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

29,922 Article Results

Nipah virus as an emerging threat: mutational dynamics, pathogenesis, and advances in vaccine development- a systematic review

10.11591/ijphs.v15i1.22365
Sadia Afrin , Md. Rezwan Ahmed Mahedi , Asma Akhter Radia , Joti Devi
Nipah virus (NiV) is an emerging zoonotic pathogen with significant pandemic potential. Large outbreaks, such as in Malaysia, required the culling of over one million pigs to control transmission. However, the epidemiology of NiV among animal hosts, including pigs, horses, and bats, remains incompletely understood. NiV infection primarily affects the respiratory and nervous systems, causing severe pneumonia, vasculitis, and meningitis, while encephalitis may be mild or infrequent in some cases. This systematic review summarizes current evidence on NiV mutational variation, pathogenesis, treatment strategies, and vaccine development up to 2022. Data were collected from major databases, including PubMed, PMC, and Cochrane Library. Due to limited therapeutic options, NiV management relies mainly on supportive care, as no approved vaccines or specific antiviral treatments are available for humans or livestock. Preventive strategies focus on reducing zoonotic transmission, particularly by minimizing contact between livestock and bat-contaminated food sources, and improving farm management practices. Early detection and continuous surveillance of high-risk populations and animal reservoirs are essential for outbreak control. Current vaccine research targets viral antigens using subunit and vector-based approaches. Overall, further studies are urgently needed to develop effective vaccines and antiviral therapies for NiV infection.
Volume: 15
Issue: 1
Page: 197-207
Publish at: 2026-03-05

Advances in dermatological imaging: enhancing skin melanoma classification for improved patient outcomes

10.11591/csit.v7i1.p111-120
Debadutta Sahoo , Soumya Mishra
The study presents an enhanced AlexNet-based deep learning system for binary classification of melanoma skin cancer as either benign or malignant using two paired dermatoscopic and clinical image datasets. The study evaluates the resilience of the models across different image sets with common preprocessing and specific data augmentation, using a melanoma dataset containing 10,000 images and a benign versus malignant dataset with 3,600 images. The AlexNet refinement exceeded several standard machine learning (ML) classifiers and other deep architectures on the two datasets with practical training times, gaining 97.12% and 96.21% in balanced accuracy. The training proceeded with SGD as optimiser and cross-entropy as loss on 256×256 images. Benchmarking against support vector machine (SVM), k-nearest neighbour (KNN), and other convolutional neural networks (CNNs) designs shows that the selected architecture and hyperparameters achieved the highest performance on cost-effective computation for the routine melanoma triage. The report highlights the need for external validation, incorporation into dermatological workflows, and explainability to improve trust, diminish dataset bias, and support the safe clinical deployment in practice.
Volume: 7
Issue: 1
Page: 111-120
Publish at: 2026-03-01

AdaWeb: a stack-adaptive framework for automated web-vulnerability assessment

10.11591/csit.v7i1.p10-19
Syed Aman Shah , Vaishali Kumar
AdaWeb was a configuration-driven framework that automated web-vulnerability assessment through four stages: technology fingerprinting, crawler selection, exploit execution, and incremental reporting. A Wappalyzer probe identified the application stack and triggered a matching crawler—hypertext preprocessor (PHP), ASP.NET, NodeJS, or a general fallback—capable of both unauthenticated and credential-based traversal. Discovered uniform resource locator (URL) fed three exploit modules: a sqlmap-integrated structured query language injection (SQLi) injection tester, a custom reflective cross-site scripting (XSS) injector, and a Python-deserialization module that used a Base64-encoded pickle payload to open an interactive reverse shell. Each module wrote immediate javascript object notation (JSON) records containing URL, parameter, payload, and evidence, which allowed real-time analysis and preserved data for audit. Empirical evaluation on four deliberately vulnerable benchmarks shows that AdaWeb cuts manual triage time by 52% and eliminates false‑negative cases that defeat generic scanners, making it a drop‑in upgrade for DevSecOps pipelines. This framework reduces manual validation effort and eliminates false negatives by leveraging stack-aligned payloads and authenticated scanning.
Volume: 7
Issue: 1
Page: 10-19
Publish at: 2026-03-01

Optimizing interconnection call routing: a machine learning approach for cost and quality efficiency

10.11591/csit.v7i1.p56-65
Ivy Anesu Mudari , Mainford Mutandavari , Kenneth Chiworera
This study presents the design and development of an automated least cost routing (LCR) model for telecommunications interconnection calls using machine learning. Leveraging a random forest regressor, the model predicts the most cost-effective call routing path based on pricing and network latency. Trained on real-world call detail records (CDRs) from TelOne Zimbabwe, the model achieved a high R² score of 0.851, with a mean absolute error (MAE) of $0.0482 per minute. Evaluation results demonstrate an average cost reduction of 46.75% compared to traditional routing methods, with prediction times under 0.1 seconds and latency remaining within acceptable thresholds. This work provides a practical, scalable, and efficient solution for telecom. operators seeking to reduce interconnection costs and maintain service quality through intelligent routing automation. The model architecture and performance to make it viable for integration into real-time telecom infrastructure.
Volume: 7
Issue: 1
Page: 56-65
Publish at: 2026-03-01

Raindrop and bit drop effects on millimeter wave network performance: a critical review

10.11591/csit.v7i1.p83-92
Victor Dela Gordon , Amevi Acakpovi , George Kwamena Aggrey , Michael Gameli Dziwornu
This PRISMA guided review examines how rain precipitation degrades 5G millimeter wave (mmWave) network performance, with emphasis on rain induced bit drop and its impact on end-to-end quality of service (QoS). From an initial corpus of 13,317 publications screened across IEEE Xplore, ACM Digital Library, ScienceDirect, Google Scholar, and ELICIT, 18 peer reviewed studies published between 2018 and 2024 met the inclusion criteria. Findings show that rainfall significantly weakens mmWave signals, with specific attenuation ranging from approximately 4 to 45 dB/km at 100 mm/h, particularly in tropical regions. When QoS outcomes are reported, these losses manifest as increased bit error rates, rain driven bit drop along the link, higher packet loss and delay, and reduced throughput. Key deficiencies identified include limited empirical validation of attenuation models against packet level QoS, lack of standardized propagation datasets for short range links, and weak treatment of bit level impairments within QoS analysis. To address these gaps, the review recommends enhancing ITU R P.530 and Mie scattering models with region specific measurements, implementing rain aware adaptive protocols, and adopting standardized benchmarking frameworks that link rain attenuation, bit drop, and QoS. This synthesis offers guidance for building climate aware mmWave systems and positions bit drop as a practical metric for precipitation resilience assessment.
Volume: 7
Issue: 1
Page: 83-92
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

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

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

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

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

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

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

A novel single-switch DC-DC converter using the coupled inductor with ultra-high voltage gain

10.11591/ijpeds.v17.i1.pp476-486
Kim-Anh Nguyen , Thai Anh Au Tran , Xuan Khanh Ho , Duong Thach Pham
This paper presents an extremely high step-up DC-DC converter using a quadratic topology and a coupled inductor (CI). The proposed converter (PC) utilizes a single switch, simplifying the control strategy and reducing switching losses. A passive clamp circuit recycles leakage energy, reducing voltage stress (VS) on the MOSFET and enabling the implementation of a low on-state resistance switch for higher efficiency. Additionally, the quadratic structure and passive clamp circuit contribute to higher voltage gain (VG) and better performance. The converter’s operating principles, steady-state analysis, and component selection criteria are discussed in detail. The influence of magnetizing inductance, duty cycle, and parasitic components on the VG is also investigated, along with the system’s dynamic response under input voltage and load variations to ensure stable operation. A comparative evaluation with existing converters highlights its advantages. The PC is verified through SIMPLIS simulations, where key performance metrics such as VG and switching stress are analyzed. Furthermore, a hardware prototype with a power rating of 300 W is built to confirm the theory and showcase the converter’s performance. Experimental results demonstrate high efficiency, stable operation, and substantial VG, validating the converter’s feasibility for renewable energy systems (RES).
Volume: 17
Issue: 1
Page: 476-486
Publish at: 2026-03-01

A new boost LED driver

10.11591/ijpeds.v17.i1.pp602-616
Dzhunusbekov Erlan , Orazbayev Sagi
Reducing the cost, increasing efficiency, and improving the reliability of LED drivers are critical due to the widespread adoption of LED lighting. This paper presents a research study on a novel boost LED driver designed to minimize voltage pulsations across power switches, thereby reducing dynamic losses in all power components. A small number of Schottky diodes were used to reduce conduction losses. To reduce switching losses in semiconductors, a quasi-resonant switching (QRS) at zero current was implemented for driving transistors. The operating principle is analyzed using computer modeling and validated experimentally in critical conduction mode (CrCM). In the initial evaluation, one version of the proposed driver achieved a high efficiency of up to 98.7% at 120 W input power. Additionally, the size and value of the main inductor were significantly reduced. The proposed driver provides an efficient and scalable solution for high-power LED lighting. Lower dynamic losses and reduced impulse voltages create opportunities for integrating the control circuit and power switches into a single chip.
Volume: 17
Issue: 1
Page: 602-616
Publish at: 2026-03-01
1 2 3
...
1994 1995
Show 22 of 1995

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