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An Improved Sparrow Search Algorithm for Node Localization in WSN

computers, Materials & Continua 2022年第4期71卷 2037-2051页

作者： RThenmozhi Abdul Wahid Nasir Vijaya Krishna Sonthi TAvudaiappan Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Computer Science and Engineering College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Electronics Communication and Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502Andhra PradeshIndia Department of Computer Science and Engineering K.Ramakrishnan College of engineeringTiruchirappalli621112India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

Wireless sensor networks(WSN)comprise a set of numerous cheap sensors placed in the target region.A primary function of the WSN is to avail the location details of the event occurrences or the node.A major challenge in WSN is node localization which plays an important role in data gathering *** GPS is expensive and inaccurate in indoor regions,effective node localization techniques are *** major intention of localization is for determining the place of node in short period with minimum *** achieve this,bio-inspired algorithms are used and node localization is assumed as an optimization problem in a multidimensional *** paper introduces a new Sparrow Search Algorithm with Doppler Effect(SSA-DE)for Node Localization in Wireless *** SSA is generally stimulated by the group wisdom,foraging,and anti-predation behaviors of ***,the Doppler Effect is incorporated into the SSA to further improve the node localization *** addition,the SSA-DE model defines the position of node in an iterative manner using Euclidian distance as the fitness *** presented SSA-DE model is implanted in MATLAB *** extensive set of experimentation is carried out and the results are examined under a varying number of anchor nodes and ranging *** attained experimental outcome ensured the superior efficiency of the SSA-DE technique over the existing techniques.

关键词： Localization wireless networks

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Machine Translation for Morphologically Rich Low-Resourced South African Languages 4

Machine Translation for Morphologically Rich Low-Resourced S...

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4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024

作者： Masethe, Hlaudi Daniel Mothapo, Lawrence M Ojo, Sunday O. Owolawi, Pius A. Masethe, Mosima Anna Giunchigilia, Fausto Tshwane University of Technology Department of Data Science South Africa Durban University of Technology Department of Information Technology South Africa Tshwane University of Technology Department of Computer Engineering South Africa Sefako Makgatho Health Sciences University Department of Computer Science and Information Technology South Africa University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350387988

In the context of machine translation, many of South Africa's languages are categorized as low-resourced, despite the country's vast linguistic diversity. Creating efficient multilingual translation systems that can support languages like Afrikaans, Sesotho sa Leboa (Sepedi), isiNdebele, Setswana, isiXhosa, Xitsonga, siSwati and isiZulu are severely hampered by the lack of high-quality training data. Low translation accuracy and fluency are the result of existing translation models' frequent inability to perform well in these languages. As a result, speakers of these languages encounter difficulties using increasingly accessible online resources for information and services. By utilizing the MarianMT model to create a reliable multilingual machine translation system, this work seeks to address these issues by enhancing the quality of translation for South African languages with limited resources. This study presents a comprehensive approach to multilingual machine translation focused on low-resourced South African languages, utilizing the MarianMT model. Our objective was to enhance translation quality for languages with limited training data, including Afrikaans, Sesotho sa Leboa (Sepedi), isiNdebele, Setswana, IsiXhosa, Xitsonga, siSwati and isiZulu. We conducted extensive experiments, resulting in a BLEU score of 62%, indicating a significant improvement in translation. Our evaluation metrics demonstrated strong performance, with precision at 78%, recall at 81%, F1 score at 79%, and overall accuracy at 82%. © 2024 IEEE.

关键词： Neural machine translation

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Indoor Sign Recognition System for Visually Impaired People 13

Indoor Sign Recognition System for Visually Impaired People

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Ahmed, Halal Abdulrahman Alizadeh, Fattah Universidad de Sevilla Minerva Data Science Lab Seville Spain Toronto Metropolitan University Department of Computer Science Toronto Canada

ISBN: (纸本)9798350395570

Vision impairment can result in a loss of independence due to the inability to fully understand the surrounding environment. Indoor signage is a crucial factor in enabling visually impaired people to navigate unfamiliar environments. This paper proposes a system that automatically recognises a variety of indoor signs, including accessibility, elevator, exit, female toilet, male toilet, no smoking, restaurant, and Wi-Fi. Developing deep-learning algorithms that can accurately recognise similar signs or significant variations is challenging. To address this, the proposed method utilises a dataset of 1141 RGB images to train and test the system. Our approach employs a pre-trained AlexNet architecture with transfer learning and a customised sequential CNN architecture. Experimental results indicate an accuracy rate of 63% for the customised sequential CNN and 80% for AlexNet with transfer learning. The proposed system has the potential to enhance the independence and freedom of visually impaired individuals significantly. © 2023 IEEE.

关键词： Convolutional neural networks

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On the Effect of Cross-Channel Normalization on Wide-Shallow Convolutional Networks 26

On the Effect of Cross-Channel Normalization on Wide-Shallow...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Alakkari, Salaheddin Mileo, Alessandra Department of Computer Science and Information Systems University of Limerick Ireland Insight Centre for Data Analytics Dublin City University Ireland

ISBN: (纸本)9781837242672

Cross-channel Normalization (CN) was first proposed in AlexNet paper as a biologically inspired normalization process mimicking the lateral inhibition phenomenon in biological neurons. However, the effect of such a normalization was not well explored in the literature due to the wide popularity of batch normalization. In this paper, we show that such a type of normalization can significantly enhance the network accuracy when applied to wide-shallow convolutional networks. Wide-shallow networks are more advantageous compared to deep CNNs since they significantly increase parallelism and reduce the sequential computation across layers. Our experiments show that using cross-channel normalization not only improves the accuracy of residual and standard convolutional networks on benchmark datasets but also significantly enhances the performance when considering limited and reduced representation examples. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Convolution

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Deep Learning Techniques for Identification of Pneumonia: A CNN Approach 2

Deep Learning Techniques for Identification of Pneumonia: A ...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Das, Ruchika Nayak, Debasish Swapnesh Kumar Rout, Chinmayee Priyadarshini Jena, Lambodar Swarnkar, Tripti Siksha 'o' Anusandhan University Department of Computer Science and Engineering Bhubaneswar India Nalanda Institute of Technology Department of Computer Application Bhubaneswar India Siksha 'O Anusandhan University Department of Data Science Bhubaneswar India Siksha 'o' Anusandhan University Department of Computer Application Bhubaneswar India

ISBN: (纸本)9798350370188

Pneumonia is an inflammatory lung disease that mostly affects the tiny air sacs known as alveoli in human bodies. Pneumonia may be caused by bacteria, viruses, or fungi. Mild to severe symptoms may include a mucus-producing cough and a fever, appals, and difficulty to breath. The approach most usually used to diagnose pneumonia is chest X-ray (CXR) imaging. Images from a CXR screening can be used as an alternative or confirmation method because they are simple and affordable to acquire. Examining the chest X-rays however is a hard task that might be vulnerable to subjective variation. We used a deep learning technique in this investigation. Convolutional neural networks (CNNs) for deep learning were employed in this paper. In his work, the proposed model is applied to the original CXR image dataset and the augmented image dataset. We generated the augmented images from the existing CXR image dataset and deployed VGG-19, a component of the CNN model to create an autonomous Pneumonia detection system. The suggested techniques that have been employed include scaling the image, adding extra information, deep learning using Keras, batch normalization, and finally using weights from the pre-trained Keras model (VGG19). Finding pneumonia sickness is difficult and takes time. In this study, we address the issue and demonstrate how to detect pneumonia disease more quickly and accurately. Our proposed model shows an accuracy of 95% on the augmented CXR image dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Constructing Multiple CISTs on BCube-Based data Center Networks in the Occurrence of Switch Failures

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IEEE Transactions on computers 2023年第7期72卷 1971-1984页

作者： Lin, Wanling Li, Xiao-Yan Chang, Jou-Ming Jia, Xiaohua Fuzhou University College of Computer and Data Science Fuzhou350108 China National Taipei University of Business Institute of Information and Decision Sciences Taipei10051 Taiwan City University of Hong Kong Department of Computer Science Hong Kong

The scale of data center networks (DCNs) has grown rapidly with the increasing popularity of cloud computing, data explosion, and the dramatic drop in setup costs. Thus, inevitable component failures (including switches and servers) will become more frequent. A DCN requires maintaining regular and reliable operation and providing efficient routing algorithms for transmitting data between servers. Particularly, fault-tolerant routing is necessary. Recently, constructing completely independent spanning trees (CISTs) on DCNs has received much attention as a dual-CIST (i.e., two CISTs) suffices to configure protection routing, which is a fault-tolerant routing. Moreover, the protection routing can additionally realize a secure mechanism if it is configured by more CISTs. BCube is a server-centric DCN with many advantages, and many variations were deformed from BCube with application requirements, such as RCube and RRect. In this paper, we provide a unified framework called BCube-based DCN (BDCN) that integrates the representation of the logic graphs of DCNs mentioned above, facilitating consistent algorithms' design. Then, we develop efficient algorithms to construct multiple CISTs on BDCN under the consideration of switch failures. Note that this is the first study that constructs multiple CISTs in DCNs with switch failures. Finally, using standard metrics, such as average path length (APL) and transmission failure rate (TFR), we evaluate the performance of the fault-tolerant routing through experiments. © 1968-2012 IEEE.

关键词： Fault tolerance

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Graph Attention Network-based Multimodal Fusion for Fake Food Reviews Detection 24

Graph Attention Network-based Multimodal Fusion for Fake Foo...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Yang, Jiajie Mao, Dianhui Zhang, Denghui Beijing Key Laboratory of Big Data Technology for Food Safety Computer Science Department Beijing Technology and Business University China

ISBN: (纸本)9798400718137

In the field of computational intelligence and data analytics, the detection of fake food reviews has emerged as a pressing challenge, exacerbated by the widespread use of social media. These fraudulent reviews, particularly prevalent in the food and beverage industry, distort consumer decision-making and disrupt market fairness. To address this, we propose GMF-R, an innovative multimodal fusion framework based on Graph Attention Network (GAT), designed to effectively detect fake food reviews. The framework features end-to-end trainability and introduces advanced multimodal feature processing mechanisms, including fine-grained cross-modal interactions, a shared feature space, and multimodal graph construction. By utilizing GAT, GMF-R can adaptively assign attention weights to key nodes and accurately capture complex inter-modal relationships. Experimental results demonstrate that GMF-R achieves a 97.4% accuracy rate and a 98.1% F1 score on the Yelp reviews website dataset, significantly outperforming previous state-of-the-art models. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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AI - Based Aircraft Recognition System 3

AI - Based Aircraft Recognition System

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3rd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2024

作者： Shiva Chandra, N.K. Rajath Anirudhan Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Karnataka Manipal576104 India

ISBN: (纸本)9798350391244

The project 'AI-Based Aircraft Recognition System' aims to develop an advanced system for automatically recognizing and identifying aircraft using AI techniques. The increasing role of artificial intelligence (AI) in various sectors, including aviation, has driven the need for more efficient and accurate recognition systems. Aircraft recognition has attracted attention due to its potential uses in areas like military operations, air traffic control, and aviation surveillance. This project proposes the use of advanced AI algorithms to address gaps in current aircraft recognition systems by enhancing accuracy, performance, and real-time application. © 2024 IEEE.

关键词： Deep learning

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Exploring Artificial Neural Network Models for c-VEP Decoding in a Brain-Artificial Intelligence Interface

Exploring Artificial Neural Network Models for c-VEP Decodin...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Miao, Zhengqing Meunier, Anja Žák, Michal Robert Grosse-Wentrup, Moritz Justus Liebig University Giessen Department of Psychology Giessen Germany University of Vienna Doctoral School Computer Science Faculty of Computer Science Vienna Austria University of Vienna Research Group Neuroinformatics Faculty of Computer Science Vienna Austria University of Vienna Vienna Cognitive Science Hub Vienna Austria University of Vienna Research Network Data Science Vienna Austria

ISBN: (纸本)9798350386226

The Conversational Brain-Artificial Intelligence Interface (BAI) is a novel brain-computer interface (BCI) that uses artificial intelligence (AI) to help individuals with severe language impairments communicate. It translates users' broad intentions into coherent, context-specific responses through an advanced AI conversational agent. A critical aspect of intention translation in BAI is the decoding of code-modulated visual evoked potentials (c-VEP) signals. This study evaluates five different artificial neural network (ANN) architectures for decoding c-VEP-based EEG signals in the BAI system, highlighting the efficacy of lightweight, shallow ANN models and pre-training strategies using data from other participants to enhance classification performance. These results provide valuable insights for the application of ANN models in decoding c-VEP-based EEG signals and may benefit other c-VEP-based BCI systems. © 2024 IEEE.

关键词： Chatbots

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Symmetric Linear Bandits with Hidden Symmetry 38

Symmetric Linear Bandits with Hidden Symmetry

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tran, Nam Phuong Ta, The Anh Mandal, Debmalya Tran-Thanh, Long Department of Computer Science University of Warwick Coventry United Kingdom CSIRO's Data61 MarsfieldNSW Australia

High-dimensional linear bandits with low-dimensional structure have received considerable attention in recent studies due to their practical significance. The most common structure in the literature is sparsity. However, it may not be available in practice. Symmetry, where the reward is invariant under certain groups of transformations on the set of arms, is another important inductive bias in the high-dimensional case that covers many standard structures, including sparsity. In this work, we study high-dimensional symmetric linear bandits where the symmetry is hidden from the learner, and the correct symmetry needs to be learned in an online setting. We examine the structure of a collection of hidden symmetry and provide a method based on model selection within the collection of low-dimensional subspaces. Our algorithm achieves a regret bound of (Equation presented), where d is the ambient dimension which is potentially very large, and d0 is the dimension of the true low-dimensional subspace such that d0 d. With an extra assumption on well-separated models, we can further improve the regret to (Equation presented). © 2024 Neural information processing systems foundation. All rights reserved.

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