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Novel technique to deblurring and blur detection techniques for enhanced visual clarity of ancient images

10.11591/ijece.v15i2.pp2314-2324
Poonam Pawar , Bharati Ainapure
Digital image quality often degrades due to various factors such as noise and blur. Many images are affected by these issues, reducing their clarity and accuracy. This degradation is especially problematic for ancient images, significantly hampers the ability to analyze historical documents and artworks. This paper presents a novel approach to both blur detection and deblur ancient images, enhancing their clarity and readability. This research introduces a technique that combines wavelet transform and convolutional neural networks (CNNs) for effective blur identification and deblurring, specifically aimed at restoring blurred ancient images, regardless of the type of blur degradation. This novel approach demonstrated an average accuracy of 98.3% in blur detection on ancient image datasets. The performance of deblurring algorithms is typically evaluated using metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index (SSIM) which quantify fidelity and quality of the deblurred images. In the deblurring, this approach produced PSNR values of 55.5 to 68.3 dB, MSE values of 2.99 to 11.1, and an SSIM of 0.9 across different types of blurs. These results show significant promise for the restoration of ancient images, providing researchers, historians, and archaeologists with valuable tool for conservation cultural heritage.
Volume: 15
Issue: 2
Page: 2314-2324
Publish at: 2025-04-01

Ensemble approach to rumor detection with BERT, GPT, and POS features

10.11591/ijict.v14i1.pp276-286
Barsha Pattanaik , Sourav Mandal , Rudra Mohan Tripathy , Arif Ahmed Sekh
As vast amounts of rumor content are transmitted on social media, it is very challenging to detect them. This study explores an ensemble approach to rumor detection in social media messages, leveraging the strengths of advanced natural language processing (NLP) models. Specifically, we implemented three distinct models: (i) generative pre-trained transformer (GPT) combined with a bidirectional long short-term memory (BiLSTM) network; (ii) a model integrating part-of-speech (POS) tagging with bidirectional encoder representations from transformers (BERT) and BiLSTM, and (iii) a model that merges POS tagging with GPT and BiLSTM. We included additional features from the dataset in all these models. Each model captures different linguistic, syntactical, and contextual features within the text, contributing uniquely to the classification task. To enhance the robustness and accuracy of our predictions, we employed an ensemble method using hard voting. This technique aggregates the predictions from each model, determining the final classification based on the majority vote. Our experimental results demonstrate that the ensemble approach significantly outperforms individual models, achieving superior accuracy in identifying rumors. To determine the performance of our model, we used PHEME and Weibo datasets available publicly. We found our model gave 97.6% and 98.4% accuracy, respectively, on the datasets and has outperformed the state-of-the-art models.
Volume: 14
Issue: 1
Page: 276-286
Publish at: 2025-04-01

Memory management of firewall filtering rules using modified tree rule approach

10.11591/ijict.v14i1.pp141-152
Dhwani Hakani , Palvinder Singh Mann
Firewalls are essential for safety and are used for protecting a great deal of private networks. A firewall’s goal is to examine every incoming and outgoing data before granting access. A notable kind of conventional firewall is the rule-based firewall. However, when it comes to job performance, traditional listed-rule firewalls are limited, and they become useless when utilized with some networks that have extremely big firewall rule sets. This study proposes a model firewall architecture called “TreeRule Firewall,” which has benefits and functions effectively in large-scale networks like “cloud.” In order to improve cloud network security, this study suggests a modified tree rule firewall (MTRF cloud) that eliminates rule discrepancies. For the matching firewall policy, this work creates a tree rule firewall. There are no duplicate rules created by the proposed improved tree rule firewall. Also, memory utilization of different size rules is compared.
Volume: 14
Issue: 1
Page: 141-152
Publish at: 2025-04-01

An improved key scheduling for advanced encryption standard with expanded round constants and non-linear property of cubic polynomials

10.11591/ijece.v15i2.pp2455-2467
Muthu Meenakshi Ganesan , Sabeen Selvaraj
The advanced encryption standard (AES) offers strong symmetric key encryption, ensuring data security in cloud computing environments during transmission and storage. However, its key scheduling algorithm is known to have flaws, including vulnerabilities to related-key attacks, inadequate nonlinearity, less complicated key expansion, and possible side-channel attack susceptibilities. This study aims to strengthen the independence among round keys generated by the key expansion process of AES—that is, the value of one round key does not reveal anything about the value of another round key—by improving the key scheduling process. Data sets of random, low, and high-density initial secret keys were used to evaluate the strength of the improved key scheduling algorithm through the National Institute of Standards and Technology (NIST) frequency test, the avalanche effect, and the Hamming distance between two consecutive round keys. A related-key analysis was performed to assess the robustness of the proposed key scheduling algorithm, revealing improved resistance to key-related cryptanalysis.
Volume: 15
Issue: 2
Page: 2455-2467
Publish at: 2025-04-01

Enhancing convolutional neural network based model for cheating at online examinations detection

10.11591/ijai.v14.i2.pp843-852
Sara Ouahabi , Rihab Aboudihaj , Nawal Sael , Kamal El Guemmat
In the last few years, e-learning has revolutioning education, giving students access to diverse and adaptable on-line resources, but it has also face a major challenge: cheating on online exams. Students now use variant cheating methods include consulting unauthorized documents, communicating with others during the exam, searching for information on the internet. Combating these cheating practices has become crucial to preserving the integrity of academic assessments. In this context, artificial intelligence (AI) has emerged as an essential tool for mitigating this fraudulent behavior. Equipped with advanced machine learning capabilities, AI can examine a wide range of data to detect student suspicious behavior. This study develops an approach based on a convolutional neural network (CNN) model designed to detect cheating by analyzing candidates' head movements during online exams. By exploiting the FEI dataset, this model achieves an interesting accuracy of 97.28%. In addition, we compare this model to the well-known transfer learning models used in the literature namely, ResNet50, VGG16, DenseNet21, MobileNetV2, and EfficientNetB0 demonstrating the out performance of our approach in detecting cheating during online exams.
Volume: 14
Issue: 2
Page: 843-852
Publish at: 2025-04-01

Diabetes mellitus diagnosis method based random forest with bat algorithm

10.11591/ijai.v14.i2.pp1140-1149
Syaiful Anam , Fidia Deny Tisna Amijaya , Satrio Hadi Wijoyo , Dian Eka Ratnawati , Cynthia Ayu Dwi Lestari , Muhaimin Ilyas
Diabetes mellitus (DM) is a very dangerous disease and can cause various problems. Early diagnosis of DM is essential to avoid severe effects and complications. An affordable DM diagnosis method can be developed by applying machine learning. Random forest (RF) is a machine learning technique that is applied to develop a DM diagnosis method. However, the optimization of RF hyperparameters determines the performance of RF approach. Swarm intelligence (SI) could be used to solve the hyperparameter optimization problem on RF. It is robust and simple to be applied and doesn’t require derivatives. Bat algorithm (BA) is one of SI techniques that gives a balance between exploration and exploitation to find a global optimal solution. This article proposes developing an RF-BA-based technique for diagnosing DM. The results of the experiment demonstrate that RF-BA can diagnose DM more accurately than conventional RF. RF-BA has higher performance compared to RF-particle swarm optimization (PSO) in terms of computational time. The RF-BA also are able to solve the overfitting problem in the conventional RF. In the future, the proposed method has a high chance of being implemented for helping people with early DM diagnosis with high accuracy, low cost, and high-speed process.
Volume: 14
Issue: 2
Page: 1140-1149
Publish at: 2025-04-01

Engraved hexagonal metamaterials resonators antenna for bio-implantable ISM-band applic

10.11591/ijeecs.v38.i1.pp204-214
Belkheir Safaa , Sabri Ghoutia Naima
This study will introduce a metamaterial antenna designing for use in biomedical implants. The antenna is compact and utilizes four slot complementary metamaterial hexagonal resonators of uniform shape and size. By incorporating the metamaterial into the antenna design, its size is reduced while the performance is enhanced. Simulation results show that the antenna achieves satisfactory peak gain values of -22.6 dBi and a 34.5% increase in bandwidth. Operating within the 2.4-2.5 GHz industrial, scientific, and medical (ISM) frequency bands, the antenna measures 7×7×1.27 mm3 and consists of substrate layers with patch radiation, four metamaterials hexagonal resonators on the upper surface, a ground layer, and a second superstrate layer. The study also addresses the challenges and problems associated with the interaction between the antenna and human tissue, while aiming to maintain antenna performance, properties, and minimize its impact on tissues. Evaluation of when using a 2.45 GHz operating frequency, the specific absorption rate (SAR) shows values of 489.87 W/kg for 1 g of averaged tissue and 53.738 W/kg for 10 g of averaged tissue. The results of placing the antenna in human skin tissue are safe for use in the human body and appropriate for biomedical applications. Simulations conducted using computer simulation technology (CST) and high frequency structure simulator (HFSS) software emphasize the excellent performance of the engraved metamaterial antenna.
Volume: 38
Issue: 1
Page: 204-214
Publish at: 2025-04-01

The 360° beach video: a supporting mindfulness intervention with virtual reality

10.11591/ijict.v14i1.pp250-258
Rohmatus Naini , Mungin Eddy Wibowo , Edy Purwanto , Mulawarman Mulawarman , E. Oos M. Anwas
This article describes optimizing virtual reality (VR) with a 360° beach video model used for mindfulness interventions. Using VR with 360° beach videos to support the presence of an immersive environment can effectively support mindfulness practices. Students are interested in the integration of technology in school counseling. VR helps in creating immersive environments such as forests, beaches, waterfalls, etc. so that students focus more on practicing mindfulness and attention in the current moment. This article focuses on optimizing 360° beach videos in the breathing mindfulness process so that it helps bring out real experiences. Obstacles to practicing mindfulness include lack of focus, mind wandering and not concentrating. through the use of 360° beach videos with VR can increase focus and be more effective in mindfulness practice.
Volume: 14
Issue: 1
Page: 250-258
Publish at: 2025-04-01

Enhancing financial cybersecurity via advanced machine learning: analysis, comparison

10.11591/ijai.v14.i2.pp1281-1289
Grace Odette Boussi , Himanshu Gupta , Syed Akhter Hossain
The financial sector is a prime target for cyber-attacks due to the sensitive nature of the data it handles. As the frequency and sophistication of cyber threats continue to rise, implementing effective security measures becomes paramount. In this paper we provide a comprehensive comparison of six prominent machine learning techniques utilized in the financial industry for cyber-attack prevention. The study aims to identify the best-performing model and subsequently compares its performance with a proposed model tailored to the specific challenges faced by financial institutions. This paper looks at using advanced machine learning methods to make cybersecurity stronger for financial institutions. The work explores the deployment of cutting-edge machine learning algorithms - logistic regression, random forest, support vector machines (SVM), K-nearest neighbour (KNN), naïve Bayes, extreme gradient boosting (XGBoost), and deep learning technique (Dense Layer) - to fortify the cybersecurity framework within financial institutions. Through a meticulous analysis and comparative study, we explore the efficacy, scalability, and practical implementation aspects of various machine learning algorithms tailored to address cybersecurity concerns. Additionally, we propose a framework for integrating the most effective machine learning models into existing cybersecurity infrastructure, offering insights into bolstering resilience against evolving cyber threats. In our comparison, XGBoost exhibited outstanding performance with an accuracy of 95%.
Volume: 14
Issue: 2
Page: 1281-1289
Publish at: 2025-04-01

Automatic detection of dress-code surveillance in a university using YOLO algorithm

10.11591/ijai.v14.i2.pp1568-1575
Benjamin Jason Tantra , Moeljono Widjaja
Dress-code surveillance is a field that utilizes an object detection model to en- sure that people wear the proper attire in workplaces and educational institutions. The case is the same within universities, where students and staff must adhere to campus clothing guidelines. However, campus security still enforces univer- sity student clothing manually. Thus, this experiment creates an object detection model that can be used in the campus environment to detect if students are wear- ing appropriate clothing. The model developed for this research has reached an f1-score of 45% with an overall 51.8% mean accuracy precision. With this, the model has reached a satisfactory state with room for further improvements.
Volume: 14
Issue: 2
Page: 1568-1575
Publish at: 2025-04-01

A survey on novel approach to semantic computing for domain specific multi-lingual man-machine interaction

10.11591/ijict.v14i1.pp1-10
Anjali Bohra , Nemi Chand Barwar
Natural language processing (NLP) helps computational linguists to understand, process, and extract information from natural languages. Linguist Panini signifies ’information coding’ in a language and explains that Karakas are semanticosyntactic relations between nouns and verbs that resemble participant roles of modern case grammar. Computational grammar maps vibhakti (inflections) of nominals and verbs to their participant roles. Karaka’s theory extracts semantic roles in the sentences which act as intermediate steps for various NLP tasks. The survey shows that NLP seeks to bridge the gap for man-machine interaction. The work presents the impact of machine learning on natural language processing with changing trends from traditional to modern scenarios with Panini’s classification scheme for semantic computing facilitating machine understanding. The study presents the significance of Karaka for semantic computing, methodologies for extracting semantic roles, and analysis of various deep learning-based language processing systems for applications like question answering. The survey covered around 50 research articles and 21 Karaka-based NLP systems performing multiple tasks like machine translation, question-answering systems, and text summaries using machine learning tools and frameworks. The work includes surveys from renowned journals, books, and relevant conferences, as well as descriptions of the latest trends and technologies in the machine learning domain.
Volume: 14
Issue: 1
Page: 1-10
Publish at: 2025-04-01

Factors influencing the integration of web accessibility in Moroccan public e-services

10.11591/ijict.v14i1.pp77-90
Chadli Fatima Ezzahra , Aniss Moumen , Driss Gretete , Zineb Sabri
Governments worldwide are increasingly digitizing their services to enhance efficiency, transparency, and accessibility for citizens. Morocco has made significant strides in adopting information and communication technology (ICT) and has implemented various initiatives to promote digital transformation across sectors. However, ensuring that digital content and e-services are accessible to everyone, including people with disabilities, is crucial to building an inclusive digital environment. Against this background, this study, based on a qualitative analysis, explores the main factors influencing the integration of web accessibility in the Moroccan public sector from the perspective of web developers and information technology (IT) managers. Through semi-structured interviews and thematic analysis, the findings reveal key barriers such as limited awareness, training deficiencies, and lack of legal framework and available guidelines. Additionally, the study highlights the need for robust managerial backing and greater collaboration with stakeholders, including people with disabilities. By raising awareness and providing actionable insights, this study offers valuable recommendations for policymakers and moves the field forward, providing a foundation for future strategies to enhance web accessibility in the Moroccan public sector.
Volume: 14
Issue: 1
Page: 77-90
Publish at: 2025-04-01

Intrusion detection based on generative adversarial network with random forest for cloud networks

10.11591/ijece.v15i2.pp2491-2498
Gnanam Jeba Rosline , Pushpa Rani
The development of cloud computing enables individuals and organizations to access a wide range of online programs and services. Because of its nature, numerous users can access and distribute cloud infrastructure. In cloud computing several security threats change the data and operations. A network's ability to detect malicious activity and possible threats is greatly aided by intrusion detection. To solve these issues, intrusion detection based on generative adversarial network with random forest (GAN-RF) for cloud networks is introduced. The function of the generative adversarial networks (GANs) based network abnormality recognition system is evaluated. It uses the CICIDS2018 dataset to detect intrusion. GAN is utilized to improve network anomaly detection in conjunction with an ensemble random forest (RF) classifier. The GAN-RF model achieved 95.01% of accuracy for intrusion detection and obtain better recall and F1-score. Extensive assessments and valuations illustrate the efficiency of the GAN-RF approach in accurately identifying network issues.
Volume: 15
Issue: 2
Page: 2491-2498
Publish at: 2025-04-01

Advanced stress detection with optimized feature selection and hybrid neural networks

10.11591/ijece.v15i2.pp1647-1655
Sangita Ajit Patil , Ajay Namdeorao Paithane
Stress impacts both mental and physical health, potentially leading to serious conditions like cardiovascular diseases and mental disorders. Early detection of stress is crucial for reducing these risks. This study aims to improve stress detection by analyzing physiological signals, specifically electroencephalography (EEG) and electrocardiogram (ECG). EEG is affordable, while ECG provides detailed insights into cardiovascular health. Feature selection is a major challenge in analyzing these signals. To address this, the research introduces a novel method that combines the Archimedes optimization algorithm (AoA) with the analytical hierarchical process (AHP) to enhance accuracy in both single and multimodal systems. The proposed multimodal system employs a parallel-structured convolutional neural network (CNN) with a deep architecture to extract spatial features and uses a long short-term memory (LSTM) network to capture temporal dynamics. Experimental results show significant improvements: ECG stress detection accuracy rises from 88.6% to 91.79%, EEG accuracy increases from 95% to 96.6%, and multimodal stress detection accuracy reaches 98.6%. These results highlight the effectiveness of the AoA-AHP-based feature selection technique in boosting stress detection accuracy, contributing to improved mental health management and overall well-being.
Volume: 15
Issue: 2
Page: 1647-1655
Publish at: 2025-04-01

Hybrid object detection and distance measurement for precision agriculture: integrating YOLOv8 with rice field sidewalk detection algorithm

10.11591/ijai.v14.i2.pp1507-1517
Anucha Tungkasthan , Nipat Jongsawat , Padma Nyoman Crisnapati , Yamin Thwe
This study aims to propose a new approach to semantic segmentation of sidewalk images in rice fields using the YOLOv8 algorithm, with the objective of enhancing agricultural monitoring and analysis. The experimental process involved preparing the development environment, extracting data from JSON, and training the model using YOLOv8. Evaluation reveals consistent and accurate sidewalk detection with a confidence score of 0.9-1.0 across various environmental conditions. Confusion matrix and precision-recall analysis confirmed the robustness and accuracy of the model. These findings validate the effectiveness of the approach in detecting and measuring sidewalks with high precision, potentially improving agricultural monitoring. The novelty of this study lies in the utilization of the RIFIS-D algorithm as an integral part of a hybrid approach with YOLOv8. This hybridization enriches the model with additional capability to detect the distance between the sidewalk and the tractor, addressing specific needs in agricultural applications. This contribution is significant in the advancement of automatic navigation and monitoring technology in agriculture, enabling the implementation of more sophisticated and efficient systems in field operations.
Volume: 14
Issue: 2
Page: 1507-1517
Publish at: 2025-04-01
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