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5th International Conference on Electrical, Control and computer engineering, InECCE 2019

作者： Zainal, Nurul Asyikin Zamli, Kamal Z. Din, Fakhrud Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Gambang KuantanPahang26300 Malaysia Department of Computer Science & IT University of Malakand Khyber Pakhtunkhwa Pakistan

ISBN: (纸本)9789811523168

To date, there are much increasing trends on adopting parameter free meta-heuristic algorithms for solving general optimization problems. With parameter free algorithms, there are no parameter controls for tuning. As such, the adoption of parameter free meta-heuristic algorithms is often straightforward. On the negative note, exploration (i.e. roaming the search space thoroughly) and exploitation (i.e. manipulating the current known best neighbor) are pre-set. As the search spaces are problem dependent, any pre-set exploration and exploitation can lead to entrapment in local optima. In this paper, we investigate the use of Lévy flight to enhance the exploration of a parameter free meta-heuristic algorithm, called Modified Symbiotic Organism Search Algorithm (MSOS), via its population initialization. Our experimentations involving the software module clustering problems have been encouraging, as MSOS gives competitive results against existing selected parameter free meta-heuristic algorithms. For all the given module clustering problems, MSOS generates overall best mean results. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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engineering Assessment in the Age of Generative Artificial Intelligence: A Critical Analysis

Engineering Assessment in the Age of Generative Artificial I...

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World engineering Education Forum - Global engineering Deans Council (WEEF-GEDC)

作者： Scott Daniel Sasha Nikolic Carolyn Sandison Rezwanul Haque Sarah Grundy Marina Belkina Sarah Lyden Ghulam M. Hassan Peter Neal School of Professional Practice & Leadership University of Technology Sydney Sydney Australia Faculty of Engineering and Information Sciences University of Wollongong Wollongong Australia School of Science Technology and Engineering University of the Sunshine Coast Sippy Downs Australia School of Chemical Engineering University of New South Wales Sydney Australia College Western Sydney University Sydney Australia School of Engineering University of Tasmania Hobart Australia School of Computer Science & Software Engineering University of Western Australia Perth Australia School of Chemical Engineering University of New South Wales Australia Sydney Australia

ISBN: (数字)9798331519803

ISBN: (纸本)9798331519810

Generative Artificial Intelligence (GenAI) has significantly impacted higher education, offering numerous benefits alongside notable risks, particularly concerning academic integrity. This paper reviews the assessment landscape in the context of GenAI and critically analyses a key GenAI assessment framework against the latest assessment integrity research in engineering. Our analysis indicates that this framework presents a valuable opportunity for advancing engineering education.

关键词： Reviews Generative AI Learning (artificial intelligence) engineering education

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Mohammad Aljaidi Ayoub Alsarhan Dimah Al-Fraihat Ahmed Al-Arjan Bashar Igried Subhieh M. El-Salhi Muhammad Khalid Qais Al-Na'amneh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Technology The Hashemite University Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince AL-Hussein bin Abdullah II of Information Technology The Hashemite University Jordan Department of Computer Science and Applications The Hashemite University Zarqa Jordan The Hashemite University Zarqa Jordan Department of Computer Sciences and Technology University of Hull Kingston upon Hull UK Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing.

关键词： Adaptation models Accuracy Phishing Identity theft Refining Machine learning Passwords

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Transformers in medical image analysis

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Intelligent Medicine 2023年第1期3卷 59-78页

作者： Kelei He Chen Gan Zhuoyuan Li Islem Rekik Zihao Yin Wen Ji Yang Gao Qian Wang Junfeng Zhang Dinggang Shen Medical School of Nanjing University NanjingJiangsu 210093China National Institute of Healthcare Data Science at Nanjing University NanjingJiangsu 210093China BASIRA Laboratory Faculty of Computer and Informatics EngineeringIstanbul Technical UniversityIstanbulTurkey School of Science and Engineering ComputingUniversity of DundeeUK State Key Laboratory for Novel Software Technology Nanjing UniversityNanjingJiangsu 210093China School of Biomedical Engineering ShanghaiTech UniversityShanghai 201210China Department of Research and Development Shanghai United Imaging Intelligence Co.Ltd.Shanghai 200030China Shanghai Clinical Research and Trial Center Shanghai 201703China

Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer *** the field of medical image analysis,transformers have also been successfully used in to full-stack clinical applications,including image synthesis/reconstruction,registration,segmentation,detection,and *** paper aimed to promote awareness of the applications of transformers in medical image ***,we first provided an overview of the core concepts of the attention mechanism built into transformers and other basic ***,we reviewed various transformer architectures tailored for medical image applications and discuss their *** this review,we investigated key challenges including the use of transformers in different learning paradigms,improving model efficiency,and coupling with other *** hope this review would provide a comprehensive picture of transformers to readers with an interest in medical image analysis.

关键词： Transformer Medical image analysis Deep learning Diagnosis Registration Segmentation Image synthesis Multi-task learning Multi-modal learning Weakly-supervised learning

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Accurate Water Quality Prediction with Attention-Based Bidirectional Lstm and Encoder-Decoder

SSRN

引用

SSRN 2022年

作者： Bi, Jing Chen, Zexian Yuan, Haitao Zhang, Jia School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Computer Science Lyle School of Engineering Southern Methodist University DallasTX75205 United States

Accurate prediction of water quality indicators can effectively prevent sudden water pollution events, and control pollution diffusion. Neural networks, e.g., Long Short-Term Memory (LSTM) and encoder-decoder network, have been widely used to predict time series data. However, as the water quality data increases, it becomes unstable and highly nonlinear. Accurate prediction of water quality becomes a big challenge. This work proposes a hybrid prediction method called VBAED to predict the water quality time series. VBAED combines Variational mode decomposition (VMD), Bidirectional input Attention mechanism, an Encoder with bidirectional LSTM (BiLSTM), and a Decoder with bidirectional temporal attention mechanism and BiLSTM. Specifically, VBAED first adopts VMD to decompose historical data of a predicted factor, and decomposed results are used as the input along with other features. Then, a bidirectional input attention mechanism is adopted to add weights to input features from both directions. VBAED adopts BiLSTM as an encoder to extract hidden features from input features. Finally, the predicted result is obtained by a BiLSTM decoder with a bidirectional temporal attention mechanism. Real-life data-based experiments demonstrate that VBAED obtains the best prediction results compared with other widely used methods. © 2022, The Authors. All rights reserved.

关键词： Variational mode decomposition

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Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting

arXiv

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arXiv 2022年

作者： Mei, Guofeng Yu, Litao Wu, Qiang Zhang, Jian Huang, Xiaoshui Bennamoun, Mohammed The Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia The Image X Institute of Faculty of Medicine and Health University of Sydney SydneyNSW2015 Australia The Department of Computer Science and Software Engineering The University of Western Australia WA6009 Australia

Learning from unlabeled or partially labeled data to alleviate human labeling remains a challenging research topic in 3D modeling. Along this line, unsupervised representation learning is a promising direction to auto-extract features without human intervention. This paper proposes a general unsupervised approach, named ConClu, to perform the learning of point-wise and global features by jointly leveraging point-level clustering and instance-level contrasting. Specifically, for one thing, we design an Expectation-Maximization (EM) like soft clustering algorithm that provides local supervision to extract discriminating local features based on optimal transport. We show that this criterion extends standard cross-entropy minimization to an optimal transport problem, which we solve efficiently using a fast variant of the Sinkhorn-Knopp algorithm. For another, we provide an instance-level contrasting method to learn the global geometry, which is formulated by maximizing the similarity between two augmentations of one point cloud. Experimental evaluations on downstream applications such as 3D object classification and semantic segmentation demonstrate the effectiveness of our framework and show that it can outperform state-of-the-art techniques. © 2022, CC BY-NC-ND.

关键词： 3D modeling

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Multi-Indicator Water Quality Prediction with Attention-Assisted Bidirectional Lstm and Encoder-Decoder

SSRN

引用

SSRN 2022年

作者： Bi, Jing Zhang, Luyao Yuan, Haitao Zhang, Jia School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Computer Science Lyle School of Engineering Southern Methodist University DallasTX75205 United States

Accurate and real-time prediction of water quality not only helps to assess the environmental quality of water, but also effectively prevents and controls water quality emergencies. In recent years, neural networks represented by Bidirectional Long Short-Term Memory (BiLSTM) and Encoder-Decoder (ED) frameworks have been shown to be suitable for the prediction of time series data. However, traditional statistical methods cannot capture nonlinear characteristics of the water quality, and deep learning models often suffer from gradient disappearance and gradient explosion problems. This work proposes a hybrid water quality prediction method called SVABEG, which combines a Savitzky-Golay (SG) filter, Variational Mode Decomposition (VMD), an Attention mechanism, BiLSTM, an ED structure, and a hybrid algorithm called Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO). SVABEG first adopts the SG filter and VMD to remove noise and deal with nonlinear features in the original time series, respectively. Then, SVABEG combines BiLSTM, the ED structure, and the attention mechanism to capture bi-directional long-term correlations, realize dimensionality reduction and extract key information, respectively. Furthermore, SVABEG adopts GSPSO to optimize its hyperparameters. Experimental results with real-life datasets demonstrate that the proposed SVABEG outperforms current state-of-the-art algorithms in terms of prediction accuracy. © 2022, The Authors. All rights reserved.

关键词： Water quality

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Machine Learning Detects Anomalies in OPS-SAT Telemetry 23rd

Machine Learning Detects Anomalies in OPS-SAT Telemetry

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23rd International Conference on Computational science, ICCS 2023

作者： Ruszczak, Bogdan Kotowski, Krzysztof Andrzejewski, Jacek Musial, Alicja Evans, David Zelenevskiy, Vladimir Bammens, Sam Laurinovics, Rodrigo Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka 16 Gliwice44-100 Poland KP Labs Konarskiego 18C Gliwice44-100 Poland European Space Agency/ESOC Robert-Bosch-Str. 5 Darmstadt64293 Germany Telespazio Germany GmbH Europaplatz 5 Darmstadt64293 Germany IrbGS ltd "Viraki" Ance Parish IrbeneLV-3601 Latvia

ISBN: (纸本)9783031359941

Detecting anomalies in satellite telemetry data is pivotal in ensuring its safe operations. Although there exist various data-driven techniques for the task of determining abnormal parts of the signal, they are virtually never validated over real telemetries. Analyzing such data is challenging due to its intrinsic characteristics, as telemetry may be noisy and affected by incorrect acquisition, resulting in missing parts of the signal. In this paper, we tackle this issue and propose a machine learning approach for detecting anomalies in single-channel satellite telemetry. To validate its capabilities in a practical scenario, we build a dataset capturing the nominal and anomalous telemetry data captured on board OPS-SAT—a nanosatellite launched and operated by the European Space Agency. Our extensive experimental study showed that the proposed algorithm offers high-quality anomaly detection in real-life satellite telemetry, reaching 98.4% accuracy over the unseen test set. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Infrastructure Sharing and Quality of Service for Telecommunication Companies in Yemen

Infrastructure Sharing and Quality of Service for Telecommun...

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2021 International Congress of Advanced Technology and engineering, ICOTEN 2021

作者： Saif, Abdu Alashwal, Ali Abdullah, Qazwan Alsamhi, S.H. Ameen, Ali Salh, Adeeb University of Malaya Department of Electrical Engineering Kuala Lumpur Malaysia Western Sydney University School of Engineering Design and Built Environment Australia Sana A University Faculty of Electrical and Electronic Engineering Yemen Ireland Ibb University Software Research Institute Athlone Institute of Technology Athlone Ibb Yemen Lincoln University College Faculty of Computer Science Kuala Lumpur Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Johor Batu Pahat86400 Malaysia

ISBN: (纸本)9781665412247

The telecommunication Quality of Service (QoS) is essential to run communication networks efficiently and smoothly. Effective costs and time management represent the most critical challenges globally to improve QoS. Infrastructure sharing in telecommunication companies (ISTC) refers to providing and delivering a high-level QoS to customers efficiently. QoS offered by telecommunication companies is not satisfactory due to the high expense of upgrading to the new technology. This paper focuses on mobile network infrastructure sharing among telecommunication companies in Yemen and improving QoS. A qualitative approach using semi-structured interviews was used to understand more about the role of ISTC in enhancing QoS in these companies. The study found that telecommunication companies in Yemen apply infrastructure sharing at a low level due to the absence of law and security policy for manage network sharing. © 2021 IEEE.

关键词： Quality of service

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Comparison of Persian Handwritten Digit Recognition in Three Color Modalities Using Deep Neural Networks 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Zohrevand, Abbas Sattari, Mahdi Sadri, Javad Imani, Zahra Suen, Ching Y. Djeddi, Chawki Department of Computer Engineering Kosar University of Bojnord Bojnord Iran Department of Computer Engineering University of Tabriz Tabriz Iran Department of Computer Science and Software Engineering Faculty of Engineering and Computer Science Concordia University MontrealQC Canada Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran Department of Mathematics and Computer Science Larbi Tebessi University Tebessa Algeria

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

Most of the methods on handwritten digit recognition in the literature are focused and evaluated on black and white image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), we investigate to see whether color modalities of handwritten digits affect their recognition rate? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten digit databases that have all three color modalities of handwritten digits. To answer this question, we select 13,330 isolated digits from novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected dataset are divided into training, validation, and testing sets. Afterward, similar conventional CNN models are trained with the samples of our training set. While the experimental results on the testing set show that CNN on the black and white digit images has a better performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modality. Also, comparisons of training times in three color modalities show that recognition of handwritten digits in black and white using CNN is much more efficient. © 2020, Springer Nature Switzerland AG.

关键词： Color

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