Intelligent Classification Research Articles

The Application of Big Data and Artificial Intelligence in Sports Industry

In the age of technology, intelligent and innovative sports events present a brand new look, which has affected how many enterprises operate worldwide. Traditional sports events are upgraded to artificial intelligence. While broadcasting baseball, basketball, and other games, the automatic tracking system is used to perform real-time technical analysis, and the player's course can be analyzed on the spot. In the event broadcast part, the simulation technology, virtual and real integration are widely used, and the events could also be broadcasted live on the streaming audio-visual platform. Therefore, how to use big data analysis has become a key factor for the success of professional teams or sports marketing companies. This research explores the application classification and significance of big data analysis and artificial intelligence in the field of sports by analyzing relevant literature. Through this study, the significance and application of big data, the importance of big data analysis and artificial intelligence in sports industry, and the development trend of sports big data are preliminarily understood. It is also expected that the application and development trend of sports intelligence technology could help sports industry-related companies improve their competitiveness in terms of development strategies, thereby enhancing the visibility of Taiwan's sports industry in the world and making the research and development strength of Taiwan's sports enterprises visible to the world.

Discrete learning-based intelligent methodology for heart disease diagnosis

Classification is one of the most frequently used data mining approaches which has been broadly applied in different fields of sciences, such as engineering, finance, energy, environments, transportation, etc., especially medicine, successfully. Over the years, various intelligent modeling techniques with different properties have been proposed to yield more accurate and more efficient classification results. However, in spite of the different appearance of all of the developed models, the same basic methodology is applied to the learning processes. Based on this methodology, a continuous distance-based cost function is considered and optimized for estimating the unknown parameters in the learning procedures. While using a continuous cost function in the classification field in which the goal function is discrete, is unreasonable or at least quite inefficient. In this paper, in contrast to conventional continuous distance-based methodologies, a novel discrete learning-based methodology is proposed for classification purposes. The main difference between the proposed learning methodology rather than conventional versions is its cost function. In the proposed learning methodology, a mismatching function is considered as a cost function, which is dissimilar to previously developed ones, which are continuous functions based on distance, is a discrete function based on direction. In this way, in the proposed learning process, unknown parameters are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the unknown parameters are continuously adjusted and step-by-step near the target. The multilayer perceptron (MLP) which is one of the most widely-used intelligent classification approaches, is exemplarily chosen in order to implement the proposed methodology. Although it can be generally demonstrated that the classification rate of the proposed discrete learning-based MLP (DIMLP) model will not be worse than its conventional continuous learning-based one. However, in order to determine the superiority of the proposed DIMLP model, it is exemplarily evaluated on the heart disease diagnosis benchmark data set and several other medical datasets, and its performance is compared to the classic multilayer perceptron model. Empirical results illustrate that, as pre-expected, the classification rate of the proposed model is higher than its conventional version in all data sets. Obtained results indicate that the proposed DIMLP classifier can yield a 94.27 % classification rate in heart disease diagnosis, which approximately indicates a 9.35 % improvement over the classic version, which can only produce an 86.21 % classification rate. Therefore, the proposed methodology is an appropriate and effective alternative learning process for intelligent classification approaches, especially when more accurate results and/or a more reasonable model are required.

Comparison Between Three Methods to Monitor Reservoir Extension in the Brazilian Semi-Arid Region

The State of Pernambuco covers an extensive semi-arid area where the Caatinga biome dominates. This region is characterized by long periods of drought, highlighting the need for water resource optimization. This paper aimed to compare three methods to assess reservoir changes: MapBiomas' products, the Normalized Difference Water Index (NDWI), and a support vector machine (SVM) algorithm. Initially, we obtained the monthly precipitation from 1987 to 2019 and calculated the yearly accumulation. Mapbiomas, Landsat 7 ETM, and Landsat 8 OLI data from 2012-2018 were accessed and processed using the Google Earth Engine platform. We obtained the annual image with the median pixel criterion to determine the NDWI and quantify the annual reservoir area. For the supervised classification with SVM, samples from different land-use types of the study area were used to train the algorithm. From 2012 to 2018, a reservoir reduction of 63.42% was observed with MapBiomas images, 69.49% with NDWI images, and 67.69% using the SVM algorithm. The results obtained using NDWI were the most similar to those from the artificial intelligence classification, indicating that NDWI can be used to monitor the reservoir conditions.

Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates.

Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived single-cell features for each population, we trained machine learning models for classification between COVID-19 and non-COVID-19 thrombosis, resulting in a patient testing accuracy of 75%. This result indicates that platelet formation differs between COVID-19 and non-COVID-19 thrombosis. All analysis steps were optimized for efficiency and implemented in an easy-to-use plugin for the image viewer napari, allowing the entire analysis to be performed within seconds on mid-range computers, which could be used for real-time diagnosis.

An Improved Vision Transformer Network with a Residual Convolution Block for Bamboo Resource Image Identification

Bamboo is an important economic crop with up to a large number of species. The distribution of bamboo species is wide; therefore, it is difficult to collect images and make the recognition model of a bamboo species with few amount of images. In this paper, nineteen species of bamboo with a total of 3220 images are collected and divided into a training dataset, a validation dataset and a test dataset. The main structure of a residual vision transformer algorithm named ReVI is improved by combining the convolution and residual mechanisms with a vision transformer network (ViT). This experiment explores the effect of reducing the amount of bamboo training data on the performance of ReVI and ViT on the bamboo dataset. The ReVI has a better generalization of a deep model with small-scale bamboo training data than ViT. The performances of each bamboo species under the ReVI, ViT, ResNet18, VGG16, Densenet121, Xception were then compared, which showed that ReVI performed the best, with an average accuracy of 90.21%, and the reasons for the poor performance of some species are discussed. It was found that ReVI offered the efficient identification of bamboo species with few images. Therefore, the ReVI algorithm proposed in this manuscript offers the possibility of accurate and intelligent classification and recognition of bamboo resource images.

Intelligent classification of coal structure using multinomial logistic regression, random forest and fully connected neural network with multisource geophysical logging data

The structure of coal indicates the degree of its fragmentation after tectonic movement, which affects the exploration and development of coalbed methane (CBM). Although coal core observations are the most convenient and intuitive way of identifying the coal structure, they are not applicable for use in unexplored coal seams without CBM wells, and they are also very time-consuming. In comparison, geophysical-logging interpretation of the coal structure is more efficient and economical. However, although qualitative methods, such as principal component analysis (PCA), can be used to identify the coal structure with geophysical logging, the interpretation is limited by the calculation ability, and improvements are required based on the structure of an empirical model. Multinomial logistic regression (MLR), random forest (RF), and deep fully connected neural network (DNN) are effective machine learning methods more accurate than the traditional method that with model-aided identification. In this respect, the MLR method is a classical method based on mathematical linear regression, and it has a low construction cost; RF is an ensemble learning algorithm based on a decision tree and use of a bagging algorithm; and DNN is a deep learning model based on self-built feature engineering that has high classification accuracy under a large amount of data training and provides obvious advantages in visual coal classification problems. In this work, the three machine learning methods, MLR, RF, and DNN, were used to identify the coal structure. Two sets of logging data comprising different quantities from the Anze Block of the southern Qinshui Basin, North China, were selected to quantitatively compare the accuracy of coal structure identification with partial coal core observation. The results showed that for 210 and 840 samples, respectively, the accuracy was 76% and 77% for MLR, 83% and 86% for RF, and 82% and 86% for DNN. These results show that the MLR and DNN methods are superior for use with minimal and maximum amounts of data, respectively, and the RF method provides overall accuracy. Furthermore, an algorithmic classification of the coal structure was established, and the geological factors controlling the predicted structure, such as geostress, coal seam thickness, and burial depth, were distinguished.

Cyber Security Risk Modeling in Distributed Information Systems

This paper deals with problems of the development and security of distributed information systems. It explores the challenges of risk modeling in such systems and suggests a risk-modeling approach that is responsive to the requirements of complex, distributed, and large-scale systems. This article provides aggregate information on various risk assessment methodologies; such as quantitative, qualitative, and hybrid methods; a comparison of their advantages and disadvantages; as well as an analysis of the possibility of application in distributed information systems. It also presents research on a comprehensive, dynamic, and multilevel approach to cyber risk assessment and modeling in distributed information systems based on security metrics and techniques for their calculation, which provides sufficient accuracy and reliability of risk assessment and demonstrates an ability to solve problems of intelligent classification and risk assessment modeling for large arrays of distributed data. The paper considers the main issues and recommendations for using risk assessment techniques based on the suggested approach.

Hybrid Filter and Genetic Algorithm-Based Feature Selection for Improving Cancer Classification in High-Dimensional Microarray Data

The advancements in intelligent systems have contributed tremendously to the fields of bioinformatics, health, and medicine. Intelligent classification and prediction techniques have been used in studying microarray datasets, which store information about the ways used to express the genes, to assist greatly in diagnosing chronic diseases, such as cancer in its earlier stage, which is important and challenging. However, the high-dimensionality and noisy nature of the microarray data lead to slow performance and low cancer classification accuracy while using machine learning techniques. In this paper, a hybrid filter-genetic feature selection approach has been proposed to solve the high-dimensional microarray datasets problem which ultimately enhances the performance of cancer classification precision. First, the filter feature selection methods including information gain, information gain ratio, and Chi-squared are applied in this study to select the most significant features of cancerous microarray datasets. Then, a genetic algorithm has been employed to further optimize and enhance the selected features in order to improve the proposed method’s capability for cancer classification. To test the proficiency of the proposed scheme, four cancerous microarray datasets were used in the study—this primarily included breast, lung, central nervous system, and brain cancer datasets. The experimental results show that the proposed hybrid filter-genetic feature selection approach achieved better performance of several common machine learning methods in terms of Accuracy, Recall, Precision, and F-measure.

Spirits quality classification based on machine vision technology and expert knowledge

By combining machine vision technology and expert knowledge, this paper proposes an online intelligent classification solution for Chinese spirits, which effectively improves the classification accuracy and production efficiency of spirits. Specifically, an intelligent spirits quality classification system is first designed, including spirits collectors, image sampling cameras, and computing devices. According to the principle that the size and shape of the bubbles in the spirits collector will change with the alcohol content in the spirits, a classification method of spirits quality based on the convolutional neural network (CNN) and bubble region of interest (ROI) selection is proposed. Furthermore, a post-processing method based on expert knowledge is proposed to improve the accuracy of the classification algorithm. A spirits quality classification dataset containing 139 119 images is created, and 15 CNNs are tested. Test results show that the highest spirits quality classification accuracy is 98.62% after using the bubble ROI selection method, and the highest classification accuracy reached 99.82% after adopting the post-processing method. Furthermore, practical application tests show that the solution proposed in this paper can improve spirits’ production quality and efficiency.

A viewpoint on evaluating ECG findings to assess heart issues: A clinical nursing review

The 12-lead EKG is a useful first-line diagnostic tool for detecting cardiovascular diseases, including arrhythmias, conduction abnormalities, and myocardial ischemia or infarction. This scoping review aims to discuss the ECG perspective for detecting heart problems. This study was conducted by following the narrative review and development framework. The literature search used ScienceDirect, Scopus, ProQuest, ClinicalKey, and SpringerLink data. The keywords used were "criteria" or "guidelines" and "ECG" or "ECG" or "electrocardiography" or "electrocardiogram" and "detection" and "heart problem" or "heart disease" or "heart problem" or "heart problem." The method uses a narrative review through eight articles spanning 2017–2022. The lighting method uses the PICO analysis framework. The results found four components, namely: 1) automatic diagnosis; 2) 3-D ECG with artificial intelligence (AI) classification; 3) use of the LSTM deep-learning model; and 4) leads using Tpeak-End (Tp-e). In patients with heart problems, the initial examination is an EKG. Thus, the ECG, with the addition of the automatic diagnosis feature, EKG-3D, and the use of a 12-lead ECG with a deep-learning LSTM model, also followed by high sensitivity and specificity, can be used as a tool to perform examinations on patients who require serial ECG examinations.
 Keywords: Electrocardiogram; nursing care; nursing review; clinical symptoms; nursing assessment

Study on Intelligent Classification of Aging Heat-Resistant Materials.

High chromium martensitic heat-resistant steel is considered as a candidate material for pressure components of the next generation of incinerators of the subcritical level or above in China due to its excellent high-temperature and corrosion resistance, but in the long-term service, aging will significantly affect the service safety of materials. So, accurate identification of its aging state is important to enhance the safety of a power plant. In this paper, an automatic aging grading model of high chromium martensite heat-resistant steel based on the depth residual network is proposed according to different scales of metallographic data. A multiscale data set is constructed by image reduction to verify the accuracy of the model in identifying microstructure images of high chromium martensitic heat-resistant steel with different scales. The experimental results show that the model using multiscale data sets performs well, and then, through feature pyramid network model training, the accuracy rate is further improved, and a relatively good prediction accuracy model is obtained. The validity of the deep learning method for the classification of damage and aging of P91 steel with different scales is verified.

UWB-Based Early Breast Cancer Existence Prediction Using Artificial Intelligence for Large Data Set

Breast cancer is the most often identified cancer among women and the main reason for cancer-related deaths worldwide. The most effective methods for controlling and treating this disease through breast screening and emerging detection techniques. This paper proposes an intelligent classifier for the early detection of breast cancer using a larger dataset since there is limited researcher focus on that for better analytic models. To ensure that the issue is tackled, this project proposes an intelligent classifier using the Probabilistic Neural Network (PNN) with a statistical feature model that uses a more significant size of data set to analyze the prediction of the presence of breast cancer using Ultra Wideband (UWB). The proposed method is able to detect breast cancer existence with an average accuracy of 98.67%. The proposed module might become a potential user-friendly technology for early breast cancer detection in domestic use.

A discrete intelligent classification methodology

A discrete intelligent classification methodology

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases.

Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning health conditions over the last two decades, after cancer around the world. The early diagnosis of COPD, particularly of lung function degradation, together with monitoring the condition by physicians, and predicting the likelihood of exacerbation events in individual patients, remains an important challenge to overcome. The requirements for achieving scalable deployments of data-driven methods using artificial intelligence for meeting such a challenge in modern COPD healthcare have become of paramount and critical importance. In this study, we have established the experimental foundations for acquiring and indeed generating biomedical observation data, for good performance signal analysis and machine learning that will lead us to the intelligent diagnosis and monitoring of COPD conditions for individual patients. Further, we investigated on the multi-resolution analysis and compression of lung audio signals, while we performed their machine classification under two distinct experiments. These respectively refer to conditions involving (1) "Healthy" or "COPD" and (2) "Healthy", "COPD", or "Pneumonia" classes. Signal reconstruction with the extracted features for machine learning and testing was also performed for securing the integrity of the original audio recordings. These showed high levels of accuracy together with the performances of the selected machine learning-based classifiers using diverse metrics. Our study shows promising levels of accuracy in classifying Healthy and COPD and also Healthy, COPD, and Pneumonia conditions. Further work in this study will be imminently extended to new experiments using multi-modal sensing hardware and data fusion techniques for the development of the next generation diagnosis systems for COPD healthcare of the future.

A novel discrete learning-based intelligent methodology for breast cancer classification purposes

Classification is one of the most significant subfields of data mining that has been successfully applied to various applications. The literature has expended substantial effort to present more efficient and accurate classification models. Despite the diversity of the proposed models, they were all created using the same methodology, and their learning processes ignored a fundamental issue. In all existing classification model learning processes, a continuous distance-based cost function is optimized to estimate the unknown parameters. The classification problem's objective function is discrete. Consequently, applying a continuous cost function to a classification problem with a discrete objective function is illogical or inefficient. This paper proposes a novel classification methodology utilizing a discrete cost function in the learning process. To this end, one of the most popular intelligent classification models, the multilayer perceptron (MLP), is used to implement the proposed methodology. Theoretically, the classification performance of the proposed discrete learning-based MLP (DIMLP) model is not dissimilar to that of its continuous learning-based counterpart. Nevertheless, in this study, to demonstrate the efficacy of the DIMLP model, it was applied to several breast cancer classification datasets, and its classification rate was compared to that of the conventional continuous learning-based MLP model. The empirical results indicate that the proposed DIMLP model outperforms the MLP model across all datasets. The results demonstrate that the presented DIMLP classification model achieves an average classification rate of 94.70 %, a 6.95 % improvement over the classification rate of the traditional MLP model, which was 88.54 %. Therefore, the classification approach proposed in this study can be utilized as an alternative learning process in intelligent classification methods for medical decision-making and other classification applications, particularly when more accurate results are required.

Kyoto classification of gastritis, virtual chromoendoscopy and artificial intelligence: Where are we going? What do we need?

Kyoto classification of gastritis, virtual chromoendoscopy and artificial intelligence: Where are we going? What do we need?

Differences in Molecular Subtype Reference Standards Impact AI-based Breast Cancer Classification with Dynamic Contrast-enhanced MRI.

Background Breast cancer tumors can be identified as different luminal molecular subtypes depending on either immunohistochemical (IHC) staining or St Gallen criteria that includes Ki-67. Purpose To characterize molecular subtypes and understand the impact of disagreement among IHC and St Gallen molecular subtype reference standards on artificial intelligence classification of luminal A and luminal B tumors with use of radiomic features extracted from dynamic contrast-enhanced (DCE) MRI scans. Materials and Methods In this retrospective study, 28 radiomic features previously extracted from DCE-MRI scans of breast tumors imaged between February 2015 and October 2017 were examined in the following groups: (a) tumors classified as luminal A by both reference standards ("agreement"), (b) tumors classified as luminal A by IHC and luminal B by St Gallen ("disagreement"), and (c) tumors classified as luminal B by both ("agreement"). Luminal A or luminal B tumor classification with use of radiomic features was conducted with use of three sets: (a) IHC molecular subtyping, (b) St Gallen molecular subtyping, and (c) agreement tumors. The Kruskal-Wallis test was followed by the Mann-Whitney U test to determine pair-wise differences of radiomic features among agreement and disagreement tumors. Fivefold cross-validation with use of stepwise feature selection and linear discriminant analysis classified tumors in each set, with performance measured with use of area under the receiver operating characteristic curve (AUC). Results A total of 877 breast cancer tumors from 872 women (mean age, 48 years [range, 19-75 years]) were analyzed. Six features (sphericity, irregularity, surface area to volume ratio, variance of radial gradient histogram, sum average, volume of most enhancing voxels) were different (P ≤ .001) among agreement and disagreement tumors. AUC (median, 0.74 [95% CI: 0.68, 0.80]) was higher than when using tumors subtyped by either reference standard (IHC, 0.66 [0.60, 0.71], P = .003; St Gallen, 0.62 [0.58, 0.67], P = .001). Conclusion Differences in reference standards can hinder artificial intelligence classification performance of luminal molecular subtypes with dynamic contrast-enhanced MRI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.

Intelligent electronic tongue system for the classification of genuine and false honeys

ABSTRACT Honey quality is a global concern since this product is highly susceptible to adulteration, given its competitive price. As a reliable strategy for honey authenticity determination, this work introduces an intelligent classification system that considers the pattern recognition point of view to develop an economical and quick analytical method to identify and differentiate genuine from adulterated honey. This work used an electronic tongue composed of three working electrodes of carbon, platinum, and gold. The system used Cyclic voltammetry to obtain data from 50 genuine and 50 adulterated honey samples. Subsequently, the system used multivariate data analysis using a pattern recognition methodology composed of three big stages, including data organization and normalization, dimensionality reduction, and k-Nearest Neighbors (k-NN) as a classification method. The process was validated with the Leave One Out Cross Validation technique (LOOCV), reaching a classification accuracy performance of 100%. The results show that it was possible the development of a combined methodology between analytical tools and chemometrics for an in-situ, quick and efficient authenticity honey evaluation.

A real-time intelligent classification model using machine learning for tunnel surrounding rock and its application

ABSTRACT Real-time and accurate prediction of surrounding rock grade is crucial for tunnel dynamic construction and design. However, the internationally accepted semi-quantitative methods (e.g. rock mass rating (RMR), Q, and basic quality (BQ)) cannot provide fast and accurate classification in construction. This study proposed an intelligent surrounding rock classification method and a tunnel information management system, which can predict the surrounding rock grade in real-time and accurately. A database is collected with 286 cases in China, including seven geological parameters and surrounding rock grades. Based on different training parameters, 12 classification models are established using VGGNet, ResNet, and support vector machine (SVM) algorithms. The accuracy of the SVM classifier is 93.02%, which performs better than the VGGNet and ResNet classifiers. Moreover, precision, recall, F-measure, receiver operating characteristic (ROC), and 20-case verification show that the SVM classification model has greater robustness in learning and generalising for small and imbalanced samples. Additionally, a tunnel information management system is developed with cloud technology, which can accurately predict the surrounding rock grade within 10 s. Overall, the achievements of this study can provide valuable references for real-time rock mass classification in traffic tunnels and underground powerhouses.

Anti-local occlusion intelligent classification method based on MobileNet for hazardous waste

Anti-local occlusion intelligent classification methods based on MobileNet and VTM for hazardous waste are investigated in this paper. Three image datasets with ten kinds of hazardous waste and 5,000.0 samples are constructed, which include the image dataset with without occlusion, the image dataset with 15% occlusion, and the image dataset with random occlusion. Based on them, the MobileNet and VTM intelligent classification model are constructed, trained, and tested, respectively. It can be seen from testing results that the classification accuracies of VTM and MobileNet are very high for the image dataset with without occlusion. But as occlusion area on images go up or randomly changes, the classification accuracies of VTM and MobileNet go down for 15% and random occlusion cases. The testing results show that classification accuracy of MobileNet model is better than that of VTM model for hazardous waste with or without occlusion.