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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Graph neural network based human detection in videos during occlusion environments Sriram, Kusuma; Purushotham, Kiran
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2616-2624

Abstract

One of the most difficult perceptual problems for many applications is accurately recognizing the human object in a variety of circumstances. This can be difficult due to obstructions, weather, complex backdrops, cast shadows, and occlusions. Occlusion is a challenging open problem where a detector can only perceive a portion of the target human because of obstacles in the surrounding. In this research, an experimental investigation was conducted using the multi object tracking (MOT17) datasets to construct a graph neural network-based solution for the detection of humans in videos while considering the possibility of occlusion. Graph neural network (GNN) is used for the construction of neural solver model for detecting human object in occlusion scenario. The results obtained shows that this proposed method offers a considerable improvement in efficiency in comparison to the ways that have been used in the past. The values obtained for the standard performance metrics are higher than the state-of-the-art methods.
A comprehensive verification of the header format and bandwidth utilization to detect distributed denial of service attack in vehicular ad hoc network Kaurav, Arun Singh; Srinivas, K.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6538-6550

Abstract

Vehicular ad hoc network (VANET) is a promising technology for controlling traffic on roads. Nowadays, heavy traffic is a major issue, and the presence of attackers exacerbates the situation. The most important challenge in VANET is its security from malicious vehicles. In order to defend against distributed denial of service DDoS attacks, we propose a comprehensive verification header format bandwidth detection (CVHB) in VANET. The behavior of a DDoS attack is unknown for all the other normal nodes in network. The header format of packer contains all the information of nodes that are actively participating in routing. The attacker infection probability measured by ???????? and ???????? or (???????? > 0.9). If both the parameters are high means attacker presence confirm in network. The CVHB scheme checks the packet header format of the attacker node, and only the attacker is one of the nodes whose sequence number is frequently changing. So, CVHB blocks the flooding of unwanted packets that consume the limited bandwidth of a wireless link and identify packets that contain no useful information. To measure the performance of the network, the basic performance metrics that are used are dropping percentage, packet delivery ratio (PDR), throughput and delay. The result of CVHB is showing improvement as compared to multilayer distributed self-organizing maps (MSOM) in VANET.
Efficient intelligent crawler for hamming distance based on prioritization of web documents Dange, Amol Subhash; Byranahalli Eraiah, Manjunath Swamy; Rao, Manju More Eshwar; Hanumanthaiah, Asha Kethaganahalli; Ganganayaka, Sunil Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1948-1958

Abstract

Search engines play a crucial role in today's Internet landscape, especially with the exponential increase in data storage. Ranking models are used in search engines to locate relevant pages and rank them in decreasing order of relevance. They are an integral component of a search engine. The offline gathering of the document is crucial for providing the user with more accurate and pertinent findings. With the web’s ongoing expansions, the number of documents that need to be crawled has grown enormously. It is crucial to wisely prioritize the documents that need to be crawled in each iteration for any academic or mid-level organization because the resources for continuous crawling are fixed. The advantages of prioritization are implemented by algorithms designed to operate with the existing crawling pipeline. To avoid becoming the bottleneck in pipeline, these algorithms must be fast and efficient. A highly efficient and intelligent web crawler has been developed, which employs the hamming distance method for prioritizing the pages to be downloaded in each iteration. This cutting-edge search engine is specifically designed to make the crawling process more streamlined and effective. When compared with other existing methods, the implemented hamming distance method achieves a high value of 99.8% accuracy.
Web Block Craft: web development for children using Google Blockly Gunaratne, Madhumini; Weerasekara, Senal; Weerakkody, Dehemi; Sashmitha, Nisal; Zoysa, Rivoni De; Kodagoda, Nuwan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5585-5592

Abstract

Web Block Craft is an innovative educational application that uses the Google Blockly framework to teach web development to children aged eleven and above. The application serves as a comprehensive learning tool, allowing users to explore both frontend project and backend project development. The frontend project includes HTML, CSS, JavaScript, and DOM manipulation, while the backend project covers server building, web app security, application programming interfaces (APIs), and database management. Web Block Craft's unique block-based interface allows users to easily drag and drop components into a dynamic working environment, resulting in an engaging experience with live output display and simultaneous code presentation. A unique feature of Web Block Craft is the integration of a platform within the application, which allows teachers to create lessons with step-by-step instructions for students. This new feature allows for a more structured learning experience, which improves understanding of web development concepts. To enhance the learning experience, the application provides extensive documentation, serving as a valuable resource for users to grasp the intricacies of web programming. By combining the power of Google Blockly with a creative user interface and educational resources, Web Block Craft provides a comprehensive learning environment that empowers creative web programming with confidence.
The preliminary study of carbon x-change rakyat using blockchain application Putro, Wahyu Sasongko; Rahmi, Nitia; Asditama, Raditya Yoga; Akbar, Nur Arifin
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp672-680

Abstract

Today’s air pollution is detrimental to the environment, particularly in Indonesia. Carbon dioxide (CO2) and nitrogen oxide (NOx) are present in the atmosphere due to air pollution. Many individuals employ reforestation to lessen the influence of CO2 and NOx gases on the atmosphere. However, in the digitalized era, lowering carbon emissions may also be accomplished through a carbon credit exchange. Thus, in this study we investigate the performance of the carbon x-change rakyat (CXR) based on blockchain platform utilizing the stress test approach. We provided four scenarios with 10,000 to 100,000 transactions evaluated on the CXR blockchain system i.e., transfer, insert, remove, and update. The outcome demonstrates CXR’s effectiveness with 100% success and 0% failure rate based on testing and statistical computations calculation. The mean absolute error (MAE), variance accounted for (VAF), and percent error (PE) are obtained with values ranging from 0.38% to 4.67%. In this study, the transaction per-second (TPS) is used to calculate include error request (IER) and exclude error request (EER) values around 312 to 746 milliseconds (ms). In addition, the TPS of CXR based on blockchain platform is a capability to create and trace database carbon certificate ownership (nonfinancial activity). It means CXR based on the blockchain platform has a fast response to process carbon certificate ownership for transactions across local and international countries in the world.
Robust parameter determination approach based on red-tailed hawk optimization used for lithium-ion battery Z. Almutair, Sulaiman; Rezk, Hegazy; Hassan, Yahia Bahaa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3729-3738

Abstract

Lithium-ion electrochemical batteries are being used more in a large number of applications, such as electric vehicles. However, increasing their efficiency lies in the accuracy of their model. For this, extracting the best values of parameters of the battery model is needed. A recent metaheuristic optimizer named the red-tail hawk (RTH) is used in the current research to extract the battery parameters. The idea of this algorithm is extracted from hunting techniques of red-tail hawks. The RTH algorithm is more likely to avoid entangled local optimums because of its high diversity, fast convergence rate, and appropriate exploitation-exploration balance. The RTH optimizer is compared with other algorithms to check and approve its performance. Using the proposed method, the root mean squared error (RMSE) between the model outputs and the measured voltage dataset was decreased to 8.12E-03, much better than all the other considered algorithms.
Predictive models in Alzheimer's disease: an evaluation based on data mining techniques Andrade-Arenas, Laberiano; Rubio-Paucar, Inoc; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2988-3002

Abstract

The increasing prevalence of Alzheimer's disease in older adults has raised significant concern in recent years. Aware of this challenge, this research set out to develop predictive models that allow early identification of people at risk for Alzheimer's disease, considering several variables associated with the disease. To achieve this objective, data mining techniques were employed, specifically the decision tree algorithm, using the RapidMiner Studio tool. The sample explore modify model and assess (SEMMA) methodology was implemented systematically at each stage of model development, ensuring an orderly and structured approach. The results obtained revealed that 45.00% of people with dementia present characteristics that identify them as candidates for confirmation of a diagnosis of Alzheimer's disease. In contrast, 52.78% of those who do not have dementia show no danger of contracting the disease. In the conclusion of the research, it was noted that most patients diagnosed with Alzheimer's are older than 65 years, indicating that this stage of life tends to trigger brain changes associated with the disease. This finding underscores the importance of considering age as a key factor in the early identification of the disease.
Optimal shortest path selection using an evolutionary algorithm in wireless sensor networks Rajkumar, Dhamodharan Udaya Suriya; Karani, Krishna Prasad; Sathiyaraj, Rajendran; Vidyullatha, Pellakuri
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6743-6752

Abstract

A wireless sensor network comprises of distributed independent devices, called sensors that monitor the physical conditions of the environment for various applications, such as tracking and observing environmental changes. Sensors have the ability to detect information, process it, and forward it to neighboring sensor nodes. Wireless sensor networks are facing many issues in terms of scalability, which necessitates numerous nodes and network range. The route chosen between the source node and the destination node with the shortest distance determines how well the network performs. In this paper, evolutionary algorithm based shortest path selection provides high end accessibility of path nodes for data transmission among source and destination. It employs the best fitness function methodology, which involves the replication of input, mutation, crossover, and mutation methods, to produce efficient outcomes that align with the best fitness function, thereby determining the shortest path. This is a probabilistic technique that receives input from learning models and provides the best results. The execution results are presented well compared with earlier methodologies in terms of path cost, function values, throughput, packet delivery ratio, and computation time.
A review on internet of things-based stingless bee's honey production with image detection framework Rohafauzi, Suziyani; Kassim, Murizah; Ja’afar, Hajar; Rustam, Ilham; Miskon, Mohamad Taib
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2282-2292

Abstract

Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A detailed analysis of deep learning-based techniques for automated radiology report generation Dhamanskar, Prajakta; Thacker, Chintan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5906-5915

Abstract

The automated creation of medical reports from images of chest X-rays has the potential to significantly reduce workloads for healthcare providers and accelerate patient care, especially in environments with limited resources. This study provides an extensive overview of deep learning-based techniques designed for radiology report generation from chest X-ray pictures automatically. By examining recent research, we delve into various deep learning architectures and techniques used for this task, including transformer-based approaches, attention mechanisms, sequence-to-sequence models, adversarial training methods, and hybrid models. We also discuss about the datasets used for evaluation and training, as well as future directions and research problems in this area. The significance of deep learning in revolutionizing radiology reporting is further emphasized by our review, which also highlights the need for additional research to address challenges such data accessibility, image quality variability, interpretation of complex findings, and contextual integration. The objective of this research is to present a comparative analysis of cutting-edge methods for developing automated medical report generation to enhance patient outcomes and healthcare delivery.

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