S. Jayanthi^*, S. Prasanna Devi

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2137-2158, 2024, DOI:10.32604/cmc.2024.045975

Abstract In healthcare, the persistent challenge of arrhythmias, a leading cause of global mortality, has sparked extensive research into the automation of detection using machine learning (ML) algorithms. However, traditional ML and AutoML approaches have revealed their limitations, notably regarding feature generalization and automation efficiency. This glaring research gap has motivated the development of AutoRhythmAI, an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of arrhythmias. Our approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection, effectively bridging the gap between data preprocessing and model selection. To validate our system, we have rigorously… More >

Samina Amin¹, Muhammad Ali Zeb¹, Hani Alshahrani^2,*, Mohammed Hamdi², Mohammad Alsulami², Asadullah Shaikh³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1167-1202, 2024, DOI:10.32604/cmes.2023.043921

Abstract Social media (SM) based surveillance systems, combined with machine learning (ML) and deep learning (DL) techniques, have shown potential for early detection of epidemic outbreaks. This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance. Since, every year, a large amount of data related to epidemic outbreaks, particularly Twitter data is generated by SM. This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM, along with the ML and DL techniques that have been configured for the… More >

Volkan Kaya¹, İsmail Akgül¹, Özge Zencir Tanır^2,*

Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2519-2534, 2023, DOI:10.32604/phyton.2023.029811

Abstract Classification and monitoring of microalgae species in aquatic ecosystems are important for understanding population dynamics. However, manual classification of algae is a time-consuming method and requires a lot of effort with expertise due to the large number of families and genera in its classification. The recognition of microalgae species has become an increasingly important research area in image recognition in recent years. In this study, machine learning and deep learning methods were proposed to classify images of 12 different microalgae species in order to successfully classify algae cells. 8 Different novel models (MobileNetV3Small-Lr, MobileNetV3SmallRf, MobileNetV3Small-Xg, MobileNetV3Large-Lr, MobileNetV3Large-Rf, MobileNetV3Large-Xg, MobileNetV3Small-Improved and… More >

P. S. Arthy^1,*, A. Kavitha²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2959-2971, 2023, DOI:10.32604/iasc.2023.032511

Abstract With the advent of Machine and Deep Learning algorithms, medical image diagnosis has a new perception of diagnosis and clinical treatment. Regrettably, medical images are more susceptible to capturing noises despite the peak in intelligent imaging techniques. However, the presence of noise images degrades both the diagnosis and clinical treatment processes. The existing intelligent methods suffer from the deficiency in handling the diverse range of noise in the versatile medical images. This paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alleviate this challenge. The proposed deep learning architecture… More >

Muhammad Adnan¹, Maryam Altalhi², Ala Abdulsalam Alarood³, M.Irfan Uddin^1,*

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1857-1872, 2022, DOI:10.32604/iasc.2022.020606

Abstract Corona Virus disease 2019 (COVID-19) has caused a worldwide pandemic of cough, fever, headache, body aches, and respiratory ailments. COVID- 19 has now become a severe disease and one of the leading causes of death globally. Modeling and prediction of COVID-19 have become inevitable as it has affected people worldwide. With the availability of a large-scale universal COVID-19 dataset, machine learning (ML) techniques and algorithms occur to be the best choice for the analysis, modeling, and forecasting of this disease. In this research study, we used one deep learning algorithm called Artificial Neural Network (ANN) and several ML algorithms such… More >

Sobia Wassan¹, Chen Xi^1,*, Nz Jhanjhi², Hassan Raza³

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 187-199, 2021, DOI:10.32604/iasc.2021.015859

Abstract Online banking is an ideal method for conducting financial transactions such as e-commerce, e-banking, and e-payments. The growing popularity of online payment services and payroll systems, however, has opened new pathways for hackers to steal consumers’ information and money, a risk which poses significant danger to the users of e-commerce and e-banking websites. This study uses the selection method of the entire e-commerce and e-banking website dataset (Chi-Squared, Gini index, and main learning algorithm). The results of the analysis suggest the identification and comparison of machine learning and deep learning algorithm performance on binary category labels (legal, fraudulent) between similar… More >