B. Ramesh, Kuruva Lakshmanna^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2513-2528, 2023, DOI:10.32604/cmes.2023.028944

Abstract Major chronic diseases such as Cardiovascular Disease (CVD), diabetes, and cancer impose a significant burden on people and healthcare systems around the globe. Recently, Deep Learning (DL) has shown great potential for the development of intelligent mobile Health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field. Type 2… More > Graphic Abstract

Jun Yang^*, Nannan Wang, Jiang Wang, Yashuai Luo

Energy Engineering, Vol.120, No.9, pp. 2079-2096, 2023, DOI:10.32604/ee.2023.028653

Abstract Distribution networks denote important public infrastructure necessary for people’s livelihoods. However, extreme natural disasters, such as earthquakes, typhoons, and mudslides, severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life. Therefore, considering the requirements for distribution network disaster prevention and mitigation, there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions. This paper accesses multi-source data, presents the data quality improvement methods of distribution networks, and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory. Furthermore, the paper… More >

Qing Wang^*, Haige Wang, Hongchun Huang, Lubin Zhuo, Guodong Ji

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2569-2578, 2023, DOI:10.32604/fdmp.2023.025349

Abstract Sticking is the most serious cause of failure in complex drilling operations. In the present work a novel “early warning” method based on an artificial intelligence algorithm is proposed to overcome some of the known problems associated with existing sticking-identification technologies. The method is tested against a practical case study (Southern Sichuan shale gas drilling operations). It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state; furthermore, the results from four groups of verification samples are also consistent with the actual downhole state. This shows that… More >

Zhihan Liu¹, Xiang Li¹, Siqi Liu², Wei Li^1,*, Xiangxu Meng¹, Jing Jia³

Computer Systems Science and Engineering, Vol.47, No.1, pp. 937-954, 2023, DOI:10.32604/csse.2023.039417

Abstract The COVID-19 virus is usually spread by small droplets when talking, coughing and sneezing, so maintaining physical distance between people is necessary to slow the spread of the virus. The World Health Organization (WHO) recommends maintaining a social distance of at least six feet. In this paper, we developed a real-time pedestrian social distance risk alert system for COVID-19, which monitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge, thus avoiding the problem of too close social distance between pedestrians in public places. We design a lightweight convolutional neural network… More >

Zhanyang Xu¹, Jianchun Cheng^1,*, Luofei Cheng¹, Xiaolong Xu^1,2, Muhammad Bilal³

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5573-5595, 2023, DOI:10.32604/cmc.2023.037287

Abstract Federated learning has been used extensively in business innovation scenarios in various industries. This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario. First, this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises (MSEs) using multi-dimensional enterprise data and multi-perspective enterprise information. The proposed model includes four main processes: namely encrypted entity alignment, hybrid feature selection, secure multi-party computation, and global model updating. Secondly, a two-step feature selection algorithm based on wrapper and filter is… More >

LIHUA CHEN^1,2,#, HONGTIAN ZHANG^1,2,#, YONG XIA^1,2, KAI SUN¹, WENJIN CHEN¹, RUXIANG XU^1,2,*

BIOCELL, Vol.47, No.5, pp. 1065-1073, 2023, DOI:10.32604/biocell.2023.025924

Abstract Purpose: The present study summarized cases of children (n = 32) with medulloblastoma (MB) who were treated using stratified therapy based on risk grading and also discussed the factors affecting prognosis. Methods: According to the risk stratification criteria, the cases were divided into the following four risk groups: low, standard, high, and very high. The 5-year overall survival (OS) and progression-free survival (PFS) rates were summarized. Further, the effects on the prognosis of tumor size, tumor stage, degree of resection, treatment mode, metastatic recurrence, molecular typing, and risk stratification were analyzed. Results: In the present study, following surgery, 3 cases… More >

Alawi Alqushaibi^1,2,*, Mohd Hilmi Hasan^1,2, Said Jadid Abdulkadir^1,2, Amgad Muneer^1,2, Mohammed Gamal^1,2, Qasem Al-Tashi³, Shakirah Mohd Taib^1,2, Hitham Alhussian^1,2

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3223-3238, 2023, DOI:10.32604/cmc.2023.035655

Abstract Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters… More >

Ashir Javeed^1,2, Ana Luiza Dallora², Johan Sanmartin Berglund^2,*, Arif Ali⁴, Peter Anderberg^2,3, Liaqat Ali⁵

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2491-2508, 2023, DOI:10.32604/cmc.2023.033783

Abstract Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic… More >

Mohsin Ghani, Ghulam Gilanie^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2835-2847, 2023, DOI:10.32604/iasc.2023.034857

Abstract The lungs are the main fundamental part of the human respiratory system and are among the major organs of the human body. Lung disorders, including Coronavirus (Covid-19), are among the world’s deadliest and most life-threatening diseases. Early and social distance-based detection and treatment can save lives as well as protect the rest of humanity. Even though X-rays or Computed Tomography (CT) scans are the imaging techniques to analyze lung-related disorders, medical practitioners still find it challenging to analyze and identify lung cancer from scanned images. unless COVID-19 reaches the lungs, it is unable to be diagnosed. through these modalities. So,… More >

YI-NONG CHEN^1,#, YING-LIN CHEN^1,#, WAN-MING CHEN^2,3, MINGCHIH CHEN^2,3, BEN-CHANG SHIA^2,3, JENQ-YUH KO^1,4, SZU-YUAN WU^{2,3,5,6,7,8,9,10,11,*}

Oncology Research, Vol.31, No.1, pp. 23-34, 2023, DOI:10.32604/or.2022.028449

Abstract This study aimed to examine the association between the use of H1-antihistamines (AHs) and head and neck cancer (HNC) risk in patients with type 2 diabetes mellitus (T2DM). Data from the National Health Insurance Research Database of Taiwan were analyzed for the period from 2008 to 2018. A propensity-score-matched cohort of 54,384 patients each in the AH user and nonuser groups was created and analyzed using Kaplan-Meier method and Cox proportional hazards regression. The results showed that the risk of HNC was significantly lower in AH users (adjusted hazard ratio: 0.55, 95% CI: 0.48 to 0.64) and the incidence rate… More >