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

Effect on saturated and unsaturated fatty acids on various vegetable oils on droplet combustion characteristic

10.11591/ijape.v14.i4.pp980-987
Dony Perdana , Muhamad Nur Rohman , Mochamad Choifin
Vegetable oils have composed of triglycerides, which one consist of 3 fatty acids combined with glycerol. Each saturated and unsaturated fatty acid has a different effect on burning characteristics. This study aimed to investigated effect of fatty acids at ceiba pentandra and jatropha oils on the flame behavior of the droplet combustion process. The combustion characteristic was observed by an ignited droplet at the junction using a thermocouple and a high-speed camera (120 fps). Results showed that a higher saturated fatty acid content resulted in long-life and steady flames. This is because more oleic and linoleic acid carbon atoms leave the droplet area and react with air. Jatropha oil produces a higher temperature of 780 °C than ceiba pentandra oil. Temperature of a vegetable oils flame is influenced by number of carbon chains, double bond, and heating value. Ceiba pentandra oil has a higher burning rate of 0.185 mm/s than jatropha oil at 0.155 mm/s. The chain content of polyunsaturated fatty acids has significant effect on rate of combustion, which is due to the weak van der Waals dispersion forces, such that heat absorption is more active and energetic. The highest flame height for ceiba pentandra oil is 55.03 mm compared to for jatropha oil it is 46.82 mm. Long-chain unsaturated double bonds and glycerol cause micro-explosions. This micro-explosion caused the shape of the flame to split and expand so that evaporation occurred faster, thus increasing the size of the flame.
Volume: 14
Issue: 4
Page: 980-987
Publish at: 2025-12-01

Implementation of a network intrusion detection system for man-in-the-middle attacks

10.11591/ijece.v15i6.pp3913-3927
Kennedy Okokpujie , William A. Abdulateef-Adoga , Oghenetega C. Owivri , Adaora P. Ijeh , Imhade P. Okokpujie , Morayo E. Awomoy
Intrusion detection systems (IDS) are critical tools designed to detect and prevent unauthorized access and potential network threats. While IDS is well-established in traditional wired networks, deploying them in wireless environments presents distinct challenges, including limited computational resources and complex infrastructure configurations. Packet sniffing and man-in-the-middle (MitM) attacks also pose significant threats, potentially compromising sensitive data and disrupting communication. Traditional security measures like firewalls may not be sufficient to detect these sophisticated attacks. This paper implements a network intrusion detection system that monitors a computer network to detect Address Resolution Protocol spoofing attacks in real-time. The system comprises three host machines forming the network. Using Kali Linux, a bash script is deployed to monitor the network for signs of address resolution protocol (ARP) poisoning. An email alert system is integrated into the bash script, running in the background as a service for the network administrator. Various ARP spoofing attack scenarios are performed on the network to evaluate the efficiency of the network IDS. Results indicate that deploying IDS as a background service ensures continuous protection against ARP spoofing and poisoning. This is crucial in dynamic network environments where threats may arise unexpectedly.
Volume: 15
Issue: 6
Page: 6027-6042
Publish at: 2025-12-01

Spth-FCM: decision support tool for speech therapist based on fuzzy cognitive mapping

10.11591/ijict.v14i3.pp845-859
Maziz Asma , Taouche Cherif
The development and integration of medical information systems into a unified information space is a significant focus in the field of information technologies. It is essential to develop decision support systems (DSS) to enhance the effectiveness of medical and diagnostic procedures. This article presents a novel decision support tool for speech therapists, which is based on fuzzy cognitive maps (FCM). The latter is a method of modeling complex systems using knowledge of human existence and experience. The proposed tool is composed of three phases. The first phase focuses on entering patient information into the graphical interface developed in JAVA based on the most precise observations. An FCM will be automatically constructed, describing the type of disorder and the patient’s case during the second phase. Finally, in the third phase, FCM-based scenarios were built during the execution of the inference process under FCM expert. The system is presented and demonstrated using a real cases study for eight weeks. The results show that the tool makes it possible to display, guide, assist, and confirm the medical decision of the speech therapist for an appropriate diagnosis and treatment.
Volume: 14
Issue: 3
Page: 845-859
Publish at: 2025-12-01

Advancements in brain tumor classification: a survey of transfer learning techniques

10.11591/ijict.v14i3.pp1002-1014
Snehal Jadhav , Smita Bharne , Vaibhav Narawade
This survey article presents a critical review of the state-of-the-art transfer learning (TL) methodologies applied in the field of brain tumor classification, with a special emphasis on their various contributions and associated performance metrics. We will discuss various pre-processing approaches, the underlying fine-tuning strategies, whether used purely or in an end-to-end training manner, and multi-modal applications. The current study specifically highlights the application of VGG16 and residual network (ResNet) methods for feature extraction, demonstrating that leveraging highorder features in magnetic resonance imaging (MRI) images can enhance accuracy while reducing training. We further analyze fine-tuning methods in relation to their role in optimizing model layers for small, domain-specific datasets, finding them particularly effective in enhancing performance on the small brain tumor dataset. It will look into end-to-end training, which means fine-tuning models that have already been trained on large datasets to make them better. It will also present multimodal TL as a way to use both MRI and computed tomography (CT) scan data to get better classification results. Comparing different pre-trained models can provide a better understanding of the strengths and weaknesses associated with the particular brain tumor classification task. This review aims to analyze the advancements in TL for medical image analysis and explore potential avenues for future research and development in this crucial field of medical diagnostics.
Volume: 14
Issue: 3
Page: 1002-1014
Publish at: 2025-12-01

Navigating predictive landscapes of cloud burst prediction approaches: insights from comparative research

10.11591/ijict.v14i3.pp1146-1155
Anil Hingmire , Sunayana Jadhav , Megha Trivedi , Karan Sankhe , Omkar Khanolkar , Yukta Patil
Cloud burst forecasting remains an evolving field that grapples with the complexities of atmospheric phenomena and their impact on local environments. Cloud bursts in hilly regions demand robust predictive models to mitigate risks. This study addresses the challenge of imbalanced cloud burst occurrences, emphasizing the need for accurate predictions to minimize damage. It develops and evaluates a machine learning-based forecasting approach that includes several weather factors such as temperature, humidity, wind speed, and atmospheric pressure. The study also tackles the imbalance in cloud burst data. A dual-axis chart visually merges cloud burst occurrences with weather parameters, providing insights into their relationships over time. The model’s overall accuracy is 0.68, with precision and recall for cloud burst events at 0.25 and 0.07, respectively, and an F1-score of 0.11. However, when it comes to forecasting non-cloud burst occurrences, it shows a high precision of 0.72. This study evaluates machine learning models for cloud burst prediction, highlighting random forest as the top performer with an accuracy of 85.43%, effectively balancing true positives and true negatives while minimizing misclassifications. This research contributes to cloud burst prediction, offering performance insights and suggesting avenues for future exploration.
Volume: 14
Issue: 3
Page: 1146-1155
Publish at: 2025-12-01

Comparative study of traditional edge detection methods and phase congruency based method

10.11591/ijict.v14i3.pp868-880
Rajendra Vasantrao Patil , Vinodpuri Rampuri Gosavi , Govind Mohanlal Poddar , Suman Kumar Swarnkar
Finding relevant and crucial details from images and effectively interpreting what they represent are two of image processing's main goals. An edge is the line that separates an object from its backdrop and shows where two things meet. Mining the picture's borders for extracting useful data remains one of the trickiest steps in understanding of an image. The borders of the objects may be used to build the image's edges, which are its basic characteristics. There are different types of traditional edge retrieval techniques that are conventionally categorized as first order and second gradient based methods such as Roberts, Prwitt, Kirsch, Robinson, canny, Laplacian and Laplacian of gaussian. The majority of research and review work on edge detection algorithms focuses on conventional algorithms and soft computing based methods, neglecting illumination invariant phase congruency based edge detector. This study aims to compare traditional derivative based edge detection algorithms with log Gabor wavelet based edge detector phase congruency. This work does a thorough examination of various edgedetecting approaches, including traditional boundary detection methods and log Gabor wavelet based method. To test effectiveness of edge detection algorithms, experimental results are obtained on images from DRIVE, STARE, and BSDS500 dataset.
Volume: 14
Issue: 3
Page: 868-880
Publish at: 2025-12-01

Performance enhancement using sensor and sensorless control techniques for a modified bridgeless Ćuk converter-based BLDC motor in EV applications

10.11591/ijape.v14.i4.pp769-782
W. Margaret Amutha , S. Premalatha , M. Karthikeyan
This work proposes a solar photovoltaic (PV)-powered, modified bridgeless Ćuk converter tailored for electric vehicle applications. It overcomes limitations such as high ripple, reduced power density, significant switching losses, and complex circuit structures in traditional designs. The system integrates a boost converter with a bridgeless Ćuk topology to ensure a reliable and efficient direct current (DC) power output. Performance evaluation includes sensor-based and sensorless speed control techniques-pulse width modulation (PWM), proportional integral derivative (PID), back electromotive force (EMF), and spider controllers-under both no-load and full-load scenarios. Key parameters such as rise time, overshoot, settling time, and steady-state error are analyzed. MATLAB/Simulink simulations indicate that the spider controller delivers superior dynamic behavior and stability. A 48 W, 1500 rpm hardware prototype confirms the simulation outcomes, demonstrating the practical viability and effectiveness of the proposed converter.
Volume: 14
Issue: 4
Page: 769-782
Publish at: 2025-12-01

Design of miniaturized dual-band bandpass filter with enhanced selectivity for GPS and RFID applications

10.11591/ijict.v14i3.pp993-1001
Thupalli Shaik Mahammed Basha , Arun Raaza , Vishakha Bhujbal , Meena Mathivanan
This article presents a miniaturized interdigital coupled dual-band bandpass filter with multiple transmission zeros/poles. Stepped impedance resonators, interdigital coupled lines, and series coupled lines make up the proposed filter design. A circuit simulator is used to analyze a proposed filter, and the magnitude and bandwidth shifts have been investigated. To confirm the proposed filter design, equations for transmission zero frequencies have been constructed and verified based on even-odd mode analysis and lossless transmission line theory. A working prototype for 2.2 GHz (RFID) and 1.38 GHz (GPS) applications is made and tested. With λg representing the guided wavelength at the first band (1.38GHz), the finished prototype is compact, measuring 0.32 λg×0.27 λg. According to the experimental findings, there is strong selectivity in the first and second passbands, with roll-off rates of 190 and 168 dB/GHz, respectively. Good isolation between the two passbands is indicated by an insertion loss of less than 20 dB.
Volume: 14
Issue: 3
Page: 993-1001
Publish at: 2025-12-01

A survey on ransomware detection using AI models

10.11591/ijict.v14i3.pp1085-1094
Goteti Badrinath , Arpita Gupta
Data centers and cloud environments are compromised as they are at great risk from ransomware attacks, which attack data integrity and security. Through this survey, we explore how AI, especially machine learning and deep learning (DL), is being used to improve ransomware detection capabilities. It classifies ransomware types, highlights active groups such as Akira, and evaluates new DL techniques effective at real-time data analysis and encryption handling. Feature extraction, selection methods, and essential parameters for effective detection, including accuracy, precision, recall, F1-score and receiver operating characteristic (ROC) curve, are identified. The findings point to the state of the art and the state of the art in AI based ransomware detection and underscore the need for robust, real-time models and collaborative research. The statistical and graphical analyses help researchers and practitioners understand existing trends and directions for future development of efficient ransomware detection systems to strengthen cybersecurity in data centers and cloud infrastructures.
Volume: 14
Issue: 3
Page: 1085-1094
Publish at: 2025-12-01

Digital control of plant development through sensors and microcontrollers in Kosova

10.11591/ijict.v14i3.pp1072-1084
Ragmi M. Mustafa , Kujtim R. Mustafa , Refik Ramadani
The plant monitoring system aims to develop an automated solution for optimizing plant growth. Using the Arduino Uno ATMEGA328P microcontroller module and various sensors, this system regulates environmental conditions to promote optimal plant development. It requires adequate software to operate effectively, enabling the microcontroller to monitor and regulate climatic conditions. The primary goal of this paper is to present a comprehensive system that continuously measures parameters such as light intensity, air humidity, and soil moisture in real time within a vegetable greenhouse or a plastic-covered plant environment. This scientific paper provides an in-depth description of the hardware components used, their electronic connections, and the implementation of program code written in C++. Based on the measured physical parameters, the plant monitoring system performs specific actions, such as watering the plants and regulating the ambient temperature. In conclusion, this system effectively supports healthy plant growth and enhances the quality and yield of plant products. The paper serves as a practical example for improving plant cultivation in the agricultural sector in the Republic of Kosova.
Volume: 14
Issue: 3
Page: 1072-1084
Publish at: 2025-12-01

A hybrid one step voltage-adjustable transformerless inverter for a one-phase grid incorporation of wind and solar power

10.11591/ijape.v14.i4.pp951-959
Bonigala Ramesh , Madhubabu Thiruveedula , Rahul Inumula , C. Poojitha Reddy , Mohammad Abdul Khadar , K. Sri Sai Hareesh
This paper presents a hybrid one-step voltage-adjustable transformerless inverter designed to efficiently integrate both solar photovoltaic (PV) and wind energy sources into a single-phase grid. The primary objective is to enhance power conversion efficiency while minimizing system complexity and cost. The proposed architecture combines a buck-boost DC-DC converter with a full-bridge inverter in a compact and modular design, enabling voltage regulation across a wide input range typical of hybrid renewable systems. By grounding the PV negative terminal, the system effectively eliminates leakage currents and ensures compliance with IEEE harmonic standards. The inverter operates with reduced switching losses and supports multiple operational modes tailored for variable solar and wind conditions. Simulation of a 300 W prototype demonstrates reliable performance, achieving a total harmonic distortion (THD) below 1%, validating its compatibility with grid requirements. Key contributions include the development of a unified topology for hybrid energy sources, in-depth analysis of energy storage components, and implementation of efficient modulation strategies. This work addresses significant challenges in renewable energy integration and provides a scalable solution for next-generation grid-connected hybrid power systems.
Volume: 14
Issue: 4
Page: 951-959
Publish at: 2025-12-01

Pneumonia detection system using convolutional neural network with DenseNet201 architecture

10.11591/ijict.v14i3.pp1172-1178
Muhammad Qomaruddin , Andi Riansyah , Hildan Mulyo Hermawan , Moch Taufik
The diagnosis of pneumonia remains a significant challenge for medical practitioners worldwide, particularly in regions with limited healthcare resources. Traditional interpretation of chest X-rays is time-consuming and often subjective, especially when images are of low quality. This study presents the development of a web-based system utilizing the DenseNet201 architecture to address these challenges. A series of experiments were conducted to evaluate three optimizers Adam, Adamax, and Adadelta over fifty epochs. Among them, Adamax yielded the best performance, achieving a training accuracy of 93.67% and a validation accuracy of 94.20%. When tested on new data, the system consistently delivered high performance, with accuracy, precision, recall, and F1 score all reaching 96%. These results suggest that the proposed system has the potential to significantly enhance the accuracy and efficiency of pneumonia diagnosis based on chest X-rays.
Volume: 14
Issue: 3
Page: 1172-1178
Publish at: 2025-12-01

Real-time posture monitoring prediction for mitigating sedentary health risks using deep learning techniques

10.11591/ijict.v14i3.pp1126-1135
D. B. Shanmugam , J. Dhilipan
Sedentary behavior has become a pressing global public health issue. This study introduces an innovative method for monitoring and addressing posture changes during inactivity, offering real-time feedback to individuals. Unlike our prior research, which focused on post-analysis, this approach emphasizes real-time monitoring of upper body posture, including hands, shoulders, and head positioning. Image capture techniques document sedentary postures, followed by preprocessing with bandpass filters and morphological operations such as dilation, erosion, and opening to enhance image quality. Texture feature extraction is employed for comprehensive analysis, and deep neural networks (DNN) are used for precise predictions. A key innovation is a feedback system that alerts individuals through an alarm, enabling immediate posture adjustments. Implemented in MATLAB, the method achieved accuracy, sensitivity, and specificity rates of 98.2%, 90.7%, and 99.2%, respectively. Comparative analysis with established methods, including support vector machine (SVM), random forest, and K-nearest neighbors (KNN), demonstrate the superiority of our approach in accuracy and performance metrics. This real-time intervention strategy has the potential to mitigate the adverse effects of sedentary behavior, reducing risks associated with cardiovascular and musculoskeletal diseases. By providing immediate corrective feedback, the proposed system addresses a critical gap in sedentary behavior research and offers a practical solution for improving public health outcomes.
Volume: 14
Issue: 3
Page: 1126-1135
Publish at: 2025-12-01

Predictive insights into student online learning adaptability: elevating e-learning landscape

10.11591/ijict.v14i3.pp892-902
Mohamed El Ghali , Issam Atouf , Kamal El Guemmat , Mohamed Talea
In Morocco’s rapidly transforming educational landscape, this study delves into students’ adaptability to online learning environments by integrating sophisticated artificial intelligence (AI) algorithms and hyperparameter optimization techniques. This research uses the comprehensive “online learning adaptivity” dataset to identify pivotal factors influencing student flexibility and effectiveness in e-learning platforms. We applied various AI models, with a particular emphasis on the CatBoost classifier, which exhibited exceptional predictive performance, achieving an accuracy rate near 98%. This high precision in predicting student adaptiveness offers essential insights into tailoring digital education systems. The results underscore the significant potential of machine learning technologies to enhance educational methodologies by catering to the diverse needs of students. Such capabilities are instrumental for educators and policymakers dedicated to refining e-learning strategies that effectively accommodate individual learning styles, ultimately improving the broader educational outcomes in Moroccan tertiary education. These findings advocate for a more nuanced understanding of the interplay between student behavior and technological solutions, providing a roadmap for developing more responsive and effective educational platforms.
Volume: 14
Issue: 3
Page: 892-902
Publish at: 2025-12-01

Attitude and intention to use chatbots in e-commerce: the moderating role of personal innovativeness

10.11591/ijict.v14i3.pp760-771
Indah Oktaviani Hardi , Ahmad Maki , Evi Rinawati Simanjuntak
Internet-based retailers employ artificial intelligence (AI) chatbots to facilitate customer communication. This research endeavored to evaluate consumers' intentions regarding the utilization of chatbots for customer service interactions, building upon the technology acceptance model (TAM). TAM-based chatbot adoption is the subject of an abundance of research. Conversely, the extent to which users' perception of the chatbot's response quality influences their intention to adopt remains uncertain. In addition to investigating the potential influence of chatbot response accuracy and completeness on users' intention to adopt the system, this study explored the relationship between users' personal innovativeness and adoption intention. A total of 312 usable responses were analyzed with PLS-SEM from survey data collected via convenience sampling from e-commerce customers. Perceived usefulness, convenience of use, accuracy, and completeness all influenced attitudes toward chatbots, as shown by hypothesis testing result. Attitude formation toward chatbots is most strongly influenced by perceived completeness. Personal innovativeness has a negative influence, which contradicts the hypothesis despite the fact that its moderating effect is statistically significant. Further comprehension of the key determinants of attitude towards chatbots is enhanced by these findings. It is advisable for organizations to empower the chatbot with the capability to conduct thorough and precise responses to inquiries.
Volume: 14
Issue: 3
Page: 760-771
Publish at: 2025-12-01
1 2 3
...
1994 1995
Show 63 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