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Towards more accurate prediction of protein folding rates: a review of the existing web-based bioinformatics approaches

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BRIEFINGS IN BIOINFORMATICS 2015年第2期16卷 314-324页

作者： Chang, Catherine Ching Han Tey, Beng Ti Song, Jiangning Ramanan, Ramakrishnan Nagasundara Monash Univ Chem Engn Selangor 46150 Malaysia Monash Univ Sch Engn Chem Engn Discipline Selangor 46150 Malaysia Monash Univ Fac Med Dept Biochem & Mol Biol Clayton Vic 3800 Australia Chinese Acad Sci Tianjin Inst Ind Biotechnol Beijing 100864 Peoples R China

The understanding of protein-folding mechanisms is often considered to be an important goal that will enable structural biologists to discover the mysterious relationship between the sequence, structure and function of proteins. The ability to predict protein-folding rates without the need for actual experimental work will assist the research work of structural biologists in many ways. Many bioinformatics tools have emerged in the past decade, and each has showcased different features. In this article, we review and compare eight web-based prediction tools that are currently available and that predominantly predict the protein-folding rate. The prediction performance, usability and utility, together with the prediction tool development and validation methodologies for these tools, are critically reviewed. This article is presented in a comprehensible manner to assist readers in the process of selecting the most appropriate bioinformatics tools to meet their needs.

关键词： prediction tool in silico prediction machine learning algorithm prediction model statistical analysis molecular biology

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A flow-based detection method for stealthy dictionary attacks against Secure Shell

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2015年第Apr.期21卷 31-41页

作者： Satoh, Akihiro Nakamura, Yutaka Ikenaga, Takeshi Kyushu Inst Technol Tobata Ku 1-1 Sensui Cho Kitakyushu Fukuoka 804 Japan

SANS has warned about the new variants of SSH dictionary attacks that are very stealthy in comparison with a simple attack. In this paper, we propose a new method to detect simple and stealthy attacks by combining two key innovations. First, on the basis of our assumptions, we employ two criteria: "the existence of a connection protocol" and "the inter-arrival time of an auth-packet and the next". These criteria are not available, though, owing to the confidentiality and flexibility of the SSH protocol. Second, we resolve this problem by identifying "the transition point of each sub-protocol "through flow features and machine learning algorithms. We evaluate the effectiveness through experiments on real network traffic at the edges in campus networks. The experimental results show that our method provides high accuracy with acceptable computational complexity. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： ssh dictionary attack Flow analysis Network operation machine learning algorithm

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Robust Approach for Medical Data Classification and Deploying Self-Care Management System for Sickle Cell Disease 15

Robust Approach for Medical Data Classification and Deployin...

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IEEE International Conference on Computer and Information

作者： Khalaf, Mohammed Hussain, Abir Jaafar Al-Jumeily, Dhiya Keenan, Russell Fergus, Paul Idowu, Ibrahim Olatunji Liverpool John Moores Univ Sch Comp & Math Sci Appl Comp Res Grp Byrom St Liverpool L3 3AF Merseyside England Alder Hey Childrens Hosp Haematol Treatment Ctr Liverpool Paediat Haemophilia Ctr Liverpool L12 2AP Merseyside England

ISBN: (纸本)9781509001545

Intelligent systems and smart devices have played the major role in improving the healthcare organisation in terms of continuous tele-monitoring therapy and maintaining telemedicine management system for sickle cell disease. The biggest challenge facing majority of patients is the fact that there is still a lack of communication with healthcare professionals. Smart home (out-of hospital care) can raise personal self-sufficiency in association with living independently for longer as this disease is considered life-long condition. By using a self-care management system, we tend to improve patient welfare and mitigate patient illness before it gets worse over time, particularly with elderly people. This paper describes the state of the art in pervasive healthcare applications and the communication technologies that assist healthcare providers to offer better services for patients. This research proposes an alert system that could send immediate information to the medical consultants once detects serious condition from the collected data of the patient. Furthermore, the system is able to track various types of symptoms through mobile application in the purpose of obtaining support from medical specialists when it is required. A machine-learning algorithm was conducted to perform the classification process. Four experiments were carried out to classify sickle cell disease patients from normal patients using machine-learning algorithm in which 99.5984% classification accuracy was achieved using Multi-layer perceptron. Classification using Core Vector Regression, Hyper Pipes and Zero-Rule based algorithms achieved classification accuracy of 95.9839 %, 87.9518% and 70.6827 %, respectively.

关键词： Sickle Cell Disease machine learning algorithm Mobile Healthcare Service Real-time data Self-care Management System E-Health

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Age Group Classification and Gender Detection Based on Forced Expiratory Spirometry 37

Age Group Classification and Gender Detection Based on Force...

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

作者： Cosgun, Sema Ozbek, I. Yucel Ataturk Univ Elect & Elect Engn Dept TR-25240 Erzurum Turkey

ISBN: (纸本)9781424492701

This paper investigates the utility of forced expiratory spirometry (FES) test with efficient machine learning algorithms for the purpose of gender detection and age group classification. The proposed method has three main stages: feature extraction, training of the models and detection. In the first stage, some features are extracted from volume-time curve and expiratory flow-volume loop obtained from FES test. In the second stage, the probabilistic models for each gender and age group are constructed by training Gaussian mixture models (GMMs) and Support vector machine (SVM) algorithm. In the final stage, the gender (or age group) of test subject is estimated by using the trained GMM (or SVM) model. Experiments have been evaluated on a large database from 4571 subjects. The experimental results show that average correct classification rate performance of both GMM and SVM methods based on the FES test is more than 99.3 % and 96.8 % for gender and age group classification, respectively.

关键词： Gaussian processes biomedical measurement feature extraction learning (artificial intelligence) medical computing mixture models pneumodynamics support vector machines GMM method Gaussian mixture model SVM method age group classification expiratory flow-volume loop forced expiratory spirometry gender detection machine learning algorithm probabilistic model support vector machine volume-time curve Databases Diseases Feature extraction Iris recognition Lungs Support vector machines Support Vector Network Feature extraction Age Groups machine learning algorithms pneumodynamics Probabilistic Model biomedical measurement medical computing iris recognition Gaussian processes Mixture models Gender Lung Database Gm(m)

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A Novel Data Mining Method on Falling Detection and Daily Activities Recognition 27

A Novel Data Mining Method on Falling Detection and Daily Ac...

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27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Peng, Yingli Zhang, Tingting Sun, Li Chen, Jie Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China Mid Sweden Univ Dept Informat Technol & Media Sundsvall Sweden

ISBN: (纸本)9781509001637

With the intensification of aging population, a growing number of elderly people have to live alone due to domestic and social reasons. Falling becomes one of the most crucial factors in threatening the elderly's lives, which is always difficult to be detected as it is instantaneous and easy to be confused with other motions, such as lying down. In this paper, a new method is proposed for accurate falling detection and activities recognition. It applies hierarchical classifiers to the time series data set including eleven activities of daily living (ADLs), collected by four wearable sensors. The new method combines two machine learning algorithms, performs concrete analysis on the original outcome and then obtains several scarcely-confused groups separately. The experiment indicates that the new method improves the accuracy of classification to a larger extent, reached to more than 90%. Furthermore, the matched algorithm for applying these classifiers, called Hierarchical Classifier algorithm (HCA), is proposed as well.

关键词： Falling detection wearable sensor machine learning algorithm Hierarchical Classifier algorithm (HCA)

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Tool Wear Condition Prediction Using Vibration Signals in High Speed Machining (HSM) of Titanium (Ti-6Al-4V) Alloy 2

Tool Wear Condition Prediction Using Vibration Signals in Hi...

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2nd International Symposium on Big Data and Cloud Computing Challenges (ISBCC)

作者： Krishnakumar, P. Rameshkumar, K. Ramachandran, K. I. Amrita Vishwa Vidyapeetham Dept Mech Engn Coimbatore 641112 Tamil Nadu India

Ti-6Al-4V is extensively used in aerospace and bio-medical applications. In an automated machining environment monitoring of tool conditions is imperative. In this study, Experiments were conducted to classify the tool conditions during High Speed Machining of Titanium alloy. During the machining process, vibration signals were monitored continuously using accelerometer. The features from the signal are extracted and a set of prominent features are selected using Dimensionality Reduction Technique. The selected features are given as an input to the classification algorithm to decide about the condition of the tool. Feature selection has been carried out using J48 Decision Tree algorithm. Classifications of tool conditions were carried out using machine learning algorithms namely J48 Decision Tree algorithm and Artificial Neural Network (ANN). From the analysis, it is found that ANN is producing comparatively better results. The methodology adopted in this study will be useful for online tool condition monitoring. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Titanium alloy High Speed machining machine learning algorithm Tool Wear

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DASyR(IR) - Document Analysis System for Systematic Reviews (in Information Retrieval) 13

DASyR(IR) - Document Analysis System for Systematic Reviews ...

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13th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Piroi, Flarina Lipani, Aida Lupu, Mihai Hanbury, Allan Vienna Univ Technol Inst Software Engn & Informat Syst Favoriten Str 9-11-188 A-1040 Vienna Austria

ISBN: (纸本)9781479918058

Creating systematic reviews is a painstaking task undertaken especially in domains where experimental results are the primary method to knowledge creation. For the review authors, analysing documents to extract relevant data is a demanding activity. To support the creation of systematic reviews, we have created DASyR-a semi-automatic document analysis system. DASyR is our solution to annotating published papers for the purpose of ontology population. For domains where dictionaries are not existing or inadequate, DASyR relies on a semi-automatic annotation bootstrapping method based on positional Random Indexing, followed by traditional machine learning algorithms to extend the annotation set. We provide an example of the method application to a subdomain of Computer Science, the Information Retrieval evaluation domain. The reliance of this domain on large scale experimental studies makes it a perfect domain to test on. We show the utility of DASyR through experimental results for different parameter values for the bootstrap procedure, evaluated in terms of annotator agreement, error rate, precision and recall.

关键词： indexing information retrieval learning (artificial intelligence) ontologies (artificial intelligence) DASyR computer science document analysis system information retrieval evaluation domain knowledge creation machine learning algorithm ontology positional random indexing retrieval information systematic semiautomatic annotation bootstrapping method semiautomatic document analysis system systematic review Data mining Graphical user interfaces Indexing Logic gates Optical character recognition software Visualization optical character recognition softwares information retrieval machine learning algorithms Review, Systematic computer science graphical user interfaces Logic gates Indexing Ontology data mining Visualization

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Impacts of traffic conditions on the performance of road freight transport 18

Impacts of traffic conditions on the performance of road fre...

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Sigakova, Ksenia Mbiydzenyuy, Gideon Holmgren, Johan Blekinge Inst Technol Dept Comp Sci & Engn Karlshamn Sweden NetPort Sci Pk AB Karlshamn Sweden Malmo Univ Dept Comp Sci SE-20506 Malmo Sweden

ISBN: (纸本)9781467365963

The efficiency of road transport is typically influenced by factors such as, weather, choice of road, and time of day, and day of the week. Knowledge about interactions between different traffic-and transport related factors and their influence on the execution of transport is important in transport planning. The purpose of this paper is to study the impact of different factors on the performance of road transport. We aim to contribute to improved transport planning by analysing traffic and transport data obtained from different sources in order to support data driven decision making. Through a review of existing literature and discussions with a Swedish road transport operator, we identified factors that could be relevant to consider when planning a transport, e.g., related to weather, location of roads where the transport will take place, and planned time of the transport. As a result of variation in size, type and volume of the data representing these factors, suitable machine learning algorithms were selected, such as Decision Stump, M5 model tree, M5 regression tree, RepTree, M5 rules, and linear regression in order to study the data. Our experimental results illustrate the complexity associated to the performance of road transport systems mainly because of the dependency between the choices of influencing factors and geographic location of the road segment.

关键词： data structures decision making learning (artificial intelligence) road traffic traffic engineering computing data representation machine learning algorithm road freight transport traffic condition transport planning Global Positioning System Meteorology Prediction algorithms Predictive models Roads Temperature Vehicles

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Cluster regularized extreme learning machine for detecting mixed-type distraction in driving

Cluster regularized extreme learning machine for detecting m...

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International IEEE Conference on Intelligent Transportation Systems

作者： Tianchi Liu Yan Yang Guang-Bin Huang Zhiping Lin Felix Klanner Cornelia Denk Ralph H. Rasshofer Sch. of Electr. & Electron. Eng. Nanyang Technol. Univ. Singapore Singapore

ISBN: (纸本)9781467365970

Distraction was previously studied within each dimension separately, i.e., physical, cognitive and visual. However real-world activities usually involve multiple distraction dimensions in terms of brain resources that might conflict with the driving task. This brings difficulties for classifying dimension/type of distraction even for human experts. On the other hand, many subsequent functional blocks do not utilize distraction type information. For example, a pre-collision system usually makes decision based on distraction level rather than distraction type. Therefore this study aims to detect distraction in general regardless of its type, and proposes an effective machine learning algorithm, i.e., Cluster Regularized Extreme learning machine (CR-ELM), to detect mixed-type distraction in driving. Compared to traditional machine learning techniques, CR-ELM is designed to handle problems with multiple clusters per class, and provides more accurate detection performance, which could be used for advanced driver assistance systems.

关键词： driver information systems learning (artificial intelligence) advanced driver assistance systems brain resources cluster regularized extreme learning machine human experts machine learning algorithm mixed-type driving distraction detection multiple distraction dimensions pre-collision system utilize distraction type information Error analysis Neurons Support vector machines Training Training data Vehicles Visualization

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Tool Wear Condition Prediction Using Vibration Signals in High Speed Machining (HSM) of Titanium (Ti-6Al-4 V) Alloy

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Procedia Computer Science 2015年 50卷 270-275页

作者： P. Krishnakumar K. Rameshkumar K.I. Ramachandran Assistant Professor Department of Mechanical Engineering Amrita Vishwa Vidyapeetham Coimbatore 641112 India Adjunct Professor Department of Mechanical Engineering Amrita Vishwa Vidyapeetham Coimbatore 641112 India Professor Department of Mechanical Engineering Amrita Vishwa Vidyapeetham Coimbatore 641112 India

Ti-6Al-4 V is extensively used in aerospace and bio-medical applications. In an automated machining environment monitoring of tool conditions is imperative. In this study, Experiments were conducted to classify the tool conditions during High Speed Machining of Titanium alloy. During the machining process, vibration signals were monitored continuously using accelerometer. The features from the signal are extracted and a set of prominent features are selected using Dimensionality Reduction Technique. The selected features are given as an input to the classification algorithm to decide about the condition of the tool. Feature selection has been carried out using J48 Decision Tree algorithm. Classifications of tool conditions were carried out using machine learning algorithms namely J48 Decision Tree algorithm and Artificial Neural Network (ANN). From the analysis, it is found that ANN is producing comparatively better results. The methodology adopted in this study will be useful for online tool condition monitoring.

关键词： Titanium alloy High Speed machining machine learning algorithm Tool Wear

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