Mohamed Elhoseny

Bio: Mohamed Elhoseny is an academic researcher from Mansoura University. The author has contributed to research in topics: Computer science & Encryption. The author has an hindex of 49, co-authored 240 publications receiving 7044 citations. Previous affiliations of Mohamed Elhoseny include Maharaja Agrasen Institute of Technology & Cairo University.

DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing

Abstract: The rapid spread of novel coronavirus pneumonia (COVID-19) has led to a dramatically increased mortality rate worldwide. Despite many efforts, the rapid development of an effective vaccine for this novel virus will take considerable time and relies on the identification of drug-target (DT) interactions utilizing commercially available medication to identify potential inhibitors. Motivated by this, we propose a new framework, called DeepH-DTA, for predicting DT binding affinities for heterogeneous drugs. We propose a heterogeneous graph attention (HGAT) model to learn topological information of compound molecules and bidirectional ConvLSTM layers for modeling spatio-sequential information in simplified molecular-input line-entry system (SMILES) sequences of drug data. For protein sequences, we propose a squeezed-excited dense convolutional network for learning hidden representations within amino acid sequences; while utilizing advanced embedding techniques for encoding both kinds of input sequences. The performance of DeepH-DTA is evaluated through extensive experiments against cutting-edge approaches utilising two public datasets (Davis, and KIBA) which comprise eclectic samples of the kinase protein family and the pertinent inhibitors. DeepH-DTA attains the highest Concordance Index (CI) of 0.924 and 0.927 and also achieved a mean square error (MSE) of 0.195 and 0.111 on the Davis and KIBA datasets respectively. Moreover, a study using FDA-approved drugs from the Drug Bank database is performed using DeepH-DTA to predict the affinity scores of drugs against SARS-CoV-2 amino acid sequences, and the results show that that the model can predict some of the SARS-Cov-2 inhibitors that have been recently approved in many clinical studies.

A Blockchain-Enabled Multi Domain Edge Computing Orchestrator

Abstract: Today, management and orchestration are considered prime components of the new network management layer Multi-domain orchestration has helped in simplifying infrastructural operations and enables better scaling and faster deployment of network services However, resource provisioning considering network optimization and fulfillment of multi-constraint quality of service (QoS) is still a major concern In this article, an architecture comprising multi-domain edge orchestration (MDEO) entrusted by blockchain designed to solve the problem of multi-constraint QoS is proposed A dynamic end-to-end (E2E) network slicing algorithm is devised to execute at the MDEO to enable multi-tenant on-demand network infrastructure provisioning isolation and security The algorithm first calculates an optimum network slice topology and then instantiates the involved virtual network functions Based on multi-constraint QoS, it fulfills the E2E slice request Blockchain is deployed to ensure trustworthiness between different telecom operators, introduce transparency and to automate the fulfillment of service-level agreements through smart contracts In addition, an experimental simulation of the system is performed and the burst and response times of the proposed framework are analyzed using different distributions The data in both cases demonstrates a lognormal distributed behavior

Quantified Self Using IoT Wearable Devices

Abstract: Nowadays, designing and developing wearable devices that could detect many types of diseases has become inevitable for E-health field. The decision-making of those wearable devices is done by various levels of analysis of enormous databases of human health records. Systems that demand a huge number of input data to decide to require real-time data collected from devices, processes, and analyzing the data. Many researchers utilize the Internet of Things (IoT) in medical wearable devices to detect different diseases by using different sensors together for one goal. The IoT promises to revolutionize the lifestyle using a wealth of new services, based on interactions between large numbers of devices data. The proposed work is human monitor system to track the human body troubles. Smart wearable devices can provide users with overall health data, and alerts from sensors to notify them on their mobile phones accordingly. The proposed system developed a technique using Internet of Things technique to decrease the load on IOT network and decrease the overall cost of the users. The simulation results proved that the proposed system could provide identical communication for IOT devices even if many nodes are used.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia the official statement of the American Thoracic Society and the Infectious Disease Society of America (特集 救急診療ガイドライン) -- (海外のガイドライン)

Abstract: Executive Summary Introduction Methodology Used to Prepare the Guideline Epidemiology Incidence Etiology Major Epidemiologic Points Pathogenesis Major Points for Pathogenesis Modifiable Risk Factors Intubation and Mechanical Ventilation Aspiration, Body Position, and Enteral Feeding Modulation of Colonization: Oral Antiseptics and Antibiotics Stress Bleeding Prophylaxis, Transfusion, and Glucose Control Major Points and Recommendations for Modifiable Risk Factors Diagnostic Testing Major Points and Recommendations for Diagnosis Diagnostic Strategies and Approaches Clinical Strategy Bacteriologic Strategy Recommended Diagnostic Strategy Major Points and Recommendations for Comparing Diagnostic Strategies Antibiotic Treatment of Hospital-acquired Pneumonia General Approach Initial Empiric Antibiotic Therapy Appropriate Antibiotic Selection and Adequate Dosing Local Instillation and Aerosolized Antibiotics Combination versus Monotherapy Duration of Therapy Major Points and Recommendations for Optimal Antibiotic Therapy Specific Antibiotic Regimens Antibiotic Heterogeneity and Antibiotic Cycling Response to Therapy Modification of Empiric Antibiotic Regimens Defining the Normal Pattern of Resolution Reasons for Deterioration or Nonresolution Evaluation of the Nonresponding Patient Major Points and Recommendations for Assessing Response to Therapy Suggested Performance Indicators