Governments rely on collected data to analyze their citizens’ health requirements and needs. Such data is usually of scattered manner gathered by several agencies and entities that fall under different business models and goals. Hence, the scattered nature of this data creates a burden on governments in precisely planning adequate health bills that guarantee the wellbeing of their citizens. Furthermore, the issue of synchronizing and having a real-time access to such data exacerbates the problem even more. Therefore, a system that guarantees the integrity, accessibility, correctness and security of medical data in a highly synchronized environment is crucial for governments to accurately project their future needs. In this paper, we present Smart-Health (SHealth), a blockchain-based health management system. In its crux, SHealth is a private multi-layered blockchain integrated with a multi-tiered addressing scheme that defines the privileges and permissions of entities in the system. Blockchain-based systems assure security, reliability, availability, resistance against tampering and malicious attacks, seamless integration, and easy data management. SHealth proposes a complete autonomy to its users. Through a user-friendly graphical interface it capitalizes on smart contracts to initiate various requests and inquiries pertaining to patients such as appointments, medical tests, medications, medical procedures, or history. SHealth is simple, robust, efficient, secured, and completely automated. All the stakeholders in the system can access the health related data stored in a distributed database without compromising its authenticity. SHealth covers all the possible scenarios in health systems from which some of them are explained in this work.

In the era of Industry 4.0 and beyond, ball bearings remain an important part of industrial systems. The failure of ball bearings can lead to plant downtime, inefficient operations, and significant maintenance expenses. Although conventional preventive maintenance mechanisms like time-based maintenance, routine inspections, and manual data analysis provide a certain level of fault prevention, they are often reactive, time-consuming, and imprecise. On the other hand, machine learning algorithms can detect anomalies early, process vast amounts of data, continuously improve in almost real time, and, in turn, significantly enhance the efficiency of modern industrial systems. In this work, we compare different machine learning and deep learning techniques to optimise the predictive maintenance of ball bearing systems, which, in turn, will reduce the downtime and improve the efficiency of current and future industrial systems. For this purpose, we evaluate and compare classification algorithms like Logistic Regression and Support Vector Machine, as well as ensemble algorithms like Random Forest and Extreme Gradient Boost. We also explore and evaluate long short-term memory, which is a type of recurrent neural network. We assess and compare these models in terms of their accuracy, precision, recall, F1 scores, and computation requirement. Our comparison results indicate that Extreme Gradient Boost gives the best trade-off in terms of overall performance and computation time. For a dataset of 2155 vibration signals, Extreme Gradient Boost gives an accuracy of 96.61% while requiring a training time of only 0.76 s. Moreover, among the techniques that give an accuracy greater than 80%, Extreme Gradient Boost also gives the best accuracy-to-computation-time ratio.

The research on the Internet of Things (IoT) has made huge strides forward in the past couple of years. IoT has its applications in almost every walk of life, and it is being regarded as the next big thing that can change the way humans perceive about their daily life. Smart IoT devices of heterogeneous nature make an essential part of modern day IoT-based systems. The security of these devices is of paramount importance as they handle an enormous amount of critical data and its breach can lead to potentially life-threatening situations. To secure the IoT devices of heterogeneous nature, we formulated a weighted optimization problem in this work. The objective function of this problem is to secure the IoT devices while finding the best trade-off between their resource usage and throughput. To achieve the objective, we consider a pool of five different implementations of Advanced Encryption Standard (AES) cryptographic schemes that offer varied resources and throughput numbers. These implementation schemes are mapped to IoT devices of heterogeneous nature. The mapping is performed through a novel adaptive framework that can consider different weights for resources and throughput to eventually find the best trade-off between the resources and throughput of an IoT-based system. This framework considers the resource and throughput requirements of different IoT devices and uses the Hungarian algorithm to adaptively map different AES implementations on them. Extensive experimentation is performed where the best trade-off is found through varying resource and throughput weight combinations. The comparison of the proposed framework with random and greedy approaches is also performed. Comparison results show that the proposed framework adaptively secures the IoT-based system while providing better resource usage and throughput results. The proposed framework provides, on average, 11% and 17% better throughput and 3% and 13% better resource usage results as compared to random and greedy approach, respectively.

High blood glucose levels are the defining characteristic of diabetes. Uncontrolled blood glucose levels in diabetic patients might result in mortality. As a result, there is a dire need to control blood glucose levels by constantly monitoring them and delivering the appropriate amount of insulin. However, insulin consumption is affected by several variables, including age, calorific intake, and body weight. The patient must see the doctor on a regular basis in order to determine the appropriate dose. Nonetheless, hospital facilities are finding it increasingly difficult to treat patients as the number of patients rises; thus, the healthcare industry is searching for an efficient method that can alleviate their burden by assisting patients with chronic conditions through remote patient care. In this work, we have developed an expert system to provide remote treatment for diabetic patients. Our expert system consists of two distinct components: one for the patient and one for the hospital. The sole requirement for the patient will be a wearable device that captures and transmits all relevant data to the cloud. On the hospital side, there should be a system in place to process that data in the cloud. The system employs a fuzzy system to handle data in two stages. A fuzzy system is initially employed to identify whether or not a patient is diabetic. In the second stage, a fuzzy system is utilized to determine the insulin dosage for a diabetic patient. Using sensors and the ESP8266 platform, we have developed a prototype of patient-side hardware. The MATLAB fuzzy toolbox is used for the processing part, which includes fuzzy systems, and the results of the MATLAB analysis are presented in the form of simulation results to demonstrate the accuracy of the proposed system in terms of determining insulin dosage. The results of the simulation using the fuzzy toolbox for the insulin dose of the diabetic patient are significantly close to the amount of dosage prescribed by the endocrinologist.

Field-Programmable Gate Array (FPGA) is at the core of System on Chip (SoC) design across various Industry 5.0 digital systems—healthcare devices, farming equipment, autonomous vehicles and aerospace gear to name a few. Given that pre-silicon verification using Computer Aided Design (CAD) accounts for about 70% of the time and money spent on the design of modern digital systems, this paper summarizes the machine learning (ML)-oriented efforts in different FPGA CAD design steps. With the recent breakthrough of machine learning, FPGA CAD tasks—high-level synthesis (HLS), logic synthesis, placement and routing—are seeing a renewed interest in their respective decision-making steps. We focus on machine learning-based CAD tasks to suggest some pertinent research areas requiring more focus in CAD design. The development of open-source benchmarks optimized for an end-to-end machine learning experience, intra-FPGA optimization, domain-specific accelerators, lack of explainability and federated learning are the issues reviewed to identify important research spots requiring significant focus. The potential of the new cloud-based architectures to understand the application of the right ML algorithms in FPGA CAD decision-making steps is discussed, together with visualizing the scenario of incorporating more intelligence in the cloud platform, with the help of relatively newer technologies such as CAD as Adaptive OpenPlatform Service (CAOS). Altogether, this research explores several research opportunities linked with modern FPGA CAD flow design, which will serve as a single point of reference for modern FPGA CAD flow design.