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27,438 Article Results

The impact of COVID-19 on e-commerce: a cross-national analysis of policy implications

10.11591/ijeecs.v38.i3.pp1946-1956
Jia Qi Cheong , Wong Hock Tsen , Samsul Ariffin Abdul Karim , Jeffrey S. S. Cheah
The field of e-commerce research has evolved over recent decades, but the coronavirus disease 2019 (COVID-19) pandemic significantly accelerated its prominence, as evidenced by extensive literature. The pandemic underscored the pivotal role of e-commerce in driving the digital transformation of the global economy. However, there remains a lack of comprehensive reviews in this area, particularly comparative analyses of how different countries leveraged e-commerce to navigate the pandemic’s challenges. This paper addresses this gap by examining the literature on e-commerce adoption and its implications during COVID-19, focusing on select countries, including China, Malaysia, and several European nations. The case of China, as a major economic power in Asia, offers particularly valuable insights.
Volume: 38
Issue: 3
Page: 1946-1956
Publish at: 2025-06-01

Intelligent transportation network-based congestion forecasting with federated learning and a convolutional neural network

10.11591/ijeecs.v38.i3.pp2041-2049
Kamaleswari Pandurangan , Krishnaraj Nagappan , B. Galeebathullah , N. Shunmuga Karpagam , N. Kumaran , S. Navaneethan
The heavy traffic in growing cities hurts the environment, commuters, and economy. Predicting such difficulties early helps increase road network capacity and efficiency and reduce congestion. Many academicians and transportation engineers ignore traffic congestion prediction despite its importance. Insufficient computationally efficient traffic forecast systems and high-quality city-wide traffic data contribute to this. Provide useful information to reduce traffic and construct shorter, more energy-efficient routes. Data storage increases traditional traffic forecasting training, storage costs, and delay. Smarter algorithms can handle today’s city expectations because sensors can now communicate with their environment. A vibrant economy requires decent roads. Improving transportation requires uninterrupted highway traffic. To overcome these issues, smart city roadway traffic flow must be monitored in real time using enhanced internet of things (IoT) capabilities. Training data may contain sensitive information, raising privacy problems. This work addresses these issues by training the prediction model near data sources using federated learning (FL). The suggested strategy was tested using Mumbai, Chennai, and Bangalore traffic data. We compared the proposed method to centralized strategies to assess its efficacy. Our experiments confirm the model’s traffic jam prediction accuracy. Our approach outperforms auto-encoder and convolutional neural network (CNN) in computer efficiency and prediction.
Volume: 38
Issue: 3
Page: 2041-2049
Publish at: 2025-06-01

Holographic-based design, building, and testing of an RRP spherical robot for olive fruits harvesting

10.11591/ijeecs.v38.i3.pp1602-1612
Osama M. Al-Habahbeh , Ayeh Arabiat , Riad Taha Al-Kasasbeh , Salam Ayoub
A revolute-revolute-prismatic (RRP) spherical robot has been designed, simulated, built, and tested. The robot is intended to perform olive fruit harvesting tasks. The design simulation is done using hologram tools. The design factors considered include reach, dexterity, accuracy, and productivity. Based on the results of the holographic simulation, a prototype was built and tested on real olive fruits. The end effector is equipped with a rake tool so that the robot can harvest multiple fruits in each stroke. The robot is controlled by Raspberry Pi while a stereovision camera enables 3-D vision. Once the camera detects the fruits, an inverse kinematics algorithm is initiated to find the location of the fruits. The fruit coordinates are commanded to the manipulator to perform the harvesting. The field tests showed that the manipulator is successful in performing the harvesting operations. To increase the harvesting efficiency, it is recommended to build a larger prototype.
Volume: 38
Issue: 3
Page: 1602-1612
Publish at: 2025-06-01

Empowering microgrids: harnessing electric vehicle potential through vehicle-to-grid integration

10.11591/ijeecs.v38.i3.pp1422-1430
Debani Prasad Mishra , Rudranarayan Senapati , Sarita Samal , Niti Rani Rai , Niharika Behera , Surender Reddy Salkuti
Electric vehicles (EVs) can potentially be integrated into microgrids via vehicle-to-grid (V2G) technology, which enhances the energy system's stability and durability. This paper provides an in-depth examination and evaluation of V2G integration in microgrid systems. It analyses the present state of research as well as possible uses, challenges, and directions for V2G technology in the future. This paper addresses the technological, economic, and regulatory aspects of implementing V2G and provides case studies and pilot projects to shed light on potential benefits and barriers associated with its adoption. The research highlights how V2G contributes to more efficient integration of renewable energy sources, grid stabilization, and cost savings for EV owners. It also addresses the latest developments in technology and proposed laws aimed at encouraging growing applications of V2G.
Volume: 38
Issue: 3
Page: 1422-1430
Publish at: 2025-06-01

For S-band WLAN applications, a patch antenna design, simulation, and optimization

10.11591/ijeecs.v38.i3.pp1613-1623
Md. Eftiar Ahmed , Biprojitt Saha Pranto , Md. Sohel Rana , Md. Omar Faruq Shakil , Md. Abul Ala Walid , Ifat Arin , Saikat Mondal , Samanta Mostafa Chooyan
A rectangular microstrip patch antenna for 2.45 GHz is designed, tested, and analyzed in this study. It uses two substrate materials (design I and II) with different permittivity levels. RT5880 (design-I) and FR-4 (design-II) substrates have a thickness of 1.57 mm and 1.6 mm, respectively. Design-I and design-II substrates have relative permittivity of 2.2 and 4.3, respectively. Performance and efficiency are considered due to the substrate material's relative permittivity and thickness; return loss (S11), voltage standing wave ratio (VSWR), gain, directivity, surface current, and efficiency. Design II and design I have 3.25 dBi and 8.089 dBi gains, respectively, and 5.92 dBi and 8.64 dBi directivity, respectively. Design I had the best antenna efficiency, 93.64%, compared to design II, 54.96%. In contrast to the design I and design II, which had return losses (S11) of -53.29 dB and -51.38 dB, each of the suggested antennas had a return loss (S11) of more than -50 dB. The VSWR for design I is 1.0043, while the Design II material is 1.0054. This study aims to reduce return loss (S11) and close the VSWR to 1. This proposed design improves antenna gain, directivity, and efficiency for future wireless applications on wireless local area networks (WLANs).
Volume: 38
Issue: 3
Page: 1613-1623
Publish at: 2025-06-01

Remove glasses diffusion model an innovative conditioned of eye glasses removal with image diffusion model

10.11591/ijeecs.v38.i3.pp1503-1516
Yuliza Yuliza , Rachmat Muwardi , Galatia Erica Yehezkiel , Mirna Yunita , Lenni Lenni
The presence of eyeglasses in facial images poses challenges for image processing, particularly in facial recognition. This paper introduces the remove glasses diffusion model (RGDM), a conditioned denoising diffusion probabilistic model (DDPM) designed for precise glasses removal. RGDM employs conditional modeling to focus on the glasses region while seamlessly restoring facial features. An eyes position accuracy mechanism, leveraging facial landmarks, ensures accurate eye restoration post-removal. Comprehensive evaluations on the CelebA dataset demonstrate RGDM’s superior performance, achieving the lowest Fréchet inception distance (FID) of 27.09 and learned perceptual image patch similarity (LPIPS) of 0.299, outperforming state-of-the-art methods such as 3D synthetic, cycleconsistent generative adversarial network (CycleGAN), and eyeglasses removal generative adversarial network (ERGAN). These results highlight the model’s effectiveness in producing natural and high-fidelity facial reconstructions. This work advances glasses removal technology and underscores the significance of conditional models in image processing. The proposed approach has practical implications for facial recognition and image enhancement, paving the way for more accurate and robust real-world applications.
Volume: 38
Issue: 3
Page: 1503-1516
Publish at: 2025-06-01

Improving farming by quickly detecting muskmelon plant diseases using advanced ensemble learning and capsule networks

10.11591/ijeecs.v38.i3.pp2090-2100
Deeba Kannan , Nagamuthu Krishnan Sundarasrinivasa Sankaranarayanan , Shanmugasundaram Venkatarajan , Rashima Mahajan , Brindha Gunasekaran , Pandi Maharajan Murugamani , Karthikeyan Dhandapani
In modern agriculture, ensuring plant health is essential for high crop yields and quality. Plant diseases pose risks to economies, communities, and the environment, making early and accurate diagnosis crucial. The internet of things (IoT) has revolutionized farming by enabling real-time crop monitoring and using drones and cameras for early disease detection. This technology helps farmers address challenges with precision and sustainability. This research propose an ensemble learning model incorporating multi-class capsule networks (MCCN) and other pre-trained model with majority voting system is implemented to predict plant diseases and pests early. The research aims to develop a robust MCCN-based ensemble prediction model for timely disease identification. To evaluate the performance of the ensemble model, various key metrics, including accuracy, and loss value, are assessed. Furthermore, a comparative analysis is conducted, benchmarking the MCCN model against other well-known pre-trained models such as residual network-101 (ResNet101), visual geometry group-19 (VGG19), and GoogleNet. This research signifies a substantial stride towards the realization of IoT-driven precision agriculture, where advanced technology and machine learning contribute to the early detection and mitigation of plant diseases, ultimately enhancing crop yield and environmental sustainability.
Volume: 38
Issue: 3
Page: 2090-2100
Publish at: 2025-06-01

Modern machine learning and deep learning algorithms for preventing credit card frauds

10.11591/ijeecs.v38.i3.pp1673-1680
Indurthi Ravindra Kumar , Shaik Abdul Hameed , Bala Annapurna , Rama Krishna Paladugu , Veeramreddy Surya Narayana Reddy , Kiran Kumar Kaveti
Credit card fraud poses a significant threat to financial institutions and consumers, particularly in the context of online transactions. Conventional rule-based systems often struggle to keep pace with the evolving tactics of fraudsters. This research paper investigates the application of advanced machine learning and deep learning algorithms for credit card fraud detection. By reviewing existing methodologies and addressing the challenges associated with fraud detection, we explore the potential of stateof-the-art techniques in enhancing detection accuracy and efficiency. Key aspects such as transaction data analysis, feature engineering, model evaluation metrics, and practical implementations are discussed. The findings underscore the importance of leveraging advanced algorithms to combat fraudulent activities effectively, thereby safeguarding the integrity of online transactions.
Volume: 38
Issue: 3
Page: 1673-1680
Publish at: 2025-06-01

Nusantara capital city sentiment analysis using support vector machine and logistic regression

10.11591/ijeecs.v38.i3.pp1708-1721
Valencia Eurelia Angelie Tania , Raymond Sunardi Oetama
The decision to move position the capital city of Indonesia to East Kalimantan has drawn people’s opinions, both pro and con, among the public, especially ahead of the presidential and vice-presidential elections. Discussions relevant to the relocation and construction of the capital city are increasingly crowded on social media, especially Twitter or X. This research aims to determine public sentiment regarding the development of the national capital to help the government and policymakers improve communication strategies, evaluate existing policies, and make more informed decisions based on public feedback. Public sentiment related to developing the Capital city of the Nusantara, including the presidential palace, toll road, and government offices, is analyzed. Support vector machine (SVM) and logistic regression (LR) algorithms are utilized for the sentiment classification. The results reveal that the SVM performs better in classifying sentiments in X data relevant to developing the Capital city of Nusantara, achieving an average accuracy of 91.97%.
Volume: 38
Issue: 3
Page: 1708-1721
Publish at: 2025-06-01

Deep learning-based secured resilient architecture for IoT-driven critical infrastructure

10.11591/ijeecs.v38.i3.pp1819-1829
Srinivas A. Vaddadi , Rohith Vallabhaneni , Sanjaikanth E. Vadakkethil Somanathan Pillai , Santosh Reddy Addula , Bhuvanesh Ananthan
While enabling remote management and efficiency improvements, the infrastructure of the smart city becomes able to advance due to the consequences of the internet of things (IoT). The development of IoT in the fields of agriculture, robotics, transportation, computerization, and manufacturing. Based on the serious infrastructure environments, smart revolutions and digital transformation play an important role. According to various perspectives on issues of privacy and security, the challenge is heterogeneous data handling from various devices of IoT. The critical IoT infrastructure with its regular operations is jeopardized by the sensor communication among both IoT devices depending upon the attacker targets. This research suggested a novel deep belief network (DBN) and a secured data dissemination structure based on blockchain to address the issues of privacy and security infrastructures. The non-local means filter performs pre-processing and the feature selection is achieved using the improved crystal structure (ICS) algorithm. The DBN model for the classification of attack and non-attack data. For the non-attacked data, the security is offered via a blockchain network incorporated with the interplanetary file system.
Volume: 38
Issue: 3
Page: 1819-1829
Publish at: 2025-06-01

Ba3GdNa(PO4)3F:Eu2+ phosphor with blue-red emission colors on white-LED properties

10.11591/ijeecs.v38.i3.pp1564-1571
Nguyen Van Dung , Nguyen Doan Quoc Anh
The blue/red-emission phosphor Ba3GdNa(PO4)3F:Eu2+ (BGN(PO)F-Eu) is used in this work for diodes emit white illumination (wLED). The phosphor is prepared using the solid-phase reaction. The suitable concentrations of Eu2+ ion dopant is about 0.7% and 0.9%. The BGN(PO)F-Eu phosphor can provide wLED light with the spectral wavelength in the region of blue (480 nm) and orange-red colors (595-620 nm). With the resulted emissions the phosphor can be appropriate for plant growing because they compatible with absorption spectra of plants’ carotenoids and chlorophylls for stimulating the photosynthesis. The phosphor influences on the wLED lighting properties depending on the doping dosages. It is possible to enhance the luminous intensity of the wLED with higher BGN(PO)F-Eu phosphor amount. Meanwhile, the color properties does not get significant improvements. Thus, the BGN(PO)F-Eu phosphor could be used with other luminescent materials to stimulate the hue rendering performance.
Volume: 38
Issue: 3
Page: 1564-1571
Publish at: 2025-06-01

Improved feature extraction method and K-means clustering for soil fertility identification based on soil image

10.11591/ijeecs.v38.i3.pp2001-2011
Agung Ramadhanu , Halifia Hendri , Sofika Enggari , Silfia Andini , Retno Devita , Eva Rianti
This research is conducting analysis of digital land images using digital image processing techniques. The main purpose of the research is to classify soil fertility based on two-dimensional RGB colored digital soil images. The research is done by extracting features and shapes from the soil image. The research uses methods of segmentation, extraction, and identification against digital soil images. This research is carried out in three stages. The first phase of this research is image pre-processing which begins with the conversion of RGB color image to Grayscale then color conversion to binary which subsequently performs noise reduction with the method Three-layer median filter. The second stage is a process that is divided into the first two stages, namely the process of segmentation by grouping RGB color images into L*a*b which is continued by clustering using the K-means clustering method. The second is the extraction of characteristics of the soil image which is characteristic of shape and texture. The final stage is the identification of soil images that are clustered into two types: fertile soils and unfertile soil. The study achieved an accuracy of 85% which could accurately identify 20 images while inaccurately classifying 5 images out of a total of 25 input images.
Volume: 38
Issue: 3
Page: 2001-2011
Publish at: 2025-06-01

Weierstrass scale space representation and composite dilated U-net based convolution for early glaucoma diagnosis

10.11591/ijeecs.v38.i3.pp1661-1672
Abdul Basith Zahir Hussain , Sulthan Ibrahim Mohamed Sulaiman
Glaucoma is one of the common causes of blindness in the current world. Glaucoma is a blinding optic neuropathy characterized by the degeneration of retinal ganglion cells (RGCs). Accurate diagnosis and monitoring of glaucoma are challenging task through eye examinations and additional tests. To achieve accurate diagnosis of glaucoma with higher sensitivity and specificity, novel method called Weierstrass scale space representation and composite dilated U-net based convolution (WSSR-CDC) is introduced. At first, the Weierstrass transform scale space representation is employed to enhance image structures at various scales with higher accuracy of region of interest (ROI) detection using Euler’s identity. Next, CDC model is utilized with several layers. In input layer, preprocessed input images are taken as input. Fragment derivative are formulated for every preprocessed input. Log cosh dice loss function and optic cup to disc ratio are computed for segmented glaucoma detected results. With this, the accurate diagnosis of glaucoma is made with minimal error. The WSSR-CDC method was evaluated using the glaucoma fundus imaging dataset with several factors. The results show that the WSSR-CDC method outperforms conventional techniques, improving accuracy by 24% and sensitivity by 18%. It demonstrates promising results in fast, accurate, diagnosis of glaucoma.
Volume: 38
Issue: 3
Page: 1661-1672
Publish at: 2025-06-01

Fabric materials classification device using YOLOv8 algorithm

10.11591/ijeecs.v38.i3.pp1479-1488
Tuti Alawiya , Muhammad Ridho Isdi , Meqorry Yusfi , Harmadi Harmadi
The fashion industry in Indonesia significantly contributes to the country’s creative economy. However, public knowledge about various types of fabric materials is still limited, often leading to fraud. This research aims to develop a device that can classify fabric materials based on their structure using computer vision techniques. The device uses a digital microscope endoscope magnifier 1600x USB camera to capture fabric structure images and the YOLOv8 algorithm to classify 17 types of fabric materials from 1,700 raw image data. The research methodology includes collecting fabric image datasets, preprocessing data, and training the YOLOv8 model. The results show that the YOLOv8 model achieves an accuracy of 98%. The classification results are displayed on an LCD connected to NodeMCU ESP8266. System testing shows that the device effectively classifies fabric materials, sends the results to the database via API, and displays the results on the LCD. Overall, this device provides an effective solution for distinguishing types of fabrics and preventing fraud in fabric purchases.
Volume: 38
Issue: 3
Page: 1479-1488
Publish at: 2025-06-01

Trust evaluation in online social networks for secured user interactions

10.11591/ijeecs.v38.i3.pp2070-2078
Anitha Yarava , C. Shoba Bindu
Online social network is a good platform, where users can share their opinions, ideas, products, and reviews with known (friends and relatives) and unknown users. The growing fame and its easy accesses of new users sometimes lead to security and privacy issues. Many methods are reported so far to address these issues but usage of high complex cryptographic algorithms creating new set of performance related challenges to the mobile users. In this paper, light weight soft security (trust) method is proposed. The proposed method “Trust evaluation in online social networks for secured user interactions-TEOSN” uses user social activities in estimation of his trustworthiness. Each user is observed in terms of followed factor-𝑓𝑑 (his interactions with others) and follower factor-𝑓𝑟 (others interaction with him). The factors 𝑓𝑑 and 𝑓𝑟 are estimated using fuzzy logic and user trust-𝜏 is estimated using beta distribution. The performance of TEOSN is verified theoretically and practically. In experimental results, TEOSN is verified against different number of users; especially it outperformed existing methods in trust computation of target users at 2 to 4-hop distances.
Volume: 38
Issue: 3
Page: 2070-2078
Publish at: 2025-06-01
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