Deep learning-based action recognition for joining and welding processes of dissimilar materials by Tao He, Xinyuan Jin, Yiming Zou

“…IntroductionJoining and welding processes for dissimilar materials present unique challenges due to the need for precise monitoring and analysis of complex physical and chemical interactions. …”
Get full text

PROFESSIONAL EDUCATION QUALITY DEVELOPMENT AND ITS ASSESSMENT IN COMPETENCE-BASED MODEL OF LEARNING by Irina N. Emelyanova

“…The paper deals with the problem of professional education assessment that has become more serious due to competence-based model of learning implementation in higher professional education. …”
Get full text

Interpretable and Robust Ensemble Deep Learning Framework for Tea Leaf Disease Classification by Ozan Ozturk, Beytullah Sarica, Dursun Zafer Seker

“…In this study, advanced deep learning architectures such as ResNet50, MobileNet, EfficientNetB0, and DenseNet121 were utilized to classify tea leaf diseases. …”
Get full text

A Deep Learning and Explainable AI-Based Approach for the Classification of Discomycetes Species by Aras Fahrettin Korkmaz, Fatih Ekinci, Şehmus Altaş, Eda Kumru, Mehmet Serdar Güzel, Ilgaz Akata

“…This study presents a novel approach for classifying Discomycetes species using deep learning and explainable artificial intelligence (XAI) techniques. …”
Get full text

Empirical Evaluation on GPU, Overclocking, and LoRA for Deep Learning on Embedded Systems by Evandro Raphaloski, Mariana Caravanti De Souza, Edson Takashi Matsubara

“…The use and optimization of deep learning models for embedded systems and mobile devices present real-world challenges, such as limited computational resources, memory size, inference latency, and high-power consumption requirements. …”
Get full text

A framework for effective construction workers safety training using flipped learning by Abdul Rehman, Muhammad Usman Hassan, Muhammad Umer Zubair, Taha Aziz, Khursheed Ahmed

“…Flipped learning transforms traditional classroom learning by introducing students to web-based videos, presentations, and readings before class, freeing up in-class time for discussions and problem-solving. …”
Get full text

A systematic review of deep learning chemical language models in recent era by Hector Flores-Hernandez, Emmanuel Martinez-Ledesma

“…In this study, we present a systematic review that offers a statistical description and comparison of the strategies utilized to generate molecules through deep learning techniques, utilizing the metrics proposed in Molecular Sets (MOSES) or Guacamol. …”
Get full text

Asynchronous Real-Time Federated Learning for Anomaly Detection in Microservice Cloud Applications by Mahsa Raeiszadeh, Amin Ebrahimzadeh, Roch H. Glitho, Johan Eker, Raquel A. F. Mini

“…The complexity and dynamicity of microservice architectures in cloud environments present substantial challenges to the reliability and availability of the services built on these architectures. …”
Get full text

A machine learning modelling for the seismicity in the region of Greece from 2000 and thereafter by Ambrosios-Antonios Savvides, Leonidas Papadopoulos, Dimitrios-Panagiotis Serris

“…Abstract In the present article, a set of artificial neural network models, following the Feed Forward Neural Network (FNN) method, are formulated for the spectrums of acceleration, velocity and displacement of a single degree of freedom system. …”
Get full text

Self-supervised multi-stage deep learning network for seismic data denoising by Omar M. Saad, Matteo Ravasi, Tariq Alkhalifah

“…However, finding an optimal balance between preserving seismic signals and effectively reducing seismic noise presents a substantial challenge. In this study, we introduce a multi-stage deep learning model, trained in a self-supervised manner, designed specifically to suppress seismic noise while minimizing signal leakage. …”
Get full text

Machine learning methods in the differential diagnosis of difficult-to-classify types of diabetes mellitus by N. V. Rusyaeva, I. I. Golodnikov, I. V. Kononenko, T. V. Nikonova, M. V. Shestakova

“…In this regard, various automated algorithms have been developed based on statistical methods and machine learning (deep neural networks, “decision trees”, etc.) to identify patients for whom an in-depth examination is most justified. …”
Get full text

Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds by Joarder Jafor Sadique, Imtiaz Nasim, Ahmed S. Ibrahim

“…We introduce two machine learning (ML)-based link scheduling techniques that model the dynamic evolution of satellite positions and link conditions over time and space. …”
Get full text

A Deep-Learning Workflow for CORONA-Based Historical Land Use Classifications by Wei Liu, Shuai Li, Di Fan, Yixin Wen, Austin Madson, Jessica Mitchell, Yaqian He, Di Yang

Get full text

Company-university intersections through service-learning (SL): a systematic review by Margarita R. Rodríguez-Gallego, Rosario Ordóñez-Sierra, Soledad Domene-Martos, Cristina de-Cecilia-Rodríguez

“…Among the possible intersections, Service-learning (SL) is an educational proposition that may help university students to develop their personal skills, offering them opportunities to learn and practice civic commitment, improving their sense of social and citizen responsibility, and combining academic and community-service learning in a constructed programme where participants train by working on real needs of the environment to optimize and transform the latter. …”
Get full text

Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis by Mohammad Hasan, Mohammad Shahriar Rahman, Helge Janicke, Iqbal H. Sarker

“…The shapley additive explanation (SHAP) method is employed to measure the contribution of each feature, and it is compatible with ensemble models. Moreover, we present rules for interpreting whether a Bitcoin transaction is anomalous or not. …”
Get full text

Masked Modeling-Based Ultrasound Image Classification via Self-Supervised Learning by Kele Xu, Kang You, Boqing Zhu, Ming Feng, Dawei Feng, Cheng Yang

“…In this paper, drawing inspiration from self-supervised learning techniques, we present a pre-training method based on mask modeling specifically designed for ultrasound data. …”
Get full text

Pedagogical strategies for supporting learning and student well-being in environmentally sustainable healthcare by Nara Jones, Nara Jones, Graeme Horton, Michelle Guppy, Georgia Brown, John Boulton

“…SDT explains the need for supporting autonomy, relatedness and competence in the learning environment. Strategies employed to address these include providing students with the opportunity to select discussion topics that they contribute to, maximizing choice of focus for the assessment task, utilizing personal reflections, case-based learning scenarios and incorporating presentations from relatable industry leaders.…”
Get full text

Application of Multiple Deep Learning Architectures for Emotion Classification Based on Facial Expressions by Cheng Qian, João Alexandre Lobo Marques, Auzuir Ripardo de Alexandria, Simon James Fong

“…Facial expression recognition (FER) is essential for discerning human emotions and is applied extensively in big data analytics, healthcare, security, and user experience enhancement. This study presents a comprehensive evaluation of ten state-of-the-art deep learning models—VGG16, VGG19, ResNet50, ResNet101, DenseNet, GoogLeNet V1, MobileNet V1, EfficientNet V2, ShuffleNet V2, and RepVGG—on the task of facial expression recognition using the FER2013 dataset. …”
Get full text

The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024) by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej, Grzegorz Wilk-Jakubowski

“…This review article presents the latest research advancements in the application of machine learning techniques to vibration and acoustic signal analysis from 2015 to 2024. …”
Get full text

Machine-learning-based analytics for risk forecasting of anaphylaxis during general anesthesia by Shuang Liu, Yasuyuki Suzuki, Toshihiro Yorozuya, Masaki Mogi

“…Logistic Regression in conjunction with Recursive Feature Elimination model also showed adequate performance, with accuracy of 0.858 and MCC of 0.541 with six features used in the classification. This study presents initial proof of the capability of a machine-learning-based strategy for forecasting low-prevalence anesthesia-related anaphylaxis from a clinical perspective. …”
Get full text