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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
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A secure communication protocol for civil drones Ayad Al-Adhami; Rajaa K. Hasoun; Ekhlas K. Gbashi; Soukaena Hassan
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1490-1501

Abstract

This paper introduces a secure communication protocol that provides secured communication pathways to manipulate drones through unsecured communication. The deployment of the proposed protocol works through providing two secured communication paths; drones to the drone’s controller path and controller to data centre path. The first secured communication path has achieved a high level of security and privacy by using a modification of SHA-1 method and an advanced encryption method. The modification of the SHA-1 is called 83SHA-1. These modifications can increase rounds in the first stage up to 83 rounds, inject each round with expansion and S-Boxes procedures that are used in DES to extend length from 160 to 240 bits then reduce it from 240 to 160 bits. After hash data from the drone then use the advanced encryption method which is called Geffe-Genetic (GG) Encryption algorithm where three types of keys will be used for deception attackers. The second accomplishment is to ensure providing secure communication between the drone’s controller and datacentre by using RNA-RADG-CBC (RRCBC) encryption algorithm where will generate an initialization vector (IV) for cipher block chaining (CBC) randomly, generate keys, and propose an encryption/decryption method. The security analysis shows a promising high security level of drones’s data.
A deep learning based system for accurate diagnosis of brain tumors using T1-w MRI Mona Ahmed; Fahmi Khalifa; Hossam El-Din Moustafa; Gehad Ahmed Saleh; Eman AbdElhalim
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1192-1202

Abstract

Detection and classification of brain tumors are of formidable importance in neuroscience. Deep learning (DL), specifically convolution neural networks (CNN), has demonstrated breakthroughs in the field of brain image analysis and brain tumors classification. This work proposes a novel CNN based model for brain tumor classification. Our pipeline starts with prepossessing and data augmentation techniques. Then, a CNN classification step is developed and utilizes ResNet50 architecture as its core. Particularly, our design modified the ResNet50 output with a global average pooling (GAP) layer to avoid over-fitting. The proposed model is trained and tested using different optimization algorithms. The final classification is achieved using a sigmoid layer. We tested the proposed structure on T1 weighted contrast-enhanced magnetic resonance images (T1-w MRI) that are collected from three datasets. A total of 3586 images containing two classes (i.e., bengin, and malignant) were used in our experiments. The proposed model reach highest accuracy 99.8%, and optimal error 0.005 using Adam when compared with other six well-known CNN architectures.
Markov random field model and expectation of maximization for images segmentation Lalaoui Lahouaoui; Djaalab Abdelhak
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.pp772-779

Abstract

Image segmentation is a significant issue in image processing. Among the various models and approaches that have been developed, some are commonly used the Markov Random Field (MRF) model, statistical techniques (MRF). In this study a Markov random field proposed is based on an EM Modified (EMM) model. In this paper, The local optimization is based on a modified Expectation-Maximization (EM) method for parameter estimation and the ICM method for finding the solution given a fixed set of these parameters. To select the combination strategy, it is necessary to carry out a comparative study to find the best result. The effectiveness of our proposed methods has been proven by experimentation. We have applied this segmented algorithm to different types of images, exhibiting the algorithm's image segmentation strength with its best values criteria for EM statics and other methods.
Cryptography based on retina information Zainab Ibrahim Abood Alrifaee; Tarik Zeyad Ismaeel
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1697-1708

Abstract

The security of message information has drawn more attention nowadays, so; cryptography has been used extensively. This research aims to generate secured cipher keys from retina information to increase the level of security. The proposed technique utilizes cryptography based on retina information. The main contribution is the original procedure used to generate three types of keys in one system from the retina vessel's end position and improve the technique of three systems, each with one key. The distances between the center of the diagonals of the retina image and the retina vessel's end (diagonal center-end (DCE)) represent the first key. The distances between the center of the radius of the retina and the retina vessel's end (radius center-end (RCE)) represent the second key. While the diagonal-radius center and the retina vessel's end (diagonal-radius center-end (DRCE)) represent the third key. The results illustrate the process's validity and applicability. Also, improve the time required to decrypt the cipher-text by a brute force attack (BFA) from (4.358e+139) year in the compared technique to (1.3074e+140) year for retina3. The BFA time will increase with increasing the number of retina vessels, as in retina1, 2, and 3, which have 24, 53, and 103 retina vessels.
Controller placement problem in software defined networks Hassan Hadi Saleh; Israa Adnan Mishkal; Dheyab Salman Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1704-1711

Abstract

The requirements for the network are increasing by the expanding and spreading the Internet. The Previous techniques of the network do not meet the modern needs, thus, a new technical presents software-defined networking (SDN). SDN recognizes as a promising new model that separates the control plane (traffic routing and network topology) from the data plane (network architecture layers). The architecture of SDN has some features that find in a single controller or many controllers instance of programmable, flexible, and scalable. In the current SDN, multiple controllers are essential. Therefore, the optimal number of the controllers and their locations is the most significant challenge, known as the controller placement problem (CPP). It deploys the optimal number of controllers within the network while meeting presentations requirements considered conflicting in nature example: credibility, load balancing, latency, energy efficiency, and computation time. Many studies researched the ways to develop solutions for improving scalability, place selection for SDN. This paper presents the CPP and gives a comprehensive review of SDN issues based on the recent well-known research to extract available solution strategies. Finally, it discusses the limitations and future study directions that can support researchers in this field.
Fine-tuning approach in metaheuristic algorithm to prolong wireless sensor networks nodes lifetime Amir Rizaan Rahiman; Temitope Betty Williams; Muhammad D. Zakaria
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp365-374

Abstract

Wireless sensor networks (WSN) have evolved a vibrant and lively research field. It comprises numerous wise and low-power consumption devices for gathering the contiguous atmosphere's data. However, the energy dissipation matter that distorts network lifetime remains the challenge since the sensor node battery is non-rechargeable and irreplaceable. Clustering and routing protocol has become the furthermost solutions and invariably minimizes depletion and prolongs the sensor node lifetime. Such protocols have adopted metaheuristic algorithms to secure the efficiency of the clustering and routing protocols. However, the cluster head's extensive task favors consuming and draining more energy. This study proposed a fine-tuning solution for the sensor node's population and generation sizes. It benefits from the modified problem-oriented genetic algorithm parameters in securing the sensor node lifetime. Besides, the solution works effectively to balance the load of the cluster head nodes. A set of simulations has been performed using MATLAB R2018b on the proposed solution, namely the energy efficient of genetic (EEG) algorithm and has revealed that the solution outperforms the network lifetime and cluster head load of the existing solution.
Review of current artificial intelligence methods and metaheuristic algorithms for wind power prediction Doha Bouabdallaoui; Touria Haidi; Mariam El Jaadi
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.pp626-634

Abstract

Due to the insufficient fossil resources and the increasing environmental challenges, the world is heading for a more use-oriented to renewable energy sources, specifically to wind energy. A number of predictive techniques are available for the efficient use of wind energy. This article, which is a review of methods of artificial intelligence (AI) and meta-heuristic algorithms for wind energy prediction, fits into this context. There are two distinct categories: the first consists of traditional methods that are commonly used in this context, like different types of artificial neural networks (ANN), support vector machines (SVM) and fuzzy logic; the second is a combined approach which mixes the classic artificial intelligence methods and the meta-heuristic algorithms for the optimization of the forecast output. Then, a summary and comparison between the methodologies are established, and the advantages and limits of each technique are defined. The combination of the classic artificial intelligence and metaheuristic algorithms has a greater performance than the utilization of classic methods only. Nevertheless, using hybrid metaheuristic algorithms with classic artificial intelligence prediction methods can provide a higher precision.
Hybrid structure of u bent optical fiber local surface plasmon resonance sensor based on graphene Saffana Zeiab Maseer; Bushra Razooky Mahdi; Nahla Abd Aljbar
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.pp644-651

Abstract

In this paper, a fiber optic sensor was designed and implemented to detect the change in refractive index of sodium chloride salt solution based on the local surface plasmon resonance (LSPR) phenomenon. This sensor was manufactured using a plastic optical fiber (POF), this optical fiber was bent in a U-shape with 0.5cm bending diameter, and then the cladding and part from core of the fiber were removed by polishing at the sensor head to become as a D-shape in cross section. The sensor was coated with 30 nm thickness of gold nano particles (GNPs) by DC plasms coating technology and it was tested with sodium chloride solution, the detection sensitivity was 466.66 nm/RIU. To enhancement the sensitivity, the latest sensor was coated with 20nm thickness of graphene nano material and retested with same samples of sodium chloride solutions. It was found that graphene improved the sensitivity by an excellent amount, where shift in wavelength was 20nm and highest sensitivity obtained was 666.666 nm/RIU.
Inheritance issues’ features extraction using Arabic text analyzer (IFAA) Abeer K. Al-Mashhadany; Marwan B. Mohammed; Mawlood Alrawi Alrawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp611-624

Abstract

Inheritance issue is part of our life. Daily, many persons may die. Person is gone, while his money stays for others. Islamic law took care of the issue of inheritance. Al-Quran has verses dedicated in inheritance issue. Al-Quran gives every person its rights, so; the share for each heir is determined. Islamic law jurists are asked frequently to solve inheritance issues. This work; inheritance issues’ features extraction using Arabic text analyzer (IFAA) hopes to analyze inheritance issue. It receives the issue as Arabic unstructured characterized text. It applies Arabic analyzer system to extract all features. Many commercial applications are constructed to solve inheritance issue; they receive the features manually, while this work is an attempt to computerize features' extracting. This work needs a good experience in analyzing Arabic text. So, this research attempts developing Arabic analyzer system dedicated in inheritance issues, which has the ability to analyze inheritance issue and extract its features. It will be shown that Arabic analyzer system is useful in converting Arabic text into data that are understandable by programming languages, and those data could be used to perform arithmetic calculations, and achieve high accuracy reaches to 100%.
A deep learning content-based image retrieval approach using cloud computing Mahmoud S. Sayed; Ahmed A. A. Gad-Elrab; Khaled A. Fathy; Kamal R. Raslan
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1577-1589

Abstract

Due to the rapid growth in multimedia content and its visual complexity, contentbased image retrieval (CBIR) has become a very challenging task. Existing works achieve high precision values at first retrieval levels such as top 10 and top 20 images, but low precision values at subsequent levels such as top 40, 50, and 70, so the goal of this paper is to propose a new CBIR approach that achieves high precision values at all retrieval levels. The proposed method combines features extracted from the pre-trained AlexNet model and discrete cosine transform (DCT). Then principal components analysis (PCA) is performed on AlexNet’s features and feeding these combination to multiclass support vector machine (SVM). The euclidean distance is used to measure the similarity between query and stored images features within the predicted class by SVM. Finally top similar images are ranked and retrieved. All above techniques require huge computational power which may not be available on client machine thus, the processing of these tasks is processed on cloud. Experimental results on the benchmark Corel-1k show that the proposed method achieves high precision value 97% along all retrieval levels top 10, 20 and 70 images and requiring less memory compared to other methods.

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