Maytham N Meqdad
@uomus.edu.iq
Computer Techniques Engineering Department
Al-Mustaqbal University College
RESEARCH INTERESTS
Cloud Computing
Computer Networking
Parallel Computing
IOT
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Yulan Zhang, Abdulrahman Jaffar Aldosky, Vishal Goyal, Maytham N. Meqdad, Tirumala Uday Kumar Nutakki, Theyab R. Alsenani, Van Nhanh Nguyen, Mahidzal Dahari, Phuoc Quy Phong Nguyen, and H. Elhosiny Ali
Elsevier BV
Yisu Ge, Guodao Zhang, Maytham N. Meqdad, and Shuzheng Chen
Elsevier BV
Yutao Li, Shahab Naghdi Sedeh, As'ad Alizadeh, Maytham N. Meqdad, Ahmed Hussien Alawadi, Navid Nasajpour-Esfahani, Davood Toghraie, and Maboud Hekmatifar
Elsevier BV
Maytham N. Meqdad, Abdullah Hasan Hussein, Saif O. Husain, Alyaa Mohammed Jawad, and Seifedine Kadry
Institute of Advanced Engineering and Science
Many studies have been conducted on human activity recognition (HAR) in the last decade. Accordingly, deep learning (DL) 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.
Maytham N. Meqdad, Saif O. Husain, Alyaa Mohammed Jawad, Seifedine Kadry, and Ahlam R. Khekan
Institute of Advanced Engineering and Science
<span lang="EN-US">Modern technologies are widely used today to diagnose epilepsy, neurological disorders, and brain tumors. Meanwhile, it is not cost-effective in terms of time and money to use a large amount of electroencephalography (EEG) data from different centers and collect them in a central server for processing and analysis. Collecting this data correctly is challenging, and organizations avoid sharing their and client information with others due to data privacy protection. It is difficult to collect these data correctly and it is challenging to transfer them to research centers due to the privacy of the data. In this regard, collaborative learning as an extraordinary approach in this field paves the way for the use of information repositories in research matters without transferring the original data to the centers. This study focuses on the use of a heterogeneous client balancing technique with an interval selection approach and classification of EEG signals with ResNet50 deep architecture. The test results achieved an accuracy of 99.14 compared to similar methods.</span>
Maytham N. Meqdad, Abdullah Hasan Hussein, Saif O. Husain, and Alyaa Mohammed Jawad
Institute of Advanced Engineering and Science
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.
Ali Mohammadiounotikandi, Hassan Falah Fakhruldeen, Maytham N. Meqdad, Banar Fareed Ibrahim, Nima Jafari Navimipour, and Mehmet Unal
MDPI AG
Concerns about fire risk reduction and rescue tactics have been raised in light of recent incidents involving flammable cladding systems and fast fire spread in high-rise buildings worldwide. Thus, governments, engineers, and building designers should prioritize fire safety. During a fire event, an emergency evacuation system is indispensable in large buildings, which guides evacuees to exit gates as fast as possible by dynamic and safe routes. Evacuation plans should evaluate whether paths inside the structures are appropriate for evacuations, considering the building’s electric power, electric controls, energy usage, and fire/smoke protection. On the other hand, the Internet of Things (IoT) is emerging as a catalyst for creating and optimizing the supply and consumption of intelligent services to achieve an efficient system. Smart buildings use IoT sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. This research proposes a new way for a smart building fire evacuation and control system based on the IoT to direct individuals along an evacuation route during fire incidents efficiently. This research utilizes a hybrid nature-inspired optimization approach, Emperor Penguin Colony, and Particle Swarm Optimization (EPC-PSO). The EPC algorithm is regulated by the penguins’ body heat radiation and spiral-like movement inside their colony. The behavior of emperor penguins improves the PSO algorithm for sooner convergences. The method also uses a particle idea of PSO to update the penguins’ positions. Experimental results showed that the proposed method was executed accurately and effectively by cost, energy consumption, and execution time-related challenges to ensure minimum life and resource causalities. The method has decreased the execution time and cost by 10.41% and 25% compared to other algorithms. Moreover, to achieve a sustainable system, the proposed method has decreased energy consumption by 11.90% compared to other algorithms.
Maytham N. Meqdad, Hafiz Tayyab Rauf, and Seifedine Kadry
MDPI AG
The most suitable method for assessing bone age is to check the degree of maturation of the ossification centers in the radiograph images of the left wrist. So, a lot of effort has been made to help radiologists and provide reliable automated methods using these images. This study designs and tests Alexnet and GoogLeNet methods and a new architecture to assess bone age. All these methods are implemented fully automatically on the DHA dataset including 1400 wrist images of healthy children aged 0 to 18 years from Asian, Hispanic, Black, and Caucasian races. For this purpose, the images are first segmented, and 4 different regions of the images are then separated. Bone age in each region is assessed by a separate network whose architecture is new and obtained by trial and error. The final assessment of bone age is performed by an ensemble based on the Average algorithm between 4 CNN models. In the section on results and model evaluation, various tests are performed, including pre-trained network tests. The better performance of the designed system compared to other methods is confirmed by the results of all tests. The proposed method achieves an accuracy of 83.4% and an average error rate of 0.1%.
Gabriel Gomes de Oliveira, Elham Moghadamnia, Reza Radfar, Mohammad Worya Khordehbinan, Mohammad Hosein Sabzalian, and Maytham N. Meqdad
Springer International Publishing
Maytham N. Meqdad, Seifedine Kadry, and Hafiz Tayyab Rauf
MDPI AG
Things receive digital intelligence by being connected to the Internet and by adding sensors. With the use of real-time data and this intelligence, things may communicate with one another autonomously. The environment surrounding us will become more intelligent and reactive, merging the digital and physical worlds thanks to the Internet of things (IoT). In this paper, an optimal methodology has been proposed for distinguishing outlier sensors of the Internet of things based on a developed design of a dragonfly optimization technique. Here, a modified structure of the dragonfly optimization algorithm is utilized for optimal area coverage and energy consumption reduction. This paper uses four parameters to evaluate its efficiency: the minimum number of nodes in the coverage area, the lifetime of the network, including the time interval from the start of the first node to the shutdown time of the first node, and the network power. The results of the suggested method are compared with those of some other published methods. The results show that by increasing the number of steps, the energy of the live nodes will eventually run out and turn off. In the LEACH method, after 350 steps, the RED-LEACH method, after 750 steps, and the GSA-based method, after 915 steps, the nodes start shutting down, which occurs after 1227 steps for the proposed method. This means that the nodes are turned off later. Simulations indicate that the suggested method achieves better results than the other examined techniques according to the provided performance parameters.
Xiaochun Cheng, Seifedine Kadry, Maytham N. Meqdad, and Rubén González Crespo
Springer Science and Business Media LLC
Maytham N. Meqdad, Fardin Abdali-Mohammadi, and Seifedine Kadry
MDPI AG
The 12 leads of electrocardiogram (ECG) signals show the heart activities from different angles of coronal and axial planes; hence, the signals of these 12 leads have functional dependence on each other. This paper proposes a novel method for fusing the data of 12-lead ECG signals to diagnose heart problems. In the first phase of the proposed method, the time-frequency transform is employed to fuse the functional data of leads and extract the frequency data of ECG signals in 12 leads. After that, their dependence is evaluated through the correlation analysis. In the second phase, a structural learning method is adopted to extract the structural data from these 12 leads. Moreover, deep convolutional neural network (CNN) models are coded in this phase through genetic programming. These trees are responsible for learning deep structural features from functional data extracted from 12 leads. These trees are upgraded through the execution of the genetic programming (GP) algorithm to extract the optimal features. These two phases are used together to fuse the leads of ECG signals to diagnose various heart problems. According to the test results on ChapmanECG, including the signals of 10,646 patients, the proposed method enjoys the mean accuracy of 97.60% in the diagnosis of various types of arrhythmias in the Chapman dataset. It also outperformed the state-of-the-art methods.
Seifedine Kadry, Venkatesan Rajinikanth, Gautam Srivastava, and Maytham N. Meqdad
Springer International Publishing
Seifedine Kadry, David Taniar, Maytham N. Meqdad, Gautam Srivastava, and Venkatesan Rajinikanth
Springer International Publishing
FerasNadhimHasoon Al Attar, Seifedine Kadry, K. Suresh Manic, and Maytham N. Meqdad
IOP Publishing
Abstract The vital organ in human physiology is the brain, and abnormality in the brain will reason for various behavioural problems. Ischemic-Stroke is a medical emergency, and early detection and action will help the patient recover quickly. This scheme aims to implement Convolutional-Neural-Network (CNN) segmentation method to extract and evaluate the infected portion from the MRI slice of the brain. In our study the pre-trained UNet scheme is adopted to extract the stroke region from the Flair modality MRI slice with axial-, coronal- and sagittal plane. In this work, the ISLES2015 database is used for the experimental investigation. The segmented portion is further evaluated to the ground-truth and the metrics such as Jaccard, Dice and Accuracy are computed. The experimental investigation is implemented using Python software. The experimental outcome of this research proves that the proposed CNN scheme aids to improve segmentation accuracy on axial-plane images compared with other images. The performance of the CNN segmentation scheme is then validated with other related results existing in the literature. The outcome of this study confirms that UNet supported technique helps extract the stroke lesion from the MRI slice with more accurate accuracy.
Geetha Achuthan, Seifedine Kadry, K. Suresh Manic, and Maytham N. Meqdad
IOP Publishing
Abstract Deep-Learning-Scheme (DLS) based medical data assessment has been widely employed in recent years due to its improved accuracy. Our goal is to study the performance of the pre-trained DLS on RGB-scale breast-histology images. The implemented idea holds these phases; (i) Data collection, pre-processing and resizing, (ii) Training the DLS with chosen test-pictures, (iii) Testing and validating the performance of the DLS with 5-fold cross-validation. This investigation considered the breast-histology pictures for the study and binary classification is employed to achieve Normal/Cancer class grouping of images. The proposed work compared the classification performance of AlexNet, VGG16 and VGG19.The experimental outcome of this study authenticates that the AlexNet with the Random-Forest (RF) classifier helps to get a higher classification accuracy (>87%) compared to VGG16 and VGG19.
Thavavel Vaiyapuri, S. Srinivasan, Mohamed Yacin Sikkandar, T. S. Balaji, Seifedine Kadry, Maytham N. Meqdad, and Yunyoung Nam
Computers, Materials and Continua (Tech Science Press)
Maytham N. Meqdad, Fardin Abdali-Mohammadi, and Seifedine Kadry
Institute of Electrical and Electronics Engineers (IEEE)
Detection of arrhythmia of electrocardiogram (ECG) signals recorded within several sessions for each person is a challenging issue, which has not been properly investigated in the past. This arrhythmia detection is challenging since a classification model that is constructed and tested using ECG signals maintains generalization when dealing with unseen samples. This article has proposed a new interpretable meta structural learning algorithm for this challenging detection. Therefore, a compound loss function was suggested including the structural feature extraction fault and space label fault with GUMBEL-SOFTMAX distribution in the convolutional neural network (CNN) models. The collaboration between models was carried out to create learning to learn features in models by transferring the knowledge among them when confronted by unseen samples. One of the deficiencies of a meta-learning algorithm is the non-interpretability of its models. Therefore, to create an interpretability feature for CNN models, they are encoded as the evolutionary trees of the genetic programming (GP) algorithms in this article. These trees learn the process of extracting deep structural features in the course of the evolution in the GP algorithm. The experimental results suggested that the proposed detection model enjoys an accuracy of 98% regarding the classification of 7 types of arrhythmia in the samples of the Chapman ECG dataset recorded from 10646 patients in different sessions. Finally, the comparisons demonstrated the competitive performance of the proposed model concerning the other models based on the big deep models.
A. Arokiaraj Jovith, Mahantesh Mathapati, M. Sundarrajan, N. Gnanasankaran, Seifedine Kadry, Maytham N. Meqdad, and Shabnam Mohamed Aslam
Computers, Materials and Continua (Tech Science Press)
Omar A. Saraereh and Ashraf Ali
Computers, Materials and Continua (Tech Science Press)
Firoz Khan, R. Lakshmana Kumar, Seifedine Kadry, Yunyoung Nam, and Maytham N. Meqdad
Institute of Advanced Engineering and Science
<span>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 <span>transmitted by these can have disastrous consequences, as human lives are at stake here. Various attacks</span> against the different type of sensors on-board an autonomous vehicle are covered.</span>
Firoz Khan, R. Lakshmana Kumar, Seifedine Kadry, Yunyoung Nam, and Maytham N. Meqdad
Institute of Advanced Engineering and Science
<span>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.</span>
Lakshmana Kumar Ramasamy, Seifedine Kadry, Yunyoung Nam, and Maytham N. Meqdad
Institute of Advanced Engineering and Science
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.