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检索条件"任意字段=5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024"

共 134 条记录，以下是91-100 订阅

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A Swift and Accurate Approach to Breast Cancer recognition Using CNN and Decision Tree Method

A Swift and Accurate Approach to Breast Cancer Recognition U...

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Global conference for Advancement in Technology (GCAT)

作者： Jeeya Sharma Deepak Kumar Abhiraj Malhotra Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Radiological examination using MRI is critical in the early diagnosis and in planning the therapy in handling cancer. Complexity and heterogeneity of tumor presentation in MRI scans usually necessitates the use of extreme machine learning strategies because the problem is beyond the capacity of conventional methodologies. It is in this work that the author puts forward a double strategy that aims at enhancing the ability of MRI image cancer diagnosis by combining CNNs and DTs. To ensure the best outcome of the classification, features the CNN recognizes patterns in the MRI scans are divided into the DT classifier. the CNN is applied for feature extraction. this method enhances the detection accuracy by a large extent by combine both the efficiencies and strengths of CNNs and DTs. For classification purpose, the dataset containing 10 thousand MRI scans split equally between the scans having cancer and those not, was collected from TCIA for the validation of the model. CNN+DT proved to be better than conventional CNN only approach with the test accuracy of 92% after the extensive trial. In identifying the cancerous regions of the mammogram, the model was competent and equally airtight in avoiding false identification stemming from false positives where records of 80% were confirmed on precision, recall and, F1-score measures. thus, these results suggest that using CNNs and DTs might be a way to improve the cancer diagnosis in MRI images. As for the advantage of this hybrid technique, it provides a better accuracy than the radial basis function model while giving better interpretability of a model, which, to the clinician, may be very important.

关键词： Accuracy Magnetic resonance imaging Atmospheric modeling Medical treatment Planning pattern recognition Convolutional neural networks Decision trees Cancer Tumors

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A Ternary Bi-Directional LSTM Classification for Brain Activation pattern recognition Using fNIRS 5

A Ternary Bi-Directional LSTM Classification for Brain Activ...

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5th international conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Wickramaratne, Sajila D. Mahmud, Md Shaad Univ New Hampshire Elect & Comp Engn Durham NH 03824 USA

ISBN: (纸本)9781728180229

Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost method used to study the brain's blood flow pattern. Such patterns can enable us to classify performed by a subject. In recent research, most classification systems use traditional machine learning algorithms for the classification of tasks. these methods, which are easier to implement, usually suffer from low accuracy. Further, a complex pre-processing phase is required for data preparation before implementing traditional machine learning methods. the proposed system uses a Bi-Directional LSTM based deep learning architecture for task classification, including mental arithmetic, motor imagery, and idle state using fNIRS data. Further, this system will require less pre-processing than the traditional approach, saving time and computational resources while obtaining an accuracy of 81.48%, which is considerably higher than the accuracy obtained using conventional machine learning algorithms for the same data set.

关键词： fNIRS deep learning LSTM mental arithmetic

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Developing a Smart IoT Solution to Monitor on-Bed Movement patterns 5

Developing a Smart IoT Solution to Monitor on-Bed Movement P...

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5th international conference on Information Technology, InCIT 2020

作者： Youngkong, Prakrankiat Panpanyatep, Worawit thamrongaphichartkul, Kitti Institute of Field Robotics King Mongkut's University of Technology Thonburi Thailand

ISBN: (纸本)9781728166940

IoT devices and systems become a part of modern living. they are mostly used to monitor daily activities, especially related to personal health and fitness. In fact, it is getting more crucial during the COVID-19 pandemic. In this study, a smart monitoring and alarming IoT system called 'NEF' was modified to recognize on-bed movement patterns including prone position applying different machine learning techniques. On-bed movement patterns were collected from 7 subjects. Considering only prone and supine positions, the models obtained from multilayer perceptron was the best. However, random forest yielded the highest overall correctly classified percentage. Further investigation is likely to include beddings such as pillows and blankets. © 2020 ieee.

关键词： pattern recognition

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Enhanced Segmentation-CNN based Finger-Vein recognition by Joint Training with Automatically Generated and Manual Labels 5

Enhanced Segmentation-CNN based Finger-Vein Recognition by J...

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5th ieee international conference on Identity, Security, and Behavior Analysis (ISBA)

作者： Jalilian, Ehsaneddin Uhl, Andreas Univ Salzburg Dept Comp Sci Jakob Haringer Str 2 Salzburg Austria

ISBN: (纸本)9781728105321

Deep learning techniques are nowadays the leading approaches to solve complex machine learning and pattern recognition problems. For the first time, we utilize state-of-the-art semantic segmentation CNNs to extract vein patterns from near-infrared finger imagery and use them as the actual vein features in biometric finger-vein recognition. In this context, beside investigating the impact of training data volume, we propose a training model based on automatically generated labels, to improve the recognition performance of the resulting vein structures compared to (i) network training using manual labels only, and compared to (ii) well established classical recognition techniques relying on publicly available software. Proposing this model we also take a crucial step in reducing the amount of manually annotated labels required to train networks, whose generation is extremely time consuming and error-prone. As further contribution, we also release human annotated ground-truth vein pixel labels (required for training the networks) for a subset of a well known finger-vein database used in this work, and a corresponding tool for further annotations.

关键词： Veins Image segmentation Feature extraction Training pattern recognition Computer architecture Decoding

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Emotion Detection of Autistic Children Using Image Processing 5

Emotion Detection of Autistic Children Using Image Processin...

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5th international conference on Image Information Processing (ICIIP)

作者： Rani, Pooja Cent Univ Punjab Dept Comp Sci & Technol Bathinda Punjab India

ISBN: (纸本)9781728108995

Facial Emotion Detection is an approach towards detecting human emotions through facial expressions. Autism Spectrum Disorder is an advance neurobehavioral disorder. Autistic people have repetitive, rude behavior. they are not ready to do social communication. People with this syndrome have problems with emotion recognition. this paper works on detecting the emotions of autistic children from the expression of their faces. this paper works on four emotions. these emotions are sad, happy, neutral, and angry. To detect the emotion of autistic children is performed with image processing and machine learning algorithms. the features are extracted from the faces of autistic children with local binary pattern. machine learning algorithms are used for classification of emotions. machine learning classifiers used in classification process are support vector machine and neural network.

关键词： machine learning Support Vector machine Neural Network Emotion Detection Image Processing Local Binary pattern

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Jamming pattern recognition Using Spectrum Waterfall: A Deep learning Method

Jamming Pattern Recognition Using Spectrum Waterfall: A Deep...

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ieee international conference on Computer and Communications

作者： Yuan Cai Kai Shi Fei Song Yifan Xu Ximing Wang Heyu Luan Coege of Counicions Engineering Ary Engineering Universiy of PLAPLA 31121 TroosCoege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLA

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

It is very important for communication equipment to realize the recognition and prediction of jamming signals in a short time. In the communication environment with variable jamming mode and noise, it is very difficult to achieve rapidly jamming recognition. To solve this problem, this paper constructs a spectrum waterfall containing white gaussian noise signals, designs a jamming pattern recognition method based on deep learning, and realizes rapid recognition of jamming patterns and important parameters. Simulation results show that the training process of this method has considerable accuracy and the test process has high recognition rate.

关键词： Jamming Convolution Training Feature extraction Receivers pattern recognition machine learning

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machine learning Methods Analysis For Calories Measuremnt of Fruits and Vegetables

Machine Learning Methods Analysis For Calories Measuremnt of...

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ieee international conference on Signal Processing, Computing and Control (ISPCC)

作者： Manisha Mittal Gittaly Dhingra Vinay Kumar Thapar Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781728139890

As people around the globe are becoming conscious about their weight, consume healthy and low-calorie food and keep away from obesity, it's an urge to establish a reliable system with high accuracy and efficiency for calorie and nutrition measurement in fruit/vegetable. the proposed model is developed to assist patients and dieticians to compute daily intake of calories. In this approach, 5 different machine learning models are used to predict classification accuracy. Our system includes camera and intelligent mat to capture the picture of the fruit/vegetable, in order to calculate the consumption of calorie. the proposed model achieves 88% accuracy with different testing-training cross validation dataset.

关键词： food products learning (artificial intelligence) medical computing pattern classification medical computing Calorific value pattern recognition Food low energy food Nutritionists Artificial Intelligence machine learning Trustworthy System Vegetable Consciousness Fruits and vegetables Globe

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pattern recognition for the Working Condition Diagnosis of Oil Well Based on Electrical Parameters 5

Pattern Recognition for the Working Condition Diagnosis of O...

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5th ieee international conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Zhuo, Jin Dang, Hua Wu, Haixia Pang, Haojun Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Shanghai Inst Spaceflight Control Technol 1555 Zhongcun RD Shanghai Peoples R China

ISBN: (纸本)9781538660058

the measured dynamometer card used to analyze working conditions of pumping units requires a large amount of manpower and material resources, and thus it is very necessary to develop a novel and simple method for the diagnosis of pumping conditions. In this paper, a pattern recognition algorithm is proposed for the classification of working conditions of oil wells based on electrical parameters. To this end, the data of electrical parameters are obtained by the acquisition system;according to the algorithm theories (logistic regression, support vector machine, principal components analysis) of pattern recognition in machine learning, the binary and multiple classifiers of condition diagnosis are used to realize the diagnosis of the working conditions of the oil wells based on electrical parameters. the improvement of the proposed algorithm has been verified by analytical and simulation results.

关键词： pattern recognition Working Condition Diagnosis Oil Well Electrical Parameter

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Environmental and Geo-Spatial Data Analytics (EnGeoData' 2018) 5

Environmental and Geo-Spatial Data Analytics (EnGeoData' 201...

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5th ieee international conference on Data Science and Advanced Analytics (ieee DSAA)

作者： Teisseire, Maguelonne Roche, Mathieu Inkpen, Diana Univ Montpellier AgroParisTech CNRS TETISIrsteaCirad Montpellier France Univ Ottawa Ottawa ON Canada

ISBN: (纸本)9781538650905

the special session "Environmental and Geospatial Data Analytics" (EnGeoData) brings together researchers interested by pre-and post-processing of environmental data, geographical information retrieval, spatial data mining and spatial data warehousing, knowledge discovery use-cases dedicated to environmental data, spatial text mining, spatial ontology, spatial recommendations and personalization, visual analytics for geospatial data, and dedicated applications. this year, the special session EnGeoData' 2018 of the 5th ieee international conference on Data Science and Advanced Analytics (DSAA' 2018) contains a collection of four papers and provides high-quality research covering part of the challenges of Spatio-Temporal Data Science, from a theoretical or experimental point of view.

关键词： Geographic information systems Data mining Data processing Data visualization machine learning learning systems Knowledge based systems pattern recognition Digital systems Information systems Database systems Remote sensing Crowdsourcing

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LBP vs SVD for Color Images Feature Extraction 5

LBP vs SVD for Color Images Feature Extraction

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5th ieee international conference on Smart Instrumentation, Measurement and Application (ICSIMA)

作者： Faridah, Y. Nasir, Haida Nikolay, S. Kushsairy, A. K. Khan, Sheroz Univ Kuala Lumpur Malaysian Inst Informat Technol 1016 Jalan Sultan Ismail Kuala Lumpur 50250 Malaysia Int Islamic Univ Malaysia Batu 8Jalan Sg Pusu Gombak 53100 Selangor Malaysia Technion Israel Inst Technol Haifa Israel

ISBN: (纸本)9781538662885

Images are important element in our daily life. We use images to identify things or associate them with others that become important in decision making or correlate with other object for identification purpose. Feature extraction need to be applied to images for them to be useful for the mentioned purpose. By definition, feature extraction is the transformation of input data into a set of features, normally distinctive properties of input patterns that differentiate between many input of patterns. It is also the process to represent raw image (important criteria or parts of) to help in decision making such as pattern detection, classification, recognition or image processing or machine learning. the process of extracting information from images is one of the important elements in authenticating process of biometric system. Many techniques for extracting features have been emerging in the current research. LBP (Local Binary pattern) has been a popular technique in biometric systems used for feature extraction. LBP is a texture feature ex fraction method which produce clearer image result. Another favorite technique known as SVD (Single Value Decomposition) has been selected, discussed and compared with the latter. A sample of still color images were tested for both methods and the results are compared and discussed further in here. this paper concentrates on these two techniques for image processing in a biometric system.

关键词： Algorithm LBP (Local Binary pattern) SVD (Single Value Decomposition) Feature Extraction Texture

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