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28,719 Article Results

Identification types of plant using convolutional neural network

10.11591/ijece.v15i6.pp5827-5836
Radityo Hendratmojo Jati Notonegoro , Hustinawaty Hustinawaty
Artificial intelligence can be implemented in fields that related to environmental education by providing knowledge for taxonomy which recognize and identify plant species based on its features. The variety of plant species that inhabit in a certain area allows many plant species to be found that look similar so that difficult to distinguish and recognize a particular plant. Convolutional neural network (CNN) often used in object detection, you only look once (YOLO), one of CNN’s object detections, could identify object in real time and obtained good performance and accuracy in several researched. However, no studies have ever identified a plant from its flowers, leaves, and fruits. Therefore, the main object of this paper is identified types of plant with CNN (YOLOv8). The YOLOv8 model with 0.01 learning rate, 32 batch size, stochastic gradient descent (SGD) optimizer obtained highest precision of 69.62% and F1 score of 61.22%, recall of 54.73%, mAP50 and mAP50 – 90 on the training data of 57.61% and 42.49%.
Volume: 15
Issue: 6
Page: 5827-5836
Publish at: 2025-12-01

Optimization of a level shifter integrated with a gate driver using TSMC 130 nm CMOS technology

10.11591/ijece.v15i6.pp5223-5233
Hicham Guissi , Khadija Slaoui
Modern electronic systems increasingly operate across multiple voltage domains, necessitating robust and efficient level shifter (LS) circuits to ensure reliable inter-domain communication. In low-power digital applications, minimizing propagation delay and transition time is critical for achieving high-speed and energy-efficient operation. This work presents a high-performance level shifter optimized for integration within Li-ion battery charger systems. The proposed design achieves a substantial reduction in propagation delays from 0.15 to 0.09062 ns while preserving signal integrity. When integrated with a gate driver, the overall structure exhibits a propagation delay of 0.20468 ns and a transition time of 0.014 ns, marking a significant improvement from the previous 0.036 ns. Furthermore, the proposed circuit occupies only 0.00039 mm² of silicon area, representing a 92% reduction compared to prior implementations (0.05 mm²). The complete design was implemented using Taiwan semiconductor manufacturing company (TSMC) 130 nm complementary metal–oxide– semiconductor (CMOS) technology, with both schematic simulation and layout carried out in the Cadence Virtuoso design environment. These results underscore the potential of the proposed solution for compact and high-efficiency system-on-chip (SoC) battery management applications.
Volume: 15
Issue: 6
Page: 5223-5233
Publish at: 2025-12-01

Integration of ultra-wideband elliptical antenna with frequency selective surfaces array for performance improvement in wireless communication

10.11591/ijece.v15i6.pp5515-5523
Saleh Omar , Chokri Baccouch , Rhaimi Belgacem Chibani
The integration of frequency selective surfaces (FSS) with antennas has gained significant attention due to its ability to enhance key radio frequency (RF) performance parameters such as gain, directivity, and bandwidth, making it highly beneficial for modern wireless communication systems. In this work, we propose and investigate an ultra-wideband (UWB) elliptical antenna operating within the 5.2 to 10 GHz frequency range. To further improve its performance, we integrate the antenna with a 13×13 FSS array. The impact of the FSS on the antenna’s characteristics is analyzed, showing a remarkable gain enhancement from 2.6 dBi (without FSS) to 10.05 dBi (with FSS). These results confirm the effectiveness of FSS integration in optimizing UWB antenna performance, making it a promising approach for advanced wireless communication applications.
Volume: 15
Issue: 6
Page: 5515-5523
Publish at: 2025-12-01

Memoryless state-recovery cryptanalysis method for lightweight stream cipher – A5/1

10.11591/ijece.v15i6.pp5453-5465
Khedkar Aboli Audumbar , Uday Pandit Khot , Balaji G. Hogade
Cryptology refers to the discipline concerned with securing communication and data in transit by transforming it into an unintelligible form, thereby preventing interpretation by unauthorized entities. Cryptanalysis is the study and practice of analyzing cryptographic systems with the aim of uncovering their weaknesses, finding vulnerabilities and obtaining unauthorized access to encrypted data. A5/1 is a lightweight stream cipher used to protect GSM communications. There are two memoryless cryptanalysis techniques used for this cipher which are Golic’s Guess-and-determine attack and Zhang’s Near Collision attack. In this paper a new guessing technique called move guessing technique used to construct linear equation filter along with Golic’s guess and determine technique is studied. Two modifications in move guessing technique are proposed for recovery of internal states S0 and S1. Further, a novel algorithm is proposed to select the modification to get minimum time complexity for recovery of internal states S0 and S1. The proposed algorithm gives minimum time complexity of 229.3138 at t = 14 for recovery of S0 state and 243.246 for recovery of S1 at t = 22.
Volume: 15
Issue: 6
Page: 5453-5465
Publish at: 2025-12-01

Data transmission technologies for the development of a drilling rig control and diagnostic system

10.11591/ijece.v15i6.pp5506-5514
Irina Rastvorova , Sergei Trufanov
This article examines telecommunication technologies used in automatic control and diagnostics systems and discusses key aspects of using telecommunication solutions for monitoring and controlling the operation processes of the electrical complex of a drilling rig, including remote access, data transmission and real-time information analysis. It provides a comprehensive overview of such communication technologies as Bluetooth, Wi-Fi, ZigBee, global system for mobile communication (GSM), RS-232, RS-422, RS-485, universal serial bus (USB), Ethernet, narrowband internet of things (NB-IoT), long range wide area network (LoRaWAN), and power line communication (PLC). Technologies that will be most effective for use in control and diagnostics systems of a drilling rig complex are proposed. The possibility of using machine learning to process a large amount of data obtained during the drilling process to optimize the controlled drilling parameters is investigated.
Volume: 15
Issue: 6
Page: 5506-5514
Publish at: 2025-12-01

Low-power and reduced delay in inverter and universal logic gates using Hvt-FinFET technology

10.11591/ijece.v15i6.pp5193-5204
Veerappa Chikkagoudar , G. Indumathi
The rapid scaling of conventional complementary metal–oxide– semiconductor (CMOS) metal–oxide–semiconductor field-effect transistors (MOSFETs) led to significantly increasing power dissipation, delay, and short channel effects (SCEs). Fin field-effect transistor (FinFET) technology is a better alternative to MOSFETs with superior electrostatic control, low power, and reduced leakage current. FinFETs have been chosen for their efficiency in overcoming these issues. This work focuses on the design of high-threshold voltage fin field-effect transistor (Hvt-FinFET) 18 nm technology-based inverter with optimized parameters and implementing universal gates NAND and NOR in Cadence Virtuoso tool. These three gates are basic building blocks for any complex digital system design. The results demonstrate significant improvement in power and reduced propagation delay in comparison with conventional CMOS technology. The Hvt-FinFET inverter obtained power dissipation and delay reduction of 13.63% and 33.33%, respectively. Power and delay optimization of 29.10% and 11.8% have been obtained in the NAND gate and 31.28% and 29.08% in the NOR gate when compared to conventional CMOS circuits. The results demonstrate significant improvements in power savings, reduced propagation delay, and superior energy efficiency, validating the effectiveness of Hvt-FinFET technology for next-generation very large scale integration (VLSI) applications.
Volume: 15
Issue: 6
Page: 5193-5204
Publish at: 2025-12-01

Optimizing radial basis function networks for harmful algal bloom prediction: a hybrid machine learning approach

10.11591/ijece.v15i6.pp5647-5654
Nik Nor Muhammad Saifudin Nik Mohd Kamal , Ahmad Anwar Zainuddin , Amir ‘Aatieff Amir Hussin , Ammar Haziq Annas , Normawaty Mohammad-Noor , Roziawati Mohd Razali
The deployment of artificial intelligence in environmental monitoring demands models balancing efficiency, interpretability, and computational cost. This study proposes a hybrid radial basis function network (RBFN) framework integrated with fuzzy c-means (FCM) clustering for predicting harmful algal blooms (HABs) using water quality parameters. Unlike conventional approaches, our model leverages localized activation functions to capture non-linear relationships while maintaining computational efficiency. Experimental results demonstrate that the RBFN-FCM hybrid achieved high accuracy (F1-score: 1.00) on test data and identified Chlorophyll-a as the strongest predictor (r = 0.94). However, real-world validation revealed critical limitations: the model failed to generalize datasets with incomplete features or distribution shifts, predicting zero HAB outbreaks in an unlabeled 11,701-record dataset. Comparative analysis with Random Forests confirmed the RBFN-FCM's advantages in training speed and interpretability but highlighted its sensitivity to input completeness. This work underscores the potential of RBFNs as lightweight, explainable tools for environmental forecasting while emphasizing the need for robustness against data variability. The framework offers a foundation for real-time decision support in ecological conservation, pending further refinement for field deployment.
Volume: 15
Issue: 6
Page: 5647-5654
Publish at: 2025-12-01

Factors influencing enrollment intention in private schools

10.11591/ijere.v14i6.35364
Lim Lee Ping , Ong Choon Hee , Tan Owee Kowang , Lim Kim Yew
The growth in private school student enrollment in Malaysia has prompted institutions to upgrade to stay competitive in the market. However, despite the increasing number of private schools, regrettably, only a few studies have focused on the factors that influence private school enrollment. This study examines the relationship between social influences (SI), school environment (SE), characteristics, parent-administration-teacher relationship (PAT), and private school enrolment intention in Malaysia. It uses a quantitative method and G*Power to determine the minimum sample size. Data was gathered from 135 respondents who have enrolled at least one child in private schools using questionnaire surveys. The statistical package for social science (SPSS) was used to analyze the data. The results showed that SI and school characteristics (SC) significantly and positively correlated with enrolment intention. The PAT was not significantly associated with enrolment intention. This study clearly shows that SI factors and SCs are crucial for enrolment intention in private schools. The management should develop and implement marketing strategies that effectively tackle current market challenges by focusing on SI and improving the SC. They can tailor the marketing strategy with electronic word-of-mouth (e-WOM) for SI and apply learning analytics for SC.
Volume: 14
Issue: 6
Page: 4509-4516
Publish at: 2025-12-01

Emotional intelligence in teaching: a key to performance and institutional climate in basic education

10.11591/ijere.v14i6.34924
Benjamin Maraza-Quispe , Victor Hugo Rosas-Iman , Giuliana Feliciano-Yucra , Atilio Cesar Martinez-Lopez , Elizabeth Katherine Ortiz-Corimaya , Walter Choquehuanca-Quispe , Frida Karina Coasaca-Hancco , Luis Elfer Nuñez-Saavedra
This study addresses the lack of understanding regarding the relationship between emotional intelligence (EI), teaching performance, and institutional climate (IC) in basic education. As a solution, the study proposes evaluating and strengthening teachers’ EI to enhance both their performance and the school environment. Using a quantitative, non-experimental, correlational design, the research analyzed a randomly selected sample of 145 teachers. Validated questionnaires measured dimensions such as self-awareness, self-regulation, motivation, empathy, and social skills, as well as teaching preparation and IC. The results reveal significant positive correlations between EI and IC (r=0.85) and between teaching performance and IC (r=0.78). This suggests that higher EI not only improves teaching effectiveness but also fosters a positive institutional environment. The study concludes that enhancing teachers’ EI can optimize both their performance and institutional dynamics, contributing to higher-quality education. The findings support the implementation of EI training programs as a key strategy to improve teaching performance and the school climate (SC).
Volume: 14
Issue: 6
Page: 5054-5066
Publish at: 2025-12-01

Explainable fault diagnosis using discrete grey wolf optimization algorithm for photovoltaic system

10.11591/ijece.v15i6.pp5286-5296
Slimani Hassina , Chouhal Ouahiba , Beddiaf Yassine , Mahdaoui Rafik , Haouassi Hichem , Hamdi Roumaissa
The present article introduces the discrete grey wolf optimization algorithm (DGWOA), a novel variant of the grey wolf optimizer (GWO). DGWOA integrates discrete optimization techniques with explainable artificial intelligence (XAI) methodologies. This approach aims to overcome limitations associated with traditional fault diagnosis methods, such as limited accuracy in identifying complex patterns and low interpretability. Furthermore, it mitigates early convergence problems commonly encountered in optimization algorithms and enhances adaptability to discrete classification challenges. The DGWOA algorithm is designed to generate interpretable classification rules for fault detection through a stochastic search strategy. The explainability provided by the model not only enhances decision-making transparency but also improves diagnostic efficiency and predictive accuracy. The proposed algorithm was evaluated using a photovoltaic system dataset and benchmarked against established rule-based classifiers. DGWOA consistently achieved a classification accuracy of 99.48% and a precision of 100%, demonstrating its effectiveness in enhancing fault detection. Moreover, the interpretability of the generated classification rules contributes to the generation of outcomes that are both actionable and comprehensible to decision-makers.
Volume: 15
Issue: 6
Page: 5286-5296
Publish at: 2025-12-01

Enhancing system integrity with Merkle tree: efficient hybrid cryptography using RSA and AES in hash chain systems

10.11591/ijece.v15i6.pp5679-5689
Irza Nur Fauzi , Farikhin Farikhin , Ferry Jie
An analysis is conducted to address the growing threats of data theft and unauthorized manipulation in digital transactions by integrating \structures within hash chain systems using hybrid cryptography techniques, specifically Rivest-Shamir-Adleman (RSA) and advanced encryption standard (AES) algorithms. This approach leverages AES for efficient symmetric data encryption and RSA for secure key exchanges, while the hash chain framework ensures that each data block is cryptographically linked to its predecessor, reinforcing system integrity. The Merkle tree structure plays a crucial role by allowing precise and rapid detection of unauthorized data changes. Empirical analyses demonstrate notable improvements in both the efficiency of cryptographic processes and the robustness of data validation, underscoring the method’s applicability in high data throughput environments such as educational institutions. This research makes a substantive contribution to information security by offering a sophisticated solution that strengthens data protection practices, ensuring greater resilience against increasingly sophisticated data threats.
Volume: 15
Issue: 6
Page: 5679-5689
Publish at: 2025-12-01

Enhancing supply chain agility with advanced weather forecasting

10.11591/ijece.v15i6.pp5904-5913
Imane Zeroual , Jaber El Bouhdidi
This article presents a solution that leverages artificial intelligence techniques to enhance urban freight transportation planning and organization through the integration of weather forecasting data. We identify key challenges in the current urban logistics landscape and introduce a range of machine learning models designed to predict delivery delays. Logistic regression serves as the foundational model, analyzing historical delivery data in conjunction with weather conditions to assess the likelihood of delays, thus enabling informed decision-making for companies. Additionally, we evaluate two other machine learning models to determine the most effective approach for our specific context, assessing their accuracy and capacity to deliver actionable insights. By improving the predictive capabilities of urban freight systems, this research aims to streamline operations, reduce costs, and enhance overall service reliability, contributing to more efficient and resilient urban transportation networks.
Volume: 15
Issue: 6
Page: 5904-5913
Publish at: 2025-12-01

Plant disease detection and classification: based on machine learning and Eig(Hess)-co-occurrence histograms of oriented gradients

10.11591/ijece.v15i6.pp5336-5346
El Aroussi El Mehdi , Barakat Latifa , Silkan Hassan
Agricultural districts provide high-quality food and contribute substantially to economic growth and population support. However, plant diseases can directly reduce food production and threaten species diversity. The use of precise, automated detection techniques for early disease identification can improve food quality and mitigate economic losses. Over the past decade, numerous methods have been proposed for plant disease classification, and in recent years the focus has shifted toward deep learning approaches because of their outstanding performance. In this study, we employ the Eig(Hess)-co-occurrence histograms of oriented gradients (CoHOG) descriptor alongside pre-trained machine-learning models to accurately identify various plant diseases. We apply principal component analysis (PCA) for dimensionality reduction, thereby enhancing computational efficiency and overall model performance. Our experiments were conducted on the popular PlantVillage database, which contains 54,305 images across 38 disease classes. We evaluate model performance using classification accuracy, sensitivity, specificity, and F1-score, and we perform a comparative analysis against state-of-the-art methods. The findings indicate that the approach we proposed achieves up to 99.83% accuracy, outperforming existing models. Additionally, we test the robustness of our method under various conditions to highlight its potential for real-world agricultural applications.
Volume: 15
Issue: 6
Page: 5336-5346
Publish at: 2025-12-01

The evolution of routing in VANET: an analysis of solutions based on artificial intelligence and software-defined networks

10.11591/ijece.v15i6.pp5388-5400
Lewys Correa Sánchez , Octavio José Salcedo Parra , Jorge Gómez
This study explored the evolution of vehicular ad hoc networks (VANET) and focused on the challenges and opportunities for routing in these dynamic environments. Despite advancements in traditional protocols, a significant gap persists in the ability to adapt to highly mobile environments with variable traffic, which limits routing efficiency and quality of service. Emerging technologies, such as artificial intelligence (AI) and software- defined networks (SDN), are discussed that have the potential to revolutionize the management of VANET. Machine learning can be used to predict traffic, optimize routes, and adapt routing protocols in real-time. Furthermore, SDN can simplify routing management and enable greater flexibility in network configurations. A comprehensive overview of the convergence of AI and SDN is presented, and the potential complementarities between these technologies to address routing challenges in VANET are explored. Finally, the implications of efficient routing in VANET for road safety, traffic management, and the development of new applications are discussed, and future research lines are identified to address challenges such as scalability, data security, and computational efficiency in vehicular environments.
Volume: 15
Issue: 6
Page: 5388-5400
Publish at: 2025-12-01

Empowering low-resource languages: a machine learning approach to Tamil sentiment classification

10.11591/ijict.v14i3.pp941-949
Saleem Raja Abdul Samad , Pradeepa Ganesan , Justin Rajasekaran , Madhubala Radhakrishnan , Peerbasha Shebbeer Basha , Varalakshmi Kuppusamy
Sentiment analysis is essential for deciphering public opinion, guiding decisions, and refining marketing strategies. It plays a crucial role in monitoring public sentiment, fostering customer engagement, and enhancing relationships with businesses' target audiences by analyzing emotional tones and attitudes in vast textual data. Sentiment analysis is extremely limited, particularly for languages like Tamil, due to limited application in diverse linguistic contexts with fewer resources. Given its global impact and linguistic diversity, addressing this gap is crucial for a more nuanced understanding of sentiments in India. In the context of Tamil, the need for sentiment analysis models is particularly crucial due to its status as one of the classical languages spoken by millions. The cultural, social, and historical nuances embedded in Tamil language usage require tailored sentiment analysis approaches that can capture the subtleties of sentiment expression. This paper introduces a novel method that assesses the performance of various text embedding methods in conjunction with a range of machine learning (ML) algorithms to enhance sentiment classification for Tamil text, with a specific focus on lyrics. Experiments notably emphasize FastText word embedding as the most effective method, showcasing superior results with a remarkable 78% accuracy when coupled with the support vector classification (SVC) model.
Volume: 14
Issue: 3
Page: 941-949
Publish at: 2025-12-01
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