Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

28,451 Article Results

Blockchain technology for optimizing security and privacy in distributed systems

10.11591/csit.v6i2.p214-224
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: 214-224
Publish at: 2025-07-01

HepatoScan: Ensemble classification learning models for liver cancer disease detection

10.11591/csit.v6i2.p169-177
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: 169-177
Publish at: 2025-07-01

Arowana cultivation water quality forecasting with multivariate fuzzy timeseries and internet of things

10.11591/csit.v6i2.p136-146
Alauddin Maulana Hirzan , April Firman Daru , Lenny Margaretta Huizen
Water quality plays a crucial role in the growth and survival of arowana fish, with imbalances in key parameters (pH, temperature, turbidity, dissolved oxygen, and conductivity) leading to increased mortality rates. While previous studies have introduced various monitoring models using Arduino IDE and intrinsic approaches, they lack predictive capabilities, leaving cultivators unable to take proactive measures. To address this gap, this study develops a predictive model integrating the internet of things (IoT) with a fuzzy time series (FTS) algorithm. Through rigorous evaluation and validation, the proposed FTS-multivariate T2 model demonstrated superior performance, achieving an exceptionally low error rate of 0.01704%, outperforming decision tree (0.13410%), FTS-multivariate T1 (0.88397%), and linear regression (20.91791%). These findings confirm that FTS-multivariate T2 not only accurately predicts water quality but also significantly reduces the mean absolute percentage error, providing a robust solution for sustainable arowana aquaculture.
Volume: 6
Issue: 2
Page: 136-146
Publish at: 2025-07-01

Classification and similarity detection of Indonesian scientific journal articles

10.11591/csit.v6i2.p147-158
Nyimas Sabilina Cahyani , Deris Stiawan , Abdiansah Abdiansah , Nurul Afifah , Dendi Renaldo Permana
The development of technology is accelerating in finding references to scientific articles or journals related to research topics. One of the sources of national aggregator services to find references is Garba Rujukan Digital (GARUDA), developed by the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek) of the Republic of Indonesia. The naïve Bayes method classifies articles into several categories based on titles and abstracts. The system achieves an F1-score of 98%, which indicates high classification accuracy, and the classification process takes less than 60 minutes. Article similarity detection is done using the cosine similarity method, and a similarity score of 0.071 reflects the degree of similarity between the title and the abstract that has been concatenated, while a score close to 1 indicates a higher similarity. Searching for similar scientific articles based on title and abstract, sort articles based on the results of the highest similarity score are the most similar articles, and generating article categories. The results of the research show that the proposed method significantly improves the classification and search processes in GARUDA, as well as accurate and efficient similarity detection.
Volume: 6
Issue: 2
Page: 147-158
Publish at: 2025-07-01

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

10.11591/csit.v6i2.p159-168
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: 159-168
Publish at: 2025-07-01

Attack detection in internet of things networks with deep learning using deep transfer learning method

10.11591/csit.v6i2.p202-213
Riki Abdillah Hasanuddin , Muhammad Subali
Cybersecurity becomes a crucial part within the information management framework of internet of things (IoT) device networks. The large-scale distribution of IoT networks and the complexity of communication protocols used are contributing factors to the widespread vulnerabilities of IoT devices. The implementation of transfer learning models in deep learning can achieve optimal performance faster than traditional machine learning models, as they leverage knowledge from previous models that already understand these features. Base model was built using the 1-dimension convolutional neural network (1D-CNN) method, using training and test data from the source domain dataset. Model 1 was constructed using the same method as base model. The test and training data used for model 1 were from the target domain dataset. This model successfully detected known attacks at a rate of 99.352%, but did not perform well in detecting unknown attacks, with an accuracy of 84.645%. Model 2 is an enhancement of model 1, incorporating transfer learning from the base model. Its results significantly improved compared to model 1 testing. Model 2 has an accuracy and precision rate of 98.86% and 99.17 %, respectively, allowing it to detect previously unknown attacks. Even with a slight decrease in normal detection, most attacks can still be detected.
Volume: 6
Issue: 2
Page: 202-213
Publish at: 2025-07-01

Real-time face detection and local binary patterns histograms-based face recognition on Raspberry Pi with OpenCV

10.11591/ijres.v14.i2.pp527-537
Bharanidharan Chandrasekaran , D. Karunkuzhali , V. Kandasamy , M. DIllibabu , K. Rama Devi
This paper presents a practical end-to-end paper demonstrating real-time face recognition using a Raspberry Pi and open source computer vision library (OpenCV) consisting of three main stages: training the recognizer, real-time recognition, and face detection and data gathering. The paper offers a comprehensive guide for enthusiasts venturing into computer vision and facial recognition. Employing the Haar Cascade classifier for accurate face detection and the local binary patterns histograms (LBPH) face recognizer for robust training and recognition, the paper ensures a thorough understanding of key concepts. The step-by-step process covers software installation, camera testing, face detection, data collection, training, and real time recognition. With a focus on the Raspberry Pi platform, this paper serves as an accessible entry point for exploring facial recognition technology. Readers will gain insights into practical implementation, making it an ideal resource for learners and hobbyists interested in delving into the exciting realm of computer vision.
Volume: 14
Issue: 2
Page: 527-537
Publish at: 2025-07-01

FPGA-based implementation of a substitution box cryptographic co-processor for high-performance applications

10.11591/ijres.v14.i2.pp587-596
Moulai Khatir Ahmed Nassim , Ziani Zakarya
The increasing demand for reliable cryptographic operations for securing current systems has given birth to well-advanced and developed hardware solutions, in this paper we consider issues within the traditional symmetric advanced encryption standard (AES) cryptographic system as major challenges. Additionally, problems such as throughput limitations, reliability, and unified key management are also discussed and tackled through appropriate hierarchical transformation techniques. To overcome these challenges, this paper presents the design and field programmable gate array (FPGA)-based implementation of a cryptographic coprocessor optimized for substitution box (S-Box) operation which is considered as a key component in many cryptographic algorithms such as AES. The architecture of the co-processor proposed in this article is based on the advanced characteristics of FPGAs to accelerate the S-Box transformation, improve throughput and reduce latency compared to software implementations. We discussed carefully the design considerations along with resource utilization, speed optimization, and energy efficiency. The obtained experimental results present significant performance improvements, the FPGA-based implementation ensured higher throughput and lower execution time compared to traditional central processing unit (CPU)-based methods. We presented in this work the effectiveness of using FPGAs for the acceleration of cryptographic operations in secure applications which will therefore be a robust solution for the next generation of secure systems.
Volume: 14
Issue: 2
Page: 587-596
Publish at: 2025-07-01

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

10.11591/csit.v6i2.p178-201
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: 178-201
Publish at: 2025-07-01

Development of a web-based application for real-time eye disease classification system using artificial intelligence

10.11591/ijres.v14.i2.pp558-574
Kennedy Okokpujie , Adekoya Tolulope , Abidemi Orimogunje , Joshua Sokowonci Mommoh , Adaora Princess Ijeh , Mary Oluwafeyisayo Ogundele
The incorporation of artificial intelligence (AI) into the field of medicine has created new strategies in enhancing the detection of disease, with a focus on the identification of eye diseases such as glaucoma, diabetic retinopathy, and macular degeneration associated with age, which can lead to blindness if not detected and treated early enough. Driven by the need to combat blindness, which affects approximately 39 million people globally, according to the World Health Organization (WHO). This research offers a web-based, real time approach to classifying eye diseases from fundus images due to user friendliness. Three pre-trained convolutional neural network (CNN) models are adopted, namely ResNet-50, Inception-v3, and MobileNetV3. The models were trained on a dataset of 8000 fundus images subdivided into four classes: cataract, glaucoma, diabetic retinopathy, and normal eyes. The performance of the models was evaluated in 3-way (normal eye and two diseases) and 4-way (normal eye and three diseases). ResNet-50 had higher performances, with 98% and 97% accuracy in the respective classifications, compared to InceptionV3 and MobileNetV3. Consequently, ResNet-50 was used in an online application that made real-time diagnoses. This research findings reveal the potential of CNNs in the healthcare industry, particularly in reducing over-reliance on specialists and increasing access to quality diagnostic technologies. Especially in critical areas such as this with limited healthcare resources, where the technology can create significant gaps in disease detection and control.
Volume: 14
Issue: 2
Page: 558-574
Publish at: 2025-07-01

Classifying IoT firmware security threats using image analysis and deep learning

10.11591/ijres.v14.i2.pp546-557
Abdelkabir Rouagubi , Chaymae El Youssofi , Khalid Chougdali
As the internet of things (IoT) grows, its embedded devices face increasing vulnerability to firmware-based attacks. The lack of robust security mechanisms in IoT devices makes them susceptible to malicious firmware updates, potentially compromising entire networks. This study addresses the classification of IoT firmware security threats using deep learning and image-based analysis techniques. A publicly available dataset of 32×32 grayscale images, derived from IoT firmware samples and categorized as benignware, hackware, and malware, was utilized. The grayscale images were converted into three-channel RGB format to ensure compatibility with convolutional neural networks (CNNs). We tested multiple pre-trained CNN architectures, including SqueezeNet, ShuffleNet, MobileNet, Xception, and ResNet50, employing transfer learning to adapt the models for this classification task. Both ResNet50 and ShuffleNet achieved exceptional performance, with 100% accuracy, precision, recall, and F1-score. These results validate the effectiveness of our methodology in leveraging transfer learning for IoT firmware classification while maintaining computational efficiency, making it suitable for deployment in resource-constrained IoT environments. T
Volume: 14
Issue: 2
Page: 546-557
Publish at: 2025-07-01

Test and measurement automation of printed circuit board assembly using digital oscilloscope

10.11591/ijres.v14.i2.pp463-471
Sanjeev Kumar , Deepak Prasad , Manoj Pandey
The testing and measurement (TM) of electrical parameters of printed circuit board assembly (PCBA) plays an integral part in the manufacturing sectors. These industries work on embedded system which widely use digital oscilloscopes (DO) for such purposes, however, operate them manually. An exponential rise in the implementation of industry 4.0 with the increasing demand for industrial products makes manual TM cumbersome. The automation of oscilloscopes (AO) remains a viable alternative to these issues requiring further investigation. An accurate and automated TM block facilitates efficient design, development, and assembly of a fully functional system hence addressed here. The AO has been carried out using generalized software that can be configured based on industry requirements. It subsequently stores the data on the server for better traceability. The automated software is developed using VB.NET and installed on a personal computer. Experiments reveal the proposed approach saves approximately 60%-70% of the time required for each PCBA operation than that of the manual system. This can enhance the productivity of the industry in terms of manpower and Resource utilization with a reduction in operating costs.
Volume: 14
Issue: 2
Page: 463-471
Publish at: 2025-07-01

Design and development of multiband multi-mode frequency reconfigurable CPW-fed antenna for 5G wireless communication

10.11591/ijres.v14.i2.pp328-338
Annu Tiwari , Muhammed Yasir Yilmaz , Gaurav Kumar Soni , Dinesh Yadav
This research develops, simulates, fabricates and measured a coplanar waveguide (CPW)-fed multiband multi-mode frequency reconfigurable antenna for 5G wireless communication. The antenna is design on Rogers RT5880 substrate with a dielectric constant of 2.2, a thickness of 0.508 mm, and a loss tangent (tanδ) of 0.0009 and the dimension is 30×28×0.508 mm3. The presented antenna has shown good impedance matching with reflection coefficients ranging from -14.82 to -50.36 dB at different frequencies between 6 GHz to 24 GHz. The presented frequency reconfigurable antenna design includes four PIN diodes, resistors, and inductors, enabling 16 different configurations. The simulated outcomes showed varied S parameter values and gains, demonstrating the antenna's flexibility. Measurements were taken using vector network analyzer (VNA) and anechoic chamber to assess reflection coefficient (|S11|) and gain, confirming the antenna's performance. The antenna's ability to reconfigure dynamically without losing signal integrity makes it suitable for 5G wireless applications. It meets and exceeds the requirements for multiband operation, validated by comprehensive simulations and measurements, showing its potential for wide use.
Volume: 14
Issue: 2
Page: 328-338
Publish at: 2025-07-01

Systematic review of a lightweight convolutional neural network architectures on edge devices

10.11591/ijres.v14.i2.pp339-352
Muhammad Abbas Abu Talib , Samsul Setumin , Siti Juliana Abu Bakar , Adi Izhar Che Ani , Denis Eka Cahyani
A lightweight convolutional neural network (CNN) has become one of the major studies in machine learning field to optimize its potential for employing it on the resource-constrained devices. However, a benchmark for fair comparison is still missing and thus, this paper aims to identify the recent studies regarding the lightweight CNN architectures including the types of CNN, its applications, edge devices usage, evaluation types and matrices, and performance comparison. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) framework was used as the main approach to collect and interpret the literature. In the process, 37 papers were identified as meeting the criteria for lightweight CNNs aimed at image classification or regression tasks. Of these, only 20 studies explored the use of these models on edge devices. To conclude, MobileNet appeared as the most used architecture, while the types of CNN focused on image classification for the general-purpose application. Following that, the NVIDIA Jetson Nano was the most utilized edge device in recent research. Additionally, performance evaluation commonly included measures like accuracy and time, along with metrics such as recall, precision, F1-Score, and other similar indicators. Finally, the average accuracy for performance comparison can serve as threshold value for future research in this scope of study.
Volume: 14
Issue: 2
Page: 339-352
Publish at: 2025-07-01

Design of a dual-band bandpass filter with shunt stubs for wireless local area network and satellite communication system

10.11591/ijres.v14.i2.pp490-496
Jacob Abraham , Kannadhasan Suriyan
High-performance radio frequency (RF) modules are required in transmitter and reception devices due to the recent expansion of wireless technology. The power amplifier, low-noise amplifier, filter, and mixer are the most crucial components in the RF transmitter/receiver chain. This work presents the design and analysis of a dual-band bandpass filter (BPF) for wireless local area network (WLAN) and C-band satellite applications. Stubs of the proper electrical length that are open and short-circuited are used to implement the filter. The low pass performance is generated by the open-circuited stubs. Short-circuited stubs achieve high-pass performance, while the combination of open and short-circuited stubs achieves bandpass performance. We confirm the filter's behaviour using the advanced design system 2022 simulation tool. The findings of return loss and insertion loss confirm the simulation-level performance analysis of the filter. The result demonstrates the suggested BPF's dual-band behaviour at 4 GHz and 6 GHz.
Volume: 14
Issue: 2
Page: 490-496
Publish at: 2025-07-01
1 2 3
...
1896 1897
Show 71 of 1897

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration