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28,428 Article Results

Strid-CNN: moving filters with convolution neural network for multi-class pneumonia classification

10.11591/ijai.v14.i4.pp3253-3261
Khushboo Trivedi , Chintan Bhupeshbhai Thacker
Millions of people around the world suffer from pneumonia, a serious lung illness. To effectively treat and manage this condition, a quick and accurate diagnosis is essential. This study thoroughly examines different ways of using transfer learning to classify pneumonia into multiple categories. We use well-known methods like DenseNet121, VGGNet-16, ResNet-50, and Inception Net, as well as a new method called Strid-CNN, which applies moving filters with convolution neural network. Through extensive testing, we show that each method effectively uses pre-learned information on a large dataset of medical images, accurately identifying pneumonia across various classes. Our results reveal subtle differences in performance among these methods, providing insights into how well they adapt to the challenging field of medical image analysis. Additionally, the Strid-CNN method shows promising results, indicating its potential as a competitive alternative. This research offers valuable guidance on choosing the right transfer learning approach for classifying pneumonia into multiple categories, contributing to improvements in diagnostic accuracy and healthcare effectiveness. Our study not only highlights the current state of transfer learning in pneumonia classification but also its potential to enhance clinical outcomes and patient care.
Volume: 14
Issue: 4
Page: 3253-3261
Publish at: 2025-08-01

Optimizing citrus disease detection: a transferrable convolutional neural network model enhanced with the fruitfly optimization algorithm

10.11591/ijai.v14.i4.pp3201-3213
Anoop Ganadalu Lingaraju , Asha Mangala Shankaregowda , Babu Kumar Sathiyamurthy , Santhrupth Budanoor Channegowda , Shruti Jalapur , Chaitra Palahalli Chennakeshava
Fungal, bacterial, and viral diseases significantly threaten citrus production and quality worldwide, prompting producers to explore technological solutions to mitigate the financial impact of these diseases. Image analysis techniques have emerged as powerful tools for detecting citrus diseases by differentiating between healthy and diseased specimens through the extraction of discriminative features from input images. This paper introduces a valuable dataset comprising 953 color images of orange leaves from the species Citrus sinensis (L.) Osbeck, which serves to train, evaluate, and compare various algorithms aimed at identifying abnormalities in citrus fruits. The development of automated detection systems is crucial for reducing economic losses in citrus production, with this research focusing on twelve specific diseases and nutrient deficiencies. We propose a novel approach to citrus plant disease detection utilizing a hyper-parameter tuned transferrable convolutional neural network (TCNN) model, referred to as the enhanced fruitfly optimization algorithm (EFOA)-TCNN model. This model optimizes the parameters of TCNN using the EFOA and enhances architectural design by incorporating three convolutional layers alongside an energy layer instead of a traditional pooling layer. Experimental results demonstrate that the proposed EFOA-TCNN model outperforms existing state-of-the-art methods, achieving a sensitivity of 0.975 and an accuracy of 0.995.
Volume: 14
Issue: 4
Page: 3201-3213
Publish at: 2025-08-01

Dual simulated annealing soft decoder for linear block codes

10.11591/ijai.v14.i4.pp2776-2787
Hicham Tahiri Alaoui , Ahmed Azouaoui , Jamal El Kafi
This paper proposes a new approach to soft decoding for linear block codes called dual simulated annealing soft decoder (DSASD) which utilizes the dual code instead of the original code, using the simulated annealing algorithm as presented in a previously developed work. The DSASD algorithm demonstrates superior decoding performance across a wide range of codes, outperforming classical simulated annealing and several other tested decoders. We conduct a comprehensive evaluation of the proposed algorithm's performance, optimizing its parameters to achieve the best possible results. Additionally, we compare its decoding performance and algorithmic complexity with other decoding algorithms in its category. Our results demonstrate a gain in performance of approximately 2.5 dB at a bit error rate (BER) of 6×10⁻⁶ for the LDPC (60,30) code.
Volume: 14
Issue: 4
Page: 2776-2787
Publish at: 2025-08-01

Interpretable machine learning for academic risk analysis in university students

10.11591/ijai.v14.i4.pp3089-3098
Mukti Ratna Dewi , Mochammad Reza Habibi , Bassam Babgei , Lovinki Fitra Ananda , Brodjol Sutijo Suprih Ulama
Higher education institutions often grapple with issues related to academic risk among their students. These academic risks encompass low academic performance, study delays, and dropouts. One approach to address these challenges is to predict students’ academic performance as accurately as possible by leveraging advanced computational techniques and utilizing academic and non-academic student data. This research aims to develop a model that accurately identifies students with high potential for academic risk while explaining the contributing factors to this phenomenon in the Faculty of Vocational Studies, Institut Teknologi Sepuluh Nopember (ITS). The prediction model is constructed using the light gradient boosting machine (LightGBM) method and is subsequently interpreted using the Shapley additive explanations (SHAP) value. Additionally, an oversampling method, based on synthetic minority oversampling technique (SMOTE), is implemented to address imbalances in the dataset. The proposed approach achieves 96% and 97% accuracy and specificity rates, respectively. Analysis based on SHAP values reveals that extracurricular activities, choice of major, smoking habit, gender, and friendship circle are among the top five factors impacting students’ academic risk.
Volume: 14
Issue: 4
Page: 3089-3098
Publish at: 2025-08-01

Solving k-city multiple travelling salesman using genetic algorithm

10.11591/ijai.v14.i4.pp2741-2752
Alikapati Prakash , Uruturu Balakrishna , Thangaraj Manogaran , Thenepalle Jayanth Kumar
This paper addresses a novel variant of the classical multiple traveling salesman problem (MTSP) i.e. k-city multiple traveling salesman problem (k-MTSP). The problem can describe as follows. Let there are n cities, m salesman positioned at depot city and a predefined positive value k. The distance between each pair of cities is known. The objective of the k-MTSP is to determine a collection of m closed tours for salesman, which covers exactly k (including depot city) of n cities such that the total distance covered is minimum. The k-MTSP can be seen as a combination of both subset selection and permutation characteristics. From the through literature review, it is found that this study on k-MTSP is first of its kind to the best of author’s knowledge. The paper introduces a zero-one integer linear programming (0-1 ILP) formulation alongside an efficient genetic algorithm (GA), designed to address k-MTSP. No comparative studies carried out due to the absence of existing studies on k-MTSP. However, the developed GA is tested over various benchmark test cases from TSPLIB and results are reported, which may potentially serve as basis for further comparative studies. Overall findings demonstrate that the GA consistently produces best solutions within reasonable computational times for relatively smaller and medium test cases, suggesting its robustness and effectiveness in tackling the k-MTSP. However, to enhance consistency and efficiency, particularly for larger datasets, further algorithm improvements are necessary.
Volume: 14
Issue: 4
Page: 2741-2752
Publish at: 2025-08-01

Optimization control design and simulation of furnace-fired boiler exit pressure: leveraging disruptive technology

10.11591/ijai.v14.i4.pp2979-2990
Ganiyat Abiodun Salawu , Glen Bright
The efficient operation of furnace-fired drum boilers is critically dependent on the precise control of downstream exit pressure, especially in the presence of stochastic heat fluctuations. This paper presents a stochastic control approach for regulating the downstream exit pressure in a furnace-fired boiler subject to random heat fluctuations. A stochastic model of the boiler dynamics is developed, incorporating heat transfer and combustion uncertainties. By leveraging disruptive technology, such as the model predictive control (MPC), strategies were designed to optimize the downstream exit pressure in real-time, and minimizing deviations from the set point. Simulation studies demonstrated the effectiveness of the proposed approach in maintaining a stable exit pressure despite random heat fluctuations. Results show significant improvements in boiler performance and efficiency compared to traditional proportional integral derivative (PID) control. The proposed stochastic control strategy offers a promising solution for reliable and efficient operation of furnace-fired boilers under uncertain conditions.
Volume: 14
Issue: 4
Page: 2979-2990
Publish at: 2025-08-01

Prediction of side effects of drug resistant tuberculosis drugs using multi-label random forest

10.11591/ijai.v14.i4.pp2899-2908
Siti Syahidatul Helma , Wisnu Ananta Kusuma , Mushthofa Mushthofa , Diah Handayani
Drug-resistant tuberculosis (DR-TB) has become a concern because anti-tuberculosis drugs (ATD) used to treat it can cause side effects in patients. This study aimed to predict the potential side effects of ATD using a multi-label classification approach with a random forest (RF) algorithm. This study used 660 medical record data, including the 14 ATD treatments prescribed to the patients and the six side effects experienced by patients. The model was trained using the best parameters based on the hyperparameter tuning process. The results show that the RF multi-label algorithm can be an alternative for building ATD side effect prediction models because it produces the most optimal performance value compared to the decision tree (DT) and extreme gradient boosting (XGBoost). The area under the curve (AUC) score of all RF multi-label models is above 0.8, which means that all RF multi-label models are considered acceptable and applicable for ATD side effect prediction. In addition, eight features influenced the models based on the average feature importance score of the RF models. This study is expected to help predict the side effects of ATD used to treat DR-TB based on ATD treatment and determine the most promising tree-based machine learning algorithm for predicting ATD side effects.
Volume: 14
Issue: 4
Page: 2899-2908
Publish at: 2025-08-01

Machine learning for global trade analysis: a hybrid clustering approach using DBSCAN, elbow, and SOM

10.11591/ijai.v14.i4.pp3033-3046
Musdalifa Thamrin , Ida Mulyadi , I Dewa Made Widia , Muhammad Faisal , Suardi Hi Baharuddin , Medy Wismu Prihatmono , Nurdiansyah Nurdiansyah , Nasir Usman
Global trade constitutes a highly complex and interdependent system influenced by diverse economic, geographic, and political factors. This study proposes a hybrid clustering framework that integrates density-based spatial clustering of applications with noise (DBSCAN), elbow, and self-organizing maps (SOM) methods to uncover latent structures in international trade patterns. Utilizing averaged trade data from 25 countries spanning the period from 2013 to 2023, the framework identifies distinct clusters based on export-import characteristics. The DBSCAN is employed to detect dense trade hubs and outlier behaviors, the elbow method determines the optimal number of clusters, and SOM facilitates the visualization of non-linear, high-dimensional trade relationships. The analysis reveals three prominent trade clusters: Global Trade Leaders, Emerging Trade Powers, and Niche Exporters, each reflecting varying degrees of trade diversification and dependency. These empirical findings align with established economic theories, including the Heckscher Ohlin model and dependency theory, and provide actionable insights for policymakers seeking to enhance trade competitiveness and regional integration strategies.
Volume: 14
Issue: 4
Page: 3033-3046
Publish at: 2025-08-01

A novel-shaped THz MIMO antenna with high bandwidth for advanced 6G wireless application

10.12928/telkomnika.v23i4.26559
Kamal Hossain; Daffodil International University Nahin , Jamal; Daffodil International University Hossain Nirob , Md. Ashraful; Daffodil International University Haque , Narinderjit Singh; INTI International University Sawaran Singh , Redwan Al Mahmud; Daffodil International University Bin Asad Ananta , Md. Kawsar; Daffodil International University Ahmed , Md. Sharif; Daffodil International University Ahammed , Liton; Pabna University of Science and Technology Chandra Paul
This article presents an industrial and innovation highly efficient drone shaped slotted graphene-based multiple input multiple output (MIMO) antenna with improved isolation, designed for high-speed short-range communication, video rate imaging, medical imaging, and explosive detection in the THz band. The proposed antenna is constructed on an 88×244 μm2 polyimide substrate. Key performance parameters such as reflection coefficient, gain, directivity, radiation pattern, and antenna efficiency are evaluated at the resonating frequencies of 1.7 THz, 3.35 THz, and 5.31 THz, covering a wide bandwidth of 4.88 THz with a reflection coefficient of less than -10 dB. The antenna achieves a maximum gain of 13.92 dB and a radiation efficiency of 95.77% within the resonating band. The MIMO design parameters include an envelope correlation coefficient (ECC) of 0.00015, a diversity gain (DG) of 9.9992, and an isolation of less than -31.55 dB between its elements across the entire bandwidth. The outcomes from CST simulations were verified by designing and simulating a similar resistance-inductance-capacitance (RLC) circuit in advanced design system (ADS), with both simulators producing comparable reflection coefficients. These features underscore the potential of the proposed antenna, utilizing simulations and an equivalent RLC circuit model, as a robust candidate for THz band applications in 6G wireless communication.
Volume: 23
Issue: 4
Page: 847-856
Publish at: 2025-08-01

Driving agricultural evolution: implementing agriculture 4.0 with Raspberry Pi and internet of things in Morocco

10.11591/ijai.v14.i4.pp3462-3473
Raja Mouachi , Elbelghiti Youssef , Sanaa El mrini , Mustapha Ezzini , Mustapha Raoufi
The purpose of this project was to investigate the use of embedded system and smartphone technologies in conjunction with Raspberry Pi and NodeMCU to create an intelligent system for smart farming (SF). By means of experiments and comparative analysis carried out in several agricultural contexts, the research evaluated the efficacy of the intelligent system. Results showed that the system was able to handle pertinent agricultural activities and effectively monitor important environmental factors including temperature, humidity, soil moisture, and climatic quality. The system's remote accessibility helped farmers by allowing them to effectively oversee agricultural operations at any time and from any location. As a consequence, SF techniques produced more production, lower costs, and maintained assets.
Volume: 14
Issue: 4
Page: 3462-3473
Publish at: 2025-08-01

Applications of artificial intelligence in indoor fire prevention and fighting

10.11591/ijai.v14.i4.pp2646-2654
Duong Huu Ai , Van Loi Nguyen , Khanh Ty Luong , Viet Truong Le
In this study, we design and analysis of artificial intelligence (AI) in indoor fire prevention and fighting. The application of image recognition processing technology has progressed from the early stages using color recognition and feature extraction methods, a newer approach is optical flow using image sequence data to identify motion regions. Image recognition processing technology, a subset of computer vision and AI, has numerous applications across different industries. It allows machines to interpret and make decisions based on visual data, such as photos, videos, or live camera feeds. Recently, AI has many applications in the field of indoor fire prevention and firefighting, leveraging real-time data analysis, predictive modeling, and automation to enhance safety and efficiency. With the application of a neural network, the simulated flame features in the laboratory are used as the input; The image containing the flame from the animation and the features of the image are fed into the artificial neural network obtained from the image from the charge-coupled device camera.
Volume: 14
Issue: 4
Page: 2646-2654
Publish at: 2025-08-01

Enhancing realism in hand-drawn human sketches through conditional generative adversarial network

10.12928/telkomnika.v23i4.26856
Imran; REVA University Ulla Khan , Depa Ramachandraiah; REVA University Kumar Raja
This research focuses on enhancing the realism of hand drawn human sketches through the use of conditional generative adversarial networks (cGAN). Addressing the challenge of translating rudimentary sketches into highfidelity images, by leveraging the capability of deep learning algorithms such as cGANs. This is particularly significant for applications in law enforcement, where accurate facial reconstruction from eyewitness sketches is crucial. Our research utilizes the Chinese University of Hang Kong Face Sketches (CUFS) dataset, a paired dataset of hand drawn human faces sketches and their corresponding realistic images to train the cGAN model. Generator network produces realistic images based on input sketches, where as discriminator network evaluates authenticity of these generated images compared to the real ones. The study involves careful preprocessing of the dataset, including normalization and augmentation, to ensure optimal training conditions. The model performance assessed through both quantitative metrics, such as frechet inception distance (FID), and qualitative evaluations, including visual inspection of generated images. The potential applications of this research extend to various fields, such as agencies of law enforcement for finding suspects and locating missing persons. Future work exploring advanced techniques for further realism, and evaluating the model’s performance across diverse datasets.
Volume: 23
Issue: 4
Page: 976-985
Publish at: 2025-08-01

Factors influencing blockchain adoption intention in Philippine small and medium enterprises

10.12928/telkomnika.v23i4.26744
Victor James; Chung Yuan Christian University C. Escolano , Wei-Jung; Chung Yuan Christian University Shiang , Alexander; Lyceum of the Philippines University A. Hernandez , Darrel; Bohol Island State University A. Cardaña
As an emerging technology, blockchain has huge potential for transforming various industries, such as small and medium enterprises (SMEs). Despite its promising impact, its application in the supply chains of SMEs in developing countries is still in its infancy. This study analyzes the key factors of blockchain adoption intention in Philippine SMEs through an integrated technology-organization-environment (TOE) and technology acceptance model (TAM) with external variables. The data were obtained through a survey of 465 SME practitioners in the national capital region (NCR), Philippines, and analyzed using partial least squares and structural equation modeling (PLS-SEM). In terms of technology dimensions, relative advantage (RLA) had a positive influence on perceived usefulness (PUS) while compatibility (COM) had a positive influence on perceived ease of use (PEU), which both subsequently led to blockchain adoption intention. As regards organization, top management support (TMS) had a significant influence on the adoption intention of blockchain among SMEs. In terms of environment, only competitive pressure (CMP) had significant influence on blockchain adoption intention. In general, most of the hypothesized relationships are significant; thus, SMEs have a positive interest in adopting blockchain technology. Finally, the study serves as baseline evidence of blockchain adoption intention among SMEs in the Philippines.
Volume: 23
Issue: 4
Page: 965-975
Publish at: 2025-08-01

A comprehensive review of interpretable machine learning techniques for phishing attack detection

10.11591/ijai.v14.i4.pp3022-3032
Pankaj Ramchandra Chandre , Pallavi Bhujbal , Ashvini Jadhav , Bhagyashree Dinesh Shendkar , Aditi Wangikar , Rajneeshkaur Sachdeo
Phishing attacks remain a significant and evolving threat in the digital landscape, demanding continual advancements in detection methodologies. This paper emphasizes the importance of interpretable machine learning models to enhance transparency and trustworthiness in phishing detection systems. It begins with an overview of phishing attacks, their increasing sophistication, and the challenges faced by conventional detection techniques. A range of interpretable machine learning approaches, including rule-based models, decision trees, and additive models like Shapley additive explanations (SHAP), are surveyed. Their applicability in phishing detection is analyzed based on computational efficiency, prediction accuracy, and interpretability. The study also explores ways to integrate these methods into existing detection systems to enhance functionality and user experience. By providing insights into the decision-making processes of detection models, interpretable machine learning facilitates human supervision and intervention, strengthening overall system reliability. The paper concludes by outlining future research directions, such as improving the scalability, accuracy, and adaptability of interpretable models to detect emerging phishing techniques. Integrating these models with real-time threat intelligence and deep learning approaches could boost accuracy while preserving transparency. Additionally, user-centric explanations and human-in-the-loop systems may further enhance trust, usability, and resilience in phishing detection frameworks.
Volume: 14
Issue: 4
Page: 3022-3032
Publish at: 2025-08-01

Graphene-based high-gain MIMO antenna for enhanced 6G wireless communication systems

10.12928/telkomnika.v23i4.26568
Narinderjit Singh; INTI International University Sawaran Singh , Md. Ashraful; Daffodil International University Haque , Jamal; Daffodil International University Hossain Nirob , Kamal; Daffodil International University Hossain Nahin , Md. Kawsar; Daffodil International University Ahmed , Md. Sharif; Daffodil International University Ahammed , Redwan; Daffodil International University A. Ananta , Liton; Pabna University of Science and Technology Chandra Paul
This paper presents a novel design and analysis of a high-performance multiple-input multiple-output (MIMO) terahertz (THz) antenna intended for next-generation sixth-generation (6G) wireless communication systems. The proposed antenna operates over a wide frequency range of 1 THz to 4.9 THz, achieving a broad bandwidth of 3.9 THz with three distinct resonant frequencies at 2.05 THz, 3.9 THz, and 4.52 THz, each exhibiting excellent return loss characteristics. The antenna features a graphene-based patch with a copper ground plane, etched on a polyimide substrate with a dielectric constant (εr) of 3.5 and a thickness of 10 micrometers (μm). Key performance metrics, including a high gain of 15.9 decibels (dB), an efficiency of 95.95%, an envelope correlation coefficient (ECC) of 0.0005, and a diversity gain (DG) of 9.997 dB, indicate outstanding performance. The measured isolation between the two antenna elements is -31.91 dB, signifying excellent isolation. An equivalent resistor-inductor-capacitor (RLC) circuit model is developed using advanced design system (ADS), validated by comparing S11 results from both computer simulation technology (CST) and ADS simulations. The proposed MIMO antenna’s wide operating range and robust performance demonstrates great potential for high-speed THz wireless communication, imaging, spectroscopy, sensing, and offers valuable contributions to industry and innovation.
Volume: 23
Issue: 4
Page: 837-846
Publish at: 2025-08-01
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