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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 9,138 Documents
 833   834   835   836   837 
Network routing and scheduling architecture in a fully distributed cloud computing environment Kumar S, Vijaya; Periyasamy, Muthusamy; Radhakrishnan, R.; Karuppiah, Tamilarasi; Elumalai, Thenmozhi
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1242-1252

Abstract

Distributed computing has turned into an indispensable application administration because of the colossal development and fame of the internet. However, determining the allocation of various tasks to suitable service nodes is crucial. For the reasons expressed over, an effective booking strategy is expected to work on the framework’s exhibition. As a result, three-layer cloud dispatching (TLCD) design is introduced to further develop mission planning execution. The assignments should be arranged into various sorts in the primary layer in radiance of about their personalities clustering selection algorithm is composed of then recommended in second layer towards dispatch the undertakings to significant help bunches. Likewise, to further develop booking effectiveness, another planning technique for third stage proposes dispatching that job here to system thinking in a central server. As a rule, the proposed TLCD design yields the quickest work finishing time. Moreover, in cloud computing network architecture, load balancing and stability can be achieved.
How does natural language processing identify the issues? Assiroj, Priati; Alam, Sirojul; Spits Warnars, Harco Leslie Hendric
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp357-366

Abstract

Product innovation and service improvement have become essential or crucial for organisations, including public service organisations. The Indonesia Immigration Directorate released the m-passport application to enhance its quality of service. The m-passport application is considered good as it has been downloaded over a million times. Like immigration officers, this application seems to be at the forefront, reflecting an increasingly better service. However, there was still a need for significant improvement in the application. Improvements can be made to the application by considering user feedback or reviews. Reviews provided by users, approximately 12K, will serve as input for improving or enhancing the application. This was made possible as users interacti directly with the application. The most common issues are one-time password or OTP verification code with a probability value of 0.044, errors when logging in with a probability value of 0.283, and slow response applications with a probability value of 0.125.
An efficient convolutional neural network for adversarial training against adversarial attack Vaddadi, Srinivas A.; Somanathan Pillai, Sanjaikanth E. Vadakkethil; Addula, Santosh Reddy; Vallabhaneni, Rohith; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1769-1777

Abstract

Convolutional neural networks (CNN) are widely used by researchers due to their extensive advantages over various applications. However, images are highly susceptible to malicious attacks using perturbations that are unrecognized even under human intervention. This causes significant security perils and challenges to CNN-related applications. In this article, an efficient adversarial training model against malevolent attacks is demonstrated. This model is highly robust to black-box malicious examples, it is processed with different malicious samples. Initially, malicious training models like fast gradient descent (FGS), recursive-FGSM (I-FGS), Deep-Fool, and Carlini and Wagner (CW) techniques are utilized that generate adversarial input by means of the CNN acknowledged to the attacker. In the experimentation process, the MNIST dataset comprising 60K and 10K training and testing grey-scale images are utilized. In the experimental section, the adversarial training model reduces the attack accuracy rate (ASR) by an average of 29.2% for different malicious inputs, when preserving the accuracy of 98.9% concerning actual images in the MNIST database. The simulation outcomes show the preeminence of the model against adversarial attacks.
A novel framework for MOOC recommendation using sentiment analysis Uthamaraj, Sujatha; Ranganathan, Gunasundari
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp603-613

Abstract

Massive open online courses (MOOC) are the largest initiative in eLearning, with the support of universities across the world. To increase course satisfaction in MOOCs, learners’ must relate to the courses that best suit their needs and interests. The goal of recommendation systems is to suggest items to users based on their preferences and past behaviour. A course recommender system makes recommendations based on the similarity of courses and past interactions with the MOOC platform. With a huge volume of online courses on multiple learning platforms, it has been difficult for learners to identify the course of their interest. To address these challenges, a novel framework for hybrid MOOC course recommendations is proposed to recommend courses from multiple learning platforms. It uses web scraping techniques to collect course data from various MOOC providers, such as Coursera, Udemy, and edX platforms. With the real time dataset, a deep learning chatbot captures the personalized learning requirements of learners and recommends using a user-user collaborative approach with the valence aware dictionary and sentiment reasoner (VADER) for sentiment analysis. It enhances the accuracy of recommendations with an root-mean-square error (RMSE) value of 0.541.
A set of embedding rules in IWT for watermark embedding in image watermarking Hafidz, Muhammad Afnan; Ernawan, Ferda; Bakar, Suraya Abu; Fakhreldin, Mohammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1512-1520

Abstract

The development of new technologies has made image watermarking crucial in the digital era to preserve and protect illegal distribution of images against unauthorized users. This paper presents a robust image watermarking technique that employs a set of embedding rules in the three-level of integer wavelet transform (IWT). The proposed method aims to achieve high robustness of image watermarking while maintaining the imperceptibility. The proposed scheme divides the red and green layers into non-overlapping 16×16 blocks. Three levels of IWT are applied to obtain 2×2 LL sub-band, four coefficients of IWT are then modified based on the proposed set of rules for embedding watermark. The experimental results demonstrate a comparison of the proposed embedding and the existing methods. The proposed scheme produced an average NC value of 0.965 against the median filter. The results also showed the imperceptibility of the the image with a PSNR of 45.1760 db and SSIM of 0.9995.
Evaluating the impact of downsampling on 3D MRI images segmentation results based on similarity metrics Fajar, Aziz; Sarno, Riyanarto; Fatichah, Chastine
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1590-1600

Abstract

Medical imaging plays a crucial role in diagnosing patient conditions, with magnetic resonance imaging (MRI) standing as a significant modality for numerous years. However, leveraging convolutional neural network (CNN) architectures like U-Net and its variations for anatomical segmentation demands considerable memory, particularly when working with full 3D image sets. Therefore, downsampling 3D MRIs proves advantageous in reducing memory consumption. Nevertheless, downsampling leads to a reduction in voxel count, potentially impacting the performance of commonly used segmentation metrics. The jaccard similarity index (JSI), dice similarity coefficient (DSC), and structural similarity index (SSIM) are extensively employed in image segmentation contexts. Hence, this study employs all three metrics to assess downsampled images and evaluate the robustness of the metrics when used to evaluate the downsampled 3D MRI images. The results show that JSI and DSC are more robust than SSIM when handling the downsampled data.
Towards automated classification of cognitive states: Riemannian geometry and spectral embedding in EEG data Siddappa, Manjunatha; Ravikumar, Kempahanumaiah M.; Madegowda, Nagendra Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1023-1029

Abstract

Our research explores the application of Riemannian geometry and spectral embedding in the context of electroencephalogram (EEG) signal analysis for cognitive state classification. Leveraging the PyRiemann library and the AlphaWaves dataset, our study employs covariance estimation and the minimum distance to mean (MDM) classifier within a machine learning pipeline. The classification accuracy is assessed through stratified k-fold cross-validation. Furthermore, we introduce a novel visualization approach by calculating the spectral embedding of covariance matrices, providing insights into the underlying structure of the EEG epochs. Our findings showcase the potential of Riemannian geometry and spectral embedding as powerful tools in the domain of EEG-based cognitive state classification, contributing to the broader field of brain signal analysis and paving the way for automated and advanced neurocognitive studies.
Bio-inspired wireless sensor networks - a protocol for an enhanced hybrid energy optimization routing Joshi, Rati D.; Banu, Sameena
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1808-1816

Abstract

Recently, there has been a focus on the significance of swarm intelligence-inspired routing algorithms for achieving optimum solutions in biologically inspired wireless sensor networks (WSNs). These protocols depict a network of wireless mobile nodes forming an infrastructure that is agile, dynamic, and independent of a central administrative facility. Among the challenges faced by bio-inspired WSNs, mobility awareness and excessive energy consumption (EC) stand out as significant hurdles, particularly in dynamic models with intermittent connections. This project seeks to tackle these obstacles by deploying the hybrid energy efficiency (HEED) approach to distributed clustering for network system cluster formation, along with fusion routing protocol of particle swarm optimization (PSO) and PIO to select cluster-heads and optimize solutions in bio-inspired WSNs. The success of the suggested approach is assessed using a variety of criteria, such as energy usage, rate of packet delivery, EC, and routing overhead and network lifetime. The methods like ad hoc on-demand distance vector's (AODV) and ant colony optimization (ACO) methods are employed in the testing and validation. In comparison to the reactive AODV routing protocol and ACO, the suggested routing protocol (HPSOPIO) reduces energy usage and increases network lifespan.
Detection of colorization based image forgeries using convolutional autoencoder method Panchal, Soumyashree Muralidhar; Hanumanthaiah, Asha Kethaganahalli; Doddasiddavanahalli, Bindushree Channabasavaraju; Eshwar Rao, Manju More; Jayaramu, Ambika Belekere
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1114-1126

Abstract

Recently, it has become difficult to recognize and easier to misuse digital images due to the large number of editing tools available. Detecting forgeries in images is crucial for security and forensic purposes. Therefore, this research implements a deep learning (DL) method of convolutional autoencoder (CAE) which improves colorization-based image forgery detection by leveraging spatial and color information, increasing the detection accuracy. At first, the pre-processed input forgery images are used with the wiener filtering-contrast restricted improved histogram equalization (WE-CLAHE) technique. Hybrid dual-tree complex wavelet trigonometric transform (H‑DTCWT) and VGG-16 are used to extract effective features from the clustered data. Improved horse herd optimization (IHH) is employed to reduce the dimensionality of a feature. At last, the CAE model is implemented to significantly recognize the image forgery. The accuracy of CASIA V1 and GRIP datasets of 99.95% and 99.97%, respectively is achieved. Hence, this implemented method obtains a high forgery detection performance than the existing methods.
Enhancing radar signal processing through LVQ-Kalman fusion: a tsunami prediction perspective Shobha, Shobha; Narasimhaiah, Nalini
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp282-289

Abstract

In radar signal processing, the pursuit of precise prediction algorithms motivates the exploration of innovative methodologies. This study introduces a pioneering fusion of learning vector quantization (LVQ)- Kalman, merging LVQ with the advanced Kalman filter. The primary aim is to enhance adaptability and robustness, vital in weather monitoring and military surveillance. LVQ, known for its efficacy in pattern recognition and prediction, adjusts prototype vectors iteratively based on input data, ideal for radar signal intricacies. Various LVQ types are incorporated, tailored meticulously for specific radar applications. The Kalman filter, originally for aerospace, excels in tracking and predicting dynamic systems, seamlessly integrated to address uncertainties in radar data. By combining LVQ’s pattern recognition with the Kalman filter’s adaptability, the fusion aims to create a versatile system navigating radar data intricacies. Applications range from airborne target tracking to weather analysis and military surveillance. The integrated approach offers adaptability and robustness, vital for real-world implementations, particularly in tsunami detection. Future research may explore deep learning to further enhance adaptability. This fusion technique presents significant potential for advancing radar signal processing, promising accurate and adaptive systems, especially in critical applications like tsunami detection.

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