Indonesian Journal of Electrical Engineering and Computer Science
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Virtual exhibition systems using virtual reality technology
Get PDFExhibitions are an activity that can bring a lot of benefits to a company. By participating in an exhibition, a company can carry out promotions to increase their sales and improve their company image. However, there are several shortcomings that can be found with conventional exhibitions held in a face-to-face manner. These exhibitions cost a lot of money, run for only a relatively short period of time, and are limited by the location of the exhibition. Because of this, the idea came up to create a virtual exhibition system which could be used as an alternative to conventional exhibitions. The development of a virtual exhibition system for this research was carried out using the Unity game engine. At the virtual exhibition, users can choose which exhibition they want to visit and enter the exhibition room view products and find information about them. Evaluation is carried out using a user acceptance test with Likert scale questions. The evaluation results show a user satisfaction level of 92.7% among the 18 users who have tested the application. With this, it can be said that the virtual exhibition system based on virtual reality technology has been successfully built
SmartSentry: a comprehensive framework for automated vulnerability discovery in Ethereum smart contracts
Get PDFIn the realm of decentralized applications, smart contracts play a pivotal role in managing an extensive array of digital assets within blockchain networks. Ensuring the security of these digital assets hinges upon the adept detection of vulnerabilities present within smart contracts. Extensive research efforts have scrutinized and elucidated numerous smart contract vulnerabilities. However, certain vulnerabilities, including signature malleability, hash collision, and inconsequential code segments, remain relatively unexplored and devoid of dedicated detection tools. In response to this research gap, this paper addresses these three previously understudied vulnerabilities. We contribute to the field by creating a labeled dataset comprising vulnerable smart contracts. This dataset serves as a valuable resource for further scientific inquiries, enabling the testing and validation of various detection frameworks. Additionally, we present SmartSentry a static vulnerability detection framework capable of identifying these vulnerabilities. Using both dataflow and control flow analysis, our framework exhibits exceptional performance, successfully identifying labeled vulnerabilities and real-world vulnerabilities within production smart contracts with speed and efficiency. These efforts collectively enhance our understanding of smart contract vulnerabilities and contribute to the broader advancement of blockchain security
A new deep learning model based on convolutional neural network and residual blocks for driver drowsiness detection
Get PDFRecognizing the pivotal importance of monitoring driver inattention in the quest to minimize accidents and enhance safety and security, it is essential to highlight the inherent danger posed by drowsiness-a specific form of inattention that can significantly contribute to accidents. To address this issue, several propositions involving artificial intelligence have been put forth to effectively monitor and identify instances of driver drowsiness. However, challenges persist in the form of real-time processing constraints, the intricate nature of model parameters, and the response time of the model. The proposed methodology focuses on using convolutional neural networks (CNNs) and Residual blocks as robust and effective deep-learning models for the real-time detection of driver drowsiness. The integration of CNNs and Residual blocks enhances the model's precision, striking a well-balanced synergy between computational efficiency and performance. Notably, this approach demonstrated an impressive accuracy of 96.09% along with a recall, f1-score, and precision all at 96% when evaluated on the publicly available eye_dataset. Furthermore, the runtime of the developed model is a mere 70 ms. To further validate the efficiency of our proposed model, we conducted a comparative analysis with various pre-trained residual neural networks, including ResNet152, ResNet50, ResNet50v2, ResNet101, and ResNet101v2
A hybrid approach for hotspot problem using load balancing and advanced ant colony algorithm
Get PDFWireless sensor networks (WSNs) are crucial in various applications such as environmental surveillance, military operations, transportation monitoring, and healthcare. However, due to a finite set of sensor nodes' resources concerning energy, memory, disk, and CPU processing, nodes in WSNs often face hotspot issues. The sensor nodes that are located near the base station, are responsible for relaying data not only from themselves but also from neighboring nodes. This leads to hotspot issues, where nodes near the base station experience higher traffic loads and faster energy depletion. This paper mainly focuses on mitigating hotspot issues in heterogeneous WSNs using unequal clustering, load balancing, and an advanced ant colony algorithm. This approach involves devising strategies for selecting cluster heads, determining clusters optimal number and formation, and optimizing data transmission processes. Central to the methodology is utilizing load balancing mechanisms and an advanced ant colony algorithm to distribute the workload among sensor nodes more evenly and find the optimum routing path. The proposed algorithm shows promise in alleviating traffic congestion and energy depletion and provides an innovative approach to enhance network performance and prolong the lifespan of sensor nodes
Machine learning models in renewable energy forecasting: a systematic literature review
Get PDFDuring the past years, the convergence of machine learning (ML) technologies with renewable energy sectors has become a significant key area of innovation as a key area of innovation, enhancing the efficiency and predictability of sustainable energy sources. ML algorithms, adept at handling complex data, have become essential in forecasting energy outputs from variable sources like solar and wind. This integration has led to the development of smarter, more adaptive grid systems, capable of efficiently managing the variability of renewable energy sources. This review paper focuses on several key areas: firstly, it provides a summary of related work, specifically focusing on ML in the renewable energy field. Secondly, it delves into ML models and evaluation metrics used for solar and wind energy forecasting. Thirdly, it analyzes 21 studies published from 2019 to 2023, primarily centered on solar energy (60%) and wind energy (40%), with an emphasis on various forecasting horizons, highlighting the results of the ML algorithms used and the performance metrics to evaluate their effectiveness. Finally, it identifies gaps and opportunities in this field. The state-of-the-art review and its findings can offer a solid foundation for future research initiatives
Simulation of ray behavior in biconvex converging lenses using machine learning algorithms
Get PDFThis study used machine learning (ML) algorithms to investigate the simulation of light ray behavior in biconvex converging lenses. While earlier studies have focused on lens image formation and ray tracing, they have not applied reinforcement learning (RL) algorithms like proximal policy optimization (PPO) and soft actor-critic (SAC), to model light refraction through 3D lens models. This study addresses that gap by assessing and contrasting the performance of these two algorithms in an optical simulation context. The findings of this study suggest that the PPO algorithm achieves superior ray convergence, surpassing SAC in terms of stability and accuracy in optical simulation. Consequently, PPO offers a promising avenue for optimizing optical ray simulators. It allows for a representation that closely aligns with the behavior in biconvex converging lenses, which holds significant potential for application in more complex optical scenarios
Gamification in work-based learning in vocational education to support students' coding abilities
Get PDFThis article studied the integration of gamification in work-based learning within vocational education as a means to support students' coding abilities. By applying game mechanics such as points, badges, leaderboards, and challenges, we aimed to motivate and engage students in coding activities that mirror real-world industry practices. The inclusion of gamified elements into the curriculum was designed to make the learning process more interactive, fostering a competitive yet collaborative environment that enhances students' interest and perseverance in coding tasks. This research employed a quasi-experimental design with pre-test and post-test measures to assess the impact of gamification on coding proficiency, comparing the outcomes of students participating in gamified learning environments with those in traditional settings. The findings indicate a significant improvement in the coding skills of students exposed to gamified work-based learning, suggesting that gamification can serve as an effective pedagogical tool in vocational education, better preparing students for industry demands
Exploring diverse prediction models in intelligent traffic control
Get PDFTraffic congestion is a major challenge that affects excellence of life for numerous people across world. The fast growth in many vehicles contributes to congestion during peak and non-peak hours. The vehicle traffic resulted in many issues like accidents and inefficiency in traffic flow. Many traffic light control systems operate on fixed time intervals leads to inefficiency. The fixed-time signals cause unnecessary delays on roads with minimum number of quantity vehicles. Intelligent transport systems (ITS) introduce new comprehensive framework that combine the advanced technologies to improve the transportation network efficiency and to optimize the traffic management. The high-traffic routes are forced to wait excessively. Machine learning (ML) methods have designed to examine the traffic control. However, the accurate detection and vehicle tracking are essential one for effective ITS. In order to mention these problems, ML and deep learning (DL) methods are introduced to improve prediction performance
Multifaceted approach for anticipating learner performance using parameter weightage and ensemble algorithm fusion
Get PDFAnticipating student performance has garnered significant attention in education research for offering early insights that enable timely interventions and personalized support, ultimately improving student success and retention rates. This research focuses on enhancing the accuracy and efficiency of student performance prediction models by employing a hybrid ensemble framework that integrates weighted feature selection with meta-learner-based approaches. A weighted feature selection method was employed to prioritize the most influential of the 23 parameters in the dataset, enhancing prediction accuracy while reducing the computational burden. These parameters were then used to build a hybrid ensemble model by combining base learners with meta-learners, systematically tuned using hyperparameter optimization. This approach aimed to further improve prediction accuracy by fusing multiple base learners, leveraging the strengths of different algorithms for more accurate predictions. The proposed hybrid model was validated across different features selected based on feature importance using random forest (RF). An accuracy of 98.38% was achieved when all 23 features were considered and an accuracy of 97.13 % was achieved when the top 10 features were used. The research highlights the significance of early prediction for prompt intervention and demonstrates how feature weighting can boost model efficacy
Modified-vehicle detection and localization model for autonomous vehicle traffic system
Get PDFThe modification of vehicles for financial gain is an evolving tendency observed in India. Recognizing and detecting of these modified illicit cars is an important but critical task in autonomous vehicles (AV). It is always possible for a cyclist or pedestrian to traverse obstacles or other fixed objects that appear in front of any moving vehicle. Vehicles that are autonomous or self-driving require a different system to quickly identify both stationary and moving objects. A deep learning model named you only look once version 5 (YOLOv5)-convolutional block attention module (CBAM) is proposed here for the Indian traffic system which is based on YOLOv5m. The proposed algorithm, YOLOv5-CBAM, has three major components. The first module, the backbone module is employed for feature extraction. The second module is to detect static as well as dynamic objects at the same time and the third CBAM module is adopted in the backbone and neck part, which mainly focuses on the more prominent features. Two cross stage partial (CSP) modules were used after every convolutional layer resulting in an additional head to the proposed model. Four head modules equipped with anchor boxes performed the final detection. For the present dataset, the proposed model showed 98.2% mean average precision (mAP), 98.4% precision, and 94.8% recall as compared to the original YOLOv5m