Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
2000
2024

Source Title

FAQ Arrow

Authors

FAQ Arrow

25,002 Article Results

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

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

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

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

Simulation of ray behavior in biconvex converging lenses using machine learning algorithms

10.11591/ijeecs.v38.i1.pp357-366
Juan Deyby Carlos-Chullo , Marielena Vilca-Quispe , Whinders Joel Fernandez-Granda , Eveling Castro-Gutierrez
This study used machine learning (ML) algorithms to investigate the simulation of light ray behavior in biconvex converging lenses. While earlier studies have focused on lens image formation and ray tracing, they have not applied reinforcement learning (RL) algorithms like proximal policy optimization (PPO) and soft actor-critic (SAC), to model light refraction through 3D lens models. This study addresses that gap by assessing and contrasting the performance of these two algorithms in an optical simulation context. The findings of this study suggest that the PPO algorithm achieves superior ray convergence, surpassing SAC in terms of stability and accuracy in optical simulation. Consequently, PPO offers a promising avenue for optimizing optical ray simulators. It allows for a representation that closely aligns with the behavior in biconvex converging lenses, which holds significant potential for application in more complex optical scenarios.
Volume: 38
Issue: 1
Page: 357-366
Publish at: 2025-04-01

Enhancing BEMD decomposition using adaptive support size for CSRBF functions

10.11591/ijeecs.v38.i1.pp172-181
Mohammed Arrazaki , Othman El Ouahabi , Mohamed Zohry , Adel Babbah
Despite their widespread development, the Fourier transform and wavelet transform are still unsuitable for analyzing non-stationary and non-linear signals. To address this limitation, bidimensional empirical mode decomposition (BEMD) has emerged as a promising technique. BEMD effectively extracts structures at various scales and frequencies but faces significant computational complexity, primarily during the extremum interpolation phase. To mitigate this, different interpolation functions were presented and suggested, with BEMD using compactly supported radial basis functions (BEMD-CSRBF) showing promising results in reducing computational cost while maintaining decomposition quality. However, the choice of support size for CSRBF functions significantly impacts the quality of BEMD. This article presents an enhancement to the BEMD-CSRBF algorithm by adjusting the CSRBF support size based on the extrema distribution of the image. Our method’s results show a significant improvement in the BEMD-CSRBF algorithm’s quality. Furthermore, when compared to the other two approaches to BEMD, it shows higher accuracy in terms of both intrinsic mode function (IMF) quality and computational efficiency.
Volume: 38
Issue: 1
Page: 172-181
Publish at: 2025-04-01

Exploring diverse prediction models in intelligent traffic control

10.11591/ijeecs.v38.i1.pp393-402
Sahira Vilakkumadathil , Velumani Thiyagarajan
Traffic congestion is a major challenge that affects excellence of life for numerous people across world. The fast growth in many vehicles contributes to congestion during peak and non-peak hours. The vehicle traffic resulted in many issues like accidents and inefficiency in traffic flow. Many traffic light control systems operate on fixed time intervals leads to inefficiency. The fixed-time signals cause unnecessary delays on roads with minimum number of quantity vehicles. Intelligent transport systems (ITS) introduce new comprehensive framework that combine the advanced technologies to improve the transportation network efficiency and to optimize the traffic management. The high-traffic routes are forced to wait excessively. Machine learning (ML) methods have designed to examine the traffic control. However, the accurate detection and vehicle tracking are essential one for effective ITS. In order to mention these problems, ML and deep learning (DL) methods are introduced to improve prediction performance.
Volume: 38
Issue: 1
Page: 393-402
Publish at: 2025-04-01

Fake review detection using enhanced ensemble support vector machine system on e-commerce platform

10.11591/ijeecs.v38.i1.pp478-485
Seenia Joseph , S. Hemalatha
Due to the quick growth of online marketing transactions, including buying and selling, fake reviews are created to promote the product market and mislead new customers. E-commerce customers can post reviews and comments on the goods or services they obtained. Before making a purchase, new customers frequently read the feedback and comments posted on the website. Nowadays customers find it very difficult to identify whether the reviews are fake or not, but doing so is essential. So, it's very crucial to develop an online spam detection system to help both consumers and producers in their decision-making. The reviewer's behaviour and important review characteristics can help you identify fake reviews. The importance of this study is to develop a fake review detection system on e-commerce platforms using an enhanced ensemble support vector machine system in which the Euclidean distance is replaced with the Mahalanobis distance metric. Review texts collected from Amazon and Yelp were given as input data sets into the constructed model and classified as fake or real.
Volume: 38
Issue: 1
Page: 478-485
Publish at: 2025-04-01

Automated handwriting analysis and personality attribute discernment using self-attention multi-resolution analysis

10.11591/ijeecs.v38.i1.pp649-656
Yashomati R. Dhumal , Arundhati A. Shinde , Roshnadevi Jaising Sapkal , Satish Bhairannawar
Handwritten document analysis is a method used in academia that examines the patterns and strokes of a person’s handwriting in order to get a deeper understanding of that person’s personality and character. In spite of the fact that there are a number of models and methods that may be used in the investigation of automated graphology, there are a few challenges that need to be solved. Among these challenges is the identification of efficient classification techniques that provide the highest possible degree of accuracy. Within the scope of this study, we propose automated handwriting analysis and personality attribute discernment using self-attention multi-resolution analysis (MRA) where the data is preprocessed using histogram equalization and the spurious line segment section is attached to the genuine line segment portion in order to segment the succeeding line from the authentic picture of the document. A deep dense network is combined with self-attention MRA in order to provide a novel approach to the investigation of authentic handwritten text. Using the most recent and cutting-edge standards that are currently in use, an evaluation is performed to determine whether or not the proposed strategy is feasible. It is observed that the proposed method obtained nearly 98% accuracy with precision of 99%.
Volume: 38
Issue: 1
Page: 649-656
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

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

Sentiment analysis based on Indonesian language lexicon and IndoBERT on user reviews PLN mobile application

10.11591/ijeecs.v38.i1.pp677-688
Yessy Asri , Dwina Kuswardani , Widya Nita Suliyanti , Yosef Owen Manullang , Atikah Rifdah Ansyari
PLN mobile application as an integrated platform for self-service among mobile consumers, facilitating easier access to various services, including receiving information such as public complaints. The application can be downloaded through the Google Play Store and App Store, and users can express their opinions through reviews and ratings. In this era of advanced technology, aspects such as reviews, ratings, and evaluations have important value for business practitioners. However, there are often inconsistencies between ratings and reviews that do not fully represent the quality of the application. In response, a study was conducted to analyze the sentiment of user reviews from January to June 2022, by collecting 1,000 review samples from the Google Play Store. The data was collected using web scraping techniques and then processed into a dataset through text pre-processing methods. Sentiments were analyzed using an automatic labeling method in Indonesian based on a lexicon known as INSET (Indonesia sentiment), which resulted in 482 positive reviews, 144 negative reviews, and 374 neutral reviews. The next step is classification using Indonesian bidirectional encoder representations from transformers (IndoBERT). In this process, the data was divided into testing, training, and validation sets with a ratio of 80:10:10. The analysis managed to achieve an impressive accuracy rate of 81%.
Volume: 38
Issue: 1
Page: 677-688
Publish at: 2025-04-01

Recognition of plant leaf diseases based on deep learning and the chemical reaction optimization algorithm

10.11591/ijeecs.v38.i1.pp447-458
Nghien Nguyen Ba , Nhung Nguyen Thi , Dung Vuong Quoc , Cuong Nguyen Cong
Agriculture plays a crucial role in developing countries such as Vietnam, where 70 percent of the population is employed in agriculture, and 57 percent of the social labor force works in the agricultural sector. Therefore, crop productivity directly affects the lives of many people. One of the primary reasons for reduced crop yields is plant leaf diseases caused by bacteria, fungi, and viruses. Hence, there is a need for a method to help farmers identify leaf diseases early to take appropriate action to protect crops and shift to smart agricultural production. This paper proposes lightweight deep learning (DL) models combined with a support vector machine (SVM), with hyperparameters fine-tuned by chemical reaction optimization (CRO), for detecting plant leaf diseases. The main advantage of the method is the simplicity of the architecture and optimization of the DL model’s hyperparameters, making it easily deployable on low hardware devices. To test the performance of the proposed method, experiments are performed on the PlantVillage dataset using Python. The superiority of the proposed method over the well-known visual geometry group-16 (VGG-16) and MobileNetV2 models is demonstrated by a 10% increase in accuracy prediction and a decrease of 5% and 66% in training time, respectively.
Volume: 38
Issue: 1
Page: 447-458
Publish at: 2025-04-01

Energy and cost-aware workload scheduler for heterogeneous cloud platform

10.11591/ijeecs.v38.i1.pp546-554
Manjunatha Shivanandappa , Naveen Kumar Chowdaiah , Swetha Mysore Devaraje Gowda , Rashmi Shivaswamy , Vadivel Ramasamy , Subramani Suryakumar Prabhu Vijay
Parallel scientific workloads, often represented as directed acyclic graphs (DAGs), consist of interdependent tasks that require significant data exchange and are executed on distributed clusters. The communication overhead between tasks running on different nodes can lead to substantial increases in makespan, energy usage, and monetary costs. Therefore, there is potential to balance communication and computation to reduce these costs. In this paper, we introduce an energy and cost-aware workload scheduler (ECAWS) tailored for executing parallel scientific workloads, generated by the internet of things (IoT), in a heterogeneous cloud environment. The performance of the proposed ECAWS model is evaluated against existing models using the Inspiral scientific workload. Results indicate that ECAWS outperforms other models in reducing makespan, costs, and energy consumption.
Volume: 38
Issue: 1
Page: 546-554
Publish at: 2025-04-01
1 2 3
...
1666 1667
Show 9 of 1667

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration