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2023 IEEE International Conference on Dependable, Autonomic and Secure computing, 2023 International Conference on Pervasive Intelligence and computing, 2023 International Conference on Cloud and Big Data computing, 2023 International Conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Ma, Jianhao Liu, Qi Zhang, Yonghong Liu, Xiaodong School of Software Nanjing University of Information Science and Technology Nanjing China School of Automation Nanjing University of Information Science and Technology Nanjing China School of Computing Edinburgh Napier University EdinburghER10 5DT United Kingdom

ISBN: (纸本)9798350304602

Precipitation forecasting is crucial given that extreme precipitation occurrences have a significant influence on human civilization. Radar echo extrapolation is a method for forecasting echo maps in the future based on observations of past echo maps, and it can forecast approaching short-term weather events. With moderate effectiveness, deep learning-based models have recently been used for radar echo extrapolation. However, high intensity radar echo values fail to be accurately predicted. In this paper, a novel generative adversarial model for weather radar echo extrapolation is proposed, which is composed of dual criss-cross transposed attention mechanism and cycle generation units. The findings show that the suggested model commonly performs better when in comparison to existing deep learning models. © 2023 IEEE.

关键词： Meteorological radar

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Weather Classification for Autonomous Vehicles under Adverse Conditions Using Multi-Level Knowledge Distillation

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Computers, Materials & Continua 2024年第12期81卷 4327-4347页

作者： Parthasarathi Manivannan Palaniyappan Sathyaprakash Vaithiyashankar Jayakumar Jayakumar Chandrasekaran Bragadeesh Srinivasan Ananthanarayanan Md Shohel Sayeed School of Computing SASTRA Deemed to be UniversityThanjavur613401India School of Computer science&Engineering Presidency UniversityBengaluru560064India Dr.A.P.J.Abdul Kalam School of Engineering Garden City UniversityBengaluru560049India Independent Researcher OxnardCA 93036USA Faculty of Information Science and Technology Multimedia UniversityMelaka75450Malaysia

Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational ***,accurately classifying diverse and complex weather conditions remains a significant *** advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather *** challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in *** address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational ***,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student *** framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous *** Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational *** proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per *** results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning *** work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,of

关键词： EfficientNetV2B3 multi-level knowledge distillation RestNet50V2 weather classification

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New Method of Song Sentiment Classification Based on Neural Network 8

New Method of Song Sentiment Classification Based on Neural ...

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8th IEEE International Conference on Big Data Security on Cloud, 8th IEEE International Conference on High Performance and Smart computing, and 8th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Yu, Ying QILU Institute of Technology School of Computing Science and Information Engineering Jinan China

ISBN: (纸本)9781665480697

In order to classify song text information more effectively, this paper proposes a new neural network classification model based on emotion vector. On the basis of Word2vec, a double word-embedding word vector containing emotion vector is constructed, and the vector is used as the input of Long Short Term Memory (LSTM). LSTM neural network is trained to extract the features of song text and realize emotional classification of song text. The experimental results show that the method can better embody the emotional features of text, and has higher accuracy than the traditional LSTM text classification. © 2022 IEEE.

关键词： Embeddings

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A Novel Predictive Analysis and Classification of Land Subsidence Vulnerability Mapping based on GIS using Hybrid Optimized Machine Learning Techniques and Computer Vision 5

A Novel Predictive Analysis and Classification of Land Subsi...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Senthil, G.A. Prabha, R. Arun, M. Sahaya Anselin Nisha, A. Prabu, S. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Land subsidence is a natural disaster caused by the extraction of the earth's material strength on the surface that causes the earth's surface to settle and sink. Land subsidence can also result in significant financial losses since it causes structural damage and expensive maintenance costs for roads, trains, barriers, pipelines, ground water, oil, natural gas, mines, and buildings. The Geographic information System (GIS) and Google Earth Platform (GEP) can be used to map this land subsidence. This provides a comprehensive view of land pattern subsidence and its interactions with other geographical elements. It applies to both progressive subsidence caused by fluid extraction (groundwater, oil, or natural gas) and rapid surface subsidence caused by the collapse of an underground mine. Risk mitigation is the most effective way to deal with the experimental model. This research study focuses on the use of Predictive Data Analysis (PDA), a powerful environment in data analytics that provides a set of tools that make it easier to evaluate optimal models. The proposed model makes use of a dataset that includes specific land subsidence circumstances as input. The prediction result is achieved using a hybrid machine learning technique that combines Stochastic Gradient Descent (SGD) with Long Short-Term Memory (LSTM) and is compared to K-Nearest Neighbor (KNN), Generative Adversarial Network (GAN), and Artificial Neural Network (ANN). The proposed system employs LSTM and SGD as optimizers, resulting in a hybrid model that improves accuracy. Among the four algorithms investigated the LSTM-SGD network has the highest accuracy (97%), followed by KNN (96%), GAN (90%), and ANN (89%). Due to their ability to capture complicated nonlinear correlations in data and manage temporal dependencies, optimized hybrid ML models LSTM-SGD and KNN models appear to be particularly well-suited for land subsidence prediction. © 2024 The Authors. Published by Elsevier B.V.

关键词： Long short-term memory

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Research and Application of Image Style Transfer Method 8

Research and Application of Image Style Transfer Method

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作者： Zhong, Luoyifan Li, Na Sun, Yi QILU Institute of Technology School of Computing Science and Information Engineering Jinan China

ISBN: (纸本)9781665480697

Image style transfer is a technology that uses specific algorithms in deep learning to separate image content and style. It combines the style of one image with the content of another. This technology can be applied to film and television special effects, artistic creation and other fields. It is a research hotspot in the field of image processing in recent years. This paper summarizes the scholars' research on image style transfer technology. Convolution neural network is used to realize image style transfer. The style image and content image are input into the convolution neural network. The loss functions of content image and style image are defined. Vgg-19 model is established, and the loss function is weighted into a complete loss function. In each iteration, the pixel value of the image is adjusted according to the total loss function. After many iterations, the fusion effect image of content and style is obtained. © 2022 IEEE.

关键词： Special effects

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A Comparative Study of Lossless Compression Technique in Quantum Secure Direct Communication 1

A Comparative Study of Lossless Compression Technique in Qua...

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1st International Conference on Cyber Security and computing, CyberComp 2024

作者： Ishak, Izlin Zuriani Wooi, Liew Seow Harun, Nur Ziadah Azahari, Nur Shahirah Ramli, Sofia Najwa Ghazali, Osman Politeknik Seberang Perai Department of Information Technology and Communication Pulau Pinang Malaysia Universiti Putra Malaysia Faculty of Computer Science and Information Technology Department of Computer Science Selangor Malaysia Raffles University Faculty of AI and Robotics Johor Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Department of Information Security and Web Technology Johor Malaysia School of Computing Universiti Utara Malaysia Kedah Malaysia

ISBN: (纸本)9798350387728

This is a comparative study to explore the performance of three lossless compression techniques, namely Run Length Encoding (RLE), Shannon-Fano Coding (SFC), and Huffman Coding (HC) in the context of a Hybrid M-Ary in Braided Single Stage (HMBSS) protocol communication scheme. The previous study on HMBSS using HC found that HC's compression technique has drawbacks, i.e., it is sensitive to errors and noise in the transmission channel and limited to certain data types. These drawbacks did affect the value of the compression rate limit. In this study, we evaluate the effectiveness of RLE, SFC, and HC in terms of data size reduction, coding efficiency, and data transmission time by comparing their results arising from the simulation experiments using Python coding in Jupiter Notebook and developed based on the HMBSS protocol algorithm. This simulation experiment starts with an initial six randomly generated datasets. Each dataset is tested using three compression techniques, which resulted in 18 different datasets. The results from the experiments reveal that SFC and HC consistently achieved a higher compression ratio and coding efficiency than RLE. These two compression techniques' algorithms effectively reduce the data size while preserving the original information. In our study, we also find that the data transmission time is increased with larger text sizes for all three compression techniques. This indicates that the larger the data size, the more transmission time is required with regard to what the compression technique's algorithm is. © 2024 IEEE.

关键词： Quantum communication

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Fault Prediction of Process Industry Based on Fuzzy Clustering 8

Fault Prediction of Process Industry Based on Fuzzy Clusteri...

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作者： Mi, Tao Zhong, Luoyifan He, Xin Sun, Yi Yan, Chenyang Yue, Hao QILU Institute of Technology School of Computing Science and Information Engineering Jinan China

ISBN: (纸本)9781665480697

In the process of process industry production, once the failure will often bring heavy losses to the enterprise and the country in terms of human resources and financial resources, so it is very important to give early warning of the failure and take corresponding solutions. In this paper, a real-time fault prediction algorithm based on incremental model update is proposed for the continuous growth of industrial data flow, real-time update of objects, complex and variable attributes, and value attenuation with time. The algorithm consists of several steps, including data preprocessing, data flow clustering based on sliding window, abnormal data judgment, fault prediction and model prediction update. In each stage of the algorithm, Spark framework is used for parallel acceleration, so as to improve the efficiency of fault prediction. © 2022 IEEE.

关键词： Fuzzy clustering

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A Contemporary Technique for Lung Disease Prediction using Deep Learning 1

A Contemporary Technique for Lung Disease Prediction using D...

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1st International Conference on Applied Artificial Intelligence and computing, ICAAIC 2022

作者： Raju, V Pradyotan Srinivasulu, Senduru Vishnu, P. Raj, Jeberson Retna Gowri, S. Jabez, J. School of Computing Sathyabama Institute of Science and Technology Department of Information Technology Chennai600 119 India

ISBN: (纸本)9781665497107

The field of computer science known as machine learning explores algorithms that learn from examples. Classification requires machine learning algorithms that understand how to apply a class label to data from the problem area. An easy-to-understand example is categorizing emails as 'spam' or 'not spam.' Binary classification predicts one of two classes, whereas multi-class classification predicts one of several classes. In most binary classification tasks, there is one type of normal state and another type of aberrant state. This Project involves how lung disease prediction using x-ray images will predict through the binary classification model implemented, and various python libraries like Tensor Flow, Keras, NumPy, etc. are used. This research project will observe the prediction of lung diseases by using x-ray images and further the output will be predicted with the detailed example and a detailed source code. The implementation will be shown step by step with possible screenshots. © 2022 IEEE.

关键词： Python

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Wearables for In-Situ Monitoring of Cognitive States: Challenges and Opportunities

Wearables for In-Situ Monitoring of Cognitive States: Challe...

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2023 IEEE International Conference on Pervasive computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Radhakrishnan, Meera Kandappu, Thivya Gulati, Manoj Misra, Archan Institute of High Performance Computing Agency for Science Technology and Research Singapore Management University Singapore School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9781665453813

We propose using wrist and ear-based sensing, via multiple novel and complementary modalities, to unobtrusively infer activity-aware, complex cognitive and affective states (such as confusion, boredom, and recall failure) of individuals. While state-of-the-art wearable devices are predominantly used (a) independently, with limited coordination among multiple devices, and (b) to capture macro-level physical activity and physiological state, we seek to expand the ambit of unobtrusive wearable sensing to capture the cognitive states while performing commonplace physical activities. Such states typically manifest via fine-grained, almost unobservable, microscopic head, face, and eye movements. We identify some of these fine-grained physical markers that serve as proxies for cognitive/affective states and show that earable-mounted pressure, EMG, and ultrasonic sensing hold promise for canturing such markers. © 2023 IEEE.

关键词： Eye movements

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Joint Optimization of Power Allocation and Beamforming for UAV-RIS Communication System

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IEEE Wireless Communications Letters 2025年第6期14卷 1733-1737页

作者： Guo, Xinying Liu, Longfei Zhang, Jiankang Chen, Sheng Ministry of Education Key Laboratory of Grain Information Processing and Control Henan University of Technology China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing and Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

This paper considers the communication system assisted by a fixed-trajectory unmanned aerial vehicle (UAV), which is equipped with a reconfigurable intelligent surface (RIS). For this UAV-RIS assisted system, we jointly optimize the power allocation and active beamforming at the base station (BS) and the passive beamforming at the RIS, by a novel phase block coordinate descent algorithm framework aimed at maximizing the system sum-rate. Specifically, the joint optimization problem is decomposed into two phases, and we propose two optimization algorithms: one for BS power allocation using fractional programming (FP) and the other for jointly optimizing active and passive beamforming using FP-manifold, which alternately optimize two phases. Simulation results not only highlight the rapid convergence and evident superiority of our proposed framework but also reveal that the optimal UAV-RIS placement is related to the flight height. © 2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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