Indonesian Journal of Electrical Engineering and Computer Science
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Educational impact and ethical considerations in using Chatbots in Academia
Get PDFChatbots are getting better every day due to the advancements in their capabilities in today’s technological age. This study aims to assess the efficacy of ChatGPT-4 and Gemini in producing scientific articles. Two types of prompts are given: direct questions and complete scenarios. Subsequently, we evaluate the educational and ethical aspects of the produced material by employing statistical analysis. We verify the credibility of references, detect any instances of plagiarism, and ensure the precision of the articles generated by the chatbot. In addition, we utilize topic modeling to assess the extent to which the content of the articles corresponds to the specified topic. According to the findings, Gemini outperformed ChatGPT-4, specifically in scenario questions, where it achieved an accuracy rate of 85%, while ChatGPT-4 only achieved 35% accuracy
Secure financial application using homomorphic encryption
Get PDFIn today’s digital age, the security and privacy of financial transactions are paramount. With the advent of technologies like homomorphic encryption, it is now possible to perform computations on encrypted data without the need to decrypt it first, offering a promising avenue for secure financial applications. This research paper explores the implementation and implications of utilizing homomorphic encryption in financial applications to safeguard sensitive data while maintaining computational integrity. By employing homomorphic encryption techniques, financial institutions can enhance the confidentiality of their clients’ information, protect against data breaches, and enable secure computations on encrypted data. The paper discusses the principles of homomorphic encryption, its applications in financial systems, challenges, and potential solutions. Additionally, it examines real-world examples and case studies where homomorphic encryption has been employed successfully, highlighting its effectiveness in ensuring the privacy and security of financial transactions. Overall, this paper aims to provide insights into the role of homomorphic encryption in creating secure financial applications and its potential to revolutionize the way sensitive financial data is handled and processed
Approach for modelling and controlling of autonomous cruise control system through machine learning algorithms
Get PDFAutomated cruise control installation is one of the utmost significant phases in the auto industry's pursuit of autonomous vehicles. The controller of choice is one of the key factors in determining whether a design will be durable and cost-effective. The model-based controller and a cutting-edge algorithmic optimization method are both presented inside the framework of this proposed study. The suggested controller may achieve the desired characteristics of the design, including a faster rise time, a faster settle time, a smaller peak overshoot, and a smaller steady-state error. A MATLAB-executed and -simulated system model using a control method based on a hybrid genetic algorithm and reinforcement learning has been used to effectively and automatically regulate the vehicle's velocity in compliance with all design parameters
Enhanced hippopotamus optimization algorithm for power system stabilizers
Get PDFThis article presents techniques for modifying the power system stabilizer's (PSS) parameters. An enhanced version of the hippocampal optimization algorithm (HO) is presented here. HO represents a novel approach in metaheuristic methodology, having been inspired by the observed clinging behavior in hippos. The notion of the HO is defined using a trinary-phase model that includes their position updates in rivers or ponds, defensive techniques against predators, and mathematically described evasive methods. To confirm the efficacy of the recommended approach, this article provides comparison simulations of the PSS objective function and transient response. This study employs validation through a comparison between Original HO and conventional methods. Simulation results demonstrate that, when compared to competing algorithms, the suggested approach yields optimal results and, in some cases, exhibits fast convergence. It is known that, in comparison to the original HO approach, the recommended way can lower the average undershoot of the rotor angel and speed by 12.049% and 26.97%, respectively
Link adaptation techniques for throughput enhancement in LEO satellites: a survey
Get PDFIn addition to the rapid geometric change of low earth orbit (LEO) satellites, the earth-to-space channel suffers from various attenuations that affect the communication link. To overcome this challenge, the link adaptation technique emerges as a key solution to optimize the transmission performance of LEO satellites, especially the data throughput. The existing contributions in the literature remain scattered across the research board, and a comprehensive survey of this research area still lacks at this stage. The present survey examines various link adaptation methods, mainly variable coding and modulation, adaptive coding and modulation, and hybrid methods using artificial intelligence. In addition, this study explains how this technique leverages a set of recommended standards and cost-effective technologies, such as software-defined radio (SDR) and field programmable gate arrays (FPGA), to fine-tune transmission strategies. Lastly, the paper provides a comparative study of the current research on this field and sheds light on future directions, where the need for higher data throughput makes emerging learning-based techniques and new experimental standards a necessity
Development of newton dynamometer instrumentation integrated with smart counter applications based on Hooke's law
Get PDFThis research presents the development of an instrumentation system that employs ultrasonic sensors for Newton dynamometer applications. A key parameter measured is the change in spring length before and after loading. The methodology implemented in this study is based on Hooke's Law, applied within the instrumentation devices. The length change data is transmitted to a smartphone via a Bluetooth module integrated into the instrument. This allows for flexible data usage and input through a calculator-based application created with MIT App Inventor, tailored to the relevant supporting parameters. Before implementation, the sensors underwent characterization to assess the linearity of their output compared to a standard measuring tool, specifically a ruler. The linearity test yielded a coefficient of 0.9998, indicating excellent performance for this application. Additionally, the system achieved an average accuracy of 94.12% and an average precision of 99.94%
Discovering solutions of economic load dispatch problem by war strategy optimization algorithm
Get PDFThis paper suggested two methods, called war strategy optimization algorithm (WSO) and tunicate swarm algorithm (TSA), to find solutions for economic load dispatch problem (ELD). Various test systems with complex restrictions and discontinuous objective functions are used to assess the efficacy and resilience of the applied methods. The test cases are ranked from the simplest to the most complicated ones, in which the last with load demands are changed from the minimum power to the maximum power of total power of all units. The result comparison indicated that WSO can always reach the best cost for all test systems, but TSA cannot achieve similar values. Namely, WSO can reduced smaller cost than TSA by 121,325 for the first and second test systems, respectively. In comparison to other previous methods, the results found by WSO are equal to or better than those from others; however, the searchability of WSO is faster. Consequently, WSO is highly effective for handling ELD problem and can be considered for applying different problems in the engineering domain
Automated adversarial detection in mobile apps using API calls and permissions
Get PDFAndroid mobile phones’ growing popularity has led to developers creating more malicious apps, which can be included in third-party arcades as protected applications. Detecting these malware applications is challenging due to time-consuming and high-cost techniques. This study proposes a robust deep learning (DL) model for detecting adversarial third-party apps using adaptive feature learning. The strategy involves preprocessing raw apk files, extracting permission behavioral features, and using the proposed spatial dropout-assisted convolutional autoencoder (SD_ConvAE) model to determine if the app is benign or malignant. The approach is simulated using a Python tool and assessed using various measures like accuracy, recall, weighted F-score (W-FS), false discovery rate (FDR), and kappa coefficient. The overall accuracies achieved by the developed techniques are about 99.6% and 99% for detecting benign and malignant apps, respectively
Implementation and analysis of temperature and gas sensor datalogger in multi-stage condenser pyrolysis
Get PDFThe development of alternative energy sources is crucial for addressing contemporary energy and environmental challenges. This study presents the implementation and analysis of a temperature and gas sensor datalogger within a multi-stage condenser pyrolysis system, designed to assess the potential of pyrolytic liquid smoke derived from Cerbera odollam (bintaro) fruit waste. The datalogger system was developed to continuously capture and retain data on temperature, air humidity, and non-condensable gases throughout the pyrolysis process. The experimental research focused on evaluating the impact of varying reactor temperatures (250 °C, 300 °C, and 350 °C) and cooling fluid flow rates on the performance of the condenser and the production of bio-oil. Results indicated that reactor temperature significantly affects bio-oil yield, with the highest output of 190 mL obtained at 350 °C. Additionally, the temperature of the smoke entering each condenser and the cooling water’s temperature were found to influence the composition of the condensates produced by each stage. This study highlights the importance of integrating sensor technologies to optimize pyrolysis conditions, thereby enhancing the efficiency of energy production from bintaro fruit waste
Forecasting virtual machine resource utilization in cloud computing: a hybrid artificial intelligence approach
Get PDFCloud computing has transformed the management of IT infrastructures by providing scalable, flexible, and cost-effective solutions. However, efficient resource management in cloud environments remains a significant challenge, as over-provisioning or under-provisioning of resources can lead to unnecessary costs or degraded performance. Accurate forecasting of virtual machine (VM) resource utilization is crucial for optimizing resource allocation, reducing operational expenses, and ensuring compliance with service level agreements (SLAs). This study aims to address these challenges by developing a hybrid forecasting model that combines the strengths of auto regressive integrated moving average (ARIMA), linear regression (LR), and long short-term memory (LSTM) techniques. By integrating these methods, our model provides more accurate predictions and better adaptability to various workload patterns, helping cloud service providers and users to make informed decisions about resource allocation, ultimately reducing costs. The data was collected from multiple EC2 instances and processed using amazon web services (AWS) Glue with Spark. The experimental results demonstrate that the hybrid model outperforms individual models such as ARIMA, LR, and LSTM in terms of accuracy for forecasting memory, CPU, and disk utilization, offering a more effective solution for managing cloud resources efficiently