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Study on neuromorphic computation and its applications

10.11591/ijeecs.v39.i1.pp272-282
Anjali Chature , A. Raganna , Venkateshappa Venkateshappa
Neuromorphic computing offers a promising alternative to traditional von Neumann architectures, especially for applications that require efficient processing in edge environments. The challenge lies in optimizing spiking neural networks (SNNs) for these environments to achieve high computational efficiency, particularly in event-driven applications. This paper investigates the integration of advanced simulation tools, such as Simeuro and SuperNeuro, to enhance SNN performance on edge devices. Through comprehensive studies of various SNN models, a novel SNN design with optimized hardware components is proposed, focusing on energy and communication efficiency. The results demonstrate significant improvements in computational efficiency and performance, validating the potential of neuromorphic architectures for executing event-driven scientific applications. The findings suggest that neuromorphic computing can transform the way edge devices handle event-driven tasks, offering a pathway for future innovations in diverse application domains.
Volume: 39
Issue: 1
Page: 272-282
Publish at: 2025-07-01

Smart brake pad early warning system: enhancing vehicle safety through real-time monitoring

10.11591/csit.v6i2.p122-135
Afif Syam Fauzi , Giva Andriana Mutiara , Muhammad Rizqy Alfarisi , Tedi Gunawan , Muhammad Aulia Rifqi Zain
A contributing factor to traffic accidents is brake pad failure, which diminishes braking system performance and extends braking distance. This work develops a prototype utilizing internet of things (IoT) to measure brake pad thickness, hence enhancing driver awareness through real-time monitoring. The system establishes the thickness detection threshold at 75% (3-4 mm) and 50% (5–6 mm) as a cautionary parameter. The thickness parameter employs an American wire gauge (AWG) 18 cable to connect to the ESP32 microcontroller. The web-based IoT monitoring interface employs Laravel. This method enables drivers to get prompt notifications regarding the decrease in brake pad thickness, hence permitting urgent preventative maintenance to mitigate the risk of accidents. The system underwent testing through friction at a rotational speed of 600 to 6,000 rpm. The test findings indicated that the sensor precisely measured the brake pad thickness with a prototype response time of a second. This system is suitable for implementation on old model vehicles that do not have an early warning system. The installation of this technology is anticipated to enhance driver knowledge of the state of the brake pads, hence potentially diminishing the danger of brake system failure caused by unmonitored pad wear.
Volume: 6
Issue: 2
Page: 122-135
Publish at: 2025-07-01

Smart enterprise architecture framework for developing patent office

10.11591/ijeecs.v39.i1.pp681-690
Yoga Prihastomo , Harjanto Prabowo , Agung Trisetyarso , Haryono Soeparno
Technology and communication’s impact on daily life makes innovation vital for economic growth, highlighting prizing intellectual property (IP) asset protection and management. Patent office, pivotal custodians of legal frameworks and repositories of IP assets, grapples with significant challenges, and backlogs stemming from escalating patent applications and outdated processes. Patent office encounters the challenge of balancing innovation and IP protection because of the convergence of rapid advancements in technologies, for instance, AI, and blockchain. This research employs a design science research methodology to generate a tailored framework addressing these multifaceted challenges. The proposed smart enterprise architecture (SEA) framework offers a strategic, multidimensional approach to modernizing the patent office. It integrates principles from enterprise architecture, information systems management, and IP law, emphasizing efficiency, scalability, and security. The framework leverages the quadruple helix model, fostering collaboration between government, industry, academia, and civil society to enhance stakeholder engagement and innovation ecosystems. Optimizing patent office functions and adapting to IP management’s evolution, the SEA framework integrates technology and organizational goals for a comprehensive approach.
Volume: 39
Issue: 1
Page: 681-690
Publish at: 2025-07-01

A comparative analysis of hybrid of traditional load flow methods for IEEE distributed power generation networks

10.11591/ijeecs.v39.i1.pp33-44
Muhammad Hafeez Mohamed Hariri , Noor Dzulaikha Daud , Nor Azizah Mohd Yusoff , Syed Muhammad Zakwan Syed Zaman , Mohd Khairunaz Mat Desa
Analyzing power flow or load flow is crucial for planning, operating, maintaining, and controlling electrical power systems. Two traditional power flow methods namely the Newton-Raphson (NR) method are known for their accuracy and robustness nevertheless high computational intensity, and the fast decoupled load flow (FD) method, is valued for its computational efficiency and speed, however, generating less accurate data. This research aims to develop a hybrid load flow technique that integrates both strengths, achieving higher accuracy and faster convergence. The validation processes are based on several IEEE standard bus systems, including the 3-bus, 9-bus, 14-bus, and 30-bus systems. These systems, with different bus types and interconnections, represent real-world operations and help generate comprehensive data on iteration count, execution time, and the accuracy of the output data results. A new hybrid method generated from this research work compared to traditional load flow methods, provides a substantially well-balanced number of iteration counts, the fastest execution times, improved by 41.55%, and produces a similar accuracy of the data set. These improvements make the hybrid method highly advantageous in practical real-time applications and large-scale systems where both accuracy and speed are critical.
Volume: 39
Issue: 1
Page: 33-44
Publish at: 2025-07-01

Blockchain technology for optimizing security and privacy in distributed systems

10.11591/csit.v6i2.p210-220
Wisnu Uriawan , Adrian Putra Pratama , Shafwan Mursyid
Blockchain technology is increasingly recognized as an effective solution for addressing security and privacy challenges in distributed systems. Blockchain ensures information security by validating data and defending against cyber threats, while guaranteeing data integrity through transaction validation and reliable storage. The research involves a literature study, problem identification, analysis of blockchain security and privacy, model development, testing, and analysis of trial results. Furthermore, blockchain enables user anonymity and fosters transparency by utilizing a distributed network, reducing the risk of fraudulent activities. Its decentralized nature ensures high reliability and accessibility, even in node failures. Blockchain enhances security and privacy by offering features like data immutability, provenance, and reduced reliance on trust. It decentralizes data storage, making tampering or deletion extremely challenging, and ensures the invalidation of subsequent blocks upon any changes. Blockchain finds applications in various domains, including supply chains, finance, healthcare, and government, enabling enhanced security by tracking data origin and ownership. Despite scalability and security challenges, the potential benefits of reduced costs, increased efficiency, and improved transparency position blockchain as a promising technology for the future. In summary, blockchain technology provides secure transaction recording and data storage, thus enhancing security, privacy, and the integrity of sensitive information in distributed systems.
Volume: 6
Issue: 2
Page: 210-220
Publish at: 2025-07-01

Artificial intelligence-powered robotics across domains: challenges and future trajectories

10.11591/csit.v6i2.p176-199
Tole Sutikno , Hendril Satrian Purnama , Laksana Talenta Ahmad
The rise of artificial intelligence (AI) in robotic systems raises both challenges and opportunities. This technological change necessitates rethinking workforce skills, resulting in new qualifications and potentially outdated jobs. Advancements in AI-based robots have made operations more efficient and precise, but they also raise ethical issues such as job loss and responsibility for robot decisions. This study explores AI-powered robotics in both of their challenges and future trajectories. As AI in robotics continues to grow, it will be crucial to tackle these issues through strong rules and ethical standards to ensure safe and fair progress. Collaborative robots in manufacturing improve safety and increase productivity by working alongside human employees. Autonomous robots reduce human mistakes during checks, leading to better product quality and lower operational expenses. In healthcare, robotic helpers improve patient care and medical staff performance by managing routine tasks. Future research should focus on improving efficiency and accuracy, boosting productivity, and creating safe environments for humans and robots to work safely together. Strong rules and ethical guidelines will be vital for integrating AI-powered robotics into different areas, ensuring technology development aligns with societal values and needs.
Volume: 6
Issue: 2
Page: 176-199
Publish at: 2025-07-01

New technic of transfer learning for detecting epilepsy by EfficientNet and DarkNet models

10.11591/ijeecs.v39.i1.pp345-352
Fatima Edderbali , Hamid El Malali , Elmaati Essoukaki , Mohammed Harmouchi
Epileptic seizures are one of the most prevalent brain disorders in the world. Electroencephalography (EEG) signal analysis is used to distinguish between normal and epileptic brain activity. To date, automatic diagnosis remains a highly relevant and significant research topic which can help in this task, especially considering that such diagnosis requires a significant amount of time to be carried out by an expert. As a result, the need for an effective seizure approach capable to classify the normal and epileptic brain signal automatically is crucial. In this perspective, this work proposes a deep neural network approach using transfer learning to classify spectrogram images that have been extracted from EEG signals. Initially, spectrogram images have been extracted and used as input to pre-trained models, and a second refinement is performed on certain feature extraction layers that were previously frozen. The EfficientNet and DarkNet networks are used. To overcome the lack of data, data augmentation was also carried out. The proposed work performed excellently, as assessed by multiple metrics, such as the 0.99 accuracy achieved with EfficientNet combined with a support vector machine (SVM) classifier.
Volume: 39
Issue: 1
Page: 345-352
Publish at: 2025-07-01

An ensemble learning approach for diabetes prediction using the stacking method

10.11591/csit.v6i2.p102-111
Elliot Kojo Attipoe , Alimatu Saadia Yussiff , Maame Gyamfua Asante-Mensah , Emmanuel Dortey Tetteh , Regina Esi Turkson
Diabetes is a severe illness characterized by high blood glucose levels. Machine learning algorithms, with their ability to detect and predict diabetes in its early stages, offer a promising avenue for research. This study sought to enhance the accuracy of predicting diabetes mellitus by employing the stacking method. The stacking method was chosen because it integrates predictions from various base models, resulting in a more precise final prediction. The stacking method enhances accuracy and generalization by utilizing the varied strengths of multiple base models. The Pima Indians diabetes dataset, a widely used benchmark dataset, was utilized in the study. The machine learning models used for the studies were logistic regression (LR), naïve Bayes (NB), extreme gradient boost (XGBoost), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM). LR, KNN, and SVM were the best-performing models based on accuracy, F1-score, precision, and area under the curve (AUC) score, and were consequently used as the base model for the stacking method. The LR model was utilized for the meta-model. The proposed ensemble approach using the stacking method demonstrated a high accuracy of 82.4%, better than the individual models and other ensemble techniques such as bagging or boosting. This study advances diabetes prediction by developing a more accurate early-stage detection model, thereby improving clinical management of the disease.
Volume: 6
Issue: 2
Page: 102-111
Publish at: 2025-07-01

Optimizing EfficientNet for imbalanced medical image classification using grey wolf optimization

10.11591/csit.v6i2.p112-121
Khusnul Khotimah , Sugiyarto Surono , Aris Thobirin
The advancement of deep learning in computer vision has result in substantial progress, particularly in image classification tasks. However, challenges arise when the model is applied to small and unbalanced datasets, such as X-ray data in medical applications. This study aims to improve the classification performance of fracture X-ray images using the EfficientNet architecture optimized with grey wolf optimization (GWO). EfficientNet was chosen for its efficiency in handling small datasets, while GWO was applied to optimize hyperparameters, including learning rate, weight decay, and dropout to improve model accuracy. Random cropping, rotation, flipping, color jittering, and random erasing, were used to expand the diversity of the dataset, and class weighting is applied to overcome class imbalance. The evaluation uses accuracy, precision, recall, and F1-score metrics. The combination of EfficientNetB0 and GWO resulted in an average 4.5% improvement in model performance over baseline methods. This approach provides benefits in developing deep learning methods for medical image classification, especially in dealing with small and imbalanced datasets.
Volume: 6
Issue: 2
Page: 112-121
Publish at: 2025-07-01

BFT water color classification in tilapia aquaculture using computer vision

10.11591/ijeecs.v39.i1.pp497-508
Bondan Suwandi , Sakinah Puspa Anggraeni , Toto Bachtiar Palokoto , Budi Sulistya , Wisnu Sujatmiko , Reza Septiawan , Nashrullah Taufik , Arief Rufiyanto , Arif Rahmat Ardiansyah
Biofloc technology (BFT) is one of the most promising aquaculture cultivation methods in the modern aquaculture era because of its high efficiency level, especially in water and fodder use. Usually, the general condition of the biofloc can be known from the color of the water. By utilizing the vision sensor, BFT color identification can be done automatically, which helps cultivators find out their BFT system’s condition. In this research, a classification was made for the watercolor of the BFT Tilapia system based on the microbial community color index (MCCI) value and the initial cultivation conditions where algae and nitrifying bacteria had not developed significantly. The color classifications of the bioflocs are clear, green, browngreen, green-brown, and deep-brown. Clear color is the new classification to indicate BFT water conditions in the initial cultivation phase. Further, two computer vision algorithm methods are introduced to classify the color of BFT system water. The first method combines the B/W algorithm and MCCI calculations, while the second algorithm uses the Manhattan distance algorithm approach. From the experiments that have been carried out, both computer vision algorithms methods for classifying biofloc colors have shown promising results.
Volume: 39
Issue: 1
Page: 497-508
Publish at: 2025-07-01

Predictive modeling for equity trading using sentiment analysis

10.11591/ijeecs.v39.i1.pp575-584
Chetan Gondaliya , Abhishek Parikh
Warren Buffett’s investment philosophy highlights the importance of generating wealth through available capital, but investors require more advanced tools for informed decision-making. Current research is focused on developing a modeling technique that leverages computer algorithms, including sentiment analysis. This method evaluates public sentiment about companies through social media, aiding investors in identifying promising stocks and safeguarding their wealth against unfavorable market conditions. In India, the banking, real estate, and pharmaceutical sectors are among the most robust and rapidly growing industries; however, deciding to invest in these sectors remains debatable. To address this, the proposed study aims to develop a hybrid prediction model that combines sentiment and technical analysis to uncover short-term trading opportunities. This model utilizes a two-layer ensemble stacking technique, training three distinct machine learning algorithms in the first layer and aggregating their outputs in the second layer. The proposed model significantly outperforms traditional methods in terms of accuracy, enabling investors to make more confident and profitable decisions in the Indian stock market.
Volume: 39
Issue: 1
Page: 575-584
Publish at: 2025-07-01

Advanced generalized integrator based phase lock loop under complex grid condition: a comparative analysis

10.11591/ijeecs.v39.i1.pp23-32
Poonam Tripathy , Banishree Misra , Byamakesh Nayak
Integration of renewable energy systems (RESs) to the grid leads to various power quality issues. A proper control approach for the interfaced inverter is required to mitigate the uncertainties caused in the grid due to the RESs association to maintain the grid stability. The presence of harmonics and DC offset in the input grid voltage of a phase lock loop (PLL) leads to inaccurate phase estimation due to fundamental frequency oscillations. Though many advanced generalized integrator (GI) based PLLs have been developed still there is a need for a robust PLL for synchronization with faster dynamic response, both the harmonics and DC offset rejection ability with precise estimation. This paper proposes some simple yet effective advanced PLLs employing low pass filters (LPFs) in the existing GI based PLLs for faster and accurate phase angle estimation for seamless synchronization under complex grid circumstances. These advanced generalized integrators with LPFs (GI-LPF) based PLLs will provide enhanced and robust synchronization for the grid integrated RESs thereby addressing multiple power quality issues like voltage unbalance, harmonics and DC offsets. The simulation based comparative analysis of the proposed controllers confirm their effective disturbance rejection capability under complex grid conditions by providing advanced and precise response.
Volume: 39
Issue: 1
Page: 23-32
Publish at: 2025-07-01

Effects of hyperparameter tuning on random forest regressor in the beef quality prediction model

10.11591/csit.v6i2.p159-166
Ridwan Raafi'udin , Yohanes Aris Purwanto , Imas Sukaesih Sitanggang , Dewi Apri Astuti
Prediction models for beef meat quality are necessary because production and consumption were significant and increasing yearly. This study aims to create a prediction model for beef freshness quality using the random forest regressor (RFR) algorithm and to improve the accuracy of the predictions using hyperparameter tuning. The use of near-infrared spectroscopy (NIRS) in predicting beef quality is an easy, cheap, and fast technique. This study used six meat quality parameters as prediction target variables for the test. The R² metric was used to evaluate the prediction results and compare the performance of the RFR with default parameters versus the RFR with hyperparameter tuning (RandomSearchCV). Using default parameters, the R-squared (R²) values for color (L*), drip loss (%), pH, storage time (hour), total plate colony (TPC in cfu/g), and water moisture (%) were 0.789, 0.839, 0.734, 0.909, 0.845, and 0.544, respectively. After applying hyperparameter tuning, these R² scores increased to 0.885, 0.931, 0.843, 0.957, 0.903, and 0.739, indicating an overall improvement in the model’s performance. The average performance increase for prediction results for all beef quality parameters is 0.0997 or 14% higher than the default parameters.
Volume: 6
Issue: 2
Page: 159-166
Publish at: 2025-07-01

Bibliometric analysis and short survey in CT scan image segmentation: identifying ischemic stroke lesion areas

10.11591/csit.v6i2.p91-101
Wahabou K. Taba Chabi , Sèmèvo Arnaud R. M. Ahouandjinou , Manhougbé Probus A. F. Kiki , Adoté François-Xavier Ametepe
Ischemic stroke remains one of the leading causes of mortality and long-term disability worldwide. Accurate segmentation of brain lesions plays a crucial role in ensuring reliable diagnosis and effective treatment planning, both of which are essential for improving clinical outcomes. This paper presents a bibliometric analysis and a concise review of medical image segmentation techniques applied to ischemic stroke lesions, with a focus on tomographic imaging data. A total of 2,014 publications from the Scopus database (2013–2023) were analyzed. Sixty key studies were selected for in-depth examination: 59.9% were journal articles, 29.9% were conference proceedings, and 4.7% were conference reviews. The year 2023 marked the highest volume of publications, representing 17% of the total. The most active countries in this area of research are China, the United States, and India. "Image segmentation" emerged as the most frequently used keyword. The top-performing studies predominantly used pre-trained deep learning models such as U-Net, ResNet, and various convolutional neural networks (CNNs), achieving high accuracy. Overall, the findings show that image segmentation has been widely adopted in stroke research for early detection of clinical signs and post-stroke evaluation, delivering promising outcomes. This study provides an up-to-date synthesis of impactful research, highlighting global trends and recent advancements in ischemic stroke medical image segmentation.
Volume: 6
Issue: 2
Page: 91-101
Publish at: 2025-07-01

HepatoScan: Ensemble classification learning models for liver cancer disease detection

10.11591/csit.v6i2.p167-175
Tella Sumallika , Raavi Satya Prasad
Liver cancer is a dangerous disease that poses significant risks to human health. The complexity of early detection of liver cancer increases due to the unpredictable growth of cancer cells. This paper introduces HepatoScan, an ensemble classification to detect and diagnose liver cancer tumors from liver cancer datasets. The proposed HepatoScan is the integrated approach that classifies the three types of liver cancers: hepatocellular carcinoma, cholangiocarcinoma, and angiosarcoma. In the initial stage, liver cancer starts in the liver, while the second stage spreads from the liver to other parts of the body. Deep learning is an emerging domain that develops advanced learning models to detect and diagnose liver cancers in the early stages. We train the pre-trained model InceptionV3 on liver cancer datasets to identify advanced patterns associated with cancer tumors or cells. For accurate segmentation and classification of liver lesions in computed tomography (CT) scans, the ensemble multi-class classification (EMCC) combines U-Net and mask region-based convolutional network (R-CNN). In this context, researchers use the CT scan images from Kaggle to analyze the liver cancer tumors for experimental analysis. Finally, quantitative results show that the proposed approach obtained an improved disease detection rate with mean squared error (MSE)-11.34 and peak signal-to-noise ratio (PSNR)-10.34, which is high compared with existing models such as fuzzy C-means (FCM) and kernel fuzzy C-means (KFCM). The classification results obtained based on detection rate with accuracy-0.97%, specificity-0.99%, recall-0.99%, and F1S-0.97% are very high compared with other existing models.
Volume: 6
Issue: 2
Page: 167-175
Publish at: 2025-07-01
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