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Near-infrared spectroscopy as a tool for rapid screening of deoxynivalenol in wheat flour and its applicability in the industry

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FOOD ADDITIVES AND CONTAMINANTS PART A-CHEMISTRY ANALYSIS CONTROL EXPOSURE & RISK ASSESSMENT 2021年第11期38卷 1958-1968页

作者： Tyska, Denize Mallmann, Adriano Gressler, Luciane Tourem Mallmann, Carlos Augusto Fed Univ Santa Maria UFSM Dept Prevent Vet Med Lab Mycotoxicol Anal LAMIC Santa Maria RS Brazil Pegasus Sci Santa Maria RS Brazil

This study aimed to evaluate the applicability and efficiency of Near-Infrared Spectroscopy (NIR) by using dispersive NIR and Fourier Transform NIR to analyse 267 samples of Brazilian wheat flour contaminated with deoxynivalenol (DON). For this, Partial Least-squares Discriminant Analysis (PLS-DA) and Principal Component Analysis-Linear Discriminant Analysis (PC-LDA) were used as discriminatory methods. Next, the samples were classified according to the maximum tolerated limits (MTL) for DON in Brazil, 750 mu g kg(-1), and two groups were established for the calibration set: category A (<= 450 mu g kg(-1)), non-contaminated or below the MTL;and category B (>450 mu g kg(-1)), contaminated or above the MTL. Validation samples through PLS-DA showed correct classification rates in the range of 85-87.5% and presented a 10-15% error;for PC-LDA, the hit rate was over 85% with an error of 10-15%. The present findings demonstrate that NIR is an excellent alternative method to classify wheat flour samples according to DON content.

关键词： Food safety mycotoxins infrared spectroscopy screening classification methods Brazil

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Distributed Kalman filtering: a bibliographic review

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IET CONTROL THEORY AND APPLICATIONS 2013年第4期7卷 483-501页

作者： Mahmoud, Magdi S. Khalid, Haris M. King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran 31261 Saudi Arabia

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this study, a bibliographical review on distributed Kalman filtering (DKF) is provided. The study contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area.

关键词： distributed sensors distributed sensor fusion DKF classification methods Sensor fusion Sensing devices and transducers sensor fusion bibliographic review Kalman filters distributed information structures Filtering methods in signal processing distributed Kalman filtering

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Using Kinect to classify Parkinson's disease stages related to severity of gait impairment

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BMC BIOINFORMATICS 2018年第1期19卷 471-471页

作者： Dranca, Lacramioara de Abetxuko Ruiz de Mendarozketa, Lopez Goni, Alfredo Illarramendi, Arantza Navalpotro Gomez, Irene Delgado Alvarado, Manuel Cruz Rodriguez-Oroz, Maria Ctr Univ Def Ctra Huesca Zaragoza Spain Univ Basque Country UPV EHU Paseo Manuel Lardizabal 1 Donostia San Sebastian 20018 Spain Biodonostia Hlth Res Inst Neurodegenerat Disorders Area Begiristain Doktorea Pasealekua Donostia San Sebastian 20014 Spain Network Ctr Biomed Res Neurodegenerat Dis CIBERNED Madrid Spain Donostia Univ Hosp Donostia San Sebastian Spain Basque Fdn Sci Ikerbasque Donostia San Sebastian Spain Basque Ctr Cognit Brain & Language BCBL Donostia San Sebastian Spain Univ Hosp Sierrallana Dept Neurol Neuroimaging Unit Valdecilla Biomed Res InstIDIVAL Santander Spain Univ Navarra Clin Dept Neurol Pamplona Spain

BackgroundParkinson's Disease (PD) is a chronic neurodegenerative disease associated with motor problems such as gait impairment. Different systems based on 3D cameras, accelerometers or gyroscopes have been used in related works in order to study gait disturbances in PD. Kinect (?) has also been used to build these kinds of systems, but contradictory results have been reported: some works conclude that Kinect does not provide an accurate method of measuring gait kinematics variables, but others, on the contrary, report good accuracy *** this work, we have built a Kinect-based system that can distinguish between different PD stages, and have performed a clinical study with 30 patients suffering from PD belonging to three groups: early PD patients without axial impairment, more evolved PD patients with higher gait impairment but without Freezing of Gait (FoG), and patients with advanced PD and FoG. Those patients were recorded by two Kinect devices when they were walking in a hospital corridor. The datasets obtained from the Kinect were preprocessed, 115 features identified, some methods were applied to select the relevant features (correlation based feature selection, information gain, and consistency subset evaluation), and different classification methods (decision trees, Bayesian networks, neural networks and K-nearest neighbours classifiers) were evaluated with the goal of finding the most accurate method for PD stage *** classifier that provided the best results is a particular case of a Bayesian Network classifier (similar to a Naive Bayesian classifier) built from a set of 7 relevant features selected by the correlation-based on feature selection method. The accuracy obtained for that classifier using 10-fold cross validation is 93.40%. The relevant features are related to left shin angles, left humerus angles, frontal and lateral bents, left forearm angles and the number of steps during *** this paper, it is

关键词： Kinect classification methods Bayesian networks Parkinson disease Gait

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Automatic writer identification from text line images

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INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION 2012年第2期15卷 85-99页

作者： Kirli, Onder Gulmezoglu, M. Bilginer 1st Air Supply & Maintenance Ctr Technol & Weapon Syst Dev Dept Eskisehir Turkey Eskisehir Osmangazi Univ Dept Elect & Elect Engn TR-26480 Eskisehir Turkey

In the present article, new techniques have been introduced for revealing the individual features of a person's handwriting pattern from the scanned images of handwritten text lines to facilitate text-independent writer identification. These techniques are aimed at designing a dynamic model which can be formalized according to any handwritten text line. Various combinations of the extracted features are applied to three well known classifiers for evaluating the contribution of features to define the correct identification rate. The K-NN, GMM, and Normal Density Discriminant Function Bayes classifiers are used in the present identification model. The experimental studies are conducted using two datasets obtained from the IAM database. The first dataset has already been proposed and used in the literature, whereas the second dataset is an expanded version of the first dataset and has been constituted for the first time in this study to analyze the performance of the extracted features under conditions such as an increased number of writers to discriminate in the database and a decreased number of text lines per writer. The remarkable identification rates obtained from the three classifiers on both datasets clearly indicate that the proposed feature extraction techniques can be effectively used in writer identification systems.

关键词： Writer identification Feature extraction Handwriting analysis classification methods Database

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A classification-Based Algorithm to Detect Forged Embedded Machines in IoT Environments

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IEEE SYSTEMS JOURNAL 2019年第1期13卷 389-399页

作者： Selis, Valerio Marshall, Alan Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

In the Internet of Things (IoT), interconnected devices manage essential information related to people's lives;hence, securing this information is essential. The number of these machines is rapidly growing;these are mostly embedded, and therefore more susceptible to attacks. Recently, thousands of subverted IoT embeddedmachines, such as surveillance cameras, were used for launching distributed denial of service (DDoS) attacks. In this scenario, attackers, who are not embeddedmachines, can emulate their behaviors to subvert themachine-to-machine network. In this paper, we present a novel algorithm to detect such forged machines. This allows detection of virtualized and emulated systems by observing their behaviors and can be used by IoT trust agents in embedded machines. With the aim of creating a machine-agnostic system, portable and applicable to future IoT machines, we propose a classification-based algorithm as the detection mechanism. Extensive experiments with different system architectures and operating systems were performed, along with a comparison of several feature selection and classification methods. The results show that our method can quickly reveal illegitimate machines with a high probability of detection, giving the opportunity for its use in power-constrained machines. Our approach is also able to detect unknown embedded systems and can be used to detect fake timing attacks.

关键词： classification methods Internet of Things (IoT) machine-to-machine (M2M) communications timing analysis trust virtual machining

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Categorizing Extent of Tumor Cell Death Response to Cancer Therapy Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2014年第6期33卷 1390-1400页

作者： Gangeh, Mehrdad J. Sadeghi-Naini, Ali Diu, Michael Tadayyon, Hadi Kamel, Mohamed S. Czarnota, Gregory J. Univ Toronto Dept Med Biophys Toronto ON M5G 2M9 Canada Univ Toronto Dept Radiat Oncol Toronto ON M5G 2M9 Canada Sunnybrook Hlth Sci Ctr Dept Radiat Oncol Toronto ON M4N 3M5 Canada Sunnybrook Hlth Sci Ctr Dept Imaging Res Phys Sci Toronto ON M4N 3M5 Canada Univ Toronto Dept Med Biophys Toronto ON M5G 2M9 Canada Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Sunnybrook Hlth Sci Ctr Dept Phys Sci Toronto ON M4N 3M5 Canada

Quantitative ultrasound (QUS) spectroscopic techniques in conjunction with maximum mean discrepancy (MMD) have been proposed to detect, and to classify noninvasively the levels of cell death in response to cancer therapy administration in tumor models. Evaluation of xenograft tumor responses to cancer treatments were carried out using conventional-frequency ultrasound at different times after chemotherapy exposure. Ultrasound data were analyzed using spectroscopic techniques and multi-parametric QUS spectral maps were generated. MMD was applied as a distance criterion, measuring alterations in each tumor in response to chemotherapy, and the extent of cell death was classified into less/more than 20% and 40% categories. Statistically significant differences were observed between "pre-" and "post-treatment" groups at different times after chemotherapy exposure, suggesting a high capability of proposed framework for detecting tumor response noninvasively. Promising results were also obtained for categorizing the extent of cell death response in each tumor using the proposed framework, with gold standard histological quantification of cell death as ground truth. The best classification results were obtained using MMD when applied on histograms of QUS parametric maps. In this case, classification accuracies of 84.7% and 88.2% were achieved for categorizing extent of tumor cell death into less/more than 20% and 40%, respectively.

关键词： Cancer treatment classification methods kernel methods nonparametric methods personalized medicine quantitative ultrasound

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A survey of underactuated mechanical systems

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IET CONTROL THEORY AND APPLICATIONS 2013年第7期7卷 921-935页

作者： Liu, Yang Yu, Hongnian Univ Aberdeen Kings Coll Sch Engn Ctr Appl Dynam Res Aberdeen Scotland Bournemouth Univ Sch Design Engn & Comp Bournemouth Dorset England

An underactuated mechanical system (UMS) is a system which has fewer independent control actuators than degrees of freedom to be controlled. Control of UMS is considered as one of the most active fields of research because of its diverse engineering applications. This survey reviews UMS from its history to the state-of-the-art research on modelling, classification, control, and to some extent, provides some unique insights for bottleneck issues and future research directions.

关键词： underactuated mechanical systems UMS control modelling methods mechanical engineering Mechanical variables control UMS degrees of freedom independent control actuators classification methods future research directions diverse engineering applications Actuating and final control devices actuators pattern classification mechanical variables control control methods

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A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2020年 108卷 101950-101950页

作者： Reyes, Oscar Perez, Eduardo Luque, Raul M. Castano, Justo Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal Cordoba Spain Maimonides Biomed Res Inst Cordoba Cordoba Spain Univ Cordoba Dept Cell Biol Physiol & Immunol Cordoba Spain

Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognosis and therapy. Determining such splicing components, however, is not a straightforward task mainly due to the heterogeneity of tumors, the variability across samples, and the fat-short characteristic of genomic datasets. In this work, a supervised machine learning-based methodology is proposed, allowing the determination of subsets of relevant splicing components that best discriminate samples. The methodology comprises three main phases: first, a ranking of features is determined by means of applying feature weighting algorithms that compute the importance of each splicing component;second, the best subset of features that allows the induction of an accurate classifier is determined by means of conducting an effective heuristic search;then the confidence over the induced classifier is assessed by means of explaining the individual predictions and its global behavior. At the end, an extensive experimental study was conducted on a large collection of transcript-based datasets, illustrating the utility and benefit of the proposed methodology for analyzing dysregulation in splicing machinery.

关键词： Transcript-based analysis Alternative Splicing Feature weighting methods classification methods Explaining classifier's predictions

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Deep transfer learning algorithms applied to synthetic drawing images as a tool for supporting Alzheimer's disease prediction

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MACHINE VISION AND APPLICATIONS 2022年第3期33卷 1-17页

作者： Cilia, Nicole D. D'Alessandro, Tiziana De Stefano, Claudio Fontanella, Francesco Univ Cassino & Southern Lazio Dept Elect & Informat Engn Cassino FR Italy Radboud Univ Nijmegen Inst Comp & Informat Sci Nijmegen Netherlands

Neurodegenerative diseases, such as Alzheimer's Disease or Parkinson's disease, are unfortunately still incurable, although there are many therapies that can slow down the progression of the disease and improve patients' lives. An essential condition, however, is the early diagnosis of these disorders to begin therapies as soon as possible: In fact, when the signs of the disease become evident, damages may be already significant and irreversible. In this context, it is generally agreed that handwriting is one of the first skills affected by the onset of cognitive disorders. For this reason, in a preliminary study, we considered a database of handwriting and drawing specimens and proposed a method for selecting the most relevant information for diagnosing neurodegenerative disorders. The basic idea was to generate, for each handwriting sample, a color image to exploit the ability of convolutional neural network to automatically extract features from raw images. In the generated images, the color of each elementary trait encodes, in the three RGB channels, the dynamic information associated with that trait. Starting from the very encouraging obtained results, the aim of this study is twofold: On the one hand, we have tried to improve the feature extraction phase, associating further dynamic information with each handwritten trait. On the other hand, we have expanded the database of handwriting samples by adding specimen derived from more complex drawing tasks. Finally, we carried out a large set of experiments for comparing the results obtained by using standard online features with those obtained with our feature extraction approach.

关键词： Alzheimer's disease prediction Feature extraction Convolutional neural networks Transfer learning classification methods

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A topic sentence-based instance transfer method for imbalanced sentiment classification of Chinese product reviews

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ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 2016年 16卷 66-76页

作者： Tian, Feng Wu, Fan Chao, Kuo-Ming Zheng, Qinghua Shah, Nazaraf Lan, Tian Yue, Jia Xi An Jiao Tong Univ Syst Engn Inst Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Shaanxi Key Lab Satellite Terr Network Tech R&D Xian 710049 Shaanxi Peoples R China Coventry Univ Dept Comp Sci & Technol Coventry CV1 2JH W Midlands England Xi An Jiao Tong Univ Dept Comp Sci & Technol Xian 710049 Shaanxi Peoples R China

The increasing interest in sentiment classification of product reviews is due to its potential application for improving e-commerce services and quality of the products. However, in realistic e-commerce environments, the review-related data are imbalanced, and this leads to a problem in which minority class information tends to be ignored during the training phase of a classification model. To address this problem, we propose a topic sentence-based instance transfer method to process imbalanced Chinese product reviews by using an auxiliary dataset (source dataset). The proposed method incorporates a rule and supervised learning hybrid approach to identify a topic sentence of each product review and adds the feature set of the topic sentence to the feature space of sentiment classification. Next, to measure the transferability of instances in source dataset, a greedy algorithm based on information gain of top-N common features is used to select common features. Then, a common feature-based cosine similarity of instances between source dataset and target dataset is introduced to select the transferable instances. Furthermore, a synthetic minority over-sampling technique (Smote) based method is adopted to overcome feature space inconsistency between the source dataset and target dataset. Finally, we immigrate the instances selected in source dataset into target dataset to form a new dataset for the training of classification model. Two datasets collected from Jingdong and Dangdang are the target dataset and source dataset. The experimental results verify that, considering the ability of generalization, our proposed method helps a support vector machine (SVM) to outperform other classification methods, such as the J48, Naive Bayes, Random Forest and Random Committee methods, when applied to datasets produced by resampling and Smote. (C) 2015 Elsevier B.V. All rights reserved.

关键词： classification methods Imbalanced sample classification Instance transfer methods Product reviews Topic sentence analysis

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