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共 46 条记录，以下是11-20 订阅

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A composite methodology for supporting collaboration pattern discovery via semantic enrichment and multidimensional analysis

A composite methodology for supporting collaboration pattern...

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international conference of soft computing and pattern recognition (socpar)

作者： Alfredo Cuzzocrea Claudia Diamantini Laura Genga Domenico Potena Emanuele Storti ICAR-CNR and University of Calabria Rende Italy

Classical process discovery approaches usually investigate logs generated by processes in order to mine and discovery corresponding process schemas. When the collaboration processes case is addressed, such approaches turn to be poorly effective, due to the fact that: (i) logs of collaboration processes are usually stored in heterogeneous data storages which also expose different data types; (ii) it is not easy and direct to derive a common analysis model from such logs. As a consequence, classical methodologies usually fail. In order to fulfill this gap, in this paper we describe a composite methodology that combines semantics-based techniques and multidimensional analysis paradigms to support effective and efficient collaboration process discovery from log data.

关键词： Collaboration Electronic mail Taxonomy Semantics Context Buildings Data mining

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Impact of Activation Functions of Neural Network in Mammographic Masses 6

Impact of Activation Functions of Neural Network in Mammogra...

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6th international conference on Trends in Electronics and Informatics (ICOEI)

作者： Agrawal, Rashmi Gupta, Neha Manav Rachna Int Inst Res & Studies Dept Comp Applicat Chennai Tamil Nadu India

ISBN: (纸本)9781665483285;9781665483292

Computational intelligence is a well-established paradigm that has been creating new hypotheses based on good biological understanding. Defining computational intelligence is not an easy task because this area belongs to multiple disciplines like Artificial Intelligence, soft computing, Machine Intelligence, Machine Learning and many others. Research in this area include the computational techniques like support vector machines, fuzzy logic, neural networks along with a wide range of applications as modelling and pattern recognition. these methods have been applied to solve many complex and diverse problems. To solve the above mentioned complex problems, neural networks are used. the present paper will focus on relevance of neural network in mammographic masses dataset to determine its performance analysis using various activation function while performing the classification task.

关键词： Computational Intelligence Classification soft computing Neural Network Activation functions

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Neuro-fuzzy and soft computing for speaker recognition

Neuro-fuzzy and soft computing for speaker recognition

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Proceedings of the 1997 6th IEEE international conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3)

作者： Jang, Jyh-Shing Roger Chen, Jiuann-Jye Tsing Hua Univ Hsinchu Taiwan

this paper describes how techniques from the discipline of neuro-fuzzy and soft computing techniques can be used, in conjunction with methodologies from pattern recognition and digital signal processing, to effectively perform speech data classification. In particular, we have applied the proposed method to automatic speaker recognition and achieved satisfactory results.

关键词： Fuzzy control

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Fused Local pattern (FLP): A Novel Local Descriptor in Pose Variations 1

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14th international conference on soft computing and pattern recognition, socpar 2022, and the 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2022

作者： Karanwal, Shekhar UK Dehradun India

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

In this paper a new local descriptor is launched for Face recognition (FR) named as Fused Local pattern (FLP). FLP feature size is characterized from MRELBP-NI, RD-LBP and 6 × 6 MB-LBP. MRELBP-NI and 6 × 6 MB-LBP captures microstructure and macrostructure information from the higher and lower patches and RD-LBP captures useful information from radial pixels. Taking merits of all three descriptors their feature size are integrated and called as FLP. Compression and matching is done by PCA and SVMs. For experiments evaluation ORL dataset used. Experiments proves the effectiveness of FLP descriptor than singly implemented ones. FLP also beats various literature work. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Compaction

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An automated method for breast mass segmentation

An automated method for breast mass segmentation

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international conference of soft computing and pattern recognition (socpar)

作者： Sourour Khouaja Hajer Jlassi Kamel Hamrouni Image and Pattern Recognition National Engineering School of Tunis Tunis Tunisia

Breast cancer continues to be a significant health problem in the world. the most familiar breast anomalies types are mass and microcalcification. However Automatic methods for detecting these abnormalities can identify breast cancer at an early stage. In this paper, we propose a marker-controlled watershed algorithm to locate breast masses. the preprocessing step has been introduced to remove all undesirable areas from mammogram. Foreground and background markers are then selected in order to apply a watershed segmentation algorithm that identifies the location of tumor region in mammogram. the proposed method was successful to segment mass anomalies. It has been tested on publicly available Mammographic Image Analysis Society (MIAS) database and it has achieved an overall mass detection rate of 90.83% and an area Az of 0.913 under the receiver operating characteristic curve ROC for mass segmentation.

关键词： Image segmentation Muscles Databases Lesions Breast cancer

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Novel hand biometric system using invariant descriptors

Novel hand biometric system using invariant descriptors

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international conference of soft computing and pattern recognition (socpar)

作者： Nesrine Charfi Hanene Trichili Adel M. Alimi Basel Solaiman REGIM-Lab.: REsearch Groups in Intelligent Machines University of Sfax ENIS Sfax Tunisia Department of Image and Information Processing (ITI) Telecom-Bretagne (ENST) Brest France

Biometrics-based hand authentication is among the most popular biometrics used to automatically characterize a person especially in forensic applications. Hand recognition systems are able to confirm or deny the identity of a claimed person because they do not cause anxiety for the users. However, different individuals may have almost similar hands. therefore, the performance of the hand verification process depends highly on the hand descriptors. In this paper, we propose a new approach for personal verification based on Scale Invariant Feature Transform (SIFT). this transform proved its high distinction and efficiency in many applications especially in object recognition and video tracking. Two public databases have been used to evaluate performances. Experimental results show promising recognition rates by achieving 94% for IITD hand database and 98% for Bosphorus hand database.

关键词： Feature extraction Databases Shape Transforms pattern recognition Biometrics (access control) Principal component analysis

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A simultaneous topic and sentiment classification of tweets 1

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9th international conference on soft computing and pattern recognition, socpar 2017

作者： Hassan, Doaa Department of Computers and Systems National Telecommunication Institute 5 Mahmoud El Miligy Street 6th district-Nasr City Cairo Egypt

ISBN: (数字)9783319763576

ISBN: (纸本)9783319763569

Social networks have been an emerging technology for communication among billions of users. One of the most popular social networks is Twitter. the popularity of Twitter comes from its simplicity since it allows users to exchange messages of short length that does not exceed 140 characters and takes the form of tweets. In this paper, we propose a model for performing a classification of tweets posted by the Twitter user based on a mixture of the topic and sentiment of those tweets. the proposed approach is new in that it creates a model that combines the processes of topic and sentiment classification of tweets simultaneously. therefore, with this model, one can categorize tweets according to their topics and simultaneously assign them into different sentiments categories. the topic of the tweets in the basic experiment of the proposed approach is classified into five main different categories including: "political", "commercials", "educational", "religious", and "sportive". Meanwhile, the sentiment of those tweets is classified into three main different categories including "positive", "negative", "neutral". the effectiveness of the proposed approach is demonstrated on a real dataset that consists of various extracted tweets with different categories of topics and opinions. the empirical results show that our approach is very powerful in categorizing tweets according to topics and simultaneously assigning them into different sentiments categories. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Sentiment analysis

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Fused Local Color pattern (FLCP): A Novel Color Descriptor for Face recognition 1

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14th international conference on soft computing and pattern recognition, socpar 2022, and the 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2022

作者： Karanwal, Shekhar Department of CSE Graphic Era University UK Dehradun India

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

Literature suggests that when color information is used then there is immense improvement in accuracy. this work presented such novel descriptor so-called Fused Local Color pattern (FLCP) by using the RGB color format. Precisely from R component MRELBP-NI is imposed for feature extraction, from G component 6 × 6 MB-LBP is imposed for feature extraction and from B component RD-LBP is imposed for feature extraction. the features extracted from all three components are joined which is called as FLCP. Compaction and matching is done by PCA and SVMs. Results on GT confirms the effectiveness of the FLCP descriptor as compared to the individually implemented gray scale based descriptors. FLCP also outperforms various literature methods. the novelty of proposed work is the development of discriminant descriptor by fusing the features extracted from RGB color format. this concept is lacking in the previous work. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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A modified hybrid naïve possibilistic Classifier for heart disease detection from heterogeneous medical data

A modified hybrid naïve possibilistic Classifier for heart ...

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international conference of soft computing and pattern recognition (socpar)

作者： Karim Baati Tarek M. Hamdani Adel M. Alimi University of Monastir Higher Institute of Computer Science of Mahdia Tunisia REGIM-Lab.: REsearch Groups on Intelligent Machines University of Sfax. National Engineering School of Sfax (ENIS) Sfax. Tunisia College of Science And arts at AI-Ula Taibah University al-Madinah al-Munawwarah KSA

In this paper, we propose a modified Hybrid Naïve Possibilistic Classifier (HNPC) for heart disease detection from the heterogeneous data (numerical and categorical) of the Cleveland dataset. the proposed classifier is based on a different pattern with regard to our former HNPC which have been recently proposed to deal with the same problem. As HNPC, the modified classifier separates data into two subsets (numerical and categorical) and then estimates possibility beliefs using the two versions of the probability-possibility transformation method of Dubois ets al. for numerical and categorical data, respectively. However, unlike HNPC which is based on two fusion steps to make decision from possibility estimations, our new classifier performs a common fusion to combine these beliefs. During this fusion, the product and the minimum as main combination operators for possibility measures are investigated. Experimental evaluations on the Cleveland dataset show that the proposed modified HNPC may outperform the former HNPC as well as the main classification techniques which have been used in recent related work.

关键词： Diseases Heart Possibility theory Data models Educational institutions conferences pattern recognition

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Local directional multi radius binary pattern: novel descriptor for face recognition application 9th

Local directional multi radius binary pattern: novel descrip...

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9th international conference on soft computing and pattern recognition, socpar 2017

作者： Kas, Mohamed El Merabet, Youssef Ruichek, Yassine Messoussi, Rochdi Laboratoire LASTID Département de Physique Faculté des Sciences Université Ibn Tofail Kénitra Morocco Le2i UMR 6306 CNRS Université de Bourgogne Franche-Comté UTBM Belfort90010 France

ISBN: (纸本)9783319763569

Face recognition becomes an important task performed routinely in our daily lives. this application is encouraged by the wide availability of powerful and low-cost desktop and embedded computing systems, while the need comes from the integration in too much real world systems including biometric authentication, surveillance, human-computer interaction, and multimedia management. this article proposes a new variant of LBP descriptor referred as Local Directional Multi Radius Binary pattern (LDMRBP) as a robust and effective face descriptor. the proposed LDMRBP operator is built using new neighborhood topology and new pattern encoding scheme. the adopted face recognition system consists of three stages: (1) face detection and alignment to normalize the input images to a common form if needed;(2) feature extraction using the proposed descriptor in order to calculate the histogram, which represents the feature vector and (3) face recognition through a supervised image classification task using the simple K-Nearest Neighbors classifier. Simulated experiments on ORL, YALE and FERET under different illumination or facial expression conditions indicate that the proposed method outperforms other texture descriptors and other existing works of the literature. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Face recognition

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