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International Conference on computer technology and Development

作者： Petch-Urai, Oraya Sombunsukho, Suwanna Department of Computer and Information Technology King Mongkut' s University of Technology Thonburi Bangkok Thailand

ISBN: (纸本)9781424488438

This study aims to develop the supply management information system of department of computer and technology. The system associates with all parcel-related items in order to reduce the processing steps and increase the effectiveness of parcel management. To accomplish this study, the logical procedure has been utilized. We firstly survey and collect data, identity objectives and scopes of this study and finally develop the system. Next, the system improvement is done. Finally, we have the system's quality evaluated by experts. The result shows that the quality of system evaluated by experts is in the very good level with the score of 4.53 on average. The experts' satisfaction toward the system rated as excellence with the score of 4.32 on average. Thus this developed system can be used in practice with high quality rated. © 2010 IEEE.

关键词： Information management

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A development of supply management information system department of computer and Information technology

A development of supply management information system Depart...

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International Conference on computer technology and Development (ICCTD)

作者： Oraya Petch-urai Suwanna Sombunsukho Department of Computer and Information Technology King Mongkut''s University of Technology Bangkok Thailand

This study aims to develop the supply management information system of department of computer and technology. The system associates with all parcel-related items in order to reduce the processing steps and increase the effectiveness of parcel management. To accomplish this study, the logical procedure has been utilized. We firstly survey and collect data, identify objectives and scopes of this study and finally develop the system. Next, the system improvement is done. Finally, we have the system's quality evaluated by experts. The result shows that the quality of system evaluated by experts is in the very good level with the score of 4.53 on average. The experts' satisfaction toward the system rated as excellence with the score of 4.32 on average. Thus this developed system can be used in practice with high quality rated.

关键词： computers

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A recover-then-discriminate framework for robust anomaly detection

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Science China(Information Sciences) 2025年第4期68卷 300-318页

作者： Peng XING Dong ZHANG Jinhui TANG Zechao LI School of Computer Science and Engineering Nanjing University of Science and Technology Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology

Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.

关键词： recovery network HOG prompt discriminative network self-correlation loss anomaly detection

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The development information system student care center project the case of computer and information technology department

The development information system student care center proje...

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作者： Sombunsukho, Suwanna Petchurai, Oraya Department of Computer and Information Technology King Mongkut's University of Technology Thonburi Bangkok 10140 Thailand

ISBN: (纸本)9789604742738

This project aims to develop an information system to student care center of computer and Information technology, University of technology Thonburi. The procedure of development followed theory of System Development Life Cycle. The evaluation of the project are done by specialists in information systems development and the success of the project are assess by the users which rank by the satisfaction of an account.

关键词： Students

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Labeling-based centrality approaches for identifying critical edges on temporal graphs

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Frontiers of computer Science 2025年第2期19卷 89-104页

作者： Tianming ZHANG Jie ZHAO Cibo YU Lu CHEN Yunjun GAO Bin CAO Jing FAN Ge YU School of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310023China School of Computer Science and Technology Zhejiang UniversityHangzhou 310013China Department of Computer Science Northeastern UniversityShenyang 110819China

Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in *** general graphs,edge closeness centrality indicates the importance of edges by the shortest distances from the edge to all the other *** betweenness centrality ranks which edges are significant based on the fraction of all-pairs shortest paths that pass through the ***,extensive research efforts go into centrality computation over general graphs that omit time ***,numerous real-world networks are modeled as temporal graphs,where the nodes are related to each other at different time *** temporal property is important and should not be neglected because it guides the flow of information in the *** state of affairs motivates the paper’s study of edge centrality computation methods on temporal *** introduce the concepts of the label,and label dominance relation,and then propose multi-thread parallel labeling-based methods on OpenMP to efficiently compute edge closeness and betweenness centralities *** types of optimal temporal *** edge closeness centrality computation,a time segmentation strategy and two observations are presented to aggregate some related temporal edges for uniform *** edge betweenness centrality computation,to improve efficiency,temporal edge dependency formulas,a labeling-based forward-backward scanning strategy,and a compression-based optimization method are further proposed to iteratively accumulate centrality *** experiments using 13 real temporal graphs are conducted to provide detailed insights into the efficiency and effectiveness of the proposed *** with state-ofthe-art methods,labeling-based methods are capable of up to two orders of magnitude speedup.

关键词： temporal graph closeness centrality between-ness centrality temporal path

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Fake news detection on social media using Adaptive Optimization based Deep Learning Approach

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Multimedia Tools and Applications 2025年第9期84卷 6091-6111页

作者： Steni Mol, T.S. Sreeja, P.S. Department of Computer Science Hindustan Institute of Technology and Science Chennai India Department of Computer Applications Vellore Institute of Technology Chennai India

Various content-sharing platforms and social media are developed in recent times so that it is highly possible to spread fake news and misinformation. This kind of news may cause chaos and panic among people. The automatic and accurate detection of fake news is a complex task. To consider this issue, an automatic and effective fake news detection approach is implemented. The proposed methodology is named Adaptive Adam Adadelta Optimizer-based Deep Bi-directional Long Short Term Memory (Bi-LSTM), in which Adaptive Adam AO is the merging of Adadelta Optimizer (AO), Adaptive concept, and Adam Optimization. The Bidirectional Encoder Representations from Transformers (BERT) is used for the tokenization and then, feature mining is done. Also, the dimensionality of the features is reduced by Singular Value Decomposition (SVD) and Moving Principal Component Analysis (MPCA). From them, the top-N feature selection is performed using Joint Quantum Entropy. Finally, for fake news detection, Adaptive Adam AO trained Deep Bi-LSTM is used, which obtained maximum specificity, sensitivity, and accuracy of 0.928, 0.945, and 0.938. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Principal component analysis

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Co-op and internship opportunities for the department of electronics, computer and information technology

Co-op and internship opportunities for the department of ele...

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114th Annual ASEE Conference and Exposition, 2007

作者： Milliken, Barbara Fatehi, Fereshteh NC A and T State University United States Department of Electronics Computer and Information Technology North Carolina A and T State University Greensboro NC United States Electronics Computer and Information Technology Department North Carolina A and T State University United States

No abstract available

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Modern Machine Learning Solution for Electricity Consumption Management in Smart Buildings

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IEEE Engineering Management Review 2025年第1期53卷 54-62页

作者： Gautam, Sandeep Kumar Shrivastava, Vinayak Udmale, Sandeep S. Singh, Amit Kumar Singh, Sanjay Kumar Department of Computer Science and Engineering Varanasi221005 India Department of Computer Engineering and Information Technology Mumbai400019 India National Institute of Technology Department of Computer Science and Engineering Patna800005 India

Effective management of electricity consumption (EC) in smart buildings (SBs) is crucial for optimizing operational efficiency, cost savings, and ensuring sustainable resource utilization. Accurate EC prediction enables proactive decision-making, ensuring that resources are allocated efficiently to meet actual demand levels while maintaining occupant comfort. Population growth, building expansion, and technology usage swiftly escalate electricity demand, thus necessitating economical EC management strategies and assist consumers to better understand and strategically plan their EC. To address these challenges, this article proposes a novel approach based on a hybrid prediction model combining temporal convolutional networks (TCNs) and gated recurrent units (GRU). This approach capitalizes on the strengths of both TCN and GRU. TCN is adept at efficiently identifying diverse patterns, particularly within the complex working environments of SBs, by effectively capturing high- and low-frequency information. Subsequently, GRU is leveraged to address the long-term dependencies within the data, enhancing the accuracy of EC prediction. In this article, the results demonstrate the effectiveness of the proposed hybrid model, outperforming competitive methods with an impressive mean absolute error score. This underscores the potential of this approach to improve energy management practices significantly within SB environments, ultimately enhancing both operational efficiency and occupant satisfaction. © 1973-2011 IEEE.

关键词： Forecasting

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Using DTL-MD with GANs and ResNet for Malicious Code Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 63-78页

作者： Li, Yiming Xie, Tao Mei, Dongdong Department of Computer Science and Technology School of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan753000 China

This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature extraction process, the weighted sum method of GIST and LBP features is used to combine the advantages of the two features. Online transfer learning is used to reduce the data distribution difference between the target domain and the source domain. The model uses ResNet50V2 as the backbone network and combines SimAM to enhance the feature extraction and representation capabilities. In addition, in order to further improve the robustness of detection, GAN is used to generate malicious code variants and expand the training data set. In the experiment, the public CICIDS 2017 data set is used for model training and testing. The performance test results show that when the threshold is 0.7, the accuracy of DTL-MD is 95.8% and the F1 score is 0.93. In a performance test involving 30,000 samples, the throughput of the DTL-MD model under Trojans, viruses, worms, and adware is 11, 12, 11, and 12 tasks/s, respectively, and the inference time is 211, 225, 239, and 234 samples/s, respectively. Compared with GAN, DTL-MD increases the throughput by about 10% and the inference speed by about 15%. The research aims to provide new ideas for improving the intelligence and automation level of malicious code detection technology, which has certain application value and practical significance. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Feature Selection

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A lightweight and efficient model for botnet detection in IoT using stacked ensemble learning

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Soft Computing 2025年第1期29卷 89-101页

作者： Esmaeilyfard, Rasool Shoaei, Zohre Javidan, Reza Department of Computer and Information Technology Engineering Shiraz University of Technology Shiraz Iran

Efficient botnet detection is of great security importance and has been the focus of researchers in recent years. Botnet detection is also a difficult task due to the difficulty in distinguishing it from normal traffic. At the same time, detecting these botnets require a lot of computation resources using traditional methods and this limitation makes it even more difficult to detect them on Internet of Things (IoT) devices. Considering the massive IoT data, an efficient and lightweight approach for detecting and predicting IoT botnet attacks is required. In this paper, multiple lightweight machine learning methods including a deep Multilayer Perceptron (MLP) method and a Random Forest (RF) method are integrated into a stacked ensemble learning model to detect botnet attacks in IoT devices. This integration is based on applying lasso regression on features and utilizing a logistic regression in ensemble learning, which leads to increasing the accuracy of botnet detection and reducing its computational complexity. The performance evaluation of the proposed model is examined from two perspectives: accuracy and lightweight characteristics;and for this purpose, a real-world UNSW (BoT-IoT) dataset is used. A comparative study with competitive neural network methods demonstrates that our approach delivers a better outcome. Experimental results reveal that this method has a higher efficiency in all metrics than competing methods including accuracy, precision, recall, and F1 score with values of 99.3%, 99.2%, 99%, and 99.1%, respectively. Besides, the results showed that the proposed method requires at least 36% less CPU and 38% less memory compared to the competing methods, which makes the proposed method to be suitable for IoT devises with limited resources. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Bot (Internet)

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