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共 729 条记录，以下是691-700 订阅

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A machine learning approach for classification of sentence polarity

A Machine Learning approach for classification of sentence p...

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international conference on signal processing and Integrated Networks (SPIN)

作者： Neethu Akkarapatty Nisha S Raj Department of Computer Science with Information Systems SCMS School of Engineering & Technology Ernakulam India

ISBN: (纸本)9781467391986

Opinion Mining is the process used to determine the attitude/opinion/emotion expressed by a person about a particular topic. Analyzing opinions is an integral part for making decisions. In the era of web, if a person wants to buy a product, he will look into the reviews and comments given by the experienced users in web. But it seems to be a tedious task to read the entire reviews available in the web. Hence people are interested in checking whether the review recommends to buy a product or not. If lot of reviews recommends to buy the product, user reach at a conclusion to buy the product, otherwise not to buy the product. In this study, machine learning approach is applied to the TripAdvisor dataset in order to develop an efficient review classification. For this work to be carried out, style markers are applied to each of the reviews. In the next stage, significant style markers are recognized with the help of some suitable feature selection method. Thus the reviews can be identified by developing a classifier using the style markers that help to characterize nature of reviews as positive or negative.

关键词： Feature extraction Data mining signal processing Sentiment analysis Tagging machine learning algorithms Syntactics

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Detection and classification of diseases of Grape plant using opposite colour Local Binary Pattern feature and machine learning for automated Decision Support System

Detection and classification of diseases of Grape plant usin...

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international conference on signal processing and Integrated Networks (SPIN)

作者： Harshal Waghmare Radha Kokare Yogesh Dandawate Dept. of Electronics and Telecommunication Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9781467391986

Plant diseases cause major economic and production losses as well as curtailment in both quantity and quality of agricultural production. Now a day's, for supervising large field of crops there is been increased demand for plant leaf disease detection system. The critical issue here is to monitor the health of the plants and detection of the respective diseases. Studies show that most of the plant disease can be diagnosed from the properties of the leaf. Thus leaf based disease analysis for plants is an exciting new domain. The technique proposed for identification of plant disease through the leaf texture analysis and pattern recognition. In this work we focus on Grapes plant leaf disease detection system. The system takes a single leaf of a plant as an input and segmentation is performed after background removal. The segmented leaf image is then analyzed through high pass filter to detect the diseased part of the leaf. The segmented leaf texture is retrieved using unique fractal based texture feature. Fractal based features are locally invariant in nature and therefore provides a good texture model. The texture of every independent disease will be different. The extracted texture pattern is then classified using multiclass SVM. The work classifies focus on major diseases commonly observed in Grapes plant which are downy mildew & black rot. The proposed approach avails advice of agricultural experts easily to farmers with the accuracy of 96.6%.

关键词： Diseases Feature extraction Support vector machines Image color analysis Agriculture Pipelines Decision support systems

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Acoustic signal processing for Anomaly Detection in machine Room Environments: Demo Abstract 16

Acoustic Signal Processing for Anomaly Detection in Machine ...

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Proceedings of the 3rd ACM international conference on Systems for Energy-Efficient Built Environments

作者： Bong Jun Ko Jorge Ortiz Theodoros Salonidis Maroun Touma Dinesh Verma Shiqiang Wang Xiping Wang David Wood IBM T.J. Watson Research Center Yorktown Heights NY United States

ISBN: (纸本)9781450342643

We present a system that uses acoustic signals to monitor equipment in commercial buildings, such as in machine rooms with HVAC system components. The system uses an ensemble of machine learning classifiers to effectively label signals as either "normal" or "abnormal". We collect audio clips from mobile devices in a machine room and an elevator shaft in the main building of IBM Research. We use these to learn the spectrum of normal sound signatures and identify abnormal sounds that fall outside this range. Abnormal sounds detected by the system are presented to the end user for anomaly confirmation. We also integrate a work-order system to automatically issue a repair work-order if the sound is abnormal.

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machine learning and BCI 3

Machine Learning and BCI

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3rd international Winter conference on Brain-Computer Interface (BCI)

作者： Mueller, Klaus-Robert TU Berlin Machine Learning Grp Marchstr 23 D-10587 Berlin Germany Korea Univ Dept Brain & Cognit Engn Seoul South Korea

ISBN: (纸本)9781479974962

A main motivation for multimodal imaging has been the possibility to enhance medical diagnosis[1]. Beyond this original medical motivation the fusion of multiple modalities has created successful interesting research opportunities that have furthered our understanding of the brain and cognition[15]. In BCI recently multimodal fusion concepts have received great attention under the label hybrid BCI[13]. Fusing information has also been a very common practice in the sciences and engineering [17]. Recently a family of novel multimodal data analysis methods have emerged that can extract nonlinear relations between data[1,2,5-10]. They are rooted in the modern machine learning and signal processing techniques that are now available for analysing EEG, for decoding mental states etc[3,11,12,14,16]. The talk will first discuss recent multimodal analysis techniques such as SPoC[5-7]. Furthermore if time permits we will discuss a novel reliable method for estimating the Hurst exponent, a quantity that has recently become popular for describing network properties and is being used for diagnostic purposes [4]. Both nonlinear techniques allow for a better and more reliable and robust analysis of complex phenomena in neurophysiological data.

关键词： Artificial Intelligence machine learning Research Opportunities signal processing Fusion reactions FUSION fusion (melting) Fusion, biological Multimodal Imaging

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Smart Traffic Optimization Using Image processing 3

Smart Traffic Optimization Using Image Processing

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IEEE 3rd international conference on MOOCs, Innovation and Technology in Education (MITE)

作者： Maheshwari, Pranav Suneja, Deepanshu Singh, Praneet Mutneja, Yogeshwar Carnegie Mellon Univ Sch Comp Sci Inst Robot Pittsburgh PA 15213 USA Infosys Ltd Bangalore Karnataka India TCS Ltd New Delhi India

ISBN: (纸本)9781467367479

Currently the traffic control system in place in our country is non-flexible and non-adaptive to the ever growing number of vehicles on the road. It does not take into account the changing density of traffic during the different hours of the day. Consequently the roads get congested frequently and intersections get blocked. Time and fuel, two highly important resources get wasted in this inefficient working of the present-day system. In this article, we propose a dynamic system that overcomes all these drawbacks. Our system uses cameras installed at the red lights and intersections to monitor the traffic dynamically. It then processes this information using image processing, computes the volume of the real time traffic, sets the timer of the signal accordingly. Simultaneously, it monitors if there is any scope of congestion at the intersection and adjusts the timer to prevent it. The entire system works autonomously and has a quick turnaround time, saving critical resources at every junction. The system also has the potential to adopt machine learning techniques in order to recognize the different emerging patterns of the future traffic and reach an optimal solution.

关键词： Operations management Edge Detection Image Analysis Congestion Control

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Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms 3

Comparative study of Principal Component Analysis based Intr...

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3rd international conference on signal processing, Communication and Networking, ICSCN 2015

作者： Chabathula, Krupa Joel Jaidhar, C.D. Ajay Kumara, M.A. Department of IT NITK Surathkal Mangalore India

ISBN: (纸本)9781467368230

This paper induces the prominence of variegated machine learning techniques adapted so far for the identifying different network attacks and suggests a preferable Intrusion Detection System (IDS) with the available system resources while optimizing the speed and accuracy. With booming number of intruders and hackers in todays vast and sophisticated computerized world, it is unceasingly challenging to identify unknown attacks in promising time with no false positive and no false negative. Principal Component Analysis (PCA) curtails the amount of data to be compared by reducing their dimensions prior to classification that results in reduction of detection time. In this paper, PCA is adopted to reduce higher dimension dataset to lower dimension dataset. It is accomplished by converting network packet header fields into a vector then PCA applied over high dimensional dataset to reduce the dimension. The reduced dimension dataset is tested with Support Vector machines (SVM), K-Nearest Neighbors (KNN), J48 Tree algorithm, Random Forest Tree classification algorithm, Adaboost algorihm, Nearest Neighbors generalized Exemplars algorithm, Navebayes probabilistic classifier and Voting Features Interval classification algorithm. Obtained results demonstrates detection accuracy, computational efficiency with minimal false alarms, less system resources utilization. Experimental results are compared with respect to detection rate and detection time and found that TREE classification algorithms achieved superior results over other algorithms. The whole experiment is conducted by using KDD99 data set. © 2015 IEEE.

关键词： Principal component analysis

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3rd international conference on Statistical Language and Speech processing, SLSP 2015

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3rd international conference on Statistical Language and Speech processing, SLSP 2015

ISBN: (纸本)9783319257884

The proceedings contain 27 papers. The special focus in this conference is on Statistical Language and Speech processing. The topics include: Towards two-way interaction with reading machines;the prediction of fatigue using speech as a biosignal;supertagging for a statistical HPSG parser for spanish;residual-based excitation with continuous F0 modeling in HMM-based speech synthesis;discourse particles in french;effects of evolutionary linguistics in text classification;evaluation of the impact of corpus phonetic alignment on the HMM-based speech synthesis quality;decoding distributed tree structures;combining continuous word representation and prosodic features for ASR error prediction;semi-extractive multi-document summarization via submodular functions;a comparison of human and machine estimation of speaker age;acoustical frame rate and pronunciation variant statistics;the influence of boundary depth on phrase-final lengthening in russian;automatic detection of voice disorders;semantic features for dialogue act recognition;conversational telephone speech recognition for lithuanian;long-term statistical feature extraction from speech signal and its application in emotion recognition;rhythm-based syllabic stress learning without labelled data;unsupervised and user feedback based lexicon adaptation for foreign names and acronyms;combining lexical and prosodic features for automatic detection of sentence modality in french;corpus based methods for learning models of metaphor in modern greek;probabilistic speaker pronunciation adaptation for spontaneous speech synthesis using linguistic features;weakly supervised discriminative training of linear models for natural language processing and merging of native and non-native speech for low-resource accented ASR.

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3rd IAPR TC3 Workshop on Multimodal Pattern Recognition of Social signals in Human-Computer-Interaction, MPRSS 2014

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3rd IAPR TC3 Workshop on Multimodal Pattern Recognition of Social signals in Human-Computer-Interaction, MPRSS 2014

ISBN: (纸本)9783319148984

The proceedings contain 18 papers. The special focus in this conference is on Pattern Recognition, machine learning, and Information Fusion Methods with Applications in Social signal processing. The topics include: Automatic image collection of objects with similar function by learning human grasping forms;client specific image gradient orientation for unimodal and multimodal face representation;multiple-manifolds discriminant analysis for facial expression recognition from local patches set;noisy speech recognition based on combined audio-visual classifiers;complementary Gaussian mixture models for multimodal speech recognition;fusion of text and audio semantic representations through CCA;a real time gesture recognition system for human computer interaction;a SIFT-based feature level fusion of iris and ear biometrics;audio-visual user identification in HCI scenarios;online smart face morphing engine with prior constraints and local geometry preservation and exploring alternate modalities for tag recommendation.

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NLP based sentiment analysis on Twitter data using ensemble classifiers

NLP based sentiment analysis on Twitter data using ensemble ...

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international conference on signal processing, Communication and Networking (ICSCN)

作者： Monisha Kanakaraj Ram Mohana Reddy Guddeti Dept. of Information Technology National Institute of Technology Karnataka Mangalore India National Institute of Technology Calicut Calicut Kerala IN

Most sentiment analysis systems use bag-of-words approach for mining sentiments from the online reviews and social media data. Rather considering the whole sentence/ paragraph for analysis, the bag-of-words approach considers only individual words and their count as the feature vectors. This may mislead the classification algorithm especially when used for problems like sentiment classification. Traditional machine learning algorithms like Naive Bayes, Maximum Entropy, SVM etc. are widely used to solve the classification problems. These machine learning algorithms often suffer from biasness towards a particular class. In this paper, we propose Natural Language (NLP) based approach to enhance the sentiment classification by adding semantics in feature vectors and thereby using ensemble methods for classification. Adding semantically similar words and context-sense identities to the feature vectors will increase the accuracy of prediction. Experiments conducted demonstrate that the semantics based feature vector with ensemble classifier outperforms the traditional bag-of-words approach with single machine learning classifier by 3-5%.

关键词： machine learning algorithms Twitter Training Sentiment analysis Semantics signal processing algorithms Support vector machines

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Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms

Comparative study of Principal Component Analysis based Intr...

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international conference on signal processing, Communication and Networking (ICSCN)

作者： Krupa Joel Chabathula C.D. Jaidhar M.A. Ajay Kumara Department of IT NITK Surathkal Mangalore India

关键词： Principal component analysis machine learning algorithms signal processing algorithms Mathematical model Accuracy Vegetation Support vector machines

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