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Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset

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JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY B-BIOLOGY 2014年 134卷 37-48页

作者： Van Wittenberghe, Shari Verrelst, Jochem Rivera, Juan Pablo Alonso, Luis Moreno, Jose Samson, Roeland Univ Antwerp Fac Sci Dept Biosci B-2020 Antwerp Belgium Univ Valencia Image Proc Lab E-46980 Valencia Spain

Biochemical and structural leaf properties such as chlorophyll content (Chl), nitrogen content (N), leaf water content (LWC), and specific leaf area (SLA) have the benefit to be estimated through nondestructive spectral measurements. Current practices, however, mainly focus on a limited amount of wavelength bands while more information could be extracted from other wavelengths in the full range (400-2500 nm) spectrum. In this research, leaf characteristics were estimated from a field-based multi-species dataset, covering a wide range in leaf structures and Chl concentrations. The dataset contains leaves with extremely high Chl concentrations (>100 mu g cm(-2)), which are seldom estimated. Parameter retrieval was conducted with the machine learning regression algorithm Gaussian Processes (GP), which is able to perform adaptive, nonlinear data fitting for complex datasets. Moreover, insight in relevant bands is provided during the development of a regression model. Consequently, the physical meaning of the model can be explored. Best estimates of SLA, LWC and Chl yielded a best obtained normalized root mean square error of 6.0%, 7.7%, 9.1%, respectively. Several distinct wavebands were chosen across the whole spectrum. A band in the red edge (710 nm) appeared to be most important for the estimation of Chl. Interestingly, spectral features related to biochemicals with a structural or carbon storage function (e.g. 1090, 1550, 1670, 1730 nm) were found important not only for estimation of SLA, but also for LWC, Chl or N estimation. Similar, Chl estimation was also helped by some wavebands related to water content (950, 1430 nm) due to correlation between the parameters. It is shown that leaf parameter retrieval by GP regression is successful, and able to cope with large structural differences between leaves. (C) 2014 Elsevier B.V. All rights reserved.

关键词： machine learning algorithm Spectral features Hyperspectral Parameter retrieval Chlorophyll Leaf structure Specific leaf area Leaf water content

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Bioinformatics approaches for improved recombinant protein production in Escherichia coli: protein solubility prediction

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BRIEFINGS IN BIOINFORMATICS 2014年第6期15卷 953-962页

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

The solubility of recombinant protein expressed in Escherichia coli often represents the production yield. However, up-to-date, instances of successful production of soluble recombinant proteins in E. coli expression system with high yield remain scarce. This is mainly due to the difficulties in improving the overall production capacity, as most of the well-established strategies usually involve a series of trial and error steps with unguaranteed success. One way to concurrently improve the production yield and minimize the production cost would be incorporating the potency of bioinformatics tools to conduct in silico studies, which forecasts the outcome before actual experimental work. In this article, we review and compare seven prediction tools available, which predict the solubility of protein expressed in E. coli, using the following criteria: prediction performance, usability, utility, prediction tool development and validation methodologies. This comprehensive review will be a valuable resource for researchers with limited prior experience in bioinformatics tools. As such, this will facilitate their choice of appropriate tools for studies related to enhancement of intracellular recombinant protein production in E. coli.

关键词： protein solubility heterologous expression in silico prediction intracellular expression machine learning algorithm prediction model

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Emotions for Accessibility Introduction to the Special Thematic Session

Emotions for Accessibility Introduction to the Special Thema...

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14th International Conference on Computers Helping People with Special Needs (ICCHP)

作者： Mohamad, Yehya Fraunhofer Inst Appl Informat Technol FIT St Augustin Germany

ISBN: (纸本)9783319085968;9783319085951

This STS on Emotions for Accessibility is targeted towards the description of affective computing approaches e.g. emotional state detection, emotional state elicitation and their potential usage in the context of users who are disabled or older people. These user groups may gain a lot of benefits from affective computing systems e.g. in support of communication especially none verbal, management of emotional states or stress, or just improving therapy procedures etc.

关键词： Affective computing Accessibility Sensors GSR PVB machine learning algorithm Disabled person Context Emotion Emotion management User interface e-learning Web-based

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A comprehensive study of named entity recognition in Chinese clinical text

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2014年第5期21卷 808-814页

作者： Lei, Jianbo Tang, Buzhou Lu, Xueqin Gao, Kaihua Jiang, Min Xu, Hua Peking Univ Ctr Med Informat Beijing 100871 Peoples R China Univ Texas Sch Biomed Informat Houston Houston TX USA Shenzhen Grad Sch Harbin Inst Technol Dept Comp Sci Shenzhen Guangdong Peoples R China

Objective Named entity recognition (NER) is one of the fundamental tasks in natural language processing. In the medical domain, there have been a number of studies on NER in English clinical notes;however, very limited NER research has been carried out on clinical notes written in Chinese. The goal of this study was to systematically investigate features and machine learning algorithms for NER in Chinese clinical text. Materials and methods We randomly selected 400 admission notes and 400 discharge summaries from Peking Union Medical College Hospital in China. For each note, four types of entity clinical problems, procedures, laboratory test, and medications were annotated according to a predefined guideline. Two-thirds of the 400 notes were used to train the NER systems and one-third for testing. We investigated the effects of different types of feature including bag-of-characters, word segmentation, part-of-speech, and section information, and different machine learning algorithms including conditional random fields (CRF), support vector machines (SVM), maximum entropy (ME), and structural SVM (SSVM) on the Chinese clinical NER task. All classifiers were trained on the training dataset and evaluated on the test set, and micro-averaged precision, recall, and F-measure were reported. Results Our evaluation on the independent test set showed that most types of feature were beneficial to Chinese NER systems, although the improvements were limited. The system achieved the highest performance by combining word segmentation and section information, indicating that these two types of feature complement each other. When the same types of optimized feature were used, CRF and SSVM outperformed SVM and ME. More specifically, SSVM achieved the highest performance of the four algorithms, with F-measures of 93.51% and 90.01% for admission notes and discharge summaries, respectively.

关键词： Medical concept recognition Chinese clinic notes Natural language processing machine learning algorithm

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An Improved K-nearest-neighbor algorithm using Genetic algorithm for Sentiment Classification

An Improved K-nearest-neighbor algorithm using Genetic Algor...

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IEEE International Conference on Circuits, Power and Computing Technologies (ICCPCT)

作者： Kalaivani, P. Shunmuganathan, K. L. Sathyabama Univ Dept CSE Chennai Tamil Nadu India RMK Engn Coll Chennai Tamil Nadu India

ISBN: (纸本)9781479923977

Sentiment classification is to find the polarity of product or user reviews. Supervised machine learning algorithms are used for opinion mining such as Navie Bayes, K-nearest neighbor and Support vector machine. KNN is simple algorithm but less efficient classification algorithm. In this paper we propose an improved KNN algorithm, genetic algorithm is developed which is a hybrid genetic algorithm that incorporates the information gain for feature selection and combined with KNN to improve its classification performance. Specifically, we compared other supervised machine learning approaches such as Navie Bayes and traditional kNN for Sentiment Classification of movie reviews and book reviews. The experimental results using genetic algorithm with improved indicate high performance levels with Fmeasure of over 87% on the movie reviews.

关键词： Opinion mining sentiment classification machine learning algorithm genetic algorithm features selection

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Recognition and Extraction of Honorifics in Chinese Diachronic Corpora 15

Recognition and Extraction of Honorifics in Chinese Diachron...

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15th Chinese Lexical Semantics Workshop (CLSW)

作者： Xiong, Dan Xu, Jian Lu, Qin Lo, Fengju Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Yuan Ze Univ Dept Chinese Linguist & Literature Zhongli Taiwan

ISBN: (纸本)9783319143316;9783319143309

Honorifics in this paper refer to names of official positions and titles of nobility or honor. They can be found in various written records in different periods and have great historical significance. This paper introduces a machine learning system to recognize the honorifics in diachronic corpora. A tagged corpus of four classic novels written in the Ming and Qing dynasties is used to train the system. The system is then used to automatically recognize and extract the honorifics in pre-Qin classics, Tang-dynasty poems, and modern Chinese news. Experimental results show that the system can achieve relatively good results in recognizing the honorifics in the pre-Qin classics and Tang-dynasty poems. This work is an attempt to improve the performance of automatic recognition of honorifics in diachronic corpora. The system can be a helpful tool in the studies on the evolution of honorifics throughout Chinese history.

关键词： Honorifics Chinese diachronic corpora machine learning algorithm

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Differential Evolution based Multiobjective Optimization for Biomedical Entity Extraction 3

Differential Evolution based Multiobjective Optimization for...

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3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Sikdar, Utpal Kumar Ekbal, Asif Saha, Sriparna Indian Inst Technol Dept CSE Patna Bihar India

ISBN: (纸本)9781479930807

In this paper, we propose multi-objective differential evolution (DE) based feature selection and ensemble learning techniques for biomedical entity extraction. The algorithm operates in two layers, first step of which concerns with the problem of automatic feature selection for a machine learning algorithm, namely Conditional Random Field (CRF). The solutions of the final best population provides different feature combinations. The classifiers generated with these feature representations are combined together using a multi-objective differential based ensemble technique. We evaluate the proposed algorithm for named entity (NE) extraction in biomedical text. Experiments on the benchmark setup yield recall, precision and F-measure values of 73.50%, 77.02% and 75.22%, respectively.

关键词： evolutionary computation feature extraction feature selection learning (artificial intelligence) medical computing pattern classification random processes text analysis CRF DE based feature selection NE extraction automatic feature selection biomedical entity extraction biomedical text classifiers conditional random field differential evolution based multiobjective optimization ensemble learning techniques feature combinations feature representations machine learning algorithm multiobjective differential based ensemble technique multiobjective differential evolution named entity Biological cells Feature extraction Linear programming Optimization Sociology Statistics Vectors

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Hybrid Modeling Attacks on Current-based PUFs 32

Hybrid Modeling Attacks on Current-based PUFs

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32nd IEEE International Conference on Computer Design (ICCD)

作者： Kumar, Raghavan Burleson, Wayne Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01003 USA

ISBN: (纸本)9781479964925

Physically Unclonable Functions have emerged as a possible candidate to replace traditional cryptography. However, majority of the strong PUFs are vulnerable to modeling attacks. In this work, we take a closer look at the possible attacks on one of the strong PUF architectures known as Current-based PUFs, which exploit irregularities in transistor currents to generate unique signatures. We demonstrate that the fault-injection attacks when coupled with a machine learning (ML) algorithm can considerably push the limits of prediction accuracies. Based on simulations, we observed that the stand-alone ML algorithms suffer from error prone CRPs especially for higher length PUFs. In such scenarios, hybrid attacks exploiting the unreliable responses pushed the prediction accuracies up to 99% for higher length Current-based PUF circuits.

关键词： cryptography learning (artificial intelligence) current-based PUF error prone CRP fault-injection attacks hybrid attacks machine learning algorithm modeling attacks physically unclonable functions strong PUF architectures transistor currents Accuracy Circuit faults Integrated circuit modeling Logic gates Prediction algorithms Security Transistors

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Comparison of ML algorithms for Identification of Automated Number Plate Recognition 3

Comparison of ML Algorithms for Identification of Automated ...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Bhardwaj, Dinesh Kaur, Harjinder Chandigarh Univ Mohali Dept Comp Sci & Engn Chandigarh India Golden Coll Engn & Tech Dept Elect & Commun Engn Gurdaspur India

ISBN: (纸本)9781479968961

Automated Number Plate Recognition (ANPR) has become essential part of traffic control system because of unconstrained increased of vehicles on roads which make it difficult to control. On or after analysis of diverse author's exertion, ANPR has six imperative parts like Image Acquisition, Pre-processing, edge detection, segmentation, feature extraction and recognition. Characters recognition is turn out to be difficult everyday jobs due to variations of number plate. The intentioned system is mainly focus on classification and recognition of characters using Kstar machine learning algorithm. The classification characters present better performance using Kstar. A comparative study is done between these classifiers based upon their considerations like average accuracy, precision, recall and F-measure. Performance is deliberated using precision, recall and F-measure. The results give you an idea about that K STAR algorithm completes better with Recognition Rate and it achieved an accuracy of 99%.

关键词： ANPR Kstar Pre-processing Segmentation Edge detection Feature extraction and recognition machine learning algorithm

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Human and machine annotation in the Orchive, a large scale bioacoustic archive

Human and machine annotation in the Orchive, a large scale b...

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Ness, Steven Tzanetakis, George Univ Victoria Dept Comp Sci Victoria BC V8W 2Y2 Canada

ISBN: (纸本)9781479970889

Advances in computer technology have enabled the collection, digitization, and automated processing of huge archives of bioacoustic sound. Many of the tools previously used in bioacoustics research work well with small to medium-sized audio collections, but are challenged when processing large collections ranging from tens of terabytes to petabyte size. The Orchive is a system that assists researchers to listen to, view, annotate and run advanced audio feature extraction and machine learning algorithms on large bioacoustic archives. Annotation is one of the biggest challenges in our work. In this paper, we describe our efforts to utilize experts as well as citizen scientists to participate in the process of annotating recordings. The Orchive contains over 23,000 hours of orca vocalizations collected over the course of 30 years, and represents one of the largest continuous collections of bioacoustic recordings in the world. Manual annotation is practically impossible and therefore we investigate the effectiveness of a semi-automatic approach for extracting information from these recordings, and show various experimental results. Finally we have been able to apply our automatic analysis over the a large portion of the archive and describe the computational resources required. To the best of our knowledge this is the largest archive of bioacoustic data that has even been automatically analyzed.

关键词： audio signal processing bioacoustics data analysis Orchive advanced audio feature extraction algorithm annotating recordings automatic analysis bioacoustic data bioacoustic recordings bioacoustic sound human annotation large scale bioacoustic archive machine annotation machine learning algorithm manual annotation medium-sized audio collections orca vocalization Accuracy Kernel Logistics machine learning algorithms Support vector machines Whales machine learning algorithms bioacoustics automatic analysis audio signal processing archives Whale Logistics Support Vector Network ANNOTATIONS Computer technology Kernel Recordings Data analysis

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