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All Journal International Journal of Electrical and Computer Engineering
Anmar Abuhamdah
Taibah University
Computer Science & IT Electrical & Electronics Engineering

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Components and Analysis Method of Enterprise Resource Planning (ERP) Requirements in Small and Medium Enterprises (SMEs) Yousef Khaleel; Anmar Abuhamdah; Mutaz Abu Sara; Bassam Al-Tamimi
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 2: April 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (303.283 KB) | DOI: 10.11591/ijece.v6i2.pp682-689

Abstract

With the fast development of information technologies and enterprise software, Enterprise Resource Planning (ERP) systems are increasingly adopted by more small and medium enterprises (SMEs). Based on this trend, it is necessary to develop ERP systems in a manner that meets and fits the SMEs requirements and needs. This paper proposes conceptual components of ERP requirements that are required for generating ERP system functions. In addition, it proposes an ERP requirements analysis method for ERP system developments in order to produce the proper ERP system functions for SMEs. The advantage of this analysis method is that it is easy to analyze and integrate the special requirements of the ERP development for distinguishing a sub-sector of SMEs. In this paper, by analyzing the components of requirements and the relationship of the business process modelling, several basic concepts are given and the method of the process analysis and modelling is also expressed.
Using deep learning to detecting abnormal behavior in internet of things Mohammed Al-Shabi; Anmar Abuhamdah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2108-2120

Abstract

The development of the internet of things (IoT) has increased exponentially, creating a rapid pace of changes and enabling it to become more and more embedded in daily life. This is often achieved through integration: IoT is being integrated into billions of intelligent objects, commonly labeled “things,” from which the service collects various forms of data regarding both these “things” themselves as well as their environment. While IoT and IoT-powered decices can provide invaluable services in various fields, unauthorized access and inadvertent modification are potential issues of tremendous concern. In this paper, we present a process for resolving such IoT issues using adapted long short-term memory (LSTM) recurrent neural networks (RNN). With this method, we utilize specialized deep learning (DL) methods to detect abnormal and/or suspect behavior in IoT systems. LSTM RNNs are adopted in order to construct a high-accuracy model capable of detecting suspicious behavior based on a dataset of IoT sensors readings. The model is evaluated using the Intel Labs dataset as a test domain, performing four different tests, and using three criteria: F1, Accuracy, and time. The results obtained here demonstrate that the LSTM RNN model we create is capable of detecting abnormal behavior in IoT systems with high accuracy.
Intelligent Arabic letters speech recognition system based on mel frequency cepstral coefficients Anas Quteishat; Mahmoud Younis; Ahmed Qtaishat; Anmar Abuhamdah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3348-3358

Abstract

Speech recognition is one of the important applications of artificial intelligence (AI). Speech recognition aims to recognize spoken words regardless of who is speaking to them. The process of voice recognition involves extracting meaningful features from spoken words and then classifying these features into their classes. This paper presents a neural network classification system for Arabic letters. The paper will study the effect of changing the multi-layer perceptron (MLP) artificial neural network (ANN) properties to obtain an optimized performance. The proposed system consists of two main stages; first, the recorded spoken letters are transformed from the time domain into the frequency domain using fast Fourier transform (FFT), and features are extracted using mel frequency cepstral coefficients (MFCC). Second, the extracted features are then classified using the MLP ANN with back-propagation (BP) learning algorithm. The obtained results show that the proposed system along with the extracted features can classify Arabic spoken letters using two neural network hidden layers with an accuracy of around 86%.
Co-Authors Ahmed Qtaishat Anas Quteishat Bassam Al-Tamimi Mahmoud Younis Mohammed Al-Shabi Mutaz Abu Sara Yousef Khaleel