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

30,185 Article Results

A novel circulant matrix-based McEliece framework for secure digital communication

10.11591/ijaas.v15.i1.pp293-302
Ravikumar Inakoti , James Stephen Meka , Padala Venkata Gopala Durga Prasad Reddy
McEliece cryptosystem is old and well-explored post-quantum cryptography system that offers superior security against quantum attacks. Though the system holds great potential and superior security, the challenge associated with large key sizes has made system impractical for most applications. The first challenge against McEliece cryptosystem remains its large key sizes, which make system impractical, especially when implementing internet of things (IoT) and mobile communication applications. Overcoming challenges and retaining superior security still remains an issue to explore. This paper presents investigation into use of circulant matrices for McEliece encryption system to achieve a considerable reduction in key sizes and enhance fast encryption processes. The use of circulant matrices’ inherent properties boosts performance without focusing much on system’s security. In addition, the paper presents security evaluation process for modified communication system to determine and mitigate weaknesses that might arise, considering use of sophisticated encryption systems. Findings and results explore use of circulant matrices, which achieve great reductions in key sizes and improve efficiency of process. Security evaluation reports that proper scrambling techniques are efficient at mending the vulnerabilities associated with circulant matrix structures. A modified McEliece cryptosystem using circulant matrices offers superior data communication, balancing both strong security and efficient computational processes, making system ideal for use in recent communication systems.
Volume: 15
Issue: 1
Page: 293-302
Publish at: 2026-03-01

Improved seizure detection using optimized time sequence based deep learning framework

10.11591/ijaas.v15.i1.pp198-208
Puspanjali Mallik , Ajit Kumar Nayak , Satyaprakash Swain
Epilepsy disease originates due to the presence of disordered neurons, and epilepsy detection stands as a challenging task for neurologists. With recent advances, electroencephalography (EEG)-based analysis is increasingly supported by deep learning and metaheuristic optimization approaches in order to improve the test results. This experiment uses a convolutional neural network (CNN) model hybridized with bidirectional long short-term memory (BiLSTM). CNN leverages the work with improved feature extraction cum classification supports, and BiLSTM keeps the time sequence of data in both the forward and backward direction for improving signal mapping purposes. To reduce the computational overhead and improve execution accuracy, a hybrid optimization algorithm called secretary bird optimization algorithm (SBOA) is used to fine-tune the execution. Key classification parameters such as accuracy, sensitivity, and specificity reflect the model’s strong predictive capability, with accuracy reaching up to 98.49%. The proposed method demonstrates the potential for high-performance EEG-based seizure detection, paving the way for future integration with edge computing devices to support remote clinical diagnostics and continuous monitoring in real-world healthcare applications.
Volume: 15
Issue: 1
Page: 198-208
Publish at: 2026-03-01

Performance enhancement of photovoltaic system integrated with a single-phase grid using advanced controllers

10.11591/ijaas.v15.i1.pp77-85
Madhu Babu Thiruveedula , Thiramdasu Chandana , Meghavath Mahesh , Avinash Udala , Yerra Praveen , Mohammed Assaduzzama
This study offers a thorough examination of a photovoltaic (PV) system using a variety of maximum power point tracking (MPPT) methods, including fuzzy logic control (FLC), adaptive neuro-fuzzy inference systems (ANFIS), perturb and observe (P&O), and artificial neural networks (ANN). Optimizing power extraction from PV systems under various environmental circumstances, including temperature variations and irradiance, is the main goal of these MPPT algorithms. Despite its widespread use and affordability, the P&O algorithm may have performance issues in dynamic circumstances. By using fuzzy logic to adjust to non-linear changes in environmental conditions, FLC improves P&O and offers more dependable and seamless operation. Although they demand a large amount of data and processing power, ANN-based MPPT approaches provide sophisticated capabilities by predicting optimal operating points by learning from historical system actions. By fusing fuzzy logic and neural networks, ANFIS offers a reliable solution that can more accurately adjust in real time to changing circumstances. These algorithms' incorporation into a PV system allows for more flexible and effective power management, guaranteeing peak performance in a range of climatic conditions. By combining many MPPT techniques, hybrid approaches can further reduce the drawbacks of individual approaches and improve the overall dependability and efficiency of PV systems.
Volume: 15
Issue: 1
Page: 77-85
Publish at: 2026-03-01

Performance comparison of feature extraction methods for electroencephalogram-based recognition of Balinese script

10.11591/ijaas.v15.i1.pp55-64
I Made Agus Wirawan , Ida Bagus Nyoman Pascima , Gede Surya Mahendra , I Made Candiasa , I Nyoman Sukajaya
Recognizing Balinese script from electroencephalogram (EEG) signals remains a challenging problem due to low signal amplitude, non-stationary dynamics, and significant inter-subject variability. Despite previous attempts, no single feature extraction method has been universally effective in addressing these limitations. To fill this gap, this study systematically evaluates five feature extraction techniques—differential entropy (DE), power spectral density (PSD), discrete wavelet transforms (DWT), Hjorth parameters, and statistical features—on the Balinese imagined spelling using electroencephalography (BISE) dataset, which contains EEG recordings specifically designed for Balinese script recognition. For classification, both artificial neural networks (ANN) and support vector machines (SVM) are applied, and their performance is validated across multiple experimental settings. Results demonstrate that DE consistently provides more stable and discriminative features than the other methods, achieving the highest classification accuracy when combined with ANN. These findings highlight the potential of DE-based approaches to advance electroencephalogram driven Balinese script recognition, offering a culturally significant contribution to brain-computer interface (BCI) research and supporting future applications in inclusive artificial intelligence, digital heritage preservation, and assistive technologies.
Volume: 15
Issue: 1
Page: 55-64
Publish at: 2026-03-01

ELLMW: an enhanced vision–language model for reliable text extraction from manually composed scripts

10.11591/ijres.v15.i1.pp194-203
Dhivya Venkatesh , Brintha Rajakumari Sivaraj
While conventional optical character recognition (OCR) systems can digitize text, they struggle with diverse handwriting styles, noisy inputs, and unstructured layouts, limiting their effectiveness. This study proposes enhanced large language model whisperer (ELLMW), a vision–language framework for accurate text extraction (TE) from fully handwritten scripts. The methodology integrates advanced preprocessing (noise reduction, binarization, and skew correction), deep learning–based handwriting recognition convolutional neural network–long short-term memory (CNN–LSTM), and LLM-based post-correction to ensure context-aware and structurally coherent outputs. The system converts scanned images, portable document formats (PDFs), and irregularly formatted answer sheets into machine-readable text, while automatically correcting errors in spelling, grammar, and layout. Experimental evaluation on a curated dataset of handwritten examination answer scripts (HEAS) demonstrates that ELLMW achieves 97.8% accuracy, 1.04%-character error rate (CER), and 3.24%-word error rate, outperforming widely used OCR tools including Tesseract, EasyOCR, Google Cloud Vision (GCV), PaddleOCR, ABBYY FineReader, and Transym OCR. The results highlight the model’s robustness across varying handwriting styles, noisy backgrounds, and complex document structures.
Volume: 15
Issue: 1
Page: 194-203
Publish at: 2026-03-01

Bridging archaeological visibility analysis and real-time 3D visualization

10.11591/csit.v7i1.p93-101
George Malaperdas , Georgia Delli
This paper investigates the integration of geographic information systems (GIS)-based visibility analysis—commonly known as viewshed analysis—with real-time 3D rendering in unreal engine, specifically within the context of archaeological and cultural heritage applications. Visibility maps are an essential tool in archaeological research, helping scholars understand the spatial relationships, sightlines, and symbolic visibility between structures, monuments, and landscapes. However, traditional GIS viewshed analysis is often static and limited to 2D environments. This project proposes a method to bring visibility analysis into immersive 3D environments by visualizing GIS-generated data within unreal engine. The methodology involves generating a viewshed from a given digital elevation model (DEM) using established GIS software. The resulting raster is then exported and processed into a texture or material mask compatible with unreal engine. Once imported, the data is mapped onto a 3D landscape model, allowing users to explore visibility dynamically, including first-person or VR-based navigation. This interdisciplinary approach contributes to the field of digital archaeology by enhancing spatial interpretation and audience engagement through immersive geovisualization. It also outlines a flexible pipeline for integrating geospatial datasets into 3D environments, potentially applicable to site management, public education, and digital preservation efforts.
Volume: 7
Issue: 1
Page: 93-101
Publish at: 2026-03-01

Advances in Parkinson’s disease diagnosis and treatment using artificial intelligence: a review

10.11591/csit.v7i1.p121-130
Mehr Ali Qasimi , Züleyha Yılmaz Acar
Parkinson’s disease (PD) diagnosis and monitoring have significantly improved because to current advancements in artificial intelligence (AI), particularly in the areas of deep learning (DL) and machine learning (ML). Early-stage insensitivity of traditional diagnostic techniques necessitates the use of clever, data-driven alternatives. AI-powered noninvasive diagnostic methods like speech recognition, handwriting analysis, and neuroimaging categorization are the main topic of this technical review. We provide a summary of comparative performance measures from recent models, highlighting their practical usefulness, data modality, and accuracy. Also covered are important issues like data variability, real-world implementation, and model interpretability. Unlike prior surveys that primarily report accuracy metrics, this review explicitly focuses on identifying the gap between experimental AI performance and real-world clinical deployment, emphasizing interpretability, validation, and scalability challenges in PD diagnosis. The purpose of this letter is to provide guidance for researchers creating deployable and clinically valid AI systems for PD detection.
Volume: 7
Issue: 1
Page: 121-130
Publish at: 2026-03-01

Cloud-based predictive analytics for pension fund performance optimization

10.11591/csit.v7i1.p46-55
Beauty Garaba , Mainford Mutandavari , Jerita Chibhabha
This study introduces a novel, cloud-based predictive analytics framework tailored for pension fund performance management in Zimbabwe. Addressing limitations in traditional actuarial models, the proposed system leverages real-time data pipelines and explainable artificial intelligence (XAI) techniques to enhance forecasting accuracy and transparency. Using regression, classification, and deep learning models, it forecasts member contributions, identifies risks of contribution drops, and predicts member churn. The system’s cloud deployment ensures scalability and interactive integration with tools like Power BI for decision support. This solution significantly advances sustainable pension fund management for emerging economies.
Volume: 7
Issue: 1
Page: 46-55
Publish at: 2026-03-01

Review on patch antenna for 5G Networks at Ka-Band

10.11591/csit.v7i1.p102-110
Md. Nurullah Al Nasib , Md. Sohel Rana
Microstrip antennas for Ka-band wireless applications will be thoroughly examined in this research. To utilize 5G wireless applications, a new research topic that has been established is the creation of microstrip patch antennas. Patch antennae are made of different shapes, such as rectangles, circular shapes, triangles, donuts, rings, etc. Many substrate materials are used in patch antenna designs. This article examines the geometric configurations of antennas, the many methods of analysis for attributes of antennas, the dimensions of antennas, the issues that antennas face, and the potential solutions to those challenges. Wireless communication technologies, such as television broadcasts, microwave ovens, mobile phones, wireless local area networks (LANs), Bluetooth, global positioning systems (GPS), and two-way radios, all use it. This article examines the geometric structures of antennas, including several characteristics and materials by which they are constructed, as well as the numerous shapes they can produce. This paper will also examine return loss (S11), bandwidth, voltage standing wave ratio (VSWR), gain, directivity, efficiency, and Bandwidth discussed in the prior studies. In the future, a novel patch antenna can be designed for 5G wireless applications.
Volume: 7
Issue: 1
Page: 102-110
Publish at: 2026-03-01

Implementation of face recognition using Python

10.11591/csit.v7i1.p1-9
Febrian Wahyu Christanto , Husnul Arifin , Christine Dewi , Teguh Prasandy
Artificial intelligence (AI)-based technology systems are developing rapidly. Along with technological development the number of criminal cases caused by facial forgery is also growing. Cases of theft and housebreaking with fake photos are a common problem in Semarang. In 2022–2023 the number of cases of theft and housebreaking reached 372,965 with a crime risk level of 137/100,000 people. To overcome this problem the facial recognition system used in the door security system uses digital image processing. This method works by imitating how nerve cells communicate with interconnected neurons, or more precisely, how artificial neural networks function in humans. As training data, image capture and facial recognition are carried out using a webcam and the Python programming language with the TensorFlow library. The image processing algorithm uses 400 facial images with an accuracy rate of 95%. However further development is needed to improve the efficiency and accuracy of the system to produce better results.
Volume: 7
Issue: 1
Page: 1-9
Publish at: 2026-03-01

Development and performance evaluation of a CNN model for seagrass species classification in Bintan, Indonesia

10.11591/csit.v7i1.p20-29
Nurul Hayaty , Hollanda Arief Kusuma
This study presents the development and evaluation of a convolutional neural network (CNN) model for automated seagrass species classification in Bintan, Indonesia. The objective of this research is to examine how different train-validation data split ratios affect model accuracy and generalization performance. The CNN was trained under four configurations (60:40, 70:30, 80:20, and 90:10) to analyze the influence of training data volume on learning convergence and predictive capability. The results indicate that all configurations achieved high validation accuracy, with the best performance reaching 98.53% when using the 90:10 split. Evaluation on unseen data demonstrated that the 60:40 configuration provided the most consistent and reliable generalization. Performance variations were also affected by the morphological similarity between the classified species, which increases the challenge in correctly distinguishing certain classes. Overall, the findings confirm the effectiveness of CNN-based classification for supporting marine biodiversity monitoring and underline the importance of dataset composition in achieving optimal performance. Future improvements will focus on expanding data variability to enhance robustness in real-world scenarios.
Volume: 7
Issue: 1
Page: 20-29
Publish at: 2026-03-01

Deep learning for sentiment analysis and topic extraction in health insurance

10.11591/csit.v7i1.p66-73
Muzondiwa Karomo , Mainford Mutandavari , Wilton Muzava
Social media has transformed into a vital channel for real-time, unsolicited feedback in healthcare, yet health insurance providers often lack the tools to mine insights from such data. This study proposes a cloud-based system leveraging deep learning for sentiment analysis and topic modeling tailored to the Commercial and Industrial Medical Aid Society (CIMAS) health insurance in Zimbabwe. Using bidirectional encoder representations from transformers (BERT), a convolutional neural network (CNN), a random forest (RF), and autoencoders, the system processes multilingual data from platforms like Twitter and Facebook, identifying customer concerns in real time. Over 15,000 posts were analyzed, with CNN achieving 91.4% accuracy in sentiment classification and BERTopic extracting coherent themes. The system detected issues such as claim delays, app navigation problems, and unreported anomalies. Findings demonstrate that AI can improve service delivery, customer satisfaction, and responsiveness in African insurance contexts.
Volume: 7
Issue: 1
Page: 66-73
Publish at: 2026-03-01

Car selection in games using multi-objective optimization by ratio analysis based on player achievement

10.11591/csit.v7i1.p30-45
Caesar Nafiansyah Putra , Fresy Nugroho , Mochamad Imamudin , Dwi Pebrianti , Jehad Abdelhamid Hammad , Tri Mukti Lestari , Dian Maharani , Alfina Nurrahman
The selection menu in some racing games usually uses a random system for vehicle selection. However, this random feature generally randomizes the selection of the index without considering factors that support the player's abilities. Therefore, this study aims to develop a racing game that can suggest vehicles that have been adjusted to the player's performance. Vehicle recommendations are made using the multi-objective optimization on the basis of ratio analysis (MOORA) method as its method. The MOORA calculation ranks vehicles based on criteria such as mileage, fuel efficiency, speed, agility, and others collected in previous games. The results of this study show the effectiveness of using the MOORA method in recommending vehicles that match the player's skills, thereby improving the overall player experience. In addition, the usability test produced a system usability scale (SUS) score of 82.4, so it is included in the very good category.
Volume: 7
Issue: 1
Page: 30-45
Publish at: 2026-03-01

An uneven cluster-based routing protocol for WSNs using a hybrid MCDM and max-min ant colony optimization

10.11591/csit.v7i1.p74-82
Man Gun Ri , Pyong Gwang Kim , JinSim Kim
In energy-constrained wireless sensor networks (WSNs) composed of sensor nodes (SNs) characterized by multi-criteria contradictory with each other, it is still one of the challenges to be solved to figure out how to combine multi-criteria with each other and how to use an intelligent optimization (IO) algorithm for developing an optimal cluster-based routing protocol. In this article, we overture a new routing protocol based on uneven cluster using the hybrid FCNP-VWA-TOPSIS (FVT) and an improved max-min ant colony optimization (ACO). This scheme uses the hybrid FVT to perform the clustering, and uses an improved max-min ACO to configure a routing tree for the relay transmission of sensed data. The extensive simulation experiments have been carried out to show that the proposed scheme greatly prolongs the network lifetime (NL) by achieving an energy consumption balance superior to the previous schemes.
Volume: 7
Issue: 1
Page: 74-82
Publish at: 2026-03-01

Video-based physical violence detection model for efficient public space surveillance

10.11591/ijict.v15i1.pp161-170
Erick Erick , Benfano Soewito
This study aims to develop an effective real-time model for detecting violence in public spaces, focusing on achieving a balance between accuracy and computational efficiency. We evaluate various model architectures, with the main comparison between the ConvLSTM2D and Conv3D models commonly used in video analysis to capture spatial and temporal features. The ConvLSTM2D model, combined with preprocessing layers such as change detection and motion blur, showed optimal performance, achieving 86% accuracy after Bayesian optimization. With a low parameter count of 25,137, this model enables fast inference in just 0.010 seconds, making it suitable for real-time applications that require efficient computation. In contrast, the Conv3D model, which is also combined with preprocessing layers such as change detection and motion blur and has more than nine million parameters, shows a lower accuracy of 77.5% as well as a slower inference time of 0.025 seconds, making it unsuitable for real-time applications. The results of this study show that the ConvLSTM2D model is promising for real-time violence detection systems in public spaces, where a fast and accurate response is essential to prevent further acts of violence.
Volume: 15
Issue: 1
Page: 161-170
Publish at: 2026-03-01
1 2 3
...
2012 2013
Show 54 of 2013

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