K. Akilandeswari¹, Nithya Rekha Sivakumar^2,*, Hend Khalid Alkahtani³, Shakila Basheer³, Sara Abdelwahab Ghorashi²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1189-1205, 2024, DOI:10.32604/cmc.2023.034815

Abstract In this present time, Human Activity Recognition (HAR) has been of considerable aid in the case of health monitoring and recovery. The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance. Although many research works conducted on Smart Healthcare Monitoring, there remain a certain number of pitfalls such as time, overhead, and falsification involved during analysis. Therefore, this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning (SPR-SVIAL) for Smart Healthcare Monitoring. At first, the Statistical Partial Regression… 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… More >

Ji-Won Baek¹, Kyungyong Chung^2,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6573-6586, 2023, DOI:10.32604/cmc.2023.035246

Abstract Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic relationships. It is possible to offer the explainable basis of decision-making for inference results. Through the causality of risk factors that have an ambiguous association in big medical data, it is possible to increase transparency and reliability of explainable decision-making that helps to diagnose disease status. In addition, the technique makes it possible to accurately predict disease risk for anomaly detection. Vision transformer for anomaly detection from image data makes classification through MLP.… More >

Saima Kanwal¹, Feng Tao^1,*, Ahmad Almogren², Ateeq Ur Rehman³, Rizwan Taj¹, Ayman Radwan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 201-219, 2023, DOI:10.32604/cmes.2022.020255

Abstract The authenticity and integrity of healthcare is the primary objective. Numerous reversible watermarking schemes have been developed to improve the primary objective but increasing the quantity of embedding data leads to covering image distortion and visual quality resulting in data security detection. A trade-off between robustness, imperceptibility, and embedded capacity is difficult to achieve with current algorithms due to limitations in their ability. Keeping this purpose insight, an improved reversibility watermarking methodology is proposed to maximize data embedding capacity and imperceptibility while maintaining data security as a primary concern. A key is generated by a… More >

Nithya Rekha Sivakumar^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2383-2398, 2023, DOI:10.32604/iasc.2023.028574

Abstract Internet of things (IoT) has brought a greater transformation in healthcare sector thereby improving patient care, minimizing treatment costs. The present method employs classical mechanisms for extracting features and a regression model for prediction. These methods have failed to consider the pollution aspects involved during COVID 19 prediction. Utilizing Ensemble Deep Learning and Framingham Feature Extraction (FFE) techniques, a smart healthcare system is introduced for COVID-19 pandemic disease diagnosis. The Collected feature or data via predictive mechanisms to form pollution maps. Those maps are used to implement real-time countermeasures, such as storing the extracted data… More >

D. Palanikkumar¹, Adel Fahad Alrasheedi², P. Parthasarathi³, S. S. Askar², Mohamed Abouhawwash^4,5,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 457-469, 2023, DOI:10.32604/csse.2023.024884

Abstract Data management becomes essential component of patient healthcare. Internet of Medical Things (IoMT) performs a wireless communication between E-medical applications and human being. Instead of consulting a doctor in the hospital, patients get health related information remotely from the physician. The main issues in the E-Medical application are lack of safety, security and privacy preservation of patient’s health care data. To overcome these issues, this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage. Patients health data is collected from physician, smart devices etc. The main goal is to store More >

Omer Iqbal^*, Tayyeba Iftakhar, Saleem Zubair Ahmad

Journal on Internet of Things, Vol.4, No.1, pp. 35-55, 2022, DOI:10.32604/jiot.2022.022256

Abstract IOT has carried out important function in converting the traditional fitness care corporation. With developing call for in population, traditional healthcare structures have reached their outmost functionality in presenting sufficient and as plenty as mark offerings. The worldwide is handling devastating developing antique population disaster and the right want for assisted-dwelling environments is turning into inevitable for senior citizens. There furthermore a determination by means of the use of way of countrywide healthcare organizations to increase crucial manual for individualized, right blanketed care to prevent and manipulate excessive coronial situations. Many tech orientated packages related… More >

R Punithavathi¹, K Venkatachalam², Mehedi Masud³, Mohammed A. AlZain⁴, Mohamed Abouhawwash^5,6,*

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 559-574, 2022, DOI:10.32604/iasc.2022.024715

Abstract The challenges in providing e-health services with the help of Internet of Medical Things (IoMT) is done by connecting to the smart medical devices. Through IoMT sensor devices/smart devices, physicians share the sensitive information of the patient. However, protecting the patient health care details from malware attack is necessary in this advanced digital scenario. Therefore, it is needed to implement cryptographic algorithm to enhance security, safety, reliability, preventing details from malware attacks and privacy of medical data. Nowadays blockchain has become a prominent technology for storing medical data securely and transmit through IoMT concept. The… More >

G. Ravikumar¹, K. Venkatachalam², Mehedi Masud³, Mohamed Abouhawwash^4,5,*

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 865-877, 2022, DOI:10.32604/iasc.2022.024278

Abstract Recently internet of medical things (IoMT) act as a smart doctor using sensor wearable’s device in human body. This smart doctor device senses necessary medical data from human and transfer via network immediately to physician. It is important to transfer sensitive data very securely. Blockchain becomes trending technology to provide high security to both end users in the network. Traditionally security structure is relying on cryptographic techniques which is very expensive and takes more time in securely transmitting data. To overcome this issue, this paper builds a cost effective, blockchain with IoMT using fog-cloud computing.… More >

Harrison Schurr, Andrei Tuluca^*, Beth Bailey

International Journal of Mental Health Promotion, Vol.24, No.2, pp. 159-167, 2022, DOI:10.32604/ijmhp.2022.017990

Abstract Suicide is a top ten cause of mortality in the United States. In previous literature the suicide rates in rural communities have been reported to be greater than those of more urban communities. Additionally, these studies have discussed many potential causes for the unfortunate disparity in rates. One cause often discussed is lack of mental health care providers in rural communities. The data for this study was gathered from the CDC’s WONDER database and the NPPES NPI Registry. The urban-rural categorization of counties used the 2013 NCHS Urban-Rural Scheme. Statistical analysis included chi-square tests, paired t-tests, More >