Regina Lai-Tong Lee^1,2,*, Anson Chiu-Yan Tang³, Ho-Yu Cheng¹, Connie Yuen-Yu Chong¹, Wilson Wai-San Tam⁴, Wai-Tong Chien¹, Sally Wai-Chi Chan⁵

International Journal of Mental Health Promotion, Vol.25, No.3, pp. 327-342, 2023, DOI:10.32604/ijmhp.2022.026388

Abstract The present study aimed to examine work environment related factors and frontline primary healthcare professionals’ mental-emotional wellbeing during the COVID-19 pandemic in school communities of Hong Kong. A total of 61 (20%) school health nurses (frontline primary healthcare professionals) participated in a cross-sectional online survey from March to June 2020. Outcomes of mental-emotional health were measured using the Mental Health Continuum-Short Form (14-item scale with three subscales related to emotional, social and psychological wellbeing); the Perceived Stress Scale (10-item scale with two subscales related to perceived helplessness and lack of self-efficacy; and the Coping Orientation to Problems Experienced Inventory (Brief… More >

Mohammad Yamin^1,*, Sarah Basahel¹, Saleh Bajaba², Mona Abusurrah³, E. Laxmi Lydia⁴

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1901-1916, 2023, DOI:10.32604/csse.2023.036455

Abstract Recently, there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy (DR). DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed world. Initial detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus images. This article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy (DLLSHDM-DR) on Retinal Fundus Images. The proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making method. In the DLLSHDM-DR technique, image preprocessing is initially… More >

J. Prabakaran^1,*, P. Selvaraj²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2141-2157, 2023, DOI:10.32604/csse.2023.034210

Abstract In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into a convenient and efficient format… More >

Sarah B. Basahel¹, Saleh Bajaba², Mohammad Yamin³, Sachi Nandan Mohanty⁴, E. Laxmi Lydia^5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1353-1369, 2023, DOI:10.32604/cmc.2023.036453

Abstract The current advancement in cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT) transformed the traditional healthcare system into smart healthcare. Healthcare services could be enhanced by incorporating key techniques like AI and IoT. The convergence of AI and IoT provides distinct opportunities in the medical field. Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population. Therefore, earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support. Lately, the emergence of IoT, AI, smartphones, wearables, and so… More >

A. S. Yusuf^1,*, A. Q. Ayinde²

Journal of Cyber Security, Vol.4, No.3, pp. 185-188, 2022, DOI:10.32604/jcs.2022.035446

Abstract Hospitals provide daily health services for thousands of patients. People, processes, and technologies drive the objectives and goals of the hospitals to ensure optimal and satisfactory health care services are rendered to their customers. Due to the sensitivity of the organization data and patient data, it is essential to ensure that the confidentiality, integrity, availability, and security of these data are considered. The leadership of the organization (managers and executives) must integrate a robust security plan when choosing the technologies that will be used to drive the organization’s processes. This paper will evaluate the existing technologies risk assessment, and the… More >

Faneela¹, Muazzam A. Khan¹, Suliman A. Alsuhibany^2,*, Walid El-Shafai^3,4, Mujeeb Ur Rehman⁵, Jawad Ahmad⁶

Computer Systems Science and Engineering, Vol.46, No.1, pp. 165-179, 2023, DOI:10.32604/csse.2023.035066

Abstract The advancements in sensing technologies, information processing, and communication schemes have revolutionized the healthcare sector. Electronic Healthcare Records (EHR) facilitate the patients, doctors, hospitals, and other stakeholders to maintain valuable data and medical records. The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks. A single attempt of a successful Denial of Service (DoS) attack can compromise the complete healthcare system. This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things (IoMT) to address the stated challenges. The proposed architecture is on the idea of a lightweight private blockchain-based network that… More >

Abdullah M. Basahel, Mohammad Yamin^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 209-224, 2023, DOI:10.32604/csse.2023.034449

Abstract Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents a new quantum-inspired differential evolution… More >

B. Karthikeyan^1,*, K. Nithya², Ahmed Alkhayyat³, Yousif Kerrar Yousif⁴

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2299-2313, 2023, DOI:10.32604/iasc.2023.032585

Abstract In today’s digital era, e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices, computers and the internet to provide high-quality healthcare services. E-healthcare decision support systems have been developed to optimize the healthcare services and enhance a patient’s health. These systems enable rapid access to the specialized healthcare services via reliable information, retrieved from the cases or the patient histories. This phenomenon reduces the time taken by the patients to physically visit the healthcare institutions. In the current research work, a new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System (SFLODL-DSS) is designed for the… More >

E. Bharath^1,*, T. Rajagopalan²

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1417-1433, 2023, DOI:10.32604/iasc.2023.032102

Abstract Parkinson’s disease (PD) is a neurodegenerative disease cause by a deficiency of dopamine. Investigators have identified the voice as the underlying symptom of PD. Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection. Machine learning (ML) models have recently helped to solve problems in the classification of chronic diseases. This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system. It includes PD classification models of Random forest, decision Tree, neural network, logistic regression and support vector machine. The feature selection is made by RF mean-decrease in accuracy… More >

Sathya Bama B^*, Bevish Jinila Y

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2085-2097, 2023, DOI:10.32604/iasc.2023.028481

Abstract Parkinson’s disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system. The cause of such brain damage seems to be fully explained in many research studies, but the understanding of its functionality remains to be impractical. Specifically, the development of a quantitative disease prediction model has evolved in recent decades. Moreover, accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson’s disease. This type of minimal infrastructure equipment helps in analyzing the Parkinson’s… More >