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检索条件"主题词=Classification Algorithms"

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Dual-modality 3D brain PET-CT image segmentation based on probabilistic brain atlas and classification fusion

Dual-modality 3D brain PET-CT image segmentation based on pr...

引用

IEEE International Conference on Image Processing

作者： Yong Xia Stefan Eberl Dagan Feng Department of PET and Nuclear Medicine Royal Prince Alfred Hospital Sydney Australia BMIT Group School of Information Technologies University of Sydney Sydney Australia Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong China

The increasing prevalence of dual medical imaging modalities, such as PET-CT scanners, poses both challenges and opportunities to image segmentation, as they provide distinct but complementary information. In this paper, we propose a novel segmentation algorithm for 3D brain PET-CT images, which classifies each voxel by fusing the voxel's memberships estimated from four points of view using the PET information, CT information, smoothness prior, and probabilistic brain atlas. All memberships having the same dynamic range greatly facilitates weighting the contribution of the four different information sources. The probabilistic brain atlas estimated for each PET-CT image from a set of training samples provides the anatomical information to the segmentation process. We compared the proposed algorithm to three single-classifier based methods, PET-based SPM algorithm, CT-based Otsu thresholding, and PET-CT based MAP-MRF algorithm. The experimental results in 11 clinical brain PET-CT studies demonstrate that the novel algorithm is capable of providing more accurate and reliable segmentation.

关键词： Image segmentation classification algorithms Positron emission tomography Brain Computed tomography Training Biomedical imaging

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Analysis of preprocessing methods on classification of Turkish texts

Analysis of preprocessing methods on classification of Turki...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Dilara Torunoğlu Erhan Çakirman Murat Can Ganiz Selim Akyokuş M. Zahid Gürbüz Department of Computer Engineering Doğuş University Istanbul Turkey

Preprocessing is an important task and critical step in information retrieval and text mining. The objective of this study is to analyze the effect of preprocessing methods in text classification on Turkish texts. We compiled two large datasets from Turkish newspapers using a crawler. On these compiled data sets and using two additional datasets, we perform a detailed analysis of preprocessing methods such as stemming, stopword filtering and word weighting for Turkish text classification on several different Turkish datasets. We report the results of extensive experiments.

关键词： Text categorization Support vector machines Training classification algorithms Filtering Text mining Information retrieval

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classification and Recognition of Underwater Target Based on MFCC Feature Extraction

Classification and Recognition of Underwater Target Based on...

引用

IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Yuze Tong Xin Zhang Yizhou Ge School of Marine Science and Technology Northwestern Polytechnical University Xi’an China Science and Technology on Communication Information Security Control Laboratory NO.36 Research Institute of CETC Jiaxing China

ISBN: (数字)9781728172026

ISBN: (纸本)9781728172033

The key to underwater target recognition is to extract the effective features of underwater target radiation noise. This paper presents an effective method for underwater target recognition and classification by extracting Mel-Frequency Cepstral Coefficients (MFCCs) features of underwater target radiation noise. Compared with traditional spectral analysis methods, MFCC makes full use of the non-linear auditory effect of the human ear with different perception capabilities for sounds of different frequencies. In this paper, the classification experiment of the radiated noise of the three types of measured underwater targets is done, where the MFCC feature vectors of the three types of targets are extracted, and the K-Nearest Neighbor (K-NN) algorithm is used to classify and identify them. Finally, the experimental results show that the method is effective.

关键词： Feature extraction Mel frequency cepstral coefficient Target recognition classification algorithms Filter banks Ear Auditory system

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Trends in Technological Areas using Patent Analytics: A Comparative Study on the Time Series Forecasting of International Patent classification

Trends in Technological Areas using Patent Analytics: A Comp...

引用

IEEE International Conference on System Engineering and Technology

作者： John Joshua F. Montañez Ralph Gerard B. Sangalang College of Engineering Bicol State College of Applied Sciences and Technology Naga City Camarines Sur Philippines College of Engineering Batangas State University the National Engineering University Batangas City Batangas Philippines

ISBN: (数字)9798331504526

ISBN: (纸本)9798331504533

Intellectual Properties like Patents, Utility Models, Industrial Designs, and Copyrights are considered the drivers of today’s modern knowledge-based economy. The number of registered and granted Intellectual Properties is one key performance indicator of a state in advancing science and technology. Thus, analyzing the trends is vital as it sets directions for the state. This study aims to analyze the patent trends in the International Patent classification by incorporating various classical statistical-based techniques and algorithms in time series forecasting. This leads to the generation of insight into the present and future of registered and granted intellectual properties. Autoregression, Seasonal Autoregressive Integrated Moving-Average and Holt Winter’s Exponential Smoothing are the classical time series forecasting algorithms that were compared using three performance indicators, i.e., Root Mean Square Errors, Mean Absolute Errors, and the Coefficient of Determination. It was concluded that the seasonal autoregressive integrated moving average achieved a high performance of several indicators per section. However, it is vital to consider the behaviour of the present data and the appropriate time series forecasting algorithms for forecasting, thus having it on a case-to-case basis.

关键词： Patents Smoothing methods Time series analysis Knowledge based systems Intellectual property Market research Systems engineering and theory classification algorithms Forecasting Root mean square

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Interest of the combination of classifiers for volumetric textures classification

Interest of the combination of classifiers for volumetric te...

引用

International Conference on Electrical Engineering and Software Applications (ICEESA)

作者： Elmoez Ben Othmen Mohamed Ali Cherni Mounir Sayadi SICISI Unit ESSTT University of Tunis Tunis Tunisia

ISBN: (纸本)9781467363020

Nowadays, classification is applied in various fields such as pattern and writing recognition, prints checking, faces identification, medical images analysis, 2D textures characterization and volumetric textures characterization. Indeed, the three-dimensional field is considered among one of the most important fields in image processing because of the great quantity of information that can be extracted. In this work, we try to improve the performances of classification for volumetric textures images by proposing a multiple classifier systems (MCS) based method combining three Euclidean classifiers: simple Euclidean classifier (ES), normal Euclidean classifier (EN) and balanced Euclidean classifier (EB). Thereafter, we compared the performance of the proposed method to the Euclidean methods (ES, EN and EB). The hybrid presented approach has proven to be more efficient in classification and mostly robust against Gaussian noise.

关键词： Support vector machine classification Robustness Gaussian noise Indexes classification algorithms

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Logistic Regression and Logistic Regression-Genetic Algorithm for classification of Liver Cancer Data

Logistic Regression and Logistic Regression-Genetic Algorith...

引用

Decision Aid Sciences and Application (DASA), International Conference on

作者： Velery Virgina Putri Wibowo Zuherman Rustam Afifah Rofi Laeli Alva Andhika Sa'id University of Indonesia Depok Indonesia

ISBN: (纸本)9781665416351

Cancer is a condition that can cause death in which abnormal cells arise and evolve in the body. Furthermore, it has a high mortality rate worldwide and its cases are expected to continue increasing rapidly every year. There are various types of cancers, and an example is Hepatocellular Carcinoma (HCC). This cancer is a general type of primary liver cancer, and is malignant in nature. It is also aggressive, thus, could spread and develop rapidly. The diagnosis of HCC is often made at a late stage because most sufferers do not show distinctive signs. Patients diagnosed at an advanced stage have a low chance of living because their liver has been damaged. Therefore, early diagnosis is needed to increase the survival rate and provide the best treatments to patients. Machine learning can be applied in the medical sector to diagnose diseases with high accuracy. Therefore, this study proposed the Logistic Regression (LR) method to classify HCC data. Based on the data, there were several features available, though, some may not be relevant. Due to this condition, feature selection was needed to increase the accuracy and determine which features were important. Genetic Algorithm (GA) was applied as a feature selection tool and Logistic Regression without feature selection (LR) was compared with Logistic Regression with Genetic Algorithm (LR-GA) to determine which method is best for classifying HCC. Based on the results, LR-GA is a better machine learning method than LR with 93.18%, 90.91 %, 95.45%, and 93.12% values for accuracy, recall, precision, and f1-score respectively.

关键词： Machine learning algorithms Liver Machine learning Feature extraction classification algorithms Medical diagnostic imaging Cancer

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Identification of Parkinson’s Disease Using Machine Learning classification Techniques

Identification of Parkinson’s Disease Using Machine Learnin...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： E Thirumagal K Harshith Savanth R M Kaushik Sagar Shaw M Tharun Deepika Dept of CSE Reva University Bangalore India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Following Alzheimer’s, Parkinson’s disease is a neurodegenerative disease that affects a majority of people in the world. This disease brings a great loss of both nerve cells and neurological capacity because the cells that generate dopamine are harmed. This paper is to inspect Parkinson’s disease using a speech feature dataset, recording features of both Parkinson-affected people and healthy individuals. The dataset is processed, and the required features are fed into different machine learning predictive models which are relatively new. The efficiency and accuracy of the same algorithms are recorded and compared to show which algorithm serves the best for early and accurate predictions.

关键词： Machine learning algorithms Automation Neurons Machine learning Predictive models Prediction algorithms classification algorithms

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Automatic classification of penicillin-induced epileptic EEG spikes

Automatic classification of penicillin-induced epileptic EEG...

引用

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

作者： Jukka Kortelainen Minna Silfverhuth Kalervo Suominen Eila Sonkajärvi Seppo Alahuhta Ville Jäntti Tapio Seppänen Department of Electrical and Information Engineering University of Oulu Finland Department of Clinical Neurophysiology Oulu University Hospital Finland Department of Anesthesiology University of Oulu Finland Department of Biomedical Engineering Department of Clinical Neurophysiology Tampere University of Technology Finland

Penicillin-induced focal epilepsy is a well-known model in epilepsy research. In this model, epileptic activity is generated by delivering penicillin focally to the cortex. The drug induces interictal electroencephalographic (EEG) spikes which evolve in time and may later change to ictal discharges. This paper proposes a method for automatic classification of these interictal epileptic spikes using iterative K-means clustering. The method is shown to be able to detect different spike waveforms and describe their characteristic occurrence in time during penicillin-induced focal epilepsy. The study offers potential for future research by providing a method to objectively and quantitatively analyze the time sequence of interictal epileptic activity.

关键词： Electroencephalography Epilepsy Clustering algorithms classification algorithms Brain modeling Data mining Discharges

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Future performance classification of high-technology venture investments with limited data

Future performance classification of high-technology venture...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Lars Krüger Matthias Walter Peter Heydebreck Engage-Key Technology Ventures AG Rostock Germany Institue for Modelling and Numerical Simualtion Rostock University Rostock Germany Inno Germany AG Karlsruhe Germany

ISBN: (纸本)9781479900213

The system we propose allows the classification of future performances of high-technology venture investments on the basis of limited, successively available information. Our system helps investors to decide whether to invest in a young High-Technology Venture (HTV) or not. In order to cope with uncertain data we apply a Fuzzy-Rule-based Classifier. As we want to attain an objective and clear decision making process we implement a learning algorithm that learns rules from given real-world examples. The availability of data on early-stage investments is typically limited. For this reason we equipped our system with a bootstrapping mechanism which multiplies the number of examples without changing their inherent quality and structure. All these features make an operational and reliable investment decision support system in the context of early stage venture capital investments possible.

关键词： Investment Training Decision support systems classification algorithms Venture capital Companies Biological system modeling

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Automated Texture Extraction From High Spatial Resolution Satellite Imagery For Land-cover classification: Concepts And Application

Automated Texture Extraction From High Spatial Resolution Sa...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： D. Marceau P.J. Howarth J.M. Dubois Earth-Observations Laboratory Institute for Space and Terrestrial Science Department of Geography University of Waterloo Waterloo ONT Canada Centre dE28099application et de recherche en télédétection (CARTEL) Université de Sherbrook Sherbrooke QUE Canada

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