to urgently tackle the mental health treatment gap currently impacting young people, and stepped care (SC) is one model that can address this need. …”
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Effects of the dyadic FirstStep2Health intervention on parents’ behaviour and anthropometric outcomes: a secondary analysis of a cluster randomised trial by Yan Shi, Nanhua Zhang, Lorraine B Robbins, Jiying Ling, Jean M Kerver, Tsui-Sui Annie Kao

“…Objectives This study aimed to examine the preliminary efficacy of the FirstStep2Health versus usual care control on improving parents’ lifestyle behaviours (moderate to vigorous physical activity, screen time, fruit/vegetable and fibre intake, skin carotenoids), nutrition and physical activity knowledge, self-efficacy, support, parenting style, feeding practices, home environment, anthropometric outcomes (body mass index, % body fat) and blood pressure from baseline to postintervention after adjusting for random cluster effects.Design A cluster randomised controlled trial with 10 Head Start daycare centres (five intervention, five control) was conducted using computer-generated randomisation after baseline data collection.Setting US Head Start daycare centres.Participants 95 parent-child dyads (53 intervention, 42 control).Interventions The 16-week, dyadic, FirstStep2Health intervention included: (1) a daycare-based child programme on healthy mindful eating and physical activity, (2) child letters to parents to connect school learning with home practice, (3) social media-based parent programme to assist parents to promote healthy eating and physical activity at home, (4) virtual group parent meetings via Zoom on topics related to healthy eating and physical activity and (5) weekly motivational messages to increase parental motivation to build a healthy home environment.Results Mixed-effect models were used to examine intervention effects, adjusting for baseline outcome and cluster effects at the daycare and classroom levels. …”
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How do help-seeking and help-abuse affect learning achievement in an interactive learning environment? by Andreas Schulz, Johannes Voermanek

“…Students' help-seeking behavior plays a central role in successful learning with interactive learning environments (ILEs), such as intelligent tutoring systems that provide on-demand help, including step-by-step hints or strategic help for solving mathematics problems. …”
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Active learning for efficient data selection in radio‐signal‐based positioning via deep learning by Vincent Corlay, Milan Courcoux‐Caro

“…Abstract The problem of user equipment positioning based on radio signals is considered via deep learning. As in most supervised‐learning tasks, a critical aspect is the availability of a relevant dataset to train a model. …”
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Does digital collective learning improve with more participants? An experiment on a collective learning platform by Santos Orejudo, Oscar Casanova, Jacobo Cano-Escoriaza, Ana Cebollero-Salinas

“…Based on the principles of collective intelligence, our collaborative learning platform proposes an interaction model in which participants gradually reach solutions to a problem through a series of interaction processes that culminate in a step where consensus is reached.MethodsIn our study, we compare results gathered from three groups of 11- to 12-year-old students (274, 56, and 69 participants) who dealt on the platform with a task related to emotional competencies in online environments. …”
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Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data by Juan Pablo Carvajal-Dossman¹, Laura Guio, Danilo García-Orjuela, Jennifer J. Guzmán-Porras, Kelly Garces, Andres Naranjo, Silvia Juliana Maradei-Anaya, Jorge Duitama

“…However, manual annotation of seizures in EEG data is a major time-consuming step in the analysis process of EEGs. Different machine learning models have been developed to perform automated detection of seizures from EEGs. …”
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