Study on Identification of Driver Steering Behavior Characteristics Based on Pattern Recognition

This study presents the development of artificial neural network (ANN) and support vector machine (SVM) classification models for predicting the safety conditions of water in distribution pipes. The study was based on 504 monthly records of water quality parameters; pH, turbidity, color and bacteria counts taken from nine different locations across the water distribution network in the city of Alesund, Norway. The models predicted the safety conditions of the water samples in the pipes with 98% accuracy and 94% respectively during testing. The high accuracy achieved in the model results indicate that contamination events in distribution systems that result in unsafe values of the water quality parameters can be detected using these classification models. This can provide water utility managers with real time information about the safety conditions of treated water at different locations of distribution pipes before water reaches consumers.

This paper introduced a novel olfactory visualization freshness sensor array prepared by dropping pH indicators on UV lithography hydrophilic–hydrophobic paper under room temperature. The sensor array was applied in Grass carp spoilage classification. The sensor array can generate different discoloration patterns that respond to the pH change of the fish spoil; therefore, it can distinguish Grass carp freshness using pattern recognition. Classification models were built with linear discriminant analysis (LDA) and backpropagation artificial neural network (BP-ANN) after extracting the sensor array’s feature color information with principal component analysis (PCA). The performance of the BP-ANN model was superior to that of the LDA model, with an optimum recognition rate of 95.45% (training set) and 90.90% (testing set). In conclusion, with a suitable algorithm choice and rational pH indicator arrangement, this olfactory visualization freshness sensor array can potentially monitor food freshness. We prepared Grass carp freshness sensor array through dropping pH-indicators on hydrophilic–hydrophobic filter paper that fabricated by UV lithography under room temperature. The hydrophobic edges of the filter paper can maintain the sensor spots' shape and provide stable and consistent colorimetric change for Grass carp freshness recognition. Colorimetric classification models are built using LDA and BP-ANN based on pattern recognition, and the recognition rate of the BP-ANN model is above 90%. Novelty impact statement We prepared Grass carp freshness sensor array through dropping pH-indicators on hydrophilic-hydrophobic filter paper that fabricated by UV lithography under room temperature. The hydrophobic edges of the filter paper can maintain the sensor spots’ shape and provide stable and consistent colorimetric change for Grass carp freshness recognition. Colorimetric classification models are built using LDA and BP-ANN based on pattern recognition, and the recognition rate of the BP-ANN model is above 90 %.

The chemical industrial park is an important source of volatile organic compounds (VOCs) emissions which is difficult to monitor due to its diversity and low concentration. In this work, a hybrid sensor array consisting of six gas sensors (four semiconductor sensors, one electrochemical sensor, and one photo ionization detector) was constructed to measure VOCs. Three most frequently detected VOCs in a Chinese fine chemical industrial park, namely toluene, dichloromethane, ethyl acetate, and their mixtures, were tested all below 5 ppm (even below 1 ppm in many cases). Support vector machine (SVM) and back-propagation artificial neural network (BP-ANN) based on different input features, including steady-state responses (SS), the first four components preprocessed by principal component analysis (PCA) and linear discriminant analysis (LDA), were trained and evaluated. The 10-fold cross validation results indicate that SS-SVM and SS-ANN are two best models with accuracy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{R}^{{2}}$ </tex-math></inline-formula> of about 94% and 0.95 for the validation set, respectively. Compared with LDA, PCA is more suitable for dimension reduction in this study, since the performance of models with the first 4 PCs are closer to that of models with SS. The results demonstrate that the proposed hybrid sensor array system with SVM and ANN models is able to identify and quantify low-concentration VOCs mixtures. This method is promising to be used for low-cost and online monitoring of VOCs emissions in chemical industrial parks.

Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification

“Artificial neural networks (ANNs)” and support vector networks have been widely used for the purpose of analysis of spatial remote sensory images. Attributes selection plays a major role in reduction of computational time for model processing and improvement in performance accuracy. In this paper, we present rough set-based artificial neural network (RS-ANN) and support vector machine classification (RS-SVM) methods for the classification of hyperspectral images. In both of these techniques, rough set (RS) is used as a feature selection mathematical tool. Further, this diminished dimensionality data is carried to artificial neural network (ANN) and support vector machine (SVM) classifiers correspondingly. The proposed procedure uses spatial information and implements the strategy for efficient processing of the image, and with utilization of the “graphics processing units (GPUs)” successfully implemented with improved efficiency. The experimental analysis achieved efficient outcome that the proposed procedures perform with improved accuracy, improved efficiency with a good amount of reduction in the execution and computing time, and enhancing the accuracy in the categorizing of spatial images in comparison with other conventional strategies.

Risk stratification of 2D ultrasound-based breast lesions using hybrid feature selection in machine learning paradigm

Due to the similar chemical composition and matrix effect, the accurate identification of mineral pigments on wall paintings has brought great challenges. This work implemented an identification study on three mineral pigments with similar chemical compositions by combining LIBS technology with the K-nearest neighbor algorithm (KNN), random forest (RF support vector machine (SVM), back propagation artificial neural network (Bp-ANN) and convolutional neural network (CNN) to find the most suitable identification method for mural research. Using the SelectKBest algorithm, 300 characteristic lines with the largest difference among the three pigments were determined. The identification models of KNN, RF, SVM, Bp-ANN and CNN were established and optimized. The results showed that, except for the KNN model, the identification accuracy of other models for mock-up mural samples was above 99%. However, only the identification accuracy of 2D-CNN models reached above 94% for actual mural samples. Therefore, the 2D-CNN model was determined as the most suitable model for the identification and analysis of mural pigments.

The capacity of pattern recognition algorithms to analyze complicated data and retrieve useful information has attracted a lot of interest in the world of medical solutions. Data mining methods have become effective tools for pattern detection in the medical field, allowing for the extraction of insightful knowledge from huge and complex datasets. In this study, a diversified convergent squirrel search optimization algorithm and support vector machine (DCSSO-SVM) are combined to provide a unique pattern recognition method for medical applications. The DCSSO algorithm draws its inspiration from squirrels' efficient search-space exploration and exploitation during foraging. The suggested method integrates this algorithm with SVM to enhance feature selection, speed up convergence, and increase classification performance. To assess the effectiveness of the suggested DCSSO-SVM strategy, we assembled a dataset of medical images for this study. Other cutting-edge methods are contrasted with the suggested methodology. The experimental findings show that, regarding the accuracy, precision, f1-score, and recall metrics, the suggested DCSSO-SVM technique performs better than the current methods. It also has great toughness and support, making it appropriate for a variety of medicinal applications.

In this study, we present a global database of ten parameters, which include measurements of rock index properties, strength stiffness and dynamic properties. Hoek–Brown constant mi, is included, and was estimated, using Hoek and Brown proposed guidelines for determining mi values for different rock types that can be used for preliminary design when triaxial tests are not available. This broad database is compiled from 96 studies and is labelled as “ROCK/10/4025”, to describe the type of geomaterial, the number of the parameters, and the number of the data samples included. It consists of 35.4 % igneous, 54.8 % sedimentary, and 9.2% metamorphic rocks. The purpose of this paper is to propose a generic soft computing model applicable to multiple lithologies, that can become more reliable and perhaps more suitable for a specific site study when used in order to densify often limited similar site-specific data. To this end four broad samples of data were selected, and served as training data sets for developed machine learning models, to develop a generic compression strength prediction model applicable to multiple lithologies. The suggested algorithms in this study are Back-Propagation Artificial Neural Networks, Artificial Neuro-Fuzzy Inference Systems, Support Vector Machines, Nearest Neighbour classifiers and Ensemble Bagged Trees. According to the findings of this study, Artificial Neuro-Fuzzy Inference Systems model performance was found to be marginally superior, while Back Propagation Artificial Neural Networks, Support Vector Machines and Ensemble Bagged Trees models were found to have good performance. Constant mi seems to be an important training parameter when training predictive models centred on data from multiple lithologies. As a result, we can suggest that these models are powerful tools that allow for a reliable estimation of compressive strength, based on the performance indicators. The performance was found to be 70%–82% when the problem of compressive strength prediction was approached as a classification problem (that is successful prediction of class from very weak to very strong), and 80%–96% when solved as a function approximation problem.

The operating temperature of silicon-based solar modules has a significant effect on the electrical performance and power generation efficiency of photovoltaic (PV) modules. It is an important parameter for PV system modeling, performance evaluation, and maximum power point tracking. The analysis shows that the results of physics-based methods always change with seasons and weather conditions. It is difficult to measure all the needed variables to build the physics-based model for the calculation of operating temperature. Due to the above problem, the paper proposes an online method to calculate operating temperature, which adopts the back propagation artificial neural network (BP-ANN) algorithm. The comparative analysis is carried out using data from the empirical test platform, and the results show that both the BP-ANN and the support vector machine (SVM) method can reach good accuracy when the dataset length was over six months. The SVM method is not suitable for the temperature modeling because its computing time is too long. To improve the performance, wind speed should be taken as one of the models’ input if possible. The proposed method is effective to calculate the operating temperature of silicon-based solar modules online, which is a low-cost soft-sensing solution.

Brain tumors are abnormal tissue that grow uncontrolled and affect a patient's neurological function. Brain tumors come in different shapes and characteristics. Moreover, its location also differs for each patient. Brain tumors can be detected using machine learning algorithms using magnetic resonance imaging (MRI) images. However, a different machine-learning comparison is limited and needs further investigation. This study aims to compare three machine-learning methods, i.e., Support Vector Machine (SVM), Naive Bayes (NB), and Artificial Neural Network Back Propagation (ANN-BP) algorithms for detecting brain tumors. Before the comparison started, MRI image quality was enhanced by performing denoising, histogram equalization, and thresholding. After that, Gray Level Co-occurrence Matrix feature extraction was performed. MRI brain images in JPEG format were acquired from an open-access database. One thousand brain tumor and 1000 normal tumor images are used as the training data, while 100 brain tumor and 100 normal tumor images are used as testing data. Each algorithm's accuracy, precision, sensitivity, and Matthews Correlation Coefficient (MCC) are evaluated and reported. The study showed that the SVM algorithm acquired the highest performance in detecting brain tumors, followed by ANN-BP and NB. The highest accuracy, precision, sensitivity, and MCC values for testing in SVM were 98,75%, 98,22%, 99,30%, and 0,9751, respectively. Meanwhile, in testing, the highest accuracy, precision, sensitivity, and MCC values were 90.50%, 98.80%, 82.00%, and 0.8220, respectively. In conclusion, this study showed the superiority of the SVM algorithm in detecting brain tumor compared to ANN-BP and NB by performing image enhancement steps and GLCM feature extraction before its detection.

The protein content is an important parameter for the estimation of the quality of cheese products. Nonlinear multivariate methods including kernel partial least squares (KPLS), support vector machine (SVM) and back propagation artificial neural network (BP-ANN) combing with the visible and near-infrared spectroscopy (Vis/NIRS) techniques are introduced for predicting the protein content of cheese. Method of augmented partial residual plot (APaRP) is performed to diagnose the nonlinearity of the raw spectral data. Multiplicative signal correction (MSC), standard normal variate (SNV) with detrending, Savitzky-Golay second-derivative and direct orthogonal signal correction (DOSC) are used to preprocess the spectral data and compared. DOSC algorithm obtains the optimal prediction performance and was chosen as the preprocess method for the following prediction models. Multivariate models including PLS, KPLS, SVM and BP-ANN are trained on the preprocessed cheese data for predicting the protein content. The square of correlation coefficient (R2), root mean squares error (RMSE) and ratio performance deviation (RPD) are used to evaluate the performance of models. DOSC-KPLS model obtains the highest scores of R2 and RPD of 0.974 and 5.587, respectively, which indicates better prediction performance than other models. Therefore, DOSC-KPLS model combined with the Vis/NIRS technique is the most promising method for accurately detecting the protein content of cheese.

Currently, irrigation decisions in coffee cultivation primarily rely on empirical knowledge, resulting in inefficient practices. Combining real-time leaf water content (LWC) data can help improve the accuracy of the irrigation planning. Spectral remote sensing is a fast, reliable, and non-invasive method to detect vegetation moisture content. In this study, a model to estimate the LWC of Coffea arabica L. was built using hyperspectral reflectance of the canopy under various irrigation levels. For this purpose, common spectral indices, two-band spectral indices [ratio spectral index (RSI); difference spectral index (DSI); and normalized difference spectral index (NDSI)], and three-band spectral indices were constructed. Feature bands were extracted using the successive projections algorithm (SPA). Optimal spectral indices were extracted using the correlation coefficient method, and the feature wavebands and spectral indices were combined into five datasets. These datasets were split into modeling and validation datasets by sample set partitioning based on the joint x-y distance (SPXY) algorithm. A linear model [partial least squares regression (PLSR)] and three non-linear models [support vector machine (SVM); extreme learning machine (ELM); back propagation artificial neural network (BPANN)] were built to estimate LWC of Coffea arabica L. The results indicated that the non-linear models surpassed the linear model. The accuracy was the highest when the modeling was performed using the dataset combination 5. Among various modeling methods, the predictive performance of ELM was the best (modeling dataset: R2=0.745, RMSE=2.241%, RRMSE=3.482%; validation dataset: R2=0.721, RMSE=2.142%, RRMSE=3.364%). ELM outperformed PLSR, SVM, and BPANN in LWC retrieval. The obtained results indicated that the dataset built by the combined use of different methods was superior to those from a single data source in accuracy. This study provides a scientific basis for the quantitative diagnosis of coffee tree water status, with significant implications for optimizing field irrigation management. Keywords: spectral index, feature band, dataset, Coffea arabica L., leaf water content, machine learning DOI: 10.25165/j.ijabe.20251805.9067 Citation: Liu X G, Peng K L, Chen S M, Tuo Y F, Zhang S, Tan S, et al. Estimating the leaf water content of Coffea arabica L. based on hyperspectral reflectance and dataset construction. Int J Agric & Biol Eng, 2025; 18(5): 287–297.

Using information technology such as search engine for collecting and monitoring of network public opinion is a practical and effective method. This paper puts forward an improved algorithm of SVM classifier based on incremental learning online, and then implements a topic crawler system for network public opinion and to grab the public opinion.

Heavy metals in submicronic particulate matter (PM1) from a Chinese metropolitan city predicted by machine learning models