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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

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

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

Classification of breast cancer using a precise deep learning model architecture

10.11591/ijict.v14i3.pp933-940
Mohammed Ghazal , Murtadha Al-Ghadhanfari , Fajer Fadhil
Breast cancer is an important topic in medical image analysis because it is a high-risk disease and the leading cause of death in women. Early detection of breast cancer improves treatment outcomes, which can be achieved by identifying it using mammography images. Computer-aided diagnostic systems detect and classify medical images of breast lesions, allowing radiologists to make accurate diagnoses. Deep learning algorithms improved the performance of these diagnoses systems. We utilized efficient deep learning approaches to propose a system that can detect breast cancer in mammograms. The proposed approach adopted relies on two main elements: improving image contrast to enhance marginal information and extracting discriminatory features sufficient to improve overall classification quality, these improvements achieved based on a new model from scratch to focus on enhancing the accuracy and reliability of breast cancer detection. The model trained on the digital database for screening mammography (DDSM) dataset and compared with different convolutional neural network (CNN) models, namely EfficientNetB1, EfficientNetB5, ResNet-50, and ResNet101. Moreover, to enhance the feature selection process, we have integrated adam optimizer in our methodology. In evaluation, the proposed method achieved 96.5% accuracy across the dataset. These results show the effectiveness of this method in identifying breast cancer through images.
Volume: 14
Issue: 3
Page: 933-940
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

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

Enhancing biodegradable waste management in Mauritius through real-time computer vision-based sorting

10.11591/ijict.v14i3.pp1119-1125
Geerish Suddul , Avitah Babajee , Nundjeet Rambarun , Sandhya Armoogum
Mauritius faces a significant waste management challenge due to the indiscriminate mixing of biodegradable and non-biodegradable waste. This practice hinders proper recycling and composting efforts, contributing to environmental pollution and resource depletion. This research proposes a computer vision-based system for real-time classification of waste into biodegradable and non-biodegradable categories. Transfer learning approach based on deep learning models, specifically DenseNet121, MobileNet, InceptionV3, VGG16 and VGG19 were evaluated with two different classifiers, the K-nearest neighbors (KNN) and support vector machine (SVM). Our experiments demonstrate that the MobileNet paired with SVM achieves a classification accuracy of 97% for detection in realtime. Compared to other studies, our results demonstrate better performance and realtime classification capabilities through the implementation of a prototype, facilitating automatic sorting of waste.
Volume: 14
Issue: 3
Page: 1119-1125
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

Does empathy and awareness of bullying affect the performance of Moroccan students in PISA?

10.11591/ijict.v14i3.pp860-867
Ilyas Tammouch , Abdelamine Elouafi , Soumaya Nouna
Socioemotional skills, such as empathy and bullying awareness, play a pivotal role in shaping students' personal and academic development. These skills are increasingly recognized as critical factors influencing educational outcomes, particularly in addressing challenges like bullying that can hinder learning. This study examines the impact of empathy and bullying awareness on the academic performance of Moroccan students, using data from the 2018 Programme for International Student Assessment (PISA). To ensure robust causal inference in high-dimensional data, the double/debiased machine learning (DML) technique is employed. The findings reveal that higher levels of empathy and awareness of bullying significantly enhance performance across reading, mathematics, and science, with the most notable improvements observed in reading. These results remain consistent across various demographic and socioeconomic groups, highlighting their robustness. The study underscores the importance of integrating socioemotional learning into educational practices to foster academic success and create supportive school environments. By contributing to the growing evidence on non-cognitive skills in education, this research offers valuable insights for educators and policymakers seeking to improve student outcomes.
Volume: 14
Issue: 3
Page: 860-867
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

Comparative analysis of u-net architectures and variants for hand gesture segmentation in parkinson’s patients

10.11591/ijict.v14i3.pp972-982
Avadhoot Ramgonda Telepatil , Jayashree Sathyanarayana Vaddin
U-Net is a well-known method for image segmentation, and has proven effective for a variety of segmentation challenges. A deep learning architecture for segmenting hand gestures in parkinson’s disease is explored in this paper. We prepared and compared four custom models: a simple U-Net, a three-layer U-Net, an auto encoder-decoder architecture, and a U-Net with dense skip pathways, using a custom dataset of 1,000 hand gesture images and their corresponding masks. Our primary goal was to achieve accurate segmentation of parkinsonian hand gestures, which is crucial for automated diagnosis and monitoring in healthcare. Using metrics including accuracy, precision, recall, intersection over union (IoU), and dice score, we demonstrated that our architectures were effective in delineating hand gestures under different conditions. We also compared the performance of our custom models against pretrained deep learning architectures such as ResNet and VGGNet. Our findings indicate that the custom models effectively address the segmentation task, showcasing promising potential for practical applications in medical diagnostics and healthcare. This work highlights the versatility of our architectures in tackling the unique segmentation challenges associated with parkinson’s disease research and clinical practice.
Volume: 14
Issue: 3
Page: 972-982
Publish at: 2025-12-01

Empowering low-resource languages: a machine learning approach to Tamil sentiment classification

10.11591/ijict.v14i3.pp941-949
Saleem Raja Abdul Samad , Pradeepa Ganesan , Justin Rajasekaran , Madhubala Radhakrishnan , Peerbasha Shebbeer Basha , Varalakshmi Kuppusamy
Sentiment analysis is essential for deciphering public opinion, guiding decisions, and refining marketing strategies. It plays a crucial role in monitoring public sentiment, fostering customer engagement, and enhancing relationships with businesses' target audiences by analyzing emotional tones and attitudes in vast textual data. Sentiment analysis is extremely limited, particularly for languages like Tamil, due to limited application in diverse linguistic contexts with fewer resources. Given its global impact and linguistic diversity, addressing this gap is crucial for a more nuanced understanding of sentiments in India. In the context of Tamil, the need for sentiment analysis models is particularly crucial due to its status as one of the classical languages spoken by millions. The cultural, social, and historical nuances embedded in Tamil language usage require tailored sentiment analysis approaches that can capture the subtleties of sentiment expression. This paper introduces a novel method that assesses the performance of various text embedding methods in conjunction with a range of machine learning (ML) algorithms to enhance sentiment classification for Tamil text, with a specific focus on lyrics. Experiments notably emphasize FastText word embedding as the most effective method, showcasing superior results with a remarkable 78% accuracy when coupled with the support vector classification (SVC) model.
Volume: 14
Issue: 3
Page: 941-949
Publish at: 2025-12-01

Chatbot for virtual medical assistance

10.11591/ijict.v14i3.pp914-922
Aravalli Sainath Chaithanya , Sampangi Lahari Vishista , Adepu MadhuSri
A healthy population is vital for societal prosperity and happiness. Amidst busy lifestyles and the challenges posed by the COVID-19 pandemic, individuals often neglect their health needs. To address this, we introduce a novel approach utilizing a chatbot for virtual medical assistance. Tailored for individuals confined indoors or hesitant to visit hospitals for minor ailments, our chatbot offers personalized medical support by diagnosing ailments based on user-reported symptoms and engaging in interactive conversations. Leveraging a robust dataset containing 132 symptoms, 41 diseases, and corresponding medications, our chatbot employs a systematic approach for symptom refinement, enhancing diagnostic precision. Upon identifying a disease, the chatbot promptly suggests basic medications tailored to the specific ailment. Furthermore, our system integrates user demographics to evaluate medication history and current state, allowing for personalized medication recommendations based on individual needs. Through extensive testing and validation, we demonstrate the effectiveness of our chatbot in accurately predicting ailments and providing timely treatment advice. Our study introduces a novel paradigm for medicine recommendation and disease prediction, with the potential to enhance healthcare accessibility and effectiveness.
Volume: 14
Issue: 3
Page: 914-922
Publish at: 2025-12-01

Scaling of Facebook architecture and technology stack with heavy workload: past, present and future

10.11591/ijict.v14i3.pp772-782
Tole Sutikno , Laksana Talenta Ahmad
Leading social media Facebook has improved its architecture to meet user needs. Facebook has improved its systems to handle millions of users with heavy workloads and large datasets using innovative architectural solutions and adaptive strategies. The study examines Facebook’s architectural and technological advances in heavy workload and big data. To understand how Facebook scaled with a growing user base and data volume, history and system architecture will be examined. It will also examine how cloud storage and high-performance computing optimize resource utilization and maintain performance during peak user activity. Facebook is managing big data and heavy workloads with new technologies like the hybrid communication model that uses PULL and PUSH strategies for real-time messaging. Facebook switched from HBase to MyRocks for message storage to improve performance as data grew. Architectural scaling and technology stack research must prioritize data storage innovations and optimized communication protocols to handle heavy workloads and big data. The messenger Sync protocol reduces network congestion and improves synchronous communication, reducing resource consumption and maintaining performance under high load. High-performance computing (HPC) and cloud storage should be studied together to support complex compute workflows. This convergence may improve large-scale application infrastructures and encourage interdisciplinary collaboration for scalable and resilient systems.
Volume: 14
Issue: 3
Page: 772-782
Publish at: 2025-12-01

Computational paradigm for advancing lung cancer drug discovery

10.11591/ijphs.v14i3.25783
Ochin Sharma , Alwalid Bashier Gism Elseed Ahmed , Mudassir Khan , Ghantasala Gnana Sudha Pradeep , Pellakuri Vidyullatha , Mohammad Mazhar Nezami
Lung cancer remainders one of the foremost causes of cancer-related impermanence worldwide. The availability of novel medicines for patients with lung cancer is restricted by the extremely lengthy timetables and high attrition rates of traditional drug discovery procedures. However, in silico drug discovery has emerged as a powerful and affordable way to identify potential treatments. This work offers well-structured paradigms for using virtual techniques to identify potential lung cancer treatments. The main concerns are virtual screening, target validation and identification, pharmacokinetic assessment, and molecular docking. The cost and time of drug development are reduced and a valuable platform for discovering novel drugs to treat lung cancer is produced by merging computational resources with proper methodologies. The current work explores the recent advancements, challenges, and possible future paths. Mann-Whitney U test says that the sampled data is different in distribution for molecular weight (MW), LogP, amount of H acceptors, and quantity of H donors for active and inactive molecules. Python tool has been utilized and identified that the CHEMBL4850929 (C31H31F2N7O4) molecule is a potential drug. It has pIC50 7.61, Lipinski values in terms of MW 603.63, LogP 3.36, amount of H donors 1, quantity of H acceptors 10.
Volume: 14
Issue: 3
Page: 1304-1312
Publish at: 2025-09-01
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