The prosperous advancement in Medical Internet of Things (MIoT) technologies has hastened the development of healthcare systems. MIoT improves the traditional medical facilities through periodically monitor of patient's health records. Electronic Medical Records (EMRs) are sensitive private data and needs efficient secure and private schemes that interchange these EMRs between healthcare providers and patients. Most of the current privacy preserving schemes do not provide the desired privacy level and suffer from computation and communication overheads. The length of an IDentity-based Strong

Cervical cancer (CC) is considered a significant global health threat to women therefore there is a need for personalized treatment strategy based on individual-specific gene expression patterns to enhance recovery and survival rates. Although a few studies have linked bisphosphoglycerate mutase (BPGM) expression with CC, its precise role in CC progression remains unclear. In this study, we conducted an integrated analysis for both bulk and single-cell RNA sequencing data to investigate the involvement of BPGM in CC. On the bulk RNA level, the Wilcoxon test result showed a significant

Application of ergonomics' rules has become a necessity in today's world. Due to the lack of knowledge of what these rules are and the resources needed to fund them, a lot of people develop health issues. One of the most common health issues relate to sitting in a wrong posture for extended period.In this document, a framework that can help in minimizing the existence of the sitting posture anomaly is proposed. This framework takes into account using limited resources as well as being able to apply it in a home environment. © 2022 IEEE.

Skin cancer is the most annoying type of cancer diagnosis according to its fast spread to various body areas, so it was necessary to establish computer-assisted diagnostic support systems. State-of-the-art classifiers based on convolutional neural networks (CNNs) are used to classify images of skin cancer. This paper tries to get the most accurate model to classify and detect skin cancer types from seven different classes using deep learning techniques; ResNet-50, VGG-16, and the merged model of these two techniques through the concatenate function. The performance of the proposed model was

—Ultrasound imaging is a unique medical imaging modality due to its clinical versatility, manageable biological effects, and low cost. However, a significant limitation of ultrasound imaging is the noisy appearance of its images due to speckle noise, which reduces image quality and hence makes diagnosis more challenging. Consequently, this problem received interest from many research groups and many methods have been proposed for speckle suppression using various filtering techniques. The common problem with such methods is that they tend to distort the edge detail content within the image and

Rice is considered a strategic crop in Egypt as it is regularly consumed in the Egyptian people’s diet. Even though Egypt is the highest rice producer in Africa with a share of 6 million tons per year [5], it still imports rice to satisfy its local needs due to production loss, especially due to rice disease. Rice blast disease is responsible for 30% loss in rice production worldwide [9]. Therefore, it is crucial to target limiting yield damage by detecting rice crops diseases in its early stages. This paper introduces a public multispectral and RGB images dataset and a deep learning pipeline

As a case study for our research, COVID-19, that was caused by a unique coronavirus, has substantially affected the globe, not only in terms of healthcare, but also in terms of economics, education, transportation, and politics. Predicting the pandemic's course is critical to combating and tracking its spread. The objective of our study is to evaluate, optimize and fine-Tune state of the art prediction models in order to enhance its performance and to automate its function as possible. Therefore, a comparison between statistical versus compartmental methods for time series-based modeling and

the diagnosis of chest disease depends in most cases on the complex grouping of clinical data and images. According to this complexity, the debate is increased between researchers and doctors about the efficient and accurate method for chest disease prediction. The purpose of this research is to enhance the first handling of the patient data to get a prior diagnosis of the disease. The main problem in such diagnosis is the quality and quantity of the images.In this paper such problem is solved by utilizing some methods of preprocessing such as augmentation and segmentation. In addition are

Heart localization holds significant importance in the process of the diagnosis and treatment of heart diseases. Additionally, it plays an important role in planning the cardiac scanning protocol. This research focuses on heart localization by employing the multi-label classification task with the utilization of RES-Net50. The primary objective is to predict the slices containing the heart and determine its endpoint. To ensure high-quality data, we implement filtering techniques and perform up-sampling during the pre-processing stage. Two experiments were conducted to assess different

The automotive industry currently is seeking to increase remote connectivity to a vehicle, which creates a high demand to implement a secure way of connecting vehicles, as well as verifying and storing their data in a trusted way. Furthermore, much information must be leaked in order to correctly diagnose the vehicle and determine when or how to remotely update it. In this context, we propose a Blockchain-based, fully automated remote vehicle diagnosis system. The proposed system provides a secure and trusted way of storing and verifying vehicle data and analyzing their performance in