Elias Hossain¹, Mohammed Alshehri², Sultan Almakdi^2,*, Hanan Halawani², Md. Mizanur Rahman³, Wahidur Rahman⁴, Sabila Al Jannat⁵, Nadim Kaysar⁶, Shishir Mia⁴

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1713-1746, 2022, DOI:10.32604/cmc.2022.024822

Abstract Diabetes Mellitus is one of the most severe diseases, and many studies have been conducted to anticipate diabetes. This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic, pre-diabetic, or non-diabetic without the assistance of any physician or medical tests. This study's methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design. The Diabetes Prediction Approach uses a novel approach, Light Gradient Boosting Machine (LightGBM), to ensure a faster diagnosis. The Proposed System Architecture Design has been combined into seven modules; the Answering Question Module is a natural… More >

Kriti¹, Mohd Anul Haq², Urvashi Garg¹, Mohd Abdul Rahim Khan^2,*, V. Rajinikanth³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 939-957, 2022, DOI:10.32604/cmc.2022.023169

Abstract A crucial task in hyperspectral image (HSI) taxonomy is exploring effective methodologies to effusively practice the 3-D and spectral data delivered by the statistics cube. For classification of images, 3-D data is adjudged in the phases of pre-cataloging, an assortment of a sample, classifiers, post-cataloging, and accurateness estimation. Lastly, a viewpoint on imminent examination directions for proceeding 3-D and spectral approaches is untaken. In topical years, sparse representation is acknowledged as a dominant classification tool to effectually labels deviating difficulties and extensively exploited in several imagery dispensation errands. Encouraged by those efficacious solicitations, sparse representation (SR) has likewise been presented… More >

Shubha Sumesh¹, John Yearwood¹, Shamsul Huda¹ and Shafiq Ahmad^2,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4503-4521, 2022, DOI:10.32604/cmc.2022.015474

Abstract

Clinical Study and automatic diagnosis of electrocardiogram (ECG) data always remain a challenge in diagnosing cardiovascular activities. The analysis of ECG data relies on various factors like morphological features, classification techniques, methods or models used to diagnose and its performance improvement. Another crucial factor in the methodology is how to train the model for each patient. Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy. This paper proposes an adaptive approach to identify performance improvement in building a training model that analyze global training… More >

Gurparsad Singh Suri¹, Gurleen Kaur¹, Sara Moein^2,*

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 723-735, 2021, DOI:10.32604/iasc.2021.015049

Abstract Schizophrenia (SZ) is a mental heterogeneous psychiatric disorder with unknown cause. Neuroscientists postulate that it is related to brain networks. Recently, scientists applied machine learning (ML) and artificial intelligence for the detection, monitoring, and prognosis of a range of diseases, including SZ, because these techniques show a high performance in discovering an association between disease symptoms and disease. Regions of the brain have significant connections to the symptoms of SZ. ML has the power to detect these associations. ML interests researchers because of its ability to reduce the number of input features when the data are high dimensional. In this… More >

Hossein ElahiShirvan¹, MohammadReza Ghotbi-Ravandi², Sajad Zare^3,*, Mostafa Ghazizadeh Ahsaee⁴

Sound & Vibration, Vol.54, No.2, pp. 99-112, 2020, DOI:10.32604/sv.2020.08839

Abstract Workers’ exposure to excessive noise is a big universal work-related challenges. One of the major consequences of exposure to noise is permanent or transient hearing loss. The current study sought to utilize audiometric data to weigh and prioritize the factors affecting workers’ hearing loss based using the Support Vector Machine (SVM) algorithm. This cross sectional-descriptive study was conducted in 2017 in a mining industry in southeast Iran. The participating workers (n = 150) were divided into three groups of 50 based on the sound pressure level to which they were exposed (two experimental groups and one control group). Audiometric tests… More >