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25,002 Article Results

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

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

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

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

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

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

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

Topographic and flow direction model: a case study of Khuan Kreng peat swamp forest, Southern Thailand

10.11591/ijece.v15i2.pp1978-1989
Panjit Musik , Nunticha Limchoowong , Phitchan Sricharoen , Jintapat Nateewattana , Tanutta Amnuaywattanakul , Woravith Chansuvarn , Uraiwun Wanthong
Floods and droughts are contrasting natural phenomena. The risk of forest fires tends to be increased by the dry and hot conditions of the dry season. A topographic and flow direction model is aimed to be created using Mathematica and ArcGIS programs. The purpose of this model is to assist in water management to prevent forest fires in the Khuan Kreng peat swamp forest located in Nakhon Si Thammarat Province, Southern Thailand. Digital elevation models obtained from the Department of Land Development, representing altitude data of the terrain at a scale of 1:4,000, are utilized in this work. Using cellular automata principles with eight sub-cell flow pathways with a precision of 5×5 meters, identification was carried out. The Universal Transverse Mercator (UTM) coordinate system can store horizontal (X, Y) and vertical (Z) data in one cell, providing information about 2D and 3D topography. Our findings regarding flow direction are comparable to reference values for summer under dry conditions, where water mass is limited. The topographic model data was found to be compatible with data obtained from ArcGIS, Google Maps, and surveys. The ArcGIS flow modeling results are found to be suitable for flood simulation. The proposed method is applicable for regulating water use during droughts and preventing forest fires.
Volume: 15
Issue: 2
Page: 1978-1989
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

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

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

Tackling the anomaly detection challenge in large-scale wireless sensor networks

10.11591/ijece.v15i2.pp2479-2490
Tamara Zhukabayeva , Aigul Adamova , Lazzat Zholshiyeva , Yerik Mardenov , Nurdaulet Karabayev , Dilaram Baumuratova
One of the areas of ensuring the security of a wireless sensor network (WSN) is anomaly detection, which identifies deviations from normal behavior. In our paper, we investigate the optimal anomaly detection algorithms in a WSN. We highlight the problems in anomaly detection, and we also propose a new methodology using machine learning. The effectiveness of the k-nearest neighbor (kNN) and Z Score methods is evaluated on the data obtained from WSN devices in real time. According to the experimental study, the Z Score methodology showed a 98.9% level of accuracy, which was much superior to the kNN 43.7% method. In order to ensure accurate anomaly detection, it is crucial to have access to high-quality data when conducting a study. Our research enhances the field of WSN security by offering a novel approach for detecting anomalies. We compare the performance of two methods and provide evidence of the superior effectiveness of the Z Score method. Our future research will focus on exploring and comparing several approaches to identify the most effective anomaly detection method, with the ultimate goal of enhancing the security of WSN.
Volume: 15
Issue: 2
Page: 2479-2490
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
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