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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

Advancements in gas leakage detection and risk assessment: a comprehensive survey

10.11591/ijeecs.v39.i1.pp614-624
Y. Bhavani , Sanjusree Vodapally , Dinesh Bokka , Harshitha Varma Muddasani , Deepika Kasturi
Gas leakage is the main problem that harms the environment, infrastructure and public safety. Technology is increasing rapidly nowadays. So, there must be advancements in the methods used. Many methods have been come across to solve this problem. This survey paper explores various methods and technology used to solve the problem. Many methodologies have been suggested to reduce the risk of gas leaks and improve detection systems. It investigates cutting-edge models for estimating the effects of liquefied natured gas (LNG) leakage accidents, comprehensive wireless sensor network (WSN) is set up for detecting gas leaks in advance, and neural network and Kalman filter-based gas leakage early warning systems. Current developments include factors like point of interest (PoI), human data movement and gas pipelines. As technology increases, there would be major threat of authentication. So, it also looks on methods for user authentication based on different patterns to mobile applications. Especially in smart home environments, there is a need to improve security. This survey provides complete understanding of present and future directions for the researchers in gas leakage detection and risk management through various methods and their evaluation.
Volume: 39
Issue: 1
Page: 614-624
Publish at: 2025-07-01

Non-contact breathing rate monitoring using infrared thermography and machine learning

10.11591/ijeecs.v39.i1.pp669-680
Anadya Ghina Salsabila , Rachmad Setiawan , Nada Fitrieyatul Hikmah , Zain Budi Syulthoni
Monitoring vital physiological parameters such as breathing rate (BR) is crucial for assessing patient health. However, current contact-based measurement methods often cause discomfort, particularly in infants or burn patients. This study aims to develop a non-contact system for monitoring BR using infrared thermography (IRT). This approach permits to detects and tracks the nose from thermal video, extracts temperature variations into a breathing signal, and processes this signal to estimate BR. The estimated BR is then classified into three health categories (bradypnea/normal/tachypnea) using k-nearest neighbors (k-NN). To evaluate system accuracy and robustness, experiments were conducted under three conditions: (i) stationary breathing, (ii) breathing with head movements, and (iii) specific breathing patterns. Results demonstrated high consistency with contact-based photoplethysmography (PPG) measurements, achieving complement of the absolute normalized difference (CAND) index values of 94.57%, 93.71%, and 96.06% across the three conditions and mean absolute BR errors of 1.045 bpm, 1.259 bpm, and 0.607 bpm. The k-NN classifier demonstrated high performance with training, validation, and testing accuracies of 100%, 100%, and 99.2%, respectively. Sensitivity, specificity, precision, and F-measure results confirm system reliability for non-contact BR monitoring in clinical and practical settings.
Volume: 39
Issue: 1
Page: 669-680
Publish at: 2025-07-01

Design and development of an automated spirulina (Arthrospira platensis) algae cultivator

10.11591/ijeecs.v39.i1.pp139-147
Miguel Q. Mariñas II , Mark Joseph B. Enojas , Daryll C. Balolong , Charissa Zandra B. Correa , Lemmuel Keith C. Roldan , Mark Lester Teves , Christian Mari Dela Cruz
The cultivation of algae has gotten more attention from algae enthusiasts who have seen the benefits of algae in many uses. To maximize productivity, the parameters for growth of this algae must be controlled, such as pH, turbidity, light intensity, and the mixture solution for optimal growth. In this paper, an automated spirulina algae cultivator is designed and developed in a small-scale pond to replace the existing manual process. The system developed is composed of compact and modular cultivation unit, ph sensor, water level sensor, turbidity sensor, light intensity sensor, and motor actuators for mixing solutions. Each parameter was controlled individually in an on-off control system. A simple nutrient addition program (SNAP) solution is also used for better growth productivity by maximizing its nutrient contents. The pH is maintained at 9 to 12 for a healthy biomass output. Daily weight measurement was conducted using an analytical balance to monitor its growth. Using the developed prototype recorded a 33% higher rate of productivity over the manual process. This setup can potentially be used as a model for mass production of spirulina algae.
Volume: 39
Issue: 1
Page: 139-147
Publish at: 2025-07-01

Effective methods for employee performance assessment

10.11591/ijeecs.v39.i1.pp509-522
Agatha Beny Himawan , Rinta Kridalukmana , Toni Prahasto
This study aims to select the most effective multi-criteria decision-making method used in an employee performance appraisal system. The approach used in this study is a comparative experiment where three multi-criteria decision-making methods simple additive weighting (SAW), analytical hierarchy process (AHP), and technique for order preference similarity to an ideal solution (TOPSIS) are compared. The dataset involves 16 employees, considering input data such as work behavior scores, and performance targets (SKP). The criteria for evaluating work behavior include service quality, accountability, competence, harmony, loyalty, adaptability, collaboration, and achievement of targets. The comparison results were tested using a one-way ANOVA to evaluate whether there are significant differences among the three methods, as well as to provide supporting evidence for the conducted research. The results indicated that the SAW method provides the most accurate and relevant performance assessments while AHP yields less precise rankings as some employees received the same scores despite having different workloads. TOPSIS also produced rankings that did not accurately reflect the relative workloads. Implementing the SAW method in the employee performance information system enhances the assessment process, making it faster, more objective, transparent, and credible. Thus, SAW emerges as the most effective method for aligning performance scores with employee roles and responsibilities.
Volume: 39
Issue: 1
Page: 509-522
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

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

Investigating the recall efficiency in abstractive summarization: an experimental based comparative study

10.11591/ijeecs.v39.i1.pp446-454
Surabhi Anuradha , Martha Sheshikala
This study explores text summarization, a critical component of natural language processing (NLP), specifically targeting scientific documents. Traditional extractive summarization, which relies on the original wording, often results in disjointed sequences of sentences and fails to convey key ideas concisely. To address these issues and ensure comprehensive inclusion of relevant details, our research aims to improve the coherence and completeness of summaries. We employed 25 different large language models (LLMs) to evaluate their performance in generating abstractive summaries of scholarly scientific documents. A recall-oriented evaluation of the generated summaries revealed that LLMs such as 'Claude v2.1,' 'PPLX 70B Online,' and 'Mistral 7B Instruct' demonstrated exceptional performance with ROUGE-1 scores of 0.92, 0.88, and 0.85, respectively, supported by high precision and recall values from bidirectional encoder representations from transformers (BERT) scores (0.902, 0.894, and 0.888). These findings offer valuable insights for NLP researchers, laying the foundation for future advancements in LLMs for summarization. The study highlights potential improvements in text summarization techniques, benefiting various NLP applications.
Volume: 39
Issue: 1
Page: 446-454
Publish at: 2025-07-01

HangeulVR: an immersive and interactive Korean alphabet learning on virtual reality

10.11591/ijeecs.v39.i1.pp545-553
Ahmad Nasikun , Muhammad Fadhil Mahendra , Achmad Rio Dessiar
Learning a new foreign language promises numerous benefits, such are career advantage, culture exposure, and traveling opportunity. However, it comes with a cost of considerably significant efforts and time commitment. The challenge intensifies when dealing with languages characterized by distinctive scripts, such as Hangeul in Korean language. The requisite mastery of Hangeul characters precedes the exploration of fundamental linguistic elements, including grammar, pronunciation, speaking, and writing. In this research, we propose an innovative, immersive, and interactive methodology for Hangeul acquisition employing virtual reality (VR). Our study transports participants into a virtual environment, guided by a gamification framework designed to facilitate Hangeul learning. Participants are able to learn basic pronunciation, listening, and Hangeul writing, three fundamental aspects of learning the Korean alphabet. Empirical findings from our experiments show the potential of its usage, indicated by its system usability scale (SUS) of 74.4.
Volume: 39
Issue: 1
Page: 545-553
Publish at: 2025-07-01

PRDTinyML: deep learning-based TinyML-based pedestrian detection model in autonomous vehicles for smart cities

10.11591/ijeecs.v39.i1.pp283-309
Norah N. Alajlan , Abeer I. Alhujaylan , Dina M. Ibrahim
Detecting pedestrians and cars in smart cities is a major task for autonomous vehicles (AV) to prevent accidents. Occlusion, distortion, and multi-instance pictures make pedestrian and rider detection difficult. Recently, deep learning (DL) systems have shown promise for AV pedestrian identification. The restricted resources of internet of things (IoT) devices have made it difficult to integrate DL with pedestrian detection. Tiny machine learning (TinyML) was used to recognize pedestrians and cyclists in the EuroCity persons (ECP) dataset. After preliminary testing, we propose five microcontroller-deployable lightweight DL models in this study. We applied SqueezeNet, AlexNet, and convolution neural network (CNN) DL models. We also use two pre-trained models, MobileNet-V2 and MobileNet-V3, to determine the optimal size and accuracy model. Quantization aware training (QAT), full integer quantization (FIQ), and dynamic range quantization (DRQ) were used. The CNN model had the shortest size with 0.07 MB using the DRQ approach, followed by SqueezeNet, AlexNet, MobileNet-V2, and MobileNet-V2 with 0.161 MB, 0.69 MB, 1.824 MB, and 1.95 MB, respectively. The MobileNet-V3 model’s DRQ accuracy after optimization was 99.60% for day photos and 98.86% for night images, outperforming other models. The MobileNet-V2 model followed with DRQ accuracy of 99.27% and 98.24% for day and night images.
Volume: 39
Issue: 1
Page: 283-309
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

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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