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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 1: October 2024" : 64 Documents clear
Artificial neural network-based intelligent sensor-based electronic nose for food applications Managuli, Manjunath; Bagyalakshmi, Kalimuthu; Shiny Malar, Francis Rosy; Rubia, Jebaraj Jency; Iderus, Samat
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.pp163-173

Abstract

Food commerce, especially for the general public, is greatly impacted by the capacity to identify and recognize chemical samples for food applications. Every chemical sample response has a unique, distinguishing smell. These advancements highlight the method of an artificial neural networks (ANN) to distinguish the distinctive fragrance from the reaction of substances. The categorization of various smell patterns has diminished confidence in ANN technology. Using an ANN technique and a sensor-based e-nose system for food applications, each chemical’s identification has been done commercially. The system comprises a 5-gas sensor selection that recognizes chemical talk while allowing for an improvement in permitting while falling gas is planned outside. To build a model of a different signal reaction, individual sensors are equally collected and merged into the innovation -favored sensor array. Demonstrates how it is related to the chemical test. The e-nose categorization has been tested with five different chemical samples and five different sensor classes. The e-nose approach, which comprises five sensors, can classify each chemical reaction model, starting with the results. With more sensors being employed, the classification accuracy of the precise chemical reaction improves. These data demonstrate that the ANN-based e-nose method promises a successful classification system for chemical sample responses for a characteristic odor sample.
Enhancing clinical decision-making with cloud-enabled integration of image-driven insights Senkamalavalli, Rajagopalan; Sankar, Singaravel; Parivazhagan, Alaguchamy; Raja, Raju; Selvaraj, Yoganand; Srinivas, Porandla; Varadarajan, Mageshkumar Naarayanasamy
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.pp338-346

Abstract

Using the complementary strengths of Bayesian networks, decision trees, artificial neural networks (ANNs), and Markov models, this endeavor intends to completely revamp clinical decision-making. In order to provide instantaneous access to image-driven insights and clinical decision support systems (CDSS), want to create a revolutionary framework that merges these cutting-edge methods with cloud-enabled technologies. The proposed framework gives a comprehensive perspective of patient data by merging the probabilistic reasoning of Bayesian networks with the interpretability of decision trees, the pattern recognition abilities of ANNs, and the temporal interdependence of Markov models. This helps doctors to make more educated judgments based on a larger spectrum of information, leading to better patient outcomes. Healthcare workers can get to vital data from any place because to the cloud-enabled architecture's seamless scalability and accessibility. This not only increases the efficiency of decision-making, but also improves communication and cooperation between different medical professionals. This uses cutting-edge modeling strategies and cloud computing to pave a new path in clinical decision-making. This system has the potential to greatly enhance healthcare by integrating image-driven insights with CDSS, to the advantage of both patients and healthcare practitioners.
Notice of Retraction An NFMF-DBiLSTM model for human anomaly detection system in surveillance videos Angadi, Sanjeevkumar; Moorthy, Chellapilla V. K. N. S. N.; Tripathi, Mukesh Kumar; Tingare, Bhagyashree Ashok; Kadam, Sandeep Uddhavrao; Misal, Kapil
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.pp647-656

Abstract

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles. We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.-----------------------------------------------------------------------In response to the increasing demand for an intelligent system to avoid abnormal events, many models for detecting and locating anomalous behaviors in surveillance videos have been proposed. Nevertheless, significant flaws of inadequate discriminating ability are present in the majority of these models. A novel newton form and monotonic function based deep bidirectional long short-term memory (NFMF-DBiLSTM) human anomaly recognition system was discussed in this paper to tackle those issues. Initially, videos are transformed into frames; after that, the duplicate frames are removed, and by utilizing the shannon entropy centered contrast limited adaptive histogram equalization (SE-CLAHE) algorithm, the contrast has been elevated. By using the probabilistic matrix factorization kernel density estimation (PMF-KDE) technique, the background is subtracted after estimating only the motion of the object. After this, the silhouette function is performed utilizing the dirac depth silhouette function (DDSF). In addition, clustering is done by sorting and average-based K-means (SA-KM). The features are extracted from the suspected human and are then chosen by utilizing Poisson Eurasian oystercatcher optimization (PEOO). For classifying normal or anomaly, the selected features are subjected directly into the NFMF-DBiLSTM. When contrasted with the prevailing methodologies, the proposed model is found to be more efficient.
A framework for reusable domain specific software component extraction based on demand Basha, N Md Jubair; Ganapathy, Gopinath; Mohammed, Moulana
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.pp274-281

Abstract

The majority of organizations use an agile software development methodology. Standard analysis and design processes are abandoned due to the enormous demand of generating the product within time and budget. This may result in a lack of high-quality software while components are not constructively reused. The components are identified at a later stage in the majority of component approaches. To address such challenges, a methodology for extracting demand-based domain-specific software components from the repository was developed. The process for reusing current components is described in depth with various domain-specific components, and the suggested framework is for extracting demand-based reusable domain-specific software components.
Hybrid optimized multi-objective honey badger algorithm and NSGA-II for feature selection problems Papasani, Anusha; Devarakonda, Nagaraju
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.pp493-500

Abstract

One of the most important aspects of classification is choosing features in such a way as to get rid of redundant or irrelevant elements in the dataset. For the most part, multi-objective feature selection strategies have been offered by a number of scholars as a strategy for this aim. On the other hand, these techniques frequently fail to simultaneously improve classification accuracy while removing redundant feature combinations. This article presents a wrapper-based feature selection strategy that strikes a compromise between classification accuracy and redundancy reduction by combining features of the multi objective (MO) based honey badger algorithm (MO-HBA) and non-dominated sorting genetic algorithm-II (NSGA-II). The technique was developed as part of this investigation. Increasing the accuracy of the classification while simultaneously reducing the number of redundant characteristics is one of the optimizations aims of this approach. The MO-HBA shows excellent performance in exploration and exploitation. A Kernel version of the extreme learning machine (KELM) is used for the process of selecting the features to use. In order to evaluate how well this method of feature selection performs, eighteen benchmark datasets are utilized, and the results are compared to four established methods of multi-objective feature selection based on different metrics.
Design and performance analysis of a long-stroke electromagnetic double-reel hammer Alkasassbeh, Jawdat S.; Pavlov, Vlademer Е.; Al-Zyoud, Khalaf Y.; Al-Awneh, Tareq A.; Alkasassbeh, Osamah; Al-Rawashdeh, Ayman Y.
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.pp137-152

Abstract

This paper comprehensively investigates the performance characteristics of a long-stroke electromagnetic double-reel hammer compared to a conventional hammer. Quantitative analysis indicates that the long-stroke hammer shows a significant increase in striker speed and impact energy. The impact energy has increased by 255%, and energy losses in copper windings have decreased by 124% per operating cycle. Additionally, the long-stroke hammer demonstrates a 105% reduction in energy consumption and a 52% improvement in overall efficiency per cycle compared to the conventional hammer. This study examines the operational characteristics of the long-stroke hammer throughout its cycle using field theory methods, MATLAB simulations, and experimental tests. Results indicate higher impact energy and speed, lower energy losses in copper windings, and higher efficiency per cycle for the long-stroke hammer. Furthermore, a mathematical model of the long-stroke hammer is developed, incorporating static parameters and oscillograms of striker movement and current flow. A comprehensive comparison of the performance indicators of both hammers reveals significant improvements in lifting height, cycle duration, impact frequency, and striker speed for the long-stroke hammer. Overall, these findings suggest that the long-stroke operating mode can significantly enhance the efficiency and performance of conventional hammers while simultaneously reducing impact frequency and machine heating.
Eye disease detection using transfer learning based on retinal fundus image data Imaduddin, Helmi; Sakina, Alivia Rahma
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.pp509-516

Abstract

The escalating global prevalence of blindness remains a pressing concern, with eye diseases representing the primary culprits behind this issue. Vision is integral to various aspects of human life, underscoring the significance of effective eye disease detection. Presently, disease detection relies largely on manual methods, which are susceptible to misdiagnosis. However, the advent of technology has paved the way for disease detection through the application of deep learning methodologies. Deep learning exhibits substantial potential in disease detection, particularly when applied to image data, as attested by its accuracy in algorithmic assessments. This research introduces a novel approach to disease detection, specifically transfer learning-based deep learning. The study seeks to evaluate and compare the performance of various models, including EfficientNetB3, DenseNet-121, VGG-16, and ResNet-152, in identifying three prevalent eye diseases: cataract, diabetic retinopathy, and glaucoma, utilizing retinal fundus image data. Extensive experimentation reveals that the DenseNet-121 model achieves the highest accuracy levels, boasting precision, recall, F1-score, and accuracy values of 96.5%, 96%, 96.25%, and 96.20%, respectively. These results demonstrate the superior performance of the employed transfer learning model, signifying its efficacy in detecting eye diseases.
Multi-objective-trust aware improved grey wolf optimization technique for uncovering adversarial attacks in WSNs Bannikuppe Srinivasiah, Venkatesh Prasad; Ranganathasharma, Roopashree Hejjaji; Ramanna, Venkatesh
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.pp375-391

Abstract

Wireless sensor network (WSN) is made of several sensor nodes (SN) that monitor various applications and collect environmental data. WSNs are essential for a wide range application, including healthcare, industrial automation, and environmental monitoring. However, these networks are susceptible to several security threats, underscoring the need for robust attack detection systems. Therefore, in this study, a multi-objective-trust aware improved grey wolf optimization (M-TAIGWO) is implemented to mitigate various attacks types. This implemented M-TAIGWO method is used to select secure cluster heads (CH) and routes to obtain secure communication through the network. The implemented M-TAIGWO provides improved security against malicious attacks by increasing the energy efficiency. The important aim of M-TAIGWO is to attain secured data transmission and maximize the WSN network lifetime. The M-TAIGWO method’s performance is evaluated through energy consumption and delay. The implemented method obtains a high PDR of 98% for 500 nodes, which is superior to the quantum behavior and gaussian mutation Archimedes optimization algorithm (QGAOA), with a delay of 15 ms for 100 nodes which is lesser than fuzzy and secured clustering algorithms. In comparison to the trust-based routing protocol for WSNs utilizing an adaptive genetic algorithm (TAGA), this implemented method achieves defense hello fold, black hole, sinkhole, and selective forwarding attacks effectively.
Exploring the tree algorithms to generate the optimal detection system of students' stress levels Yamasari, Yuni; Qoiriah, Anita; Rochmawati, Naim; Prapanca, Aditya; Prihanto, Agus; Suartana, I Made; Ahmad, Tohari
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.pp548-558

Abstract

The significant changes in the world of education after the coronavirus disease 2019 (COVID-19) pandemic have increased students' anxiety levels. This anxiety can trigger stress which can interfere with students' academic performance. Therefore, this condition is a critical problem that needs to be addressed immediately. However, researchers have not previously conducted much research to detect post-COVID stress levels. Apart from that, the existence of a system capable of carrying out this detection is still lacking. Therefore, this research focuses on building a system for detecting student stress levels. First, an exploration of the tree algorithm was carried out to find the most optimal method for recognizing student stress levels. Then a detection system is built using this optimal method. The research results show that the tree ID3 (Iterative Dichotomiser 3) algorithm achieves the highest accuracy value of 95% compared to other tree algorithms with the scenario of dividing training data into test data of 80%:20%. Moreover, this telegram bot-based detection system works well in recognizing three categories of stress, namely: light-, moderate-, and heavy stress based on black-box testing techniques.
A novel distributed generation integrated MFUPQC for active-power regulation with enhanced power quality features Seshu, Moturu; Sundaram, Kalyana; Ramesh, Maddukuri Venkata
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.pp26-40

Abstract

The distributed generation (DG) scheme has become significant and advanced energy generation corridor for present power distribution system. This advanced DG scheme offers several merits such as flexible active power transfer, low transmission losses, maximize power efficiency, reduce transmission cost, expanding grid capacity, so on. It is motivated that, integration of such DG system in to multi-parallel feeder distribution system with enhanced power-quality features is considered as major problem statement. The proposed multi-functional unified power-quality conditioner (MFUPQC) device has robust design, reliable performance; specifically for addressing the voltage-current affecting PQ issues, regulation of active-power in multi-parallel distribution system. The fundamental goal of the MFUPQC device has been to operate as both a PQ improvement device and a DG integration device by implementing a new universal fundamental vector reference (UFVR) control algorithm. The suggested innovative control algorithm extracts the fundamental voltage and current reference signals with low computational response delay, simple mathematical formulations and without additional transformations which are also major problems identified in classical control schemes. This work focuses on design, operation and performance of MFUPQC device has been evaluated in both PQ and DG operations in a multi-parallel feeder distribution system through MATLAB/Simulink computing platform. The simulation results are illustrated with possible interpretation and analysis.

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