Deciphering plant transcriptomes: Leveraging machine learning for deeper insights by Bahman Panahi, Rasmieh Hamid, Hossein Mohammad Zadeh Jalaly

“…We analyzed different ML methods and their applications in the identification of differentially expressed genes (DEGs), the elucidation of functional annotations and the reconstruction of regulatory networks. …”
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Novel distance-fitness learning scheme for ameliorating metaheuristic optimization by Emre Çelik, Essam H. Houssein, Mahmoud Abdel-Salam, Diego Oliva, Ghanshyam G. Tejani, Nihat Öztürk, Sunil Kumar Sharma, Mohammed Baljon

“…Empirical results from solving 23 standard benchmark functions, 10 Congresses on Evolutionary Computation (CEC) 2020 test functions, and 2 real-world engineering problems reveal that DFL boosts the capability of RSA significantly. …”
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Machine Learning for Selecting High-Energy Phosphate Cathode Materials by Yongchun Dang, Zechen Li, Yongchao Yu, Xiwei Bai, Li Wang, Xuelei Wang, Peng Liu, Chen Sun, Xunli Zhou, Zhenpo Wang, Yongjie Zhao, Xiangming He, Lei Li

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Optimizing the learning rate for adaptive estimation of neural encoding models. by Han-Lin Hsieh, Maryam M Shanechi

“…We derive explicit functions that predict the effect of learning rate on error and convergence time. …”
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Electronic learning at a university as a new educational paradigm by Irina Anzaurovna Udzhukhu, Ruslan Kazbekovich Meshvez

“…The purpose of the research is to reveal the essence of e-learning at a university, its goals, components and functions. …”
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New approach methodologies for risk assessment using deep learning by Enol Junquera, Irene Díaz, Susana Montes, Ferdinando Febbraio

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QUALIFICATION DATA AS A BASIS FOR TEACHER TRAINING DISTANCE LEARNING by Natalia V. Nikulicheva

“…At present, the professional status of the teacher of distance education as well as his professional requirements and characteristics is not officially presented in Russian pedagogics: either his functions, or requirements to knowledge and requirements to qualification. …”
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Optimizing Deep Learning Models with Improved BWO for TEC Prediction by Yi Chen, Haijun Liu, Weifeng Shan, Yuan Yao, Lili Xing, Haoran Wang, Kunpeng Zhang

“…However, these deep learning models usually contain a large number of hyperparameters. …”
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Smart deep learning model for enhanced IoT intrusion detection by Faisal S. Alsubaei

“…This superb performance of the OSNN model can be explained by the careful optimization of hyperparameters like strong activation functions (ReLU, GeLU, LeakyReLU), learning rates, dropout rates, and regularization techniques that enable it to learn intricate intrusion patterns efficiently using various datasets. …”
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Evolutionary fuzzy learning for Chinese medicine liver syndrome differentiation by Jia-Yu Yan, Peng-Wei Zhang, Wei-Guo Sheng, Jun-Ping Shi, Wei Ni, Li Li, Yu-Jun Zheng

“…To determine the most appropriate fuzzy membership functions of the fuzzy learning machines, an evolutionary algorithm was employed to optimize the types and parameters of the fuzzy functions simultaneously. …”
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Machine learning-assisted image analysis techniques for glaucoma detection by Vaibhav Yadav, Barnali Dey, Udayan Baruah, Saumya Das, Om Prakash

“…Many research experiments used traditional machine learning and deep learning techniques to detect glaucoma. …”
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Interdisciplinary cooperation in the integrated information learning environment of technical university by S. Yu. Rzheutskaya, M. V. Kharina

“…By now all the structural and functional components of the model have been embedded in the learning process and proved to be viable. …”
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Mathematical Perspective on Piaget’s Theory and Its Implications for Teaching and Learning by Chinedu Obasi, Patricia Obi

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E-learning course for creating descriptions of information about occupations by Ewelina Sikora

“…The article presents an e-learning course designed for creating descriptions of information about occupations. …”
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Dissociable processes for orientation discrimination learning and contextual illusion magnitude. by Charlotte Elizabeth Holmes Wilks, Geraint Rees, Dietrich Samuel Schwarzkopf

“…To test whether these perceptual functions are inherently linked, we measured both orientation discrimination sensitivity and the magnitude of the tilt illusion before and after participants had been trained for three days on an orientation discrimination task. …”
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Neutrosophic Clustering in Adaptive AI Learning for Students with Down Syndrome by Rania Lutfi

“…This short communication introduces an innovative conceptual framework that integrates neutrosophic error clustering into adaptive AI systems, utilizing neutrosophic logic to define membership functions for truth, indeterminacy, and falsity. The framework enables real-time personalization and modifications to learning by grouping errors into conceptual, procedural, or attention-related clusters. …”
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Break the Limits: INGOs Limitation and Learning from Amnesty International by Vinesha Anindita Anindita

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Synaptic Plasticity and Learning in Animal Models of Tuberous Sclerosis Complex by Timo Kirschstein

“…Both the clinical appearance of this condition including tumors, cognitive decline, and epileptic seizures and the molecular understanding of the mTOR signaling pathway, not only involved in cell growth, but also in neuronal functioning, have inspired numerous studies on learning behavior as well as on synaptic plasticity which is the key molecular mechanism of information storage in the brain. …”
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Using Machine Learning to Detect Vault (Anti-Forensic) Apps by Michael N. Johnstone, Wencheng Yang, Mohiuddin Ahmed

“…While these apps may serve legitimate functions, they unequivocally present significant challenges for law enforcement. …”
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Exploring the Potential of the Bicameral Mind Theory in Reinforcement Learning Algorithms by Munavvarkhon Mukhitdinova, Mariana Petrova

“…This study explores the potential of Julian Jaynes’ bicameral mind theory in enhancing reinforcement learning (RL) algorithms and large language models (LLMs) for artificial intelligence (AI) systems. …”
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