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Patterns of brain structural connectivity differentiate normal weight from overweight subjects

NEUROIMAGE-CLINICAL 2015年 7卷 506-517页

作者： Gupta, Arpana Mayer, Emeran A. Sanmiguel, Claudia P. Van Horn, John D. Woodworth, Davis Ellingson, Benjamin M. Fling, Connor Love, Aubrey Tillisch, Kirsten Labus, Jennifer S. Univ Calif Los Angeles Gail & Gerald Oppenheimer Family Ctr Neurobiol St IBOP Los Angeles CA USA Univ Calif Los Angeles David Geffen Sch Med Los Angeles CA 90095 USA Univ Calif Los Angeles Div Digest Dis Los Angeles CA USA Univ Calif Los Angeles Ahmanson Lovelace Brain Mapping Ctr Los Angeles CA USA Univ Calif Los Angeles GLA VHA Integrat Med Los Angeles CA USA Univ So Calif Keck Sch Med Inst Neuroimaging & Informat Los Angeles CA 90033 USA Univ Calif Los Angeles David Geffen Sch Med Radiol Los Angeles CA 90095 USA

Background: Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. Aim: To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Methods: Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. Results: 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achi

关键词： Obesity Overweight Morphological gray-matter Anatomical white-matter connectivity Reward network Multivariate analysis classification algorithm

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The Application of Negative Selection algorithm in Multi-angle Infrared Vehicle Images Recognition 38

The Application of Negative Selection Algorithm in Multi-ang...

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38th International Conference on Telecommunications and Signal Processing (TSP)

作者： Yu, Xi Fu, Dongmei Yang, Tao Riha, Kamil Univ Sci & Technol Beijing Sch Infommt & Engeering Xueyuan Rd Beijing 100083 Peoples R China Brno Univ Technol Dept Telecommun Tech 12 Brno 61600 Czech Republic

ISBN: (纸本)9781479984985

At present, the objects of the vehicle recognition based on image processing are mostly visible images with rich details and limited camera angles. Few studies have been done on the recognition of multi-angle infrared vehicle images with less detail. However, it has important implications for transportation and especially public security. This paper extracts object contour of the vehicle from infrared vehicle images. After image correction and normalization, the geometric features and moment invariants are extracted. Based on the tolerance process of biological immune cells, this paper proposes a new Multi-classification Artificial Immunity Negative Selection (MAINS) algorithm. This algorithm can recognize vehicle types by the objects' geometric features and moment invariants. It has a stable performance and global search ability, also a high correct recognition rate in infrared vehicle image recognition.

关键词： classification algorithm Infrared Images Negative Selection Vehicle Recognition

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ARSkNN-A k-NN Classifier Using Mass Based Similarity Measure

ARS<i>k</i>NN-A <i>k</i>-NN Classifier Using Mass Based Simi...

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International Conference on Information and Communication Technologies (ICICT)

作者： Kumar, Ashish Bhatnagar, Roheet Srivastava, Sumit Manipal Univ Jaipur Jaipur 303007 Rajasthan India

k-Nearest Neighbor (k-NN) classification technique is one of the most elementary and straightforward classification methods. Although distance learning is in the core of k-NN classification, similarity can be preferred upon distance in several practical applications. This paper proposes a novel algorithm for learning a class of an instance based on a similarity measure which does not calculate distance, for k-Nearest Neighbor (k-NN) classification. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： classification algorithm Data Mining Nearest Neighbor Similarity Measures

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The data retrieval optimization from the perspective of evidence-based medicine

The data retrieval optimization from the perspective of evid...

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3rd International Conference on Innovative Network Systems and Applications (iNetSApp) held in conjunction with Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Dobrynin, Vladimir Balykina, Julia Kamalov, Michael Kolbin, Alexey Verbitskaya, Elena Kasimova, Munira St Petersburg State Univ Univ Skaya 7-9 St Petersburg 199034 Russia Pavlov First St Petersburg State Med Univ St Petersburg Russia Tashkent Inst Postgrad Med Educ Tashkent Uzbekistan

ISBN: (纸本)9788360810668

The paper is devoted to classification of MEDLINE abstracts into categories that correspond to types of medical interventions - types of patient treatments. This set of categories was extracted from *** web site. Few classification algorithms were tested including Multinomial Naive Bayes, Multinomial Logistic Regression, and Linear SVM implementations from sklearn machine learning library. Document marking was based on the consideration of abstracts containing links to the *** Web site. As the result of an automatical marking 3534 abstracts were marked for training and testing the set of algorithms metioned above. Best result of multinomial classification was achieved by Linear SVM with macro evaluation precision 70.06%, recall 55.62% and F-measure 62.01%, and micro evaluation precision 64.91%, recall 79.13% and F-measure 71.32%.

关键词： Bayes methods Web sites information retrieval learning (artificial intelligence) patient treatment regression analysis support vector machines Clinicaltrials.gov web site classification algorithm data retrieval optimization document marking evidence-based medicine linear SVM multinomial logistic regression multinomial naive Bayes sklearn machine learning library Biology Databases Drugs Entropy Support vector machines Testing Training

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Blood Donor's safety using Data Mining 1

Blood Donor's safety using Data Mining

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International Conference on Green Computing and Internet of Things (ICGCIoT)

作者： Patil, Rohini Pawar, Pooja Poi, Madhu Patil, Tejashree Ghuse, Namrata SITRC Dept Comp Sci Nasik India

ISBN: (纸本)9781467379106

Blood is most essential constituent in human body. Blood centers collect, process and transport blood to hospitals and other health care centers. Sometimes blood is collected directly from blood donors rather than taking it from blood bank. Nowadays donor safety is major issue as blood is collected from different donors. Small amount of donors face reactions after donating blood and if not treated on time then it may lead to serious injury. The reactions that occur in the donor during blood donation at different hospitals and blood banks is collected, organized and analyzed by the DonorHART tool [1, 3]. The donor reactions information are captured and analyzed by DonorHART system[1]. The tool also monitors and researches the risks involved for donors at the time of blood donation or after the donation process. Data mining makes an effort to reveal the patterns in data that are difficult to detect and recognize with automatic pattern recognition. It is analyzing of data and then summarizing it into different form which is called as information and is taken from different databases. The proposed system is a web based application which helps to reduce the human mistakes or errors and different techniques can be applied for blood type classification, diagnosing diabetic symptoms, classifying blood type and identifying different donors reaction and apply preventive measures against them.

关键词： Blood Bank DonorHart tool Data Mining Database classification algorithm

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Classifying and Passing 3D Obstacles for Autonomous Driving 18

Classifying and Passing 3D Obstacles for Autonomous Driving

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Delp, Michael Nagasaka, Naoki Kamata, Nobuhide James, Michael R. Toyota Res Inst North Amer 1555 Woodridge Ave Ann Arbor MI 48105 USA

ISBN: (纸本)9781467365963

Highly automated driving in real world situations requires perception and planning to account for other forms of human locomotion, principally bicycles, and pedestrians, in addition to vehicles. Here we present a complete perception-planning-control system with high quality perception including object classification and tracking, and a path planning algorithm that makes use of probabilistic classification results. Our classification algorithm is fast enough to provide results for the vast majority of objects in on-line operation, but to ensure output for real-time decision-making the computation and integration of classification results is done using an anytime algorithm. Our focus is on safe driving behavior in the presence of dynamic obstacles on the side of the road, which are typically bicycles, using LIDAR data. Our resulting system is extensively tested in real-world situations, and shows good performance on a large test set of interactions with bicycles.

关键词： bicycles image classification intelligent transportation systems object tracking path planning pedestrians road safety road vehicles 3D obstacles LIDAR data anytime algorithm automated driving autonomous driving classification algorithm dynamic obstacles high quality perception human locomotion object classification path planning algorithm perception-planning-control system probabilistic classification real-time decision-making safe driving behavior vehicles Bicycles Laser radar Radar tracking Robot sensing systems Three-dimensional displays

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Dynamic Partial Reconfiguration implementation of the SVM/KNN multi-classifier on FPGA for Bioinformatics application 37

Dynamic Partial Reconfiguration implementation of the SVM/KN...

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

作者： Hussain, Hanaa M. Benkrid, Khaled Seker, Huseyin PAAET Coll Technol Studies Elect Engn Technol Dept Shuwaikh 70654 Kuwait Univ Edinburgh Sch Engn & Elect Kings BldgMayfield Rd Edinburgh EH9 3JL Midlothian Scotland Northumbria Univ Dept Comp Sci & Digital Technol Fac Engn & Environm Newcastle Upon Tyne NE1 8ST Tyne & Wear England

ISBN: (纸本)9781424492701

Bioinformatics data tend to be highly dimensional in nature thus impose significant computational demands. To resolve limitations of conventional computing methods, several alternative high performance computing solutions have been proposed by scientists such as Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). The latter have shown to be efficient and high in performance. In recent years, FPGAs have been benefiting from dynamic partial reconfiguration (DPR) feature for adding flexibility to alter specific regions within the chip. This work proposes combing the use of FPGAs and DPR to build a dynamic multi-classifier architecture that can be used in processing bioinformatics data. In bioinformatics, applying different classification algorithms to the same dataset is desirable in order to obtain comparable, more reliable and consensus decision, but it can consume long time when performed on conventional PC. The DPR implementation of two common classifiers, namely support vector machines (SVMs) and K-nearest neighbor (KNN) are combined together to form a multi-classifier FPGA architecture which can utilize specific region of the FPGA to work as either SVM or KNN classifier. This multi-classifier DPR implementation achieved at least similar to 8x reduction in reconfiguration time over the single non-DPR classifier implementation, and occupied less space and hardware resources than having both classifiers. The proposed architecture can be extended to work as an ensemble classifier.

关键词： bioinformatics field programmable gate arrays support vector machines DPR feature FPGA K-nearest neighbor KNN classifier SVM-KNN multiclassifier bioinformatics application bioinformatics data classification algorithm dynamic partial reconfiguration implementation graphical processing units support vector machine Bioinformatics classification algorithms Computer architecture Field programmable gate arrays Hardware Support vector machines Training

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Improving Spam Detection in Online Social Networks 1

Improving Spam Detection in Online Social Networks

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2015 International Conference on Cognitive Computing and Information Processing (CCIP)

作者： Gupta, Arushi Kaushal, Rishabh Indira Gandhi Delhi Tech Univ Women Dept Informat Technol Delhi India

ISBN: (纸本)9781479971718

Online Social Networks (OSNs) are deemed to be the most sought-after societal tool used by the masses world over to communicate and transmit information. Our dependence on these platforms for seeking opinions, news, updates, etc. is increasing. While it is true that OSNs have become a new medium for dissemination of information, at the same time, they are also fast becoming a playground for the spread of misinformation, propaganda, fake news, rumors, unsolicited messages, etc. Consequently, we can say that an OSN platform comprises of two kinds of users namely, Spammers and Non-Spammers. Spammers, out of malicious intent, post either unwanted (or irrelevant) information or spread misinformation on OSN platforms. As part of our work, we propose mechanisms to detect such users (Spammers) in Twitter social network (a popular OSN). Our work is based on a number of features at tweet-level and user-level like Followers/Followees, URLs, Spam Words, Replies and HashTags. In our work, we have applied three learning algorithms namely Naive Bayes, Clustering and Decision trees. Furthermore, to improve detection of Spammers, a novel integrated approach is proposed which "combines" the advantages of the three learning algorithms mentioned above. Improvement of spam detection is measured on the basis of Total Accuracy, Spammers Detection Accuracy and Non-Spammers Detection Accuracy. Results, thus obtained, show that our novel integrated approach that combines all algorithms outperforms other classical approaches in terms of overall accuracy and detect Non-Spammers with 99% accuracy with an overall accuracy of 87.9%.

关键词： Spam detection classification algorithm twitter online social networking

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An Empirical Comparison of Classifiers to Analyze Intrusion Detection 5

An Empirical Comparison of Classifiers to Analyze Intrusion ...

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5th International Conference on Advanced Computing & Communication Technologies (ACCT)

作者： Aggarwal, Preeti Sharma, Sudhir Kumar Ansal Univ Sch Engn & Technol Gurgaon India

ISBN: (纸本)9781479984886

The massive data exchange on the web has deeply increased the risk of malicious activities thereby propelling the research in the area of Intrusion Detection System (IDS). This paper aims to first select ten classification algorithms based on their efficiency in terms of speed, capability to handle large dataset and dependency on parameter tuning and then simulates the ten selected existing classifiers on a data mining tool Weka for KDD' 99 dataset. The simulation results are evaluated and benchmarked based on the generic evaluation metrics for IDS like F-score and accuracy.

关键词： Intrusion detection syste NSL-KDD classification algorithm

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Detection and Prevention of Code Injection Attacks on HTML5-based Apps 3

Detection and Prevention of Code Injection Attacks on HTML5-...

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3rd International Conference on Advanced Cloud and Big Data (CBD)

作者： Xiao, Xi Yan, Ruibo Ye, Runguo Li, Qing Peng, Sancheng Jiang, Yong Tsinghua Univ Grad Sch Shenzhen Shenzhen Peoples R China China Elect Standardizat Inst Beijing Peoples R China Zhaoqing Univ Sch Comp Sci Zhaoqing Peoples R China

ISBN: (纸本)9781467385374

Security on mobile devices is becoming increasingly important. HTML5 are widely used to develop mobile applications due to its portability on multi platforms. However it is allowed to mix data and code together in web technology. HTML5-based applications are prone to suffer from code injection attacks that are similar to XSS. In this paper, at first, we introduce a more hidden type of code injection attacks, coding-based attacks. In the new type of code injection attacks, JavaScript code is encoded in a human-unreadable form. Then we use classification algorithms of machine learning to determine whether an app suffers from the code injection attack or not. The experimental result shows that the Precision of our detection method reaches 95.3%. Compare with the other method, our approach improves a lot in detection speed with the precision nearly unchanged. Furthermore, an improved access control model is proposed to mitigate the attack damage. In addition, filters are adopted to remove JavaScript code from data to prevent the attacks. The effectiveness and rationality have been validated through extensive simulations.

关键词： code injection classification algorithm machine learning access control model filter

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