The challenge for deployment of low-cost and high-speed ubiquitous Smart Health services has prompted us to propose new framework design for providing excellent healthcare to humankind. So, there exists a very high demand for developing an Internet of Medical Things (IoMT) based Ubiquitous Real-Time LoRa (Long Range) Healthcare System using Convolutional Neural Networks (CNN) to agree if a sequence of frames contains a person falling. To model the video motion and make the system scenario sovereign, in this research, we use optical flow images as input to the networks. Right now hospital and home falls are a noteworthy medical services concern overall on account of the aging populace. Current observational information, vital signs and falls history give the necessary data identified with the patient's physiology, and movement information give an additional utensil in falls risk evaluation. The proposed framework utilizes Real-Time Vital signs monitoring and emergency alert message to caregivers or doctors. In this context, we introduce "LoRaWAN based Next Generation Ubiquitous Healthcare System (NXTGeUH), an intelligent middleware platform. In addition, this proposed method is evaluated with different public hospital datasets achieving the state-of-the-art outcomes in all aspects.

The rapid evolution of telehealth, or telemedicine, has spurred crucial technological advancements aimed at addressing the early stages of complex cancer conditions, where conventional diagnostic methods face challenges. This research introduces a cancer detection system that utilizes Internet of Things (IoT)-based patient records and machine learning. The primary objective is to automate real-time breast cancer monitoring and detection in residential institutions and smart hospitals, thus enhancing the delivery of quality cancer healthcare. Background: Traditional diagnostic methods, particularly physical inspection, exhibit inherent limitations in identifying breast cancer at early stages. This research responds to this challenge by leveraging innovative technologies, such as IoT and deep learning-based techniques, to overcome the constraints of conventional approaches. Objective: The primary goal of this study is to develop and implement a cancer detection system that integrates IoT-based patient records and machine learning for real-time breast cancer monitoring in residential and healthcare settings. Method: The research employs a synergistic combination of IoT technology for collecting images of residential users and Convolutional Neural Network (CNN), a deep learning technique, for early cancer prediction. The focus lies on contributing to the overall well-being of individuals who may unknowingly be living with cancer. Result: Simulated outcomes after 25 epochs are presented, emphasizing the training accuracy of the model and its validation accuracy using the proposed VGG16 classifier. Graphical representations of the results indicate consistent performance metrics, with both validation and training accuracy exceeding 99%. Specifically, the training accuracy measures at an impressive 99.64%, while the validation accuracy stands at 99.12%. Main Findings: The study demonstrates the effectiveness of the integrated IoT and deep learning techniques in achieving high accuracy rates for early breast cancer prediction. The findings affirm the potential of this approach to assist dermatologists in identifying breast malignancies at treatable stages. Conclusion: This research establishes a foundational framework for the integration of IoT and deep learning techniques, presenting a promising avenue for advancing early cancer detection in smart healthcare systems. The proposed cancer detection system holds significant potential for improving healthcare outcomes and contributing to the overall well-being of individuals at risk of breast cancer.

Majority of recommender systems have their basis on either of the aspectual factors or contextual factors where as very few systems endeavours to demonstrate the use of both factors collectively. Very few works have been done to identify more fine-grained aspect level contextual preferences and their significance in generating accurate predictions for the user. Accuracy has constantly been the centre of all the works performed in improving this system. The purpose of this study is to introduce the use of such a technique that can integrate well into a system that is based on both contextual and non-contextual user preferences. For this purpose, use of a standard machine learning technique, Support Vector Machine was suggested in this paper. SVM facilitates in separating the data via hyperplane, in the finest manner and then classify these data. Users’ preferences are further classified using training set produced as a result of SVM classification. Finally a real-life dataset is experimented to demonstrate that our method is proficient in dealing with contextual as well as non-contextual preferences of users with higher accuracy

In the India, the number of individuals living with self-restricting conditions, for example, Dementia, Parkinson's infection and wretchedness, is expanding. The subsequent strain on national HealthCare assets implies that giving 24-hour observing to patients is a test. As this issue heightens, administering to a maturing populace will turn out to be likewise all the more requesting throughout the following decade. Advanced Health Care is progressively developing in developing nations like India, the accessibility of ease cell phones and the adequately broad scope of GSM systems in India is a great chance to give benefits that would trigger improvement and enhance individuals' HealthCare with the utilization of Internet of Things(IoT) and wireless sensor networks. By Using particular sensors, the information will be recorded and contrasted and a configurable predefined threshold employing microcontroller which delineated by a specific doctor who takes after the patient, regardless of emergency using GSM and GPS devices, SMS would transfer it to particular doctor's phone number with accurate values and patient's data. Our framework i-MsRTRM (Intelligent Medicare system for Real-Time Remote Health Monitoring) uses equipped models, which are conveyed as web services utilizing cloud computing and IoT gateways with different advanced sensors, to give a remote health monitoring effectively.