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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Ramprasath, M. Dhanasekaran, K. Karthick, T. Velumani, R. Sudhakaran, P. Srm Institute of Science and Technology Department of Data Science and Business Systems School of Computing TamilNadu Chennai India Department of Computer Science and Engineering Andhra Pradesh Madhrawada India Srm Trp Enginerring College Department of Computer Science and Engineering Tamil Nadu Trichy India

ISBN: (纸本)9781665484251

In recent years, Pretraining Language Models based on Transformers -Natural Language Processing (PLMT-NLP) have been highly successful in nearly every NLP task. In the beginning, Generative Pre-trained model-based Transformer, BERT- Bidirectional Encoder model Representations using Transformers was used to develop these models. Models constructed on transformers, Self-supervise knowledge acquiring, and transfer learning establish the foundation of these designs. Transformed-based pre-trained models acquire common linguistic illustrations from vast amounts of textual information through self-supervised model and apply this information to downstream tasks. To eliminate the need to retrain downstream models, these models provide a solid foundation of knowledge. In this paper, the enhanced learning on PLMT-NLP has been discussed. Initially, a quick introduction to self-supervised learning is presented, then diverse core concepts used in PLMT-NLP are explained. Furthermore, a list of relevant libraries for working with PLMT-NLP has been provided. Lastly, the paper discusses about the upcoming research directions that will further improve these models. Because of its thoroughness and relevance to current PLMT-NLP developments, this survey study will positively serve as a valuable resource for those seeking to understand both basic ideas and new developments better. © 2022 IEEE.

关键词： Natural language processing systems

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Establishment of an Effective Brain Tumor Classification System through Image Transformations and Optimization Techniques 1

Establishment of an Effective Brain Tumor Classification Sys...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Arora, Gaurav Kumar Taj Mahaboob, Shaik Adilakshmi, S. Rani, Sasi Kala Kasthuri, A. Mamodiya, Udit IBM India Pvt. Ltd Uttar Pradesh Greater Noida India Jntua College of Engineering Department of ECE Andhra Pradesh Pulivendula India Vignana Bharathi Institute of Technology Department of Data Science Telangana India Sri Krishna College of Engineering and Technology Department Computer Science and Engineering Coimbatore India Vinayaka Mission's Kirupananda Variyar Engineering College Department Computer Science and Engineering Salem India Poornima Institute of Engineering and Technology Department of Electrical Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350345957

In recent years, researchers and health care professionals have been interested in medical image fusion, which combines images of the human body, organs, and cells with general information to address medical difficulties. computer-aided imaging techniques offer quantitative assessment of images under validation, which helps doctors make unbiased, objective decisions quickly. Multi-sensor and multi-source image fusion models also diversify healthcare analysis characteristics. It led to strong information processing that reveals otherwise invisible details. The fused pictures can help pinpoint anomalies. Multimodal image fusion combines complementing pictures into one. The fused image is ideal for visual perception or computer segmentation, feature extraction, and object recognition. The goal is to create a single fused image with more information than the source photos alone. The fused image combines the source images' complementary and shared elements, delivering enhanced subjective and objective detail. In biomedical imaging, fusing multimodal images has shown useful for disease characterisation. Medical image analysis uses CT, MRI, and PET (PET). In this research paper, we developed a Multimodality Image Fusion using Centre based genetic algorithm (CBGA) and fuzzy logic (FL) technique with lifting wavelet transform (LWT) for fusion of spatially registered MRI and CT images. We also proposed an efficient Multimodality Medical Image Fusion system based on DWT and binary crow search optimization (BCSO) algorithm for fusing MRI and CT images. The performance of the suggested models is validated against many benchmark medical datasets. The experiment showed that the proposed models outperformed all others. © 2023 IEEE.

关键词： Magnetic resonance imaging

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DFD-Net:lung cancer detection from denoised CT scan image using deep learning

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Frontiers of computer science 2021年第2期15卷 119-131页

作者： Worku J.SORI Jiang FENG Arero W.GODANA Shaohui LIU Demissie J.GELMECHA School of Electrical Engineering and Computing Department of Computer Science and EngineeringAdama Science and Technology UniversityAdama1888Ethiopia Faculty of Natural Computational Science Department of Mathematics Bule Hora UniversityBule HoraHagere Maryam144Ethiopia School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China Peng Cheng Laboratory Shenzhen518052China School of Electrical Engineering and Computing Department of Electronics and Communications EngineeringAdama Science and Technology UniversityAdama1888Ethiopia

The availability of pulmonary nodules in CT scan image of lung does not completely specify *** noise in an image and morphology of nodules,like shape and size has an implicit and complex association with cancer,and thus,a careful analysis should be mandatory on every suspected nodules and the combination of information of every *** this paper,we introduce a“denoising first”two-path convolutional neural network(DFD-Net)to address this *** introduced model is composed of denoising and detection part in an end to end ***,a residual learning denoising model(DR-Net)is employed to remove noise during the preprocessing ***,a two-path convolutional neural network which takes the denoised image by DR-Net as an input to detect lung cancer is *** two paths focus on the joint integration of local and global *** this end,each path employs different receptive field size which aids to model local and global *** further polish our model performance,in different way from the conventional feature concatenation approaches which directly concatenate two sets of features from different CNN layers,we introduce discriminant correlation analysis to concatenate more representative ***,we also propose a retraining technique that allows us to overcome difficulties associated to the image labels *** found that this type of model easily first reduce noise in an image,balances the receptive field size effect,affords more representative features,and easily adaptable to the inconsistency among nodule shape and *** intensive experimental results achieved competitive results.

关键词： medical image discriminant correlation analysis features fusion image detection denoising

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Predict-and-Update Network: Audio-Visual Speech Recognition Inspired by Human Speech Perception

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2024年 33卷 11-22页

作者： Jiadong Wang Xinyuan Qian Haizhou Li Shenzhen Research Institute of Big data School of Data Science The Chinese University of Hong Kong Shenzhen China Electrical and Computer Engineering National University of Singapore Singapore Department of Computer Science and Technology University of Science and Technology Beijing Beijing China

Audio and visual signals complement each other in human speech perception, and the same applies to automatic speech recognition. The visual signal is less evident than the acoustic signal, but more robust in a complex acoustic environment, as far as speech perception is concerned. It remains a challenge how we effectively exploit the interaction between audio and visual signals for automatic speech recognition. There have been studies using visual signals as redundant or complementary information to audio input in a synchronous manner. However, human studies suggest another mechanism that visual signal primes the listener in advance, indicating when and which frequency to attend to. To simulate such a visual cueing mechanism, we propose a Predict-and-Update Network (P&U net) for Audio-Visual Speech Recognition (AVSR). In particular, we first predict the character posteriors of the spoken words, i.e. the visual embedding, based on the visual signal. The audio signal is then conditioned on the visual embedding via a novel cross-modal Conformer, that updates the character posteriors. We validate the effectiveness of the visual cueing mechanism through extensive experiments. The proposed P&U net outperforms the state-of-the-art AVSR methods on both LRS2-BBC and LRS3-BBC datasets, with the relative Word Error Rate (WER) reductions exceeding 10% and 40% under clean and noisy conditions, respectively.

关键词： Visualization Speech recognition Noise measurement Lips Feature extraction Interference Fuses Vectors Signal to noise ratio Sensors

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Guest Editorial Special Section on Generative AI and Large Language Models Enhanced 6G Wireless Communication and Sensing

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 0-2页

作者： Jiacheng Wang Geng Sun Dusit Niyato Hina Tabassum Gabriel-Miro Muntean Nelson Fonseca College of Computing and Data Science Nanyang Technological University Jurong West Singapore College of Computer Science and Technology Jilin University Changchun China Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University Toronto ON Canada School of Electronic Engineering Dublin City University Dublin 9 Ireland Institute of Computing State University of Campinas Campinas Brazil

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Distributed Estimation of Anomalies using a Low Complexity PSO Algorithm

Distributed Estimation of Anomalies using a Low Complexity P...

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2024 International Conference on Emerging Trends in Smart Technologies, ICETST 2024

作者： Saeed, Muhammad Omer Bin Khan, Salman A. Butt, Naveed R. Mohammad, Nazeeruddin Zerguine, Azzedine Gik Institute Of Engineering Sciences & Technology Faculty Of Engineering Sciences Topi Pakistan Karachi Institute Of Economics & Technology College Of Computing & Information Sciences Karachi Pakistan Prince Mohammad Bin Fahd University College Of Computer Engineering And Science AlKhobar Saudi Arabia King Fahd University Of Petroleum & Minerals Department Of Electrical Engineering Dhahran Saudi Arabia

ISBN: (纸本)9798331515850

Any wireless sensor network can experience anomalies that can hinder performance. This work presents a distributed estimation algorithm that estimates any such anomalies using a PSO algorithm. PSO algorithms are computationally expensive. This work presents a low complexity version of the algorithm that removes redundant computations and achieves excellent performance as well. Low complexity algorithms are essential for a longer battery life of wireless sensor so this algorithm provides an excellent alternative to the basic linear adaptive filtering algorithms that have low complexity but require a lot more time to achieve similar results. © 2024 IEEE.

关键词： Consensus algorithm

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Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

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computers, Materials & Continua 2024年第9期80卷 4895-4916页

作者： Brij Bhooshan Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPuneMaharashtra411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Ronin Institute MontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)HomantinHong KongChina

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection *** machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 *** WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation *** results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

关键词： Phishing detection Recurrent Neural Network(RNN) Whale Optimization Algorithm(WOA) cybersecurity machine learning optimization

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Real-time Agrochemical Management Solutions using Cloud computing and K-Nearest Neighbors Algorithm 8

Real-time Agrochemical Management Solutions using Cloud Comp...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Sivanesan, T.M. Mohankumar, N. Shibu, N.V. Nandagopal, V. Rajasekaran, B. Srinivasan, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Nagpur Campus Maharastra Pune India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Mohan Babu University School of Engineering Department of Electrical and Electronics Engineering Andhra Pradesh Tirupathi India Vinayaka Missions Kirupananda Variyar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Optimizing pesticides to ensure crop health and productivity while limiting environmental effect is one of the many issues faced by modern agriculture. In this research, it provides a cloud-based, K-Nearest Neighbors (KNN) algorithm-based real-time agrochemical management system. The proposed method allows for effective data processing and analysis by using the computing power of cloud platforms. Environmental factors, crop type, soil properties, and historical data are used to inform the KNN algorithm's prediction of appropriate pesticide application rates. To provide the most current insights and suggestions, the system incorporates real-time data from a variety of sources, including sensors, satellites, and weather predictions. Enhanced crop yields, decreased costs, and less environmental impact may result from farmers making informed decisions about the use of agrochemicals via continuous monitoring and analysis. The system can be easily deployed and adjusted to many agricultural contexts because of the scalability and flexibility of cloud computing. This method provides a viable strategy for improving pesticide management practices, which in turn promotes sustainable farming and increases food safety. © 2024 IEEE.

关键词： Smart agriculture

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Large Language Model (LLM)-enabled Graphs in Dynamic Networking

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IEEE Network 2024年

作者： Sun, Geng Wang, Yixian Niyato, Dusit Wang, Jiacheng Wang, Xinying Poor, H. Vincent Letaief, Khaled B. Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Hong Kong University of Science and Technology Department of Electrical and Computer Engineering Hong Kong

Recent advances in generative artificial intelligence (AI), and particularly the integration of large language models (LLMs), have had considerable impact on multiple domains. Meanwhile, enhancing dynamic network performance is a crucial element in promoting technological advancement and meeting the growing demands of users in many applications areas involving networks. In this article, we explore an integration of LLMs and graphs in dynamic networks, focusing on potential applications and a practical study. Specifically, we first review essential technologies and applications of LLM-enabled graphs, followed by an exploration of their advantages in dynamic networking. Subsequently, we introduce and analyze LLM-enabled graphs and their applications in dynamic networks from the perspective of LLMs in different roles. On this basis, we propose a novel framework of LLM-enabled graphs for networking optimization, and then present a case study on UAV networking, concentrating on optimizing UAV trajectory and communication resource allocation to validate the effectiveness of the proposed framework. Finally, we outline several potential future extensions. © 2024 IEEE.

关键词： Generative adversarial networks

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Building Intelligent Automated Real-Time data Analysis Systems Through Machine Learning 2

Building Intelligent Automated Real-Time Data Analysis Syste...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Rathee, Sahil Vigenesh, M. Priydarshini, Kala Vijaykumar, R. Singh, Shweta Seth, Jyoti Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350308846

This paper explores the capacity of using gadget-get-to-know-based facts evaluation structures to build intelligent, automated real-time statistics analysis systems. It examines the demanding situations present points evaluation systems pose and proposes a unique remedy. The proposed method leverages supervised and unsupervised devices and learning strategies to enable deep information on complex datasets. It then makes use of predictive skills to make clever selections with the aid of comparing input facts about databases. ultimately, the paper describes a case to demonstrate the effectiveness of this proposed machine and outlines implications for comparable implementations in the enterprise. This work is precious for information scientists and commercial enterprise decision-makers, as it outlines an ability to deploy reliable and accurate records analysis structures. © 2023 IEEE.

关键词： Accurate Deploying Enterprise Structures

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