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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 111 Documents
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Search results for , issue "Vol 14, No 6: December 2024" : 111 Documents clear
A comparative study of long short-term memory based long-term electrical load forecasting techniques with hyperparameter optimization Mani, Geetha; Seetharaman, Suresh; Kandasamy, Jothinathan; Ladha, Lekshmy Premachandran; Mohandas, Anish John Paul; Sivasubramoniam, Swamy; Renugadevi, Valarmathi Iyappan
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.pp7080-7089

Abstract

Long-term load forecasting (LTLF) is crucial for reliable electricity supply, infrastructure planning, and informed energy policies, ensuring grid stability and efficient resource allocation. Traditional methods, like statistical models and expert judgment, rely on historical data but may struggle with dynamic changes in technology, regulations, and consumer behavior. Addressing challenges such as economic uncertainties, seasonal variations, data quality, and integrating renewable energy requires advanced forecasting models and adaptive strategies. This research aims to develop an efficient LTLF model for the Coimbatore region in Tamil Nadu, India, using long short-term memory (LSTM) networks. While LSTM has limitations in capturing long- term dependencies and requires high data quality and complex management, optimizing hyperparameters, including through the opposition-based hunter- prey optimization (OHPO) technique, is explored to enhance its predictive performance. The results show that the proposed OHPO-configured LSTM model for LTLF achieves superior performance compared to other techniques, with a mean square error (MSE) of 0.25, root mean square error (RMSE) of 0.5 and mean absolute percentage error (MAPE) of 0.27. This research underscores the significance of improving LTLF precision for informed decision-making in infrastructure planning and energy policy formulation.
Precipitation and water discharge for internet of things based flood disaster prediction improvement Efendi, Rissal; Widiasari, Indrastanti R.
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.pp6773-6785

Abstract

Floods are a major global problem affect communities and businesses. For these effects to be mitigated and emergency measures to be improved, accurate prediction is essential. Conventional flood prediction models frequently fail because the models ignore important hydrological elements like water discharge and instead solely use rainfall data. This limitation was addressed by the combination of rainfall and water discharge data on internet of things (IoT)-based technologies. It focuses on analyzing historical records from flood-prone areas in Semarang using gated recurrent unit (GRU) models. The findings demonstrate how effectively the GRU model performs when rainfall and water discharge factors are taken into account, resulting in very accurate and dependable predictions of flood events. Precision, Recall, and F1-score are evaluation metrics that demonstrate the accuracy on which the model determines flood emergency statuses. This study advances flood prediction methods and highlights the value of integrating internet of things data to improve preparedness and resilience against flood disasters.
A passive sonar based underwater acoustic channel model for improved search and rescue operations in deep sea Abbas, Afsar Ali Mohamed; Mohideen, Kaja Mohideen Sultan; Narayanaswamy, Vedachalam
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.pp6148-6159

Abstract

Active and passive sonar are the two types of empirical underwater acoustic channel models (UWACMs). Passive sonar UWACMs have applications in military, ocean exploration, and search and rescue (SAR) activities. However, high transmission loss (TL), multipath propagation, and ambient noise pose significant challenges to signal-to-noise ratio (SNR) and communication effectiveness. To address these challenges, this paper develops a UWACM based on the passive sonar equation method to determine SNR in deep-sea environments, specifically for SAR operations. Determining SNR involves characterizing signal propagation in terms of TL. Existing models lack analysis of TL and SNR for various deep-sea multipath propagation scenarios relevant to SAR applications. Therefore, this paper analyses TL and SNR for both direct and various multipath propagation modes, including surface reflection (SR), surface duct (SD), bottom bounce (BB), convergence zone (CZ), deep sound channel (DSC), and reliable acoustic paths (RAPs) in the deep sea. This work aims to quantify the detection capabilities of underwater location beacons (ULBs) under various deep-sea scenarios and configurations. By analyzing ULB signal propagation characteristics, this research seeks to address key challenges related to ULB performance and ultimately improve SAR operations. The results of the proposed model significantly correlate with existing literature, confirming its accuracy.
A semantic similarity search engine for movies Mustafa, Ahmad; Mheidat, Hammam; Shatnawi, Adam
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.pp7137-7144

Abstract

Semantic similarity has been gaining traction in the field of natural language processing. It is a measure of how similar two pieces of text are in terms of their meaning. It can be used to improve search engine results. We propose a deep learning-based approach to build a semantic similarity search engine for movies based on a movie summary. Filmmakers can gain insight into audience preferences and trends, allowing them to create more engaging and successful films. The dataset used in this study was gathered from internet movie database (IMDb), it includes movie summaries along with their corresponding name movies. The test dataset was generated using ChatGPT to be very close to human input. The universal sentence encoder (USE) model presented promising results in semantic similarity, the model results show that for the top 5 similar movies, the model returned 176 out of 300 movies (58.6%). For the top 10 similar movies, the model returned 211 out of 300 movies (70.3%). Additionally, for the top 15 similar movies, the model returned 229 out of 300 movies (76.3%). And, for the top 20 similar movies, the model returned 249 of 300 movies (83%). This method can be applied to movie recommendation systems or to organize films in a collection automatically.
A study of Tobacco use and mortality by data mining Arenas, Laberiano Andrade; Paucar, Inoc Rubio; Yactayo-Arias, Cesar
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.pp6861-6873

Abstract

The use of data mining to address the issue of people who consume tobacco and other harmful substances for their health has led to a significant dependence among smokers, which over time causes illnesses that may result in the addict's death. As a result, the research's goal is to apply a data mining study whose findings showed that the confidence intervals are less than 0.355. However, the lift and conviction in the last three rules are also lower, making it unlikely that these rules will be followed. On the other hand, the knowledge discovery in data bases method was used. It consists of the following stages: data selection, preparation, data mining, and evaluation and interpretation of the results. To that end, comparisons of agile data mining methodologies like crisp-dm, knowledge discovery in data, and Semma are also done. As a result, using specific criteria, dimensions are segmented to allow for the differentiation of these methodologies. As a result, a comparison graph of models such as naive Bayes, decision trees, and rule induction is used. To sum up, it can be said that the rules of association apply to men, the number of admissions, and the cancers that can be brought on by smoking. Also, the percentage of male patients admitted with cancers that can be brought on by smoking Last but not least, the number of admissions and cancers that can be brought on by smoking
Text encryption using secure and expeditious multiprocessing SerpentCTR using logistic map Elshoush, Huwaida T.; Ahmed, Duaa M.; Ishag, Abdalmajid A.; Elsadig, Muawia A.; Altigani, Abdelrahman
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.pp6753-6772

Abstract

Unarguably performance is a critical factor to the success of any cipher. Al-Beit Serpent is more secure than advanced encryption standard (AES), it faces limitations such as speed and memory requirement. Hence, this paper proffers a text encryption method S  that ameliorates the performance by running Serpent in parallel using the counter (CTR) encryption mode and further enhances the security by generating sub-keys for each block using logistic map. The intricate logistic map generated keys adds robustness to the proposed algorithm. Comprehensive experiments using Python 3.9 on commonly used metrics verify the efficacy of the proposed method in terms of execution time, central processing unit (CPU) usage, security analysis including key space, strict avalanche effect and its randomness. The encryption/decryption reduction rate reached up to 80.81%. It is worthy of note that it is effectually resistant to brute force attacks having a large key space in addition to its dependency on the number of blocks besides the randomly generated keys. The enhanced Serpent was examined using the statistical test suite (STS) recommended by the National Institute of Standards and Technology (NIST) and verified its randomness by passing all tests. Furthermore, it efficaciously resisted statistical analysis, particularly histogram and correlation coefficient analysis. Moreover, it prevails over current methods when juxtaposed with them in terms of performance, key space, key sensitivity, avalanche effect, histogram analysis and correlation coefficient, ergo affirming its efficiency.
A significant features vector for internet traffic classification based on multi-features selection techniques and ranker, voting filters Munther, Alhamza; Abualhaj, Mosleh M.; Alalousi, Alabass; Fadhil, Hilal A.
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.pp6958-6968

Abstract

The pursuit of effective models with high detection accuracy has sparked great interest in anomaly detection of internet traffic. The issue still lies in creating a trustworthy and effective anomaly detection system that can handle massive data volumes and patterns that change in real-time. The detection techniques used, especially the feature selection methods and machine learning algorithms, are crucial to the design of such a system. The fundamental difficulty in feature selection is selecting a smaller subset of features that are more related to the class but are less numerous. To reduce the dimensionality of the dataset, this research offered a multi-feature selection technique (MFST) using four filter techniques: fast correlation-based filter, significance feature evaluator, chi-square, and gain ratio. Each technique's output vector is put via ranker and Borda voting filters. The feature with the highest number of votes and rank values will be selected from the dataset. The performance of the given MFST framework was the best when compared to the four strategies listed above functioning alone; three different classifiers were employed to test the accuracy. C4.5, nave Bayes, and support vector machine. The experiment outcomes employed ten datasets of different sizes with 10,000-300,000 instances. Only 8 out of 248 characteristics were chosen, with classifiers percentages averaging 65%, 93.8%, and 95.5%.
Mobile application for the prevention and self-care of varicose veins Andrade-Arenas, Laberiano; Retuerto, Margarita Giraldo; Yactayo-Arias, Cesar
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.pp6560-6571

Abstract

Details the process of creating a prototype of a mobile application designed to promote prevention and self-care of varicose veins in patients at high vascular risk. The objective is to investigate the experience of patients at high vascular risk when using a mobile application created for the prevention and self-care of varicose veins. The methodology used is design thinking, a user-centered approach that seeks to solve complex challenges through creativity, design and problem solving. The results obtained from the expert judgment, based on ATLAS.ti 23, provide valuable insight into the feasibility and potential of the technological tools as the interface has the highest variability among the criteria evaluated, followed by interaction and quality, while usability presents the lowest variability. This suggests that usability evaluations tend to be more consistent compared to the other criteria. In conclusion, the present work analyzes how mobile applications can play a crucial role in promoting prevention and self-care of varicose veins in patients at high vascular risk. The good reception of the prototype confirms the importance of technology in the field of vascular health and highlights the value of this approach to improve quality of life and health management in this demographic group.
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.
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.

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