jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Michael Fop | Pierre‐Alexandre Mattei | Charles Bouveyron | Thomas Brendan Murphy
Source: Advances in Data Analysis and Classification (preprint, 2022-03-01)
Field: Chemistry — Analytical Chemistry

Unobserved classes and extra variables in high-dimensional discriminant analysis In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase. Moreover, the same units in the test data may be measured on a set of additional variables recorded at a subsequent stage with respect to when the learning sample was collected. In this situation, the classifier built in the learning phase needs to adapt to handle potential unknown classes and the extra dimensions. We introduce a model-based discriminant approach, Dimension-Adaptive Mixture Discriminant Analysis (D-AMDA), which can detect unobserved classes and adapt to the increasing dimensionality. Model estimation is carried out via a full inductive approach based on an EM algorithm. The method is then embedded in a more general framework for adaptive variable selection and classification suitable for data of large dimensions. A simulation study and an artificial experiment related to classification of adulterated honey samples are used to validate the ability of the proposed framework to deal with complex situations. Linear discriminant analysis ; Curse of dimensionality ; Discriminant ; Classifier (UML) ; Artificial intelligence ; Optimal discriminant analysis ; Computer science ; Dimension (graph theory) ; Machine learning ; Sample (material) ; Feature selection ; Pattern recognition (psychology) ; Set (abstract data type) ; Dimensionality reduction ; Variable (mathematics) ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ana Umaquinga | Marco Naranjo-Toro | Omar Godoy-Collaguazo
Source: INNOVATION & DEVELOPMENT IN ENGINEERING AND APPLIED SCIENCES (article, 2022-07-26)
Field: Materials Science — Polymers and Plastics

Textile and machine learning: A Bibliometric analysis Abstract. In the present research, a bibliometric analysis of scientific publications in the area of textile sciences from the area of machine learning is carried out. The search equation identifies as keywords (i) textile and (ii) machine learning from the scopus database, obtaining as initial result 308 publications, being 281 the final publications to be studied from 1991 to 2022. The bibliometric analysis was carried out using bibliometrix and VOS Viewer, showing the growing interest of the scientific community and authors in this area of research. Scopus ; Textile ; Bibliometrics ; Computer science ; Artificial intelligence ; Data science ; Library science ; Geography ; Archaeology ; Political science ; MEDLINE Textile materials and evaluations
Authors: E. Strauß | D. Bulatov
Source: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences (article, 2022-10-14)
Field: Materials Science — Materials Chemistry

A REGION-BASED MACHINE LEARNING APPROACH FOR SELF-DIAGNOSIS OF A 4D DIGITAL THERMAL TWIN Abstract. In this paper, we explore the applicability of machine learning as a tool for self-diagnosis of 4D digital twins with a focus on simulated surface temperatures. Generation of digital twins involves abstractions, simplification, and the closed-world assumption. Hence, performing thermal simulation in order to obtain surface temperatures involves not only mathematical modeling of the physical phenomena, but also temporal uncertainties on external conditions. To identify the types of simulation inaccuracies, our proposed method is based on thermal image comparison, i.e. the corresponding measured thermal image and the simulated thermal image resulting from the 4D digital twin. First, a statistical necessary condition is defined to obtain regions of interest in the simulated image. Second, after manual labeling of these regions into the two inaccuracy classes, we conduct a detailed feature analysis and subsequently train our Random Forest classifier. The results show a good separability of the two classes despite the limited training data, allowing to achieve values of overall accuracy around 93.5%. Computer science ; Thermal ; Artificial intelligence ; Focus (optics) ; Digital image ; Image (mathematics) ; Classifier (UML) ; Feature (linguistics) ; Surface (topology) ; Machine learning ; Pattern recognition (psychology) ; Computer vision ; Image processing ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced machining processes and optimization ; Industrial Vision Systems and Defect Detection
Authors: K. Madeshwaran | A. Eswari | M. R. Duraisamy | S Praneetha | B. Selvi
Source: International Journal of Environment and Climate Change (article, 2022-08-05)
Field: Chemistry — Inorganic Chemistry

Comparison of Linear and Non-linear Models for Coconut Yield Prediction in Coimbatore Using Weather Parameters and External Factors Coconut is the world's most significant plantation crop, and it is grown in practically every country. As Coimbatore is the leading producer of coconut in Tamil Nadu, followed by Thanjavur and Kanyakumari, this study is centred on the Coimbatore area. West Coast Tall is a popular cultivar that produces more than other types. The West Coast Tall (WCT) cultivar was used in this research. In this paper, four models were developed such as Ridge, Least Absolute Shrinkage Selection Operator (LASSO), Elastic net (ELNET) regression methods and Artificial Neural Networks (ANN). Further, we validate this model using field-level data from TNAU coconut research farm for two years. The purpose of this communication is to find the best fit model for prediction of coconut yield using weather parameters and external factors in Coimbatore district. The models were selected based on different performance metrics such as RMSE, MAPE, MAE, and R2. Among the four models developed in the study, the ELNET model is found to be best model for prediction of coconut yield based on weather and external factors for the available data in the studied region. Yield (engineering) ; Ridge ; Mathematics ; Cultivar ; Artificial neural network ; Selection (genetic algorithm) ; Linear regression ; Tamil ; Agriculture ; Statistics ; Agricultural engineering ; Geography ; Agronomy ; Engineering ; Biology ; Computer science ; Machine learning ; Ecology Coconut Research and Applications ; Natural Products and Biological Research ; Agricultural Economics and Practices
Authors: Beverly A. Malabag | Cereneo S. Santiago | Erwin L. Cahapin | Jocelyn L. Reyes | Gemma S. Legaspi
Source: International Journal of Emerging Technology and Advanced Engineering (article, 2022-10-01)
Field: Chemistry — Analytical Chemistry

Fuzzy Logic-Based Size and Ripeness Classification of Banana using Image Processing Technique The grading of banana ripeness and size are currently done manually by farmers and vendors following a certain process. It will assist producers grade the fruit, sellers set the best price, and customers get the most for their money if a system to automatically categorize bananas is proposed and developed. In this study, the size and maturity of bananas were classified using image processing and fuzzy logic techniques in MATLAB. This paper is segmented into five stages which are the image pre-processing, image processing, determination of length and thickness, size classification and ripeness classification. The metrics of length and thickness were used to classify banana sizes, while methods of image processing such as color segmentation and calculating the intensity of mean color based on RGB were used to classify banana ripeness. The classification of the banana was done using fuzzy logic in the decision-making process. Keywords—banana classification, fuzzy logic, image processing, defuzzification, banana grading Ripeness ; Artificial intelligence ; Image processing ; Fuzzy logic ; Mathematics ; Pattern recognition (psychology) ; RGB color model ; Contextual image classification ; Computer science ; Computer vision ; Image (mathematics) ; Horticulture Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Banana Cultivation and Research
Authors: Fernando Ribeiro Cassiano | Thelma Sáfadi | Paulo Henrique Sales Guimarães
Source: Research Society and Development (article, 2022-10-27)
Field: Chemistry — Analytical Chemistry

Wavelets in the analysis of seed image similarity: an approach using the Hurst directional exponent Modernization is present in all fields of knowledge. The wavelet transform and the Hurst exponent are tools that have fundamental importance in many of these advances. In the present study, the wavelet decomposition technique was combined with the Hurst exponent calculation to analyze X-ray images of seeds and thus classify them as full, slightly damaged or damaged. To calculate the Hurst exponent the mean and median were used as measurements of position. A support vector machine was used to validate the proposed method. For the full, damaged and slightly damaged seed groups, the average accuracy of the method, using the mean as measure position, was 74.5%, and using the median was 57.05%. For the full and damaged seed groups, the average accuracy using the mean was 99.76%, and using the median was 80.93%. For the slightly damaged and damaged seed groups, the average accuracy, using the mean as measure of position, was 99.26%, and the median was 76.22%. When analyzing seeds with slight damage, we observed a decrease in accuracy because the classification of the X-rays was subjective. Therefore, for the image database used in this study, the proposed methodology is efficient for automatic classification. Hurst exponent ; Wavelet ; Mathematics ; Similarity (geometry) ; Pattern recognition (psychology) ; Position (finance) ; Detrended fluctuation analysis ; Artificial intelligence ; Wavelet transform ; Exponent ; Statistics ; Image (mathematics) ; Measure (data warehouse) ; Support vector machine ; Computer science ; Data mining ; Geometry Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Studies ; Machine Learning in Bioinformatics
Authors: Goran Demboski | Maja Jankoska | Miguel Carvalho
Source: Industria Textila (article, 2022-04-30)
Field: Materials Science — Polymers and Plastics

Seam pucker detection through presser foot displacement monitoring The paper investigates the relation between seam pucker, woven fabric formability, and the presser foot displacement during stitch formation. Superimposed seams of class 1 were sewn in warp and weft direction using woven fabrics for tailored garments. The presser foot height was continuously monitored during stitch formation for different sewing machine speeds. The foot displacement during the sewing cycle was correlated with seam pucker. The obtained results showed various patterns of pressure foot displacement regarding the sewing machine speeds. It was found that at a lower machine speed there is no bouncing of the presser foot and the ratio of the height of the presser foot before and after rising and declining movement versus double thickness of the fabric (h/T1), correlates significantly with the seam pucker percentage. The fabrics having a higher h/T1 ratio showed a higher percentage of seam pucker. The obtained results suggest that online monitoring of this parameter can be a useful indication of on-seam pucker appearance, providing reliable means to obtain a quality seam. Displacement (psychology) ; Foot (prosody) ; Formability ; Materials science ; Structural engineering ; Engineering ; Composite material ; Art Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Ergonomics and Musculoskeletal Disorders
Authors: L. J. Frasinski
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Cumulant mapping as the basis of multi-dimensional spectrometry Cumulant mapping employs a statistical reconstruction of the whole by sampling its parts. The theory developed in this work formalises and extends <i>ad hoc</i> methods of 'multi-fold' or 'multi-dimensional' covariance mapping. Explicit formulae have been derived for the expected values of up to the 6th cumulant and the variance has been calculated for up to the 4th cumulant. A method of extending these formulae to higher cumulants has been described. The formulae take into account reduced fragment detection efficiency and a background from uncorrelated events. Number of samples needed for suppressing the statistical noise to a required level can be estimated using Matlab code included in Supplemental Material. The theory can be used to assess the experimental feasibility of studying molecular fragmentations induced by femtosecond or X-ray free-electron lasers. It is also relevant for extending the conventional mass spectrometry of biomolecules to multiple dimensions. Cumulant ; Covariance ; Sampling (signal processing) ; Femtosecond ; Algorithm ; Noise (video) ; Signature (topology) ; Basis (linear algebra) ; Computer science ; Statistical physics ; Statistics ; Mathematics ; Physics ; Laser ; Optics ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Ion-surface interactions and analysis
Authors: Md. Ameenuddin | Bavireddy Vishwanth Kumar | S. P. Yashwanth | Kushal Sahu | Ganjikunta Teja
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2022-11-17)
Field: Chemistry — Analytical Chemistry

Quality Testing of Rice Grains Using Image Processing Applications Abstract: Quality Testing of Rice Grains is testing of grain to evaluate the planting value and the authenticity of the certified lot. There are certain limitations to human eye to observe the Grain. So, the electronic world helps us to separate the faulty Grains from quality Grains. The specific target to be achieved is the development of a rice quality detection system that can assess the quality of rice using digital image processing. The evaluation of the rice grains on the basic grain size and shape using image processing edge detection algorithm is used to find the region of boundaries in each grain. Grain testing is done mainly for moisture, germination and physical purity of Grains. The performance of Image Processing reduces the time of operation. Image processing ; Agricultural engineering ; Quality (philosophy) ; Digital image processing ; Grain quality ; Computer vision ; Germination ; Computer science ; Digital image analysis ; Enhanced Data Rates for GSM Evolution ; Artificial intelligence ; Image (mathematics) ; Agronomy ; Engineering ; Biology Spectroscopy and Chemometric Analyses
Authors: Francisco O. Borges | Vanessa M. Lopes | Catarina Frazão Santos | Pedro Reis Costa | Rui Rosa
Source: Research Square (preprint, 2022-11-29)
Field: Materials Science — Biomaterials

Impacts of Climate Change Impacts on the Biogeography of three Amnesic Shellfish Toxin-producing Diatom species Diatom ; Climate change ; Biogeography ; Ecology ; Shellfish ; Environmental science ; Geography ; Biology ; Fishery ; Fish <Actinopterygii> ; Aquatic animal Diatoms and Algae Research ; Cephalopods and Marine Biology ; Marine Toxins and Detection Methods
Authors: Bilal Barış Alkan | Işıl Ünaldı
Source: Journal of Applied Mathematics Statistics and Informatics (article, 2022-05-01)
Field: Chemistry — Analytical Chemistry

Robust sparse principal component analysis: situation of full sparseness Abstract Principal Component Analysis (PCA) is the main method of dimension reduction and data processing when the dataset is of high dimension. Therefore, PCA is a widely used method in almost all scientific fields. Because PCA is a linear combination of the original variables, the interpretation process of the analysis results is often encountered with some difficulties. The approaches proposed for solving these problems are called to as Sparse Principal Component Analysis (SPCA). Sparse approaches are not robust in existence of outliers in the data set. In this study, the performance of the approach proposed by Croux et al. (2013), which combines the advantageous properties of SPCA and Robust Principal Component Analysis (RPCA), will be examined through one real and three artificial datasets in the situation of full sparseness. In the light of the findings, it is recommended to use robust sparse PCA based on projection pursuit in analyzing the data. Another important finding obtained from the study is that the BIC and TPO criteria used in determining lambda are not much superior to each other. We suggest choosing one of these two criteria that give an optimal result. Principal component analysis ; Sparse PCA ; Robust principal component analysis ; Outlier ; Dimensionality reduction ; Pattern recognition (psychology) ; Projection pursuit ; Dimension (graph theory) ; Projection (relational algebra) ; Artificial intelligence ; Computer science ; Sparse approximation ; Data mining ; Mathematics ; Algorithm Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Face and Expression Recognition
Authors: Tine Claeys | Maxime Menu | Robbin Bouwmeester | Kris Gevaert | Lennart Martens
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-10-05)
Field: Chemistry — Spectroscopy

Machine learning on large-scale proteomics data identifies tissue- and cell type-specific proteins Abstract Using data from 183 public human data sets from PRIDE, a machine learning model was trained to identify tissue and cell-type specific protein patterns. PRIDE projects were searched with ionbot and tissue/cell type annotation was manually added. Data from physiological samples were used to train a Random Forest model on protein abundances to classify samples into tissues and cell types. Subsequently, a one-vs-all classification and feature importance were used to analyse the most discriminating protein abundances per class. Based on protein abundance alone, the model was able to predict tissues with 98% accuracy, and cell types with 99% accuracy. The F-scores describe a clear view on tissue-specific proteins and tissue-specific protein expression patterns. In-depth feature analysis shows slight confusion between physiologically similar tissues, demonstrating the capacity of the algorithm to detect biologically relevant patterns. These results can in turn inform downstream uses, from identification of the tissue of origin of proteins in complex samples such as liquid biopsies, to studying the proteome of tissue-like samples such as organoids and cell lines. Proteome ; Proteomics ; Cell type ; Random forest ; Computational biology ; Computer science ; Cell ; Artificial intelligence ; Biology ; Bioinformatics ; Biochemistry Advanced Proteomics Techniques and Applications ; Gene expression and cancer classification ; Forensic and Genetic Research
Authors: Malwina Janiszewska
Source: Biometrical Letters (article, 2022-12-01)
Field: Chemistry — Analytical Chemistry

Structures of the covariance matrix: An overview Summary In this paper, some multivariate and double multivariate modelling approaches are presented. Moreover, this article provides an overview of the modelling of the structure of the covariance matrix. Furthermore, some methods of covariance structure identification are given. Covariance ; Covariance matrix ; Multivariate statistics ; Estimation of covariance matrices ; Identification (biology) ; Scatter matrix ; CMA-ES ; Covariance function ; Mathematics ; Matrix (chemical analysis) ; Covariance intersection ; Multivariate analysis ; Matérn covariance function ; Computer science ; Applied mathematics ; Statistics ; Chemistry Spectroscopy and Chemometric Analyses ; Neural Networks and Applications ; Control Systems and Identification
Authors: Muhammad M.A.S. Mahmoud
Source: Preprints.org (preprint, 2022-02-08)
Field: Materials Science — Materials Chemistry

New Method to Estimation Electrical Stresses on 300kV OHTL/Cable Joints Due to Switching Operation Using ANN to Determine the Insulation Lifetime This article discuss the estimation of electrical stresses input that is required to determine lifetime degradation of the HV cables installed within overhead transmission lines (OHTL&amp;rsquo;s). Abort from lightning effects, these electrical stresses are mainly generated due to repetitive switching operation of the transmission line that produces transient overvoltage (TOV) stress and high frequency stress. The transient overvoltage (TOV) due to switching-off the transmission line at the interface points between the cable and OHTL is estimated using artificial neural network (ANN). PSCAD software is used to build the transmission line model for 749 different case in order to build the required database for ANN training. Two different algorithm input to train ANN with one hidden layer and one output layer using different number of nodes are modeled in MATLAB. The results performance is continuously compared and evaluated till acceptable Regression is achieved to insure the error is less than +/-1.5%. The high frequency due to switching-on the transmission line is also recorded and analyzed. The results show that this new method is efficient, accurate and useful as there is no cable monitoring system is available for each HV cable installed within the transmission line. Overvoltage ; Transmission line ; Transient (computer programming) ; Overhead line ; Transmission (telecommunications) ; Electric power transmission ; Overhead (engineering) ; Artificial neural network ; Electronic engineering ; Line (geometry) ; Stress (linguistics) ; Engineering ; MATLAB ; Electrical engineering ; Computer science ; Voltage ; Structural engineering ; Artificial intelligence ; Mathematics High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Thermal Analysis in Power Transmission
Authors: Chunyan Ma | Jinhui Dong | Fan Ding | Changchun Li
Source: Research Square (preprint, 2022-03-02)
Field: Chemistry — Analytical Chemistry

Monitoring and prediction of wheat stripe rust based on hyperspectral and meteorological data Abstract This study integrated spectral sensitivity values and absorption characteristic parameters based on canopy hyperspectral data and meteorological data, e.g. air volume, average air temperature, relative humidity, sunshine hours, and precipitation. These include the total area of absorption peaks, the area at the left end thereof, absorption depth, and symmetry. This study analyses correlations between these factors and a disease index based on partial least squares method and constructs the small of flag picking period, early and middle grouting period, and the entire growth period. The disease condition inversion model of wheat stripe rust was used to verify the accuracy of the model. The correlation of NDVI, RDVI, TVI, DVI, RVI, and meteorological factors with stripe rust were analysed simultaneously. DVI and average temperature values with a high correlation degree and stripe rust occurrence probability were selected, a prediction model of wheat stripe rust occurrences was established, and the accuracy of the model was verified. Results showed that the average R 2 coefficients of the inversion model for the disease index in three growth periods and the entire growth period were 0.94 and 0.92, respectively; the average RMSE values were 0.068 and 0.063, respectively, with high inversion accuracy,which indicated that the prediction model based on spectral data and meteorological data had high prediction accuracy and good prediction effect. It provides new technology and method for monitoring and forecasting crop diseases and insect pests. Environmental science ; Inversion (geology) ; Hyperspectral imaging ; Relative humidity ; Mathematics ; Canopy ; Atmospheric sciences ; Meteorology ; Statistics ; Remote sensing ; Geography ; Geology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Horticultural and Viticultural Research