PLANT DISEASE DETECTION USING DEEP LEARNING by Amrita, Sanjay Kumar Nayak, Rajiv Kumar, Sakshi Tomar

“…In this paper, several deep learning (DL) models are proposed to recognize the multiple classes of diseases present in plants from the images of leaves taken under various resolutions and different environmental conditions. …”
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Neuromorphic devices assisted by machine learning algorithms by Ziwei Huo, Qijun Sun, Jinran Yu, Yichen Wei, Yifei Wang, Jeong Ho Cho, Zhong Lin Wang

“…It features parallel interconnected neural networks, high fault tolerance, robustness, autonomous learning capability, and ultralow energy dissipation. …”
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Pseudorandom Function from Learning Burnside Problem by Dhiraj K. Pandey, Antonio R. Nicolosi

“…We present three progressively refined pseudorandom function (PRF) constructions based on the learning Burnside homomorphisms with noise (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>B</mi><mi>n</mi></msub></semantics></math></inline-formula>-LHN) assumption. …”
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Evaluation of hydraulic fracturing using machine learning by Ali Akbari, Ali Karami, Yousef Kazemzadeh, Ali Ranjbar

“…Despite its effectiveness, traditional methods used to evaluate HF performance often struggle to capture the complex, nonlinear interactions among operational and geological parameters. This study presents a comprehensive machine learning (ML)-based framework to address this challenge by predicting HF efficiency using three widely used algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN). …”
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Modeling PROTAC degradation activity with machine learning by Stefano Ribes, Eva Nittinger, Christian Tyrchan, Rocío Mercado

“…In this work, we present a strategy for curating open-source PROTAC data and an open-source deep learning tool for predicting the degradation activity of novel PROTAC molecules. …”
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Nursing students' experience of learning cultural competence. by Isabel Antón-Solanas, Elena Tambo-Lizalde, Nadia Hamam-Alcober, Valérie Vanceulebroeck, Shana Dehaes, Indrani Kalkan, Nuran Kömürcü, Margarida Coelho, Teresa Coelho, Antonio Casa Nova, Raul Cordeiro, Lucía Sagarra-Romero, Ana B Subirón-Valera, Isabel Huércanos-Esparza

“…Theme 4, learning cultural competence, integrates the participants' learning experiences as part of their nursing curricula, as part of other academic learning opportunities and as part of extra-academic activities. …”
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A refined lion optimizer for deep learning by Jian Rong, Chenhao Ma, Qinghui Zhang, Yong Cao, Weili Kou

“…Compared to the AdamW and Lion optimizer, the loss and accuracy present the RLion can promote the classification validation accuracy about $$0 \sim +20\%$$ higher than AdamW on many models even the learning rate is as high as AdamW. …”
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Assessing the impact of virtual workplaces on collaboration and learning by Irene Sánchez Rodríguez, Luca Bailo, Folco Panizza, Valerie A. A. van Es, Marzio Alessi, Monica Betta, Emiliano Ricciardi

“…While these technologies offer opportunities for enhanced remote interaction, they also present challenges related to communication, security, and social dynamics. …”
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Accelerating Giant-impact Simulations with Machine Learning by Caleb Lammers, Miles Cranmer, Sam Hadden, Shirley Ho, Norman Murray, Daniel Tamayo

“…To accelerate giant-impact simulations, we present a machine learning (ML) approach to predicting collisional outcomes in multiplanet systems. …”
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Abnormality-aware multimodal learning for WSI classification by Thao M. Dang, Qifeng Zhou, Yuzhi Guo, Hehuan Ma, Saiyang Na, Thao Bich Dang, Jean Gao, Junzhou Huang

“…To address these limitations, we propose the Abnormality-Aware MultiModal (AAMM) learning framework, which integrates abnormality detection and multimodal feature learning for WSI classification. …”
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Neuroeducation: understanding neural dynamics in learning and teaching by K. Pradeep, Rajalakshmi Sulur Anbalagan, Asha Priya Thangavelu, S. Aswathy, V. G. Jisha, V. S. Vaisakhi

“…Furthermore, the integration of technology into educational practices, ranging from brain-computer interfaces to immersive virtual reality experiences, presents new possibilities for enhancing learning engagements and accommodating diverse learning styles and curricula. …”
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Environmental adaptations in metagenomes revealed by deep learning by Johanna C. Winder, Simon Poulton, Taoyang Wu, Thomas Mock, Cock van Oosterhout

“…Abstract Background Deep learning has emerged as a powerful tool in the analysis of biological data, including the analysis of large metagenome data. …”
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Мethods of Machine Learning in Ophthalmology: Review by D. D. Garri, S. V. Saakyan, I. P. Khoroshilova-Maslova, A. Yu. Tsygankov, O. I. Nikitin, G. Yu. Tarasov

“…Machine learning is applied in every field of human activity using digital data. …”
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Evaluation of Learning Management Systems for Success Factors. by Muhaise, Hussein, Adeyemi, A. L., Muteb,I.J.

“…The results of the study presented a model of success factors for the Learning Management System, eLearning specifically. …”
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Galaxy Cluster Characterization with Machine Learning Techniques by M. Sadikov, J. Hlavacek-Larrondo, L. Perreault-Levasseur, C. L. Rhea, M. McDonald, M. Ntampaka, J. ZuHone

“…We present an analysis of the X-ray properties of the galaxy cluster population in the z = 0 snapshot of the IllustrisTNG simulations, utilizing machine learning techniques to perform clustering and regression tasks. …”
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Measuring the Intracluster Light Fraction with Machine Learning by Louisa Canepa, Sarah Brough, Francois Lanusse, Mireia Montes, Nina Hatch

“…However, to process this increased amount of data, we need faster, fully automated methods to streamline the measurement process. This paper presents a machine learning model designed to automatically measure the ICL fraction in large samples of images, with no manual preprocessing required. …”
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MOODLE – COMPUTERIZED DISTANT LEARNING SUPPORT SYSTEM by V. A. Tunda, E. A. Tunda

“…In most cases, Moodle is used to support and connect learning face-to-face with online training, as well as with other types of learning. …”
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Organization of Distance Learning in School, College, University by N. V. Nikulicheva, O. I. Dyakova, O. S. Glukhovskaya

“…The new learning environment has different capabilities and requires the lecturer to plan differently, present information, and organize monitoring and record results.The results of the study allowed us to identify general trends in the organization of DL at all levels of education and specific problems typical for particular levels. …”
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TOWARDS STRATEGIC SELF-REGULATION IN SECOND/FOREIGN LANGUAGE LEARNING. PART III. LEARNER STRATEGY CHOICES AND LANGUAGE LEARNING SUCCESS by Małgorzata Dąbrowska

“…In Part I., the author reviews a number of research studies on the ‘Good Language Learner’ issue; she also attempts to summarize the main characteristics, strategies, and behaviours of successful and unsuccessful learners. Part II. presents an overview of studies focused on the role of selected individual differences and shows how the variables may influence the process and outcomes of language learning; it also indicates which strategies and behaviours of ‘good’ learners can be taught and learnt in the classroom. …”
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ABOUT THE INTEGRATION OF B-LEARNING IN EPP TO REINFORCE LEARNER AUTONOMY ABOUT THE INTEGRATION OF B-LEARNING IN EPP TO REINFORCE LEARNER AUTONOMY by Irina Argüelles Álvarez

“…Qualitative data concerning the<br />benefits of MOODLE within an autonomous learning context are presented. To conclude, it is suggested that<br />prior analysis of each particular teaching situation is very important to decide whether MOODLE will in fact<br />fulfil previous expectations.…”
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