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25,002 Article Results

A constrained convolutional neural network with attention mechanism for image manipulation detection

10.11591/ijece.v15i2.pp2304-2313
Kamagate Beman Hamidja , Fatoumata Wongbé Rosalie Tokpa , Vincent Mosan , Souleymane Oumtanaga
The information disseminated by online media is often presented in the form of images, in order to quickly captivate readers and increase audience ratings. However, these images can be manipulated for malicious purposes, such as influencing public opinion, undermining media credibility, disrupting democratic processes or creating conflict within society. Various approaches, whether relying on manually developed features or deep learning, have been devised to detect falsified images. However, they frequently prove less effective when confronted with widespread and multiple manipulations. To address this challenge, in our study, we have designed a model comprising a constrained convolution layer combined with an attention mechanism and a transfer learning ResNet50 network. These components are intended to automatically learn image manipulation features in the initial layer and extract spatial features, respectively. It makes possible to detect various falsifications with much more accuracy and precision. The proposed model has been trained and tested on real datasets sourced from the literature, which include MediaEval and Casia. The obtained results indicate that our proposal surpasses other models documented in the literature. Specifically, we achieve an accuracy of 87% and a precision of 93% on the MediaEval dataset. In comparison, the performance of methods from the literature on the same dataset does not exceed 84% for accuracy and 90% for precision.
Volume: 15
Issue: 2
Page: 2304-2313
Publish at: 2025-04-01

Buffers balancing of buffer-aided relays in 5G non-orthogonal multiple access transmission internet of things networks

10.11591/ijece.v15i2.pp1774-1782
Mohammad Alkhwatrah , Nidal Qasem
Buffer-aided cooperative non-orthogonal multiple access (NOMA) enhances the efficiency of utilizing the spectral by allowing more users to share the same re- sources to establish massive connectivity. This is remarkably attractive in the fifth generation (5G) and beyond systems, where a massive number of links is essential like in the internet of things (IoT). However, the capability of buffer co-operation in reducing the outage is limited due to empty and full buffers, where empty buffers can not transmit and full buffers can not receive data packets. Therefore, in this paper, we propose balancing the buffer content of the inter-connected relays, so the buffers that are more full send packets to the emptier buffers, hence all buffers are more balanced and farther from being empty or full. The simulations show that the proposed balancing technique has improved the network outage probability. The results show that the impact of the balancing is more effective as the number of relays in the network is increased. Further- more, utilizing the balancing with a lower number of relays may lead to better performance than that of more relays without balancing. In addition, giving the balancing different levels of priorities gives different levels of enhancement.
Volume: 15
Issue: 2
Page: 1774-1782
Publish at: 2025-04-01

Model of semiconductor converters for the simulation of an asymmetric loads in an autonomous power supply system

10.11591/ijece.v15i2.pp1332-1347
Saidjon Tavarov , Mihail Senyuk , Murodbek Safaraliev , Sergey Kokin , Alexander Tavlintsev , Andrey Svyatykh
This article is devoted to the development of computer model with semiconductor converters for the simulation of asymmetric loads allowing to solve the voltage symmetry problems under asymmetric loads (active and active-inductive) for isolated electric networks with renewable energy sources (mini hydroelectric power plants). A model of a symmetry device has been developed in the MATLAB/Simulink environment based on a proportional-integral controller and a relay controller - P. The effectiveness of their use depends on the load's nature. The implementation of a voltage converter is presented considering a three-phase inverter with discrete key switching at 120, 150, and 180 degrees with a purely active load. Based on the harmonic analysis of the three-phase voltage at discrete conversion, the value of the first harmonic is determined. Voltage transformations under active-inductive load at 120, 150, and 180 degrees are mathematically described. To determine the harmonic spectrum, an analysis of the fast Fourier transform for the three-phase voltage of a MATLAB/Simulink semiconductor converter was carried out. It is established that the alternating current output voltage is generated on the output side of the inverter of a three-phase voltage source through a three-phase load connected by a star with a harmonic suppression method.
Volume: 15
Issue: 2
Page: 1332-1347
Publish at: 2025-04-01

OCNet-23: a fine-tuned transfer learning approach for oral cancer detection from histopathological images

10.11591/ijece.v15i2.pp1826-1833
Amatul Bushra Akhi , Abdullah Al Noman , Sonjoy Prosad Shaha , Farzana Akter , Munira Akter Lata , Rubel Sheikh
Oral squamous cell carcinoma (OSCC) is emerging as a significant global health concern, underscoring the need for prompt detection and treatment. Our study introduces an innovative diagnostic method for OSCC, leveraging the capabilities of artificial intelligence (AI) and histopathological images (HIs). Our primary objective is to expedite the identification process for medical professionals. To achieve this, we employ transfer learning and incorporate renowned models such as VGG16, VGG19, MobileNet_v1, MobileNet_v2, DenseNet, and InceptionV3. A key feature of our approach is the meticulous optimization of the VGG19 architecture, paired with advanced image preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and median blur. We conducted an ablation study with optimized hyperparameters, culminating in an impressive 95.32% accuracy. This groundbreaking research ensures accurate and timely diagnoses, leading to improved patient outcomes, and represents a significant advancement in the application of AI for oral cancer diagnostics. Utilizing a substantial dataset of 5,192 meticulously categorized images into OSCC and normal categories, our work pioneers the field of OSCC detection. By providing medical professionals with a robust tool to enhance their diagnostic capabilities, our method has the potential to revolutionize the sector and usher in a new era of more effective and efficient oral cancer treatment.
Volume: 15
Issue: 2
Page: 1826-1833
Publish at: 2025-04-01

Kafka-machine learning based storage benchmark kit for estimation of large file storage performance

10.11591/ijece.v15i2.pp1990-1999
Sanjay Kumar Naazre Vittal Rao , Anitha Chikkanayakanahalli Lokesh Kumar , Subhash Kamble
Efficient storage and maintenance of big data is important with respect to assuring accessibility and cost-friendliness to improve risk management and achieve an effective comprehension of the user requirements. Managing the extensive data volumes and optimizing storage performance poses a significant challenge. To address this challenge, this research proposes the Kafka-machine learning (ML) based storage benchmark kit (SBK) designed to evaluate the performance of the file storage system. The proposed method employs Kafka-ML and a drill-down feature to optimize storage performance and enhance throughput. Kafka-ML-based SBK has the capability to optimize storage efficiency and system performance through space requirements and enhance data handling. The drill-down search feature precisely contributes through reducing disk space usage, enabling faster data retrieval and more efficient real-time processing within the Kafka-ML framework. The SBK aims to provide transparency and ease of utilization for benchmarking purposes. The proposed method attains maximum throughput and minimum latency of 20 MBs and 70 ms, respectively on the number of data bytes is 10, as opposed to the existing method SBK Kafka.
Volume: 15
Issue: 2
Page: 1990-1999
Publish at: 2025-04-01

Secure financial application using homomorphic encryption

10.11591/ijeecs.v38.i1.pp595-602
Vijaykumar Bidve , Aruna Pavate , Rahul Raut , Shailesh Kediya , Pakiriswamy Sarasu , Koteswara Rao Anne , Aryani Gangadhara , Ashfaq Shaikh
In today’s digital age, the security and privacy of financial transactions are paramount. With the advent of technologies like homomorphic encryption, it is now possible to perform computations on encrypted data without the need to decrypt it first, offering a promising avenue for secure financial applications. This research paper explores the implementation and implications of utilizing homomorphic encryption in financial applications to safeguard sensitive data while maintaining computational integrity. By employing homomorphic encryption techniques, financial institutions can enhance the confidentiality of their clients’ information, protect against data breaches, and enable secure computations on encrypted data. The paper discusses the principles of homomorphic encryption, its applications in financial systems, challenges, and potential solutions. Additionally, it examines real-world examples and case studies where homomorphic encryption has been employed successfully, highlighting its effectiveness in ensuring the privacy and security of financial transactions. Overall, this paper aims to provide insights into the role of homomorphic encryption in creating secure financial applications and its potential to revolutionize the way sensitive financial data is handled and processed.
Volume: 38
Issue: 1
Page: 595-602
Publish at: 2025-04-01

Tree-based models and hyperparameter optimization for assessing employee performance

10.11591/ijeecs.v38.i1.pp569-577
Rendra Gustriansyah , Shinta Puspasari , Ahmad Sanmorino , Nazori Suhandi , Dewi Sartika
The Palembang city fire and rescue service (FRS) is encountering challenges in adhering to national standards for fire response time. Hence, the Palembang city FRS is committed to enhancing employee performance through quarterly performance assessments based on various criteria such as attendance, work targets, behavior, education, and performance reports. This study proposes tree-based models in machine learning (ML) and hyperparameter optimization to assess the performance of Palembang city FRS employees. Tree-based models encompass decision trees (DT), random forests (RF), and extreme gradient boosting (XGB). The predictive performance of each model was evaluated using the confusion matrix (CM), the area under the receiver operating characteristic (AUROC), and the kappa coefficient (KC). The results indicate that RF performs better than DT and XGB in the sensitivity, AUROC, and KC metrics by 1.0000, 0.9874, and 0.8584, respectively.
Volume: 38
Issue: 1
Page: 569-577
Publish at: 2025-04-01

Flexible hybrid graphene-based NFC tag antenna for temperature monitoring application

10.11591/ijeecs.v38.i1.pp227-242
Najwa Mohd Faudzi , Ahmad Rashidy Razali , Asrulnizam Abd Manaf , Nurul Huda Abd Rahman , Ahmad Azlan Ab Aziz , Syed Muhammad Hafiz , Suraya Sulaiman , Nora’zah Abdul Rashid , Amirudin Ibrahim , Aiza Mahyuni Mozi
A hybrid graphene-based material, composed of reduced graphene oxide (rGO) and silver nanoparticle (AgNP), has been proposed for a near field communication (NFC) tag antenna with an integrated, flexible temperature monitoring circuit. The limited availability of high-conductivity graphene-based materials in the market has restricted the use of graphene in NFC tag applications. Therefore, this paper proposes a hybrid graphene-based composition featuring a high conductivity of 3.95×106 S/m. The feasibility of this material for NFC tags had not been validated previously, which is the main motivation for this research. The synthesis of the materials, along with the design, fabrication, and characterization of the NFC tag, is also presented. Results show that the inkjet-printed tag achieves a good reading range of up to 3 cm and demonstrates robustness against bending from 60⁰ to 190⁰, maintaining a maximum reading range of 1.3 cm. Performance on various materials, such as plastic, paper, and carton, also shows minimal impact on frequency shifting. Additionally, the graphene-based NFC tag integrates well with the temperature circuit, effectively monitoring temperatures in the 20-60 ⁰C range in real-time. This makes the developed tag suitable for applications such as food safety monitoring systems through NFC-integrated packaging.
Volume: 38
Issue: 1
Page: 227-242
Publish at: 2025-04-01

Plant disease detection using vision transformers

10.11591/ijece.v15i2.pp2334-2344
Mhaned Ali , Mouatassim Salma , Mounia El Haji , Benhra Jamal
Plant diseases present a major risk to worldwide food security and the sustainability of agriculture, leading to substantial economic losses and hindering rural livelihoods. Conventional methods for disease detection, including visual inspection and laboratory-based techniques, are limited in their scalability, efficiency, and accuracy. This paper addresses the critical problem of accurately detecting and diagnosing plant diseases using advanced machine learning techniques, specifically vision transformers (ViTs), to overcome these limitations. ViTs leverage self-attention mechanisms to capture intricate patterns in plant images, enabling accurate and efficient disease classification. This paper reviews the literature on deep learning techniques in agriculture, emphasizing the growing interest in ViTs for plant disease detection. Additionally, it presents a comprehensive methodology for training and evaluating ViT models for plant disease classification tasks. Experimental results demonstrate the effectiveness of ViTs in accurately identifying various plant diseases across a balanced 55 classes dataset, highlighting their potential to revolutionize precision agriculture and promote sustainable farming practices.
Volume: 15
Issue: 2
Page: 2334-2344
Publish at: 2025-04-01

IDCCD: evaluation of deep learning for early detection caries based on ICDAS

10.11591/ijeecs.v38.i1.pp381-392
Rina Putri Noer Fadilah , Rasmi Rikmasari , Saiful Akbar , Arlette Suzy Setiawan
Dental caries is a common oral disease in children, influenced by environmental, psychological, behavioral, and biological factors. The American academy of pediatric dentistry recommends screening from the time the first tooth erupts or at one year of age to prevent caries, which mostly affects children from racial and ethnic minorities. In Indonesia, the 2023 health survey reported a caries prevalence of 84.8% in children aged 5-9 years. This research introduces early caries detection using three deep learning models: faster-RCNN, you only look once (YOLO) V8, and detection transformer (DETR), using Indonesian dental caries characteristic datasets (IDCCD) focused on Indonesian data with international caries detection and assessment system (ICDAS) classification D0 to D6. The results showed that YOLO V8-s and DETR gave good results, with mean average precision (mAP) of 41.8% and 41.3% for intersection over union (IoU) 50, and 24.3% and 26.2% for IoU 50:90. Precision-recall (PR) curves show that both models have high precision at low recall (0 to 0.2), but precision decreases sharply as recall increases. YOLO V8-s showed a slower and more regular decrease in precision, indicating a more stable performance compared to DETR.
Volume: 38
Issue: 1
Page: 381-392
Publish at: 2025-04-01

Enhancing patient navigation and referral through tele-referral system with geographical information systems

10.11591/ijeecs.v38.i1.pp281-291
Winston G. Domingo , Virdi C. Gonzales , Jennifer A. Gamay
A tele-referral system with a geographic information system (GIS) integrates telehealth services with spatial data to enhance healthcare delivery. Resource constraints can significantly impact the effectiveness of a tele-referral system with GIS. Addressing delayed or missed referrals is critical to ensuring timely patient care and improving health outcomes. Implementing a tele-referral system with GIS can significantly enhance healthcare delivery by leveraging spatial data and telehealth technologies to improve access, efficiency, and outcomes. One major issue is the lack of access to specialists, particularly in underprivileged communities. Patients face accessing specialized care due to a cumbersome referral process or long wait times, as well as the lack of patient engagement. The results showed that the GIS-enabled tele-referral system significantly reduced patient waiting times and improved the coordination of care. By incorporating these functionalities and strategies, the tele-referral system with GIS can effectively address issues related to delayed or missed referrals, ensuring timely patient care and improving overall health outcomes. By incorporating these strategies and functionalities, the tele-referral system with GIS can effectively address limited access to specialists, ensuring timely patient care and optimal use of available resources.
Volume: 38
Issue: 1
Page: 281-291
Publish at: 2025-04-01

A comprehensive overview of LLM-based approaches for machine translation

10.11591/ijeecs.v38.i1.pp344-356
Bhuvaneswari Kumar , Varalakshmi Murugesan
Statistical machine translation (SMT) used parallel corpora and statistical models, to identify translation patterns and probabilities. Although this method had advantages, it had trouble with idiomatic expressions, context-specific subtleties, and intricate linguistic structures. The subsequent introduction of deep neural networks such as recurrent neural networks (RNNs), long short-term memory (LSTMs), transformers with attention mechanisms, and the emergence of large language model (LLM) frameworks has marked a paradigm shift in machine translation in recent years and has entirely replaced the traditional statistical approaches. The LLMs are able to capture complex language patterns, semantics, and context because they have been trained on enormous volumes of text data. Our study summarizes the most significant contributions in the literature related to LLM prompting, fine-tuning, retrieval augmented generation, improved transformer variants for faster translation, multilingual LLMs, and quality estimation with LLMs. This new research direction guides the development of more efficient and innovative solutions to address the current challenges of LLMs, including hallucinations, translation bias, information leakage, and inaccuracy due to language inconsistencies.
Volume: 38
Issue: 1
Page: 344-356
Publish at: 2025-04-01

Identification of Android APK malware through local and global feature extraction using meta classifier

10.11591/ijece.v15i2.pp1834-1849
Yoga Herawan , Imas Sukaesih Sitanggang , Shelvie Nidya Neyman
Android, the most widely used mobile operating system, is also the most vulnerable to malware due to its high popularity. This has significantly focused on Android malware detection in mobile security. While extensive research has been conducted using various methods, new malware’s emergence underscores this field’s dynamic nature and the need for continuous research. The motivation that drives malware developers to create Android malware constantly is the potential to access Android devices, thereby gaining access to sensitive user information. This study, which is a complex and in-depth exploration, aims to detect Android malware using a meta-classifier that combines the single-classifier light gradient boosting machine, support vector machine, and random forest. The process involves converting disassembled malware codes into grey images for global and local feature extraction. The classification accuracy is 97% at best on a malware dataset of 3,963 samples. The main contribution of this paper is to produce an Android APK malware detector model that works by combining multiple machine learning algorithms trained using the dataset resulting from local and global feature extraction algorithms.
Volume: 15
Issue: 2
Page: 1834-1849
Publish at: 2025-04-01

A new data imputation technique for efficient used car price forecasting

10.11591/ijece.v15i2.pp2364-2371
Charlène Béatrice Bridge-Nduwimana , Aziza El Ouaazizi , Majid Benyakhlef
This research presents an innovative methodology for addressing missing data challenges, specifically applied to predicting the resale value of used vehicles. The study integrates a tailored feature selection algorithm with a sophisticated imputation strategy utilizing the HistGradientBoostingRegressor to enhance efficiency and accuracy while maintaining data fidelity. The approach effectively resolves data preprocessing and missing value imputation issues in complex datasets. A comprehensive flowchart delineates the process from initial data acquisition and integration to ultimate preprocessing steps, encompassing feature engineering, data partitioning, model training, and imputation procedures. The results demonstrate the superiority of the HistGradientBoostingRegressor for imputation over conventional methods, with boosted models eXtreme gradient boosting (XGBoost) regressor and gradient boosting regressor exhibiting exceptional performance in price forecasting. While the study’s potential limitations include generalizability across diverse datasets, its applications include enhancing pricing models in the automotive sector and improving data quality in large-scale market analyses.
Volume: 15
Issue: 2
Page: 2364-2371
Publish at: 2025-04-01

Ensemble learning weighted average meta-classifier for palm diseases identification

10.11591/ijeecs.v38.i1.pp303-311
Sofiane Abden , Mostefa Bendjima , Soumia Benkrama
Crop diseases lead to significant losses for farmers and threaten the global food supply. The date palm, valued for its nutritional benefits and drought resistance in desert climates, is a vital export crop for many countries in the Middle East and North Africa, second only to hydrocarbons. However, various diseases pose a threat to this important plant. Therefore, early disease prediction using deep learning (DL) is essential to prevent the deterioration of date palm crops. The aim of this paper is to apply a robust ensemble method (EL) combining tree transfer learning (TL) models Resnet50, DenseNet201, and InceptionV3, and compares its performance with the CNN-SVM model and the tree TL models mentioned previously. The models were applied to a date palm dataset containing three classes: White scale, brown spot, and healthy leaf. The training and validation sets were applied to a public dataset, while the testing set was applied to a local dataset captured manually to check the model’s performance. As a result, we considered that the ensemble method gave very satisfactory results compared to other methods. Our hybrid model reached a testing accuracy of 98% while achieving an amazing training and validation accuracy of 99.94% and 98.14%, respectively.
Volume: 38
Issue: 1
Page: 303-311
Publish at: 2025-04-01
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