M
Mohamed Adel Serhani
Researcher at College of Information Technology
Publications - 101
Citations - 2272
Mohamed Adel Serhani is an academic researcher from College of Information Technology. The author has contributed to research in topics: Web service & Cloud computing. The author has an hindex of 18, co-authored 89 publications receiving 1451 citations. Previous affiliations of Mohamed Adel Serhani include United Arab Emirates University & University College West.
Papers
More filters
Journal ArticleDOI
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †
TL;DR: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area.
Proceedings ArticleDOI
A QoS broker based architecture for efficient Web services selection
TL;DR: This paper presents a QoS broker-based architecture for Web services to support the client in selecting Web services based on his/her required QoS, and proposes a two-phase verification technique that is performed by a third party broker.
Journal ArticleDOI
ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges.
TL;DR: A generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG Monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends.
Journal ArticleDOI
Novel Cloud and SOA-Based Framework for E-Health Monitoring Using Wireless Biosensors
TL;DR: This paper proposes a framework to collect patients' data in real time, perform appropriate nonintrusive monitoring, and propose medical and/or life style engagements, whenever needed and appropriate, which allows a seamless integration of different technologies, applications, and services.
Journal ArticleDOI
Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting
TL;DR: Metaheuristic-search-based algorithms are used, known by their ability to alleviate search complexity as well as their capacity to learn from the domain where they are applied, to find optimal or near-optimal values for the set of tunable LSTM hyperparameters in the electrical energy consumption domain.