To diagnose an illness in healthcare, doctors typically conduct physical exams and review the patient’s medical history, followed by diagnostic tests and procedures to determine the underlying cause of symptoms. Chronic kidney disease (CKD) is currently the leading cause of death, with a rapidly increasing number of patients, resulting in 1.7 million deaths annually. While various diagnostic methods are available, this study utilizes machine learning due to its high accuracy. In this study, we have used the hybrid technique to build our proposed model. In our proposed model, we have used the Pearson correlation for feature selection. In the first step, the best models were selected on the basis of critical literature analysis. In the second step, the combination of these models is used in our proposed hybrid model. Gaussian Naïve Bayes, gradient boosting, and decision tree classifier are used as a base classifier, and the random forest classifier is used as a meta‐classifier in the proposed hybrid model. The objective of this study is to evaluate the best machine learning classification techniques and identify the best‐used machine learning classifier in terms of accuracy. This provides a solution for overfitting and achieves the highest accuracy. It also highlights some of the challenges that affect the result of better performance. In this study, we critically review the existing available machine learning classification techniques. We evaluate in terms of accuracy, and a comprehensive analytical evaluation of the related work is presented with a tabular system. In implementation, we have used the top four models and built a hybrid model using UCI chronic kidney disease dataset for prediction. Gradient boosting achieves around 99% accuracy, random forest achieves 98%, decision tree classifier achieves 96% accuracy, and our proposed hybrid model performs best getting 100% accuracy on the same dataset. Some of the main machine learning algorithms used to predict the occurrence of CKD are Naïve Bayes, decision tree, K‐nearest neighbor, random forest, support vector machine, LDA, GB, and neural network. In this study, we apply GB (gradient boosting), Gaussian Naïve Bayes, and decision tree along with random forest on the same set of features and compare the accuracy score.

Wireless Body Area Network is an emerging technology that is used primarily in the area of healthcare applications. It is a low-cost network having the capability of transportability and adaptability. It can be used in location independent and long-term remote monitoring of people without disturbing their daily activities. In a typical WBAN system, sensing devices are either implanted or etched into the human body that continuously monitors his physiological parameters or vital signs. In such a network, trusts among the stakeholders (healthcare providers, users, and medical staff, etc.) are found of high importance and regarded as the critical success factor for the reliability of information exchange among them. In remote patient monitoring, the implementation of trust and privacy preservation is crucial, as vital parameters are being communicated to remote locations. Nonetheless, its widespread use, WBAN, has severe trust and privacy risks, limiting its adaptation in healthcare applications. To address trust and privacy-related issues, reliable communication solutions are widely used in WBANs. Given the motivation, in this paper, we have proposed a trust-based communication scheme to ensure the reliability and privacy of WBAN. To ensure reliability, a cooperative communication approach is used, while for privacy preservation, a cryptography mechanism is used. The performance of the proposed scheme is evaluated using MATLAB simulator. The output results demonstrated that the proposed scheme increases service delivery ratio, reliability, and trust with reduced average delay. Furthermore, a fuzzy-logic method used for ranking benchmark schemes, that has been concluded that the proposed scheme has on top using comparative performance ranking.

The tremendous advancement in embedded systems, miniaturization, and wireless technology had allowed Wireless Body Area Networks (WBAN) to have overwhelming applications in e-healthcare, entertainment, sports/games training, etc. WBAN is a special type of wireless sensor network where bio-sensors are attached or embedded to a single human-body designed to connect various bio-sensors and applications, operate autonomously and observe different vital signs of a human body remotely. Despite its enormous benefits and applications, some of the key challenges in designing heterogeneous WBAN is their energy-efficiency, reliability, and fault-tolerance among the installed bio-sensors. Due to the criticality of services related to WBAN applications, it is imperative to have a high degree of reliability and fault-tolerance, especially in the case of health-care monitoring applications where continuous monitoring of patient’s vital information is required for diagnosis. However, in health-care applications, interference and body fading occur, which affect the communication among nodes and gateway, which reduces the reliability and fault-tolerance of the network. To address these issues, in this paper, we have proposed an energy-efficient fault-tolerant scheme to improve the reliability of WBANs. The proposed scheme adopted the cooperative communication and network coding strategy to minimize channel impairment and body fading effect and hence reduces the ensued faults, bit error rate, and energy consumption. Based on the proposed scheme, a case study was designed for remote Sepsis monitoring. The system identifies tracking indicators using cooperative communication to reduce hospital re-admissions and mortality rates. The proposed scheme performance is also evaluated via extensive simulations using various metrics. From the results obtained, it is evident that the proposed scheme reduces energy consumption, delay, and bit error rate, thereby increasing the throughput and reliability in WBAN.

Cryptocurrency mining and blockchain technology using renewable energy as the main electricity source has gained attention for sustainable development in financial areas. However, very few studies have been reported concerning the power usage of cryptocurrencies using renewable energy. In this article, we report the effect of overclocking and undervolting on power usage and the hash rate for mining dogecoin with solar energy as renewable energy. The mining rig used in this work consists of different graphics processing units (GPUs) and non-LHR (lite hash rate) cards. The UnMineable software has been used for mining dogecoin as well as for wallet integration. The results indicate that mining dogecoin with solar energy as renewable energy consumes 2000 Watts power with overclocking and 1700 Watts power with undervolting technique. This work implicates the potential future of crypto-mining with renewable energy and the hardware configuration associated with it, which is expected to reduce e-waste and improve sustainable development. To reduce the e-waste and high electricity consumption, we have introduced two important techniques named GPU optimization and use of renewable energy for mining, which helps the miners to reduce the e-waste and electricity consumption significantly at the same time getting most out of the GPU by not having any impact on the environment.

Today is the era of super-connectivity, where real-world things connect, gather real-time data from their surroundings and disseminate the recorded data into the environment. The users can access services without understanding the basic composite structure of heterogeneous devices and hybrid IoT infrastructure. Data is collected, managed, and processed by minute and plug-able sensors for the IoT paradigm. Due to the resource-constrained nature of these sensors, massive and recurrent tasks create congestion in the network and drain the energy of sensors. Sending unnecessary and redundant data packets is life-threatening and affects the availability of other resources. This paper proposes a novel scheme, an “Energy-Efficient Dynamic and Adaptive State-based Scheduling” (EDASS) for Wireless Sensor Network. The suggested method switches nodes between states dynamically and adapts to new states based on the contents of sensed data packets. Four distinct states of energy are derived from a combination of internal modules of the sensor. The typical sequence of operation is abruptly changed, and all sensors become active when a new event occurs. EDASS decreases energy consumption by 29% in live nodes, 41% in message overhead and 33% in the cluster head selection process. At the same time, the average delay in EDASS increases from 1.26 ms to 1.39 ms due to control message overhead.