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27,438 Article Results

TextBugger: an extended adversarial text attack on NLP-based text classification model

10.11591/ijeecs.v38.i3.pp1735-1744
Sanjaikanth E. Vadakkethil Somanathan Pillai , Srinivas A. Vaddadi , Rohith Vallabhaneni , Santosh Reddy Addula , Bhuvanesh Ananthan
Recently, adversarial input highly negotiates the security concerns in deep learning (DL) techniques. The main motive to enhance the natural language processing (NLP) models is to learn attacks and secure against adversarial text. Presently, the antagonistic attack techniques face some issues like high error and traditional prevention approaches accurately secure data against harmful attacks. Hence, some attacks unable to increase more flaws of NLP models thereby introducing enhanced antagonistic mechanisms. The proposed article introduced an extended text adversarial generation method, TextBugger. Initially, preprocessing steps such as stop word (SR) removal, and tokenization are performed to remove noises from the text data. Then, various NLP models like Bi-directional encoder representations from transformers (BERT), robustly optimized BERT (ROBERTa), and extreme learning machine neural network (XLNet) models are analyzed for outputting hostile texts. The simulation process is carried out in the Python platform and a publicly available text classification attack database is utilized for the training process. Various assessing measures like success rate, time consumption, positive predictive value (PPV), Kappa coefficient (KC), and F-measure are analyzed with different TextBugger models. The overall success rate achieved by BERT, ROBERTa, and XLNet is about 98.6%, 99.7%, and 96.8% respectively.
Volume: 38
Issue: 3
Page: 1735-1744
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

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

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 todayโ€™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 modelโ€™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

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 peopleโ€™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

A comprehensive access control model integrating zero trust architecture

10.11591/ijeecs.v38.i3.pp1896-1904
Pattabhi Mary Jyosthna , Konala Thammi Reddy
In contemporary IT landscapes, trust in entities, whether internal or external, within organizations has become obsolete. Establishing and enforcing strict access controls, alongside continuous verification, is imperative to safeguard organizational resources from potential insider and outsider threats. The emergence of zero trust architecture (ZTA) addresses this need by advocating for a paradigm shift in security. This research proposes a comprehensive access control model aligned with the fundamental ZTA security principles, namely least privilege, conditional access, and continuous monitoring. The model integrates well-established access control paradigms, including role-based access control (RBAC) to uphold the least privilege principle, attribute-based access control (ABAC) to support conditional access, and trust-based access control (TBAC) to enable continuous monitoring. To determine the trust level of a user requesting access, an analysis of the user's log activities is conducted using the Nmedian outlier detection (NMOD) technique. This analysis aids in evaluating the trustworthiness of the user seeking access to resources. Furthermore, this research assesses the efficiency and efficacy of the proposed integrated access control model in comparison to existing access control models, primarily focusing on their respective functionalities.
Volume: 38
Issue: 3
Page: 1896-1904
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

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

Brain tumor classification for optimizing performance using hybrid RNN classifier

10.11591/ijeecs.v38.i3.pp1905-1913
Boya Nethappa Gari Kalavathi , Umadevi Ramamoorthy
Tumor is the uncontrolled growth of cancer cells in any part of the human body. Brain tumoris the leading cause of cancer deaths worldwide among adults and childrens. Early detection of brain cancers is essential. To prevent more issues, early defect detection is essential. Healthcare physicians may discover and categorize brain tumors with the use of computational intelligence-focused tools. An essential task for diagnosing tumors and choosing the right type of therapy is classifying brain tumors. Brain tumor identification and segmentation using magnetic resonance imaging (MRI) scans is now recognized as one of the most significant and difficult research areas in the world of medical image processing. The field of medical imaging has gained greatly from the use of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL). DL has shown significant presentation, especially in the areas of brain tumor classification and segmentation. In this work, brain tumor classification for optimizing performance using hybrid recurrent neural network (RNN) classifier is presented. Different types of brain tumors are classified using a mix of RNN and inception residual neural network (ResNet). This strategy will produce improved F1-score, precision, accuracy, and recall scores.
Volume: 38
Issue: 3
Page: 1905-1913
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

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

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 โ€œTrust evaluation in online social networks for secured user interactions-TEOSNโ€ uses user social activities in estimation of his trustworthiness. Each user is observed in terms of followed factor-๐‘“๐‘‘ (his interactions with others) and follower factor-๐‘“๐‘Ÿ (others interaction with him). The factors ๐‘“๐‘‘ and ๐‘“๐‘Ÿ are estimated using fuzzy logic and user trust-๐œ 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

Blue light therapy device for wound healing

10.11591/ijeecs.v38.i3.pp1527-1539
Minahil Kamal , Aleena Kamal , Azka Abid , Sarah Ahmed , Syed Muddusir Hussain , Jawwad Sami Ur Rahman , Sathish Kumar Selvaperumal
Cuts, diabetic ulcers, and pressure sores are examples of chronic skin wounds that pose a serious healthcare danger because of their delayed healing rates. This problem emphasizes the necessity of creating noninvasive, economical, and successful wound treatment plans. Conventional treatments, such as skin grafting, negative pressure wound therapy, and hyperbaric oxygen therapy, have demonstrated effectiveness; nevertheless, they are frequently costly, intrusive, and have possible side effects. On the other hand, blue light treatment has become a viable substitute due to its antimicrobial characteristics and capacity to encourage cellular restoration. However, there is a crucial gap in the development of a portable, noninvasive, and cost-effective photobiomodulation device for wound treatment and monitoring, despite its demonstrated potential in wound healing. This work aims to address this gap by creating a novel blue light therapy tool specifically suited for wound healing. The gadget allows for controlled blue light exposure and real-time temperature monitoring to minimize overheating. It has a portable arm housing with integrated blue light strips, a temperature sensor, and an integrated fan. An STM 32 microcontroller powers the systemโ€™s pulse width modulation (PWM) technology, which modifies light intensity and therapy duration in response to conditions unique to each wound. This novel strategy seeks to improve the effectiveness of wound healing, lower the likelihood of adverse effects, and offer patients and healthcare providers a workable alternative that is noninvasive, inexpensive, and easy to use.
Volume: 38
Issue: 3
Page: 1527-1539
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 plantsโ€™ 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
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