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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Dhanalakshmi, R. Vijayaraghavan, N. Narasimhan, S. Rajesh, R. School. of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India Kcg College of Technology Dept. of Mathematics Chennai India School. of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350315127

With the rapid advancement of biological information, accurate analysis of treatment data aids in early disease detection. To uncover knowledge for medical research, advanced Machine Learning algorithms are applied. Here in this research paper, to determine if a patient is affected by any Infectious disorders, we use the Naive Bayes Algorithm method. © 2023 IEEE.

关键词： Machine learning

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A Demonstrative Study on Prediction of Disease Using Bayesian Machine Learning Techniques

A Demonstrative Study on Prediction of Disease Using Bayesia...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R. Dhanalakshmi N. Vijayaraghavan S. Narasimhan R Rajesh Centre for Cyber Physical Systems School. of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Mathematics KCG College of Technology Chennai India School. of Computer Science and Engineering Vellore Institute of Technology Chennai India

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Spatial and Channel-wise Attention in Multimodality Feature Fusion for Multispectral Pedestrian Detection

Spatial and Channel-wise Attention in Multimodality Feature ...

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2020 International Conference on Internet of Things and Intelligent Applications, ITIA 2020

作者： Wei, Hao Shen, Jifeng Zuo, Xin Yang, Wankou Jiangsu University School. of Electronic and Informatics Engineering Zhenjiang Jiangsu212013 China Jiangsu University of Science and Technology School. of Computer Science and Engineering Zhenjiang Jiangsu China Southeast University School. of Automation Nanjing Jiangsu210096 China

ISBN: (纸本)9781728193014

Multispectral pedestrian detection with feature fusion of thermal and visible images has achieved great success in all-day scenarios, but few attentions have been paid to the quality of feature fusion. In this paper, we have proposed an improved attention aware dual-stream Faster R-CNN algorithm to ameliorate the feature quality. Quantitative analysis of three different feature fusion methods demonstrate that the performance of detector is highly related to the quality of feature fusion. Therefore, an improved multi-step spatial and channel-wise attention method is proposed to improve the feature fusion quality step by step based on the dual-stream Faster R-CNN framework. With the aid of attention mechanism, the model learning is optimized in a more smooth way, which can effectively enhance the response of fused feature map in each stage. Experiments based on the KAIST and CVC-14 dataset demonstrate that the proposed method has achieved better performance compare to thebaseline method with a decrease of 4% and 8% in AMR respectively. © 2020 IEEE.

关键词： Feature extraction

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Spatial and Channel-wise Attention in Multimodality Feature Fusion for Multispectral Pedestrian Detection

Spatial and Channel-wise Attention in Multimodality Feature ...

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Internet of Things and Intelligent Applications (ITIA), International Conference on

作者： Hao Wei Jifeng Shen Xin Zuo Wankou Yang School. of Electronic and Informatics Engineering Jiangsu University Zhenjiang Jiangsu China School. of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang Jiangsu China School. of automation Southeast University Nanjing Jiangsu China

ISBN: (数字)9781728193014

ISBN: (纸本)9781728193021

关键词： Feature extraction Detectors Object detection Proposals Fuses Convolution Conferences

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Experimental comparisons of multi-class classifiers

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Informatica (Slovenia) 2015年第1期39卷 71-85页

作者： Li, Lin Wu, Yue Ye, Mao Institute of Intelligent Computing and Information Technology Chengdu Normal University No.99 East Haike Road Wenjiang District Chengdu China School. of Computer Science and Engineering University of Electronic Science and Technology of China No.2006 Xiyuan Ave Chengdu China

The multi-class classification algorithms are widely used by many areas such as machine learning and computer vision domains. Nowadays, many literatures described multi-class algorithms, however there are few literature that introduced them with thorough theoretical analysis and experimental comparisons. This paper discusses the principles, important parameters, application domain, runtime performance, accuracy, and etc. of twelve multi-class algorithms: decision tree, random forests, extremely randomized trees, multi-class adaboost classifier, stochastic gradient boosting, linear and nonlinear support vector machines, K nearest neighbors, multi-class logistic classifier, multi-layer perceptron, naive Bayesian classifier and conditional random fields classifier. The experiment tested on five data sets: SPECTF heart data set, Ionosphere radar data set, spam junk mail filter data set, optdigits handwriting data set and scene 15 image classification data set. Our major contribution is that we study the relationships between each classifier and impact of each parameters to classification results. The experiment shows that gradient boosted trees, nonlinear support vector machine, K nearest neighbor reach high performance under the circumstance of binary classification and minor data capacity;Under the condition of high dimension, multi-class and big data, however, gradient boosted trees, linear support vector machine, multi-class logistic classifier get good results. At last, the paper addresses the development and future of multi-class classifier algorithms.

关键词： Support vector machines

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Multi-class image classification based on fast stochastic gradient boosting

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Informatica (Slovenia) 2014年第2期38卷 145-153页

作者： Li, Lin Wu, Yue Ye, Mao School. of Computer Science and Engineering University of Electronic Science and Technology of China No.2006 Xiyuan Ave West Hi-Tech Zone Chengdu China Sichuan TOP IT Vocational Institute No.2000 Xixin Ave West Hi-Tech Zone Chengdu China

Nowadays, image classification is one of the hottest and most difficult research domains. It involves two aspects of problem. One is image feature representation and coding, the other is the usage of classifier. For better accuracy and running efficiency of high dimension characteristics circumstance in image classification, this paper proposes a novel framework for multi-class image classification based on fast stochastic gradient boosting. We produce the image feature representation by extracting PHOW descriptor of image, then map the descriptor though additive kernels, finally classify image though fast stochastic gradient boosting. In order to further boost the running efficiency, We propose method of local parallelism and an error control mechanism for simplifying the iterating process. Experiments are tested on two data sets: Optdigits, 15-Scenes. The experiments compare decision tree, random forest, extremely random trees, stochastic gradient boosting and its fast versions. The experiment justifies that (1) stochastic gradient boosting and its extensions are apparent superior to other algorithms on overall accuracy;(2) our fast stochastic gradient boosting algorithm greatly saves time while keeping high overall accuracy.

关键词： Image classification

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A functional Link Artificial Neural Network for Location Management in Cellular Network

A functional Link Artificial Neural Network for Location Man...

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International Conference on Information Communication and Embedded Systems

作者： Smita Parija Prasanna.Kumar Sahu. Sudhansu. Sekhar Singh Santosh Kumar Nanda Dept. of Electrical engineering National Institute of Technology Rourkela India School. of Electrical Science KIIT University BBSR India Dept. of Computer Science engineering Eastern Academy of Science Technology Bhubaneswar India.

ISBN: (纸本)9781467357869

Mobility management is one of the major issues in mobile networks to provide an efficient and low-cost service. In this paper, we intend a prediction-based location management scheme for locating a mobile host (MH) or mobile station, which depends on its history of movement pattern of a mobile subscriber. A multilayer neural network (MNN) model for mobile movement prediction is designed to predict the future movement of a mobile host. For predicting the location of a mobile host the MNN is trained with respect to the data obtained from the movement pattern. The difficulties associated with the location management can be solved by nonlinear neural network that is computationally efficient. The major issue in feed forward neural network such as Multilayer perceptron (MLP) trained with back Propagation (BP) is that it requires a large amount of computation time for learning the network. Functional Link Neural Network (FLNN) is proposed here and that is simpler than MLP-BP. This is basically a single layer structure in which nonlinearity is introduced where the input pattern is enhanced with nonlinear functional expansion. The novelty of the proposed work is it requires less computation than that of MLP-BP. With proper choice of functional expansion in case of FLANN, this network performs better than multilayer perceptron with back propagation .It is observed from the simulation result that FLANN outperforms MLP-BP in terms of performance error. It is also shown that proposed network is computationally cheap and gives better classification accuracy than that of MLP classifier.

关键词： Location prediction Neural Network MLP FLANN

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Hybrid heuristics based on harmony search to minimize total flow time in no-wait flow shop

Hybrid heuristics based on harmony search to minimize total ...

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The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

作者： K. Z. Gao H. Li Q. K. Pan J. Q. Li J.J. Liang College of Computer Science Liaocheng University Liaocheng 252059 PR China School. Electrical Engineering Zhengzhou University

This paper proposes an effective harmony search (HS) based heuristics method for the no-wait flow-shop scheduling optimization. The purpose of this paper is to minimize the total flow time. Firstly, the basic HS optimization mechanism, FSSP is presented and the total flow time is calculated by a novel method. Secondly, a largest-order-value rule is used to transform harmony in harmony memory from real vectors to job sequence so that the harmony search can be applied for FSSP. Improvising rule of new harmony is expatiated and high effective algorithm parameters are set for optimization object. Thirdly, HS and Insert-and interchange-based neighbor search, is proposed to solve the FSSP. At last, simulations and comparisons demonstrate the efficiency, effectiveness and robustness of harmony search algorithm for no-wait FSSP.

关键词： Harmony search total flow time no-wait flow shop

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A Novel Discrete Harmony Search Algorithm for Scheduling Lot-streaming Flow Shops

A Novel Discrete Harmony Search Algorithm for Scheduling Lot...

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The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

作者： Quan-Ke Pan Jun-hua Duan Junqing Li Kaizhou Gao J. J. Liang College of Computer Science Liaocheng University Liaocheng 252059 PR China School. Electrical Engineering Zhengzhou University

This paper proposes a novel discrete harmony search (DHS) algorithm to minimize makespan for a lot-streaming flow shop scheduling problem with sequence dependent setup times. Unlike the traditional harmony search (HS) algorithm, the proposed DHS algorithm utilizes job permutations to represent harmonies and applies a job-permutation-based improvisation to generate new harmonies. To enhance the algorithm’s searching ability, an effective initialization scheme based on the NEH heuristic is developed to construct an initial harmony memory with certain quality and diversity, and an efficient local search algorithm based on the insert neighborhood structures is fused to stress the local exploitation. Extensive computational simulations and comparisons are provided, which demonstrate the effectiveness of the proposed DHS against the best performing algorithms from the literature.

关键词： Flow shop scheduling Lot-streaming Harmony search algorithm Makespan

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Multi-Agent Architecture for Information Fusion Platform in Coal Enterprise

Multi-Agent Architecture for Information Fusion Platform in ...

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International Conference on Information science and engineering (ICISE)

作者： Sheng-li Hu Qun Jiang School. of Computer Science and Engineering Anhui University of Science and Technology Huainan China School. of Mechanical Electronic & Information Engineering China University of Mining &Technology Beijing China

How to integrate and analyse vast production and management information plays a very important role for coal enterprise. A multi-level coal enterprise integrated information fusion platform is put forward based on multi-agent system technology, a distributed information fusion model is proposed, Multi-agent framework and coordination mechanism of different agents in the architecture are discussed. The platform can integrate the isolated application system of coal enterprise and fuse the various information under heterogeneous environment efficiently.

关键词： computer architecture Production systems computer science Technology management Multiagent systems Isolation technology Application software Fuses Intelligent systems Monitoring

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