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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

SmartSentry: a comprehensive framework for automated vulnerability discovery in Ethereum smart contracts

10.11591/ijeecs.v38.i1.pp657-667
Oualid Zaazaa , Hanan El Bakkali
In the realm of decentralized applications, smart contracts play a pivotal role in managing an extensive array of digital assets within blockchain networks. Ensuring the security of these digital assets hinges upon the adept detection of vulnerabilities present within smart contracts. Extensive research efforts have scrutinized and elucidated numerous smart contract vulnerabilities. However, certain vulnerabilities, including signature malleability, hash collision, and inconsequential code segments, remain relatively unexplored and devoid of dedicated detection tools. In response to this research gap, this paper addresses these three previously understudied vulnerabilities. We contribute to the field by creating a labeled dataset comprising vulnerable smart contracts. This dataset serves as a valuable resource for further scientific inquiries, enabling the testing and validation of various detection frameworks. Additionally, we present SmartSentry a static vulnerability detection framework capable of identifying these vulnerabilities. Using both dataflow and control flow analysis, our framework exhibits exceptional performance, successfully identifying labeled vulnerabilities and real-world vulnerabilities within production smart contracts with speed and efficiency. These efforts collectively enhance our understanding of smart contract vulnerabilities and contribute to the broader advancement of blockchain security.
Volume: 38
Issue: 1
Page: 657-667
Publish at: 2025-04-01

An enhanced least recently used page replacement algorithm

10.11591/ijeecs.v38.i1.pp417-427
Afaf Tareef , Khawla Al-Tarawneh , Omar Alhuniti
Page replacement algorithms play a crucial role in enhancing the performance issue brought on by variations in processor speeds and memory by effectively removing pages from computer memory to improve overall efficiency. The majority of these algorithms can address the page replacement problems, but their implementation is challenging. This paper introduces a new efficient page replacement algorithm, i.e., enhanced least-replacement (E-LRU) based on two introduced features used to select the victim page. By incorporating elements of traditional algorithms such as first in first out (FIFO) and least recently used (LRU), E-LRU presents itself as a new approach with potential benefits for memory management. This study evaluates the effectiveness of E-LRU in reducing power consumption by reducing cache faults and compares its performance to existing algorithms in various settings. The results provide insight into the advantages and disadvantages of E-LRU and essential perspectives on its potential benefits for contemporary memory management algorithms. Furthermore, the study puts E-LRU into the perspective of evolving algorithms and provides directions for future investigation and improvement in the ever-changing field of memory management. The study proved that E-LRU works better than FIFO and LRU algorithms.
Volume: 38
Issue: 1
Page: 417-427
Publish at: 2025-04-01

Diabetes detection and prediction through a multimodal artificial intelligence framework

10.11591/ijeecs.v38.i1.pp459-468
Gururaj N. Kulkarni , Kelapati Kelapati
Diabetes detection and prediction are crucial in modern healthcare, requiring advanced methodologies and comprehensive data analysis. This study aims to review the application of multi-parameters and artificial intelligence (AI) techniques in diabetes assessment, identify existing research limitations and gaps, and propose a novel multimodal framework for enhanced detection and prediction. The research objectives include evaluating current AI methodologies, analyzing multi-parameter integration, and addressing challenges in early detection and model evaluation. The study utilizes a systematic review approach, analyzing recent literature on AI-based diabetes detection and prediction, focusing on diverse data sources and machine learning (ML) techniques. Findings reveal a significant lack of integration of diverse data sources, limited focus on early detection strategies, and challenges in model evaluation. The study concludes with a proposed innovative framework for more accurate and personalized diabetes detection, contributing to the advancement of diabetes research and highlighting the potential of AI-driven healthcare interventions. This research underscores the importance of comprehensive data integration and robust evaluation methods in enhancing diabetes detection and prediction.
Volume: 38
Issue: 1
Page: 459-468
Publish at: 2025-04-01

Machine learning-based classification of corn seed viability using electrical impedance spectroscopy

10.11591/ijeecs.v38.i1.pp333-343
Perrie Lance Perocho , Ronnie Concepcion II
Corn (Zea mays L.), an essential global commodity, plays an ever-increasing role in agri-food systems. To support growing demand, rapid and noninvasive methods for determining seed germination rates are crucial alongside invasive techniques such as dissection, germination paper tests, and chemical assays. This study introduces electrical impedance spectroscopy (EIS) as a novel, non-invasive approach for classifying viable and non-viable corn seeds. Non-viable corn seeds were prepared by exposing them to 100 °C convection heat for 30 minutes. Impedance spectra were measured using the EVAL-AD5933EBZ evaluation board from 400 kHz to 1 MHz frequency range within 30 seconds. Furthermore, a comparison of six optimized supervised machine learning (ML) algorithms, including shallow and deep networks, was performed, setting this apart from other studies. The trained model was deployed to assess the viability of new seed samples effectively. Key impedance metrics, including their frequencies, were extracted to train and test the algorithms. The regression tree (RTree) model outperformed deep learning classifiers, achieving 95% accuracy, 90% precision, and 100% sensitivity. The results indicated an upward trend in viable seed impedance, increasing by 0.000164 Ω/Hz, peaking at 990 kHz. This approach offers a rapid, non-invasive solution for seed viability assessment, with significant potential to enhance agricultural productivity.
Volume: 38
Issue: 1
Page: 333-343
Publish at: 2025-04-01

Machine learning-based intelligent result compilation RPA bot for higher education institutions

10.11591/ijeecs.v38.i1.pp587-594
Neelam Yadav , Supriya P. Panda
Educators are essential for societal progress, and well-educated students are pivotal for a promising future. Higher education faces challenges such as budget constraints, limited time, and a shortage of trained personnel, leading to faculty stress. Emerging technologies such as artificial intelligence (AI), machine learning (ML), and block chain provide solutions, with robotic process automation (RPA) bots a notable advanced AI subfield-automating repetitive tasks, thereby freeing teachers to focus on more essential responsibilities. RPA bots automate various educational processes, including examinations, admissions, marks updating, student record management, result compilation, human resources, resume screening, and administration. This research examines robotic automation in higher education institutions (HEIs), selecting and prioritizing RPA tasks through a survey involving subject matter experts (SMEs) from different HEIs, including professors and RPA experts. The research aims to develop a “virtual software bot” for automating “result compilation” post-examination. Using tools like XPATH, Whisper, and the web-based automation program Selenium web in Python, the bot automates this process. The ML library “Whisper” addresses the reCAPTCHA problem. The automated bot generates comma separated values (CSV) files in specific formats, completing the task 58 times faster than humans and saving 43 man-hours by compiling results for 653 students in 45 minutes.
Volume: 38
Issue: 1
Page: 587-594
Publish at: 2025-04-01

Virtual exhibition systems using virtual reality technology

10.11591/ijeecs.v38.i1.pp367-380
P. M. Winarno , Wirawan Istiono , Rajendra Abhinaya
Exhibitions are an activity that can bring a lot of benefits to a company. By participating in an exhibition, a company can carry out promotions to increase their sales and improve their company image. However, there are several shortcomings that can be found with conventional exhibitions held in a face-to-face manner. These exhibitions cost a lot of money, run for only a relatively short period of time, and are limited by the location of the exhibition. Because of this, the idea came up to create a virtual exhibition system which could be used as an alternative to conventional exhibitions. The development of a virtual exhibition system for this research was carried out using the Unity game engine. At the virtual exhibition, users can choose which exhibition they want to visit and enter the exhibition room view products and find information about them. Evaluation is carried out using a user acceptance test with Likert scale questions. The evaluation results show a user satisfaction level of 92.7% among the 18 users who have tested the application. With this, it can be said that the virtual exhibition system based on virtual reality technology has been successfully built.
Volume: 38
Issue: 1
Page: 367-380
Publish at: 2025-04-01

A multi-scale convolutional neural network and discrete wavelet transform based retinal image compression

10.11591/ijeecs.v38.i1.pp243-253
Dalila Chikhaoui , Mohammed Beladgham , Mohamed Benaissa , Abdelmalik Taleb-Ahmed
The different applications of medical images have contributed significantly to the growing amount of image data. As a result, compression techniques become essential to allow real-time transmission and storage within limited network bandwidth and storage space. Deep learning, particularly convolutional neural networks (CNN) have marked rapid advances in many computer vision tasks and have progressively drawn attention for being used in image compression. Therefore, we present a method for compressing retinal images based on deep CNN and discrete wavelet transform (DWT). To further enhance CNN capabilities, multi-scale convolutions are introduced into the network architecture. In this proposed method, multiscale CNNs are used to extract useful features to provide a compact representation at the encoding stage and guarantee a better reconstruction quality of the image at the decoding stage. Based on compression efficiency and reconstructed image quality, a wide range of experiments have been conducted to validate the proposed technique performance compared with popular image compression standards and existing deep learning-based methods. At a compression ratio (CR) of 80, the proposed method achieved an average peak signal-to-noise ratio (PSNR) value of 38.98 dB and 96.8% similarity in terms of multi-scale structural similarity (MS-SSIM), demonstrating its effectiveness.
Volume: 38
Issue: 1
Page: 243-253
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

Project QSUeVoto: distributed electronic voting system based on blockchain technology

10.11591/ijeecs.v38.i1.pp272-280
Winston G. Domingo , Manuel De Guzman , Charmaine Ruth G. Abella , Dennie T. Ruma , Rishelle B. Nucaza , Eduard P. Alip , Selino S. Malunao
Students' voting experience can be made far more secure, transparent, and effective with an electronic voting system based on blockchain. But for it to be implemented successfully, technological issues must be resolved, accessibility must be guaranteed, and student trust must be developed. Resilient security protocols, intuitive user interfaces, and unambiguous dissemination of the advantages and functionality of the system are vital for surmounting possible obstacles and optimizing favorable outcomes. System development techniques and a descriptive research design were used in this study. The developed systems are accepted and compliant as determined by the IT experts, as evidenced by the grand mean of 4.63 and the descriptive rating of conformity to a very high level. It can be deduced that the SG Advisor, SAS Director, students, and Canvasser Board from Maddela and Diffun Campus gave the generated application great approval and acceptance. This indicates that there is a notable discrepancy between the users' and IT specialists' perceptions of the system's adoption and compliance levels. This procedure can be made better with a safe voting system that has cutting-edge features. Blockchain technology is regarded as a disruptive breakthrough with substantial potential to improve the electronic voting system.
Volume: 38
Issue: 1
Page: 272-280
Publish at: 2025-04-01
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