Indonesian Journal of Electrical Engineering and Computer Science
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Plagiarism detection in verilog and textual content using linguistic features
Get PDFThe illicit act of appropriating programming code has long been an appealing notion due to the immediate time and effort savings it affords perpetrators. However, it is universally acknowledged that concerted efforts are imperative to identify and rectify such transgressions. This is particularly crucial as academic institutions, including universities, may inadvertently confer degrees for work tainted by this form of plagiarism. Consequently, the primary objective of this research is to scrutinize the feasibility of identifying plagiarism within pairs of Verilog algorithms and texts. this study aims to detect plagiarism in textual content and Verilog code by leveraging diverse linguistic characteristics from the WordNet lexical database. The primary objective is to achieve optimal accuracy in identifying instances of plagiarism, incorporating features such as modifications to text structure, synonym substitution, and simultaneous application of these strategies. The system's architecture is intricately designed to unveil instances of plagiarism in both textual content and Verilog code by extracting nuanced characteristics. The systematic process includes preprocessing, detailed analysis, and post-processing, supported by a feature-rich database. Each entry in the database represents a distinctive similarity case, contributing to a thorough and comprehensive approach to plagiarism detection
A simple machine learning technique for sensor network wireless denial-of-service detection
Get PDFWireless sensor networks (WSNs) are integral to numerous applications but are vulnerable to denial-of-service (DoS) attacks, which can severely compromise their functionality. This research proposes a lightweight machine learning approach to detect DoS attacks in WSNs. Specifically, we investigate the efficacy of decision tree (DT) algorithms with the Gini feature selection method, alongside random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbor (KNN) classifiers. Data collected from normal and DoS attack scenarios are preprocessed and used to train these models. Experimental results showcase the effectiveness of the proposed approach, with the DT algorithm exhibiting high accuracy exceeding 90%, surpassing other classifiers in computational efficiency and interpretability. This study contributes to enhancing the security and reliability of WSNs, offering insights into potential future optimizations and algorithmic explorations for robust DoS attack detection
Optimal placement of wind turbine to minimize voltage variance in distributed grid considering harmonic distortion
Get PDFThis paper suggested an algorithm to choose the optimal location of wind turbines (WT) in a distribution grid. The optimal position is calculated so that the maximal voltage variance in the distribution grid is minimized. This paper considers the harmonic current emitted by WT and the limitation of total harmonic distortion of voltage waves at nodes in the distribution grid. This proposed approach is written in MATLAB software and validated through a sample distribution grid, IEEE 33-bus. The verifying results demonstrated that by applying the suggested algorithm, the maximal voltage variance due to the variation of the power output of WT is minimized, the total harmonic distortion value at all buses remains within the operating range, and the electrical loss in the grid is reduced. Moreover, by considering the limitation of total harmonic distortion, the number of WT allowed to be installed in the grid is able to limited
A deep learning approach to detect DDoS flooding attacks on SDN controller
Get PDFSoftware-defined networking (SDN), integrated into technologies like internet of things (IoT), cloud computing, and big data, is a key component of the fourth industrial revolution. However, its deployment introduces security challenges that can undermine its effectiveness. This highlights the urgent need for security-focused SDN solutions, driving advancements in SDN technology. The absence of inherent security countermeasures in the SDN controller makes it vulnerable to distributed denial of service (DDoS) attacks, which pose a significant and pervasive threat. These attacks specifically target the controller, disrupting services for legitimate users and depleting its resources, including bandwidth, memory, and processing power. This research aims to develop an effective deep learning (DL) approach to detect such attacks, ensuring the availability, integrity, and consistency of SDN network functions. The proposed DL detection approach achieves 98.068% accuracy, 98.085% precision, 98.067% recall, 98.057% F1-score, 1.34% false positive rate (FPR), and 1.713% detection time
An improved efficientnet-B5 for cucurbit leaf identification
Get PDFPlant diseases significantly impact the quality and productivity of crops, leading to substantial economic losses. This paper introduces two enhanced EfficientNet-B5 architectures, EfficientNetB5-sigca and EfficientNetB5- sigbi, specifically designed to detect and classify diseases in cucurbit leaves. We employ EfficientNet-B5 for feature extraction, using a 456×456×3 input and omitting the top layer to generate feature maps with Swish activation. A global average pooling 2D layer replaces the conventional fully connected layer, producing a flattened vector. This is followed by a dense layer with four output units, L2 regularization, and sigmoid activation, using either categorical or binary cross-entropy as the loss function. We also developed a novel image dataset targeting cucumber and cantaloupe leaves, including 11,425 augmented images categorized into four disease classes: anthracnose, powdery mildew, downy mildew, and fresh leaf. Our experiments dataset demonstrates that the EfficientNetB5-sigbi achieves an accuracy of 97.07%, marking a significant improvement in classifying similar diseases in cucurbit leaves
A comparative study on electricity load forecasting using statistical and deep learning approaches
Get PDFLoad forecasting has become reproving aspect of an energy management system (EMS). It gives basic advantage to grid stability, cost effectiveness and battery storage system (BSS). For this purpose, machine learning (ML) is widely adopted to forecast the electricity load. This research paper investigates the performances of various time series estimating models applied to electricity load data for an Irish company. The research mainly adopts the autoregressive integrated moving average (ARIMA) model, long short-term memory (LSTM) networks and transformer neural network (TNN) to forecast the electricity load. A comparison evaluation is conducted encompassing various quantifying measures such as root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE). The results are then compared to get an understanding whether the TNN using attention-based mechanism is better than the two state of the art models. Hence provides a complete understanding about which of the model needs improvements in its architecture for enhancement of operational efficiency and cost effectiveness in the realm of EMS
Speed drives control using particle swarm optimization for PMSM drives
Get PDFThe paper presents a contemporary method for controlling the speed of a permanent magnet synchronous machine (PMSM) by optimizing the parameters of a proportional-integral (PI) controller using the particle swarm optimization (PSO) algorithm. This approach aims to enhance the robustness and dynamic performance of the drive system, resulting in improved accuracy and sensitivity to load changes and wide range of speed. The study evaluates two tuning techniques for the PI controller, which are the traditional trial-and-error method and the PSO optimization method. The performance of the PMSM is assessed based on speed response performance, including rise time, overshoot, and settling time. The PSOtuned controller significantly minimizes overshoot compared to the trialand-error method. And also achieves a shorter settling time, indicating a more stable response. However, the rise time is slightly longer with the PSO-tuned controller compared to the conventional tuning method just for the medium speed. For the rated speed, PSO still having shorter rise time compared to trial-and-error PI method. These findings imply that while the PSO method may result in a longer rise time, its overall advantages in reducing overshoot and settling time make it a more effective option for speed control in PMSMs. This is consistent with other research suggesting that PSO can outperform traditional methods in optimizing control parameters across various applications
Robust spoken word detection in assamese language using BiLSTM with data augmentation for noisy environments
Get PDFThis study focuses on enhancing spoken word detection in the Assamese language using bidirectional long shor term memory (BiLSTM). The primary objective is to improve the model’s robustness in noisy environments by using various data augmentation methods. The research addresses the challenges of keyword detection in low-resource languages like Assamese. A BiLSTM model was trained and tested using a speech corpus sourced from the Indian Language Technology Proliferation and Development Center (ILTP-DC), comprising 32,335 utterances from 1,000 speakers and 262 unique Assamese words. The model was trained on 10 specific keywords. Feature extraction was conducted using 39 coefficients, including MFCC, ΔMFCC, and ΔΔMFCC. The model’s performance was evaluated on clean and augmented noisy datasets. The application of data augmentation techniques significantly improved the model’s performance in noisy environments. This model achieved an average accuracy of 98.01% and a word error rate (WER) of 19.94% on noisy data, showcasing the effectiveness of augmentation in enhancing keyword detection. This work introduces a novel approach to Assamese spoken word detection by integrating BiLSTM with data augmentation techniques, making the model more noise-resilient. This study sets a benchmark for Assamese speech recognition and showcases augmentation techniques’ effectiveness in low-resource languages
Unveiling critical factors of test automation adoption in software testing
Get PDFThis paper aims to observe the adoption of test automation in Indonesia and examine the determining factors that influence the use of this technology in organizations. The study focuses on five critical factors: technology acceptance model, task-technology fit, managerial support (MS), individual performance, and organizational performance. A survey of 109 QA community members was conducted to collect data, and partial least squares structural equation modeling was used for data processing. Based on the study, Selenium is the top test automation framework used for organizations in Indonesia, followed by Appium and Postman. The result showed that out of twelve (12) examined relationships, nine (9) of them were accepted. This data indicates the strong influence of task technology fit (TTF), computer self-efficacy (CSE), perceived ease of use, perceived usefulness, and MS towards behavioral intention and actual use of test automation. Additionally, the actual use of test automation was found to have a positive impact on individual and organizational performance. The study contributes valuable insights for decision-makers by identifying critical factors influencing automation adoption and offers a replicable methodology for evaluating similar technologies
For S-band WLAN applications, a patch antenna design, simulation, and optimization
Get PDFA 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)