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25,002 Article Results

Optimization techniques applied on image segmentation process by prediction of data using data mining techniques

10.11591/ijece.v15i2.pp2161-2171
Ramaraj Muniappan , Srividhya Selvaraj , Rani Vanathi Gurusamy , Velumani Thiyagarajan , Dhendapani Sabareeswaran , David Prasanth , Varadharaj Krithika , Bhaarathi Ilango , Dhinakaran Subramanian
The research work presents an enhanced method that combines rule-based color image segmentation with fuzzy density-based spatial clustering of applications with noise (FDBSCAN). This technique enhances super-pixel robustness and improves overall image quality, offering a more effective solution for image segmentation. The study is specifically applied to the challenging and novel task of predicting the age of tigers from camera trap images, a critical issue in the emerging field of wildlife research. The task is fraught with challenges, particularly due to variations in image scale and thickness. Proposed methods demonstrate that significant improvements over existing techniques through the broader set of parameters of min and max to achieve superior segmentation results. The proposed approach optimizes segmentation by integrating fuzzy clustering with rule-based techniques, leading to improved accuracy and efficiency in processing color images. This innovation could greatly benefit further research and applications in real-world scenarios. Additionally, the scale and thickness variations of the present barracuda panorama knowledge base offer many advantages over other enhancement strategies that have been proposed for the use of these techniques. The experiments show that the proposed algorithm can utilize a wider range of parameters to achieve better segmentation results.
Volume: 15
Issue: 2
Page: 2161-2171
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

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

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

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

Towards a standardized enterprise architecture: enhancing decision-making in oncology multidisciplinary team meetings

10.11591/ijece.v15i2.pp2224-2236
Nassim Bout , Hicham Belhadaoui , Nadia Afifi , Mounia Abik , Mohamed El-Hfid , Ali Azougaghe
This study proposes a novel enterprise architecture (EA) designed to enhance the efficiency and decision-making processes of multidisciplinary team meetings (MDTMs) in oncology by integrating advanced artificial intelligence (AI) technologies. The architecture addresses current inefficiencies in MDTMs, particularly the lack of real-time data integration and limited decision support, by providing a structured framework that improves interoperability and standardizes clinical workflows. Developed using the open group architecture framework (TOGAF) framework and the ArchiMate modelling language, this conceptual architecture lays the groundwork for future empirical research, offering a scalable solution that can be adapted to various healthcare settings. The AI component, centered on generative pretrained transformer (GPT) models, is designed to support oncologists by providing evidence-based treatment recommendations tailored to individual patient cases. Although the study focusses on the theoretical development of this architecture, it opens the door for subsequent empirical testing and validation, with the aim of ultimately improving patient outcomes and streamlined oncology care through enhanced decision support systems.
Volume: 15
Issue: 2
Page: 2224-2236
Publish at: 2025-04-01

Key management for bitcoin transactions using cloud based key splitting technique

10.11591/ijece.v15i2.pp1861-1867
Amar Buchade , Nakul Sharma , Varsha Jadhav , Jagannath Nalavade , Suhas Sapate , Rajani Sajjan
Bitcoin wallet contains the information which is required for making transactions. To access this information, user maintains the secret key. Anyone with the secret key can access the records stored in bitcoin wallet. The compromise of the key such as physical theft, side channel attack, sybil attack, DoS attack and weak encryption can cause the access of transactional details and bitcoins stored in the wallet to the attacker. The cloud-based key split up technique is proposed for securing the key in blockchain technology. The key shares are distributed across virtual machines in cloud computing. The approach is compared to the existing key management approaches such as local key storage, keys derived from password and hosted wallet. It is observed that our approach is most suitable among the other key management approaches.
Volume: 15
Issue: 2
Page: 1861-1867
Publish at: 2025-04-01

Machine learning based stator-winding fault severity detection in induction motors

10.11591/ijeecs.v38.i1.pp182-192
Partha Mishra , Shubhasish Sarkar , Sandip Saha Chowdhury , Santanu Das
Approximately 35% of all induction motor defects are caused by stator inter-turn faults. In this paper a novel algorithm has been proposed to analyze the three-phase stator current signals captured from the motor while it is in operation. The suggested method seeks to identify stator inter-turn short circuit faults in early stage and take the appropriate action to prevent the motor's condition from getting worse. Three-phase current signals have been captured under healthy and faulty conditions of the motor. Involving discrete wavelet transform (DWT) based decomposition followed by reconstruction using inverse DWT (IDWT), 50 Hz fundamental component has been removed from the captured raw current signals. Subsequently, from each phase current 15 statistical parameters have been retrieved. The statistical parameters include mean, standard deviation, skewness, kurtosis, peak-to-peak, root mean square (RMS), energy, crest factor, form factor, impulse factor, and margin factor. At the end, a standard machine learning algorithm namely error correcting output codes-support vector machine (ECOC-SVM) has been employed to classify six different severity of stator winding faults. The proposed fault diagnosis method is load and motor-rating independent.
Volume: 38
Issue: 1
Page: 182-192
Publish at: 2025-04-01

SMOTE tree-based autoencoder multi-stage detection for man-in-the-middle in SCADA

10.11591/ijeecs.v38.i1.pp133-144
Freska Rolansa , Jazi Eko Istiyanto , Afiahayati Afiahayati , Aufaclav Zatu Kusuma Frisky
Security incidents targeting supervisory control and data acquisition (SCADA) infrastructure are increasing, which can lead to disasters such as pipeline fires or even lost of lives. Man-in-the-middle (MITM) attacks represent a significant threat to the security and reliability of SCADA. Detecting MITM attacks on the Modbus SCADA networks is the objective of this work. In addition, this work introduces SMOTE tree-based autoencoder multi-stage detection (STAM) using the Electra dataset. This work proposes a four-stage approach involving data preprocessing, data balancing, an autoencoder, and tree classification for anomaly detection and multi-class classification. In terms of attack identification, the proposed model performs with highest precision, detection rate/recall, and F1 score. In particular, the model achieves an F1 score of 100% for anomaly detection and an F1 score of 99.37% for multi-class classification, which is preeminence to other models. Moreover, the enhanced performance of multi-class classification with STAM on minority attack classes (replay and read) has shown similar characteristics in features and a reduced number of misclassifications in these classes.
Volume: 38
Issue: 1
Page: 133-144
Publish at: 2025-04-01

Innovating household efficiency: the internet of things intelligent drying rack system

10.11591/ijeecs.v38.i1.pp99-106
Norhalida Othman , Zakiah Mohd Yusoff , Mohamad Fadzli Khamis @ Subari , Nur Amalina Muhamad , Noor Hafizah Khairul Anuar
The intelligent drying rack system (IIDRS) proposes an innovative approach to modernize clothes drying practices using internet of things (IoT) technology. Combining an Arduino Uno microcontroller, ESP8266 for data transmission, and an array of sensors including limit switches, light dependent resistors (LDRs), rain sensors, and temperature/humidity sensors, the IIDRS enables automated control of the drying rack and fan. Its remote accessibility via Blynk apps allows users to conveniently adjust settings and monitor drying progress. By autonomously adjusting drying cycles based on real-time environmental conditions, the IIDRS enhances efficiency and minimizes inconveniences such as wet clothes during rainfall. Moreover, it contributes to sustainable living by optimizing energy consumption through weather-based operation. With its intuitive interface and compatibility with modern lifestyles, the IIDRS represents a significant advancement in smart home solutions, showcasing the transformative potential of IoT technologies in everyday tasks.
Volume: 38
Issue: 1
Page: 99-106
Publish at: 2025-04-01

HorseNet: a novel deep learning approach for horse health classification

10.11591/ijeecs.v38.i1.pp555-568
Nesrine Atitallah , Ahmed Abdel-Wahab , Anas A. Hadi , Hussein Abdel-Jaber , Ali Wagdy Mohamed , Mohamed Elsersy , Yusuf Mansour
In equestrian sports and veterinary medicine, horse welfare is paramount. Horse tiredness, lameness, colic, and anemia can be identified and classified using deep learning (DL) models. These technologies analyze horse images and videos to help vets and researchers find symptoms and trends that are hard to see. Early detection and better treatment of certain disorders can improve horses’ health. DL models can also improve with new data, improving diagnosis accuracy and efficiency. This study comprehensively evaluates three convolutional neural network (CNN) models to distinguish normal and abnormal horses using the generated horse dataset. For this study, a unique dataset of horse breeds and their normal and abnormal states was collected. The dataset includes mobility patterns from this study’s initial data collection. DL models like CNNs and transfer learning (TL) models (visual geometry group (VGG)16, InceptionV3) were employed for categorization. The InceptionV3 model outperformed CNN and VGG16 with over 97% accuracy. Its depth and multi-level structure allow the InceptionV3 model to recognize characteristics in images of varied scales and complexities, explaining its excellent performance.
Volume: 38
Issue: 1
Page: 555-568
Publish at: 2025-04-01

Solar-powered irrigation and monitoring system for okra cultivation

10.11591/ijeecs.v38.i1.pp469-477
Mohamad Syfiq Soekarno , Roslina Mohamad , Norashikin M. Thamrin , Wan Norsyafizan W. Muhamad
Eco-friendly and cost-effective irrigation systems are essential for sustainable agriculture. Traditional irrigation systems are unsustainable due to the high cost of operation and environmental pollution associated with fossil fuels. A possible solution for farmers is the use of solar-powered irrigation systems. This research aims to develop a solar-powered irrigation and a real-time monitoring system for okra cultivation. The irrigation system was powered by a monocrystalline solar panel and controlled by a Node MicroController Unit ESP8266 microcontroller unit. A 12 V pneumatic diaphragm water pump was utilized to irrigate the okra plants efficiently. The real-time monitoring system using Blynk allowed for the remote monitoring of the system's performance. The irrigation system was deployed on an okra farm, and the results showed that the system could sustain the soil moisture level for the okra plants, with an average soil moisture sensor reading of over 80%. The system delivered power effectively, with an average voltage measurement exceeding 12 V, average current readings above 180 mA, and average power readings exceeding 2 W. These results demonstrate that the solar-powered irrigation system is a viable and sustainable solution for farmers, researchers, and engineers to enhance the performance of conventional irrigation systems.
Volume: 38
Issue: 1
Page: 469-477
Publish at: 2025-04-01

Advancing supply chain management through artificial intelligence: a systematic literature review

10.11591/ijeecs.v38.i1.pp321-332
Ouahbi Younesse , Ziti Soumia , Lagmiri Najoua Souad
This study evaluates the role and impact of artificial intelligence (AI) in supply chain management (SCM). Following a five-step process, the review covered academic publications from 2000 to 2024, drawing from different databases. The review identified 426 relevant articles for analysis, focusing on AI techniques. The analysis explored their applications, advantages, and barriers to adoption in SCM. The study also discussed key challenges, including financial, organizational, strategic, technological, and legal barriers. The findings suggest that while AI techniques offer significant potential for improving SCM, several obstacles hinder their broader implementation. Addressing these obstacles requires investments in infrastructure, skills development, and effective change management.
Volume: 38
Issue: 1
Page: 321-332
Publish at: 2025-04-01

Customized dataset-based machine learning approach for black hole attack detection in mobile ad hoc networks

10.11591/ijece.v15i2.pp2138-2149
Houda Moudni , Mohamed Er-rouidi , Mansour Lmkaiti , Hicham Mouncif
This article explores the application of machine learning (ML) algorithms to classify the black hole attack in mobile ad hoc networks (MANETs). Black hole attacks threaten MANETs by disrupting communication and data transmission. The primary goal of this study is to develop an intrusion detection system (IDS) to detect and classify this attack. The research process involves feature selection, the creation of a custom dataset tailored to the characteristics of black hole attacks, and the evaluation of four machine learning models: random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and decision tree (DT). The evaluation of these models demonstrates promising results, with significant improvements in accuracy, precision, F1-score, and recall metrics. The findings underscore the potential of machine learning in enhancing the security of MANETs by providing an effective means of attack classification.
Volume: 15
Issue: 2
Page: 2138-2149
Publish at: 2025-04-01
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