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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

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A scaled three-term conjugate gradient method for convex-constrained monotone nonlinear equations and application 3

A scaled three-term conjugate gradient method for convex-con...

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3rd international conference on Recent Advances in Fundamental and Applied sciences, RAFAS 2021

作者： Abdullahi, H. Awasthi, A.K. Waziri, M.Y. Halilu, A.S. Department of Mathematics School of Chemical Engineering and Physical Sciences Lovely Professional University Phagwara144411 India Department of Mathematical Sciences Bayero University Kano Nigeria Department of Mathematics and Computer Science Sule Lamido University Jigawa Kafin Hausa Nigeria

One of the fastest, old, and most adopted method for solving unconstrained optimization problems is the conjugate gradient method (cg). Over the decades, several types of research have been put in place to extend the methods (cg) to solving constrained monotone nonlinear equations. This paper presents a scaled three-term cg for convex-constrained monotone nonlinear equations. The proposed method fulfills descent (sufficient) property as well as trust-region feature. Two sets of numerical experiments were carried off and demonstrate the effectiveness of the proposed method by comparison with existing methods in the literature. In the first experiment, the proposed method was applied and solved some convex-constrained monotone nonlinear equations using some benchmark test functions. For the second experiment, a signal problem;that arose from compressed sensing was restored by applying the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Conjugate gradient method

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Prediction of Silicon Content in Molten Iron Based on EMD-GA-LSTM 1

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3rd international conference on 3D Imaging Technologies - Multidimensional signal processing and Deep Learning, 3DIT-MSP and DL 2021

作者： Yang, Junqi Wang, Haoran Wang, Xiuhe Zhang, Lintong University of Science and Technology Beijing Beijing100083 China

ISBN: (数字)9789811924521

ISBN: (纸本)9789811924514

Stable and accurate prediction of molten iron silicon content in blast furnace is of much concern to production scheduling and stable, safe, and efficient operation of blast furnace ironmaking. To make the most of the information gathered during process of the blast furnace ironmaking and improve the stability and validity of predictions, this paper proposes a new model that combines empirical mode decomposition (EMD), genetic algorithm (GA), and long short-term memory neural network (LSTM) to predict the silicon content in molten iron. First, EMD algorithm is used to decompose the original silicon content sequence;respectively, several IMF decomposition components are obtained. Then, GA algorithm is used to optimize the batch size parameters and neuron parameters of LSTM network, so as to predict each IMF component. Finally, the predicted value of IMF component is reconstructed to obtain the final forecast result. This paper uses the measured data for verification. The results prove that the proposed method can make an accurate prediction of the multivariable time series of molten iron and silicon content and have higher prediction accuracy and less margin of error than autoregressive integrated moving average model (ARIMA), support vector regression model (SVR), and standard LSTM neural network model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Empirical mode decomposition

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Machine Learning Approaches for Rumor Detection on Social Media Platforms: A Comprehensive Survey 3rd

Machine Learning Approaches for Rumor Detection on Social Me...

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3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Gongane, Vaishali U. Munot, Mousami V. Anuse, Alwin SCTR’s Pune Institute of Computer Technology SPPU Pune411046 India School of ECE Dr. Vishwanath Karad MIT-WPU Pune411038 India

ISBN: (纸本)9789811908392

The inescapable utilization of Internet and social media has opened doors of social event continuous data across the globe in an exceptionally limited capacity to focus time. Social media networks are becoming a focal point for disseminating news that take the form of fake news and rumors. A rumor refers to a statement or a post that is false and is circulated fast on social media without verifying its truthfulness. Rumors have an adverse effect not only on an individual, but also it extends to the entire society. While manual detection and screening of rumors are becoming a dire need, automatic detection of rumors is an emerging research with various machine learning algorithms. The paper summarizes the utility of various machine learning algorithms for automatic rumor detection. Various steps like feature engineering and classification are also thoroughly studied and detailed in this paper. This paper coins to further research directions. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Social networking (online)

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Interaction-Enhanced Textual Similarity Matching Distance Measurement – Exploration on Chinese

Interaction-Enhanced Textual Similarity Matching Distance Me...

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computer science and Management Technology (ICCSMT), international conference on

作者： Yijia Zhang Jiyao Wang Dan Xiong School of Engineering The Hong Kong University of Science and Technology Hong Kong SAR China Department of Information Technology GF Securities CO.LTD Guangzhou China

Textual similarity matching is one of the most valuable tasks in natural language processing. Meanwhile, due to the wide development of applications and increase in requirements, various subtasks are proposed and expected to be resolved. In addition, Chinese, which owns the largest population of native speakers, introduced more challenges because of its different grammar and characteristics. In this project, we explore the best design of representation, interaction, and prediction layers for Chinese textual similarity matching by extensive experiments on two public datasets. Results prove that the precise interaction paradigm and unlearnable distance metric is more suitable for matching task considering effectiveness and efficiency. And for the option of representation structure, we should simultaneously consider textual information distribution within datasets and computational cost.

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Anomaly-Based Intrusion Detection System Through Deep Neural Network 3rd

Anomaly-Based Intrusion Detection System Through Deep Neural...

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3rd international conference on Communication, Computing and Electronics Systems, ICCCES 2021

作者： Afzal, Sabeeha Deen, Anjna Jayant Department of Computer Science and Engineering University Institute of Technology-Rajiv Gandhi Proudyogiki Vishwavidyalaya Madhya Pradesh Bhopal462033 India

ISBN: (纸本)9789811688614

Intrusion detection has a crucial responsibility in securing our network, and also the main aim is to precisely identify each type of attacks. In this paper, efforts have been made to study about the datasets KDD-CUP’99, NSL-KDD properly;further these datasets are used after pre-processing for training machine learning models such as SVM, DT, RF and then for testing and calculating the accuracy and other metrics of machine learning models. Apart from these, focus was to build a Deep Learning approach for Intrusion Detection using Deep Neural Network on the CIC-IDS 2017 Benchmark dataset. In this model, functions made use of are Adam optimizers, Rectified Linear Unit (ReLU), sigmoid classification function for binary classification and softmax classification for multi-class classification. Overall, the intention of making this paper was to do an exhaustive study so in future we can use the best of the experimental results to enhance and detect real-time attacks. Then it has come to my notice that handling lots of datasets is easier for deep learning models by giving a little knowledge about the important features and that there is improvement in the detection rate. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Land Use Land Cover Classification Using Different ML Algorithms on Sentinel-2 Imagery 3rd

Land Use Land Cover Classification Using Different ML Algori...

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3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Bayas, Shiwani Sawant, Suraj Dhondge, Ishwari Kankal, Priyanka Joshi, Amit Department of Computer Engineering and IT College of Engineering Pune Shivajinagar Maharashtra Pune411005 India

ISBN: (纸本)9789811908392

Land use land cover analysis is important in many remote sensing applications such as change detection and resource monitoring. It is an efficient means to manage and analyse land transformation. Best machine learning classifier is needed to increase the accuracy of the assessment for different terrains. The study comprises of accuracy assessment of four different ML algorithms—Naive Bayes algorithm (NB), classification and regression trees (CART), gradient tree boost (GTB) and random forest (RF) for land use/land cover mapping on Sentinel-2 imagery. Sentinel-2 has 13 spectral bands and freely available high-resolution data. The aim is to find the best classifier for Indian states' undulating terrain. Results show that with minor variations, RF performed best four of four selected based on kappa coefficient and overall accuracy (0.92), whereas NB showed least accuracy 0.81. Individual class accuracies were also compared. The three algorithms RF, CART, GTB showed least variation, whereas NB mostly had lower values. For this study, in Indian terrain, RF was considered to be best-suited machine learning classifier for LULC modelling. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Remote Sensing Scene Classification Based on Covariance Pooling of Multi-layer CNN Features Guided by Saliency Maps 3rd

Remote Sensing Scene Classification Based on Covariance Pool...

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3rd international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Akodad, Sara Bombrun, Lionel Germain, Christian Berthoumieu, Yannick Université de Bordeaux CNRS IMS UMR 5218 Groupe Signal et Image Talence33405 France

ISBN: (纸本)9783031090363

The new generation of remote sensing imaging sensors enables high spatial, spectral and temporal resolution images with high revisit frequencies. These sensors allow the acquisition of multi-spectral and multi-temporal images. The availability of these data has raised the interest of the remote sensing community to develop novel machine learning strategies for supervised classification. This paper aims at introducing a novel supervised classification algorithm based on covariance pooling of multi-layer convolutional neural network (CNN) features. The basic idea consists in an ensemble learning approach based on covariance matrices estimation from CNN features. Then, after being projected on the log-Euclidean space, an SVM classifier is used to make a decision. In order to give more strength to relatively small objects of interest in the scene, we propose to incorporate the visual saliency map in the process. For that, inspired by the theory of robust statistics, a weighted covariance matrix estimator is considered. Larger weights are given to more salient regions. Finally, some experiments on remote sensing classification are conducted on the UC Merced land use dataset. The obtained results confirm the potential of the proposed approach in terms of classification scene accuracy. It demonstrates, besides the interest of exploiting second order statistics and adopting an ensemble learning approach, the benefit of incorporating visual saliency maps. © 2022, Springer Nature Switzerland AG.

关键词： Remote sensing

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Fusion recommendation algorithm based on DeepWalk and Item-Based collaborative filtering

Fusion recommendation algorithm based on DeepWalk and Item-B...

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computer science and Management Technology (ICCSMT), international conference on

作者： Yicheng Yu Rongbin Li Jun Yin School of Computing Qinghai Normal University Xi’Ning China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xi’Ning China

Collaborative filtering techniques have been applied to personalised recommendation systems in recommender systems. However, with the gradual increase in the number of platform users and goods, the user rating data on the entire project matrix is extremely sparse, resulting in the inaccuracy of the prediction results. DeepWalk, on the other hand, still performs well when the data is sparse. Therefore, DeepWalk is introduced into collaborative filtering, and a fusion recommendation algorithm based on DeepWalk and collaborative filtering is proposed to reduce the impact of cold start and sparsity on the recommendation results. Experiments show that the proposed fusion recommendation algorithm has higher recommendation accuracy when the data is sparse compared with other traditional algorithms, which improves the recommendation quality to a certain extent.

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Intelligent system for governing risks in smart cities applications

Intelligent system for governing risks in smart cities appli...

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The 3rd international conference on Distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： A. Darwiesh M. Elhoseny A. H. El-Baz Department of Mathematics Faculty of Science Damietta University New Damietta Egypt University of Sharjah Sharjah UAE Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt

This paper presents an intelligent system to manage risks in smart cities. This system is based on mining social media big perceptions by using natural language processing in identifying and assessing risks. It will help the authority of smart cities in managing risks processes. In addition, a related survey for the most relevant research articles in the area of risk management in smart cities is included. Besides, we provide a framework to the proposed system and describe it mathematically as well as explicit mathematical expressions such as identifying risks in tweets are driven. Moreover, a real data is collected from profiles of London citizens on Twitter, and risk analysis and assessment are performed. Furthermore, performance measures are included to show the validity of the proposed system.

关键词： data mining natural language processing risk analysis risk management social networking (online)

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A new double scrambling color image encryption algorithm based on multi chaotic systems and image segmentation

A new double scrambling color image encryption algorithm bas...

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computer science and Management Technology (ICCSMT), international conference on

作者： Junfeng Tian Ke Wang Cheng Liu Key Laboratory of Big Data Analysis and Processing of Henan Province Henan University Kaifeng PR China

Aiming at low security of traditional encryption methods, this paper give a new encryption method by using multi chaotic systems and image segmentation. Firstly, use Arnold transform to perturb the RGB component matrices of the source image. Subsequently, the chaotic image chunking algorithm is applied to the mixed component matrix, and the chunked mixed matrix is perturbed by Hyperchaotic Chen system and Logistic mapping. Moreover, Diffusion of pixel transformations to the whole image by inverse transform operations. Finally, logistic system is applied to the secondary pixels scrambling, which achieves image encryption. The experimental data indicate that this encryption method encryption effect is good, and can effectively prevent ciphertext attack and noise attack.

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