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Bearings are critical components in modern manufacturing, yet they are prone to failures in induction machines. Detecting these faults early can reduce repair costs. To achieve efficient and accurate fault detection, we explore vibration-based analysis. Traditional methods rely on manual feature extraction, which is time-consuming. In contrast, our work leverages deep learning, particularly convolutional neural networks, to automatically extract fault features from raw data. We investigate various image sizes (16 × 16, 32 × 32, 64 × 64, 128 × 128, 256 × 256) and their performance in bearing fault diagnosis. Our convolutional neural networks-based approach is compared to traditional methods such as support vector machine, nearest neighbors, and artificial neural networks. Results demonstrate the superior performance of our data-driven fault diagnosis using convolutional neural networks. [ABSTRACT FROM AUTHOR]

The effect of aluminum oxide nanoparticles (Al2O3) on the 60/70 penetration of asphalt cement (AC) was investigated in terms of the physical and rheological characteristics by using the Superpave testing procedures. Al2O3 at 3, 5, and 7% concentrations were blended with 60/70 penetration of grade AC. Conventional testing procedures were adopted regarding the physical characteristics, while dynamic shear rheometer (DSR) testing procedures were conducted to evaluate the high and low temperature failure parameters. In addition, heuristic modelling techniques, artificial neural networks (ANN), and support vector machines (SVM) were employed to predict the performance characteristics of AC by using the mechanical testing conditions. The frequency sweep test and multiple stress creep recovery (MSCR) test results revealed that the optimum composition of Al2O3 was at 5% concentration considering the high temperature performance characteristics since further addition of the Al2O3 resulted in degradation in the enhanced properties due to agglomeration of the nanoparticles in the blend. On the contrary, Al2O3 5% demonstrated the lowest viscoelastic behavior at intermediate temperatures. The higher complex modulus (G ∗) and lower phase angle (δ) parameters indicated that the increase in stiffness due to the modification process was at the cost of losing elastic properties against fatigue cracking. Moreover, based on the statistical performance indicator, coefficient of determination (R2), it was observed that the ANN models for predicting G ∗ and δ achieved a prediction accuracy of 0.989 and 0.911 while SVM models were able to achieve 0.984 and 0.929, respectively, considering the training datasets. On the other hand, it was noted that SVM models outperformed the ANN models in terms of a smaller gap between the results obtained from the training and testing datasets. The difference between the training and testing datasets for G ∗ and δ parameters for the SVM models were 3.2% and 6.8% while for the ANN models, the differences were 11.6% and 9.5%, respectively, indicating that the ANN models were more prone to the overfitting phenomenon. [ABSTRACT FROM AUTHOR]

The use of computer-aided diagnostic (CAD) models has been proposed to aid in the detection and classification of breast cancer. In this work, we evaluated the performance of multilayer perceptron neural network and nonlinear support vector machine models to classify breast cancer nodules. From the contour of 569 samples, ten morphological features were used as input to the classifiers. The average results obtained in the set of 50 simulations performed show that the proposed models showed good performance (all exceeded 90.0%) in terms of accuracy in the test set. The nonlinear support vector machine algorithm stands out when compared to the proposed multilayer perceptron neural network algorithm, with 99% accuracy and a 2% false-negative rate. The neural network model presented lower performance than the nonlinear support vector machine classifier. With the application of the proposed models, the average results obtained are promising in the classification of breast cancer. [ABSTRACT FROM AUTHOR]

Purpose. We want to develop a model for predicting lymph node status based on positron emission computed tomography (PET) images of untreated ovarian cancer patients. We use the feature map formed by wavelet transform and the parameters obtained by image segmentation to build the model. The model is expected to help clinicians and provide additional information about what to do with first-visit patients. Materials and Methods. Our study included 224 patients with ovarian cancer. We have chosen two main methods to extract information from images. On the one hand, we segmented the image to extract the parameters to evaluate the clustering effect. On the other hand, we used wavelet transform to extract the image's texture information to form the image's feature map. Based on the above two kinds of information, we used residual neural network and support vector machine for modeling. Results. We established a model to predict lymph node metastasis in patients with primary ovarian cancer using PET images. On the training set, our accuracy was 0.8854, AUC: 0.9472, CI: 0.9098-0.9752, sensitivity was 0.9865, and specificity was 0.7952. On the test set, our accuracy was 0.9104, AUC: 0.9259, CI: 0.8417-0.9889, sensitivity was 0.8125, and specificity was 1.0000. Conclusions. We used wavelet transform to process the preoperative medical images of ovarian cancer patients, and the residual neural network can effectively predict the lymph node metastasis of ovarian cancer patients, which is undoubted of great significance for patients' staging and treatment options. [ABSTRACT FROM AUTHOR]

Artificial intelligence has rapidly grown and has made the scenario that no field can function without it. Like every field, it also plays a vital role in the sports field nowadays. In certain sports, injuries happen very often due to heavy training and sudden speedy actions, especially in athletics and football. Here arises a need to analyze the effect of physical training in sportsperson by collecting data from their daily training. With the help of artificial intelligence, a recurrent neural model is developed to analyze the effect of physical training and treatment concerning sports injury. A Recurrent Neural Network (RNN) can be a subsection of Artificial Neural Networks (ANN) that uses the neural nodes connected in a temporal sequence. The temporal sequence is one of the essential terms in this research, which denotes a data sequence of events in a given timeframe. The recurrent neural model is an intelligent machine learning method that comprises a neural schema replicating humans. This neural schema studies the data it collects from the athletes/players and processes it by analyzing previous injuries. Sports injuries have to be analyzed because, in some cases, it becomes more dangerous to the sportsperson that they may even lose their career due to disability. Sometimes it may cause a massive loss to the club or company that hired the sportsperson for the sport. The prediction process can give the player rest until he recovers, thus becoming the safest approach in sports. Therefore, it is essential to analyze the sportsperson's track data to keep an eye on his health. In this research, RNN model is compared with the existing Support Vector Machine (SVM) in concerning to the effect of physical training and treatment for sports. The results show that the proposed model has achieved 99% accuracy, which is higher than the existing algorithm. [ABSTRACT FROM AUTHOR]

Researchers in the past discussed the psychological issue like stress, anxiety, depression, phobias on various forms, and cognitive issues (e.g., positive thinking) together with personality traits on traditional research methodologies. These psychological issues vary from one human to other human based on different personality traits. In this paper, we discussed both psychological issues together with personality traits for predicting the best human capital that is mentally healthy and strong. In this research, we replace the traditional methods of research used in the past for judging the mental health of the society, with the latest artificial intelligence techniques to predict these components for attaining the best human capital. In the past, researchers have point out major flaws in predicting psychological issue and addressing a right solution to the human resource working in organizations of the world. In order to give solution to these issues, we used five different psychological issues pertinent to human beings for accurate prediction of human resource personality that effect the overall performance of the employee. In this regard, a sample of 500 data has been collected to train and test on computer through python for selecting the best model that will outperform all the other models. We used supervised AI techniques like support vector machine linear, support vector machine radial basis function, decision tree model, logistic regression, and neural networks. Results proved that psychological issue data from employee of different organizations are better means for predicting the overall performance based on personality traits than using either of them alone. Overall, the novel traditional techniques predicted that sustainable organization is always subject to the control of psychological illness and polishing the personality traits of their human capital. [ABSTRACT FROM AUTHOR]

Heart disease is among the leading causes of mortality globally. Predicting cardiovascular disease is a major difficulty in clinical data analysis. AI has been demonstrated to be powerful in deciding and anticipating an enormous measure of information created by the health domain. We provide a unique method for finding essential traits employing machine learning approaches in this paper, which enhances the effectiveness of identifying heart diseases. Decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) are the classification techniques used to create the proposed system. Ensemble stacking integrates the four classification models to create a single best-fit predictive model using logistic regression. Many explorations have been directed at the identification of cardiac infection; however, the exactness of the outcomes is poor. Accordingly, to further enhance the efficiency, Moth-Flame Optimization (MFO) algorithm is proposed. The feature selection strategies are used to improve the classification accuracy while shortening the execution time of the classification system. Medical data are used to assess the probability of heart disease based on BP, age, gender, chest ache, cholesterol, blood sugar, and other variables. Results revealed that the proposed system excelled other existing models, obtaining 99% accuracy in the Cleveland dataset. [ABSTRACT FROM AUTHOR]

At present, the economic development of the world's major economies is showing a positive and positive state. Driven by the development of related industries, the development of the financial field is also changing with each passing day. Various activities in the financial industry are in full swing, and the forecasts of related prospects are also full of uncertainties. Summarizing the laws of financial activities through technical means and making accurate predictions of future trends and trends is a hot research direction that relevant researchers pay attention to. Accurate financial forecasts can provide reference for financial activities and decision-making to a certain extent, promote the steady development of the market, and improve the conversion rate of financial profits. As an algorithm model that can simulate the biological visual system, the convolutional neural network can predict the numerical trend of the next period of time based on known data. Therefore, this paper integrates the support vector machine with the established model by establishing a convolutional neural network model and applies the prediction model to the prediction of financial time series data. The experimental results show that the model proposed in this paper can more accurately predict the trend of the stock index. [ABSTRACT FROM AUTHOR]

In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods. [ABSTRACT FROM AUTHOR]

In this paper, a CNN model for color element data analysis of the urban spatial environment is constructed through an in-depth study of color element data analysis. This paper investigates a high-order structure formed by a few nodes; it proposes a motif-based graph autoencoder MODEL, combining redefined first- and second-order similarities and perfectly integrating motif structure and autoencoder. The algorithm first proposes an efficient graph transformation method to add the influence of central nodes. It then offers a primary awareness mechanism to aggregate the information of noncentral neighbors. Cen GCN_D and Cen GCN_E outperform the latest algorithms in node classification, link prediction, node clustering, and network visualization. As the number of network layers increases, the advantages of these two variants become progressively more prominent. This paper uses a support vector machine to implement classification validation based on CNN. The experimental results show that when 450 images are randomly selected as training data, the classification accuracy obtained by using the features of different CNN output layers is distributed between 91.4% and 95.2%. When the training set of the experiment reaches more than 300, the accuracy can exceed 90%, and the experimental results corresponding to different training sets a more stable trend. Finally, the trained classifier model is obtained in this thesis, which achieves the purpose of fast classification prediction based on CNN for color element data analysis of urban spatial environments. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet loans and the demand for Internet loans, Internet-based loan default prediction is particularly important. P2P online lending is based on Internet technology. With the popularization of personal PCs and mobile terminals, the borrower's financing cost has been reduced to a large extent, and the efficiency of the borrower's capital utilization has also been improved to a considerable level. Making full use of the existing data of the online lending platform, integrating third-party data, and predicting the default behavior of users are the major directions of future development. This paper mainly studies the network loan default prediction model based on DPNN. This paper first analyzes the problems and risks of the P2P online lending platform, then introduces the principle and characteristics of BPNN in detail, and determines the credit risk rating process for online lending based on BPNN. With the help of data analysis and processing software, after cleaning and variable selection of credit customer data provided by lending clubs, a set of corresponding online lending default risk assessment models are established through BPNN. This paper simulates the network loan default assessment model of the BPNN model and compares it with the support vector machine and regression model. The experimental results show that the highest accuracy rate of the BPNN model is 98.01% and the highest recall rate is 99.82%, which is better than the other two models; the AUC value of BPNN is 0.79, which is significantly higher than that of support vector machine and regression model. The above results show that the online loan default prediction model based on DPNN has high application value in practice. Predicting the probability of customer default risk in advance will help reduce the risk of P2P companies and lenders, improve the competitiveness of P2P lending institutions, and promote the development of domestic P2P platforms to be more stable. [ABSTRACT FROM AUTHOR]

Manufacturing industry has always occupied this very important proportion in the national economy. However, with the economic problems in the past two years, some enterprises began to have a deficit crisis. In order to fundamentally alleviate and solve the financial problems of enterprises, this paper studies the financial model prediction of enterprises based on principal component analysis and support vector machine. The multiple financial analysis trajectories are constructed by using PCA-SVM model, and the results are compared with those of logistic model, BP neural network model, and single support vector machine. Experiments show that the prediction level of PCA-SVM model is the best, and the accuracy of the second model is not as good as this model. The error rates of the second type are 14.81%, 14.54%, and 7.67%, respectively, which are higher than those of PCA-SVM model. After comparing the models in this paper, it is found that the components of the model should be extracted first, and then the data operation should be carried out. Such calculation trajectory has a high level of accuracy. The research of this paper provides reference value for solving the financial problems of manufacturing enterprises. [ABSTRACT FROM AUTHOR]

Several problems remain, despite the evident advantages of sentiment analysis of public opinion represented on Twitter and Facebook. On complicated training data, hybrid approaches may reduce sentiment mistakes. This research assesses the dependability of numerous hybrid approaches on a variety of datasets. Across domains and datasets, we compare hybrid models to singles. Text tweets and reviews are included in our deep sentiment analysis learning systems. The support vector machine (SVM), Long Short-Term Memory (LSTM), and ghost model convolution neural network (CNN) are combined to get the hybrid model. The dependability and computation time of each approach were evaluated. On all datasets, hybrid models outperform single models when deep learning and SVM are combined. The traditional models were less trustworthy, and deep learning algorithms have recently shown their enormous promise in sentiment analysis. Linear transformations are used in feature maps to eliminate duplicate or related features. The ghost unit makes ghost features by taking away attributes that are both similar and duplicated from each intrinsic feature. LSTM produces higher results but takes longer to process, while CNN needs less hyperparameter adjusting and monitoring. The effectiveness of the integrated model varies depending on the work, and all performed better than the others. For hybrid deep sentiment analysis learning models, LSTM networks, CNNs, and SVMs are needed. Hybrid models are used to compare SVM, LSTM, and CNN, and we tested each method's accuracy and errors. Deep learning-SVM hybrid models improve sentiment analysis accuracy. Experimental results have shown the accuracy of the proposed model shown 91.3 percent and 91.5 percent for datasets type 1 and 8, respectively. [ABSTRACT FROM AUTHOR]

Dyslexia is among the most common neurological disorders in children. Detection of dyslexia therefore remains an important pursuit for the research works across various domains which is illustrated by the plethora of work presented in diverse scientific articles. The work presented herein attempted to utilize the potential of a unified gaming test of subjects (dyslexia/controls) in tandem with principal components derived from data to detect dyslexia. The work aims to build a machine learning model for dyslexia detection using comprehensive gaming test data. We have attempted to explore the potential of various kernel functions of the Support Vector Machine (SVM) on different number of principal components to reduce the computational complexity. A detection accuracy of 92% is obtained from the radial basis function with 5 components, and the highest detection accuracy obtained from the radial basis function with 3 components is 93%. On the contrary, the Artificial Neural Network(ANN) shows an added advantage with minimal number of hyperparameters with 3 components for obtaining an accuracy of 95%. The comparison of the proposed method with some of the existing works shows efficacy of this method for dyslexia detection. [ABSTRACT FROM AUTHOR]

As the competition between enterprises intensifies, employees have witnessed a decline of psychological health year by year and severe anxiety and depression. To ensure the normal work of employees, it is important to implement psychological occupational therapy intervention (POTI). The previous studies on corporate occupational values focus on the behavioral variables and attitude variables related to occupations. The research paradigm does not suit a specific group of employees. Therefore, this paper explores the individual differences in POTI guided by the realization of occupational values. For a specific group of employees, the correlations of the psychological problems with job attitude, job involvement, and psychological health level were reasoned logically, a theoretical model was established, and the full-model map was plotted to illustrate the influence of the realization of occupational values over POTI. Then, the evaluation data on the psychological intervention effect of 2,800 employees were explored based on multikernel learning, and an individual difference prediction model was constructed through multikernel learning. Finally, the predicted results of different groups of employees were compared, revealing the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

The median grain size of rock is the main basis for the identification of sedimentary facies, and the variation of the median grain size of rock can be used to obtain the stratum sedimentary rhythm and thus to classify the flow unit. Therefore, the median grain size of rock is an extremely important parameter for reservoir evaluation. However, there is no petrophysical method that can directly evaluate the median grain size of rock in the logging data. The predecessors used natural gamma logging data to calculate the median rock grain size (Md) based on linear and statistical analysis for medium-high porosity and permeability sandstone reservoirs work. However, for low-permeability sandstone reservoirs, the error in the fitted median grain size of rock using linear multiple regression methods is too large for the calculated results to be applied. Therefore, the calculation of the median grain size of low-permeability sandstone reservoirs is a difficult problem to be solved. In this paper, the sensitivity logging parameters of median rock grain size are optimized for low permeability sandstone reservoirs using principal component analysis obtained the grain size direction correlation curves (DEN, CNL,GR, and RD) in the study area, and the corresponding loss and activation functions are selected based on the learning characteristics of the nonlinear mapping of the logging data and the BP neural network to ensure that overfitting occurs. The best model was obtained by using decision tree, support vector machine, shallow and deep neural networks to model the median rock grain size and predict neighboring wells, and a comparative analysis showed that for the problem of predicting the median rock grain size in low-permeability sandstone reservoirs, the deep neural network improved significantly over the shallow one and was much stronger than other machine learning methods. The best model obtained a coefficient of determination ( R 2 ) of 0.9831. Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. This method takes into account the nonlinear mapping relationship between the logging data and the fitting of small sample data and provides a systematic way of thinking for the logging curve to predict the grain size of low-permeability sandstone. [ABSTRACT FROM AUTHOR]

Most international academic papers are written in English, and the use of tenses in English academic papers often follows some conventional rules. Automatically extracting and analyzing English tenses in scientific papers have begun to attract researchers' attention for the global environment. In the analysis of the English tense of scientific papers, consider that the neural network model that combines attention mechanism and sequential input network such as Long Short-Term Memory (LSTM) network has a long training time, low extraction accuracy, and cannot parallelize text input. We propose an environmental affection-driven English tense analysis model, which includes an attention mechanism and LSTM model and conducts a temporal analysis of English texts based on an affective computing model. In this paper, our proposed method is verified based on the self-built healthcare exercise-based corpus over public English environment. By comparison, the experimental results show that the method proposed in this paper has better performance than ordinary Convolutional Neural Network (CNN), Support Vector Machine (SVM), and LSTM based on attention mechanism. [ABSTRACT FROM AUTHOR]

Accurate assessment of wet aggregate stability is critical in evaluating soil quality. However, a few general models are used to assess it. In this work, we use the support vector machine to evaluate wet aggregate stability and compare it with a benchmark model based on artificial neural networks. One hundred thirty-four soil samples from various land uses, such as crops, grasslands, and bare land are adopted to verify the effectiveness of the proposed method and confirm the valid input parameters. We select 107 samples for calibrating the prediction model and the rest for evaluation. Experiments show that organic carbon is the main control parameter of wet aggregate stability, although the most influential factors for different land use are various. Comparing the determination coefficient and the root mean square error, it proves that the support vector machine method is superior to the artificial neural network method. In addition, the relative importance analysis shows that contents of organic carbon, silt, and clay are the primary input parameters. Finally, the impact of land use and management types is evaluated. [ABSTRACT FROM AUTHOR]

This work addresses the prediction and optimization of average surface roughness (Ra) and maximum flank wear (Vbmax) of 6061 aluminum alloy during high-speed milling. The investigation was done using a DMU 50 CNC 5-axis machine with Ultracut FX 6090 fluid. Four factors were examined: the table feed rate, cutting speed, depth of cut, and cutting length. Three levels of each factor were examined to conduct 81 experiment runs. The response parameters in these experiments were measurements of Ra and Vbmax. We applied a two-pronged approach that combines machine learning (ML) and a Nondominated Sorting Genetic Algorithm (NSGA-II) to model and optimize Ra and Vbmax. Four ML models were used to predict Ra and Vbmax: linear regression (LIN), support vector machine regression (SVR), a gradient boosting tree (GBR), and an artificial neural network (ANN). The input variables were the significant factors that affect the surface quality and tool wear: the feed rate, depth of cut, cutting speed, and cutting time. Several quality metrics were employed to quantify the performance of the models, such as the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). As a result, SVR and ANN were found to have the best predictive performance for Ra and Vbmax. These models and the NSGA-II-based approach were then employed for multiobjective optimization of cutting parameters during high-speed milling of aluminum 6061. Fifty Pareto solutions were found with Ra in the range of 0.257 to 0.308 µm and Vbmax in the range of 136.198 to 137.133 μm. Experimental validations were then conducted to confirm that the optimum solution was within an acceptable error range. More precisely, the absolute percentage errors for Ra and Vbmax were 2.5% and 1.5%, respectively. This work proposes an effective strategy for efficiently combining machine learning techniques and the NSGA-II multiobjective optimization algorithm. The experimental validations have reflected the potential for applying this strategy in various machining-optimization problems. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology, an increasing number of researchers try to apply different machine learning and deep learning methods to quantitative trading fields to obtain more stable and efficient trading models. As a typical quantitative trading strategy, stock selection has also attracted a lot of attention. There are many studies and applications on stock selection. However, the existing research and application cannot meet the continuous expansion of the scale and dimension of stock selection data set and cannot meet the needs in terms of efficiency and accuracy of stock selection. A convolutional neural network has been applied to image classification and achieved better results than the traditional methods. In this study, we first constructed a multifactor stock selection data set based on China's stock market. Then, we apply the convolutional neural network model to analyze stock selection data and select stocks. The main contribution of this study is that we build a stock multifactor data set, construct a "factor picture," and classify them by convolutional neural network to select stocks. This study also makes comparative experiments on the decision tree, support vector machine, and feedforward neural network in stock selection on the same data set constructed in this study. The results show that the stock selection method based on the convolutional neural network outperforms other methods in terms of the annual return, sharp ratio, and max drawdown. [ABSTRACT FROM AUTHOR]

Heart disease is a very common high-incidence disease. Due to the wide variety of pathology of heart disease, how to improve the medical diagnosis of heart disease and carry out earlier intervention and treatment is a problem that needs to be solved urgently. The paper adds the decision tree algorithm and its comparison and proposes an optimized classification algorithm Co-SVM. Based on the establishment of a heart disease diagnosis classifier based on data mining algorithms, it is aimed at exploring which of these four algorithms is more suitable for heart disease diagnosis problems and optimizing them. A brief description of the cause, influencing factors, and acquired data of heart disease can be seen from the accuracy and scientificity of the data, which further enhances the authenticity and reliability of the clinical diagnosis model of heart disease. At the same time, the ultrasound diagnosis technology of heart disease is introduced, and the important role of ultrasound diagnosis technology in the medical diagnosis of heart disease is discussed. This thesis uses the heart disease clinical data set to establish a heart disease diagnosis classifier based on the decision tree algorithm, neural network algorithm, support vector machine algorithm, and Co-SVM algorithm. Through experimental comparison and analysis, the optimal classification is selected according to the obtained results. The algorithm is Co-SVM algorithm. The experimental results show that the proposed Co-SVM algorithm has a higher accuracy rate than the other three classic algorithms, and the effectiveness of the Co-SVM algorithm is verified by the evaluation results of multiple algorithms. By applying the Co-SVM algorithm in the medical diagnosis system, it is helpful to assist doctors in making more accurate and precise diagnosis of the condition. [ABSTRACT FROM AUTHOR]

Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated. [ABSTRACT FROM AUTHOR]

Student performance is crucial to the success of tertiary institutions. Especially, academic achievement is one of the metrics used in rating top-quality universities. Despite the large volume of educational data, accurately predicting student performance becomes more challenging. The main reason for this is the limited research in various machine learning (ML) approaches. Accordingly, educators need to explore effective tools for modelling and assessing student performance while recognizing weaknesses to improve educational outcomes. The existing ML approaches and key features for predicting student performance were investigated in this work. Related studies published between 2015 and 2021 were identified through a systematic search of various online databases. Thirty-nine studies were selected and evaluated. The results showed that six ML models were mainly used: decision tree (DT), artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), linear regression (LinR), and Naive Bayes (NB). Our results also indicated that ANN outperformed other models and had higher accuracy levels. Furthermore, academic, demographic, internal assessment, and family/personal attributes were the most predominant input variables (e.g., predictive features) used for predicting student performance. Our analysis revealed an increasing number of research in this domain and a broad range of ML algorithms applied. At the same time, the extant body of evidence suggested that ML can be beneficial in identifying and improving various academic performance areas. [ABSTRACT FROM AUTHOR]

Brain cancer is one of the cell synthesis diseases. Brain cancer cells are analyzed for patient diagnosis. Due to this composite cell, the conceptual classifications differ from each and every brain cancer investigation. In the gene test, patient prognosis is identified based on individual biocell appearance. Classification of advanced artificial neural network subtypes attains improved performance compared to previous enhanced artificial neural network (EANN) biocell subtype investigation. In this research, the proposed features are selected based on improved gene expression programming (IGEP) with modified brute force algorithm. Then, the maximum and minimum term survivals are classified by using PCA with enhanced artificial neural network (EANN). In this, the improved gene expression programming (IGEP) effectual features are selected by using remainder performance to improve the prognosis efficiency. This system is estimated by using the Cancer Genome Atlas (CGA) dataset. Simulation outputs present improved gene expression programming (IGEP) with modified brute force algorithm which achieves accurate efficiency of 96.37%, specificity of 96.37%, sensitivity of 98.37%, precision of 78.78%, F -measure of 80.22%, and recall of 64.29% when compared to generalized regression neural network (GRNN), improved extreme learning machine (IELM) with minimum redundancy maximum relevance (MRMR) method, and support vector machine (SVM). [ABSTRACT FROM AUTHOR]

In order to predict financial distress in 3424 Chinese listed companies, we incorporate a novel time windows optimization model into a convolutional neural network and use 576 financial/nonfinancial/macroindicators as the model input data. Our prediction accuracy can reach 94.5%, at least 2% higher than known classifiers (e.g., support vector machine, decision tree, logistic regression, neural network). In terms of AUC and the Kolmogorov–Smirnov statistic, our model also outperformed these classifiers. The introduction of the optimization model in our model can combine indicator information in different time windows, leading to the best prediction performance. [ABSTRACT FROM AUTHOR]

Most patients with diabetes mellitus are asymptomatic, which leads to delayed and more complex treatment. At the same time, most individuals are routinely subjected to standard clinical laboratory examinations, which create large health datasets over a lifetime. Computer processing has been used to search for health anomalies and predict diseases using clinical examinations. This work studied machine learning models to support the screening of diabetes through routine laboratory tests using data from laboratory tests of 62,496 patients. The classification and regression models used were the K-nearest neighbor, support vector machines, Bayes naïve, random forest models, and artificial neural networks. Glycated hemoglobin, a test used for diabetes diagnosis, was used as the target. Regression models calculated glycated hemoglobin directly and were later classified. The performance of classification computer models has been studied under various subdataset partitions and combinations (e.g., healthy, prediabetic, and diabetes, as well as no healthy and no diabetes). The best single performance was achieved with the artificial neural network model when detecting prediabetes or diabetes. The artificial neural network classification model scored 78.1%, 78.7%, and 78.4% for sensitivity, precision, and F1 scores, respectively, when identifying no healthy group. Other models also had good results, depending on what is desired. Machine learning-based models can predict glycated hemoglobin values from routine laboratory tests and can be used as a screening tool to refer a patient for further testing. [ABSTRACT FROM AUTHOR]

The slope angle of a slope is one of the important parameters affecting the stability of rocky slopes. In this paper, a new method based on the random forest (RF) algorithm is proposed to study the slope angle of rocky slopes. Based on the international typical rocky slope actual measurement data, the RF model for predicting the foot of the rocky slope is constructed by determining ten influencing factors affecting the slope angle of the rocky slope, namely, rock strength, rock quality designation (RQD), joint spacing, continuity, openness, roughness, filling, weathering, groundwater, and engineering direction as independent variables. The experimental results show that (1) the RF model has the smallest out-of-bag error when the number of decision trees ntree is four and the number of features in the split feature set mtry is five hundred; (2) engineering direction, fill degree, RQD, groundwater, and joint spacing have a large influence on the foot of a rocky slope; (3) relative to artificial neural networks (BP), artificial neural networks optimized by genetic algorithm (GA-BP), support vector machine (SVM), and multiple linear regression (MLR), the RF regression model has obvious advantages in terms of prediction accuracy and model stability, which provides an effective method for achieving accurate prediction of slope angle of rocky slopes. [ABSTRACT FROM AUTHOR]

Heart failure is the most common cause of death in both males and females around the world. Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30% of all fatalities in the United States and 45% in Europe. Artificial intelligence (AI) approaches such as machine learning (ML) and deep learning (DL) models are playing an important role in the advancement of heart failure therapy. The main objective of this study was to perform a network meta-analysis of patients with heart failure, stroke, hypertension, and diabetes by comparing the ML and DL models. A comprehensive search of five electronic databases was performed using ScienceDirect, EMBASE, PubMed, Web of Science, and IEEE Xplore. The search strategy was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. The methodological quality of studies was assessed by following the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) guidelines. The random-effects network meta-analysis forest plot with categorical data was used, as were subgroups testing for all four types of treatments and calculating odds ratio (OR) with a 95% confidence interval (CI). Pooled network forest, funnel plots, and the league table, which show the best algorithms for each outcome, were analyzed. Seventeen studies, with a total of 285,213 patients with CVDs, were included in the network meta-analysis. The statistical evidence indicated that the DL algorithms performed well in the prediction of heart failure with AUC of 0.843 and CI [0.840–0.845], while in the ML algorithm, the gradient boosting machine (GBM) achieved an average accuracy of 91.10% in predicting heart failure. An artificial neural network (ANN) performed well in the prediction of diabetes with an OR and CI of 0.0905 [0.0489; 0.1673]. Support vector machine (SVM) performed better for the prediction of stroke with OR and CI of 25.0801 [11.4824; 54.7803]. Random forest (RF) results performed well in the prediction of hypertension with OR and CI of 10.8527 [4.7434; 24.8305]. The findings of this work suggest that the DL models can effectively advance the prediction of and knowledge about heart failure, but there is a lack of literature regarding DL methods in the field of CVDs. As a result, more DL models should be applied in this field. To confirm our findings, more meta-analysis (e.g., Bayesian network) and thorough research with a larger number of patients are encouraged. [ABSTRACT FROM AUTHOR]

Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. In this paper, various methods such as statistical analysis, logistic regression, machine learning, and deep learning methods were used in the prediction and detection of SAH which are reviewed. The advantages and limitations of SAH prediction and risk assessment methods are also being reviewed. Most of the existing methods were evaluated on the collected dataset for the SAH prediction. In some researches, deep learning methods were applied, which resulted in higher performance in the prediction process. EEG data were applied in the existing methods for the prediction process, and these methods demonstrated higher performance. However, the existing methods have the limitations of overfitting problems, imbalance data problems, and lower efficiency in feature analysis. The artificial neural network (ANN) and support vector machine (SVM) methods have been applied for the prediction process, and considerably higher performance is achieved by using this method. [ABSTRACT FROM AUTHOR]

This study applies response surface methodology (RSM) to the hyperparameter fine-tuning of three machine learning (ML) algorithms: artificial neural network (ANN), support vector machine (SVM), and deep belief network (DBN). The purpose is to demonstrate RSM effectiveness in maintaining ML algorithm performance while reducing the number of runs required to reach effective hyperparameter settings in comparison with the commonly used grid search (GS). The ML algorithms are applied to a case study dataset from a food producer in Thailand. The objective is to predict a raw material quality measured on a numerical scale. K-fold cross-validation is performed to ensure that the ML algorithm performance is robust to the data partitioning process in the training, validation, and testing sets. The mean absolute error (MAE) of the validation set is used as the prediction accuracy measurement. The reliability of the hyperparameter values from GS and RSM is evaluated using confirmation runs. Statistical analysis shows that (1) the prediction accuracy of the three ML algorithms tuned by GS and RSM is similar, (2) hyperparameter settings from GS are 80% reliable for ANN and DBN, and settings from RSM are 90% and 100% reliable for ANN and DBN, respectively, and (3) savings in the number of runs required by RSM over GS are 97.79%, 97.81%, and 80.69% for ANN, SVM, and DBN, respectively. [ABSTRACT FROM AUTHOR]

Accurate prediction of airborne equipment failure rate can provide correct repair and maintenance decisions and effectively establish a health management mechanism. This plays an important role in ensuring the safe use of the aircraft and flight safety. This paper proposes an optimal combination forecasting model, which mixes five single models (Multiple Linear Regression model (MLR), Gray model GM (1, N), Partial Least Squares model (PLS), Artificial Neural Network model (BP), and Support Vector Machine model (SVM)). The combined model and its single model are compared with the other three algorithms. Seven classic comparison functions are used for predictive performance evaluation indicators. The research results show that the combined model is superior to other models in terms of prediction accuracy. This paper provides a practical and effective method for predicting the airborne equipment failure rate. [ABSTRACT FROM AUTHOR]

In this paper, the IoT-based adaptive mutation PSO-BPNN algorithm is used to conduct in-depth research and analysis of the entrepreneurship evaluation model for college students and practical applications. This paper details the principle, implementation, and characteristics of each BP algorithm and PSO algorithm. When classifying college students' entrepreneurship evaluation based on BP neural network, because BP algorithm is a local optimization-seeking algorithm, it is easy to fall into local minima in the training phase of the network and the convergence speed is slow, which leads to the reduction of classifier recognition rate. To address the above problems, this paper proposes the algorithm of PSO optimized BP neural network (PSO-BPNN) and establishes a classification and recognition model based on this algorithm for college students' entrepreneurship evaluation. The predicted values obtained from the particle swarm optimization neural network model are used to calculate the gray intervals, and the modeling samples are further screened using the gray intervals and the correlation principle, while the hyperspectral particle swarm optimization neural network model of soil organic matter based on the gray intervals is established afterward; and the estimation results are compared and analyzed with those of traditional modeling methods. The results showed that the coefficient of determination of the gray interval-based particle swarm optimization neural network model was 0.8826, and the average relative error was 3.572%, while the coefficient of determination of the particle swarm optimization neural network model was 0.853, and the average relative error was 4.34%; the average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model were 8.79%, 6.717%, and 9.9%, respectively. The average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model are 8.79%, 6.717%, and 9.468%, respectively. In general, the entrepreneurial ability of college students is at a good level (83.42 points), among which the entrepreneurial management ability score (84.30 points) and entrepreneurial spirit (84.16 points) are basically the same, while the entrepreneurial technology ability is relatively low (82.76 points), and the evaluation results are further verified by the double case analysis method. The current problems encountered by university students in entrepreneurship are mainly the lack of practicality, which indicates that universities, industries, and national strategy implementation levels are not sufficiently focused and collaborative in entrepreneurship development to varying degrees. [ABSTRACT FROM AUTHOR]

Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and correlation coefficient (R) were used to evaluate and compare performance of the models. The results show that all the three models performed well (R > 0.8) for the prediction of permeability coefficient of soil, but the RF model (RMSE = 0.0084, MAE = 0.0049, and R = 0.851) is more efficient compared with the other two models, namely, ANN (RMSE = 0.001, MAE = 0.005, and R = 0.845) and SVM (RMSE = 0.0098, MAE = 0.0064, and R = 0.844). Thus, it can be concluded that the RF model can be used for accurate estimation of the permeability coefficient (K) of the soil. [ABSTRACT FROM AUTHOR]

Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and correlation coefficient (R) were used to evaluate and compare performance of the models. The results show that all the three models performed well (R > 0.8) for the prediction of permeability coefficient of soil, but the RF model (RMSE = 0.0084, MAE = 0.0049, and R = 0.851) is more efficient compared with the other two models, namely, ANN (RMSE = 0.001, MAE = 0.005, and R = 0.845) and SVM (RMSE = 0.0098, MAE = 0.0064, and R = 0.844). Thus, it can be concluded that the RF model can be used for accurate estimation of the permeability coefficient (K) of the soil. [ABSTRACT FROM AUTHOR]

Rock blasting often has an irreversible impact on the surrounding environment and threatens the safety of life and property. Therefore, accurate prediction of blast-induced ground vibration (BIGV) is a prerequisite for safe construction. In view of the fact that traditional blasting peak particle velocity (PPV) empirical formulas cannot be accurately predicted, this study selected 88 sets of blasting monitoring data, based on distance from the blast-face, maximum charge per delay, total charge, hole depth, spacing, burden, stemming length, and powder factor being used as input variables and PPV being used as output variable to characterize BIGV. First, a nonlinear mapping relationship between input variables and output variable is established through the Gaussian process (GP). The differential evolution algorithm (DE) is used to optimize the hyperparameters σf, σn, and l of the GP, and a blasting PPV model based on the DE-GP is constructed. The proposed model is compared with the empirical formulas, least square support vector machine (LSSVM), artificial neural network (ANN), and GP model, and its prediction performance is evaluated by statistical indicators such as root mean square error (RMSE). Finally, the cosine amplitude method (CAM) is used to analyze the sensitivity of blasting parameters. The results show that the DE-GP algorithm for blasting vibration velocity prediction has higher precision and accuracy, which is significantly better than other models, and is the closest to the measured PPV. Distance from the blast-face, total charge, and maximum charge per delay have a greater impact on the prediction of PPV, while stemming length and powder factor have a smaller impact on the prediction of PPV. The DE-GP model proposed by this research has certain reference value for the prediction and control of PPV in blasting construction. [ABSTRACT FROM AUTHOR]

It is difficult to determine the main control factors owing to the complex geological conditions of heavy oil reservoirs, including high viscosity, a wide range of variation of crude oil, and the great difference in production between different recovery methods. In this context, main control factors of heavy oil production in different recovery methods are analyzed and obtained based on the Apriori algorithm. The prediction of heavy oil production is faced with problems such as low prediction precision and insufficient data usage. Therefore, a novel intelligent simulation and prediction model of data-driven heavy oil production with time-varying characteristics is established based on differential simulation, machine learning, and intelligent optimization theory, which overcomes the defects of nonlinear, multifactor, and low fitting precision of dynamic data of heavy oil development. The parameters of the heavy oil production time-varying simulation model are identified by the least square support vector machine (LSSVM) to realize the intelligent prediction of the production. Numerical experiments show that the prediction result of the novel intelligent simulation and prediction model is better than the BP neural network model and the GM (1, N) model. This study provides a novel feasible method for data-driven heavy oil production prediction, and it can be helpful in further study of data-driven heavy oil production. [ABSTRACT FROM AUTHOR]

Objective. To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse. Methods. We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome (n = 163), patients with Yin deficiency syndrome (n = 174), and healthy controls (n = 185) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively. Results. Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t4 and t5. The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806. Conclusions. It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer. [ABSTRACT FROM AUTHOR]

Fiber optic gyroscope (FOG) inertial measurement unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in positioning and navigation of military and aerospace fields, due to its simple structure, small size, and high accuracy. However, noise such as temperature drift will reduce the accuracy of FOG, which will affect the resolution accuracy of IMU. In order to reduce the FOG drift and improve the navigation accuracy, a long short-term memory recurrent neural network (LSTM-RNN) model is established, and a real-time acquisition method of the temperature change rate based on moving average is proposed. In addition, for comparative analysis, backpropagation (BP) neural network model, CART-Bagging, classification and regression tree (CART) model, and online support vector machine regression (Online-SVR) model are established to filter FOG outputs. Numerical simulation based on field test data in the range of -20°C to 50°C is employed to verify the effectiveness and superiority of the LSTM-RNN model. The results indicate that the LSTM-RNN model has better compensation accuracy and stability, which is suitable for online compensation. This proposed solution can be applied in military and aerospace fields. [ABSTRACT FROM AUTHOR]

The progress of construction and safe production in mining, water conservancy, tunnels, and other types of deep underground engineering is seriously affected by rockburst disasters. This makes it essential to accurately predict rockburst intensity. In this paper, the ratio of maximum tangential stress of surrounding rock to rock uniaxial compressive strength (σθ/σc), the ratio of rock uniaxial compressive strength to rock uniaxial tensile strength (σc/σt), and the elastic energy index of rock (Wet) were chosen as input indices, and rockbursts were graded as level I (none rockburst), level II (light rockburst), level III (medium rockburst), and level IV (strong rockburst). A total of 104 groups of rockburst engineering samples, collected widely from around the world, were divided into a training set (84 groups of samples) and a test set (20 groups of samples). Based on the kernel principal component analysis (KPCA), the adaptive particle swarm optimization (APSO) algorithm, and the support vector machine (SVM), the KPCA-APSO-SVM model was established. The proposed model showed satisfactory classification performance: the prediction accuracies of the training set and test set were 98.81% and 95%, respectively. In addition, the trained prediction model was applied to five rockburst engineering cases and compared with the BP neural network model, SVM model, and APSO-SVM model. The comparative results show that the KPCA-APSO-SVM model has a higher prediction accuracy; as such, it provides a new reliable method for rockburst prediction. [ABSTRACT FROM AUTHOR]

With accumulation of data and development of artificial intelligence, human activity recognition attracts lots of attention from researchers. Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. However, these algorithms have their own limitations and their prediction accuracy still has space to improve. In this study, we focus on K-nearest neighbors (KNN) and solve its limitations. Firstly, kernel method is employed in model KNN, which transforms the input features to be the high-dimensional features. The proposed model KNN with kernel (K-KNN) improves the accuracy of classification. Secondly, a novel reduced kernel method is proposed and used in model K-KNN, which is named as Reduced Kernel KNN (RK-KNN). It reduces the processing time and enhances the classification performance. Moreover, this study proposes an approach of defining number of K neighbors, which reduces the parameter dependency problem. Based on the experimental works, the proposed RK-KNN obtains the best performance in benchmarks and human activity datasets compared with other models. It has super classification ability in human activity recognition. The accuracy of human activity data is 91.60% for HAPT and 92.67% for Smartphone, respectively. Averagely, compared with the conventional KNN, the proposed model RK-KNN increases the accuracy by 1.82% and decreases standard deviation by 0.27. The small gap of processing time between KNN and RK-KNN in all datasets is only 1.26 seconds. [ABSTRACT FROM AUTHOR]

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%). [ABSTRACT FROM AUTHOR]

Only using single feature information as input feature cannot fully reflect the transformer fault classification and improve the accuracy of transformer fault diagnosis. To address the above problem, the convolution neural networks' model is applied for transformer fault assessment designed to implement an end-to-end "different space feature extraction + transformer state diagnosis classification" to enable information from possibly heterogeneous sources to be integrated. This method integrates various feature information of the power transformer operation state to form the isomeric feature, and the model can be used to automatically extract different feature spaces' information from isomeric feature quantity using its unique one-dimensional convolution and pooling operations. The performance of the proposed approach is compared with that of other models, such as a support vector machine (SVM), backpropagation neural network (BPNN), deep belief network (DBNs), and others. The experimental results show that the proposed one-dimensional convolution neural networks based on an isomeric feature (IF-1DCNN) can accurately classify the fault state of transformer and reduce the adverse interaction between different feature space information in the mixed feature, which has a good engineering application prospect. [ABSTRACT FROM AUTHOR]

Training image (TI) has a great influence on reservoir modeling as a spatial correlation in the multipoint geostatistics. Unlike the variogram of the two-point geostatistics that is mathematically defined, there is a high degree of geological uncertainty to determine a proper TI. The goal of this study is to develop a classification model for determining the proper geological scenario among plausible TIs by using machine learning methods: (a) support vector machine (SVM), (b) artificial neural network (ANN), and (c) convolutional neural network (CNN). After simulated production data are used to train the classification model, the most possible TI can be selected when the observed production responses are put into the trained model. This study, as far as we know, is the first application of CNN in which production history data are composed as a matrix form for use as an input image. The training data are set to cover various production trends to make the machine learning models more reliable. Therefore, a total of 800 channelized reservoirs were generated from four TIs, which have different channel directions to consider geological uncertainty. We divided them into training, validation, and test sets of 576, 144, and 80, respectively. The input layer comprised 800 production data, i.e., oil production rates and water cuts for eight production wells over 50 time steps, and the output layer consisted of a probability vector for each TI. The SVM and CNN models reasonably reduced the uncertainty in modeling the facies distribution based on the reliable probability for each TI. Even though the ANN and CNN had roughly the same number of parameters, the CNN outperformed the ANN in terms of both validation and test sets. The CNN successfully classified the reference model's TI with about 95% probability. This is because the CNN can grasp the overall trend of production history. The probabilities of TI from the SVM and CNN were applied to regenerate more reliable reservoir models using the concept of TI rejection and reduced the uncertainty in the geological scenario successfully. [ABSTRACT FROM AUTHOR]

It is crucial to carry out the fault diagnosis of rotating machinery by extracting the features that contain fault information. Many previous works using a deep convolutional neural network (CNN) have achieved excellent performance in finding fault information from feature extraction of detected signals. They, however, may suffer from time-consuming and low versatility. In this paper, a CNN integrated with the adaptive batch normalization (ABN) algorithm (ABN-CNN) is developed to avoid high computing resource requirements of such complex networks. It uses a large-scale convolution kernel at the grassroots level and a multidimensional 3 × 1 small convolution nuclear. Therefore, a fast convergence and high recognition accuracy under noise and load variation environment can be achieved for bearing fault diagnosis. The performance results verify that the proposed model is superior to Support Vector Machine with Fast Fourier Transform (FFT-SVM) and Multilayer Perceptron with Fast Fourier Transform (FFT-MLP) models and Deep Neural Network with Fast Fourier Transform (FFT-DNN). [ABSTRACT FROM AUTHOR]

In the construction of rock tunnels, the penetration rate of the tunnel boring machine (TBM) is influenced by many factors (e.g., geomechanical parameters), some of which are highly uncertain. It is difficult to establish a precise model for predicting the penetration rate on the basis of the influencing factors. Thus, this work proposed a useful method, based on the relevance vector machine (RVM) and particle swarm optimization (PSO), for the prediction of the TBM penetration rate. In this method, the RVM played a vital role in establishing a nonlinear mapping relationship between the penetration rate and its influencing factors through training-related samples. Then, the penetration rate could be predicted using some collected data of the influencing factors. As for the PSO, it helped to find the optimum value of a key parameter (called the basis function width) that was needed in the RVM model. Subsequently, the validity of the proposed RVM-PSO method was checked with the data monitored from a rock tunnel. The results showed that the RVM-PSO method could estimate the penetration rate of the TBM, and it proved superior to the back-propagation artificial neural network, the least-squares support vector machine, and the conventional RVM methods, in terms of the prediction performance. Moreover, the proposed RVM-PSO method could be applied to identify the difference in the importance of the various factors affecting the TBM penetration rate prediction for a tunnel. [ABSTRACT FROM AUTHOR]

In the context of COVID-19, many companies have been affected by the financial crisis. In order to carry out a comparative study on the accuracy of the company's financial crisis early warning method, this study used RPROP artificial neural network and support vector machine, with 162 listed companies' two-year panel financial indicator data as a model sample, and the test sample established a financial crisis early warning model. The theory of comprehensive evaluation combining two kinds of neural network methods is put forward innovatively. The predicted results can strengthen the supervision of the listed companies with risks by themselves and others and have important economic and social significance to ensure the stable operation of the listed companies, the securities market, and the national economy. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. Due to the highly nonlinear and dynamic nature of travel time series, it is necessary to consider time dependence and the spatial reliance of travel time series for predicting the travel time of road networks. The dynamic feature of the travel time series can be revealed by the EDM method, a nonlinear approach based on Chaos theory. Further, the spatial characteristic of urban traffic topology can be reflected from the perspective of complex networks. To fully guarantee the reasonability and validity of spatio-temporal features, which are dug by empirical dynamic modeling and complex networks (EDMCN), for urban traffic travel time prediction, an XGBoost prediction model is established for those characteristics. Through the in-depth exploration of the travel time and topology of a particular road network in Guiyang, the EDMCN-XGBoost prediction model's performance is verified. The results show that, compared with the single XGBoost, autoregressive moving average, artificial neural network, support vector machine, and other models, the proposed EDMCN-XGBoost prediction model presents a better performance in forecasting. [ABSTRACT FROM AUTHOR]

Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K -Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is one of the main diseases that endanger men's health worldwide. In developing countries, due to the large number of patients and the lack of medical resources, there is a big conflict between doctors and patients. To solve this problem, an auxiliary medical decision system for prostate cancer was constructed. The system used six relevant tumor markers as the input features and employed classical machine learning models (support vector machine and artificial neural network). Stacking method aimed at different ensemble models together was used for the reduction of overfitting. 1,933,535 patient information items had been collected from three first-class hospitals in the past five years to train the model. The result showed that the auxiliary medical system could make use of massive data. Its performance is continuously improved as the amount of data increases. Based on the system and collected data, statistics on the incidence of prostate cancer in the past five years were carried out. In the end, influence of diet habit and genetic inheritance for prostate cancer was analyzed. Results revealed the increasing prevalence of PCa and great negative impact caused by high-fat diet and genetic inheritance. [ABSTRACT FROM AUTHOR]