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检索条件"主题词=Support Vector Machine Classifier"

共 235 条记录，以下是131-140 订阅

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Diabetic Retinopathy Related Lesions Detection and Classification Using machine Learning Technology 15th

Diabetic Retinopathy Related Lesions Detection and Classific...

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15th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Saha, Rituparna Chowdhury, Amrita Roy Banerjee, Sreeparna West Bengal Univ Technol Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9783319393841

A novel Computer Aided Diagnosis System for early diagnosis of Diabetic Retinopathy is proposed for the detection and classification of Bright lesion classes and Dark lesion classes of Fundus Retina images using machine learning mechanisms. In the proposed methodology, the detection procedure is based on Fuzzy C Means (FCM) clustering technique to segment the candidate region areas. In the Dark lesion category, attempts are being made to modify the Micro aneurysms detection and Blood vessel elimination with the help of improvised algorithms. For the classification of each Bright and Dark lesion classes a classification system is built using machine learning algorithms namely Naive Bayes and support vector machine. A comparative study between the two machine learning algorithms yield accuracy of 97.0588% for Bright lesion classification using Naive Bayes classifier and accuracy of 88.8889% for Dark lesion classification using support vector machine classifier.

关键词： Diabetic retinopathy Fuzzy C means Nave Bayes classifier support vector machine classifier

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3rd IEEE Global Conference on Consumer Electronics (GCCE)

作者： Kinoshita, Shohei Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Sch Engn Kita Ku N-13W-8 Sapporo Hokkaido 0608628 Japan Hokkaido Univ Grad Sch Informat Sci & Technol Kita Ku Sapporo Hokkaido 0600814 Japan

ISBN: (纸本)9781479951468

This paper presents popular music estimation based on a topic model using time information and audio features. The proposed method calculates latent topic distribution using Latent Dirichlet Allocation to obtain more accurate music features. In this approach, we also use release date information of each music as time information for concerning the relationship between music trends and each age. Then, by using the obtained latent topic distribution features, the estimation of the popular music becomes feasible based on a support vector machine classifier. Experimental results show the effectiveness of our method.

关键词： music pattern classification support vector machines audio features latent Dirichlet allocation music estimation popular music estimation support vector machine classifier time information topic model Educational institutions Estimation Market research Multiple signal classification Music support vector machines vectors

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Integrating recursive Bayesian estimation with support vector machine to map probability of flooding from multispectral Landsat data

Integrating recursive Bayesian estimation with support vecto...

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International Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Sarker, Chandrama Alvarez, Luis Mejias Woodley, Alan Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld Australia

ISBN: (纸本)9781509028962

This paper addresses the challenge of introducing a Bayes rules to measure flood probability from multispectral data. machine learning classifiers were applied to map the extent of flood inundation from multispectral remote sensing imagery. The paper applies Extended support vector machine classifier along with linear spectral un-mixing to obtain the classification output. K-means clustering is applied on pre and post flood images to select SVM training samples from clustering outcome of the most informative spectral band. Experiments were conducted by dividing training and testing samples into two groups. The efficiency of classifier was enhanced by introducing the Bayesian probability measure and performance was assessed by using precision and recall metrics on the pre and post Bayesian flood probability estimation. It has been observed that for some test cases in this study there was a substantial improvement in precision-recall curve with high precision values and low recall rate. The optimal flood probability threshold value has also been easily calculated by calculating and analising iteratively precision and recall.

关键词： remote sensing K-means clustering support vector machine classifier Bayesian estimation

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Object Categories Specific Brain Activity Classification with Simultaneous EEG-fMRI 37

Object Categories Specific Brain Activity Classification wit...

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37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Ahmad, Rana Fayyaz Malik, Aamir Saeed Kamel, Nidal Reza, Faruque Univ Teknol Petronas CISIR Dept Elect & Elect Engn Tronoh 31750 Malaysia Univ Sains Malaysisa Dept Neurosci Kota Baharu 16150 Malaysia

ISBN: (纸本)9781424492701

Any kind of visual information is encoded in terms of patterns of neural activity occurring inside the brain. Decoding neural patterns or its classification is a challenging task. Functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) are non-invasive neuroimaging modalities to capture the brain activity pattern in term of images and electric potential respectively. To get higher spatiotemporal resolution of human brain from these two complementary neuroimaging modalities, simultaneous EEG-fMRI can be helpful. In this paper, we proposed a framework for classifying the brain activity patterns with simultaneous EEG-fMRI. We have acquired five human participants' data with simultaneous EEG-fMRI by showing different object categories. Further, combined analysis of EEG and fMRI data was carried out. Extracted information through combine analysis is passed to support vector machine (SVM) classifier for classification purpose. We have achieved better classification accuracy using simultaneous EEG-fMRI i.e., 81.8% as compared to fMRI data standalone. This shows that multimodal neuroimaging can improve the classification accuracy of brain activity patterns as compared to individual modalities reported in literature.

关键词： bioelectric potentials biomedical MRI electroencephalography image classification image coding image resolution neurophysiology spatiotemporal phenomena support vector machines electric potential functional magnetic resonance imaging multimodal neuroimaging neural activity pattern decoding spatiotemporal resolution specific brain activity classification support vector machine classifier visual information encoding Accuracy Brain Data acquisition Electroencephalography Neuroimaging support vector machines Visualization

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Diagnosis of Diabetic Retinopathy Using Morphological Process and SVM classifier

Diagnosis of Diabetic Retinopathy Using Morphological Proces...

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2nd IEEE International Conference on Communications and Signal Processing (ICCSP)

作者： Gandhi, Mahendran Dhanasekaran, R. Syed Ammal Engn Coll Dept Elect & Commun Engn Ramanathapuram Tamil Nadu India

ISBN: (纸本)9781467348669;9781467348652

Diabetic Retinopathy (DR), the most common eye disease of the diabetic patients, occurs when small blood vessels gets damaged in the retina, due to high glucose level. It affects 80% of all patients who have had diabetes for 10 years or more, which can also lead to vision loss. Detection of diabetic retinopathy in advance, protects patients from vision loss. The major symptom of diabetic retinopathy is the exudates. Exudate is a fluid that filters from the circulatory system into lesions or area of inflammation. Detecting retinal fundus diseases in an early stage, helps the ophthalmologists apply proper treatments that might eliminate the disease or decrease the severity of it. This paper focuses on automatic detection of diabetic retinopathy through detecting exudates in colour fundus retinal images and also classifies the rigorousness of the lesions. Decision making of the severity level of the disease was performed by SVM classifier.

关键词： Diabetic Retinopathy Erosion Exudates support vector machine classifier

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Predict Driver Fatigue Using Facial Features

Predict Driver Fatigue Using Facial Features

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Berkati, Oussama Srifi, Mohamed Nabil Ibn Tofail Univ Natl Sch Appl Sci Elect & Telecommun Res Grp Kenitra Morocco

ISBN: (纸本)9781538673287

Fatigue on board is amoung the causes of fatal accidents today, however drained driver means that there a metal box without control that threatens lives in our roads. Nowadays, there is no effective non-intrusive method to detect driver tiredness. This paper presents a method for early prediction signs of fatigue during driving. Luckily our face characteristics reflects our current state, therefore we will base on facial features to monitor the driver state. To localize driver's face we had chosen Viola-Jones for faces detection and for face tracking Kanade-Lucas-Tomasi is the best alternative due to their implementation simplicity using just standard phone camera equipped by IR LED. Then all extracted frame characteristics will be presented to SVM for classification that separate the normal state from the critical state. Our objective is to avoid false alerts and early fatigue detection in real-time, for this reason we will combine HOG+SVM, eyes blink rate/duration and PERCLOS. The driver state detection and fatigue alert are not the final steps in our method because a bad reaction can cause disasters that is why we include different road users notification message.

关键词： Fatigue detection support vector machine classifier Viola-Jones Kanade-Lucas-Tomasi Histograms of Oriented Gradients

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Hierarchical Computer Aided Diagnostic System for Seizure Classification 2

Hierarchical Computer Aided Diagnostic System for Seizure Cl...

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2nd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Sood, Meenakshi Bhooshan, Sunil V. Jaypee Univ Informat Technol Dept ECE Solan Himachal Prades India J Jaypee Univ Informat Technol Dept ECE Solan Himachal Prades India

ISBN: (纸本)9789380544168

EEG is the most economical and effective tool for understanding the complex dynamic behavior of the brain and studying its physiological states. In the present work, hierarchical computer aided diagnostic system (HCAD) for classification of normal, ictal and inter-ictal of EEG signals is proposed. In the present work, three different HCAD systems comprising of SVM, KNN and PNN classifiers are proposed. It is observed that the SVM based CAD system results in highest classification accuracy of 96% in comparison with 94% and 93.3% as obtained from KNN and PNN based HCAD systems. The promising results obtained from the present work indicate that the proposed SVM based HCAD system can be routinely used for seizure classification in clinical practice.

关键词： Hierarchical Computer-aided diagnostic system K-Nearest Neighbor Probabilistic Neural Network Seizure support vector machine classifier

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Exploration of a Hybrid Design Based on EEG and Eye Movement 10th

Exploration of a Hybrid Design Based on EEG and Eye Movement

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10th International Conference on Intelligent Robotics and Applications (ICIRA

作者： Yang, Junyou Hao, Yuan Bai, Dianchun Jiang, Yinlai Yokoi, Hiroshi Shenyang Univ Technol Sch Elect Engn Shenyang Liaoning Peoples R China Univ Electrocommun Sch Intelligent Robot Chofu Tokyo Japan

ISBN: (纸本)9783319652894;9783319652887

This study presents a novel hybrid interface based on both electroencephalography (EEG) and eye movement. The detection of combination EEG with eye movement provides a new means of communication for patients whose muscular damage are unable to communicate. And this method can translate some brain responses into actions. In this paper, based on the motor imagery, event related synchronization/desychronization (ERS/ERD) were tested by using time-frequency spectrum and brain topographic mapping. A features extraction algorithm is proposed based on common spatial pattern (CSP), then the support vector machine (SVM) were carried out to classificate data. An EEG recording device integrated with an eye tracker can be complementary to attain improved performance and a better efficiency. The eye movement signals (via eye tracker of Tobbi) and EEG signals of ERS/ERD are as the input of hybrid BCI system simultaneously while subjects follow movement of the arrows in each direction. The recognition accuracy of the entire system reaches to 86.1%. The results showed that the proposed method was efficient in the classification accuracy.

关键词： Brain-computer interface (BCI) Eye tracker Electroencephalography (EEG) Prosthesis Common spatial pattern support vector machine classifier Hybrid BCI

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Detection of Brain Tumors Using ROF Filter with Feature Extraction and Classification

Detection of Brain Tumors Using ROF Filter with Feature Extr...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Monisha, B. Kumar, R. Sathish Sanjayprabu, S. Bhuvaneswari, N. Karthikamani, R. Sri Ramakrishna Misson Vidyalaya College of Arts and Science Department of Mathematics Coimbatore641020 India Sri Ramakrishna Engineering College Department of Electrical and Electronic Engineering Coimbatore641022 India

ISBN: (纸本)9798350313987

Brain tumor detection is a challenging task in the field of medicine. Due to its complexity and diversity, each Brain tumor develops in various forms. It is determined by the distribution of tumor cells in the brain, which causes the problems, as well as its size and structure. This research study proposes a novel method for detecting brain tumors from MRI images, which is then compared to the healthy brain images to provide precise recommendations. In this work, the ROF filter is used to provide the best results when compared to other filters, and further the ROF filter is efficient in providing suggestions, after which the NGTDM feature is used and the results are determined by using SVM classification. © 2023 IEEE.

关键词： Brain tumor Magnetic Resonance Index Scan Neighboring Grey Tone Difference Matrix Osher and Fatemi) Filter ROF (Rudin support vector machine classifier

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A Novel Cough Detection Algorithm for COVID-19 Surveillance at Public Places 8

A Novel Cough Detection Algorithm for COVID-19 Surveillance ...

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8th International Conference on Smart Computing and Communications (ICSCC)

作者： Sreedharan, Deepak Raj, Subodh M. S. George, Sudhish N. Ashok, S. Natl Inst Technol Calicut Dept Elect Engn Kozhikode India Natl Inst Technol Calicut Dept Elect & Commun Engn Kozhikode India

ISBN: (纸本)9781728196879

A worldwide pandemic, COVID-19 has been caused by a newly discovered strain of coronavirus SARS-Cov-2. Its common symptoms are high fever, coughing, and shortness of breath. With the rising number of COVID-19 cases, manual detection of infectious individuals at public spaces is a hectic task. Artificial Intelligence (AI) based detection systems can be deployed at public places like airports, railway stations, etc. for continuous monitoring of potential infectious individuals and screening based on common symptoms exhibited. In this paper, a new algorithm is developed for detecting repetitive coughing action which is the main symptom in COVID-19 cases, and thus detecting people with COVID-19 based on it. The performance of the proposed system is tested on an existing sneeze-cough dataset and also on a real-time dataset. The evaluation shows that the proposed method has superior performance over the state-of-the-art methods.

关键词： Artificial Intelligence Spatial Pyramid Matching support vector machine classifier

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