An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

Hybrid e-rehabilitation services: SMART-system for remote support of rehabilitation activities and services

One of the most effective solutions in medical rehabilitation assistance is remote patient / person-centered rehabilitation. Rehabilitation also needs effective methods for the “Physical therapist – Patient – Multidisciplinary team” system, including the statistical processing of large volumes of data. Therefore, along with the traditional means of rehabilitation, as part of the “Transdisciplinary intelligent information and analytical system for the rehabilitation processes support in a pandemic (TISP)” in this paper, we introduce and define: the basic concepts of the new hybrid e-rehabilitation notion and its fundamental foundations; the formalization concept of the new Smart-system for remote support of rehabilitation activities and services; and the methodological foundations for the use of services (UkrVectores and vHealth) of the remote Patient / Person-centered Smart-system. The software implementation of the services of the Smart-system has been developed

INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life

Remote Health Monitoring IoT Framework using Machine Learning Prediction and Advanced Artificial Intelligence (AI) Model

Real intervention and treatment standards drew attention to remote health monitoring frameworks. Remote monitoring frameworks for disease detection at an early stage are opposed by most conventional works. Even so, it ran into issues like increased operational complexity, higher resource costs, inaccurate predictions, longer data collection times, and a lower convergence rate. A remote health monitoring framework that uses artificial intelligence (AI) to predict heart disease and diabetes from medical datasets is the goal of this project. Patients' health data is collected via smart devices, and the resulting data is then combined using a variety of nodes, including a detection node, a visualisation node, and a prognostic node. People with long-term illnesses (such as the elderly and disabled) are in such greater demand than ever before that a new approach to healthcare delivery is essential. In the evolved paradigm, conventional physical medical services foundations like clinics, nursing homes, and long haul care offices will be old. Due to recent advancements in modern technology, such as artificial intelligence (AI) and machine learning (ML), the smart healthcare system has become increasingly necessary (ML). This paper will discuss wearable and smartphone technologies, AI for medical diagnostics, and assistive structures, including social robots, that have been created for the surrounding upheld living climate. The review presents programming reconciliation structures that are urgent for consolidating information examination and other man-made consciousness instruments to develop brilliant medical care frameworks (AI)

Cloud Computing with Artificial Intelligence Techniques for Effective Disease Detection

With the current rapid advancement of cloud computing (CC) technology, which enabled the connectivity of many intelligent objects and detectors and created smooth data interchange between systems, there is now a strict need for platforms for data processing, the Internet of Things (IoT), and data management. The field of medicine in CC is receiving a lot of attention from the scientific world, as well as the private and governmental sectors. Thousands of individuals now have a digital system due to these apps where they may regularly obtain helpful medical advice for leading a healthy life. The use of artificial intelligence (AI) in the medical field has several advantages, including the ability to automate processes and analyze large patient databases to offer superior medicine more quickly and effectively. IoT-enabled smart health tools provide both internet solutions and a variety of features. CC infrastructure improves these healthcare solutions by enabling safe storage and accessibility. We suggest a novel Cloud computing and artificial intelligence (CC-AI) premised smart medical solution for surveillance and detecting major illnesses to provide superior solutions to the users. For disease detection, we suggested AI-based whale optimization (WO) and fuzzy neural network (FNN) (WO-FNN). Patients' IoT wearable sensor data is gathered for detection. The accuracy, sensitivity, specificity, and computation time are evaluated and compared with existing techniques

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table