LSTM-Based Neural Network to Recognize Human Activities Using Deep Learning Techniques by Sunitha Sabbu, Vithya Ganesan

“…The present study proposed a deep network architecture based on one of the techniques of deep learning named as residual bidirectional long-term memory (LSTM). …”
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The Learning Inquiry with Mnemonic Strategy for the Formation of Long-Term Memory and Creative Thinking Ability by Nurul Azizah, Aditya Marianti, Wiwi Isnaeni, Endah Peniati

“…This study implies that the use of an inquiry learning model with mnemonic strategies can be an alternative learning strategy to improve the formation of LTM and students' creative thinking ability at the secondary school level. …”
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Machine learning approach for studying the neural underpinnings of dyslexia on a phonological awareness task by Christoforos Christoforou, Timothy C. Papadopoulos, Maria Theodorou

“…We proposed a novel machine-learning-based approach to extract neural activity from EEG to identify neural differences at the group level. …”
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How the Covid-19 Pandemic was Experienced by Slovenian and German Adolescents with Specific Learning Difficulties by Karmen Javornik, Marija Kavkler, Sven Lychatz, Milena Košak Babuder

“…The spring phase of the pandemic made the education of adolescents with specific learning difficulties (SpLD) challenging. In the present study, which included 122 adolescents with SpLD (50% from Slovenia, 50% from Germany), we investigated how Slovenian and German adolescents with SpLD perceived and solved some of the challenges of distance learning. …”
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scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Single-cell RNA sequencing now profiles whole transcriptomes for hundreds of thousands of cells, yet existing trajectory-inference tools rarely pinpoint where and when fate decisions are made. We present single-cell reinforcement learning (scRL), an actor–critic framework that recasts differentiation as a sequential decision process on an interpretable latent manifold derived with Latent Dirichlet Allocation. …”
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Machine learning-based multiphysics model for corrosion fatigue crack propagation in aluminum alloy by Hongkun Wang, Zhenshuang Wu, Dongmei Jiang, Haitao Wang, En-Hou Han

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A survey of reinforcement and deep reinforcement learning for coordination in intelligent traffic light control by Aicha Saadi, Noureddine Abghour, Zouhair Chiba, Khalid Moussaid, Saadi Ali

“…Reinforcement learning (RL) enables a single agent to learn and perform optimal actions independently, whereas multi-agent reinforcement learning (MARL) enables traffic light controllers to learn, exchange and optimize their actions. …”
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Research Progress on Atmospheric Turbulence Perception and Correction Based on Adaptive Optics and Deep Learning by Qinghui Liu, Yihang Di, Mengmeng Zhang, Zhenbo Ren, Jianglei Di, Jianlin Zhao

“…Particular emphasis is placed on deep learning‐enabled intelligent correction paradigms, while critical analysis is provided regarding prospective research trajectories and implementation challenges.…”
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The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learningZenodo by Gerardo Roa Dabike, Trevor J. Cox, Alex J. Miller, Bruno M. Fazenda, Simone Graetzer, Rebecca R. Vos, Michael A. Akeroyd, Jennifer Firth, William M. Whitmer, Scott Bannister, Alinka Greasley, Jon P. Barker

“…This paper presents the Cadenza Woodwind Dataset. This publicly available data is synthesised audio for woodwind quartets including renderings of each instrument in isolation. …”
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ASSUME: An agent-based simulation framework for exploring electricity market dynamics with reinforcement learning by Nick Harder, Kim K. Miskiw, Manish Khanra, Florian Maurer, Parag Patil, Ramiz Qussous, Christof Weinhardt, Marian Klobasa, Mario Ragwitz, Anke Weidlich

“…To address the complexity of electricity markets and their interactions, we present ASSUME, an open-source agent-based simulation framework that incorporates multi-agent deep reinforcement learning for modeling adaptive market participants. …”
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Investigating the Effects of Discovery Learning on Daycare Pupils’ Environmental Skills Using EcoExplorer by Irish lores, Mikka Cassandra Abat, Kyla Marie Duran, Charisse Galboy, Daniel Ong Jr.

“…Early childhood education, particularly in daycare settings in the Philippines, plays a pivotal role in shaping the foundation of a child's lifelong learning. The integration of discovery learning into children's education in urban areas with limited space and opportunities for exploration remains uncertain, presenting numerous challenges that continue to hinder the development of learners’ skills in physical science and the natural environment. …”
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Real-Time American Sign Language Interpretation Using Deep Learning and Keypoint Tracking by Bader Alsharif, Easa Alalwany, Ali Ibrahim, Imad Mahgoub, Mohammad Ilyas

“…To bridge this gap, assistive technologies leveraging artificial intelligence (AI) and deep learning have gained prominence. This study presents a real-time American Sign Language (ASL) interpretation system that integrates deep learning with keypoint tracking to enhance accessibility and foster inclusivity. …”
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Nutriacción+: A Tool for Learning About Healthy Eating for Economically and Educationally Vulnerable Children by Diana Arce-Cuesta, Evelyn Pesántez, Pablo Cevallos-Larrea, Cato Van Strijdonck, Michael Peralta

“…This article presents the development and evaluation of an educational game designed to promote learning about healthy eating for children and adolescents in situations of economic and educational vulnerability in Ecuador. …”
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Transformer optimization with meta learning on pathology images for breast cancer lymph node micrometastasis by Jing Huang, Jingtao Wang, Junhai Shi, Hengli Ni, Shan Xu, Ping Wu, Yuexiang Ren, Lijuan Bian, Chenhan Su, Yuxuan Xu, Xinyu He, Xinjian Chen, Jianming Li

“…However, the limited size of these hidden lesions restricts dataset expansion, presenting a significant challenge for manual examination and conventional deep learning techniques. …”
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Local kernel renormalization as a mechanism for feature learning in overparametrized convolutional neural networks by R. Aiudi, R. Pacelli, P. Baglioni, A. Vezzani, R. Burioni, P. Rotondo

“…This finding highlights a simple mechanism for feature learning that can take place in overparametrized shallow convolutional neural networks, but not in shallow fully-connected architectures or in locally connected neural networks without weight sharing.…”
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Ionospheric Electron Density and Temperature Profiles Using Ionosonde-like Data and Machine Learning by Jean de Dieu Nibigira, Richard Marchand

“…Predicting the behaviour of the Earth’s ionosphere is crucial for the ground-based and spaceborne technologies that rely on it. This paper presents a novel way of inferring ionospheric electron density profiles and electron temperature profiles using machine learning. …”
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An interactive art activity to promote student reflection and learning about host-microbe interactions by Camila Souza Beraldo

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Enhancing phishing detection with dynamic optimization and character-level deep learning in cloud environments by Vishnukumar Ravula, Mangayarkarasi Ramaiah

“…The presented DLPC approach is capable of learning new forms of phishing attacks in real time and reduce false positives. …”
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Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models by Marta Terrados-Cristos, Marina Diaz-Piloneta, Francisco Ortega-Fernández, Gemma Marta Martinez-Huerta, José Valeriano Alvarez-Cabal

“…Atmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. …”
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RISKS REDUCING THROUGH INTELLIGENT HEADLIGHT MANAGEMENT: OPTIMIZING Q-LEARNING FOR ELECTRIC VEHICLES by Pitchaya Jamjuntr, Chanchai Techawatcharapaikul, Pannee Suanpang

“…Evaluation of the performance of the adaptive Q-learning system is presented in this study in terms of safety metrics such as visibility distance and energy efficiency indicators such as power consumption through comprehensive simulations across various turning scenarios. …”
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