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Irish Signals and systems Conference (ISSC)

作者： Peter Hassard Dermot Kerr School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland

ISBN: (数字)9798350352986

ISBN: (纸本)9798350352993

This paper demonstrates how supervised machine learning algorithms can be leveraged to predict match outcomes from 2015/16 football season for matches in the top professional leagues of 5 countries (Spain - La Liga, England - Premier League, Italy - Serie A, Germany - 1. Bundesliga and France - Ligue 1). In the summer of 2023 StatsBomb released this free data; once important features on game play activity are generated and processed. A clear correlation between the match outcome and the location of a team's events on the pitch will be shown, moreover the ability to predict a match outcome based on the team's previous matches, this was done using their mean values for relevant features. For optimal prediction levels, the machine learning models in this paper had their hyperparameters selectively adjusted.

关键词： Machine learning algorithms Correlation Machine learning Games Predictive models Sports

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Web Application Vulnerabilities

Web Application Vulnerabilities

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International Symposium on Computer Science and intelligent Controls (ISCSIC)

作者： Stephen McNally Kevin Curran School of Computing Engineering & Intelligent Systems Ulster University Londonderry UK

ISBN: (数字)9798350380286

ISBN: (纸本)9798350380293

In the digital age web applications have become a staple of our daily lives due to their convenience and efficiency. It is our main form of communication, transactions and how we access information. With the abilities of web applications, we have become reliant on them for our day-to-day tasks. Although this is not inherently bad, it has become clear that web application security is a paramount tool for online use, particularly among businesses and organisations. Web application security is the practice of protecting websites, applications, and APIs from attacks. This form of security aims to ensure that web applications function smoothly and remain unhindered by cyber vandalism, data theft, and other malicious attacks. This report will explore the top three vulnerabilities highlighted by the most recent OWASP top ten vulnerabilities report, last updated in 2021. These vulnerabilities include broken access control, cryptography failures and injection attacks. Moreover, each section will highlight how these security techniques defend, can be exploited and what measures developers can take to better defend web applications. The paper addresses the future of web application security in the context of emerging technologies such as artificial intelligence (AI) and machine learning. It speculates on how these technologies might be integrated into security strategies to enhance threat detection and response capabilities. The concept of DevSecOps is introduced as an evolutionary approach to integrate security into the development process of web applications, emphasizing the shift towards proactive security practices. In conclusion, the paper highlights the critical nature of web application security in the digital age. It provides a detailed examination of the most pressing vulnerabilities and offers guidance on implementing effective security measures. The aim is to equip developers, security professionals, and organizations with the knowledge and tools necessary to build

关键词： Access control Knowledge engineering Pressing Organizations Machine learning Information age Application security Threat assessment Security intelligent control

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ScaDLES: Scalable Deep Learning over Streaming data at the Edge

ScaDLES: Scalable Deep Learning over Streaming data at the E...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Tyagi, Sahil Swany, Martin Computing and Engineering Indiana University Luddy School of Informatics Department of Intelligent Systems Engineering BloomingtonIN United States

ISBN: (纸本)9781665480451

Distributed deep learning (DDL) training systems are designed for cloud and data-center environments that assumes homogeneous compute resources, high network bandwidth, sufficient memory and storage, a s w ell a s independent and identically distributed (IID) data across all nodes. However, these assumptions don't necessarily apply on the edge, especially when training neural networks on streaming data in an online manner. computing on the edge suffers from both systems and statistical heterogeneity. systems heterogeneity is attributed to differences in compute resources and bandwidth specific to each device, while statistical heterogeneity comes from unbalanced and skewed data on the edge. Different streaming-rates among devices can be another source of heterogeneity when dealing with streaming data. If the streaming rate is lower than training batch-size, device needs to wait until enough samples have streamed in before performing a single iteration of stochastic gradient descent (SGD). Thus, low-volume streams act like stragglers slowing down devices with high-volume streams in synchronous training. On the other hand, data can accumulate quickly in the buffer if the streaming rate is too high and the devices can't train at line-rate. In this paper, we introduce ScaDLES to efficiently train on streaming data at the edge in an online fashion, while also addressing the challenges of limited bandwidth and training with non-IID data. We empirically show that ScaDLES converges up to 3.29× faster compared to conventional distributed SGD. © 2022 IEEE.

关键词： Digital storage

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Autism Spectrum Disorder Detection from EEG Through Hjorth Parameters and Classification Using Neural Network 1st

Autism Spectrum Disorder Detection from EEG Through Hjorth P...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Peya, Zahrul Jannat Zaman, Bipasha Akhand, M.A.H. Siddique, Nazmul Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna9203 Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland Londonderry United Kingdom

ISBN: (纸本)9783031346217

Autism spectrum disorders (ASD) are a collection of neurodevelopmental disorders. Even though ASD has no cure, early detection and intervention can help developing language, behavior, and communication skills. Research shows that ASD can be diagnosed from brain signals, particularly electroencephalography (EEG) where the brain activity is recorded over time as an EEG signal from humans’ scalp and then used to study neuropsychiatric disorders. This study investigates the classification performance of the Neural Networks (NN) model with Hjorth features namely activity, mobility, and complexity extracted from EEG signals through selected channels using the XGBoost algorithm. The classification accuracy is 80% using all 19 channels of EEG data whereas the accuracy of the model using activity, mobility and complexity reached 100% with selected channels. Hjorth parameter-based NN model with channel selection seems to be promising that improves the computation complexity significantly. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Electroencephalography

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An Empirical Study on Genetic Algorithm for 2D Path Planning of Unmanned Aerial Vehicles with Obstacle Avoidance 23

An Empirical Study on Genetic Algorithm for 2D Path Planning...

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2023 International Conference on Computer, Vision and intelligent Technology, ICCVIT 2023

作者： Zhao, Ya-Hong Lan, Yu-Lin Li, Shang-Lin Zhang, Wen-Fen School of Computer and Artificial Intelligence Xiangnan University China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Chenzhou China

ISBN: (纸本)9798400708701

Unmanned aerial vehicle (UAV), with high efficiency, low manpower costs, and flexible convenience, has attracted widespread attention in logistics. UAV trajectory planning is an essential component in this context. Genetic algorithm(GA) is an effective tool for solving complex route optimization problems. However, selecting the optimal combination of parameters for UAV path planning remains a challenging problem, especially in complex urban environments with numerous obstacles. This paper proposes a genetic algorithm-based UAV path planning approach(UAV_GA) that considers obstacle avoidance on a two-dimensional grid-based map. Furthermore, to investigate the general patterns in selecting parameters for GA in such problems, a comprehensive empirical study is conducted. The study analyzes the effects of population size, number of iterations, crossover, and mutation probabilities on algorithm performance. It aims to identify universal patterns for optimal parameter combinations and provide guidance for parameter selection in GA for UAV trajectory planning with obstacle avoidance. © 2023 ACM.

关键词： Motion planning

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A survey for light field super-resolution

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High-Confidence computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Device-Friendly Guava Fruit and Leaf Disease Detection Using Deep Learning 1st

Device-Friendly Guava Fruit and Leaf Disease Detection Using...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Nandi, Rabindra Nath Palash, Aminul Haque Siddique, Nazmul Zilani, Mohammed Golam BJIT Limited Dhaka Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom Swinburne Online Australia Melbourne Australia

ISBN: (纸本)9783031346187

This research presents a machine learning model that detects plant disease from images of fruits and leaves. Five state-of-the-art machine learning models are used in this research, which achieved high accuracy in detecting plant disease. The problem with such models with high accuracy is that the models are of larger size which do not allow them to be applied to end-user devices. In this research, model quantization techniques such as float16 and dynamic range quantization are applied to the model architectures. The experimental results show that the GoogleNet model reached the size of 0.143 MB with an accuracy of 97% and the EfficientNet model reached the size of 4.2 MB with an accuracy of 99%. The source codes are available at https://***/CompostieAI/Guava-disease-detection. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Fruits

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Impulsive Control of Heterogeneous Multi-agent systems with Saturation Constraint and Time Delay

Impulsive Control of Heterogeneous Multi-agent Systems with ...

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2024 China Automation Congress, CAC 2024

作者： Jiao, Ranran Li, Bo Chen, Xiangyong Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China

ISBN: (纸本)9798350368604

This paper addresses the problem of leader-following consensus in heterogeneous multi-agent systems (HMAS) with input saturation and communication delay. Given the practical differences between leader and follower dimensions, we design virtual leaders to address this problem by allowing actual leaders and followers to communicate with virtual leaders separately, thus enabling indirect communication between actual leaders and followers. Additionally, the control protocol proposed in this paper takes into account the effects of input saturation and time delay that exist in real communication. At last, a simulation example is presented to demonstrate the effectiveness of the designed control protocol. © 2024 IEEE.

关键词： Multi agent systems

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Feature-based Approaches to Multi-View Image Stitching

Feature-based Approaches to Multi-View Image Stitching

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Computational Intelligence in Image, Signal Processing and Synthetic Media Companion (CISM Companion), IEEE Symposium on

作者： Samuel Venezia Sonya Coleman Dermot Kerr John Fegan School of Computing Engineering and Intelligent Systems Ulster University Londonderry Derry Northern Ireland

ISBN: (数字)9798331508517

ISBN: (纸本)9798331508524

Image stitching, the technique of merging multiple images with overlapping regions, has various applications in real-life scenarios such as security systems, autonomous vehicles, and sports analytics. Several image stitching methodologies have been developed, resulting in a wide range of traditional feature-based and deep learning approaches. In this paper, we utilise the Adobe Panoramic Dataset to assess eight approaches for stitching four overlapping images. We develop six novel feature-based stitching approaches, including AKAZE-SIFT-BF KNN-RANSAC. As an example approach, KAZE-SIFT-BF KNN-RANSAC utilises Accelerated KAZE (AKAZE) for feature detection, Scale Invariant Feature Transformation (SIFT) to generate feature descriptors, Brute Force with k-Nearest Neighbours and a ratio test for keypoint matching, and Random Sample Consensus (RANSAC) for matched keypoint outlier removal. Evaluation is performed using the CombSIQE metric and qualitative comparison to evaluate the quality of the resulting stitched images. Performance evaluation shows that our proposed approaches produce comparable quality stitched images to the optimised SOTA SuperPoint and SuperGlue approach in outdoor scenes, while remaining consistent in indoor scenes.

关键词： Performance evaluation Object detection Nearest neighbor methods Signal processing Feature extraction Real-time systems Security Image stitching Optimization Sports

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