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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 66 Documents
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Search results for , issue "Vol 37, No 1: January 2025" : 66 Documents clear
Random forest algorithm with hill climbing algorithm to improve intrusion detection at endpoint and network Sekar, Satheesh Kumar; Parvathy, Palaniraj Rajidurai; Pinjarkar, Latika; Latha, Raman; Sathish, Mani; Reddy, Munnangi Koti; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp134-142

Abstract

Cloud computing is a framework that enables end users to connect highly effective services and applications over the internet effortlessly. In the world of cloud computing, it is a critical problem to deliver services that are both safe and dependable. The best way to lessen the damage caused by entry into this environment is one of the primary security concerns. The fundamental advantage of a cooperative approach to intrusion detection system (IDS) is a superior vision of an action of network attack. This paper proposes a random forest (RF) algorithm with a hill-climbing algorithm (RFHC) to improve intrusion detection at the endpoint and network. Initially, it is used for feature selection, and the next process is to separate the intrusions detection. The feature selection is maintained by the hill climbing (HC) algorithm that chooses the best features. Then, we utilize the RF algorithm to separate the intrusion efficiently. The experimental results depict that the RFHC mechanism reached more acceptable results regarding recall, precision, and accuracy than a baseline mechanism. Moreover, it minimizes the miss detection ratio and enhances the intrusion detection ratio.
Enhancing energy efficiency and reliability in wireless sensor networks using BioGAT optimization Shareef, D. K.; Jyothsna, Veeramreddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp601-612

Abstract

The BioGAT model, as proposed, presents a novel methodology for enhancing the efficiency of wireless sensor networks (WSNs), which are essential elements of contemporary communication and sensing systems. For real-time monitoring and data analysis, WSNs are comprised of autonomous sensor nodes that are outfitted with processing, wireless communication, and sensing capabilities. These nodes are deployed in a variety of environments. By means of an advanced optimization model, this work aims to address critical challenges in WSNs, specifically in the areas of node placement, energy efficiency, and network reliability. By utilizing biogeography-based optimization (BBO) and graph attention networks (GAT), the BioGAT model endeavors to dynamically adapt to network changes while achieving a balance between efficient coverage and energy consumption. Cluster heads (CHs), which are essential for the aggregation of data, have a significant impact on improvements in energy efficiency and the longevity of networks. By means of comprehensive simulations and evaluation, this study presents exceptional outcomes. The BioGAT model outperforms prior approaches by attaining a 95% packet delivery ratio and an enhanced throughput. In addition, the model effectively decreases mean energy consumption, underscoring its capacity to improve the sustainability and dependability of networks in a variety of WSN applications.
A novel boundary adaptive oversampling approach for intrusion detection Kaur, Ritinder; Gupta, Neha
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp518-529

Abstract

Managing unbalanced datasets is a significant challenge in intrusion detection, since uncommon assaults are often obscured by the bulk of regular network traffic. In order to mitigate the effects of class imbalance and improve intrusion detection system (IDS) performance, it is necessary to use a variety of imbalanced learning algorithms. Methods of data augmentation such as adaptive synthetic sampling (ADASYN) and synthetic minority oversampling technique (SMOTE) are useful in addressing class imbalance. This paper introduces a novel technique to data resampling where decision tree-generated decision boundaries are used to conduct ADASYN on complicated and unusual samples. When this method’s efficacy was evaluated using the standard NSL-KDD dataset, the accuracy of the unusual class u2r was increased to 42% and, for r2l it was improved to 83%, respectively. The UNSW-NB 15 dataset has been used for further validation of the method, and its statistical significance has been asserted by comparing the suggested method to other oversampling techniques.
A three-phase model to keyword detection in Arabic corpora Namly, Driss; Bouzoubaa, Karim; Tachicart, Ridouane
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp206-213

Abstract

The exponential growth of Arabic text data in recent years has created an urgent demand for sophisticated keyword detection techniques that are specifically tailored to the nuances of the Arabic language. This study addresses the critical need for efficient tools capable of swiftly and accurately identifying keywords within a collection of Arabic documents, particularly when analyzing multiple documents in a corpus. To meet this challenge, we present a novel corpus specifically designed for keyword detection in Arabic texts, along with an innovative approach that integrates three distinct candidate keyword lists: a frequency-based list, a vector space model list, and a machine learning-based list. This hybrid methodology leverages the strengths of each technique, enabling a more comprehensive and effective keyword identification process. We conducted extensive experimental validation to assess the performance and computational efficiency of our proposed pipeline. The results demonstrate that our approach consistently achieves robust performance across a variety of domains, with evaluation metrics indicating F1-scores that consistently surpass 91%. Overall, this study contributes to the advancement of automated keyword detection in Arabic, paving the way for enhanced information retrieval and text analysis capabilities.
Optimizing 2D-to-3D image conversion for precise flat surface detection using laser triangulation and HSV masking Rahayu Purwanti, Bernadeta Siti; Akhinov, Ihsan Auditia; Mulyono, Raden Sugeng; Nurtanto, Muhammad; Hamid, Mustofa Abi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp111-122

Abstract

This study tackles a critical challenge in converting two-dimensional (2D) images into three-dimensional (3D) representations, focusing on the precise detection of flat surfaces. The research utilizes a triangulation method involving laser and camera systems, emphasizing the optimization of laser shooting angles and camera positioning to accurately determine z-coordinates. The methodology employs hue, saturation, and value (HSV) color masking, which has proven superior to traditional red, green, blue (RGB) methods for isolating red line objects. Key findings indicate that the optimal laser angle, β1=70.65°, significantly minimizes root mean square (RMS) error, thereby enhancing the accuracy of 3D imaging. Additionally, the use of three laser lines at different angles enables a more comprehensive detection of z-coordinates by creating multiple reference points across the surface. This arrangement improves the robustness and precision of the 3D reconstruction process, as the intersecting laser lines generate detailed coordinate data that is critical for accurately mapping surface irregularities. These results not only support existing theories in digital feature extraction but also offer a robust framework for practical applications in manufacturing and quality control, particularly in surface defect detection. The study’s innovative approach advances the field of computer vision, providing new insights and methodologies for optimizing image conversion techniques.
A hybrid firefly algorithm for the sales representative planning problem Bouatouche, Mourad; Belkadi, Khaled
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp406-415

Abstract

In the rapidly increasing pharmaceutical sector, sales representatives are employed by pharmaceutical manufacturers and distributors to inform and educate physicians. To convince providers to prescribe the medications to their patients, these representatives rely on their product expertise and people’s abilities to close deals. Instead of making direct sales, pharmaceutical sales representatives help medical professionals get the medications, treatments, and information they need to give their patients the best care possible. Furthermore, they inform the public about novel and occasionally life-saving treatments and share interesting medical developments. This study presents a hybrid methodology that integrates the benefits of local search and the firefly algorithm (FA) to determine the optimal planning for a sales representative. The objective is to maximize the rewards while adhering to certain constraints. The objective is to maximize the rewards while adhering to certain limits. Utilizing local search, the hybrid algorithm enhances firefly’s global search behaviour and produces the best possible sales presentation planning. The experimental findings demonstrate the superior performance of the suggested algorithm compared to the FA and other literature methods in the sense of enhancing the convergence rate and preventing local minima. Furthermore, it can enhance the best-known solution for most benchmark instances.
MODIS-NDVI and wheat yield patterns and predictions in Taounate, Morocco Bouskour, Sara; Zaggaf, Mohamed Hicham; Bahatti, Lhoussain; Zayrit, Soumaya
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp648-659

Abstract

This study is devoted to the use of varied analytical methods to elucidate the complex relationship between normalized difference vegetation index (NDVI) and wheat production in Taounate, Morocco based on MODIS Satellite data. Linear regression (LR), with a coefficient of determination (R²) of 0.93, provided a solid basis, while the decision tree (DT) showed significant performance with an R² of 0.81. Support vector regression (SVR) performed well with an R² of 0.96, highlighting its ability to capture the non-linear nuances of the data. Given the complexity inherent in the observed relationships, characterized by non-linear variations, we opted for a combined approach. K-means, closely linked to SVR, was integrated for its ability to identify homogeneous subgroups in the data (R2 up to 0.98). This combination made it possible to circumvent the limits of strictly linear methods, thus reinforcing the robustness of our analysis. These results underline the capacity of the chosen methodology to decode the interactions between NDVI and wheat production in the complex context of Taounate. By providing clear and nuanced perspectives, this study helps inform agricultural decisions and build resilience to climate challenges in the region.
Enhanced SMS spam classification using machine learning with optimized hyperparameters Hafidi, Nasreddine; Khoudi, Zakaria; Nachaoui, Mourad; Lyaqini, Soufiane
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp356-364

Abstract

Short message service (SMS) text messages are indispensable, but they face a significant issue with spam. Therefore, there is a need for robust models capable of classifying SMS messages as spam or non-spam. Machine learning offers a promising approach for this classification, based on existing datasets. This study explores a comparison of several techniques, including logistic regression (LR), support vector machines (SVM), gradient boosting (GB), and neural networks (NN). Hyperparameters play a crucial role in the performance of these models, and their optimization is essential for achieving high accuracy. To this end, we employ an evolutionary programming approach for hyperparameter optimization. This approach evaluates the performance of these models before and after hyperparameter optimization, aiming to identify the most effective model for SMS spam classification.
Detection and severity classification of ataxia using gait features and a hybrid model Pushpalatha, Srikantaswamy; Jayaprakash, Vidyarani Hamppayanamaligae; Krishnamurthy, Sunitha
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp560-568

Abstract

Ataxia, a neurological disorder characterized by impaired coordination and unsteady movements, presents significant challenges for accurate diagnosis and classification. traditional machine-learning (ML) and deep-learning (DL) models often struggle to achieve high accuracy in predicting and classifying this complex condition. This study addresses these limitations by introducing a novel hybrid model, XGBoost-multi-layer-perceptron (XGB-MLP), specifically designed to enhance the accuracy of ataxia prediction and classification. The objective of this research is to develop a more reliable and precise diagnostic tool that outperforms existing ML and DL approaches. The methodology involved integrating the strengths of XGBoost, known for its powerful gradient boosting, with the multi-layer perceptron (MLP) neural network, creating a robust hybrid model. The proposed XGB-MLP model was rigorously tested against conventional models like random forest (RF), logistic regression (LR), support vector machine (SVM), MLP, and standalone XGBoost. The findings reveal that the XGB-MLP model achieves outstanding accuracy rates of 98.91% for ataxia prediction and 97.91% for classification, significantly surpassing the performance of the traditional models.
Archimedes assisted LSTM model for blockchain based privacy preserving IoT with smart cities Somanathan Pillai, Sanjaikanth E Vadakkethil; Vallabhaneni, Rohith; Vaddadi, Srinivas A; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp488-497

Abstract

Presently, the emergence of internet of things (IoT) has significantly improved the processing, analysis, and management of the substantial volume of big data generated by smart cities. Among the various applications of smart cities, notable ones include location-based services, urban design and transportation management. These applications, however, come with several challenges, including privacy concerns, mining complexities, visualization issues and data security. The integration of blockchain (BC) technology into IoT (BIoT) introduces a novel approach to secure smart cities. This work presents an Archimedes assisted long short-term memory (LSTM) model intrusion detection for BC based privacy preserving (PP) IoT with smart cities. After the stage of pre-processing, the LSTM is utilized for automated feature extraction and classification. At last, the Archimedes optimizer (AO) is utilized to optimize the LSTM’s hyper-parameters. In addition, the BC technology is utilized for securing the data transmission.

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