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

Flavonoids are plant-derived compounds that have several health benefits, including antioxidative, anti-inflammatory, anti-mutagenic, and anti-carcinogenic effects. Quercetin is a flavonoid that is widely present in various fruits, vegetables, and drinks. Accurate determination of quercetin in different samples is of great importance for its potential health benefits. This review, is an overview of sample preparation and determination methods for quercetin in diverse matrices. Previous research on sample preparation and determination methods for quercetin are summarized, highlighting the

Computational Intelligence for Medical Internet of Things (MIoT) Applications: Machine Intelligence Applications for IoT in Healthcare explores machine intelligence techniques necessary for effective MIoT research and practice, taking a practical approach for practitioners and students entering the field. This book investigates advanced concepts and applications in the MIoT field, guiding readers through emerging developments and future trends. A wide range of international authors guide readers through advanced concepts, including deep learning, neural network, and big data analytic

Introduction: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. Method: In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic

In symptomatic patients, a positive COVID-19 test is critical for securing life-saving services such as ICU care and ventilator support; it may cause septic shock, septic pneumonia, respiratory failure, heart difficulties, liver issues, and even death. CAD systems help people in rural places and doctors in the early detection of COVID-19. A diagnostic and severity detection technique utilizing transfer learning and a backpropagation neural network has been developed with the aid of a computer for this purpose. This study aims to compare and analyze multiple deep learning-enhanced strategies

Diabetic patients need a management tool that combines multiple features and tracks and views detailed data time-efficiently. Effective food logging is an important element of health monitoring. In this paper, we propose 'Suger.ly', a lightweight mobile application with artificial intelligence food recognition for diabetes management. The system has been trained to recognize 101 distinct types of food, with a focus on Egyptian cuisine. The app can then get nutritional value and insulin calculations. The results obtained from the Single-Shot multibox Detection (SSD) MobileNet-V1 food detection

The global rise in Metabolic dysfunction-associated steatotic liver disease (MASLD)/Metabolic dysfunction-associated steatohepatitis (MASH) highlights the urgent necessity for noninvasive biomarkers to detect these conditions early. To address this, we endeavored to construct a diagnostic model for MASLD/MASH using a combination of bioinformatics, molecular/biochemical data, and machine learning techniques. Initially, bioinformatics analysis was employed to identify RNA molecules associated with MASLD/MASH pathogenesis and enriched in ferroptosis and exophagy. This analysis unveiled specific

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

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

Multiple Sclerosis (MS) is a long-term autoimmune disorder affecting the central nervous system, marked by inflammation, demyelination, and neurodegeneration. While the exact cause of MS remains unknown, recent research indicates that environmental factors, particularly diet, may influence the disease's risk and progression. As a result, the potential neuroprotective effects of coffee, one of the most popular beverages worldwide, have garnered significant attention due to its rich content of bioactive compounds. This chapter explores the impact of coffee consumption on patients with Multiple