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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Maytham N. Meqdad
Al-Mustaqbal University College
Computer Science & IT Electrical & Electronics Engineering Engineering

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Cyber physical systems: A smart city perspective Firoz Khan; R. Lakshmana Kumar; Seifedine Kadry; Yunyoung Nam; Maytham N. Meqdad
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3609-3616

Abstract

Cyber-physical system (CPS) is a terminology used to describe multiple systems of existing infrastructure and manufacturing system that combines computing technologies (cyber space) into the physical space to integrate human interaction. This paper does a literature review of the work related to CPS in terms of its importance in today’s world. Further, this paper also looks at the importance of CPS and its relationship with internet of things (IoT). CPS is a very broad area and is used in variety of fields and some of these major fields are evaluated. Additionally, the implementation of CPS and IoT is major enabler for smart cities and various examples of such implementation in the context of Dubai and UAE are researched. Finally, security issues related to CPS in general are also reviewed.
Autonomous vehicles: A study of implementation and security Firoz Khan; R. Lakshmana Kumar; Seifedine Kadry; Yunyoung Nam; Maytham N. Meqdad
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3013-3021

Abstract

Autonomous vehicles have been invented to increase the safety of transportation users. These vehicles can sense their environment and make decisions without any external aid to produce an optimal route to reach a destination. Even though the idea sounds futuristic and if implemented successfully, many current issues related to transportation will be solved, care needs to be taken before implementing the solution. This paper will look at the pros and cons of implementation of autonomous vehicles. The vehicles depend highly on the sensors present on the vehicles and any tampering or manipulation of the data generated and transmitted by these can have disastrous consequences, as human lives are at stake here. Various attacks against the different type of sensors on-board an autonomous vehicle are covered.
Performance analysis of sentiments in Twitter dataset using SVM models Lakshmana Kumar Ramasamy; Seifedine Kadry; Yunyoung Nam; Maytham N. Meqdad
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2275-2284

Abstract

Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models.
Smart agriculture management system using internet of things Kaushik Sekaran; Maytham N. Meqdad; Pardeep Kumar; Soundar Rajan; Seifedine Kadry
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14029

Abstract

In the world of digital era, an advance development with internet of things (IoT) were initiated, where devices communicate with each other and the process are automated and controlled with the help of internet. An IoT in an agriculture framework includes various benefits in managing and monitoring the crops. In this paper, an architectural framework is developed which integrates the internet of things (IoT) with the production of crops, different measures and methods are used to monitor crops using cloud computing. The approach provides real-time analysis of data collected from sensors placed in crops and produces result to farmer which is necessary for the monitoring the crop growth which reduces the time, energy of the farmer. The data collected from the fields are stored in the cloud and processed in order to facilitate automation by integrating IoT devices. The concept presented in the paper could increase the productivity of the crops by reducing wastage of resources utilized in the agriculture fields. The results of the experimentation carried out presents the details of temperature, soil moisture, humidity and water usage for the field and performs decision making analysis with the interaction of the farmer.
New prediction method for data spreading in social networks based on machine learning algorithm Maytham N. Meqdad; Rawya Al-Akam; Seifedine Kadry
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 6: December 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i6.16300

Abstract

Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the graph neural network algorithm, which involves the selection of inactive vertices for activation based on the neighboring vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices byactive vertices. The method is tested on three scientific bibliography datasets: The Digital Bibliography and Library Project (DBLP), Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of thenext article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBL Pand Pubmed datasets, respectively.
Design of optimal search engine using text summarization through artificial intelligence techniques Kaushik Sekaran; P. Chandana; J. Rethna Virgil Jeny; Maytham N. Meqdad; Seifedine Kadry
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14028

Abstract

Natural language processing is the trending topic in the latest research areas, which allows the developers to create the human-computer interactions to come into existence. The natural language processing is an integration of artificial intelligence, computer science and computer linguistics. The research towards natural Language Processing is focused on creating innovations towards creating the devices or machines which operates basing on the single command of a human. It allows various Bot creations to innovate the instructions from the mobile devices to control the physical devices by allowing the speech-tagging. In our paper, we design a search engine which not only displays the data according to user query but also performs the detailed display of the content or topic user is interested for using the summarization concept. We find the designed search engine is having optimal response time for the user queries by analyzing with number of transactions as inputs. Also, the result findings in the performance analysis show that the text summarization method has been an efficient way for improving the response time in the search engine optimizations.
Implementation of face and eye detection on DM6437 board using simulink model B. Vijayalaxmi; Kaushik Sekaran; N. Neelima; P. Chandana; Maytham N. Meqdad; Seifedine Kadry
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (311.925 KB) | DOI: 10.11591/eei.v9i2.1703

Abstract

Driver Assistance system is significant in drriver drowsiness to avoid on road accidents.  The aim of this research work is to detect the position of driver’s eye for fatigue estimation. It is not unusual to see vehicles moving around even during the nights. In such circumstances there will be very high probability that a driver gets drowsy which may lead to fatal accidents. Providing a solution to this problem has become a motivating factor for this research, which aims at detecting driver fatigue. This research concentrates on locating the eye region failing which a warning signal is generated so as to alert the driver. In this paper, an efficient algorithm is proposed for detecting the location of an eye, which forms an invaluable insight for driver fatigue detection after the face detection stage. After detecting the eyes, eye tracking for input videos has to be achieved so that the blink rate of eyes can be determined.
Image processing based eye detection methods a theoretical review B. Vijayalaxmi; Chavali Anuradha; Kaushik Sekaran; Maytham N. Meqdad; Seifedine Kadry
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (458.912 KB) | DOI: 10.11591/eei.v9i3.1783

Abstract

Lately, many of the road accidents have been attributed to the driver stupor. Statistics revealed that about 32% of the drivers who met with such accidents demonstrated the symptoms of tiredness before the mishap though at varying levels. The purpose of this research paper is to revisit the various interventions that have been devised to provide for assistance to the vehicle users to avert unwarranted contingencies on the roads. The paper tries to make a sincere attempt to encapsulate the body of work that has been initiated so far in this direction. As is evident, there are numerous ways in which one can identify the fatigue of the driver, namely biotic or physiological gauges, vehicle type and more importantly the analysis of the face in terms of its alignment and other attributes.
Classification of electrocardiogram signals based on federated learning and a gaussian multivariate aggregation module Maytham N. Meqdad; Abdullah Hasan Hussein; Saif O. Husain; Alyaa Mohammed Jawad
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp936-943

Abstract

Categorization of cardiac abnormalities received from several centers is not possible within the quickest time because of privacy and security restrictions. Today, individuals’ security problem is considered as one of the most important research fields in most research sciences. This study provides a novel approach for detection of cardiac abnormalities based on federated learning (FL). This approach addresses the challenge of accessing data from remote centers and presents the possibility of learning without the need for transferring data from the main center. We present a novel aggregation approach in the FL for addressing the challenge of imbalanced data using the averaging stochastic weights (SWA) optimizer and a multivariate Gaussian in order to make a better and more accurate detection possible. The advantage of the present proposed approach is robust and secure aggregation for unbalanced electrocardiogram (ECG) data from heterogeneous clients. We were able to achieve 87.98% accuracy in testing with the robust VGG19 architecture.
Human activity recognition method using joint deep learning and acceleration signal Maytham N. Meqdad; Abdullah Hasan Hussein; Saif O. Husain; Alyaa Mohammed Jawad; Seifedine Kadry
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1459-1467

Abstract

Many studies have been conducted on human activity recognition (HAR) in the last decade. Accordingly, deep learning algorithms have been given more attention in terms of classification of human daily activities. Deep neural networks (DNNs) compute and extract complex features on voluminous data through some hidden layers that require large memory and powerful graphics processing units (GPUs). So, this study proposes a new joint learning (JL) approach to classify human activities using inertial sensors. To this end, a large complex donor model based on a convolutional neural network (CNN) is used to transfer knowledge to a smaller model based on CNN referred to as the acceptor model. The acceptor model can be deployed on mobile devices and low-power hardware due to decreased computing costs and memory consumption. The wireless sensor data mining (WISDM) dataset is used to test the proposed model. According to the experimental results, the HAR system based on the JL algorithm outperforms than other methods.
Co-Authors Abdullah Hasan Hussein Abdullah Hasan Hussein Ahlam R. Khekan Alyaa Mohammed Jawad B. Vijayalaxmi B. Vijayalaxmi Chavali Anuradha Firoz Khan J. Rethna Virgil Jeny Kaushik Sekaran Kaushik Sekaran Lakshmana Kumar Ramasamy N. Neelima P. Chandana P. Chandana Pardeep Kumar R. Lakshmana Kumar Rawya Al-Akam Saif O. Husain Seifedine Kadry Seifedine Kadry Seifedine Kadry Seifedine Kadry Seifedine Kadry Soundar Rajan Yunyoung Nam