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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431-440 订阅

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A Performance Analysis Approach for Network Intrusion Detection Algorithms 12th

A Performance Analysis Approach for Network Intrusion Detect...

引用

12th EAI international conference on Simulation Tools and Techniques, SIMUtools 2020

作者： Wang, Zhihao Jiang, Dingde Wang, Yuqing Zhang, Junyang School of Astronautics and Aeronautic University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9783030727918

With the development of mobile Internet and cloud computing, the amount of network traffic has been significantly increased. Security problems have drawn a lot of attention, while traditional methods are becoming increasingly unsuitable for it. In this paper, three machine learning algorithms are employed to detect network intrusion, including KNN, Random Forest, and Multilayer Perceptron. Performance evaluation and comparison between them are conducted, in terms of precision, recall, training time, etc. Simulation results on the NSL-KDD, a benchmark data set of network intrusion detection, show that the Random Forest algorithm exhibits higher detection accuracy and remarkably shorter training time. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021.

关键词： machine learning

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SoC Kohonen Maps Based on Stochastic computing

SoC Kohonen Maps Based on Stochastic Computing

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international Joint conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Moran, Alejandro Rossello, Josep L. Roca, Miquel Canals, Vincent Univ Balearic Isl Dept Phys Palma Of Majorca Spain

ISBN: (纸本)9781728169262

Mobile systems and by extension Internet of Things (IoT) applications requests more and more machine learning functions, thus requiring a big computational power with a small power available. These demands have led to renewed interest in unconventional hardware computing methods capable of to implement complex functions in a simple way, with a very small power consumption. This work proposes a novel System-on-Chip (SoC) implementation of a Kohonen Map based on stochastic computing. In turn, to support this development, several stochastic block designs are presented as Winner-Take-All (WTA) similarity check and the squared Euclidian distance. The capabilities and performance of the proposed SoC solution is tested over a well-known classification task over the Fisher's Iris data set, archiving the same classification performance than the software. The proposed solution requests few hardware resources and low power, due to its inherent capacity to implement complex functions in a simple way. This enables to implement large self-learning classifiers based on Kohonen maps on tiny systems.

关键词： Stochastic logic Unsupervised learning Kohonen maps Field programmable gate arrays System-on-chip

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Accidental Face Recognition and Detection Using machine learning

Accidental Face Recognition and Detection Using Machine Lear...

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Advanced Innovations in Smart Cities (ICAISC), international conference on

作者： Ashish Sharma Piyansh Agrawal Krishan Bhisham Sharma Imed Ben Dhaou Department of Computer Engineering & Applications GLA University Mathura UP India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

The data collected from all the States and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms.

关键词： Resistance Technological innovation Road accidents Social networking (online) Smart cities Face recognition Roads

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Active learning for Road Lane Landmark Inventory with Random Forest in Highly Uncontrolled LiDAR Intensity Based Image 15th

Active Learning for Road Lane Landmark Inventory with Random...

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

作者： Izquierdo, Asier Lopez-Guede, Jose Manuel Airestudio Geoinformation Technologies Scoop Albert Einstein Kalea 44 E6 Oficina 8 Vitoria-Gasteiz01510 Spain Nieves Cano 12 Vitoria-Gasteiz01006 Spain San Sebastián Spain

ISBN: (纸本)9783030578015

Road landmark inventory is becoming an important industry for the maintenance of transport infrastructures among others. Several commercial sensors are available witch include LiDAR sensors allowing to capture up to 1.5 million data point per second. We obtain an intensity based image from the LiDAR point cloud intensity. The landmark detection is posed as a two class classification problem that may be solved by some standard approaches, for example, Random Forest (RF). Besides model parameter selection, a central problem is the construction of the labeled dataset due to human labor cost and the highly uncontrolled conditions of the data capture. We propose an open ended Active learning approach with a human operator in the loop who can start the Active learning process when detection quality is degraded by the change in data condition in order to achieve adaptation to them. As an additional contribution, we have assessed the ability of Active learning to overcome the issues raised by highly class imbalanced dataset, reaching a True Pixel Ratio value of 0.98. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Classification of Grain Discoloration via Transfer learning and Convolutional Neural Networks 2019

Classification of Grain Discoloration via Transfer Learning ...

引用

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

作者： Nghia Duong-Trung Luyl-Da Quach Minh-Hoang Nguyen Chi-Ngon Nguyen FPT Univ Can Tho City Vietnam Tay Do Univ Dept Engn Technol Can Tho City Vietnam Univ Trento Dept Informat Engn & Comp Sci Trento Italy Can Tho Univ Coll Engn Technol Can Tho City Vietnam

ISBN: (纸本)9781450366120

Grain discoloration disease of rice is an emerging threat to rice harvest in Vietnam as well as all over the world and it acquires specific attention as it results in qualitative loss of harvested crop. An accurate classification is preliminary to any kind of intervention. Unfortunately, collecting enough grain discoloration data as well as building and training a machine learning model from scratch is next to impossible due to the lack of hardware infrastructure and finance support. It painfully restricts the needs of rapid solutions to deal with the disease. For this purpose, this paper exploits the idea of transfer learning which is the improvement of learning in a new prediction task through the transfer of knowledge from a related prediction task that has already been learned. By utilizing convolutional neural networks trained with our collected data, our experiment shows that the proposed idea performs perfectly and achieves the classification accuracy of 88.2% with the acceptable training time on a normal laptop.

关键词： Image Classification Grain Discoloration Transfer learning Convolutional Neural Networks

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Towards a transfer learning approach to food recommendations through food images 2019

Towards a transfer learning approach to food recommendations...

引用

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

作者： Hasan, Hafiz Muhammad Zubair Khan, Hammad Asif, Talha Hashmi, Sohaib Rafi, Muhammad Natl Univ Comp & Emerging Sci FAST Natl Highway Karachi Sindh Pakistan

ISBN: (纸本)9781450366120

User generated text/multimedia content are increasingly shared in online businesses systems and their effective use in user modelling and recommendation strategies is consequently growing too. In restaurant businesses the food menu along with images are a common practice and users also shared the food images they ordered and feel good about. Yelp data set challenge in their round 9 and onward introduced such rich images data for their competition. In this paper, we motivated from this rich images data of food for semantically incorporating image-specific features to the star-rating and recommendation process. We first applied a transfer learning approach with pre-trained CNNs (Convolution Neural Networks) which were used to label the Yelps food images of the restaurants using the Food101 data-set. We defined star-rating for restaurants by capturing a correlation between restaurant images and users shared images. Our proposed strategy works on discovering hidden aspects of food images and labels to be used in recommendation strategy. We performed an extensive set of experiments by creating a baseline using standard rating provided in the Yelp data-set. The proposed approach produced better Root Mean Square Error (RMSE), which is a clear indication of high-quality recommendation strategy

关键词： recommendation system transfer learning image classification Yelp round 10 Food101 root mean square error

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Optimization and performance analysis of extreme learning machine by l2-norm regularization

Optimization and performance analysis of extreme learning ma...

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international conference on Applications and Techniques in Cyber Intelligence, ATCI 2020

作者： Wang, Ya Wang, Qingqing Guo, Xingchen Li, Huan Niu, Lei Wang, Huiling Wang, Xianchuan Fuyang Normal University FuyangAnhui236037 China

ISBN: (纸本)9783030539795

Extreme learning machine is a new feedforward neural network. Compared with the traditional neural network, it has the advantages of simple network construction and fast learning. However, as the least square method is used to solve the optimal output weight, the extreme learning machine has some problems such as weak anti-interference ability, poor stability and over-fitting. For the above problems, L2-norm regularization is adopted to optimize the extreme learning machine model, and minimize the training error and output weight by determining the regular parameters in this paper. Simulation results on standard data sets shows that optimization model by L2-norm regularization can significantly improve the stability and anti-interference of model. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Feedforward neural networks

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Supervised and Unsupervised learning Applied to Crowdfunding 1

引用

international conference on Computational Vision and Bio-Inspired computing (ICCVBIC)

作者： Torralba Quitian, Oscar Ivan Paola Lis-Gutierrez, Jenny Viloria, Amelec Univ Cent Bogota Colombia Univ Nacl Colombia Bogota Colombia Fdn Univ Konrad Lorenz Bogota Colombia Univ Costa CUC Calle 58 55-66 Atlantico Baranquilla Colombia

ISBN: (数字)9783030372187

ISBN: (纸本)9783030372187;9783030372170

This paper aims to establish the participation behavior of residents in the city of Bogota between 25 and 44 years of age, to finance or seek funding for entrepreneurial projects through crowdfunding? In order to meet the proposed objective, the focus of this research is quantitative, non-experimental and transactional (2017). Through data collection and data analysis, we seek patterns of behavior of the target population. Two machine learning techniques will be used for the analysis: supervised learning (using the learning algorithm of the decision tree) and unsupervised learning (clustering). Among the main findings are that (i) most of the people who would participate as an entrepreneur and donor and entrepreneur simultaneously belong to stratum 3;(ii) Crowdfunding projects based on donations do not have a high interest on the part of Bogotans, but those in which they aspire to recover the investment.

关键词： Supervised learning Unsupervised learning Crowdfunding Bogota Decision tree Clustering machine learning

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Initial Research on Fruit Classification Methods Using Deep Neural Networks

Initial Research on Fruit Classification Methods Using Deep ...

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11th international Image Processing and Communications conference (IP and C)

作者： Nasarzewski, Zbigniew Garbat, Piotr Warsaw Univ Technol Inst Radioelect & Multimedia Technol Warsaw Poland Warsaw Univ Technol Inst Microelect & Optoelect Warsaw Poland

ISBN: (纸本)9783030312541;9783030312534

In the following paper, the methods from deep learning area, with the classical method of machine learning, have been compared. The fine-tuned networks: VGG, ResNet and MobileNet, as well as SVM, have been put together. Models of neural networks were trained using own dataset of fruit photos taken in laboratory conditions with the help of high-speed industrial cameras.

关键词： Fruit classification Object recognition Image recognition Image processing machine learning Deep learning Deep neural network SVM classifcator Image processing applications

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COOPERATIVE learning VIA FEDERATED DISTILLATION OVER FADING CHANNELS

COOPERATIVE LEARNING VIA FEDERATED DISTILLATION OVER FADING ...

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IEEE international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ahn, Jin-Hyun Simeone, Osvaldo Kang, Joonhyuk Korea Adv Inst Sci & Technol Daejeon South Korea Kings Coll London London England

ISBN: (纸本)9781509066315

Cooperative training methods for distributed machine learning are typically based on the exchange of local gradients or local model parameters. The latter approach is known as Federated learning (FL). An alternative solution with reduced communication overhead, referred to as Federated Distillation (FD), was recently proposed that exchanges only averaged model outputs. While prior work studied implementations of FL over wireless fading channels, here we propose wireless protocols for FD and for an enhanced version thereof that leverages an offline communication phase to communicate "mixed-up" covariate vectors. The proposed implementations consist of different combinations of digital schemes based on separate source-channel coding and of over-the-air computing strategies based on analog joint source-channel coding. It is shown that the enhanced version FD has the potential to significantly outperform FL in the presence of limited spectral resources.

关键词： Distributed training machine learning federated learning joint source-channel coding

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