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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Computer Science
Seifedine Kadry
Noroff University College
Computer Science & IT Electrical & Electronics Engineering Engineering

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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.
Hybrid Reality-Based Education Expansion System for Non-Traditional Learning Firoz Khan; R.Lakshmana Kumar; Seifedine Kadry
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 1 (2021): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i1.20568

Abstract

Many educators utilize conventional coaching methods to coach and study behaviors in a classroom with face-to-face, verbal contact. But, the coaching with learning atmosphere has developed further than the classroom. The incorporation of technology at the coaching with learning procedure is the novel tendency at teaching, by a favorable result. Technologies present surroundings for learning behaviors to happen anytime also everywhere to advantages instructors with students universal. One of the skills to have been demonstrating feasibilities of the appliance at learning surroundings is Hybrid Reality (HR), which includes together Virtual Reality (VR) with Augmented Reality (AR). This work attempts to construct ahead the recent condition of hybrid reality also its appliance at learning. The initial section depicts the fundamental formation of hybrid reality also its various divisions. The subsequent sections provide the superior construction of a few innovative appliances that are implemented for the hybrid reality. Lastly, the paper shows the benefits of those applications over the traditional teaching methods and the essential user reactions. The outcomes have highly in assistance of taking mobile applications based on Hybrid Reality into a contemporary teaching scheme.
Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability Prakash Shanmurthy; Poongodi Thangamuthu; Balamurugan Balusamy; Seifedine Kadry
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp504-512

Abstract

Semantic web technology is adapted to the internet of things (IoT) for web - based applications to globally connect the services. Web ontology language (OWL) domain ontology is a powerful machine - readable language for domain knowledge representation. The developer stored the IoT application relevant ontology in a repository or catalogue. Hence, IoT application - related ontology files are available for reus e, but many of the IoT application - relevant ontology files are publicly not available or inaccessible. The proposed idea is to extract the contextual knowledge of IoT applications that contain inaccessible ontology files. The context - wise specific domain I oT applications are not obtainable, hence respective ontology - based research papers are identified and their frequent terms are computed. The selected contextual dominant frequent terms from the transport domain are passed into the skip - gram flavour of wor d2vector modelled n atural language processing ( NLP ) corpus which produces most similar terms. The domain experts select the appropriate terms to annotate in OWL ontology for contextual knowledge augmentation. Finally, 1422 contextual terms were generated b ased on dominant terms of selected IoT applications.
ThreatNet: advanced threat detection, region-based convolutional neural network framework Anurag Singh; Naresh Kumar; Seifedine Kadry
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1007-1015

Abstract

It is critical for many countries to ensure public safety in detecting and identifying threats in a night, commercial places, border areas and public places. Majority of past research in this area has focused on the use of image-level categorization and object-level detection techniques. As an X-ray and thermal security image analysis strategy, object separation can considerably improve automatic threat detection when used in conjunction with other techniques. In order to detect possible threats, the effects of introducing segmentation deep learning models into the threat detection pipeline of a large imbalanced X-ray and thermal dataset were investigated. With the purpose of boosting the number of true positives discovered, a faster regional convolutional neural network (R-CNN) model was trained on a balanced dataset to identify probable hazard zones in X-ray and thermal security pictures. In order to get the final results, we combined the two models i.e faster R-CNN with Mask RCNN into a single detection pipeline using the transfer learning technique, which outperforms baseline and end-to-end instance segmentation methods using less number of the practical dataset, with mAPs ranging from 94.88 percent to 91.40 percent helps in detecting the person with guns, knives, pliers to avoid cross border threats.
An investigation of machine learning techniques in speech emotion recognition Anu Saini; Amit Ramesh Khaparde; Sunita Kumari; Salim Shamsher; Jeevanandam Joteeswaran; Seifedine Kadry
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp875-882

Abstract

The natural languages are medium of communication from the inception of civilization. As the technology improves, the text messages, voice messages and videos are the addons in medium of communication. In long distance communication, the analysis of expression is modern area of research. The parameters of assessment are subjective hence the emotion recognition is challenging task. This article furnishes the investigation of various machine learning techniques and novel methods for speech emotion recognition (SER) to determine the feeling/sentiments in a speech. Here, we investigate the three machine learning methods named multinominal Naive Bayes (MNB), logistic regression (LR), and linear support vector machine (LSVM). Further, these techniques are incorporated with the proposed method. The performance of these machine learning techniques is investigated on two different datasets.  The datasets consist of voice and text data samples. The prosed method is trained and tested on these datasets. As per the experimentation, it has been observed that the LSVM has outperformed the other two machine learning techniques.
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.
Classification of electroencephalography using cooperative learning based on participating client balancing Maytham N. Meqdad; Saif O. Husain; Alyaa Mohammed Jawad; Seifedine Kadry; Ahlam R. Khekan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4692-4699

Abstract

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.
Co-Authors Abdullah Hasan Hussein Ahlam R. Khekan Alyaa Mohammed Jawad Amit Ramesh Khaparde Anu Saini Anurag Singh Balamurugan Balusamy Firoz Khan Jeevanandam Joteeswaran Maytham N. Meqdad Naresh Kumar Poongodi Thangamuthu Prakash Shanmurthy R. Lakshmana Kumar R.Lakshmana Kumar Saif O. Husain Salim Shamsher Sunita Kumari Yunyoung Nam