レーザー誘導破壊分光法を用いた水中で計測した熱水海底岩石の分類のための機械学習アルゴリズムの検討と統計解析

The aim is to study the use of machine learning algorithms for the classification hydrothermal seafloor rocks measured underwater using Laser-Induced Breakdown Spectroscopy. The rocks were classified concerning their labels assigned to each rock, and geological groups formed ternary diagram with the relative ratio of Cu-Pb-Zn. In this research the target rocks are obtained from deep-ocean in Okinawa Japan. These were hydrothermal deposit sea-floor rocks. Further, these rocks were brought into the laboratory and broken into pieces and made the pellets. The experimental setup which resembles the ocean, then to test using ChemiCam device which is a LIBS device which is specifically designed for the chemical elemental analysis in the deep ocean is used to fire the laser beams on rocks. The proposed methods for classification of rocks with respect to their labels and for geological group are evaluated using with and without linear detrend along with the principal component analysis (PCA) as a pre-processing step which significantly reduces the dimensionality of the data, with classification algorithms such as the support vector machine (SVM), k-nearest neighbor search (KNN) and artificial neural network (ANN) methods. The performance of the classification algorithms depends on the size of the dataset, to prove this the dataset has been divided into two sets of 100 laser shots of each rock and 300 laser shots of each rock. Additionally, removing the noise from the spectra such as linear trend using linear detrend operation from the data enhances the performance of the classification in terms of sensitivity. The best classification performance concerning the rock label concerning sensitivity is obtained using an SVM linear kernel algorithm with 95%. The best classification performance concerning the geological group is obtained using the SVM method with 98% accuracy. The one-sided Wilcoxon signed rank test is applied to the classification results in the rock label and group cases, and the results indicate that the SVM algorithm has statistical significance over the other algorithms while classifying the rock labels and rock group.九州工業大学博士学位論文 学位記番号：生工博甲第345号 学位授与年月日：平成31年3月25日1 Introduction|2 Machine Learning Models for Classification|3 Result and Discussion|4 Conclusion and Future Scope九州工業大学平成30年

Target and Non-Target Approaches for Food Authenticity and Traceability

Over the last few years, the subject of food authenticity and food fraud has received increasing attention from consumers and other stakeholders, such as government agencies and policymakers, control labs, producers, industry, and the research community. Among the different approaches aiming to identify, tackle, and/or deter fraudulent practices in the agri-food sector, the development of new, fast, and accurate methodologies to evaluate food authenticity is of major importance. This book, entitled “Target and Non-Target Approaches for Food Authenticity and Traceability”, gathers original research and review papers focusing on the development and application of both targeted and non-targeted methodologies applied to verify food authenticity and traceability. The contributions regard different foods, among which some are frequently considered as the most prone to adulteration, such as olive oil, honey, meat, and fish. This book is intended for readers aiming to enrich their knowledge through reading contemporary and multidisciplinary papers on the topic of food authentication

Digital soil mapping of soil physical and chemical properties using proximal and remote sensed data in Australian cotton growing areas

In Australian cotton-growing areas, information of soil physical and chemical properties is required as they decide soil structure, nutrient availability and water holding capacity. However, using conventional laboratory methods to determine these properties is impractical as they are time-consuming and costly. This is especially the case when considering samples from different depths and across heterogenous fields and districts. Thus, there is a need for efficient and affordable methods to enable data generation. To answer this need, digital soil mapping (DSM) can be used, in which limited laboratory measured soil data is coupled with cheaper-to acquire digital data through models and then the model and spatially exhaustive digital data are used to predict soil properties on unsampled locations. This thesis evaluates DSM methods for the prediction of soil physical (e.g., clay content) and chemical (e.g., cation exchange capacity [CEC] and exchangeable [exch.] cations) properties at various depths across cotton growing areas in south-eastern Australia, at field and district scales. Chapter 1 is the general introduction where research problems are defined, and research objectives are introduced. To point out gaps in the application of DSM on the prediction of soil properties, Chapter 2 comprehensively reviews DSM concepts, the applicability of proximally (e.g., electromagnetic induction (EM), visible near-infrared spectroscopy (vis-NIR)) or remotely (e.g., γ-ray spectrometer) sensed digital data for prediction of soil properties at various depths and the modelling techniques. The first research chapter (Chapter 3) compares various strategies to build the vis-NIR spectral library for clay content prediction at two depths across seven cotton growing areas using Cubist model. The results show that the area-specific vis-NIR library achieve the best results. The improvement in model performance is possible using spiking. The Chapter 4 compares multivariate methods for estimating clay content and its uncertainty map at two depths and the effect of weighted model averaging is evaluated. The results show that random forest (RF) model generally performs the best and model averaging could further improve the prediction accuracy. The Chapter 5 evaluates the potential of vis-NIR as a tool for the simultaneous prediction of soil physical and chemical properties across cotton growing areas and considering two calibration models. The results show that satisfactory predictions of clay and CEC are achieved with silt and sand prediction moderate, while the prediction of pH and exchangeable sodium percentage (ESP) are unsatisfactory. A multi-depth vis-NIR library generally performs better than depth-specific libraries on prediction of soil properties. The Chapter 6 builds a topsoil (0 – 0.3 m) vis-NIR spectral library to predict topsoil exch. cations considering four different calibration models and explores the applicability of the topsoil library to predict exch. cations at deeper depths considering spiking or not. The results show that the vis-NIR could provide satisfactory prediction of exch. calcium and magnesium. Topsoil spectral library could be used to predict exch. cations at deeper depth with spiking further improving the result. The Chapter 7 estimates spatial variation of CEC at various depths using quasi-3d joint inversion of EM38 and EM31 data in an irrigated cotton field. The results indicate that the joint-inversion approach developed in this study could generate accurate 3D predictions of soil CEC in the cotton growing field. This thesis explores DSM methods for the prediction of soil physical and chemical properties in Australian cotton growing areas and the results deliver new evidence of the potential to use proximally and remotely sensed digital data and state-to-art models for rapid and efficient generation of soil information. New findings will serve to advance the existing knowledge on application of DSM at field and district scales

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine