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Autoencoder and GAN-aided plant disease detection in rice and cotton via hybrid feature extraction and decision tree classification

10.11591/ijai.v15.i1.pp707-724
Anandraddi Naduvinamani , Jayashri Rudagi , Mallikarjun Anandhalli
In agriculture, crop diseases caused by pathogens, including bacteria, viruses, and fungi, pose a significant threat to the effectiveness of agricultural productivity. Some major crops in India such as rice and cotton are adversely impacted, leading to economic loss and loss of production. Timely intervention and sustainable agriculture depend on proper and early identification of diseases. In this paper, we propose a novel plant disease detection framework that integrates generative adversarial network (GAN) based image denoising with feature extraction and decision tree (DT) classification. The GAN module effectively removes noise from agricultural images, enhancing quality and stability under challenging imaging conditions. Following denoising, a combination of color, texture, and gradient features is extracted to obtain rich and discriminative patterns, which are then used to train a DT classifier for disease identification. Experiments are conducted on benchmark datasets comprising rice and cotton leaf images. The proposed system achieves superior performance, with 98.70% accuracy, 98.20% precision, 97.22% recall, and 98.50% F1 score, outperforming existing methods. These results demonstrate that the GAN-based denoising approach, combined with traditional feature-based classification, offers a robust, efficient, and practical solution for modern agricultural disease monitoring systems.
Volume: 15
Issue: 1
Page: 707-724
Publish at: 2026-02-01

Deep learning-based cervical cancer detection via colposcopy images integrated into an Android mobile application

10.11591/ijai.v15.i1.pp338-349
Retno Supriyanti , Arsil Kultura Anzil , Yogi Ramadhani , Suroso Suroso , Wahyu Widanarto , Muhammad Alqaaf , Kartika Dwi Hapsari , Futiat Diana Kartika
Cervical cancer is a form of cancer that develops in the cells of the cervix, the lower part of the uterus that connects the uterus to the vagina. Early detection is essential for improving the chances of recovery from cervical cancer. One method for early detection is colposcopy image analysis, a medical procedure that examines the cervix and captures images for evaluation. These images were analyzed to observe color changes after the visual inspection with acetic acid (VIA) process. However, this analysis requires experienced and specially trained medical personnel. To address this challenge, a system that can automatically classify cervical cancer images is needed. Therefore, researchers proposed designing and developing an Android mobile application to enable early detection of cervical cancer using the convolutional neural network (CNN) algorithm. The CNN model was tested using test data to evaluate its performance. The optimized CNN model utilizing the ResNet50 architecture achieved 86% test accuracy, 85% precision, and 87% recall. The test results indicate that the model's accuracy is consistent before and after its implementation on the mobile application, confirming the effectiveness of both the model and its implementation as diagnostic tools.
Volume: 15
Issue: 1
Page: 338-349
Publish at: 2026-02-01

Real-time object detection to classify export quality of mangosteen using variants of you only look once version 8

10.11591/ijai.v15.i1.pp116-128
Dian Sa'adillah Maylawati , Mi’raj Fuadi , Kurniawan Yniarto , Yuhendra AP , Rizky Rahmat Nugraha , Akbar Hidayatullah Harahap , Agung Wahana
Mangosteen is one of the leading export commodities from Indonesia. Despite its great economic potential, only about 25% of Indonesian mangosteens meet export standards, mainly due to visual defects such as yellow sap and spots on the skin of the fruit. The process of sorting export worthy mangosteens has been done manually, which tends to be time consuming and inconsistent. Therefore, this study aims to utilize artificial intelligence technology in building a real-time image recognition model to improve the efficiency and accuracy of the export-quality mangosteen sorting process. This study uses you only look once version 8 (YOLOv8) as an image recognition model with YOLOv8 variants, including nano, small, medium, large, and extra large variants. The results of the study using 4,014 primary and 255 secondary data of mangosteen, the highest performance is reached by YOLOv8 medium 82% of accuracy, 0.856 of mean average precision (mAP)50, and 0.616 of mAP50-95. This result is obtained from 70% training, 20% validation, and 10% testing data with epoch stop 85. These results indicate that the model can provide good performance in mangosteen export quality classification. This research contributes to the fields of agricultural technology and artificial intelligence by offering an innovative solution to a practical problem, enhancing efficiency, accuracy, and scalability in export-quality mangosteen sorting.
Volume: 15
Issue: 1
Page: 116-128
Publish at: 2026-02-01

Gradient descent optimization based weighted federated learning for privacy-preserving framework

10.11591/ijai.v15.i1.pp878-887
Gururaj Prakash Murthy , Chandrashekhar Pomu Chavan
Federated learning (FL) is a disseminated machine learning (ML) paradigm that gained significant consideration in modern days, particularly in a domain of the internet of things (IoT). FL saves communication bandwidth when compared to centralized ML processes by eliminating the need to transmit raw client data to a central server, thereby enhancing data privacy. Nevertheless, participant privacy is still compromised through inference attacks and similar threats. Additionally, a data excellence provided through clients can differs significantly, and excessive inclusion of low-quality data during training may degrade the overall performance of the global model. Hence, this research introduces a gradient descent optimization assisted weighted federated learning (GDO-WFL) method for privacy preservation. The proposed GDO-WFL approach is significantly efficient as it strengthens privacy preservation through reducing exposure to inference attacks and optimises gradient updates for secure learning. Through weighting client contributions based on data quality, an undesirable effect of low-quality data can be minimised, helping to maintain a strength as well as accuracy of the global model. The experimental results illustrate a proposed GDO-WFL approach maintains an overall accuracy of 99.3 and 91.5% on MNIST and CIFAR-10 datasets as compared to the existing method of FedlabX method.
Volume: 15
Issue: 1
Page: 878-887
Publish at: 2026-02-01

Artificial intelligence in writing: unveiling a research landscape

10.11591/ijai.v15.i1.pp66-75
Wan Rusydiah Salehudin , Zilal Saari , Hafiza Abas
This study examines the expanding research landscape of artificial intelligence (AI) in writing, a field that continues to reshape the way ideas are produced, refined, and communicated. While AI has been widely examined in education and technology, limited research has mapped its thematic evolution and ethical dimensions in writing. To address this gap, 1,596 publications indexed in Scopus between 2021 and 2024 were analyzed using bibliometric mapping tools such as Scopus analyzer and VOSviewer. The analysis covers publication patterns, collaboration networks, and keyword relationships to trace the intellectual structure of the field. The results indicate a sharp increase in scholarly output over the past four years, supported by contributions from multiple disciplines, including computer science, social sciences, and education. Several thematic clusters were identified, centering on AI-assisted creative writing, authorship ethics, educational use, and cross-sector innovation. Despite these advances, ethical frameworks and responsible AI applications in writing remain underexplored. This paper offers a comprehensive overview of current trends and presents a foundation for future research on how AI can be integrated into writing practices responsibly and in ways that uphold human creativity and academic integrity.
Volume: 15
Issue: 1
Page: 66-75
Publish at: 2026-02-01

Evaluating the detected communities using traditional algorithms on keyword co-occurrence networks

10.11591/ijai.v15.i1.pp919-928
Kiruthika R. , Krishnaveni Sakkarapani
Community detection is one of the most significant research areas in network analysis, which helps to understand the internal structure of large networks. This work utilizes the traditional community detection methods on a keyword co-occurrence graph derived from the Scopus bibliographic database. This research article primarily focused on the index keywords of deep learning driven publications obtained from three major network Scopus bibliometric datasets (SBD), namely SBD_1 as 2006-2013, SBD_2 as 2014-2016, and SBD_3 as 2017. For this proposed model framework, the existing traditional algorithms, including Louvain, greedy modularity optimization (GMO), Leiden, Infomap, speaker-listener label propagation algorithm (SLPA), Walktrap, SpinGlass, K-Clique, and Clauset, Newman and Moore (CNM) methods are applied to detect communities from the network and carried out through Python. Comparisons among these algorithms, Leiden, SpinGlass, and Louvain are considered as better algorithms for our work based on the detected communities, modularity score and other metrics to evaluate the performance of detected communities from the network. This research proposes an ideology for the selection process of algorithms that depends on different factors like network characteristics, network structure, dataset size, and computational efficiency. This analysis suggests a unique perspective on the effectiveness of each method in the Scopus bibliometric network and its potential to enhance research topic exploration.
Volume: 15
Issue: 1
Page: 919-928
Publish at: 2026-02-01

Interpretable artificial intelligence system for personalized cognitive stimulation

10.11591/ijai.v15.i1.pp164-176
Rubén Baena-Navarro , Yulieth Carriazo-Regino , Mario Macea-Anaya
The growing need to preserve cognitive health in aging populations has intensified interest in adaptive digital interventions that provide personalized and interpretable support. This study presents a web-based cognitive stimulation system for older adults integrating a multilayer perceptron (MLP) classifier, expert-derived symbolic rules, and explainable artificial intelligence (XAI) techniques, including Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME). The platform was evaluated through a 24-week intervention involving 150 participants aged 65 years and older, combining baseline cognitive profiling, rule-guided recommendation logic, and neural prediction to support individualized task allocation. Compared with a control group, participants in the intervention arm showed statistically significant improvements in cognitive outcomes (p <0.05), with measurable gains in memory- and attention-related tasks. The explainability component enabled examination of model behavior at the level of individual features through feature attribution analysis and symbolic consistency checks, supporting interpretation beyond aggregate performance metrics. Unlike approaches dependent on high-end extended reality (XR) infrastructures or game centered interaction, the system was implemented to operate under low connectivity conditions and was tested with participants from diverse educational backgrounds. This hybrid configuration provides an interpretable basis for cognitive support initiatives adaptable to community settings contexts.
Volume: 15
Issue: 1
Page: 164-176
Publish at: 2026-02-01

Detection and forecasting of mental health disorders using machine learning models on social media data

10.11591/ijai.v15.i1.pp672-680
Chaithra Indavara Venkateshagowda , Roopashree Hejjajji Ranganathasharma , Yogeesh Ambalagere Chandrashekaraiah , Narve Lakshminarayan Taranath
The detection and classification of depression and other mental disorders have become crucial in the modern era, particularly with the growing reliance on social media for self-expression. Existing systems often face challenges like limited prediction accuracy, difficulty forecasting future mental illnesses, and handling both clinical and non-clinical data. This study proposes a novel analytical model that not only screens individuals' current mental health status from social media content but also predicts the likelihood of future mental health issues. The proposed methodology integrates classical machine learning (ML) models, ensemble learning approaches, and pretrained models for enhanced detection and forecasting accuracy. The outcome shows that pre-trained language models accomplished maximized F1-score and overall performance significantly better than conventional ML and ensemble models. The system outperforms existing methods with a significant accuracy improvement, achieving 90.9% overall accuracy, a 7.2% improvement over traditional ML classifiers, 5.8% over ensemble models, and 11.3% over language models.
Volume: 15
Issue: 1
Page: 672-680
Publish at: 2026-02-01

A review of modern techniques for plant disease identification and weed detection in precision agriculture

10.11591/ijai.v15.i1.pp998-1008
Mohammad Naseera , Arpita Gupta
Plant disease identification and weed detection are critical components of precision agriculture, aimed at ensuring high crop yields and sustainable farming practices. These processes involve the use of advanced machine learning and deep learning techniques to automatically identify and classify plant diseases and distinguish between crops and weeds in agricultural fields. Traditional methods for managing these challenges are often labor intensive, prone to errors, and environmentally unsustainable, necessitating the development of automated, accurate, and scalable solutions. This survey provides a comprehensive review of the state-of-the-art approaches, including pixel-based, region-based, and spectral-based methods, and evaluates their effectiveness in various agricultural contexts. Additionally, it identifies significant challenges such as data scarcity, model generalization, and computational constraints, while proposing potential research directions to address these gaps. The findings aim to guide future research in developing more robust and interpretable models that can be deployed in real-world agricultural environments, ultimately contributing to more efficient, precise, and sustainable farming practices.
Volume: 15
Issue: 1
Page: 998-1008
Publish at: 2026-02-01

A comprehensive survey of cyberbullying on social media: challenges, detection, and AI-based prevention

10.11591/ijai.v15.i1.pp86-96
Ammar Odeh , Osama Alhaj Hassan , Anas Abu Taleb , Abobakr Aboshgifa , Nabil Belhaj
Cyberbullying is a pervasive issue in the digital landscape, particularly on social media platforms, where individuals engage in online harassment, intimidation, and abuse. Unlike traditional bullying, cyberbullying has a broader reach, anonymity, and persistence, making it a growing concern for mental health, social well-being, and online safety. This paper provides a comprehensive survey of cyberbullying trends, its psychological and social impacts, and the role of social media in amplifying the problem. It explores existing detection and prevention strategies, including artificial intelligence (AI)-driven approaches, policy frameworks, and platform-based moderation techniques. Furthermore, it discusses challenges in enforcement, the limitations of automated detection systems, and the need for improved legal measures. This paper uniquely contributes an integrated perspective on cyberbullying detection and prevention by synthesizing current research across psychological, sociocultural, and technical dimensions. It emphasizes underexplored gaps such as multilingual detection, real-time moderation, and cross-platform enforcement, and proposes a layered framework to guide future research and policy.
Volume: 15
Issue: 1
Page: 86-96
Publish at: 2026-02-01

Efficient data streaming in dynamic vehicular networks: a hybrid controller for seamless connectivity

10.11591/ijai.v15.i1.pp229-236
Prathibha Thimmappa , Mayuri Kundu
The demand for highly efficient data transmission is being increasingly demanded for dynamic vehicular networks, especially in the case of internet-of-vehicle (IoV). The current data transmission methods are known to encounter inefficiencies in terms of unreliable routing and restricted scalability. Evolving studies have found artificial intelligence (AI) based schemes more suitable to address these issues; however, there are no significant innovations towards developing a potential framework that can not only increase data transmission performance but also minimize the analytical overheads of AI. Hence, this paper presents a novel baseline framework by introducing an optimized controller structure at anchor points with the inclusion of novel ideologies of orientation degree and selection of mediating node. The proposed model witnesses 32.3 dB of signal quality, 857 kbps throughput, 81 ms delay, and 171 ms of response time, exhibiting much better performance in contrast to the frequently used data transmission method. The proposed model contributes to a solid foundation for any futuristic AI model for efficient and reliable data transmission in IoV.
Volume: 15
Issue: 1
Page: 229-236
Publish at: 2026-02-01

Deep intelligence for sustainable farming: a swarm-empowered data analytics architecture

10.11591/ijai.v15.i1.pp901-908
Kiran Muniswamy Panduranga , Roopashree Hejjaji Ranganathasharma
The inclusion of complex patterns of data in precision agriculture (PA) induces a greater degree of challenges from the perspective of carrying out conventional analytical operations. Although proliferated use of artificial intelligence (AI) has been noticed to yield some promising results to address such issues, yet they too have many shortcomings. Hence, the current manuscript introduces an innovative hybrid AI scheme towards enhancing the analytical operations necessary for decision-making in smart farming. The proposed scheme hybridizes a deep neural network (DNN) with a novel swarm intelligence (SI) model for optimizing the performance of its adopted deep learning (DL) model. Tested on a standard dataset of agriculture, the proposed model exhibited a 10% increase in accuracy and 40% faster response time when compared with conventional machine learning (ML) models, DL models, and SI models. The study contributes to a novel benchmark towards time-efficient, scalable, and intelligent analytics on PA.
Volume: 15
Issue: 1
Page: 901-908
Publish at: 2026-02-01

Development of generalized principal component analysis using multiple imputation genetic algorithm

10.11591/ijai.v15.i1.pp454-468
Fahrezal Zubedi , I Made Sumertajaya , Khairil Anwar Notodiputro , Utami Dyah Syafitri
In this study, we propose an innovative method called the integrated GPCA MIGA, which integrates the multiple imputation genetic algorithm (MIGA) and generalized principal component analysis (GPCA) to perform missing value imputation and data dimensionality reduction simultaneously. The approximated original data produced by GPCA serves as the basis for MIGA to update missing values in the next iteration. At the same time, GPCA refines the low-dimensional representation using the latest imputation results from MIGA, thereby balancing the accuracy of missing value imputation and the stability of dimensionality reduction. The objective of this study is to evaluate the performance of the integrated GPCA-MIGA and analyze trends in human development at the district/city level in Indonesia. The findings of this study show that the integrated GPCA-MIGA effectively reduces the dimensionality of data containing missing values compared to other methods. The integrated GPCA-MIGA method was applied to human development data. The results were then visualized using a biplot, which revealed that human development trends in Jayawijaya from 2019 to 2022 indicate progress in school enrollment rates for ages 16–18 years.
Volume: 15
Issue: 1
Page: 454-468
Publish at: 2026-02-01

An efficient method to improve machine learning decoders using automorphisms group

10.11591/ijai.v15.i1.pp547-558
Imrane Chemseddine Idrissi , Said Nouh , El Mehdi Bellfkih , Mohammed El Assad , Abdelaziz Marzak
The decoding of error-correcting codes (ECCs) is a critical aspect of communication systems, yet traditional decoding techniques can often be computationally demanding or ineffective for certain codes, necessitating innovative approaches. In this study, we introduce a hybrid approach that combines machine learning and automorphism techniques to optimize the decoding process. Specifically, we train multilayer perceptron (MLP) models to learn the mapping between error syndromes and their corresponding errors. While these models exhibit robust learning capabilities, their performance sometimes does not reach 100%. To mitigate this limitation, we exploit the automorphism group of the code—a set of structure-preserving transformations—to convert the errors that the MLP struggles to decode into ones it can process more effectively. We use a minimum number of p permutations, pre-calculating and storing all possible automorphisms to ensure computational efficiency. Our experimental results reveal that this hybrid approach substantially enhances the decoding performance of the MLP model, presenting a promising avenue for decoding ECCs. Importantly, this approach is not limited to MLP models and can be applied to any machine learning model with a learning score less than 100%, broadening its applicability and impact. By integrating machine learning with traditional algebraic coding theory, we propose a new paradigm that holds the potential to revolutionize the design of decoding systems, making them more efficient and effective.
Volume: 15
Issue: 1
Page: 547-558
Publish at: 2026-02-01

Predicting trapped victims in debris using signal analysis ensemble classification

10.11591/ijai.v15.i1.pp493-505
Enoch Adama Jiya , Ilesanmi B. Oluwafemi , Olayinka O. Ogundile , Oluwaseyi P. Babalola
One major difficulty in pervasive computing is trapped human detection in search and rescue (SAR) scenarios. Accurately identifying trapped individuals is challenging due to noisy data and the curse of dimensionality. When non-line-of-sight (NLOS) conditions are present during catastrophic occurrences, the curse of dimensionality can result in blind spots in detections because of noise and uncorrelated data. Because machine learning algorithms are incredibly accurate, this work focuses on using ultra wideband (UWB) radar waves to detect individuals in NLOS scenarios and leveraging wireless communication to harmonize information. The paper uses ensemble methods to extract features using independent component analysis (ICA) and evaluate classification performance on both static and dynamic datasets. The testing results confirm the effectiveness of the proposed strategy, with classification accuracies of 87.20% for dynamic data and 88.00% for static data. Lastly, during SAR operations, our approach can assist engineers and scientists in making quick decisions.
Volume: 15
Issue: 1
Page: 493-505
Publish at: 2026-02-01
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