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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 64 Documents
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Search results for , issue "Vol 36, No 2: November 2024" : 64 Documents clear
An interactive visualization tool for the exploration and analysis of multivariate ocean data K. G., Preetha; S., Saritha; Jeevan, Jishnu; Sachidanandan, Chinnu; Maheswaran, P. A.
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.pp1329-1337

Abstract

Ocean data exhibits great heterogeneity from variances in measuring methods, formats, and quality, making it extremely complicated and diverse due to a variety of data kinds, sources, and study elements. A few examples of data sources are satellites, buoys, ships, self-driving cars, and distant systems. The processing of data is made more challenging by the significant regional and temporal variations in oceanic characteristics including temperature, salinity, and currents. This work presents an interactive tool for multivariate ocean parameter visualisation, specifically overlays, based on Python. In ocean data visualisation, overlays are extra visual layers or data points that are layered to improve comprehension over a basic map. Based on the available data and the visualisation goals, these overlays are chosen and blended. Users can customise overlays with this tool, which also supports formatting, 2D and 3D visualisation, and data preparation. In order to reduce artefacts, it uses kriging interpolation for 3D visualisation and a modified version of the ray casting algorithm for representing octree data. By integrating overlays like as bathymetry, currents, temperature, and marine life, users can produce visually appealing and comprehensive depictions of ocean data. This method provides a thorough grasp of intricate marine processes by making it easier to see patterns, trends, and abnormalities in the data.
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.
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.
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.
RecommendRift: a leap forward in user experience with transfer learning on netflix recommendations Anuradha, Surabhi; Jyothi, Pothabathula Naga; Sivakumar, Surabhi; Sheshikala, Martha
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.pp1218-1225

Abstract

In today’s fast-paced lifestyle, streaming movies and series on platforms like  Netflix is a valued recreational activity. However, users often spend considerable time searching for the right content and receive irrelevant recommendations, particularly when facing the “cold start problem” for new users. This challenge arises from existing recommender systems relying on factors like casting, title, and genre, using term frequency-inverse document frequency (TF-IDF) for vectorization, which prioritizes word frequency over semantic meaning. To address this, an innovative recommender system considering not only casting, title, and genre but also the short description of movies or shows is proposed in this study. Leveraging Word2Vec embedding for semantic relationships, this system offers recommendations aligning better with user preferences. Evaluation metrics including precision, mean average precision (MAP), discounted cumulative gain (DCG), and ideal cumulative gain (IDCG) demonstrate the system’s effectiveness, achieving a normalized DCG (NDCG)@10 of 0.956. A/B testing shows an improved click-through rate (CTR) of recommendations, showcasing enhanced streaming experience.
Development of explainable machine intelligence models for heart sound abnormality detection Ramakrishna, Jeevakala Siva; Venkateswarlu, Sonagiri China; Kumar, Kommu Naveen; Shreya, Parikipandla
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.pp846-853

Abstract

Developing explainable machine intelligence (XAI) models for heart sound abnormality detection is a crucial area of research aimed at improving the interpretability and transparency of machine learning algorithms in medical diagnostics. In this study, we propose a framework for building XAI models that can effectively detect abnormalities in heart sounds while providing interpretable explanations for their predictions. We leverage techniques such as SHapley additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) to generate explanations for model predictions, enabling clinicians to understand the rationale behind the algorithm’s decisions. Our approach involves preprocessing heart sound data, training machine learning models, and integrating XAI techniques to enhance the interpretability of the models. We evaluate the performance of our XAI models using standard metrics and demonstrate their effectiveness in accurately detecting heart sound abnormalities while providing insightful explanations for their predictions. This research contributes to the advancement of XAI in medical applications, particularly in the domain of cardiac diagnostics, where interpretability is crucial for clinical decision-making.
Inset-fed microstrip patch antenna optimization for 2.4 GHz using surrogate model assisted differential evolution machine learning algorithm Alaba, Magnoudewa; Onyango Konditi, Dominic Bernard; Oduol, Vitalice Kalecha
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.pp901-912

Abstract

In this work, we have used the surrogate model assisted differential evolution (SADEA) to model a one and two-element inset-fed patch antenna array to optimize its parameters for efficiency and usability. The microstrip patch antennas operates in a frequency band of 2.4 GHz. The optimization process focused on fine-tuning the patch length, patch width, and notch width to enhance key performance metrics directivity, return loss, and bandwidth. The design is made in CST software with an FR-4 substrate and simulated in the ADE1.0 software a MATLAB toolbox. Significant enhancements were achieved including a directivity gain of 3.04 dB, and 5.58 dB a return loss of -19 dB, -16 dB, and an expanded impedance bandwidth from 0.0798 GHz, 0.0588 GHz to 0.0951 GHz, 0.0824 GHz respectively. The antenna was constructed and then measured. The findings showed that the measurements and the fabrication process closely matched, especially in terms of return loss.
Comparative efficiency analysis of RF power amplifiers with fixed bias and envelope tracking bias Babu, Ambily; Shivaleelavathi, Bangalore Gangadharaiah; Yatnalli, Veeramma
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.pp808-816

Abstract

RF power amplifier (RF PA) finds its application in almost all the areas of electronics, mobile communication being identified as a major area. The paper performs a comparative efficiency analysis of RF power amplifiers operating with a fixed bias and an envelope tracking bias. Simulations are performed using Keysight advanced design system (ADS) tool. A class a RF PA operating at a 12 dB gain is fixed for the work. 16 QAM LTE signal operating at 5 MHz input frequency, with a peak to average power ratio (PAPR) of 6.0 dB is used as input signal. An envelope simulation at 2.5 GHz is performed on the RF power amplifier. Simulation result shows an improvement of 12% in power added efficiency (PAE) at 6 dB back-off and 6.422% in mean PAE while using envelope tracking power amplifiers, compared to RF PA with fixed supply. Envelope tracking power amplifiers reduced AM/AM distortions also by a factor of 0.248. The results obtained are much better than that obtained using a conventional RF PA with fixed bias. RF PA being the most power dissipative block in a mobile handset, improving its efficiency contributes directly to a great improvement in the battery lifetime of mobile phones. The major challenges faced by envelope tracking PA (ETPA) designers in achieving this efficiency improvement is also delineated in the paper.
Classification of endometrial adenocarcinoma using histopathology images with extreme learning machine method Rulaningtyas, Riries; Rahaju, Anny Setijo; Dewi, Rosa Amalia; Hanifah, Ummi; Purwanti, Endah; Rahma, Osmalina Nur; Katherine, Katherine
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.pp961-971

Abstract

As many as 70-80% of endometrial cancer cases are endometrial adenocarcinoma. Histopathological assessment is based on the degree of differentiation, into well-differentiated, moderate-differentiated, and poorly-differentiated. Management and prognosis differ between grades, so differential diagnosis in determining the degree of tumor differentiation is crucial for appropriate treatment decisions. Histopathological image analysis offers detailed diagnostic results, but manual analysis by a pathologist is very complicated, error-prone, quite tedious, and time-consuming. Therefore, an automatic diagnostic system is needed to assist pathologists in grading the tumor. This research aims to determine the degree of differentiation of endometrial adenocarcinoma based on histopathological images. The extreme learning machine (ELM) method performs image classification with gray level run long matrix (GLRLM) features and a combination of local binary pattern (LBP)-GLRLM features as input. Experimental results show that the ELM model can achieve satisfactory performance. Training accuracy, testing accuracy, and model precision with GLRLM features were 97.13%, 91.33%, and 80% and combined LBPGLRLM features were 91.03%, 71.33%, and 100%. Overall, the model created can determine the degree of tumor differentiation and is useful in providing a second opinion for pathologists.
DDoS-attacks prevention using MinE-DT an adaptive security and energy optimization integration of NIPS in wireless sensor networks Ramachandra, Bharathi; Surekha, T. P.
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.pp1226-1233

Abstract

Wireless sensor networks (WSNs) have revolutionized data collection in diverse environments, from industrial settings to natural ecosystems. However, their decentralized nature and energy constraints pose unique security and operational challenges. Previous research provided foundational insights into WSN security but lacked comprehensive strategies for real-time intrusion prevention and efficient energy utilization. Our work employs a multi-layered approach, integrating network intrusion prevention systems (NIPS) with WSNs and leveraging machine learning for threat detection. We developed MinE-DT (minimum energy-direct transmission) hybrid routing an integrated WSN model that not only identifies and mitigates distributed denial-of-service (DDoS) attack but also optimizes energy consumption, ensuring prolonged network longevity without compromising security. The proposed model's distinctiveness lies in its fusion of NIPS with energy-saving algorithms, offering a dual advantage of enhanced security and energy efficiency. Utilizing a combination of simulations and theoretical analysis, our methodology yielded promising results, showcasing significant improvements in threat detection rates and energy conservation.

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