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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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Simulation of Data Acquisition System Using SimulIDE, Virtual Serial Port and Python 9

Simulation of Data Acquisition System Using SimulIDE, Virtua...

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9th International Conference on Education and Technology, ICET 2023

作者： Puji, Muhammad Nurul Astuti, Winda Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Engineering Program Computer Engineering Department Jakarta Indonesia

ISBN: (纸本)9798350358292

Data acquisition systems are commonplace in the current era of digitalization and Industry 4.0. These systems are typically used to obtain data from sensors, whether analog or digital, which is then stored for processing in subsequent stages. However, the implementation of hardware for creating data acquisition systems can be challenging due to constraints such as laboratory facilities, budget limitations, and other factors. Therefore, in this research, SimulIDE simulation software was employed to simulate electronic components and the Arduino Uno. To make this virtual Arduino appear as a physical one to the host computer, PyVirtualSerialPorts was used as a virtual serial port. The data acquisition system was created using Python programming with several key features: it can store data in CSV format, present data plots and graphs, and display the frequency spectrum of signals. The system achieved a sampling speed of 1000 data points per second, with input signals limited to a maximum of 10 Hz. This study also shows that signal distortion can occur due to excessively frequent data plotting and Fast Fourier Transform (FFT) processing. Therefore, the use of SimulIDE simulator, virtual serial port, and Python can be utilized in the learning process of a simple data acquisition system. © 2023 IEEE.

关键词： Python

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Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1287-1301页

作者： Takiddin, Abdulrahman Ismail, Muhammad Atat, Rachad Davis, Katherine R. Serpedin, Erchin Department of Electrical and Computer Engineering Texas A and M University College StationTX77843 United States Department of Computer Science Tennessee Tech University CookevilleTN38505 United States Department of Electrical and Computer Engineering Program Texas A and M University Qatar Doha23874 Qatar

Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the adopted datasets for training have correct labeling information. However, such an assumption is not always valid as training data might include measurement samples that are incorrectly labeled as benign, namely, adversarial data poisoning samples, which have not been detected before. Neglecting such an aspect makes detectors susceptible to data poisoning. Our investigations revealed that detection rates (DRs) of existing detectors significantly deteriorate by up to 9-29% when subject to data poisoning in generalized and topology-specific settings. Thus, we propose a generalized graph neural network-based anomaly detector that is robust against FDIAs and data poisoning. It requires only benign datasets for training and employs an autoencoder with Chebyshev graph convolutional recurrent layers with attention mechanism to capture the spatial and temporal correlations within measurement data. The proposed convolutional recurrent graph autoencoder model is trained and tested on various topologies (from 14, 39, and 118-bus systems). Due to such factors, it yields stable generalized detection performance that is degraded by only 1.6-3.7% in DR against high levels of data poisoning and unseen FDIAs in unobserved topologies. Impact Statement-Artificial Intelligence (AI) systems are used in smart grids to detect cyberattacks. They can automatically detect malicious actions carried out bymalicious entities that falsifymeasurement data within power grids. Themajority of such systems are data-driven and rely on labeled data for model training and testing. However, datasets are not always correctly labeled since malicious entities might be carrying out cyberattacks without being detected, which leads to training on mislabeled datasets. Such actions might degrade the d

关键词： Cybersecurity

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Trade Ease with Machine Learning and AWS

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IEEE Access 2023年 11卷 25893-25905页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research. © 2013 IEEE.

关键词： Deep neural networks

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Car Engine and Chassis Assembly Training Simulation Using VR Technology

Car Engine and Chassis Assembly Training Simulation Using VR...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Noya, Miguel Yehezkiel Asy'ari, Muhammad Zacky Kuswandi, Christian Bernard Sukra, Zener Lie Automotive and Robotics Program Binus Aso School of Engineering Bina Nusantara University Computer Engineering Department Jakarta Indonesia

ISBN: (纸本)9798350381290

The process of educating and learning about the automotive industry is still ongoing, and one of the most effective techniques is the provision of direct practice. Such a system is regarded as effective, albeit with certain downsides. One of them is the restricted area and time required to conduct the exercise, as well as the significant risk of accidents during vehicle engine assembly. Based on these justifications, Vehicle Engine Assembly Simulation with Virtual Reality (VR) Technology is now available as an alternate means of educating students about the automotive industry, particularly car engine assembly. This concept employs a virtual environment that allows users to perceive the automobile world in real time through an immersive experience. VR allows users to directly disassemble automobile engines. This virtual reality application was created utilizing the Oculus Quest 2 platform and the Unity Game Engine. © 2023 IEEE.

关键词： Virtual reality

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Modeling a fuzzy expert system for predicting customers’ behavioral intention for restaurant business through parallel prediction of perceived values and satisfactions

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International Journal of Information Technology (Singapore) 2025年第2期17卷 1007-1014页

作者： Akhtaruzzaman, M. Shahryer, Fahim Halder, Sachitra Tanvin, Jamal Uddin Department of Computer Science and Engineering Military Institute of Science and Technology (MIST) Mirpur Cantonment Dhaka 1216 Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh

This paper focuses on the modeling of a recommendation process for the restaurant industry. It is a challenge job for a restaurant owner to ascertain the intentions of customers to evaluate their services, further improve their profit margin. So, the objective of this study is to design a fuzzy-based prediction model of customers’ intentions for a fine dining experience. To facilitate a comparative analysis and to identify the optimal approach, various membership functions (MF) and de-fuzzification methods are employed along with the analysis of the correlational behavior among various input subsets. Results have shown that CoA method with triangular MF are well enough to implement the model. The fuzzy-correlation analysis presents various levels of membership strengths for a same recommendation score in some cases. Moreover, the evaluation of the proposed model based on a custom dataset shows prominent results with as 0.938. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Customer intension Customer satisfaction FIS Perceived value Recommendation

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Explorative Study on Motor Interference During Synchronous Human and Robot Arm Movements Under Varied Presence of a Robot Head

Explorative Study on Motor Interference During Synchronous H...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Kaya, Mertcan Kuhnlenz, Kolja Coburg University of Applied Sciences and Arts Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg Germany

ISBN: (纸本)9781665481090

This paper investigates the influence of a static robot head on deviations of human hand movements from task direction (motor interference) during simultaneous human and robot arm movements using a collaborative robot arm. Synchronous vertical and horizontal human and robot arm movements are conducted in all combinations (within-subjects factors direction and congruency). The presence of the head (head/ no head) is varied as a between-subjects factor. In this first step, only its motionless presence is deliberately focused at, in order to exclude contributions by eye- or head-movement behavior or facial expressions being difficult variables to generalize from. Participants' hands are tracked optically marker-based and deviations from task-related vertical, respectively, horizontal movements are evaluated. In contrast to expectations, results do not show a significant impact of the robot head on motor interference. Thus, there is evidence, that appearance might not be an activating factor for motor interference in HRI. © 2024 IEEE.

关键词： Robotic arms

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A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Huang, Xiang-Rui Chen, Yi-Cheng Chen, Liang-Bi Hu, Wu-Chih National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798331530839

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection. © 2024 IEEE.

关键词： Aquaculture

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Lighting the Smart Way: A Technological Odyssey in Urban Street Illumination 10

Lighting the Smart Way: A Technological Odyssey in Urban Str...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vinu, M.S. Priya, P Kanaga Thilakaveni, P. Hermina, J. M. Ashforn Prashanthini, K. Misba, M. Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Psg Polytechnic College Department of Computer Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Ramakrishna Engineering College Department of Robotics and Automation Coimbatore India Bethlahem Institute of Engineering Department of Computer Science and Engineering Nagercoil Karungal India

ISBN: (纸本)9798350384369

The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things (IoT) technologies to increase the functionality of street lights. The major components include Light Dependent Resistors (LDR) for automatic light management, an MQ135 sensor for air quality monitoring, and a fog detection system employing a laser module and LDR. These sensors are integrated with the ESP8266, providing cloud integration for real-time data visualization and monitoring. Additionally, solar panels are employed to enhance energy efficiency. The proposed methodology digs into the precise implementation, data analysis, and the potential of this system to alter urban lighting infrastructure © 2024 IEEE.

关键词： Air quality

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Energy Efficiency in Sustainable Software Development: Clean Code Approaches 4

Energy Efficiency in Sustainable Software Development: Clean...

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4th International Conference on Electrical engineering and computer science, ICECOS 2024

作者： Inayatullah Widhiarso, Wijang Stiawan, Deris Universitas Multi Data Palembang Faculty of Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Multi Data Palembang Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Palembang Indonesia

ISBN: (纸本)9798350368253

The use of technology and information devices contributes to global warming. This issue has also become a concern for UN institutions, as stated in international environmental agreements, which aim to stabilize greenhouse gas emissions in the atmosphere at a level that prevents anthropogenic and dangerous disturbances to the environmental system. This research uses a line of code derived from the tanjak dataset, a student project from 2003 to 2020, numbering 500. This research aims to apply energy consumption efficiency to reduce CO2 emissions produced using computer devices, especially in developing software. The approach used in this research is clean code, which can provide benefits in the form of a line of code that is easy to read, understand, test, and maintain so that it can create savings in energy use. In the test, the line of code used was 1,072,089, producing an energy of 82,152.18 Joules, minus the line of code comments, which was 76,750, producing an energy of 2,971.25 Joules. Initial research results show that the clean code approach can be used to save energy usage, with the result of the line of code used being 995,339 and producing 79,180 Joules of energy, so it can affect reducing CO2 emissions when implementing software development. © 2024 IEEE.

关键词： Greenhouse gas emissions

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