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Malware detection using convolutional neural network-di strategy polar fox optimization algorithm

10.11591/ijai.v15.i1.pp140-153
Parvathi Sathenahalli Jayaprakash , Yogeesh Ambalagere Chandrashekaraiah
Malware attacks have escalated significantly with an increase of internet users and connected devices. With the rise of various types of malwares released by the hackers, constructing new competitive methods are necessary to identify the advanced malware. However, conventional malware detection struggles to identify new and evolving malware variants accurately because of its dependence on handcrafted features and static-signature based methods. To address this problem, this research proposes convolutional neural network (CNN) based di strategy polar fox optimization algorithm (DSPFOA) for malware detection to fine-tune the CNN parameters effectively which later assists to overcome the limitations of CNN. The model integrates the sine chaotic mapping and Cauchy operator mutation as DSPFOA prevents the model from local optima issue, and extends search space solution, also enhance convergence. This ensures that the CNN learns highly discriminative features which makes the system more accurate and robust in detecting both known and evolving malware variants. The CNN DSPFOA achieves a high accuracy of 99.65 and 99.76% by utilizing BIG2015 and Malimg dataset respectively compared to existing methods like masked self-supervised model with swin transformer (MalSort).
Volume: 15
Issue: 1
Page: 140-153
Publish at: 2026-02-01

Unmanned aerial vehicle systems: key management and intrusion response techniques

10.11591/ijai.v15.i1.pp972-986
Reshma C. Sonawane , A. Muthukrishnan
Protecting information is crucial for unmanned aerial vehicle (UAV) network communications, particularly during delicate tasks such as surveillance and reconnaissance. While encryption safeguards data privacy, managing and distributing keys in UAV settings poses significant challenges due to the vehicles' mobility and limited processing power. This research proposes an efficient key management scheme utilizing elliptic curve cryptography (ECC) and bilinear pairings, complemented by a lightweight intrusion detection system (IDS). The method employs behavior-based anomaly detection, using cluster-based watchdogs and trust assessment to identify and isolate harmful nodes. Additionally, applying compression techniques before encryption helps to reduce transmission load. Simulation in NS2 demonstrates performance improvements of 6-10% in throughput, 4-6% in packet delivery ratio (PDR), and a 13-17% reduction in delay compared to elliptic curve cryptography Diffie–Hellman (ECCDH) and pairwise encryption methods.
Volume: 15
Issue: 1
Page: 972-986
Publish at: 2026-02-01

Artificial intelligence framework for multi-stage lung disease detection with audio signals

10.11591/ijai.v15.i1.pp106-115
Bandreddi Venkata Seshukumari , Jyothirmayi Tayi , Rajeshkhanna Bhuthkuri , Bhavani Madireddy , Jhansi Yellapu , Bodapati Venkata Rajanna , Nitalaksheswara Rao Kolukula , Siva Sairam Prasad Kodali , Jayasree Pinajala , James Stephen Meka , Chilakala Rami Reddy
Automated diagnostic systems are increasingly pivotal in advancing the accuracy and efficiency of medical diagnostics. Due to abnormal changes in human life and pollution, lung disease and cancer cases increasing in huge number. Identification and prediction of lung diseases may help to increase the human life span. This study introduces a robust framework for automatic lung disease detection using respiratory sound signals. The methodology brings together a series of activities like preprocessing, feature extraction, selection, and classification to improve diagnostic accuracy. The adaptive empirical stockwell-transform (AEST) is used to enhance the quality of the signal, whereby extracting and refining features, mainly Mel-frequency cepstral coefficients (MFCC), and Mel-spectrograms, are used. The scalable convolutional geyser network (SCGN) helps to mitigate challenges posed by imbalanced datasets, redundant features, and overfitting, ensuring reliable classification of the features. The model is validated when using the International Conference on Biomedical and Health Informatics (ICBHI) dataset, which validates the performance indicators of the model (F1-score 0.94, accuracy 0.95, precision 0.93, recall 0.94). This is shown superior performance compared to other existing models and demonstrates the framework's ability to diagnose a serviceable and reliable medical diagnosis; which indicates the strengths of combining advances in signal processing and scalable deep learning (DL) in healthcare applications.
Volume: 15
Issue: 1
Page: 106-115
Publish at: 2026-02-01

Comparison of image enhancement methods for pratima theft detection using artificial intelligence

10.11591/ijai.v15.i1.pp213-228
Made Sudarma , Ni Wayan Sri Ariyani , I Putu Agus Eka Darma Udayana , Ida Bagus Gde Pranatayana , Lie Jasa
The theft of pratima in Balinese temples threatens the spiritual and cultural balance of the community. These sacred objects, regarded as manifestations of God in Hinduism, hold profound religious significance, and their loss represents both material and spiritual desecration. To address this issue, this study investigates a security system that leverages image enhancement for low-light detection. Four techniques—contrast limited adaptive histogram equalization (CLAHE), adaptive histogram equalization (AHE), histogram equalization (HE), and gamma correction—were evaluated to improve image quality. CLAHE yielded the lowest mean squared error (MSE) of 21.16 and the highest peak signal-to-noise ratio (PSNR) of 38.13 dB. For object detection, VGG-19 and AlexNet were assessed. The best configuration, VGG-19 with HE, reached 83.33% accuracy and 93.75% recall, and achieved a receiver operating characteristic area under the curve (ROC AUC) of 0.90±0.02 across five runs. Thresholds derived from the ROC analysis were selected using the Youden J statistic to balance sensitivity and specificity. The approach outperformed lightweight and classical baselines in AUC, indicating superior discrimination under low illumination. These findings show that superior image quality does not always align with higher detection accuracy, and they highlight the importance of pairing effective enhancement with robust detectors for temple security. The study contributes practical insights for preserving Balinese cultural and spiritual heritage by strengthening efforts to protect pratima against theft.
Volume: 15
Issue: 1
Page: 213-228
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

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

Predicting university student dropouts in Latin America using machine learning

10.11591/ijai.v15.i1.pp628-641
Laberiano Andrade-Arenas , Inoc Rubio Paucar , Margarita Giraldo Retuerto , Cesar Yactayo-Arias
In the university context, student dropout has become one of the most recurring problems, both in the short and long term. The objective of this research was to develop a predictive model using the random forest (RF) algorithm to identify patterns associated with university dropout. To achieve this, the knowledge discovery in databases (KDD) methodology was applied, which encompasses the stages of selection, preprocessing, transformation, data mining, and interpretation of results. The RF model demonstrated superior performance compared to other evaluated models, achieving an accuracy of 87%, a precision of 86%, a recall of 85%, an F1-score of 85%, and an receiver operating characteristic (ROC) area under the curve (AUC) of 0.91, highlighting its high predictive capability compared to other techniques analyzed. Therefore, the application of the proposed model is recommended in various university institutions in order to identify potential dropout cases at an early stage.
Volume: 15
Issue: 1
Page: 628-641
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

Secure and interoperable electronic health record exchange using blockchain and ECDHE-based access control

10.11591/ijai.v15.i1.pp310-321
Krishna Prasad Narasimha Rao , Chinnaiyan Selvan
Electronic health records (EHRs) act as comprehensive records of health related transactions and essential resources of data in the healthcare sector. However, the integrity and security problems of EHR continue to be inflexible. The architecture of blockchain-enabled EHR addresses the issues of integrity efficiently. This paper developed the decentralized patient centric healthcare data management (PCHDM) system using blockchain enabled EHR architecture for addressing problems with access control, record privacy and data confidentiality. This framework places the patient at the control center, ensuring secure storage of EHR records and obtaining efficient data management through the integration of blockchain and interplanetary file system (IPFS). To prevent access by unauthorized users, the proposed elliptic curve Diffie-Hellman ephemeral (ECDHE) mechanism incorporates smart contract-enabled access control for managing EHR transactions and enforcing access strategies. This architecture incorporates hyperledger fabric endorsement policies (HFRP) to address scalability problems while preserving patient privacy and securing medical data. The developed method secures the EHR data and facilitates the data exchange across heterogeneous healthcare platforms, ensuring standard communication among different EHR systems. The architecture is assessed with parameters of time for block creation, the computational overhead of transaction with encryption key size and EHR upload and download time.
Volume: 15
Issue: 1
Page: 310-321
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

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

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

Change detection and classification of satellite images using convolutional neural network

10.11591/ijai.v15.i1.pp329-337
Raghavendra Srinivasaiah , Santosh Kumar Jankatti , Manjunath Ramanna Lamani , Niranjana Shravanabelagola Jinachandra
Satellite and airborne imagery, collectively known as earth observation imagery, are images of the earth collected from spaceborne or airborne platforms such as satellites and aircraft. Over the last 100 years, with the fast development of aviation, space exploration, and imaging technologies, the coming together of these technologies has been inevitable. Earth observation imagery has many applications in regional planning, geology, reconnaissance, fishing, meteorology, oceanography, agriculture, biodiversity conservation, forestry, landscape, intelligence, cartography, education, and warfare. With the rise in the number of these airborne and spaceborne imaging platforms being deployed by government and private entities alike, the capability to sift through and analyze vast amounts of data generated by these platforms is the need of the hour. With the exponential improvement in the computational capabilities of computers over the last half a century, analysts are exceedingly moving towards the practice of artificial intelligence, machine learning (ML), and computer vision solutions to automate a large part of the processes employed in analyzing earth observation imagery. This work recommends a workflow to perceive and classify changes in earth observation imagery of a given area by utilizing the vast flexibility that convolutional neural networks (CNN) provide.
Volume: 15
Issue: 1
Page: 329-337
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
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