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29,061 Article Results

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

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

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

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

Determinants of integrated teaching capacity among teachers in ethnic minority primary schools in northern Vietnam

10.11591/ijere.v14i6.30087
Hang Nguyen Thi Thu , Chuyen T. H. Nguyen
This study explores factors affecting the integrated teaching capacity of primary school teachers in ethnic minority schools in the Northern mountainous regions of Vietnam. Given the challenges of linguistic and cultural diversity in this context, the research aims to address gaps in current practices and propose measures for improvement. A quantitative approach was adopted, surveying 280 teachers and administrators using exploratory factor analysis (EFA) and multivariate regression. The results identify four primary factors influencing teaching capacity: i) language, culture, and parent coordination; ii) teacher capacity and community participation; iii) teaching materials, equipment, and teacher attitudes; and iv) policies and support from management agencies. Among these, language, culture, and parent coordination are the most impactful. The study underscores the need for targeted teacher training programs and improved collaboration with local communities to enhance teaching outcomes. These findings provide actionable insights for policymakers and educators to improve integrated teaching in ethnically diverse and economically challenged regions.
Volume: 14
Issue: 6
Page: 4295-4306
Publish at: 2025-12-01

The role of social support and academic self-efficacy in enhancing academic engagement among undergraduates

10.11591/ijere.v14i6.33999
Elizabeth Ifeoma Anierobi , Amjad Islam Amjad , Favour Amarachi Ubani , Sarfraz Aslam , Mohamad Ahmad Saleem Khasawneh , Huda Alshamsi
Academic engagement is vital to students’ academic success, especially in higher education settings where motivation and support systems vary widely. This study investigated the influence of social support and academic self-efficacy on academic engagement among undergraduates of Nnamdi Azikiwe University. The main objectives were to determine the extent to which parental and peer support and students’ belief in their academic abilities correlate with their level of academic engagement. A correlational research design was used to guide the study. From a total population of about 20,000 undergraduate students enrolled in the 2023/2024 academic session, a sample of 403 students was randomly selected using a simple random sampling technique to ensure equal representation. Data were collected using three standardized instruments: the social support questionnaire (SSQ), the academic self-efficacy questionnaire (ASEQ), and the academic engagement questionnaire (AEQ). The data were analyzed using SPSS software. Pearson’s product-moment correlation and multiple regression analysis were used to test the research questions and hypotheses. Findings showed significant positive relationships between social support (both parental and peer), academic self-efficacy, and students’ academic engagement. These results highlight the importance of fostering supportive learning environments and building students’ confidence in their academic abilities. Practical implications suggest that universities should implement structured peer mentoring, parental involvement strategies, and workshops that enhance academic self-efficacy to improve student engagement and academic outcomes.
Volume: 14
Issue: 6
Page: 4689-4699
Publish at: 2025-12-01

The level of scientific research skills of the biology students in the Philippines

10.11591/ijere.v14i6.34323
Chillet G. Credo , Justin Vianey M. Embalsado , Jed V. Madlambayan , Rich Paulo S. Lim , Maica S. Pineda , Ricardo C. Salunga , Arnel A. Diego , Tracy John A. Credo
Scientific research is essential in advancing human knowledge and in driving technological advancements. Students in the bachelor of science in biology program are expected to accomplish scientific research as a curriculum requirement. Possessing scientific research skills is essential for producing high-quality research outputs. A scale for assessing scientific research skills among senior high school students is available, however, there is an instrumentation gap in evaluating these skills at the tertiary level. In this regard, a research gap also exists in the assessment of students’ scientific research skills. Confirmatory factor analysis (CFA) using the JAMOVI software was utilized to establish the validity and reliability of the scientific research skill scale. The study included 133 officially enrolled biology students who voluntarily agreed to participate. The results provided compelling evidence that the tool effectively assesses scientific research skills in three key areas: scientific information development skills, scientific research management skills, and scientific research processing skills. This also affirmed the relevance of the three key areas in the biology program. The results also revealed that the level of scientific research skills of the students is on the average level across all three areas. This reflected an existing issue in the field of scientific research as mastery of skills is crucial in producing quality output, hence the study has significant implications for curriculum developers and policymakers of higher education institutions. There is a need to revisit the curriculum and to incorporate opportunities to enhance scientific research skills across various science subjects.
Volume: 14
Issue: 6
Page: 4517-4527
Publish at: 2025-12-01

Efficient design of approximate carry-based sum calculating full adders for error-tolerant applications

10.11591/ijict.v14i3.pp1189-1198
Badiganchela Shiva Kumar , Galiveeti Umamaheswara Reddy
Approximate computing is an innovative circuit design approach which can be applied in error-tolerant applications. This strategy introduces errors in computation to reduce an area and delay. The major power-consuming elements of full adder are XOR, AND, and OR operations. The sum computation in a conventional full adder is modified to produce an approximate sum which is calculated based on carry term. The major advantage of a proposed adder is the approximation error does not propagate to the next stages due to the error only in the sum term. The proposed adder was coded in verilog HDL and verified for different bit sizes. Results show that the proposed adder reduces hardware complexity with delay requirements.
Volume: 14
Issue: 3
Page: 1189-1198
Publish at: 2025-12-01

Performance analysis of D2D network in heterogeneous multitier interference scenarios

10.11591/ijict.v14i3.pp811-821
Dhilipkumar Santhakumar , Arunachalaperumal Chellaperumal , Jenifer Suriya Lazer Jessie , Jerlin Arulpragasam
The trade-off between boosting network throughput and minimizing interference is a critical issue in fifth generation (5G) networks. Diverting the data traffic around the network access point in device-to-device (D2D) communication is an important step in realizing the vision of 5G. Though the D2D network improves the network performance, they complicate the interference management process. Interference is an invisible physical phenomenon occurring in wireless communication which happens when multiple transmissions happen simultaneously over a general wireless medium. Enormous growth in usage of mobile phone and other wireless gadgets in recent years has paved the way for significant role in Interference analysis over multi-tier network. Interference could affect communication systems performance and it might even prevent systems functioning properly. 3G and 4G wireless devices coexisted with reverse compatibility in a coverage area. Also, after their widespread adoption, 5G devices have become prevalent across the globe and this reaffirms interference coexistence as a significant field of research. Multiple systems operating in a region will cause severe interference and ultimately reduce the quality of received signal. A simulation environment for cellular standards coexistence considering real-time parameters is created and experimented. Various research works earlier addresses the interference mitigation techniques in multi-tier networks but none of them present the analysis of scenarios and interfering signal power levels in the respective contexts. In this paper various scenarios with different network interference coexistence were studied, simulated, and analyzed quantitatively.
Volume: 14
Issue: 3
Page: 811-821
Publish at: 2025-12-01

Solar-powered boost-fly back converter for efficient warehouse monitoring with flack droid

10.11591/ijict.v14i3.pp802-810
S. Sivajothi Kavitha , D. Usha , V. Jamuna
Warehouses serve as essential infrastructure for storing a wide array of goods and are utilized by various entities. Implementing a sophisticated warehouse management system (WMS) represents a pinnacle of technological advancement. Effective warehouse maintenance is paramount, benefiting both consumers and producers alike. Typically, warehouses store items such as medicine, chemicals, food, and electronics, requiring controlled conditions of temperature and humidity. Monitoring these factors is essential to comply with regulations and maintain internal quality standards. This paper focuses on optimizing warehouse management to meet customer demands and streamline processes for packaging and production teams. Additionally, it proposes the integration of droid technology within warehouses to monitor the parameters and mitigate fire hazards, thereby enhancing the efficiency and safety of goods storage. This proactive approach not only ensures the integrity of stored products but also contributes to cost-saving measures within the warehouse. This paper introduces an innovative method to achieve a substantial increase in voltage output in a DC-DC converter while avoiding the need for excessively high duty ratios. The converter’s operation is governed by a single pulse width modulation (PWM) signal, employing a fractional-order proportional-integral-derivative controller (FOPID) for regulating the power switch. By merging boost-forward-fly back (BFF) converter topologies, the design achieves a remarkable voltage gain. Moreover, the converter efficiently recycles energy stored in the leakage inductance of the coupled inductor, thereby reducing voltage stress and minimizing power losses and thus enhancing overall converter efficiency.
Volume: 14
Issue: 3
Page: 802-810
Publish at: 2025-12-01

Novel multilevel local binary texture descriptor for oral cancer detection

10.11591/ijict.v14i3.pp837-844
Vijaya Yaduvanshi , Raman Murugan
Categorizing texture medical images is an extensive job in most of the fields of computer vision, pattern recognition and biomedical imaging. For the past few years, the texture analysis system model, especially for biological images, has been brought to attention because of its ever-growing requirements and characteristics. This research shows its novelty by using a multilevel local binary texture descriptor (MLBTD) algorithm with support vector machine (SVM), k-nearest neighbor (KNN), and CT Classifiers to investigate the texture features of the oral cancer samples. The simulation work is done in MATLAB2021a environment by employing the MLBTD algorithm. A Mendeley dataset, containing 89 oral cavity histopathological images and 439 OSCC images in 100x magnification, is used. A statistical comparative study of local binary pattern (LBP) and MLBTD with linear SVM, KNN, CT classifier is performed in which results show the better performance of MLBTD and linear SVM with 89.94% of accuracy and by applying MLBTD algorithm over 90.57% accuracy is obtained whereas LBP algorithm only provides 86.16% of accuracy.
Volume: 14
Issue: 3
Page: 837-844
Publish at: 2025-12-01

A recommendation system for teaching strategies according to learning styles

10.11591/ijict.v14i3.pp983-992
Juan Francisco Figueroa-Pérez , Manuel Rodríguez-Guerrero , Alan Ramírez-Noriega , Yobani Martínez-Ramírez
Teaching strategies (TS) are resources, procedures, techniques, and/or methods that teachers use as instruments to promote meaningful learning in students and that have proven to be efficient as support in classroom teaching. This paper describes a recommendation system (RS) for teaching strategies according to learning styles (RSTSLS) that helps to determine the most appropriate TS to use according to the learning style (LS) of the students based on Felder and Silverman’s learning styles model (FSLSM). A working example of the system is provided, as well as the results of its functional and non-functional tests, which were satisfactory. It is concluded that the system can be useful as a support tool for teachers, allowing them to adapt their TS according to the LS of their students.
Volume: 14
Issue: 3
Page: 983-992
Publish at: 2025-12-01

Quality of service optimization for 4G LTE upload and download throughput

10.11591/ijict.v14i3.pp1024-1033
Afrizal Yuhanef , Siska Aulia , Lefenia Indriani
Demand for mobile data services and people’s dependence on 4G LTE networks continue to increase. However, the quality of service (QoS) of this network still requires improvement, especially regarding the effect of QoS on throughput at specific frequencies. The research gap lies in the lack of indepth analysis of the impact of QoS parameters on network performance at frequencies of 2,100 MHz and 2,300 MHz. This study evaluates the effect of QoS parameters, such as delay, jitter, and packet loss, on throughput in 4G LTE networks at both frequencies. The research methodology uses an experimental approach with throughput, delay, jitter, and packet loss measurements in various network conditions. The results showed that delay (17.2174 ms to 37.0322 ms), jitter, and packet loss significantly influence throughput, ranging from 624.5 Kbps to 1,322.4 Kbps. The 2,100 MHz frequency tends to show better performance than 2,300 MHz. This study concludes that optimizing QoS parameters, especially delay and jitter, can significantly improve 4G LTE network performance. These findings provide practical contributions for mobile operators in improving network quality and customer satisfaction and open opportunities for further research on other frequencies or newer network technologies.
Volume: 14
Issue: 3
Page: 1024-1033
Publish at: 2025-12-01

Influences of educational and personal contexts on self-efficacy and job satisfaction of public elementary school teachers

10.11591/ijere.v14i6.33510
Ellaine Joy G. Eusebio , Philip R. Baldera , Aljay Marc C. Patiam , Charton F. Sombria , Jacel Ruz F. Gan , Connie G. Castillo
Enhancing teachers’ performance and sense of fulfillment in their roles is essential for advancing educational quality and promoting their overall well-being. This study investigates the determinants of teachers’ self-efficacy within a supportive school culture, as well as the factors influencing their job satisfaction, focusing on both educational and personal contexts among public elementary school teachers within a supportive school culture, focusing on educational and personal contexts. Utilizing a sample of 97 teachers from 13 schools in the Philippines, the research employs a causal-comparative design and surveys to gather data. The Kruskal-Wallis test results indicate no significant differences in self-efficacy and job satisfaction across age groups. The Mann-Whitney U test reveals a significant difference in self-efficacy between male and female teachers, with the latter reporting higher levels, while no significant gender differences were observed in job satisfaction. Likewise, no significant differences were found across career stages in both efficacy and satisfaction. A multivariate analysis of variance reveals that a supportive school culture has a significant impact on teachers’ self-efficacy and also on their job satisfaction. These results emphasize the critical role of nurturing a supportive school environment to enhance teacher well-being and effectiveness. The study provides valuable insights and practical recommendations for improving educational quality and teacher satisfaction through targeted interventions in school culture and opportunities for career advancement.
Volume: 14
Issue: 6
Page: 4468-4477
Publish at: 2025-12-01

The fundamental context of SEL for early childhood student teachers in private higher education institutions

10.11591/ijere.v14i6.34818
Yupawadee Promsatien , Ariyabhorn Kuroda , Suwadee Aerarunchot , Sungwean Pinagalang
This research examined the learning needs and the current social and emotional conditions of early childhood student teachers in private higher education institutions. The sample group responding to the questionnaire comprised 343 undergraduate students majoring in early childhood education. The target group for interviews included nine participants, which consisted of 3 deans, 3 department heads, and 3 lecturers. The research instruments consisted of a questionnaire on the state and needs of social and emotional learning (SEL) and an interview guide on the current social and emotional conditions of early childhood student teachers. The data were analyzed using percentage, mean, standard deviation, and content analysis. The findings revealed that the state of learning among pre-service teachers was at a low level, while their learning needs were high. Furthermore, the current conditions indicated that pre-service teachers require enhanced SEL opportunities. Activities fostering such learning should be designed, including group work, experiential learning, listening to others, and engaging in discussions.
Volume: 14
Issue: 6
Page: 4254-4263
Publish at: 2025-12-01
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