检索结果-内蒙古大学图书馆

1st international Multimodal Sentiment Analysis in Real-Life Media Challenge and workshop, MuSe 2020

作者： Amiriparian, Shahin Winokurow, Pawel Karas, Vincent Ottl, Sandra Gerczuk, Maurice Schuller, Björn Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Augsburg Germany

ISBN: (纸本)9781450381574

Motivated by the attention mechanism of the human visual system and recent developments in the field of machine translation, we introduce our attention-based and recurrent sequence to sequence autoencoders for fully unsupervised representation learning from audio files. In particular, we test the efficacy of our novel approach on the task of speech-based sleepiness recognition. We evaluate the learnt representations from both autoencoders, and conduct an early fusion to ascertain possible complementarity between them. In our frameworks, we first extract Mel-spectrograms from raw audio. Second, we train recurrent autoencoders on these spectrograms which are considered as time-dependent frequency vectors. Afterwards, we extract the activations of specific fully connected layers of the autoencoders which represent the learnt features of spectrograms for the corresponding audio instances. Finally, we train support vector regressors on these representations to obtain the predictions. On the development partition of the data, we achieve Spearman's correlation coefficients of .324, .283, and .320 with the targets on the Karolinska Sleepiness Scale by utilising attention and non-attention autoencoders, and the fusion of both autoencoders' representations, respectively. In the same order, we achieve .311, .359, and .367 Spearman's correlation coefficients on the test data, indicating the suitability of our proposed fusion strategy. © 2020 ACM.

关键词： learning systems

来源：评论

学校读者我要写书评

暂无评论

A Survey on machine learning Approaches in Water Analysis 13th

A Survey on Machine Learning Approaches in Water Analysis

引用

13th mining Humanistic data workshop, MHDW 2024, 9th workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 international workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Tsimpidi, Ilektra Sartjärvi, Rosa Juntunen, Petri Nikolakopoulos, George Robotics and AI Team Department of Computer Science Space and Electrical Engineering Luleå University of Technology Luleå Sweden Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9783031632266

The aim of this article is to present a survey on machine learning approaches for performing water analysis as in general integrating Artificial Intelligence in water analysis has a transformative potential for optimizing and sustaining water resources. Recent trends in water treatment and desalination applications effectively address pollution and scarcity challenges, while by combining AI with technologies like data analytics, water management complexities are simplified, ensuring sustainability and cost-effectiveness through predictive data assessment. This survey presents examples of the ML’s significance in water treatment and in water quality analysis, such as for enhancing accuracy in water quality index (WQI) assessments and examples of exploring and predicting pollutants in water processes, and aiding in the early detection and categorization of contaminants. Furthermore, this survey will reveal the immense potential in leveraging ML algorithms to enhance water analysis accuracy, speed, and efficiency, paving the way for reduced chemical usage and improved understanding of microbiological processes. As it will be presented, ML approaches offer valuable tools across various stages of water analysis, from identifying critical indicators to accurately measuring and predicting water parameters. The survey concludes by emphasizing the potential of AI and particularly ML to revolutionize water resource management, offering precision, efficiency, and foresight in addressing challenges of water scarcity and sustainable resource utilization. © IFIP international Federation for Information Processing 2024.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Experimental evaluation on machine learning techniques for human activities recognition in digital education context 1

引用

1st international workshop on Social Computing in Digital Education, SOCIALEDU 2015

作者： Leitão, Gabriel Colonna, Juan Ribeiro, Erick Barreto, Raimundo Araujo, Thierry-Yves Martins, Anny Koster, Andrew Koch, Fernando Manaus Brazil Samsung Research Institute CampinasSP France

ISBN: (数字)9783319396729

ISBN: (纸本)9783319396712

This paper describes an experimental evaluation of the main machine learning supervised techniques to be used for the human activities recognition in the context of technological education using data collected from smartphones sensors. The overall goal is to use the recognition of activities to identify students with attention deficit or hyperactivity problems, by recognizing three activities: walking, standing and sitting. Hence, this work focuses on developing activities recognition method of the students. The methodology consists in: collecting data where the user explicitly states what activity he/she is doing;applying various techniques to automatically recognize the activities;and measuring the degree of accuracy of each technique. The results shows that techniques such as Bayesian inference and SVM (Support Vector machine) have smaller accuracy than techniques based on decision tree and kNN (k-nearest neighbors). Furthermore, the techniques based on decision trees have a constant computational cost, while the kNN depends on the number of samples. © Springer international Publishing Switzerland 2016.

关键词： students

来源：评论

学校读者我要写书评

暂无评论

Combining fisher linear discriminants for dissimilarity representations 1

引用

1st international workshop on Multiple Classifier Systems, MCS 2000

作者： Pȩkalska, Elzbieta Skurichina, Marina Duin, Robert P.W. Pattern Recognition Group Department of Applied Physics Faculty of Applied Sciences Lorentzweg 1 2628 CJ Delft Netherlands

ISBN: (数字)9783540450146

ISBN: (纸本)3540677046

Investigating a data set of the critical size makes a classification task difficult. studying dissimilarity data refers to such a problem, since the number of samples equals their dimensionality. In such a case, a simple classifier is expected to generalize better than the complex one. Earlier experiments [9,3] confirm that in fact linear decision rules perform reasonably well on dissimilarity representations. For the Pseudo-Fisher linear discriminant the situation considered is the most inconvenient since the generalization error approaches its maximum when the size of a learning set equals the dimensionality [10]. However, some improvement is still possible. Combined classifiers may handle this problem better when a more powerful decision rule is found. In this paper, the usefulness of bagging and boosting of the Fisher linear discriminant for dissimilarity data is discussed and a newmethod based on random subspaces is proposed. This technique yields only a single linear pattern recognizer in the end and still significantly improves the accuracy. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

ENSIOT: A stacking Ensemble learning Approach for IoT Device Identification 32

ENSIOT: A Stacking Ensemble Learning Approach for IoT Device...

引用

IEEE/ACM 32nd international Symposium on Quality of Service (IWQoS)

作者： Niu, Kangli Liu, Shenghao Yi, Lingzhi Deng, Xianjun Chen, Suning Yang, Laurence T. Cheng, Minmin Hubei Key Lab Distributed Syst Secur Wuhan Peoples R China Hubei Engn Res Ctr Big Data Secur Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan Peoples R China

ISBN: (纸本)9798350350128

In order to resist network attacks on IoT devices, identifying IoT devices is the first step for ensuring device security. The traditional passive method identifies IoT devices by mining the potential relationship between traffic characteristics and devices. However, the form of selected traffic features are too singular without considering device behavioral characteristics and the classifier used is too specific with simple structure in these methods. This paper proposes a stacking ensemble learning approach for IoT device identification, ENSIOT, which fully considering the behavioral characteristics of devices and integrating the advantages of various machine learning methods to achieve efficient identification of IoT devices. Firstly, in the process of traffic processing, our method selects features from activity cycles, port numbers, signalling patterns, and cipher suites. Then, in model integration, many machine learning methods are used as base models to learn features selected, and output preliminary recognition results. Finally, the meta model learns the relationship between label and the recognition results of each base model and outputs the final device identification result. This stacking structure stacks the base models and the meta model to make a classifier with strong identification and generalization ability. Incremental learning is used to improve identification accuracy when traffic pattern changing. Comparative experiments are conducted on two datasets of UNSW and TMA-2021. The experimental results verify the effectiveness of ENSIOT, which achieve the accuracy of over 98% on two dataset and bring a noticeable improvement in terms of both accuracy and macro F1 score.

关键词： IoT security ensemble learning IoT device identification

来源：评论

学校读者我要写书评

暂无评论

Support vector learning for gender classification using audio and visual cues

引用

international JOURNAL OF pattern recognition AND ARTIFICIAL INTELLIGENCE 2003年第3期17卷 417-439页

作者： Walavalkar, L Yeasin, M Narasimhamurthy, A Sharma, R Penn State Univ Dept Comp Sci Pond Lab 220 University Pk PA 16802 USA

Computer vision systems for monitoring people and collecting valuable demographic information in a social environment is an important research problem. It is expected that such a system will play an increasingly important role in enhancing user's experience and can significantly improve the intelligibility of a human computer interaction (HCI) system. For example, a robust gender classification system can provide a basis for passive surveillance and access to a smart building using demographic information or can provide valuable consumer statistics in a public place. The option of an audio cue in addition to the visual cue promises a robust solution with high accuracy and ease-of-use in human computer interaction systems. This paper investigates gender classification using Support Vector machines (SVMs). The visual (thumbnail frontal face) and the audio (features from speech data) cues were considered for designing the classifier. Three different representations of the data, namely, raw data, principle component. analysis (PCA) and non-negative matrix factorization (NMF) were used for the experimentation with visual signal. For speech, mel-cepstral coefficient and pitch were used for the experimentation. It was found that the best overall classification rates obtained using the SVM for the visual and speech data were 95.31% and 100%, respectively, on data set collected in laboratory environment. The performance of the SVM was compared with two simple classifiers namely, the nearest prototype neighbor and the k-nearest neighbor on all feature sets. It was found that the SVM outperformed the other two classifiers on all datasets. To further understand the robustness issues, the proposed approach has been applied on a large balanced (roughly equal distribution of gender, ethnicity and age group) data-base consisting of 8000 faces collected in real world environment. While, the results are very promising it indicates more to be done to make a statistically meaningful co

关键词： gender classification support vector machines (SVMs) PCA NMF mel-cepstrum linear and nonlinear classifies

来源：评论

学校读者我要写书评

暂无评论

The massive data classifiers based on reduced set vectors method 1

The massive data classifiers based on reduced set vectors me...

引用

1st international Conference on Communications, Circuits and Systems, ICCCAS 2002

作者： Ding, Ai-Ling Liu, Fang Zhao, Xiang-Mo Computer School Xidian University Xi'an710071 China Institute of Information and Engineering Chang-An University Xi'an710064 China

ISBN: (纸本)0780375475

A support vector machine (SVM) is a universal learning machine whose decision surface is parameterized by a set of support vectors. SVMs find application in pattern recognition, regression estimation, and operator inversion for ill-posed problems. SVMs are currently considerably slower in the test phase than other approaches with similar generalization performance. To this important problem, we propose the reduced vector set method to significantly decrease the complexity of the decision rules obtained by the SVM. The proposed method made the SVM test speed competitive with that of other methods. Simulation results for massive and complex data turns out to support our idea. © 2002 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Leveraging Semi-Supervised learning to Enhance data mining for Image Classification under Limited Labeled data 4

Leveraging Semi-Supervised Learning to Enhance Data Mining f...

引用

4th international Conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Shen, Aoran Dai, Minghao Hu, Jiacheng Liang, Yingbin Wang, Shiru Du, Junliang University of Michigan Ann Arbor United States Columbia University New York United States Tulane University New Orleans United States Northeastern University Seattle United States Dartmouth College Hanover United States Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331534622

In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. This study optimizes data mining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. The experimental results demonstrate that the proposed method significantly outperforms traditional machine learning techniques such as Support Vector machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios. © 2024 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

A fast SVM training algorithm 1st

A fast SVM training algorithm

引用

1st international workshop on pattern recognition with Support Vector machines, SVM 2002

作者： Dong, Jian-Xiong Krzyżak, Adam Suen, Ching Y. Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 1M8 Canada Department of Computer Science Concordia University 1455 de Maisonneuve Blvd. W MontrealQCH3G 1M8 Canada

ISBN: (纸本)354044016X

A fast support vector machine (SVM) training algorithm is proposed under the decomposition framework of SVM’s algorithm by effectively integrating kernel caching, digest and shrinking policies and stopping conditions. Extensive experiments on MNIst handwritten digit database have been conducted to show that the proposed algorithm is much faster than Keerthi et al.’s improved SMO, about 9 times. Combined with principal component analysis, the total training for ten oneagainst-the-rest classifiers on MNIst took just 0. 77 hours. The promising scalability of the proposed scheme can make it possible to apply SVM to a wide variety of problems in engineering. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Multi-agent monitoring system for heat loss mapping of multi-story buildings 1

Multi-agent monitoring system for heat loss mapping of multi...

引用

1st international workshop on Information-Communication Technologies and Embedded Systems, ICT and ES 2019

作者： Burlachenko, Ivan Zhuravska, Iryna Tohoiev, Oleksii Ukhan, Yehor Tiutiunyk, Yevhen Petro Mohyla Black Sea National University Mykolaiv Ukraine LeadsMarket LLC Woodland HillsCA United States

In this paper, the problem of developing a multi-agent method for detecting the places of heat energy leaks on the multi-story buildings using machine learning is solved. Efficient data processing of scanning areas for the heat energy leak monitoring was achieved using the multi-agent monitoring system (MAMS) that can perform calculations in the cloud conditionally. Features of the monitoring system with the integration of an analytical model for presenting a heat loss map with an account of multiple autonomous separated UAV's for temperature measurements were contained. The MAMS reliability of the synchronization model between simultaneous localization and mapping method and generated heat loss map based on temperature measurements was confirmed. It has been experimentally proven that theoretical assumptions and accuracy for experimental usage during the multi-story building leaks analysis are sufficient. The recognition time of markers of the front of the building is in the range from 0 to 27 s. In this case, with the proposed model ?NN, the CPU load during the execution of tasks did not exceed 26%. Copyright © 2019 for this paper by its authors.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：