The Internet of Things (IoT) and blockchain (BC) are reliable technologies widely employed in various contexts. IoT devices have a lot of potential for data sensing and recording without human intervention, but they also have processing and security issues. Due to their limited computing power, IoT devices cannot use specialized cryptographic security mechanisms. There are various challenges when using traditional cryptographic techniques to transport and store medical records securely. The general public’s health depends on having an electronic health record (EHR) system that is current. In the era of e-health and m-health, problems with integrating data from various EHRs, preserving data interoperability, and ensuring that all data access is in the patient’s hands are all obstacles to creating a dependable EHR system. If health records get into the wrong hands, they could endanger the lives of patients and their right to privacy. BC technology has become a potent tool for ensuring recorded data’s immutability, validity, and confidentiality while enabling decentralized storage. This study focuses on EHR and other types of e-healthcare, evaluating the advantages of complementary technologies and the underlying functional principles. The major BC consensus mechanisms for BC-based EHR systems are analyzed in this study. It also examines several IoT-EHR frameworks’ current infrastructures. A breakdown of BC integration’s benefits with the IoT-EHR framework is also offered. A BC-based IoT-EHR architecture has been developed to enable the automated sensing of patient records and to store and retrieve these records in a secure and reliable environment. Finally, we conduct a security study to demonstrate the security of our suggested EHR framework.

Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer’s disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to diagnose AD, with an accuracy of only 85 per cent. This did not allow for a correct diagnosis, which could be validated only through postmortem examination. Diagnosis of AD can be sped up, and the course of the disease can be predicted by applying machine learning (ML) techniques to Magnetic Resonance Imaging (MRI) techniques. Dementia in specific seniors could be predicted using data from AD screenings and ML classifiers. Classifier performance for AD subjects can be enhanced by including demographic information from the MRI and the patient’s preexisting conditions. In this article, we have used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In addition, we proposed a framework for the AD/non-AD classification of dementia patients using longitudinal brain MRI features and Deep Belief Network (DBN) trained with the Mayfly Optimization Algorithm (MOA). An IoT-enabled portable MR imaging device is used to capture real-time patient MR images and identify anomalies in MRI scans to detect and classify AD. Our experiments validate that the predictive power of all models is greatly enhanced by including early information about comorbidities and medication characteristics. The random forest model outclasses other models in terms of precision. This research is the first to examine how AD forecasting can benefit from using multimodal time-series data. The ability to distinguish between healthy and diseased patients is demonstrated by the DBN-MOA accuracy of 97.456%, f-Score of 93.187 %, recall of 95.789 % and precision of 94.621% achieved by the proposed technique. The experimental results of this research demonstrate the efficacy, superiority, and applicability of the DBN-MOA algorithm developed for the purpose of AD diagnosis.

In this article, a low-profile, compact, quad-port super-wideband (SWB) multiple-input–multiple-output (MIMO) antenna is presented for the internet of things (IoT) applications. The proposed antenna comprises four identical sickle-shaped resonating elements, which are excited by tapered coplanar waveguide (CPW) feed lines. The antenna elements are arranged in rotational symmetry (mutually orthogonal to each other) to achieve high port isolation. A complementary slot, which matches the sickle-shaped radiator, is etched from the ground of the proposed monopole antenna element to achieve massive bandwidth. The MIMO antenna possesses a resonating bandwidth ( $\vert S_{11}\vert \le -10$ dB) of 1.3–40 GHz and a bandwidth ratio of 31:1. In addition, an L-shaped slit and a complementary split-ring resonator (CSRR) are introduced in the sickle-shaped radiator to reject Bluetooth (2.4 GHz), WLAN (5.5 GHz), and downlink of X-band satellite communication (7.5 GHz) signals from the SWB. The proposed MIMO antenna is fabricated and experimental results are found in agreement with the simulated results.

(1) Background, Travel characteristics of Saudi women contrast significantly from those in the west. This is not only because they have different culture, attitudes and preferences but also until recently, Saudi women were not allowed to drive. In 2018, they were granted the right to drive. It has been anticipated that enabling women to drive will improve their mobility and employability. (2) Methods: This study presents a qualitative study into factors affecting Saudi women’s travel decisions “before” and “after” enabling women to drive in the Kingdom. Two six “before” and “after” focus groups have been carried out to investigate the decision-making process associated with Saudi women’s travel, available options of travel and perception of Saudi women towards private car driving. (3) Results: The results reveal that main travelling options for professional and high-income women is a private driver in the “before” scenario and a ride-share option with a family member. In the “after” scenario, high income professional women prefer “drive own car” option. Moreover, many of the participants indicated that it is likely that they might keep private drivers as well. (4) Conclusion. The results from this research indicate that there has been significant change in travel characteristics, attitudes and behaviour of Saudi women since they were granted the right to drive. This is likely to have significant implications for decision and policy makers. Further research into potential impacts of the current situation on car ownership and use, impacts on public transport system, environmental impacts and sustainability is needed.

This paper presents a compact 1 × 4 antipodal Vivaldi antenna (AVA) array for 5G millimeter-wave applications. The designed antenna operates over 24.19 GHz–29.15 GHz and 30.28 GHz–40.47 GHz frequency ranges. The proposed antenna provides a high gain of 8 dBi to 13.2 dBi and the highest gain is obtained at 40.3 GHz. The proposed antenna operates on frequency range-2 (FR2) and covers n257, n258, n260, and n261 frequency bands of 5G communication. The corrugations and RT/Duroid 5880 substrate are used to reduce the antenna size to 24 mm × 28.8 mm × 0.254 mm, which makes the antenna highly compact. Furthermore, the corrugations play an important role in the front-to-back ratio improvement, which further enhances the gain of the antenna. The corporate feeding is optimized meticulously to obtain an enhanced bandwidth and narrow beamwidth. The radiation pattern does not vary over the desired operating frequency range. In addition, the experimental results of the fabricated antenna coincide with the simulated results. The presented antenna design shows a substantial improvement in size, gain, and bandwidth when compared to what has been reported for an AVA with nearly the same size, which makes the proposed antenna one of the best candidates for application in devices that operate in the millimeter frequency range.

Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer’s disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages. In order to leverage the capabilities of the pre-trained GoogLeNet model, transfer learning is employed. The GoogLeNet model, which is pre-trained for image classification tasks, is fine-tuned for the specific purpose of multi-class AD classification. Through this process, a better accuracy of 98% is achieved. As a result, a local cloud web application for Alzheimer’s prediction is developed using the proposed architectures of GoogLeNet. This application enables doctors to remotely check for the presence of AD in patients.