The advantages of distance learning technologies: students’ and university lecturers’ views by A. I. Nazarov, O. V. Sergeeva

“…The aim of the research is to analyze the results of the implementation of network education module in Physics into the bachelor education process, which is carried out with the use of distance learning technologies and e-learning platform Blackboard.In accordance with the Federal Law «On Education in the Russian Federation» at the present stage the organization of educational process for bachelors of engineering specialties of the University may be carried out through an integrated combination of traditional full-time study with the possibilities of distance learning technologies.In order to realize the benefits of such an education the authors of the article have been already using e-learning platform Blackboard for four years. …”
Get full text

The role of evaluation in iterative learning and implementation of quality of care interventions by Nikhil Shah, Sharon Mathew, Amanda Pereira, April Nakaima, Sanjeev Sridharan

“…A framework is presented to help guide the iterative learning, and includes the dimensions of clinical care, person-centered care, continuum of care, and ‘more than medicine. …”
Get full text

Machine Learning for Non-Destructive Prediction of Sunflower Leaf Area by Joao Everthon da Silva Ribeiro, Antonio Gideilson Correia da Silva, Pablo Henrique de Almeida Oliveira, Josiana Micarla da Silva Oliveira, Alessandra Nunes da Silva, John Victor Lucas Lima, Ivan Euzebio da Silva, Ester Dos Santos Coelho, Isaque de Oliveira Leite, Elania Freire da Silva, Toshik Iarley da Silva, Lindomar Maria da Silveira, Aurelio Paes Barros Junior

“…Therefore, the present study aimed to develop and compare linear regression models and machine learning algorithms for the non-destructive prediction of leaf area in four sunflower cultivars (BRS 323, Altis 99, Sany 66, and BRS 442). …”
Get full text

A Deep Learning Approach to Measure Visual Function in Zebrafish by Manjiri Patil, Annabel Birchall, Hammad Syed, Vanessa Rodwell, Ha-Jun Yoon, William H. J. Norton, Mervyn G. Thomas

“…Traditional methods for OKR analysis often rely on binarization techniques (threshold-based conversion of images to black and white) or costly software, which limits their utility in low-contrast settings or hypopigmented disease models. Here, we present a novel deep learning pipeline for OKR analysis, using ResNet-50 within the DeepLabCut framework in a Python Version 3.10 environment. …”
Get full text

Deep learning based quantitative cervical vertebral maturation analysis by Fulin Jiang, Abbas Ahmed Abdulqader, Yan Yan, Fangyuan Cheng, Tao Xiang, Jinghong Yu, Juan Li, Yong Qiu, Xin Chen

“…Existing methods are often subjective and time-consuming, while deep learning alternatives withstand the complex anatomical variations. …”
Get full text

Deep Learning Models for Predicting the Recurrence of Idiopathic Granulomatous Mastitis by Li L, Yang W, Jia H

“…This study aims to evaluate and compare the performance of different machine learning models, including logistic regression, random forest, and neural networks, in predicting IGM recurrence using patient data.Methods: A retrospective analysis was conducted on 212 patients diagnosed with IGM. …”
Get full text

Automatic Selection of Machine Learning Models for Armed People Identification by Alonso Javier Amado-Garfias, Santiago Enrique Conant-Pablos, Jose Carlos Ortiz-Bayliss, Hugo Terashima-Marin

“…A multilayer perceptron-based APD4F, which combines an MLP-APD, a NB-APD, and a LR-APD, presented the best performance, achieving an accuracy of 95.84%, a recall of 99.28% and an F1 score of 96.07%.…”
Get full text

Unmasking the Fake: Machine Learning Approach for Deepfake Voice Detection by Muhammad Usama Tanveer Gujjar, Kashif Munir, Madiha Amjad, Atiq Ur Rehman, Amine Bermak

“…These synthetic voices can be used to convincingly imitate someone, making them nearly indistinguishable from genuine recordings. We present an advanced method for deepfake voice detection, leveraging a custom model named MFCC-GNB XtractNet. …”
Get full text

Machine-learning-based probabilistic forecasting of solar irradiance in Chile by S. Baran, J. C. Marín, J. C. Marín, O. Cuevas, O. Cuevas, M. Díaz, M. Szabó, O. Nicolis, M. Lakatos

“…However, ensemble forecasts still tend to be uncalibrated or biased, thus requiring some form of post-processing. The present work investigates probabilistic forecasts of solar irradiance for regions III and IV in Chile. …”
Get full text

Formation of Individual Educational Trajectory in Adaptive Learning Management Systems by T. M. Shamsutdinova

“…This study includes a review of bibliographic sources on the formation of an individual learning path and the implementation of adaptive learning in e-learning courses. …”
Get full text

Machine Learning Framework for Conotoxin Class and Molecular Target Prediction by Duc P. Truong, Lyman K. Monroe, Robert F. Williams, Hau B. Nguyen

“…We have previously demonstrated that the inclusion of post-translational modifications, collisional cross sections values, and other structural features, when added to the standard primary sequence features, improves the prediction accuracy of conotoxins against non-toxic and other toxic peptides across varied datasets and several different commonly used machine learning classifiers. Here, we present the effects of these features on conotoxin class and molecular target predictions, in particular, predicting conotoxins that bind to nicotinic acetylcholine receptors (nAChRs). …”
Get full text

Network-based intrusion detection using deep learning technique by Muhammad Farhan, Hafiz Waheed ud din, Saadat Ullah, Muhammad Sajjad Hussain, Muhammad Amir Khan, Tehseen Mazhar, Umar Farooq Khattak, Ines Hilali Jaghdam

“…Most traditional Network-based Intrusion Detection Systems (NIDS) can become weak at detecting new patterns of attacks due to the use of obsolete data or traditional machine learning models. To overcome the mentioned constraints, the current research presents a new deep learning solution that combines Sequential Deep Neural Networks (DNN) and Rectified Linear Unit (ReLU) activation unit with an Extra Tree Classifier feature selection procedure. …”
Get full text

Explainable machine learning for predicting lung metastasis of colorectal cancer by Zhentian Guo, Zongming Zhang, Limin Liu, Yue Zhao, Zhuo Liu, Chong Zhang, Hui Qi, Jinqiu Feng, Peijie Yao

“…Our study seeks to construct and verify a predictive model utilizing machine learning (ML) that can evaluate the risk of lung metastasis with newly diagnosed colorectal cancer (CRC) using Shapley Additive exPlanations (SHAP). …”
Get full text

Machine learning enabled assessment of the vacuum freeze-drying of the kiwifruit by Uzair Sajjad, Farzana Bibi, Imtiyaz Hussain, Naseem Abbas, Muhammad Sultan, Hafiz Muhammad Asfahan, Muhammad Aleem, Wei-Mon Yan

Get full text

imageseg: An R package for deep learning‐based image segmentation by Jürgen Niedballa, Jan Axtner, Timm Fabian Döbert, Andrew Tilker, An Nguyen, Seth T. Wong, Christian Fiderer, Marco Heurich, Andreas Wilting

“…Abstract Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. …”
Get full text

Improving Satellite Imagery Masking Using Multitask and Transfer Learning by Rangel Daroya, Luisa Vieira Lucchese, Travis Simmons, Punwath Prum, Tamlin Pavelsky, John Gardner, Colin J. Gleason, Subhransu Maji

“…Our model leverages multitask learning to improve accuracy while sharing computation across tasks for added efficiency. …”
Get full text

The effectiveness of blended learning technologies in higher education: Assessment methodology by N. V. Bordovskaia, E. A. Koshkina, M. A. Tikhomirova, M. P. Iskhakova

“…The present research aims to theoretically substantiate the methodology for studying the blended learning technologies effectiveness, to develop and approbate appropriate methodological tools, followed by its verification for reliability and validity.Methodology and research methods. …”
Get full text

Managing Diversity in History Learning Based on the Perspective of Kakawin Ramayana by Nur Fatah Abidin, Fakrul Islam Laskar

“…Along with the inclusive curriculum, history textbooks should provide alternate narratives in the form of personal or biographical history as the third way to counter the grand narratives and present the multi-narratives in learning history. …”
Get full text

Enhancing Anomaly Detection Performance: Deep Learning Models Evaluation by Yunusa Mohammed Jeddah, Aisha Hassan Abdalla Hashim, Othman Omran Khalifa, Khmaies Ouhada

“…In contrast to other state-of-the-art models such as DenseNet121 and ResNet50, InceptionV3 presents enhanced precision and adaptability in handling the variability found in video anomaly datasets. …”
Get full text

FORMATION OF KEY COMPETENCIES OF EDUCATION SEEKERS IN CONDITIONS OF BLENDED LEARNING by Iryna Lovyanova, Oleksii Kozachenko, Andriy Krasnoshchok

“…The presented work substantiates the peculiarities of the formation of key competencies of education seekers in conditions of blended learning. …”
Get full text