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8th International Conference on electrical, Electronics and Information engineering, ICEEIE 2023

作者： Akbar, Muhammad Iqbal Smaragdina, Azhar Ahmad Ningrum, Gres Dyah Kusuma Fritama, Muhammad Anandha Pratama, Wahyu Setyo Universitas Negeri Malang Electrical and Informatics Engineering Department Malang Indonesia

ISBN: (纸本)9798350340501

In this study, Tech Zone Plaza conducted a testing phase using the system usability scale method, which is used with the aim of measuring how good a user experience is from software. The results of this test is used as a reference for improving and adjusting the user interface based on the result and input from the test. Testing Tech Zone Plaza e-commerce uses System Usability Scale (SUS) Methods. There are several steps that must be followed in conducting a system usability scale. The steps are divided into 3 parts, Preparation, Execution, and Analysis The overall average SUS score is 74, on a scale of 0 to 100, indicating a moderately positive perception of the system's usability. According to the findings of this study, SUS ratings and individual responder feedback provide essential information for evaluating the system's usability. They serve as critical indications of strengths and shortcomings, highlighting areas for improvement and informing decision-making to improve the user experience. A full knowledge of the system's usability may be obtained by supplementing quantitative data from SUS scores with qualitative input from users, resulting in a system that better matches user expectations and requirements. © 2023 IEEE.

关键词： User interfaces

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A Comparative Study of Machine Learning Algorithms for Graduation Prediction in electrical engineering and informatics department

A Comparative Study of Machine Learning Algorithms for Gradu...

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International Conference on informatics and Computing (ICIC)

作者： Azhar Ahmad Smaragdina Didik Dwi Prasetya Wahyu Nur Hidayat Gulsun Kurubacak Cakir Utomo Pujianto Putrinda Inayatul Maula Department of Electrical Engineering and Informatics State University of Malang Malang Indonesia Distance Education Department Anadolu University Eskisehir Turkiye Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Accurate prediction of students’ graduation time is a significant challenge for academic institutions, especially in the context of optimizing educational outcomes and resource allocation. However, there is a research gap in identifying which machine learning algorithms are best suited for this task, particularly in the electrical and informatics engineering department. This study addresses this gap by evaluating the performance of various machine learning algorithms in predicting students’ graduation time. Several algorithms, including Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), and Naive Bayes, were applied to a dataset consisting of academic and demographic records of students from a university in Indonesia. The evaluation used performance metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that LR, KNN, DT, RF, and SVM exhibited comparable accuracy rates of 74%, with a weighted average F1-score of 0.85, indicating these algorithms are effective in classifying data. In contrast, Naive Bayes, while showing superior speed with an execution time of 0.018322 seconds, achieved lower performance with an accuracy of only 39% and a weighted average F1-score of 0.44. These findings suggest that selecting an algorithm should balance the trade-off between accuracy and time efficiency. For scenarios where both are important, LR and DT are optimal choices, while Naive Bayes may be suitable for faster processing at the expense of accuracy.

关键词： Support vector machines Radio frequency Accuracy Machine learning algorithms Nearest neighbor methods Prediction algorithms Bayes methods Classification algorithms Resource management informatics

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Measuring the Level of e-Commerce Usability in the electrical and informatics engineering department Using the System Usability Scale

Measuring the Level of e-Commerce Usability in the Electrica...

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International Conference on electrical, Electronics and Information engineering (ICEEIE)

作者： Muhammad Iqbal Akbar Azhar Ahmad Smaragdina Gres Dyah Kusuma Ningrum Muhammad Anandha Fritama Wahyu Setyo Pratama Electrical and Informatics Engineering Department Universitas Negeri Malang Malang Indonesia

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Evaluate Recent Numerical Methods for Long-Term Simulation to Study the Effect of Different Shapes of Thermal Bridges in Walls

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Defect and Diffusion Forum 2024年 436卷 77-90页

作者： Omle, Issa Kovács, Endre Department of Fluid and Heat Engineering Faculty of Mechanical Engineering and Informatics University of Miskolc Miskolc3515 Hungary Institute of Physics and Electrical Engineering Faculty of Mechanical Engineering and Informatics University of Miskolc Miskolc3515 Hungary

According to previous studies, the most effective, stable, and explicit numerical methods to deal with problems of heat transfer in building walls are the two recently published approaches, which are the modified Dufort-Frankel and leapfrog-hopscotch techniques, which are used in this study to make transient and long-term simulations (three months of the winter season) of 2-D space systems that enable us to execute these simulations with relatively short computational times to evaluate the two most effective versions of these methods. Our solution to a real-world engineering challenge involves investigating thermal bridges of different forms inside multilayer walls of buildings exposed to environmental factors specific to Hungary's climate, such as the outside temperature and sun radiation, to improve energy efficiency. The distributions of temperatures and the total heat loss (across the walls) are calculated for all cases (three layers without a thermal bridge three layers with thermal bridges in straight, bent, and L-shaped shapes). © 2024 Trans Tech Publications Ltd, All Rights Reserved.

关键词： Walls (structural partitions)

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Designing a pre-cooling model for air conditioning to avoid an electricity price spike for different building characteristics

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Journal of King Saud University - engineering Sciences 2023年第7期35卷 485-494页

作者： Marwan, Marwan Marwan, Muhammad Dihyah Electrical Engineering Department Polytechnic State of Ujung Pandang Makassar Indonesia Department of Electrical Engineering and Informatics The University of Gadjah Mada Yogyakarta Indonesia

Air conditioning (AC) is a key driver to produce electricity consumption, particularly in residential buildings. In Indonesia, the electricity bill during pandemic covid-19 has increased to a high level on hot days. This is because during these periods many consumers applied their AC for a long time at the same time. It is therefore essential to develop an innovative model to control the electricity peak price and keep a comfortable room during hot days. Many previous studies have confirmed that applied evaporative cooling or other technologies can minimize the whole consumption and cost. This research aims to design a pre-cooling model (PM) for AC to avoid high electricity prices at midday during hot days. To achieve this goal a mathematical model was developed to define the energy cost (EC) and total cost (TC) for AC during the hot season. Under numerical optimization, the EC can be minimized when a spike may occur due to increased temperature at midday for a half hour (0.5 h) and one hour (1 h) spike cases. To justify this model, a PM was applied for two different building characteristics. As a result, the benefits of a PM if a spike occurs for 0.5 h and 1 h were achieved, such as IDR 10,061 (30.43 %) and IDR 10,693 (24.44 %) for building-1;IDR 12,071 (37.40 %) and IDR 14,844 (34.97 %) for building-2. In addition, the TC considering the probability of a spike of 0.5 h and 1 h if the spike occurs every five minutes were IDR 45,247 and IDR 38,287 for building-1 and building-2, respectively. © 2021 King Saud University

关键词： Air conditioning

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Practical autoencoder based anomaly detection by using vector reconstruction error

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Cybersecurity 2023年第2期6卷 1-13页

作者： Hasan Torabi Seyedeh Leili Mirtaheri Sergio Greco Department of Electrical and Computer Engineering Faculty of EngineeringKharazmi UniversityTehranIran Department of Informatics ModelingElectronics and System EngineeringUniversity of CalabriaArcavacataItaly.

Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be *** are several techniques for dealing with network *** this end,network anomaly detection systems are widely used as an effective countermeasure against network *** anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of *** anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning *** paper presents an efficient model based on autoencoders for anomaly detection in cloud computing *** autoencoder learns a basic representation of the normal data and its reconstruction with minimum ***,the reconstruction error is used as an anomaly or classification *** addition,to detecting anomaly data from normal data,the classification of anomaly types has also been *** have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction *** the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a *** enables our model to use the reconstruction error of every input feature as an anomaly or classification *** further propose a multi-class classification structure to classify the *** use the CIDDS-001 dataset as a commonly accepted dataset in the *** evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score

关键词： Cloud Practical Anomaly detection Autoencoder Reconstruction error Machine learning

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Double-crossed Bowtie Antenna-coupled Carbon Nanotube Microbolometer with Meander Structures at 1THz

Double-crossed Bowtie Antenna-coupled Carbon Nanotube Microb...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Hasiholan, Matthew Gabriel Pangesti Aji, Arie Apriono, Catur Rahardjo, Eko Tjipto Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Indonesia Department of Electrical Engineering Faculty of Engineering and Informatics Universitas Dian Nusantara Indonesia

ISBN: (纸本)9798350364774

A high reception of THz waves is important in the design of an antenna-coupled microbolometer, which is indicated by the ability to detect THz waves effectively from all directions. In this study, we implement the meander line structure in a carbon nanotube (CNT) based bolometer coupled with a double-crossed bowtie antenna. The structure is designed on a SiO2 substrate with an additional metal reflector at the bottom. The simulation results reveal the potential high responsivity of 281.5 kV/W and could effectively minimize the NEP down to 0.0145 pW/Hz1/2. These results demonstrate the prospect of a meander line bolometer structure to enhance THz wave absorption with great potential in the application of real-time imaging. © 2024 IEEE.

关键词： Terahertz waves

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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Motion Detection Methods Applied on RGB-D Images for Vehicle Classification on the Edge Computing

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IEEE Internet of Things Journal 2025年第12期12卷 19631-19645页

作者： Micko, Kristian Papcun, Peter Technical University in Košice Faculty of Electrical Engineering and Informatics Department of Cybernetics and Artificial Intelligence Košice Slovakia

Intelligent Transportation Systems rely on data processing methods in real-time conditions. There are many methods to process the data. However, the progress of hardware computational power forces us to reassess the effectiveness of some methods. The first idea for discussion is whether cloud or fog computing is necessary to process Computer Vision methods in real-time conditions. The second idea for discussion is whether a depth map gained from the 2D image monocular estimation is suitable as an extra feature to process. Another scientific question is the categorization of the vehicles based on 3D data by extracted volume or height features. This approach could be useful for ToF camera output. This study simulates the output of a ToF camera with a resolution higher than VGA via the monocular depth map estimation with the Convolutional Neural Network model MiDaS. This article proposes the computational architectures for data processing between SBCs with various computational power. Data processing includes obtaining, feature extraction, and classification via various methods. These methods are image loading, background subtraction, shadow removal, monocular depth estimation, pointcloud calculation, 3D convex hull, and volume thresholding. Volume thresholding is a reliable approach for categorization into light and heavy vehicles. Background subtractors connected with K-Means are reliable for vehicle detection without shadows. © 2014 IEEE.

关键词： Fog computing

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Muli-Quay Combined Berth and Quay Crane Allocation Using the Cuckoo Search Algorithm 10

Muli-Quay Combined Berth and Quay Crane Allocation Using the...

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10th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2024

作者： Aslam, Sheraz Michaelides, Michalis P. Herodotou, Herodotos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Cyprus

ISBN: (纸本)9789897587030

This study investigates the combined berth allocation problem (BAP) and quay crane allocation problem (QCAP) while considering a multi-quay setting. First, a mixed integer linear programming mathematical model is developed based on various constraints and real port settings. Then, the multi-quay combined BAP and QCAP is solved using both the exact method and a metaheuristic optimization method, namely, the cuckoo search algorithm (CSA). This analysis pertains to a one-week planning scenario, utilizing data from a real port. The results of the comparative analysis show that the proposed CSA can provide a near-optimal solution ( Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Integer programming

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