Energy-detection (ED) pulse-position modulation (PPM) receivers exhibit poor performance and low rates. Coherent receivers do not have such problems but their complexity is unacceptable. We propose two detection schemes to increase the performance of non-coherent PPM receivers. Unlike the ED-PPM receiver, the first proposed receiver cubes the absolute value of the received signal before demodulation and achieves a considerable performance gain. This gain is obtained because the absolute-value cubing (AVC) operation reduces the effect of low-SNR samples and increases the effect of high-SNR samples on the decision statistic. To further increase energy efficiency and rate of the non-coherent PPM receivers at almost the same complexity, we use the weighted-transmitted reference (WTR) system instead of the ED-based receiver. The WTR system has adequate robustness to weight coefficients and integration interval variations. To generalize the AVC concept to the WTR-PPM receiver, the reference pulse is first passed through a polarity-invariant squaring (PIS) operation and then is correlated with the data pulses. In this paper, the performance of different receivers employing the binary PPM (BPPM) is investigated at data rates of 2.08 and 9.1 Mbps over in-vehicle channels in the presence of noise, inter-block interference, inter-pulse interference, and inter-symbol interference (ISI). Simulations show that the proposed AVC-BPPM receiver outperforms the ED-based one in the absence of ISI and offers the same performance in the presence of strong ISI, the WTR-BPPM system considerably outperforms the ED-BPPM system (especially at high rates), and the proposed PIS-based WTR-BPPM system considerably outperforms the conventional WTR-BPPM system.

Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients' health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients' vitals and storing them securely have risen more than ever. Analyzing the stored patients' data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients' data, and processing/analyzing the stored patients' data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.

In recent years, there have been concentrations on the Digital Twin from researchers and companies due to its advancement in IT, communication systems, Cloud Computing, Internet-of-Things (IoT), and Blockchain. The main concept of the DT is to provide a comprehensive tangible, and operational explanation of any element, asset, or system. However, it is an extremely dynamic taxonomy developing in complication during the life cycle that produces an enormous quantity of the engendered data and information from them. Likewise, with the development of the Blockchain, the digital twins have the potential to redefine and could be a key strategy to support the IoT-based digital twin's applications for transferring data and value onto the Internet with full transparency besides promising accessibility, trusted traceability, and immutability of transactions. Therefore, the integration of digital twins with the IoT and blockchain technologies has the potential to revolutionize various industries by providing enhanced security, transparency, and data integrity. Thus, this work presents a survey on the innovative theme of digital twins with the integration of Blockchain for various applications. Also, provides challenges and future research directions on this subject. In addition, in this paper, we propose a concept and architecture for integrating digital twins with IoT-based blockchain archives, which allows for real-time monitoring and control of physical assets and processes in a secure and decentralized manner. We also discuss the challenges and limitations of this integration, including issues related to data privacy, scalability, and interoperability. Finally, we provide insights into the future scope of this technology and discuss potential research directions for further improving the integration of digital twins with IoT-based blockchain archives. Overall, this paper provides a comprehensive overview of the potential benefits and challenges of integrating digital twins with IoT-based blockchain and lays the foundation for future research in this area.

The Internet of Things (IoT) in the healthcare system is rapidly changing from the conventional hospital and concentrated specialist behavior to a distributed, patient-centric approach. With the advancement of new techniques, a patient needs sophisticated healthcare requirements. IoT-enabled intelligent health monitoring system with sensors and devices is a patient analysis technique to monitor the patient 24 h a day. IoT is swapping the architecture and has improved the application of different complex systems. Healthcare devices are one of the most remarkable applications of the IoT. Many patient monitoring techniques are available in the IoT platform. This review presents an IoT-enabled intelligent health monitoring system by analyzing the papers reported between 2016 and 2023. This survey also discusses the concept of big data in IoT networks and the IoT computing technology known as edge computing. This review concentrated on sensors and smart devices used in intelligent IoT based health monitoring systems with merits and demerits. This survey gives a brief study based on sensors and smart devices used in IoT smart healthcare systems.

Healthcare systems throughout the world are under a great deal of strain because to the continuing COVID-19 epidemic, making early and precise diagnosis critical for limiting the virus's propagation and efficiently treating victims. The utilization of medical imaging methods like X-rays can help to speed up the diagnosis procedure. Which can offer valuable insights into the virus's existence in the lungs. We present a unique ensemble approach to identify COVID-19 using X-ray pictures (X-ray-PIC) in this paper. The suggested approach, based on hard voting, combines the confidence scores of three classic deep learning models: CNN, VGG16, and DenseNet. We also apply transfer learning to enhance performance on small medical image datasets. Experiments indicate that the suggested strategy outperforms current techniques with a 97% accuracy, a 96% precision, a 100% recall, and a 98% F1-score.These results demonstrate the effectiveness of using ensemble approaches and COVID-19 transfer-learning diagnosis using X-ray-PIC, which could greatly aid in early detection and reducing the burden on global health systems.

The worldwide pandemic of COVID-19 illness has wreaked havoc on the health and lives of countless individuals in more than 200 countries. More than 44 million individuals have been afflicted by October 2020, with over 1,000,000 fatalities reported. This disease, which is classified as a pandemic, is still being researched for diagnosis and therapy. It is critical to diagnose this condition early in order to save a person's life. Diagnostic investigations based on deep learning are speeding up this procedure. As a result, in order to contribute to this sector, our research proposes a deep learning-based technique that may be employed for illness early detection. Based on this insight, gaussian filter is applied to the collected CT images and the filtered images are subjected to the proposed tunicate dilated convolutional neural network, whereas covid and non-covid disease are categorized to improve the accuracy requirement. The hyperparameters involved in the proposed deep learning techniques are optimally tuned using the proposed levy flight based tunicate behaviour. To validate the proposed methodology, evaluation metrics are tested and shows superiority of the proposed approach during COVID-19 diagnostic studies.

Modern information and communication technologies have intensively reformed the teaching process in higher education, expanding new opportunities for learning and access to educational resources, compared to those used in traditional learning. Taking into account the specifics of the application of these technologies in different scientific disciplines, the aim of this paper is to analyse the impact of the teachers' scientific field on the effects of the application of these technologies in selected higher education institutions. The research included teachers from 10 faculties and three schools of applied studies, who provided answers to 20 survey questions. After the survey and statistically processed results, the attitude of teachers from different scientific fields to the effects of the implementation of these technologies in selected higher education institutions was analysed. In addition, the forms of application of ICT in the conditions during the Covid 19 pandemic were analysed. The obtained results indicate various effects, as well as certain shortcomings, in the implementation of these technologies in the analysed higher education institutions, provided by teachers that belong to various scientific fields.

Covid19 corona virus has caused widespread disruption across the world, in terms of the health, economy, and society problems. X-ray images of the chest can be helpful in making an accurate diagnosis because the corona virus typically first manifests its symptoms in patients' lungs. In this study, a classification method based on deep learning is proposed as a means of identifying lung disease from chest X-ray images. In the proposed study, the detection of covid19 corona virus disease from chest X-ray images was made with MobileNet and Densenet models, which are deep learning methods. Several different use cases can be built with the help of MobileNet model and case modelling approach is utilized to achieve 96% accuracy and an Area Under Curve (AUC) value of 94%. According to the result, the proposed method may be able to more accurately identify the signs of an impurity from dataset of chest X-ray images. This research also compares various performance parameters such as precision, recall and F1-Score.

Leukemia can be detected by an abnormal rise in the number of immature lymphocytes and by a decrease in the number of other blood cells. To diagnose leukemia, image processing techniques are utilized to examine microscopic peripheral blood smear (PBS) images automatically and swiftly. To the best of our knowledge, the initial step in subsequent processing is a robust segmentation technique for identifying leukocytes from their surroundings. The paper presents the segmentation of leukocytes in which three color spaces are considered in this study for image enhancement. The proposed algorithm uses a marker-based watershed algorithm and peak local maxima. The algorithm was used on three different datasets with various color tones, image resolutions, and magnifications. The average precision for all three-color spaces was the same, i.e. 94% but the Structural Similarity Index Metric (SSIM) and recall of HSV were better than other two. The results of this study will aid experts in narrowing down their options for segmenting leukemia. Based on the comparison, it was concluded that when the colour space correction technique is used, the accuracy of the proposed methodology improves.

This work proposes a stochastic model of the coordinator units of each wireless body area network (WBAN) in a multi-WBAN scenario. In a Smart Home environment, multiple patients can come into the vicinity of each other while each of them is wearing a WBAN configuration for monitoring body vitals. Thus, while multiple WBANs coexist, the individual WBAN coordinators require adaptive transmission strategies in order to balance between maximizing the likelihood of data transmission and minimizing the chances of packet loss due to inter-BAN interference. Accordingly, the proposed work is divided into two phases. In the offline phase, each WBAN coordinator is modeled stochastically and the problem of their transmission strategy has been modeled as a Markov Decision Process(MDP). The channel conditions and buffer status that influence the transmission decision are taken to be the state parameters in MDP. The formulation is solved offline, prior to deployment of the network to find out the optimal transmission strategies for various input conditions. Such transmission policies for inter-WBAN communication are then incorporated into the coordinator nodes in the post-deployment phase. The work is simulated using Castalia and the results demonstrate the robustness of the proposed scheme in handling both favorable and unfavorable operating conditions.

In the Covid-19 pandemic situation, the world is looking for immunity-boosting techniques for fighting against coronavirus. Every plant is medicine in one or another way, but Ayurveda explains the uses of plant-based medicines and immunity boosters for specific requirements of the human body. To help Ayurveda, botanists are trying to identify more species of medicinal immunity-boosting plants by evaluating the characteristics of the leaf. For a normal person, detecting immunity-boosting plants is a difficult task. Deep learning networks provide highly accurate results in image processing. In the medicinal plant analysis, many leaves are like each other. So, the direct analysis of leaf images using the deep learning network causes many issues for medicinal plant identification. Hence, keeping the requirement of a method at large to help all human beings, the proposed leaf shape descriptor with the deep learning-based mobile application is developed for the identification of immunity-boosting medicinal plants using a smartphone. SDAMPI algorithm explained numerical descriptor generation for closed shapes. This mobile application achieved 96%accuracy for the 64 × 64 sized images.

Urine infections are one of the most prevalent concerns for the healthcare industry that may impair the functioning of the kidney and other renal organs. As a result, early diagnosis and treatment of such infections are essential to avert any future complications. Conspicuously, in the current work, an intelligent system for the early prediction of urine infections has been presented. The proposed framework uses IoT-based sensors for data collection, followed by data encoding and infectious risk factor computation using the XGBoost algorithm over the fog computing platform. Finally, the analysis results along with the health-related information of users are stored in the cloud repository for future analysis. For performance validation, extensive experiments have been carried out, and results are calculated based on real-time patient data. The statistical findings of accuracy (91.45%), specificity (95.96%), sensitivity (84.79%), precision (95.49%), and f-score(90.12%) reveal the significantly improved performance of the proposed strategy over other baseline techniques.

A serious effect on people's life, social communication, and surely on medical staff who were forced to monitor their patients' status remotely relying on the available technologies to avoid potential infections and as a result reducing the workload in hospitals. this research tried to investigate the readiness level of healthcare professionals in both public and private Iraqi hospitals to utilize IoT technology in detecting, tracking, and treating 2019-nCoV pandemic, as well as reducing the direct contact between medical staff and patients with other diseases that can be monitored remotely.A cross-sectional descriptive research via online distributed questionnaire, the sample consisted of 113 physicians and 99 pharmacists from three public and two private hospitals who randomly selected by simple random sampling. The 212 responses were deeply analyzed descriptively using frequencies, percentages, means, and standard deviation.The results confirmed that the IoT technology can facilitate patient follow-up by enabling rapid communication between medical staff and patient relatives. Additionally, remote monitoring techniques can measure and treat 2019-nCoV, reducing direct contact by decreasing the workload in healthcare industries. This paper adds to the current healthcare technology literature in Iraq and middle east region an evidence of the readiness to implement IoT technology as an essential technique. Practically, it is strongly advised that healthcare policymakers should implement IoT technology nationwide especially when it comes to safe their employees' life.Iraqi medical staff are fully ready to adopt IoT technology as they became more digital minded after the 2019-nCoV crises and surely their knowledge and technical skills will be improved spontaneously based on diffusion of innovation perspective.

The variety of diseases is increasing day by day, and the demand for hospitals, especially for emergency and radiology units, is also increasing. As in other units, it is necessary to prepare the radiology unit for the future, to take into account the needs and to plan for the future. Due to the radiation emitted by the devices in the radiology unit, minimizing the time spent by the patients for the radiological image is of vital importance both for the unit staff and the patient. In order to solve the aforementioned problem, in this study, it is desired to estimate the monthly number of images in the radiology unit by using deep learning models and statistical-based models, and thus to be prepared for the future in a more planned way. For prediction processes, both deep learning models such as LSTM, MLP, NNAR and ELM, as well as statistical based prediction models such as ARIMA, SES, TBATS, HOLT and THETAF were used. In order to evaluate the performance of the models, the symmetric mean absolute percentage error (sMAPE) and mean absolute scaled error (MASE) metrics, which have been in demand recently, were preferred. The results showed that the LSTM model outperformed the deep learning group in estimating the monthly number of radiological case images, while the AUTO.ARIMA model performed better in the statistical-based group. It is believed that the findings obtained will speed up the procedures of the patients who come to the hospital and are referred to the radiology unit, and will facilitate the hospital managers in managing the patient flow more efficiently, increasing both the service quality and patient satisfaction, and making important contributions to the future planning of the hospital.

Around the world, the educational system is evolving. The new trend can be found in traditional classroom systems as well as digitalization systems. Cloud-based Learning Management Systems (LMS) will accelerate the educational industry forward in the next years because they can provide end-user with a versatile, convenient, secure, and cost-effective learning process. The cloud-based LMS approach is the most effective and proper learning model in the worldwide educational sector, particularly if the organization is in a state of depression owing to a global pandemic. It can be utilized over the internet with several users on the same platform. As a result, the initial requirement is important to enable to the LMS model. Despite its many advantages, LMS confronts challenges such as confidentiality, user acceptance, and traffic. In a pandemic like Covid 19, the entire planet depends on a safe LMS platform to establish student and instructor trust. Therefore, with this work, the attempt has been made to explain one LMS model that may provide its users with optimal security, a user-friendly environment, and quick access. This paper discusses the use of the cloud attack, and also cryptographic and steganographic security models and techniques to address these issues. There's also information on what kinds of security vulnerabilities or operations on cloud data are feasible, and also how to deal with them using various algorithms.

Node failure in the Wireless Sensor Networks (WSN) topology may lead to economic loss, endanger people, and cause environmental damage. Node reliability can be achieved by adequately managing network topology using structural approaches, where the critical nodes are precisely detected and protected. This paper addresses the problem of critical node detection and presents two-phase algorithms (ABCND). Phase-I, a 2 Critical Node (-) detection algorithm, is proposed, which uses only the neighbor's Received Signal Strength Indicator () information. In Phase II, a correlation-based reliable RSSI approach is proposed to increase the node resilience against the adversary. The proposed algorithms () require time for convergence and for Critical Node detection, represents the number of IoT devices, and is the cost required to forward the message. We compare our algorithm (ABCND) with the current state-of-the-art on - detection algorithms. The simulation result shows that the proposed algorithm consumes 50% less energy to detect - with 90% to 95% accurate Critical Nodes (-).

In recent times, providing privacy to the medical dataset has been the biggest issue in medical applications. Since, in hospitals, the patient's data are stored in files, the files must be secured properly. Thus, different machine learning models were developed to overcome data privacy issues. But, those models faced some problems in providing privacy to medical data. Therefore, a novel model named Honey pot-based Modular Neural System (HbMNS) was designed in this paper. Here, the performance of the proposed design is validated with disease classification. Also, the perturbation function and the verification module are incorporated into the designed HbMNS model to provide data privacy. The presented model is implemented in a python environment. Moreover, the system outcomes are estimated before and after fixing the perturbation function. A DoS attack is launched in the system to validate the method. At last, a comparative assessment is made between executed models with other models. From the comparison, it is verified that the presented model achieved better outcomes than others.

In this technique, it has been proposed to agree the session keys that have been generated through dual artificial neural networks based on the Telecare Health COVID-19 domain. Electronic health enables secure and protected communication between the patients and physicians, especially during this COVID-19 pandemic. Telecare was the main component which served the remote and non-invasive patients in the crisis period of COVID-19. Neural cryptographic engineering support in terms of data security and privacy is the main theme for Tree Parity Machine (TPM) synchronization in this paper. The session key has been generated on different key lengths and key validation done on the proposed set of robust session keys. A neural TPM network receives a vector designed through same random seed and producing a single output bit. Duo neural TPM networks' intermediate keys would be partially shared between the patient and doctor for the purpose neural synchronization. Higher magnitude of co-existence has been observed at the duo neural networks at the Telecare Health Systems in COVID-19. This proposed technique has been highly protective against several data attacks in the public networks. Partial transmission of the session key disables the intruders to guess the exact pattern, and highly randomized through different tests. The average -values of different session key lengths of 40 bits, 60 bits, 160 bits, and 256 bits were observed to be 221.9, 259.3, 242, and 262.8 (taken under multiplicative of 1000) respectively.

The COVID-19 pandemic has created an emergency across the globe. The number of corona positive and death cases is still rising worldwide. All countries' governments are taking various steps to control the infection of COVID-19. One step to control the coronavirus's spreading is to quarantine. But the number of active cases at the quarantine center is increasing daily. Also, the doctors, nurses, and paramedical staff providing service to the people at the quarantine center are getting infected. This demands the automatic and regular monitoring of people at the quarantine center. This paper proposed a novel and automated method for monitoring people at the quarantine center in two phases. These are the health data transmission phase and health data analysis phase. The health data transmission phase proposed a geographic-based routing that involves components like Network-in-box, Roadside-unit, and vehicles. An effective route is determined using route value to transmit data from the quarantine center to the observation center. The route value depends on the factors such as density, shortest path, delay, vehicular data carrying delay, and attenuation. The performance metrics considered for this phase are E2E delay, number of network gaps, and packet delivery ratio, and the proposed work performs better than the existing routing like geographic source routing, anchor-based street traffic aware routing, Peripheral node based GEographic DIstance Routing . The analysis of health data is done at the observation center. In the health data analysis phase, the health data is classified into multi-class using a support vector machine. There are four categories of health data: normal, low-risk, medium-risk, and high-risk. The parameters used to measure the performance of this phase are precision, recall, accuracy, and F-1 score. The overall testing accuracy is found to be 96.8%, demonstrating strong potential for our technique to be adopted in practice.

Wireless Body Area Network (WBAN) is an emerging and promising specialized area in Wireless networks that deals with crucial health-related datasets. Unlike other wireless networks, as this type of network deals with medical facts, losing it is fatal. WBAN is a highly constrained network. Reducing energy consumption and enhancing lifetime are the two most important challenges of WBANs. One way to achieve these is by deploying relay nodes optimally in WBANs. Generally, a relay node is placed at the midpoint of the line joining the source and the destination () nodes. We show that such simplistic deployment of the relay nodes is not the optimal deployment, which can hamper the overall lifetime of WBANs. In this paper, we have investigated the best location to deploy a relay node on a human body. We assume that an adaptive decode and forward relay node () can move linearly between the source () and the destination () nodes. Moreover, the assumption is that a relay node can be deployed linearly and that the body part of a human is a flat surface and hard. We have investigated the most energy-efficient data payload size based on the optimally placed relay location. The impact of such a deployment on different system parameters, such as distance (), payload (), modulation scheme, specific absorption rate, and an end to end outage () are examined as well. It is observed that in every aspect optimal deployment of the relay node performs an important role to enhance the lifetime of wireless body area networks. Sometimes linear relay deployment is very difficult to implement, especially on the different body parts of the human body. To address these issues, we have examined the optimal region for the relay node based on a 3D non-linear system model. The paper provides guidance for both linear and non-linear relay deployment along with the optimal data payload size under various circumstances and also considered the impact of specific absorption rates on the human body.

The COVID-19 has affected and threatened the world health system very critically throughout the globe. In order to take preventive actions by the agencies in dealing with such a pandemic situation, it becomes very necessary to develop a system to analyze the impact of environmental parameters on the spread of this virus. Machine learning algorithms and artificial Intelligence may play an important role in the detection and analysis of the spread of COVID-19. This paper proposed a twinned gradient boosting machine (GBM) to analyze the impact of environmental parameters on the spread, recovery, and mortality rate of this virus in India. The proposed paper exploited the four weather parameters (temperature, humidity, atmospheric pressure, and wind speed) and two air pollution parameters (PM2.5 and PM10) as input to predict the infection, recovery, and mortality rate of its spread. The algorithm of the GBM model has been optimized in its four distributions for best performance by tuning its parameters. The performance of the GBM is reported as excellent (where R2 = 0.99) in training for the combined dataset comprises all three outcomes i.e. infection, recovery and mortality rates. The proposed approach achieved the best prediction results for the state, which is worst affected and highest variation in the atmospheric factors and air pollution level.