Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning by Renata Retkute, Kathleen S. Crew, John E. Thomas, Christopher A. Gilligan

“…Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. …”
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Perspectives of Medical Students in Using Blended Learning: A Thematic Analysis by Almohammadi NH, Elnugomi NM, Atta Elmannan AA, Zalat MM

“…The students’ adherence to school schedules, which required them to be disciplined regarding time, travel, and class preparation, contributed to their development of excellent habits. However, this presents a challenge for institutions that continue to offer an outstanding education despite the increasing acceptance of blended learning, as it may influence the motivation and learning outcomes of future doctors. …”
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Unveiling nature's secrets: Deep learning for enhanced biogenic emission resolution by Antonio Giganti

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Non-Invasive Painting Pigment Classification Through Supervised Machine Learning by Michal Piotr Markowski, Solongo Gansukh, Mateusz Madry, Robert Borowiec, Jaroslaw Rogoz, Boguslaw Szczupak

“…Accurate pigment classification is essential for the analysis and conservation of historical paintings. This study presents a non-invasive approach based on supervised machine learning for classifying pigments using image data acquired under three distinct spectral illumination conditions: visible-light reflectography (VIS), ultraviolet false-color imaging (UVFC), and infrared false-color imaging (IRFC). …”
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Self-adaptation of e-learning software based on observing the information environment by A. M. Bershadskiy, A. S. Bozhday, A. A. Gudkov, Yu. I. Evseeva

“…Determining a user’s personal characteristics can be done in a variety of ways (for example, using psychological testing or by analyzing learning outcomes). Results. The main results of the study are: 1) universal principles of building a self-adaptive e-learning system 2) a way of presenting the self-adaptive structure of a software system in the form of a characteristics model relevant to a wide range of software 3) a new universal method of self-adapting applied software used in E-Learning the main differences of which from the existing ones are, firstly, in using the opinions of the users of the system themselves to adjust with self-adaptive behavior, secondly, in the possibility of generating new states of the system throughout the entire period of its operation. …”
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Mo.Se.: Mosaic image segmentation based on deep cascading learning by Andrea Felicetti, Marina Paolanti, Primo Zingaretti, Roberto Pierdicca, Eva Savina Malinverni

“…(Mosaic Segmentation), an algorithm that exploits deep learning and image segmentation techniques; the methodology combines U-Net 3 Network with the Watershed algorithm. …”
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Deep learning based detection of silicosis from computed tomography images by Hamit Aksoy, Ümit Atila, Sertaç Arslan

“…These results show that Xception outperformed the other algorithms and was the most successful algorithm in the automatic detection of silicosis with an accuracy rate of 97.29 %.Additionally, a new dataset consisting of tomography images from silicosis patients is presented in this study. Experimental results have shown that transfer learning algorithms can significantly benefit the diagnosis of silicosis by successfully classifying CT images. …”
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A Method for the Automatic Selection of Training Tasks in Learning Environment for IT Students by S. U. Rzheutskaya, M. V. Kharina

“…The research, the results of which are presented in this article, was carried out in order to activate and improve the efficiency of independent work of students in the information environment of learning by rational individual selection of training tasks. …”
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Recognizing and localizing chicken behaviors in videos based on spatiotemporal feature learning by Yilei Hu, Jinyang Xu, Zhichao Gou, Di Cui

“…This limitation highlights the insufficient temporal resolution of video-based behavior recognition models. This study presents a chicken behavior recognition and localization model, CBLFormer, which is based on spatiotemporal feature learning. …”
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Usability Evaluation of Electronic Learning Management Systems in the University of Medical Sciences by Maryam Okhovati, Elham Sharifpoor, Fatemeh Karami Robati, Zohreh Oghabian, Leila Namdar

“…Background: With the Covid-19 pandemic, higher education communities almost worldwide have switched from traditional face-to-face education to distance learning and the use of electronic learning management systems (ELMS) in universities increased greatly; so that today online and electronic education is being done as an important part of education. …”
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Assessing groundwater drought in Iran using GRACE data and machine learning by Ali Kashani, Hamid R. Safavi

“…Although GRACE data offers valuable insights, its large-scale nature presents challenges for localized basin and aquifer studies, compounded by data gaps resulting from a 15-month interruption during the transition to the GRACE-FO project. …”
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Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization by Chuanwei Zhang, Dingshuai Liu, Paraskevas Tsangaratos, Ioanna Ilia, Sijin Ma, Wei Chen

“…The present study examines the application of four machine learning models—Multi-Layer Perceptron, Naive Bayes, Credal Decision Trees, and Random Forests—to assess landslide susceptibility using Mei County, China, as a case study. …”
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A generalized deep learning model to detect and classify volcano seismicity by David Fee, Darren Tan, John Lyons, Mariangela Sciotto, Andrea Cannata, Alicia Hotovec-Ellis, Társilo Girona, Aaron Wech, Diana Roman, Matthew Haney, Silvio De Angelis

“…Etna (Italy); and Kīlauea, Hawai`i (USA). These volcanoes present a wide range of volcano seismic signals, source-receiver distances, and eruption styles. …”
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An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detection by Mohammad Rifat Ahmmad Rashid, Md. AL Ehtesum Korim, Mahamudul Hasan, Md Sawkat Ali, Mohammad Manzurul Islam, Taskeed Jabid, Raihan Ul Islam, Maheen Islam

“…To address this challenge, we present an Ensemble Learning Framework with Explainable AI (XAI) tailored to disease detection, using cucumber leaf diagnosis as a key use case. …”
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Leveraging UAV Data and Deep Learning Models for Detecting Waste in Rivers by Bipun Man Pati, Bishnu Khadka, Ukesh Thapa, Sujay Kumar Pal, Subarna Sakya, Anup Shrestha, Hemant Joshi, Dhiraj Pyakurel, Prem Chandra Roy

“…The transfer learning fine-tuned DeepLabv3+ model obtained an mIoU score of 0.849 for Bagmati to Bishnumati and 0.841 for Bishnumati to Bagmati transfer learning. …”
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GuardianML: Anatomy of Privacy-Preserving Machine Learning Techniques and Frameworks by Nges Brian Njungle, Eric Jahns, Zhenqi Wu, Luigi Mastromauro, Milan Stojkov, Michel A. Kinsy

“…Machine learning has become integral to our lives, finding applications in nearly every aspect of our daily routines. …”
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DIDACTIC POTENTIAL OF MOBILE APPLICATIONS FOR LEARNING ENGLISH AS A FOREIGN LANGUAGE by Blynova Neliia, Kyrylova Oksana, Dolzhenko Maryna

“…Application developers pay the most attention to grammatical material and thematic vocabulary. Learned topics can be consolidated with the help of tests, often presented in a game form. …”
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Efficient sepsis detection using deep learning and residual convolutional networks by Ahmed S. Almasoud, Ghada Moh Samir Elhessewi, Munya A. Arasi, Abdulsamad Ebrahim Yahya, Menwa Alshammeri, Donia Badawood, Faisal Mohammed Nafie, Mohammed Assiri

“…Although recent technological advancements have aided sepsis detection, challenges remain in timely diagnosis using standard clinical practices. In this article, we present a new deep learning model to detect the occurrence of sepsis and the African vulture optimization algorithm (AVOA) to enhance the model performance. …”
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Competency self-assessment for a learning-based autonomous aircraft system by Nicholas Conlon, Aastha Acharya, Jamison McGinley, Trevor Slack, Camron A. Hirst, Marissa D’Alonzo, Mitchell R. Hebert, Christopher Reale, Eric W. Frew, Rebecca Russell, Nisar R. Ahmed

“…As a relevant example, we develop a competency-aware learning-based autonomous uncrewed aircraft system (UAS) and evaluate it within a multi-target ISR mission.ResultsWe present an analysis of the computational cost and performance of GOA-based competency reporting. …”
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Students’ Perceptions of Learning Analytics for Mental Health Support: Qualitative Study by Aglaia Freccero, Miriam Onwunle, Jordan Elliott, Nathalie Podder, Julia Purrinos De Oliveira, Lindsay H Dewa

“… Abstract BackgroundPoor mental health among higher education students is a global public health concern. Learning analytics, which involves collecting and analyzing big data to support learning, could detect changes in behavior, learning patterns, as well as mental health and well-being. …”
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