S
Samaher Al-Janabi
Researcher at University of Babylon
Publications - 48
Citations - 1249
Samaher Al-Janabi is an academic researcher from University of Babylon. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 13, co-authored 29 publications receiving 751 citations. Previous affiliations of Samaher Al-Janabi include Information Technology University.
Papers
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Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications
TL;DR: This paper reviewed WBAN communication architecture, security and privacy requirements and security threats and the primary challenges in WBANs to these systems based on the latest standards and publications and covers the state-of-art security measures and research.
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A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation
Samaher Al-Janabi,Ayad F. Alkaim +1 more
TL;DR: This paper, attempting to search the capability of building a novel tool to estimate missing values of various datasets called developed random forest and local least squares (DRFLLS), finds the optimal number of neighborhoods of missing values is associated with the highest value of PC and a smaller value of NRMSE.
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A new method for prediction of air pollution based on intelligent computation
TL;DR: The aim of the work presented herein is to design an intelligent predictor for the concentrations of air pollutants over the next 2 days based on deep learning techniques using a recurrent neural network (RNN) and a particle swarm optimization (PSO) algorithm.
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An Innovative synthesis of deep learning techniques (DCapsNet & DCOM) for generation electrical renewable energy from wind energy
TL;DR: The MORE-G is characterized by addressing one of the real problems, saving on material costs, reducing the need for manpower and reducing dependence on other countries in importing electric power) and upgrading the scope of the ministry of electricity.
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Design and evaluation of a hybrid system for detection and prediction of faults in electrical transformers
TL;DR: A hybrid system based on Genetic Neural Computing (GNC) for analyzing and interpreting the data derived from the concentration of the dissolved gases and generates the necessary decision rules to assist the system’s operator in identifying the exact fault in the transformer and its fault status.