Jiadong Wu, Yinan Wang^*, Zhiwei Li^*, Lun Lu, Qingjiang Li

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2909-2939, 2024, DOI:10.32604/cmc.2024.047240

Abstract Artificial neural networks (ANNs) have led to landmark changes in many fields, but they still differ significantly from the mechanisms of real biological neural networks and face problems such as high computing costs, excessive computing power, and so on. Spiking neural networks (SNNs) provide a new approach combined with brain-like science to improve the computational energy efficiency, computational architecture, and biological credibility of current deep learning applications. In the early stage of development, its poor performance hindered the application of SNNs in real-world scenarios. In recent years, SNNs have made great progress in computational performance and practicability compared with the… More >

Lifang Fu¹, Huanxin Peng^2,*, Changjin Ma², Yuhan Liu²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4399-4416, 2024, DOI:10.32604/cmc.2024.047053

Abstract In recent years, how to efficiently and accurately identify multi-model fake news has become more challenging. First, multi-model data provides more evidence but not all are equally important. Secondly, social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical. Unfortunately, existing approaches fail to handle these problems. This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues (TD-MMC), which utilizes three valuable multi-model clues: text-model importance, text-image complementary, and text-image inconsistency. TD-MMC is dominated by textural content and… More >

Rabia Abid¹, Muhammad Rizwan², Abdulatif Alabdulatif^3,*, Abdullah Alnajim⁴, Meznah Alamro⁵, Mourade Azrour⁶

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3413-3429, 2024, DOI:10.32604/cmc.2024.046880

Abstract Explainable Artificial Intelligence (XAI) has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning (ML) and Deep Learning (DL) based algorithms. In this paper, we chose e-healthcare systems for efficient decision-making and data classification, especially in data security, data handling, diagnostics, laboratories, and decision-making. Federated Machine Learning (FML) is a new and advanced technology that helps to maintain privacy for Personal Health Records (PHR) and handle a large amount of medical data effectively. In this context, XAI, along with FML, increases efficiency and improves the security of e-healthcare systems. The… More >

Xiaoying Chen^1,*, Jie Kang¹, Cong Hu²

Journal of New Media, Vol.6, pp. 1-16, 2024, DOI:10.32604/jnm.2024.045833

Abstract With the development of cities and the prevalence of networks, interpersonal relationships have become increasingly distant. When people crave communication, they hope to find someone to confide in. With the rapid advancement of deep learning and big data technologies, an enabling environment has been established for the development of intelligent chatbot systems. By effectively combining cutting-edge technologies with human-centered design principles, chatbots hold the potential to revolutionize our lives and alleviate feelings of loneliness. A multi-topic chat companion robot based on a state machine has been proposed, which can engage in fluent dialogue with humans and meet different functional requirements.… More >

Ghulam Gilanie^1,*, Sana Cheema¹, Akkasha Latif¹, Anum Saher¹, Muhammad Ahsan¹, Hafeez Ullah², Diya Oommen³

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 57-71, 2024, DOI:10.32604/iasc.2024.041535

Abstract Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient. It affects a large percentage of people globally, who fluctuate between depression and mania, or vice versa. A pleasant or unpleasant mood is more than a reflection of a state of mind. Normally, it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio, so automated procedures are the best options to diagnose and verify the severity of bipolar. In this research work, facial micro-expressions have been used for bipolar detection using… More >

Wanxiu Zhu^*

Psycho-Oncologie, Vol.18, No.1, pp. 69-77, 2024, DOI:10.32604/po.2023.046593

Abstract Exploring the relationship between social support and the mental health of cancer patients is a vital endeavor for enhancing mental well-being. The objective of this study was to explore the relationship between social support and psychological well-being in cancer patients and to provide new empirical evidence for psychological improvement in cancer patients. This research holds significant practical implications for optimizing China’s public health system and strengthening the development of a healthy China. Using data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) database, we empirically examined the influence of social support on the mental health of cancer patients.… More >

Kenneth M. Armijo, Van P. Carey^*

Frontiers in Heat and Mass Transfer, Vol.1, No.2, pp. 1-6, 2010, DOI:10.5098/hmt.v1.2.3003

Abstract This paper investigates the impact of Marangoni phenomena for low concentrations of 2-propanol/water and methanol/water mixtures. In real systems the addition of small levels of surface-active contaminants can affect the surface tension of the liquid-vapor interface and thermodynamic conditions in this region. Analysis was performed for three widely accepted binary mixture correlations to predict heat flux and superheat values for subatmospheric experimental data using bulk fluid and film thermodynamic properties. Due to the non-ideal nature of these alcohol/water mixtures, this study also employs an average pseudo single-component (PSC) coefficient in place of an ideal heat transfer coefficient (HTC) to improve… More >

Albert Osom¹, Krysta S. Barton², Katie Sexton^3,4, Lyndia Brumback¹, Joyce P. Yi-Frazier⁴, Abby R. Rosenberg^5,6, Ruth Engelberg⁷, Jill M. Steiner^8,*

Congenital Heart Disease, Vol.19, No.1, pp. 33-48, 2024, DOI:10.32604/chd.2024.046656

Abstract Social determinants of health (SDOH) affect quality of life. We investigated SDOH impacts on self-perceived resilience among people with adult congenital heart disease (ACHD). Secondary analysis of data from two complementary studies: a survey study conducted May 2021–June 2022 and a qualitative study conducted June 2020–August 2021. Resilience was assessed through CD-RISC10 score (range 0–40, higher scores reflect greater self-perceived resilience) and interview responses. Sociodemographic and SDOH (education, employment, living situation, monetary stability, financial dependency, area deprivation index) data were collected by healthcare record review and self-report. We used linear regression with robust standard errors to analyze survey data and… More > Graphic Abstract

Yamixa Delgado^1,*, Caliani Gaytan¹, Naydi Perez², Eric Miranda³, Bryan Colón Morales¹, Mónica Santos¹

Congenital Heart Disease, Vol.19, No.1, pp. 19-31, 2024, DOI:10.32604/chd.2024.046339

Abstract Background: Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States, there is a compelling need to investigate the intricate interplay among body mass index (BMI), pregestational, and gestational maternal diabetes, and their potential impact on the occurrence of congenital heart defects (CHD) during neonatal development. Methods: Using the comprehensive System of Vigilance and Surveillance of Congenital Defects in Puerto Rico, we conducted a focused analysis on neonates diagnosed with CHD between 2016 and 2020. Our assessment encompassed a range of variables, including maternal age, gestational age, BMI, pregestational diabetes, gestational diabetes, hypertension,… More >

Koren Hyogene Kwag^1,*, Ibrahim Kak¹, Ying Li², Walid Khass¹, Alec McKechnie¹, Oksana Nulman¹, Brande Brown¹, Manoj Chhabra¹

Congenital Heart Disease, Vol.19, No.1, pp. 107-121, 2024, DOI:10.32604/chd.2024.045953

Abstract Objective: Accurate measurement of QT interval, the ventricular action potential from depolarization to repolarization, is important for the early detection of Long QT syndrome. The most effective QT correction (QTc) formula has yet to be determined in the pediatric population, although it has intrinsically greater extremes in heart rate (HR) and is more susceptible to errors in measurement. The authors of this study compare six different QTc methods (Bazett, Fridericia, Framingham, Hodges, Rautaharju, and a computer algorithm utilizing the Bazett formula) for consistency against variations in HR and RR interval. Methods: Descriptive Retrospective Study. We included participants from a pediatric… More >