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检索条件"任意字段=2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017"

共 2746 条记录，以下是391-400 订阅

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Target Recognition and Grab Location Based on machine Vision and Deep learning 10th

Target Recognition and Grab Location Based on Machine Vision...

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10th international conference on Frontier computing, FC 2020

作者： Yao, Jin Feng, Jing Liu, Yuzhou Chen, Licheng You, Rentang Sun, Jiaxing Zhang, Xiaofei Xiang, Yongzhi Chen, Xiaoyun Li, Hui School of Mechanical and Material Engineering Wuzhou University Guangxi Wuzhou543000 China

ISBN: (纸本)9789811601149

The application of target recognition and sorting has greatly improved the level of industrial automation, liberated human hands, and effectively saved resources and improved efficiency. The purpose of this paper is to study target recognition and location based on machine vision and deep learning. Firstly, the development of machine vision is summarized, the algorithm of spatial structure feature extractor is proposed, and the overall architecture of fusion model is explained simply. After comparing the network model of this paper with that of SSD, the network positioning accuracy is analyzed. The experimental results show that when the threshold IoU is less than 0.5, the detection performance of faster R-CNN is better than that of SSD network, and when the threshold IoU is more than 0.5, the situation is the opposite. The experimental results show that the positioning accuracy of SSD network is higher than that of faster R-CNN, and the comprehensive detection performance gap between the two network models will reverse with the improvement of task requirements for positioning accuracy. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Intrusion Detection System Using soft computing Techniques: A Review 1

引用

1st international conference on Data Science, machine learning and Applications, 2019

作者： Agarwal, Pooja Dr. Shakuntala Misra National Rehabilitation University LucknowUttar Pradesh India

ISBN: (数字)9789811514203

ISBN: (纸本)9789811514197

In the proposed study, the past research work has been reviewed which was based on soft computing techniques for IDS and played a better role to detect the intrusion in computer networks. This study reviewed various research articles during 2009–2017 on the intrusion detection system by using soft computing techniques and tried to present a more comprehensive study. In the review process, the data collected has been related to the tools used, dataset class, approaches, performance metrics related details of the soft computing technique and summarizes it with effectiveness. Finally, considering collected statistics, strengths and weaknesses of reviewed articles the findings of this review shall be useful for future researchers in the field of intrusion detection system in their research. © 2020, Springer Nature Singapore Pte Ltd.

关键词： soft computing

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A PHP and JSP Web Shell Detection System With Text Processing Based On machine learning 19

A PHP and JSP Web Shell Detection System With Text Processin...

引用

19th IEEE international conference on Trust, Security and Privacy in computing and Communications (IEEE TrustCom)

作者： Zhang, Han Liu, Ming Yue, Zihan Xue, Zhi Shi, Yong He, Xiangjian Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9781665403924

Web shell is one of the most common network attack methods, and traditional detection methods may not detect complex and flexible variants of web shell attacks. In this paper, we present a comprehensive detection system that can detect both PHP and JSP web shells. After file classification, we use different feature extraction methods, i.e. AST for PHP files and bytecode for JSP files. We present a detection model based on text processing methods including TF-IDF and Word2vec algorithms. We combine different kinds of machine learning algorithms and perform a comprehensively controlled experiment. After the experiment and evaluation, we choose the detection machine learning model of the best performance, which can achieve a high detection accuracy above 98%.

关键词： Web Shell Opcode Bytecode AST machine learning XGBoost

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HOPFIELD NETWORKS IS ALL YOU NEED 9

HOPFIELD NETWORKS IS ALL YOU NEED

引用

9th international conference on learning Representations, ICLR 2021

作者： Ramsauer, Hubert Schäfl, Bernhard Lehner, Johannes Seidl, Philipp Widrich, Michael Adler, Thomas Gruber, Lukas Holzleitner, Markus Kreil, David Kopp, Michael Klambauer, Günter Brandstetter, Johannes Hochreiter, Sepp ELLIS Unit Linz LIT AI Lab Institute for Machine Learning Johannes Kepler University Linz Austria Austria

We introduce a modern Hopfield network with continuous states and a corresponding update rule. The new Hopfield network can store exponentially (with the dimension of the associative space) many patterns, retrieves the pattern with one update, and has exponentially small retrieval errors. It has three types of energy minima (fixed points of the update): (1) global fixed point averaging over all patterns, (2) metastable states averaging over a subset of patterns, and (3) fixed points which store a single pattern. The new update rule is equivalent to the attention mechanism used in transformers. This equivalence enables a characterization of the heads of transformer models. These heads perform in the first layers preferably global averaging and in higher layers partial averaging via metastable states. The new modern Hopfield network can be integrated into deep learning architectures as layers to allow the storage of and access to raw input data, intermediate results, or learned prototypes. These Hopfield layers enable new ways of deep learning, beyond fully-connected, convolutional, or recurrent networks, and provide pooling, memory, association, and attention mechanisms. We demonstrate the broad applicability of the Hopfield layers across various domains. Hopfield layers improved state-of-the-art on three out of four considered multiple instance learning problems as well as on immune repertoire classification with several hundreds of thousands of instances. On the UCI benchmark collections of small classification tasks, where deep learning methods typically struggle, Hopfield layers yielded a new state-of-the-art when compared to different machine learning methods. Finally, Hopfield layers achieved state-of-the-art on two drug design datasets. The implementation is available at: https://***/ml-jku/hopfield-layers. © 2021 ICLR 2021 - 9th international conference on learning Representations. All rights reserved.

关键词： learning systems

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Face Reconstruction with Generative Adversarial Network 2019

Face Reconstruction with Generative Adversarial Network

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3rd international conference on machine learning and soft computing (icmlsc)

作者： Putra, Dino Hariatma Basaruddin, T. Univ Indonesia Fac Comp Sci Depok Indonesia

ISBN: (纸本)9781450366120

Generative Adversarial Network (GAN) is a framework of deep learning in generative models. The generative model aims to synthesize a new data so that it has a distribution of distribution according to the original data distribution. In the current development, GAN is not only used to synthesize data from noise alone, but in the current development it has begun to be used to translate data from a domain to data with a different domain. Several studies have been developed, such as CycleGAN, and Pix2pix. In this study, the face has not been used as an object of translation. In this study a model for translating images of face sketches into face images will be made.

关键词： Generative Adversarial Network Deep learning Synthesize Translation Face Image Face Sketch CycleGAN

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Unsupervised Description of 3D Shapes by Superquadrics Using Deep learning 1

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international conference on Computer Vision and machine Intelligence, CVMI 2022

作者： Eltaher, Mahmoud Breuß, Michael Brandenburg University of Technology Cottbus Germany Al-Azhar University Cairo Egypt

ISBN: (数字)9789811978678

ISBN: (纸本)9789811978661

The decomposition of 3D shapes into simple yet representative components is a very intriguing topic in computer vision as it is very useful for many possible applications. Superquadrics may be used with benefit to obtain an implicit representation of the 3D shapes, as they allow to represent a wide range of possible forms by few parameters. However, in the computation of the shape representation, there is often an intricate trade-off between the variation of the represented geometric forms and the accuracy in such implicit approaches. In this paper, we propose an improved loss function, and we introduce beneficial computational techniques. By comparing results obtained by our new technique to the baseline method, we demonstrate that our results are more reliable and accurate, as well as much faster to obtain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Time Series Data Augmentation and Dropout Roles in Deep learning Applied to Fall Detection 15th

Time Series Data Augmentation and Dropout Roles in Deep Lear...

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15th international conference on soft computing Models in Industrial and Environmental Applications, SOCO 2020

作者： González, Enol García Villar, José Ramón de la Cal, Enrique Computer Science Department University of Oviedo Oviedo Spain

ISBN: (纸本)9783030578015

Fall Detection is one of the most interesting and challenging research topics in the world today because of its implications in society and also because the complexity of processing Time Series (TS). Plenty of research has been published in the literature, several of them introducing Deep learning (DL) Neural Network (NN) as the modelling element. In this study we analyse one of these contributions and address several enhancement using TS data augmentation and dropout. Moreover, the possibility of reducing the NN to make it lighter has been studied. The NN has been implemented using Keras in Python and the experimentation includes an staged fall publicly available data set. Results show the TS data augmentation together with dropout helped in learning a more robust and precise model. Future work includes introducing different types of cross-validation as well as introducing other types of DL models more suitable for TS. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fall detection

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Enhancing Explainability of machine learning-based Intrusion Detection Systems

Enhancing Explainability of Machine Learning-based Intrusion...

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IEEE international conference on Research, Innovation and Vision for the Future

作者： Thi-Ngoc-Phuc Ho Thuy-Linh Nguyen Xuan-Ha Nguyen Kim-Hung Le University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (纸本)9781665461672

Over the past decade, the anomaly-based Intrusion Detection System (IDS) has established itself with many studies proving its effectiveness, especially with deep learning models. However, these models have become more complex, thus making them difficult for humans to explain the system's decisions. Meanwhile, research to increase the transparency of IDSs receives insufficient attention from the research community. Therefore, this study proposes an Explainable NIDS capable of accurately detecting attacks and providing explicit explanations for its decisions. Our proposed IDS employs the Shapley Additive exPlanations (SHAP) framework to account for IDS decisions. It assists our IDS in self-explain its decisions at both the local and global levels. The local explanation explains the IDS decisions for each specific sample, while the global level provides the feature's importance and shows the attacks' characteristics. To demonstrate the effectiveness, we evaluate our proposal on two well-known IDS datasets: KDD Cup 99 and CICIDS2017. In addition, we experiment with interpreting not only the machine learning model but also the deep learning model. This research is expected to enhance the transparency of anomaly-based IDS and provide deeper insights for security experts.

关键词： Deep learning Additives Computational modeling Intrusion detection Communications technology Security Reliability

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Optimizing Digital Coupon Assignment Using Constrained Reinforcement learning 2019

Optimizing Digital Coupon Assignment Using Constrained Reinf...

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3rd international conference on machine learning and soft computing (icmlsc)

作者： Yao, Xinlin Lu, Xianghua Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China Fudan Univ Sch Management Shanghai Peoples R China

ISBN: (纸本)9781450366120

Coupon marketing is a traditional but effective way to retain customers and stimulate new purchases. Recently, digital coupons have been widely used in e-commerce and distributed to almost everyone. However, the decision on when and whom to issue the coupon is often based on managers' experience and calling for optimization and automation. Collaborated with a leading e-commerce platform, we propose an exploratory constrained reinforcement learning modeling to optimize digital coupon distribution policy under the constraint of maximum offering number. Our experimental results showed that the optimal policy could increase the cumulative total sales about 6% comparing to the original policy of the platform. This work enriches the applications of reinforcement learning in real-world business practices and provides useful implications for future study on constrained reinforcement learning.

关键词： Digital coupon assignment Constrained reinforcement learning Online marketing

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Utilizing Spatio-temporal Satellite Images and machine learning to Examine the Impacts of Changing Land Use and Land Cover on Sohar City

Utilizing Spatio-temporal Satellite Images and Machine Learn...

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Information Technology (ACIT)

作者： Muhammad Shafi Faculty of Computing and Information Technology Sohar University Sohar Oman

The growing urbanization of cities and towns in the Gulf Cooperation Council (GCC) states as a result of socioeconomic change has primarily resulted in a great deal of strain on the few natural resources and the loss of productive areas. In fact, there hasn't been much focus on the spatial patterns of urbanization and how they affect the environment and agricultural resources, especially in Oman. It may be possible to better understand the connection between spatial growth patterns and its effects on agricultural production by predicting urban growth in Sohar City. This study aims to analyze spatiotemporal dynamics of land use/land cover (LULC) for the last six years (2017–2022) and its impacts especially on the reduction of agricultural land. The spatiotemporal data was obtained from Sentinel-2 with a cell size of 10 meters which was then cropped using the vector boundaries of Sohar city. Part of the data was labelled manually which was used to train supervised machine learning technique for classifying the data into different land covers such as water bodies, trees, crops, etc. Accuracy assessment was carried out by com-paring the automatic classification results with ground truth and an accuracy of 93.4% was obtained. It has been found that built up area is increasing rapidly while hugely affecting the arable land. It's also interesting to see that during the COVID19 pandemic, this urban growth was stopped as evident from the satellite data. The results of this study could address the dangers and declines of urban sustainability for Sohar city, as well as provide geographical guidelines for observing future changes in LULC dynamics. The national strategy for future urban development in Oman also benefits from identifying regions of bare soil and vegetation that are amenable to urbanization.

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