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31,456 Article Results

Design and enhancement of microstrip patch antenna with frequency selective surface backing for vehicle-to-vehicle communication

10.11591/ijece.v14i6.pp6445-6454
Ikram Troudi , Chokri Baccouch , Rhaimi Belgacem Chibani
This research investigates the application of frequency selective surface (FSS) reflectors to enhance vehicle-to-vehicle (V2V) communication performance. A compact antenna measuring 32×24×1.6 mm, derived from an FSS 10×10×1.6 mm unit cell, was studied. Integration of FSS technology with the antenna resulted in significant performance improvements. The gain increased from 3.9 to 5.4 dB at 5.9 GHz, representing a 1.5-fold enhancement. Additionally, the bandwidth extended to 230.94 MHz. These advancements demonstrate the efficacy of FSS technology for antenna gain enhancement in V2V systems. The compact design, coupled with improved performance, makes this approach particularly suitable for vehicular applications where space is limited. This study not only showcases the potential of FSS technology in antenna design but also suggests its broader applicability in enhancing V2V communication systems, potentially contributing to the development of more efficient and safer transportation networks.
Volume: 14
Issue: 6
Page: 6445-6454
Publish at: 2024-12-01

Enhanced Vigenere encryption technique for color images acting at the pixel level

10.11591/ijece.v14i6.pp6675-6688
Abdelhakim Chemlal , Hassan Tabti , Hamid El Bourakkadi , Hicham Rrghout , Abdellatif Jarjar , Abdelhamid Benazzi
The pixel unit is an essential component of many encryption schemes. In the beginning two substitution tables, separately constructed from chaotic maps namely, the logistic map, slanted tent map, and the AJ map, which has a very high Lyapunov exponent and is very sensitive to start factors, are used to make modifications at the pixel level. These S-Boxes have a maximum period and are produced from several linear congruential generators. This approach uses newly developed confusion and diffusion functions connected to the recently built substitution tables to perform a refined Vigenere strategy. The purpose of this chaining is to defend the system from differential assaults. Extensive simulations on a variety of image formats and sizes confirm our process’s robustness against identified dangers.
Volume: 14
Issue: 6
Page: 6675-6688
Publish at: 2024-12-01

Batak Toba language-Indonesian machine translation with transfer learning using no language left behind

10.11591/ijaas.v13.i4.pp834-843
Cevin Samuel , Irsan Taufik Ali
This study focuses on neural machine translation (NMT) for low-resource languages (LRLs) pair, Batak Toba-Indonesian (bbc↔ind). The Batak Toba language is a critically endangered dialect of an Indonesian ethnic group, Batak. Recent advances in machine translation offer potential solutions, with transfer learning emerging as a promising approach for this language pair. We used a publicly available bbc↔ind parallel corpora from the Hugging Face datasets hub and employed the NLLB-200's distilled 600M variant model as the baseline model. Our models achieved sacreBLEU scores as follows: i) for bbc→ind, it achieved a score of 37.10 (+25.67, up from 11.43) and ii) for ind→bbc, it achieved a score of 30.84 (+25.82, up from 5.02). These results outperform all previous works in the task bbc↔ind machine translation and prove the validity of our approach.
Volume: 13
Issue: 4
Page: 834-843
Publish at: 2024-12-01

A semantic similarity search engine for movies

10.11591/ijece.v14i6.pp7137-7144
Ahmad Mustafa , Hammam Mheidat , Adam Shatnawi
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.
Volume: 14
Issue: 6
Page: 7137-7144
Publish at: 2024-12-01

An ensemble framework augmenting surveillance cameras for detecting intruder clusters as potential mobs

10.11591/ijai.v13.i4.pp4557-4571
Omobayo Ayokunle Esan , Isaac O. Osunmakinde
Many developing nations around the world curtail crimes through video surveillance technology, but the crime rate is still high. This is compounded by short-staffed security operatives and a deficiency of security infrastructure to assist security operatives with knowledge-driven decision support systems in the low-resource constraint environment. In a public environment, it is challenging to detect intruder clusters accurately as potential mobs for early warning. Previous research investigated some classical techniques, but their recommendations were insufficient. This research develops a machine learning 3-tiers ensemble framework, which integrates gray level co-occurrence matrices (GLCM) principles to enhance the capabilities of surveillance cameras and security operatives to effectively discern and respond to potential mob formations. The University of California San Diego (UCSD) pedestrian datasets that are publicly available were used for the experiments. With an improved overall average precision of 0.98, recall of 0.98, and accuracy of 98.52% on the UCSD dataset, the suggested framework outperforms the widely used methods for the detection of intruder clusters. The reduction in computational time on processors showcases the framework's significant advancements as a promising solution for robust real-time threat assessment applications.
Volume: 13
Issue: 4
Page: 4557-4571
Publish at: 2024-12-01

The study of requirements for the workforce of the digital industries using web scraping techniques

10.11591/ijaas.v13.i4.pp864-881
Rachawit Tipsena , Wirapong Chansanam , Kulthida Tuamsuk , Tossapon Boongoen
This study investigates workforce requirements in Thailand's digital industries, focusing on qualification requirements across five industry groups: hardware and smart devices, software and software services, digital service, digital content, and telecommunication. Employing Python-based web scraping from selected job websites during 2022–2023, the data undergoes natural language processing (NLP) for analysis. Within Group 1 (hardware and smart devices), electrical engineers dominate with 92 positions, emphasizing a demand for engineering expertise. Group 2 (software and software services) sees a surge in programmer roles, totaling 244 positions, showcasing a need for robust programming skills. Group 3 (digital service) prioritizes information technology (IT) support, claiming 354 positions, indicating high demand for IT support qualifications. Graphic design leads Group 4 (digital content) with 587 positions, highlighting the need for a workforce in digital content production. In Group 5 (telecommunication), network engineers dominate with 37 positions, signaling a demand for top-tier network engineering skills. Most positions across groups specify a bachelor's degree and often require prior experience, highlighting the industry's preference for both academic and practical qualifications. This study underscores the digital industry's rapid growth and the sustained demand for a qualified workforce, emphasizing the importance of academic credentials and specialized skills for future employment in these sectors.
Volume: 13
Issue: 4
Page: 864-881
Publish at: 2024-12-01

Traffic signs detection and prohibitor signs recognition in Morocco road scene

10.11591/ijece.v14i6.pp6313-6321
Imane Taouqi , Abdessamad Klilou , Kebir Chaji , Assia Arsalane
Traffic sign detection is a crucial aspect of advanced driver assistance systems (ADAS) for academic research and the automotive industry. seeing that accurate and timely detection of traffic signs (TS) is essential for ensuring the safety of driving. However, TS detection methods encounter challenges like slow detection speed and a lack of robustness in complex environments. This paper suggests addressing these limitations by proposing the use of the you only look one version 7 (YOLOv7) network to detect and recognize TS in road scenes. Furthermore, the k-means++ algorithm is used to acquire anchor boxes. Additionally, a tiny version of YOLOv7 is used to take advantage of its real-time and low model size, which are required for real-time hardware implementation. So, we conducted an experiment using our proprietary Morocco dataset. According to the experimental results, YOLOv7 achieves 85% in terms of mean average precision (mAP) at 0.5 for all classes. And YOLOv7-tiny obtains 90% in the same term. Afterward, a recognition system for the prohibitive class using the convolutional neural network (CNN) is trained and integrated inside the YOLOv7 algorithm; its model achieves an accuracy of 99%, which leads to a good specification of the prohibitive sign meaning.
Volume: 14
Issue: 6
Page: 6313-6321
Publish at: 2024-12-01

A passive sonar based underwater acoustic channel model for improved search and rescue operations in deep sea

10.11591/ijece.v14i6.pp6148-6159
Afsar Ali Mohamed Abbas , Kaja Mohideen Sultan Mohideen , Vedachalam Narayanaswamy
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.
Volume: 14
Issue: 6
Page: 6148-6159
Publish at: 2024-12-01

Self-reported worry and action among Vietnamese citizens during the COVID-19 pandemic in 2021: latent class analysis

10.11591/ijphs.v13i4.24101
Hung Pham Quoc , Son Dao Anh , Tung Pham Anh
Describe the prevalence of and factors associated with self-reported worry and actions of Vietnamese citizens during the COVID-19 pandemic in 2021. A secondary data analysis was implemented using a cross-sectional survey conducted in 2021 among 447 adults living in Hanoi City from July to December 2021 and Ho Chi Minh City from May to October 2021. The hidden layers assessment results retained the 3-class model as the best-fitting one in the study. In this model, 28.4% of participants were in Class 2 - "Low Self-reported Worry and Moderate Action" group, 45.9% of the participants were classified into Class 1-"Moderate Self-reported Worry and Low Action" group, and 25.7% in Class 3 - "High Self-reported Worry and High Action" group. The multinomial logistics model showed that worrying about losing the house because of COVID-19 makes the expected participation risk of staying in latent class 1 lower for subjects high in latent class 3 (p<0.05). Our study identified the latent classes of self-protection action and awareness among Vietnamese citizens. The associated factor that impacted the classes was the fear of losing a house.
Volume: 13
Issue: 4
Page: 1641-1646
Publish at: 2024-12-01

Factors influencing the intention to use m-commerce in Malaysian: an extended IS success model

10.11591/ijaas.v13.i4.pp961-973
Moussa Barry , Ahasanul Haque , Muhammad Tahir Jan
The progress of mobile technology has undergone substantial development in recent years, leading to the emergence of new and creative ideas. This paper investigates the factors influencing consumers' intentions to use mobile commerce in Malaysia. The DeLone and McLean updated information system success model served as the basis for this study's proposed model. A convenience sampling method was employed to collect 310 surveys from smartphone owners who conduct mobile commerce activities in Malaysia. A “two-stage structural equation modeling” (SEM) technique assessed the research model and the study's assumptions. The findings revealed that “information quality”, “service quality”, “system quality”, and “trust” significantly influence consumers' “intention to use mobile commerce in Malaysia”. The findings further reveal that “system quality” is the strongest factor influencing the “intention to use mobile commerce in Malaysia”. Therefore, the research outcomes will benefit academicians, researchers, policymakers, and practitioners in the mobile commerce industry in Malaysia. To the best of the authors’ knowledge, this is the first empirical study that expanded the “information system success model” by including “trust” in the context of Malaysian mobile commerce users’ “intentions”. However, further research is recommended to explore the factors influencing consumers' “intention to use mobile commerce in Malaysia”.
Volume: 13
Issue: 4
Page: 961-973
Publish at: 2024-12-01

Enhancing video anomaly detection for human suspicious behavior through deep hybrid temporal spatial network

10.11591/ijai.v13.i4.pp4121-4128
Kusuma Sriram , Kiran Purushotham
Abnormal behavior exhibited by individuals with particular intentions is common, and when such behavior occurs in public places, it can cause physical and mental harm to others. Considering the rise in the automated approach for anomaly detection in videos, accuracy becomes essential. Most existing models follow a deep learning architecture, which faces challenges due to variations in motion. This research work develops a deep learning based hybrid architecture with temporal and spatial features. The hybrid temporal spatial network (HTSNet) consists of two customized architectures: a graph neural network (GNN) and a convolutional neural network (CNN). HTSNet combined with a novel classifier to extract features and classify normal and abnormal behavior. The performance of HTSNet is rigorously evaluated using the University of California, San Diego-Pedestrian 1 (UCSD Ped1) dataset, a benchmark in computer vision research for anomaly detection in video surveillance. The effectiveness of HTSNet is demonstrated through a comparative analysis with current state-of-the-art methods, using the area under the curve (AUC) metric as a standard measure of performance. This paper contributes to the advancement of video surveillance technology, providing a robust framework for enhancing public safety and security in an increasingly digital world.
Volume: 13
Issue: 4
Page: 4121-4128
Publish at: 2024-12-01

Indonesian sentiment towards global economic recession in 2023 using optimized hyperparameters of support vector machine kernels

10.11591/ijai.v13.i4.pp4948-4956
Dairatul Maarif , Adinda Aulia Hafizha , Andi Kurniawan
The potential for the 2023 global recession has troubled people worldwide, particularly in light of the COVID-19 pandemic. This study employs a sentiment analysis approach to examine how the Indonesian internet community, particularly on Twitter, perceives the topics related to the global economic recession. We collected 11,017 uploaded tweets that were analyzed using support vector machine classifier with linear, radial basis function (RBF), sigmoid, and polynomial kernel schemes. Furthermore, we optimized the classifiers with C, Gamma, and degree hyperparameters. Empirical evidence indicates a lack of preparedness to face a global recession, evidenced by most responses towards 2023 global recession exhibiting concerns about high inflation and economic instability. The finding also suggests that the optimized RBF is a superior modeling kernel relative to others. Collectively, these results provide insights with significant implications for sentiment analysis, natural language processing, and the study of behavioural economics.
Volume: 13
Issue: 4
Page: 4948-4956
Publish at: 2024-12-01

Improved impressed current cathodic protection systems by incorporating a pulse-feeding technique integrated with internet of things capabilities

10.11591/ijece.v14i6.pp7254-7265
Mohd Rafi Adzman , Aiman Arif Mohd Sallehuddin , Shaiful Rizam Shamsudin , Nusaybah Mohd Husin , Nur Syakirah Mohammed Haniff , Mahalaksmi Gunasilan , Muhd Hafizi Idris , Melaty Amirruddin
This paper introduces an innovative improvement to impressed current cathodic protection (ICCP) systems by integrating a pulse-feeding technique designed to address metal protection degradation during off-potential periods, a common issue in conventional systems. The proposed system enhances the overall effectiveness and reliability of ICCP, providing consistent corrosion protection for critical metal structures. A notable advantage of this method is its simplicity, utilizing a cost-effective microcontroller for pulse feeding. This approach simplifies integration processes and enhances cost-effectiveness, making it an attractive solution for improving cathodic protection system performance without substantial additional costs. The method addresses conventional ICCP weaknesses by applying a high-frequency pulse current during off-potential periods. This reduces excessive negative charge buildup on metal surfaces during interruptions, boosting the system’s effectiveness and stability. Research laboratory experiments were conducted using pulse width modulation (PWM) on an ATmega328P microcontroller to demonstrate the method’s effectiveness. Additionally, an IoT-monitored ICCP system was developed using an ESP32 microcontroller and the Blynk application. Results highlight the superiority of a 50 kHz pulse feeding frequency in preventing corrosion compared to lower frequencies. Overall, this advancement significantly enhances ICCP systems, providing improved corrosion protection and durability in harsh environments.
Volume: 14
Issue: 6
Page: 7254-7265
Publish at: 2024-12-01

Faculty Assistant Bot-automation of administrative activities using robotic process automation

10.11591/ijece.v14i6.pp6797-6806
Mamidyala Durga Prasad , Balusu Nandini
In this paper, a process workflow for Bot is created using robotic process automation (RPA), associated with artificial intelligence that is used to stream line the administrative tasks and alleviate stress levels of faculty in handling administrative tasks while teaching in higher education. These activities are must for National Academic Audit Council (NAAC) accreditation and All India Survey on Higher Education (AISHE) surveys, which strive to bring quality in teaching higher education by shaping educational policies. Therefore, ensuring the accuracy of this data is paramount to avoid misleading decisions. The Bot automatically gathers student results from the website and saves them into individual files, eliminating the need for human intervention. It is trained to find the related file of student and update his results of upcoming semesters or backlogs. The Bot efficiently manages folders during file saving to enhance retrieval. Additionally, it maintains pertinent student details such as community, caste, and religion, beneficial for educational policy surveys aiming for improved quality. Moreover, it generates and updates reports post each process execution, ensuring data integrity, and can be trained for statistical analysis to predict student outcomes. The UiPath tool is used in the design and testing of the developed Bot
Volume: 14
Issue: 6
Page: 6797-6806
Publish at: 2024-12-01

A novel semi-supervised consensus fuzzy clustering method for multi-view relational data

10.11591/ijece.v14i6.pp6883-6893
Hoang Thi Canh , Pham Huy Thong , Phung The Huan , Vu Thuy Trang , Nguyen Nhu Hieu , Nguyen Tien Phuong , Nguyen Nhu Son
Multi-view data is widely employed in various domains, highlighting the need for advanced clustering methodologies to efficiently extract knowledge from these datasets. Consequently, multi-view clustering has emerged as a prominent research topic in recent years. In this paper, we propose a novel approach: the semi-supervised consensus fuzzy clustering method for multi-view relational data (SSCFMC). This method combines the advantages of fuzzy clustering and consensus clustering to address the challenges posed by multi-view data. By leveraging available labeled information and the relational structure among views, our method aims to enhance clustering performance. Extensive experiments on benchmark datasets demonstrate that our method surpasses existing single-view and multi-view relational clustering algorithms in terms of accuracy and stability. Specifically, the SSCFMC algorithm exhibits superior clustering performance across various datasets, achieving an adjusted rand index (ARI) of 0.68 on the multiple features dataset and an F-measure of 0.91 on the internet dataset, highlighting its robustness and efficiency. Overall, this study advances multi-view clustering techniques for relational data and provides valuable insights for researchers in this field.
Volume: 14
Issue: 6
Page: 6883-6893
Publish at: 2024-12-01
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