Predictive estimations of health systems resilience using machine learning by Alessandro Jatobá, Paula de Castro-Nunes, Paloma Palmieri, Omara Machado Araujo de Oliveira, Patricia Passos Simões, Valéria da Silva Fonseca, Paulo Victor Rodrigues de Carvalho

“…Abstract Operationalizing resilience in public health systems is critical for enhancing adaptive capacity during crises. This study presents a Machine Learning (ML) -based approach to assess resilience of the health system. …”
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Non-Invasive Biomarkers in the Era of Big Data and Machine Learning by Konstantinos Lazaros, Styliani Adam, Marios G. Krokidis, Themis Exarchos, Panagiotis Vlamos, Aristidis G. Vrahatis

“…Computational advancements, particularly in artificial intelligence and machine learning, are addressing these limitations by uncovering complex patterns in multi-modal datasets, enhancing diagnostic accuracy and facilitating personalized medicine. …”
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Intelligent waste sorting for urban sustainability using deep learning by Gulzar Ahmad, Fizza Muhammad Aleem, Tahir Alyas, Qaiser Abbas, Waqas Nawaz, Taher M. Ghazal, Abdul Aziz, Saira Aleem, Nadia Tabassum, Aidarus Mohamed Ibrahim

“…Abstract Smart cities’ have experienced an increasingly higher rate of urbanization and increase of the population leading to strengthening the pressing needs in waste management. In this paper, we present an intelligent waste classification system that utilises Convolutional Neural Networks (CNNs) for automatic segregation into twelve categories of waste, employing image data. …”
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Deep learning and explainable AI for classification of potato leaf diseases by Sarah M. Alhammad, Doaa Sami Khafaga, Walaa M. El-hady, Farid M. Samy, Khalid M. Hosny

“…The accurate classification of potato leaf diseases plays a pivotal role in ensuring the health and productivity of crops. This study presents a unified approach for addressing this challenge by leveraging the power of Explainable AI (XAI) and transfer learning within a deep Learning framework. …”
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Explainable AI and machine learning for robust cybersecurity in smart cities by Shruti Gupta, Jyotsna Singh, Rashmi Agrawal, Usha Batra

“…This article explores the urban promise of AI and presents a new framework infusion of AI into cityscapes. …”
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A survey on deep learning-based lidar place recognition by Weizhong Jiang, Shubin Si, Hanzhang Xue, Yiming Nie, Zhipeng Xiao, Qi Zhu, Liang Xiao

“…With the rapid advancements in deep learning, deep learning-based LiDAR place recognition (DL-LPR) has emerged as the dominant research direction in this field. …”
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Machine learning classification meets migraine: recommendations for study evaluation by Igor Petrušić, Andrej Savić, Katarina Mitrović, Nebojša Bačanin, Gabriele Sebastianelli, Daniele Secci, Gianluca Coppola

“…Abstract The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classification of migraine types and subtypes. …”
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Resolution Enhancement of Brain MRI Images Using Deep Learning by Minakshi Roy, Biraj Upadhyaya, Jyoti Rai, Kalpana Sharma

“…A widely used deep learning (DL) technique, accessible brain MRI dataset, and quantity evaluation matrices have been presented, mostly used for image super resolution. …”
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Foundations and Innovations in Data Fusion and Ensemble Learning for Effective Consensus by Ke-Lin Du, Rengong Zhang, Bingchun Jiang, Jie Zeng, Jiabin Lu

“…We present a comparative analysis of ensemble learning and deep learning, highlighting their respective strengths, limitations, and synergies. …”
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An introduction to Self-Aware Deep Learning for medical imaging and diagnosis by Paolo Dell’Aversana

“…Aim: This study represents preliminary research for testing the effectiveness of the Self-Aware Deep Learning (SAL) methodology in the context of medical diagnostics using various types of attributes. …”
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regAL: Python package for active learning of regression problems by Elizaveta Surzhikova, Jonny Proppe

“…In this work, we present our Python package regAL , which allows users to evaluate different active learning strategies for regression problems. …”
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E-learning in logistics of production – business process management perspective by Joanna Łabędzka, Mariusz Siczek

“…The main goal of the paper is to present a process approach to e-learning of automated transportation used in production logistics. …”
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Ventricular segmentation algorithm for echocardiography based on transfer learning and GAN by Jin Wang, Xiaoning Bo, Guoqin Li, Yanli Tan

“…With the swift progression of computer technology, utilizing deep learning for left ventricular image segmentation in echocardiography is of great significance for automated cardiac function assessment. …”
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Predicting Quail Egg Quality Using Machine Learning Algorithms by BI Yildiz, K Eskioğlu, D Özdemir, M Akşit

“…ABSTRACT This study evaluates the effectiveness of machine learning algorithms in predicting quail egg quality based on nine key parameters, including egg weight, egg width, egg length, yolk height, yolk width, yolk weight, albumen height, albumen width, and albumen length. …”
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Improving Weather Forecasting in Remote Regions Through Machine Learning by Kaushlendra Yadav, Saket Malviya, Arvind Kumar Tiwari

“…By leveraging advanced machine learning and deep learning techniques on available data from well-documented regions, this paper propose a framework for generating reliable weather forecasts for remote territories. …”
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Interpretable machine learning models classify minerals via spectroscopy by R. Smith, Tyler L. Spano, Marshall McDonnell, Lance Drane, Ian Gibbs, Andrew Miskowiec, J. L. Niedziela, Ashley E. Shields

“…Here, we developed interpretable machine learning models that can classify uranium minerals by secondary oxyanion chemistry and other physicochemical properties based solely on Raman spectra. …”
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FAFIT APPLICATION: SCRATCH WEB-BASED ENGLISH LEARNING INNOVATION by Fatma Yuniarti, Fitri wulandari, Renita Sugesti

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Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration by Yazan Otoum, Chaosheng Hu, Eyad Haj Said, Amiya Nayak

“…Federated learning offers a framework for developing local models across institutions while safeguarding sensitive data. …”
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Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. by Lisa M Breckels, Sean B Holden, David Wojnar, Claire M Mulvey, Andy Christoforou, Arnoud Groen, Matthew W B Trotter, Oliver Kohlbacher, Kathryn S Lilley, Laurent Gatto

“…Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. …”
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Machine Learning Decision Support for Macronutrients in Neonatal Parenteral Nutrition by Setareh Derakhshandara, Valentina Franzoni, Daniele Mezzetti, Valentina Poggioni

“…Using a dataset of 1210 neonatal intensive-care unit patients of the University Hospital of Perugia, collected over 17 years, this work aims to establish the basis for an evidence-based decision support system to reduce the time and effort required for manual calculations from doctors working in such a critical emergency environment. After the data was presented, we compared different machine learning techniques (i.e., random forest, gradient boosting machine, support vector machine, multilayer perceptron) that could predict nutritional requirements. …”
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