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Different number of broken wires produce different grooves on the surface of steel wire rope. Based on the local structural features of these grooves, a new broken wire identification method is proposed. By comparing the processing effects of various image enhancement methods, a processing method called adaptive histogram equalization is selected to process the broken wire image. Aiming at a large amount of useless information in structural features extracted by HOG algorithm, a encoder-decoder neural network is designed to reduce the dimension of features. In addition, to effectively avoid information loss caused by the output layer of the BP neural network, a joint algorithm of the BP neural network and the support vector machine is proposed. The experimental results show that using image enhancement technology to process broken wire images can effectively improve the recognition rate of broken wires; The structural features extracted by HOG algorithm are more beneficial to the quantitative recognition of broken wires than the texture features extracted by LBP operator; Compared with various dimensionality reduction methods, neural network can retain more effective information; The joint algorithm can improve the recognition rate of broken wire by at least 0.25% on the basis of BP neural network. [ABSTRACT FROM AUTHOR]

This study attempts to develop the adaptive neuro-fuzzy inference system (ANFIS) with biogeography-based optimization (BBO) (ANFIS-BBO) for a case study of the actual number of COVID-19 vaccinations in a medical center, considering the variables of the date and time of vaccination, the brand of vaccine, and the number of open appointments on the government network platform in Taiwan. The COVID-19 has brought about a great burden on the health and economy of the world since the end of 2019. Many scholars have proposed a prediction model for the number of confirmed cases and deaths. However, there is still a lack of research in the prediction model for mass vaccination. In this study, ANFIS-BBO is developed to predict the number of COVID-19 vaccination, and three other forecasting models, support vector machines (SVM), least-square support vector machines (LSSVM) and general regression neural network (GRNN) are employed for forecasting the same data sets. Empirical results show that the ANFIS-BBO with trapezoidal membership function model can achieve better performance than other methods and provide robust predictions for the actual number of COVID-19 mass vaccination. [ABSTRACT FROM AUTHOR]

Dealing with classification problems requires the crucial step of feature selection (FS), which helps to reduce data dimensions and shorten classification time. Feature selection and support vector machines (SVM) classification method for banknote dirtiness recognition based on marine predator algorithm (MPA) with mathematical functions was proposed. The mathematical functions were mainly used to improve the optimizatio of MPA for feature parameter selection, and the loss function and kernel function parameters of the SVM are optimized by slime mold optimization algorithm (SMA) and marine predator algorithm. According to the experimental results, the accuracy of identifying dirtiness on the entire surface of the banknote reaches 89.07%. At the same time, according to the image pattern distribution of the banknoteS, the white area image in the middle left of the collected banknote is selected by the same method to select the feature parameters and identify the dirtiness of the banknoteS. The accuracy of dirtiness recognition in the middle left white area reached 86.67%, this shows that the white area in the middle left can basically completely replace the entire banknote. To confirm the effectiveness of the feature selection method, the proposed optimization method has been compared with four other swarm intelligent optimization algorithms to verify its performance. The experiment results indicate that the enhanced strategy is successful in improving the performance of MPA. Moreover, the robustness analysis proves its effectiveness. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANET) have become one of the hottest research areas in computer science, including in military and civilian applications. Such applications have formed a variety of security threats, particularly in unattended environments. An Intrusion detection system (IDS) must be in place to ensure the security and reliability of MANET services. These IDS must be compatible with the characteristics of MANETs and competent in discovering the biggest number of potential security threats. In this work, a specialized dataset for MANET is implemented to identify and classify three types of Denial of Service (DoS) attacks: Blackhole, Grayhole and Flooding Attack. This work utilized a cluster-based routing algorithm (CBRA) in MANET.A simulation to gather data, then processed to create eight attributes for creating a specialized dataset using Java. Mamdani fuzzy-based inference system (MFIS) is used to create dataset labelling. Furthermore, an ensemble classification technique is trained on the dataset to discover and classify three types of attacks. The proposed ensemble classification has six base classifiers, namely, C4.5, Fuzzy Unordered Rule Induction Algorithm (FURIA), Multilayer Perceptron (MLP), Multinomial Logistic Regression (MLR), Naive Bayes (NB) and Support Vector Machine (SVM). The experimental results demonstrate that MFIS with the Ensemble classification technique enables an enhancing security in MANET's by modeling the interactions among a malicious node with number of legitimate nodes. This is suitable for future works on multilayer security problem in MANET. [ABSTRACT FROM AUTHOR]

A software defect is a common cyberspace security problem, leading to information theft, system crashes, and other network hazards. Software security is a fundamental challenge for cyberspace security defense. However, when researching software defects, the defective code in the software is small compared with the overall code, leading to data imbalance problems in predicting software vulnerabilities. This study proposes a heterogeneous integration algorithm based on imbalance rate threshold drift for the data imbalance problem and for predicting software defects. First, the Decision Tree-based integration algorithm was designed following sample perturbation. Moreover, the Support Vector Machine (SVM)-based integration algorithm was designed based on attribute perturbation. Following the heterogeneous integration algorithm, the primary classifier was trained by sample diversity and model structure diversity. Second, we combined the integration algorithms of two base classifiers to form a heterogeneous integration model. The imbalance rate was designed to achieve threshold transfer and obtain software defect prediction results. Finally, the NASA-MDP and Juliet datasets were used to verify the heterogeneous integration algorithm's validity, correctness, and generalization based on the Decision Tree and SVM. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a supervised binary classifier with good generalization ability and excellent computational properties. It has been widely used in many fields such as image recognition, bioinformatics and so on. However, the traditional SVM requires the input data to be clear and knowable, while in the actual application process, there will be many cases that the input data is uncertain. In order to solve this problem, a new SVM model is proposed in this paper by combining the uncertainty theory with the SVM theory. The uncertainty theory was proposed by Liu in 2007, and it is often used to describe the uncertainty of things. The uncertain set in uncertainty theory is often used to model unsharp concepts. Therefore, this paper regards each uncertain data as an uncertain set and establishes a SVM model with uncertain chance constraints. However, the uncertain chance constraints are non-convex. Therefore, this paper gives the equivalent transformation process of constraint conditions when the input data are triangular uncertain sets. Finally, the non-convex constraint conditions are transformed into the linear constraint conditions, so that the model is transformed into a nonlinear programming model. In the numerical experiment, the Particle Swarm Optimization (PSO) algorithm is used to solve the problem, which proves the feasibility of the model. [ABSTRACT FROM AUTHOR]

Foods are very essential for living beings for providing energy, development and preserve their existence. It plays a vital role in promoting health and preventing illness. Nowadays, many people are suffered from obesity, they tend to maintain their body weight by consuming a sufficient number of calories in their routine life. In this research, a novel Modified Deep Learning-based Food Item Classification (MDEEPFIC) approach has been proposed to categorize the different food items from the dataset with their calorie values. Initially, the images are processed using the sigmoid stretching method to enhance the image quality and remove the noises. Consequently, the pre-processed images are segmented using Improved Watershed Segmentation (IWS2) algorithm. Recurrent Neural Network (RNN) is used to extract features like shape, size, textures, and color. The extracted features are then normalized using the modified dragonfly technique for same food calorie calculation. Bidirectional Long Short-Term Memory (Bi-LSTM) is utilized to classify food products based on these pertinent aspects. Finally, using food area volume and calorie and nutrition measures based on mass value, the calorie value of the categorized food item is calculated. The efficiency of the proposed method was calculated in terms of specificity, precision, accuracy, and recall F-measure. The proposed method improves the overall accuracy of 4.99%, 8.72%, and 10.4% better than existing Deep Convolution Neural Network (DCNN), Faster Recurrent convolution neural network (FRCNN), Local Variation Segmentation based Support Vector Machine (LSV-SVM) method respectively. [ABSTRACT FROM AUTHOR]

A machine learning model intends to produce a secure model with low bias and variance. Finding the optimal machine learning model for a dataset is a challenging task. A suitable machine learning model is yet to be specified for the Arthritis Profile Data dataset. Autoimmune disease is widely spread all over the world. Some autoimmune arthritis diseases are Rheumatoid Arthritis, Psoriatic Arthritis, Juvenile Arthritis, etc. These diseases come under both categories autoimmune and inflammatory diseases. The proposed work is designed to suggest the best machine learning model with the highest observed accuracy for the Arthritis Profile Data. Many authors do not compare newly created datasets with previously used datasets. This can lead to inaccurate results due to the lack of reliable comparison. Additionally, it can prevent researchers from detecting potential bias in the data. Comparing datasets can help to identify and address any potential issues and improve the accuracy of the results. It is important to review existing datasets before beginning a new project to ensure the accuracy of the results. This article is the first study on the topic that analysis the accuracy behavior of each machine learning model concerning the Arthritis Profile Data and various benchmark disease datasets with different hold-out and k-fold cross-validation methods. The study concludes with a glimpse of whether dataset and feature size affect model prediction accuracy and proffers a machine learning model for the Arthritis Profile Data. The proposed research explores base learning classification algorithms and ensemble methods such as Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Extreme Gradient Boosting from machine learning. Our empirical evidence clearly states XGBoost ensemble technique shows the highest accuracy for the Arthritis Profile Data. [ABSTRACT FROM AUTHOR]

Support Vector Machine (SVM) is a machine learning with excellent classification performance, which has been widely used in various fields such as data mining, text classification, face recognition and etc. However, when data volume scales to a certain level, the computational time becomes too long and the efficiency becomes low. To address this issue, we propose a parallel balanced SVM algorithm based on Spark, named PB-SVM, which is optimized on the basis of the traditional Cascade SVM algorithm. PB-SVM contains three parts, i.e., Clustering Equal Division, Balancing Shuffle and Iteration Termination, which solves the problems of data skew of Cascade SVM and the large difference between local support vector and global support vector. We implement PB-SVM in AliCloud Spark distributed cluster with five kinds of public datasets. Our experimental results show that in the two-classification test on the dataset covtype, compared with MLlib-SVM and Cascade SVM on Spark, PB-SVM improves efficiency by 38.9% and 75.4%, and the accuracy is improved by 7.16% and 8.38%. Moreover, in the multi-classification test, compared with Cascade SVM on Spark on the dataset covtype, PB-SVM improves efficiency and accuracy by 94.8% and 18.26% respectively. [ABSTRACT FROM AUTHOR]

In recent decades, network security for organizations and individuals has become more and more important, and intrusion detection systems play a key role in protecting network security. To improve intrusion detection effect, different machine learning techniques have been widely applied and achieved exciting results. However, the premise that these methods achieve reliable results is that there are enough available and well-labeled training data, training and test data being from the same distribution. In real life, the limited label data generated by a single organization is not enough to train a reliable learning model, and the distribution of data collected by different organizations is difficult to be the same. In addition, various organizations protect their privacy and data security through data islands. Therefore, this paper proposes an efficient intrusion detection method using transfer learning and support vector machine with privacy-preserving (FETLSVMP). FETLSVMP performs aggregation of data distributed in various organizations through federated learning, then utilizes transfer learning and support vector machines build personalized models for each organization. Specifically, FETLSVMP first builds a transfer support vector machine model to solve the problem of data distribution differences among various organizations; then, under the mechanism of federated learning, the model is used for learning without sharing training data on each organization to protect data privacy; finally, the intrusion detection model is obtained with protecting the privacy of data. Experiments are carried out on NSL-KDD, KDD CUP99 and ISCX2012, the experimental results verify that the proposed method can achieve better results of detection and robust performance, especially for small samples and emerging intrusion behaviors, and have the ability to protect data privacy. [ABSTRACT FROM AUTHOR]

BACKGROUND: Hyperspectral brain tissue imaging has been recently utilized in medical research aiming to study brain science and obtain various biological phenomena of the different tissue types. However, processing high-dimensional data of hyperspectral images (HSI) is challenging due to the minimum availability of training samples. OBJECTIVE: To overcome this challenge, this study proposes applying a 3D-CNN (convolution neural network) model to process spatial and temporal features and thus improve performance of tumor image classification. METHODS: A 3D-CNN model is implemented as a testing method for dealing with high-dimensional problems. The HSI pre-processing is accomplished using distinct approaches such as hyperspectral cube creation, calibration, spectral correction, and normalization. Both spectral and spatial features are extracted from HSI. The Benchmark Vivo human brain HSI dataset is used to validate the performance of the proposed classification model. RESULTS: The proposed 3D-CNN model achieves a higher accuracy of 97% for brain tissue classification, whereas the existing linear conventional support vector machine (SVM) and 2D-CNN model yield 95% and 96% classification accuracy, respectively. Moreover, the maximum F1-score obtained by the proposed 3D-CNN model is 97.3%, which is 2.5% and 11.0% higher than the F1-scores obtained by 2D-CNN model and SVM model, respectively. CONCLUSION: A 3D-CNN model is developed for brain tissue classification by using HIS dataset. The study results demonstrate the advantages of using the new 3D-CNN model, which can achieve higher brain tissue classification accuracy than conventional 2D-CNN model and SVM model. [ABSTRACT FROM AUTHOR]

Multi-modal information outbreak is consistently increasing in social media. Classification of tweet sentiments using various information modalities will help the recommender systems to achieve success in digital marketing. Moreover, aspect-level sentiment analysis categorizes a target's sentiment polarity in a specific environment. Using topic modelling in aspect-level sentiment analysis enables the identification of more accurate aspect-based tweet sentiments. The existing sentiment classification techniques used for the development of recommendation systems do not focus on the aspect-based approach modelled using deep learning classifier with temporal analysis on the social media data. Hence, this paper proposes an efficient sentiment classification model that highlights the impact of topic modelling-based word feature embedding for improvising the classification of Twitter sentiments and product reviews based on temporal reasoning and analysis for performing predictive analysis. For tweets context analysis, Latent Dirichlet Allocation based topic modelling is used in this work which generates the topics. For each topic, the sentiment is calculated separately and the topic guided feature expansion is done using Senti-wordnet. Moreover, an extended deep learning classification algorithm called Long Short-Term Memory (LSTM) with word embedding and temporal reasoning(LSTMWTR) is proposed in this paper for improving the classification accuracy. Finally, the labelled data are classified using the existing machine learning algorithms namely Naïve Bayes, Support Vector Machines and also using the deep learning models such as Convolution Neural Network(CNN),LSTM, Recurrent Neural Networks (RNN) and the transformer model namelyBi-directional Encoder Representation from Transformers (BERT),Convolution Bi-directional Recurrent Neural Network (CBRNN) and the proposed deep learning algorithm namelyLSTMWTR. These sentiment classification algorithms have been evaluated with word embedding for tweet sentiment classification and product review classification. The results obtained from this work show that the proposed LSTMWTR algorithm emerges as the highly accurate model for tweet sentiment and product review classification. [ABSTRACT FROM AUTHOR]

Mechanical fault detection has an important influence on production schedule and efficiency. With the development of intelligent technology, more and more intelligent detection technologies are applied to mechanical fault detection. In order to detect mechanical faults more efficiently and accurately, this experiment proposes a production knowledge base model based on genetic algorithm (GA algorithm). The model uses the unique biological genetics principle of genetic algorithm to evolve the interested population, and can conduct spatial search to find the global optimal solution. By comparing the performance of GA algorithm model with other similar detection models, it is found that the model proposed in the experiment has obvious advantages in mechanical fault detection performance. The experimental results show that the maximum accuracy of the GA algorithm is 0.935, 0.074 higher than the support vector machine (SVM) model, 0.118 higher than the linear discriminant analysis (LDA) model, 0.032 higher than the random forest (RF) model, and 0.166 higher than the K nearest neighbor (KNN) model. In addition, the error value of GA algorithm is the lowest among these models, which is 0.028. This proves that the genetic algorithm model has higher diagnostic accuracy and can play an important role in mechanical fault detection. [ABSTRACT FROM AUTHOR]

When using the support vector regression method to predict grain storage temperature, it is challenging to choose the appropriate model parameters. Generally, it is effective to examine the trend of grain storage temperature in different layers after ventilation intervention. To enhance the performance of a support vector machine, it is necessary to choose an appropriate parameter optimization algorithm. The adaptive particle swarm optimization algorithm completes the operation by continuously updating the particles in the spatial domain; after discussing its application principle in detail, the convergence effect is more optimal; and the algorithms are applied to parameter optimization for support vector regression models. After employing the adaptive particle swarm optimization algorithm, the evaluation indicators and experimental prediction results demonstrate that the APSO model has fewer errors, a higher tracking degree, superior generalization performance, and greater prediction accuracy. This is a useful resource for forecasting grain temperature trends. [ABSTRACT FROM AUTHOR]

Traffic safety is directly affected by poor road conditions. Automating the detection of road defects allows improvements in the maintenance process. The identification of defects such as cracks and potholes can be done using computer vision techniques and supervised learning. In this paper, we propose the detection of cracks and potholes in images of paved roads using machine learning techniques. The images are subdivided into blocks, where Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor Filter's texture descriptors are used to extract features of the images. For the classification task, the Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Multi-Layer Perceptron (MLP) models are compared. We performed two experiments on a dataset built with images of Brazilian highways. In the first experiment, we obtained a F-measure of 75.16% when classifying blocks of images that have cracks and potholes, and 79.56% when comparing roads with defects and without defects. In the second experiment, a F-measure of 87.06% was obtained for the equivalent task. Thus, it is possible to state that the use of the techniques presented is feasible for locating faults in highways. [ABSTRACT FROM AUTHOR]

BACKGROUND: Liver metastases is a pivotal factor of death in patients with colorectal cancer. The longitudinal data of colorectal liver metastases (CRLM) during treatment can monitor and reflect treatment efficacy and outcomes. OBJECTIVE: The objective of this study is to establish a radiomic model based on longitudinal magnetic resonance imaging (MRI) to predict chemotherapy response in patients with CRLM. METHODS: This study retrospectively enrolled longitudinal MRI data of five modalities on 100 patients. According to Response Evaluation Criteria in Solid Tumors (RECIST 1.1), 42 and 58 patients were identified as responders and non-responders, respectively. First, radiomic features were computed from different modalities of image data acquired pre-treatment and early-treatment, as well as their differences (Δ). Next, the features were screened by a two-sample t-test, max-relevance and min-redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO). Then, several ensemble radiomic models that integrate support vector machine (SVM), k-nearest neighbor (KNN), gradient boost decision tree (GBDT) and multi-layer perceptron (MLP) were established based on voting method to predict chemotherapy response. Data samples were divided into training and verification queues using a ratio of 8:2. Finally, we used the area under ROC curve (AUC) to evaluate model performance. RESULTS: Using the ensemble model developed using featue differences (Δ) computed from the longitudinal apparent diffusion coefficient (ADC) images, AUC is 0.9007±0.0436 for the training cohort. Applying to the testing cohort, AUC is 0.8958 and overall accuracy is 0.9. CONCLUSIONS: Study results demonstrate advantages and high performance of the ensemble radiomic model based on the radiomics feature difference of the longitudinal ADC images in predicting chemotherapy response, which has potential to assist treatment decision-making and improve clinical outcome. [ABSTRACT FROM AUTHOR]

BACKGROUND: Fatty liver disease is a common condition caused by excess fat in the liver. It consists of two types: Alcoholic Fatty Liver Disease, also called alcoholic steatohepatitis, and Non-Alcoholic Fatty Liver Disease (NAFLD). As per epidemiological studies, fatty liver encompasses 9% to 32% of the general population in India and affects overweight people. OBJECTIVE: An Optimized Support Vector Machine with Support Vector Regression model is proposed to evaluate the volume of liver fat by image analysis (LFA-OSVM-SVR). METHOD: The input computed tomography (CT) liver images are collected from the Chennai liver foundation and Liver Segmentation (LiTS) datasets. Here, input datasets are pre-processed using Gaussian smoothing filter and bypass filter to reduce noise and improve image intensity. The proposed U-Net method is used to perform the liver segmentation. The Optimized Support Vector Machine is used to classify the liver images as fatty liver image and normal images. The support vector regression (SVR) is utilized for analyzing the fat in percentage. RESULTS: The LFA-OSVM-SVR model effectively analyzed the liver fat from CT scan images. The proposed approach is activated in python and its efficiency is analyzed under certain performance metrics. CONCLUSION: The proposed LFA-OSVM-SVR method attains 33.4%, 28.3%, 25.7% improved accuracy with 55%, 47.7%, 32.6% lower error rate for fatty image classification and 30%, 21%, 19.5% improved accuracy with 57.9%, 46.5%, 31.76% lower error rate for normal image classificationthan compared to existing methods such as Convolutional Neural Network (CNN) with Fractional Differential Enhancement (FDE) (CNN-FDE), Fully Convolutional Networks (FCN) and Non-negative Matrix Factorization (NMF) (FCN-NMF), and Deep Learning with Fully Convolutional Networks (FCN) (DL-FCN). [ABSTRACT FROM AUTHOR]

The energy consumption prediction of the chiller is an important means to reduce the energy consumption of buildings. Therefore, a novel energy consumption prediction model for chillers based on an improved support vector machine (ICA-DE-SVM) is proposed. The imperialist competitive algorithm (ICA) is used to optimize the penalty coefficient and kernel function width of SVM, greatly improving the generalization ability and prediction accuracy of the SVM model. The assimilation process is very important in ICA. Colonies of empires move randomly toward imperialists during the assimilation process in ICA, which decreases population diversity and can lead to premature convergence. Therefore, to create more new locations for colonies and increase population diversity, the idea of differential mutation proposed by differential evolution (DE) was applied to ICA. The established model was experimentally verified in an actual multi-chiller system in a building, and the results showed that the ICA-DE-SVM model could obtain good prediction results. Finally, the proposed model was compared with SVM model, PSO-SVM model, GA-SVM model, WOA-SVM model, and ICA-SVM model. With an MAPE of 0.6%, an MSE of 2.3, and an R2 of 0.9998, the findings demonstrate that the ICA-DE-SVM model has a greater prediction accuracy than the other models. [ABSTRACT FROM AUTHOR]

The diversity of attribute categories brings certain difficulties to data feature detection. In order to improve the accuracy and efficiency of feature detection, a hybrid attribute feature detection method for power system intelligent operation and maintenance big data based on improved random forest algorithm is proposed. Clustering processing power system intelligent operation and maintenance big data, based on data clustering results to reduce the characteristics of data mixed attributes, reduce the scale of data processing, and discretize the data mixed attributes; BP neural network is used to preprocess the results. Make corrections to improve the accuracy of feature detection, use the improved random forest algorithm to classify the data, and improve the convergence speed of the method. Finally, the support vector machine method is used to realize the feature detection of data mixed attributes. The experimental results show that the feature detection accuracy and efficiency of the method designed in this paper are high, and more features can be detected, which verifies its effectiveness. The method designed in this paper has the minimum RMSE value and the maximum value is only 0.12, which is far lower than the RMSE value of the improved spectral clustering algorithm and multi-domain feature extraction method, and has high detection accuracy. [ABSTRACT FROM AUTHOR]

A Computer Aided Diagnosis (CAD) framework to diagnose Pulmonary Edema (PE) and covid-19 from the chest Computed Tomography (CT) slices were developed and implemented in this work. The lung tissues have been segmented using Otsu's thresholding method. The Regions of Interest (ROI) considered in this work were edema lesions and covid-19 lesions. For each ROI, the edema lesions and covid-19 lesions were elucidated by an expert radiologist, followed by texture and shape extraction. The extracted features were stored as feature vectors. The feature vectors were split into train and test set in the ratio of 80 : 20. A wrapper based feature selection approach using Squirrel Search Algorithm (SSA) with the Support Vector Machine (SVM) classifier's accuracy as the fitness function was used to select the optimal features. The selected features were trained using the Back Propagation Neural Network (BPNN) classifier. This framework was tested on a real-time PE and covid-19 dataset. The BPNN classifier's accuracy with SSA yielded 88.02%, whereas, without SSA it yielded 83.80%. Statistical analysis, namely Wilcoxon's test, Kendall's Rank Correlation Coefficient test and Mann Whitney U test were performed, which indicates that the proposed method has a significant impact on the accuracy, sensitivity and specificity of the novel dataset considered. Comparative experimentations of the proposed system with existing benchmark ML classifiers, namely Cat Boost, Ada Boost, XGBoost, RBF SVM, Poly SVM, Sigmoid SVM and Linear SVM classifiers demonstrate that the proposed system outperforms the benchmark classifiers' results. [ABSTRACT FROM AUTHOR]

The foul odor of foul gas has many harmful effects on the environment and human health. In order to accurately assess this impact, it is necessary to identify specific malodorous components and levels. In order to meet the qualitative and quantitative identification of the components of malodorous gas, an electronic nose system is developed in this paper. Both principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the dimensionality of the collected data. The reduced-dimensional data are combined with a support vector machine (SVM) and backpropagation (BP) neural network for classification and recognition to compare the recognition results. Regarding qualitative recognition, this paper selects the method of LDA combined with the BP neural network after comparison. Experiments show that the qualitative recognition rate of this method in this study can reach 100%, and the amount of data after LDA dimensionality reduction is small, which speeds up the pattern speed of recognition. Regarding quantitative identification, this paper proposes a prediction experiment through Partial least squares (PLS) and BP neural networks. The experiment shows that the average relative error of the trained BP network is within 6%. Finally, the experiment of quantitative analysis of malodorous compound gas by this system shows that the maximum relative error of this method is only 4.238%. This system has higher accuracy and faster recognition speed than traditional methods. [ABSTRACT FROM AUTHOR]

Most existing classifiers are better at identifying majority classes instead of ignoring minority classes, which leads to classifier degradation. Therefore, it is a challenge for binary classification to imbalanced data, to address this, this paper proposes a novel twin-support vector machine method. The thought is that majority classes and minority classes are found by two support vector machines, respectively. The new kernel is derived to promote the learning ability of the two support vector machines. Results show that the proposed method wins over competing methods in classification performance and the ability to find minority classes. Those classifiers based-twin architectures have more advantages than those classifiers based-single architecture in classification ability. We demonstrate that the complexity of imbalanced data distribution has negative effects on classification results, whereas, the advanced classification results and the desired boundaries can be gained by optimizing the kernel. [ABSTRACT FROM AUTHOR]

Premature mortality from cardiovascular disease can be reduced with early detection of heart failure by analysing the patients' risk factors and assuring accurate diagnosis. This work proposes a clinical decision support system for the diagnosis of congenital heart failure by utilizing a data pre-processing approach for dealing missing values and a filter-wrapper based method for selecting the most relevant features. Missing values are imputed using a missForest method in four out of eight heart disease datasets collected from the Machine Learning Repository maintained by University of California, Irvine. The Fast Correlation Based Filter is used as the filter approach, while the union of the Atom Search Optimization Algorithm and the Henry Gas Solubility Optimization represent the wrapper-based algorithms, with the fitness function as the combination of accuracy, G-mean, and Matthew's correlation coefficient measured by the Support Vector Machine. A total of four boosted classifiers namely, XGBoost, AdaBoost, CatBoost, and LightGBM are trained using the selected features. The proposed work achieves an accuracy of 89%, 84%, 83%, 80% for Heart Failure Clinical Records, 81%, 80%, 83%, 82% for Single Proton Emission Computed Tomography, 90%, 82%, 93%, 80% for Single Proton Emission Computed Tomography F, 80%, 80%, 81%, 80% for Statlog Heart Disease, 80%, 85%, 83%, 86% for Cleveland Heart Disease, 82%, 85%, 85%, 82% for Hungarian Heart Disease, 80%, 81%, 79%, 82% for VA Long Beach, 97%, 89%, 98%, 97%, for Switzerland Heart Disease for four classifiers respectively. The suggested technique outperformed the other classifiers when evaluated against Random Forest, Classification and Regression Trees, Support Vector Machine, and K-Nearest Neighbor. [ABSTRACT FROM AUTHOR]

ITK inhibitor is used for the treatment of asthma and activity of inhibitor prediction helps to provide better treatment. Few researches were carried out for the analysis and prediction of kinases activity. Existing methods applied for the inhibitor prediction have limitations of imbalance dataset and lower performance. In this research, the Posterior Probabilistic Weighted Average Based Ensemble voting (PPWAE)ensemble method is proposed with various classifier for effective prediction of kinases activity. The PPWAE model selects the most probable class from the classification method for prediction. The co-train model has two advantages: Features are trained together to increases the learning rate of model and probability is measured for each model to select the efficient classifier. Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Classification and Regression Tree (CART), and Nave Bayes were among the classifiers employed. The results suggest that the Probabilistic Co-train ensemble technique performs well in kinase activity prediction. In the prediction of ITK inhibitor activity, the suggested ensemble method has a 74.27 percent accuracy, while the conventional SVM method has a 60% accuracy. [ABSTRACT FROM AUTHOR]

Early diagnosis of breast cancer plays an important role in improving survival rate. Physiological changes of breast tissue can be observed and measured through medical electrical impedance, and the results can be used as a preliminary diagnosis by doctors before treatment. In this paper, quantum genetic algorithm (QGA) and support vector machine (SVM) were combined to classify breast tissues to help clinicians in diagnosis. The algorithm uses QGA to optimize the parameters of SVM and improve the classification performance of SVM. In this experiment, the electrical impedance data measured from breast tissue provided by UCI [58] was used as the data set. Objectively speaking, the data volume of the data set is small and the representativeness is not strong enough. However, the experimental results show that QGA-SVM shows better classification performance, and it is better than SVM. [ABSTRACT FROM AUTHOR]

Granaries should have good airtightness to reduce grain loss in storage. Prediction of granary airtightness at the design stage is beneficial in improving granary design. This paper proposes a method for the prediction interval (PI) of granary airtightness by using small sample data, which can guide designers with granary design. PI that the probability of the true target falling in it is markedly close or larger compared with the confidence level can be the decision basis of the granary design scheme. This study adopts support vector machine as the regression model trained by the airtightness data set of built granaries, and obtains the probability distribution of regression errors through information diffusion. The probability interval of errors is derived using a search algorithm, and PIs of granary airtightness can be acquired thereafter. Assessment indexes of PIs with confidence levels of 0.8 and 0.9 indicate that the proposed method can achieve confidence level and is superior to the comparative method using artificial neural network and bootstrap for PIs in cases of only a few samples. Thus, an innovative and feasible method is proposed for the computer-aided design of granary airtightness. [ABSTRACT FROM AUTHOR]

As a supervised learning algorithm, Support Vector Machine (SVM) is very popularly used for classification. However, the traditional SVM is error-prone because of easy to fall into local optimal solution. To overcome the problem, a new SVM algorithm based on Relief algorithm and particle swarm optimization-genetic algorithm (Relief-PGS) is proposed for feature selection and data classification, where the penalty factor and kernel function of SVM and the extracted feature of Relief algorithm are encoded as the particles of particle swarm optimization-genetic algorithm (PSO-GA) and optimized by iteratively searching for optimal subset of features. To evaluate the quality of features, Relief algorithm is used to screen the feature set to reduce the irrelevant features and effectively select the feature subset from multiple attributes. The advantage of Relief-PGS algorithm is that it can optimize both feature subset selection and SVM parameters including the penalty factor and the kernel parameter simultaneously. Numerical experimental results indicated that the classification accuracy and efficiency of Relief-PGS are superior to those of other algorithms including traditional SVM, PSO-GA-SVM, Relief-SVM, ACO-SVM, etc. [ABSTRACT FROM AUTHOR]

BACKGROUND: Liver metastases is a pivotal factor of death in patients with colorectal cancer. The longitudinal data of colorectal liver metastases (CRLM) during treatment can monitor and reflect treatment efficacy and outcomes. OBJECTIVE: The objective of this study is to establish a radiomic model based on longitudinal magnetic resonance imaging (MRI) to predict chemotherapy response in patients with CRLM. METHODS: This study retrospectively enrolled longitudinal MRI data of five modalities on 100 patients. According to Response Evaluation Criteria in Solid Tumors (RECIST 1.1), 42 and 58 patients were identified as responders and non-responders, respectively. First, radiomic features were computed from different modalities of image data acquired pre-treatment and early-treatment, as well as their differences (Δ). Next, the features were screened by a two-sample t-test, max-relevance and min-redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO). Then, several ensemble radiomic models that integrate support vector machine (SVM), k-nearest neighbor (KNN), gradient boost decision tree (GBDT) and multi-layer perceptron (MLP) were established based on voting method to predict chemotherapy response. Data samples were divided into training and verification queues using a ratio of 8:2. Finally, we used the area under ROC curve (AUC) to evaluate model performance. RESULTS: Using the ensemble model developed using featue differences (Δ) computed from the longitudinal apparent diffusion coefficient (ADC) images, AUC is 0.9007±0.0436 for the training cohort. Applying to the testing cohort, AUC is 0.8958 and overall accuracy is 0.9. CONCLUSIONS: Study results demonstrate advantages and high performance of the ensemble radiomic model based on the radiomics feature difference of the longitudinal ADC images in predicting chemotherapy response, which has potential to assist treatment decision-making and improve clinical outcome. [ABSTRACT FROM AUTHOR]

The spectrum scarcity problem in today's wireless communication network is addressed through the use of a cognitive radio network (CRN). Detection in the spectrum is made easier by cooperative spectrum sensing (CSS), which is a tool developed by the military. The fusion centre receives the sensing information from each secondary user and uses it to make a global conclusion about the presence of the principal user. Literature has offered several different methods for decision making that lack scalability and robustness. CSS censoring is inspected in the attendance of faded settings in the current study. Rayleigh fading, which affects reporting channels (R), is examined in detail. Multiple antennae and an energy detector (ED) are used by each secondary user (SU). A selection combiner (SC) combines the ED outputs with signals from the primary user (PU), which are established by several antennas on SU, before the joint signal is utilised to make a local result. SUs are expurgated at the fusion centre (FC) using a hybrid Support Vector Machine (SVM) that significantly improves detection performance and reduces the number of false positives. With a minimum false alarm probability of 0.1, error rate of 0.04, spectrum utilization of 99%, throughput of 2.9kbps and accuracy of 99%, proposed model attains better performance than standard SVM and Artificial Neural Network (ANN) models. [ABSTRACT FROM AUTHOR]

Controlling and managing city traffic is one of them. In order to use image processing to prevent accidents on the road, vehicle tracking and detection are essential. By following moving objects, surveillance video monitoring and human activity recording are carried out. By taking this into account, a useful technique for image processing that detects automobiles from the image is suggested. For numerous vehicle tracking and detection systems, the ECNN-SVM (Enhanced Convolution Neural Network with Support Vector Machine) has just been introduced. However, the larger dimensional data space and inaccurate edge recognition make this system's performance difficult. The WHOSVD (Weight High Order Singular Value Decomposition) approach, which reduces the dimension and breaks up the positive and negative training picture samples, is established to improve training speed and visual vehicle recognition. To effectively identify the edges at corners, improved canny edge detection is used for edge detection. Mean Kernel Fuzzy C Means (MKFCM) clustering algorithm-based three-dimensional bounding box estimation is used to identify the vehicle items. By merging the feature value of samples with their class labels, the Speed Factor Based Cuckoo Search Algorithm (SFCSA) is introduced for feature selection. The WHOSVD algorithm was used as the input for the enhanced convolutional neural network (ECNN), which is introduced for low-dimensional space and is used for vehicle detection and tracking. Occlusion problems are resolved and target features are further identified using a machine learning classifier. For common algorithms like CNN+SVM, Support Vector Machine (SVM), and the proposed technique, experimentation is done in regards to the metrics of accuracy, f-measure, precision, and recall for performance evaluation. [ABSTRACT FROM AUTHOR]

Cervical cancer is one of the most common causes of death in women in the world, and early screening is an effective means of diagnosis and treatment, which can greatly improve the survival rate. Cervical cell classification model is an effective means to assist screening. However, the existing single model, including CNNs and machine learning methods, still has shortcomings such as unclear feature meaning, low accuracy and insufficient supervision. To solve the shortcomings of a single model, a novel framework based on strong feature Convolutional Neural Networks (CNN)-Lagrangian Support Vector Machine (LSVM) model is proposed for the accurate classification of cervical cells. Strong features extracted by hybrid methods are fused with the abstract ones from hidden layers of LeNet-5, then the fused features are processed with dimension reduction and fed into the LSVM classifier optimized by Adaboost for classification. Proposed model is evaluated using the augmented Herlev and private dataset with the metrics including accuracy (Acc), sensitivity (Sn), and specificity (Sp), which outperformed the baselines and state-of-the-art approaches with the Acc of 99.5% and 94.2% in 2&7-class classification, respectively. [ABSTRACT FROM AUTHOR]

In recent years, the lack of thermal images and the difficulty of thermal feature extraction have led to low accuracy and efficiency in the fault diagnosis of circuit boards using thermal images. To address the problem, this paper presents a simple and efficient intelligent fault diagnosis method combined with computer vision, namely the bag-of-SURF-features support vector machine (BOSF-SVM). Firstly, an improved BOF feature extraction based on SURF is proposed. The preliminary fault features of the abnormally hot components are extracted by the speeded-up robust features algorithm (SURF). In order to extract the ultimate fault features, the preliminary fault features are clustered into K clusters by K-means and substituted into the bag-of-features model (BOF) to generate a bag-of-SURF-feature vector (BOSF) for each image. Then, all of the BOSF vectors are fed into SVM to train the fault classification model. Finally, extensive experiments are conducted on two homemade thermal image datasets of circuit board faults. Experimental results show that the proposed method is effective in extracting the thermal fault features of components and reducing misdiagnosis and underdiagnosis. Also, it is economical and fast, facilitating savings in labour costs and computing resources in industrial production. [ABSTRACT FROM AUTHOR]

In reality, the data generated in many fields are often imbalanced, such as fraud detection, network intrusion detection and disease diagnosis. The class with fewer instances in the data is called the minority class, and the minority class in some applications contains the significant information. So far, many classification methods and strategies for binary imbalanced data have been proposed, but there are still many problems and challenges in multi-class imbalanced data that need to be solved urgently. The classification methods for multi-class imbalanced data are analyzed and summarized in terms of data preprocessing methods and algorithm-level classification methods, and the performance of the algorithms using the same dataset is compared separately. In the data preprocessing methods, the methods of oversampling, under-sampling, hybrid sampling and feature selection are mainly introduced. Algorithm-level classification methods are comprehensively introduced in four aspects: ensemble learning, neural network, support vector machine and multi-class decomposition technique. At the same time, all data preprocessing methods and algorithm-level classification methods are analyzed in detail in terms of the techniques used, comparison algorithms, pros and cons, respectively. Moreover, the evaluation metrics commonly used for multi-class imbalanced data classification methods are described comprehensively. Finally, the future directions of multi-class imbalanced data classification are given. [ABSTRACT FROM AUTHOR]

The diagnosis cycle of schizophrenia is long, there is no objective diagnostic basis. The over-energy entropy product of the speech fluency rectangular parameter is designed in the paper, the fuzzy clustering is used to double locate speech pause areas and to assist in the diagnosis of schizophrenia. The pause area of speech is located based on the low speech fluency and flat energy in schizophrenia patients, an extraction algorithm is given for speech fluency quantification parameters, support vector machine (SVM) classifier is used in the approach. The fluency acoustic features of speech are taken from 28 schizophrenia patients and 28 normal controls, these are used to verify the effect of the method in schizophrenia recognition, there is a correct rate of over 85%. The automatic schizophrenia identification based on energy entropy product and fuzzy clustering can provide objective, effective and non-invasive auxiliary for clinical diagnosis of schizophrenia. [ABSTRACT FROM AUTHOR]

Protein Remote Homology and fold Recognition (PRHR) is the most crucial task to predict the protein patterns. To achieve this task, Sequence-Order Frequency Matrix-Sampling and Deep learning with Smith-Waterman (SOFM-SDSW) were designed using large-scale Protein Sequences (PSs), which take more time to determine the high-dimensional attributes. Also, it was ineffective since the SW was only applied for local alignment, which cannot find the most matches between the PSs. Hence, in this manuscript, a rapid semi-global alignment algorithm called SOFM-SD-GlobalSW (SOFM-SDGSW) is proposed that facilitates the affine-gap scoring and uses sequence similarity to align the PSs. The major aim of this paper is to enhance the alignment of SW algorithm in both locally and globally for PRHR. In this algorithm, the Maximal Exact Matches (MEMs) are initially obtained by the bit-level parallelism rather than to align the individual characters. After that, a subgroup of MEMs is obtained to determine the global Alignment Score (AS) using the new adaptive programming scheme. Also, the SW local alignment scheme is used to determine the local AS. Then, both local and global ASs are combined to produce a final AS. Further, this resultant AS is considered to train the Support Vector Machine (SVM) classifier to recognize the PRH and folds. Finally, the test results reveal the SOFM-SDGSW algorithm on SCOP 1.53, SCOP 1.67 and Superfamily databases attains an ROC of 0.97, 0.941 and 0.938, respectively, as well as, an ROC50 of 0.819, 0.846 and 0.86, respectively compared to the conventional PRHR algorithms. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a natural language processing (NLP) technique for determining emotional tone in a body of text. Using product reviews in sentiment analysis and opinion mining various methods have been developed previously. Although, existing product review analyzing techniques could not accurately detect the product aspect and non-aspect. Hence a novel Detach Frequency Assort is proposed to detect the product aspect term using TF-ISF (Term frequency-inverse sentence frequency) with Part of Speech (POS) tags for sentence segmentation and additionally using Feedback Neural Network to combine product aspect feedback loop. Furthermore, decision-making problem occurs during classification of sentiments. Hence, to solve this problem a novel technique named, Systemize Polarity Shift is proposed in which flow search based Support Vector Machine (SVM) with Bag of Words model classifies pre-trained review comments as positive, negative, and neutral sentiments. Moreover, the identification of specific products is not focused in sentiment analysis. Hence, a novel Revival Extraction is proposed in which a specific product is extracted based on thematic analysis method to obtain accurate data. Thus, the proposed Product Review Opinion framework gives effective optimized results in sentiment analysis with high accuracy, specificity, recall, sensitivity, F1-Score, and precision. [ABSTRACT FROM AUTHOR]

Twin proximal support vector regression is a new regression machine designed by using twin support vector machine and proximal support vector regression. In this paper, we use the above models framework to build a new regression model, called the twin proximal least squares support vector regression model based on heteroscedastic Gaussian noise (TPLSSVR-HGN). The least square method is introduced and the regularization terms b 1 2 and b 2 2 are added respectively. It transforms an inequality constraint problem into two simpler equality constraint problems, which not only improves the training speed and generalization ability, but also effectively improves the forecasting accuracy. In order to solve the parameter selection problem of model TPLSSVR-HGN, the particle swarm optimization algorithm with fast convergence speed and good robustness is selected to optimize its parameters. In order to verify the forecasting performance of TPLSSVR-HGN, it is compared with the classical regression models on the artificial data set, UCI data set and wind-speed data set. The experimental results show that TPLSSVR-HGN has better forecasting effect than the classical regression models. [ABSTRACT FROM AUTHOR]

Accident severity prediction is a hot topic of research aimed at ensuring road safety as well as taking precautionary measures for anticipated future road crashes. In the past decades, both classical statistical methods and machine learning algorithms have been used to predict traffic crash severity. However, most of these models suffer from several drawbacks including low accuracy, and lack of interpretability for people. To address these issues, this paper proposed a hybrid of Balanced Bagging Classification (BBC) and Light Gradient Boosting Machine (LGBM) to improve the accuracy of crash severity prediction and eliminate the issues of bias and variance. To the best of the author's knowledge, this is one of the pioneer studies which explores the application of BBC-LGBM to predict traffic crash severity. On the accident dataset of Great Britain (UK) from 2013 to 2019, the proposed model has demonstrated better performance when compared with other models such as Gaussian Naïve Bayes (GNB), Support vector machines (SVM), and Random Forest (RF). More specifically, the proposed model managed to achieve better performance among all metrics for the testing dataset (accuracy = 77.7%, precision = 75%, recall = 73%, F1-Score = 68%). Moreover, permutation importance is used to interpret the results and analyze the importance of each factor influencing crash severity. The accuracy-enhanced model is significant to several stakeholders including drivers for early alarm and government departments, insurance companies, and even hospitals for the services concerned about human lives and property damage in road crashes. [ABSTRACT FROM AUTHOR]

Among malignant tumors, lung cancer has the highest morbidity and fatality rates worldwide. Screening for lung cancer has been investigated for decades in order to reduce mortality rates of lung cancer patients, and treatment options have improved dramatically in recent years. Pathologists utilize various techniques to determine the stage, type, and subtype of lung cancers, but one of the most common is a visual assessment of histopathology slides. The most common subtypes of lung cancer are adenocarcinoma and squamous cell carcinoma, lung benign, and distinguishing between them requires visual inspection by a skilled pathologist. The purpose of this article was to develop a hybrid network for the categorization of lung histopathology images, and it did so by combining AlexNet, wavelet, and support vector machines. In this study, we feed the integrated discrete wavelet transform (DWT) coefficients and AlexNet deep features into linear support vector machines (SVMs) for lung nodule sample classification. The LC25000 Lung and colon histopathology image dataset, which contains 5,000 digital histopathology images in three categories of benign (normal cells), adenocarcinoma, and squamous carcinoma cells (both are cancerous cells) is used in this study to train and test SVM classifiers. The study results of using a 10-fold cross-validation method achieve an accuracy of 99.3% and an area under the curve (AUC) of 0.99 in classifying these digital histopathology images of lung nodule samples. [ABSTRACT FROM AUTHOR]

A modification to the gear tooth surface is crucial for reducing whistling noise generated by gearboxes of electric vehicles. Using the orthogonal test method, this paper studies geometry parameters of gear pair and the effect of load conditions on modification parameters of tooth surface. To achieve this, the paper develops various test models of gear pairs based on MASTA and then corrects the gear profiles according to load distribution state. The influential factors of gear transmission to modification parameters of tooth surface are thereby intuitively analyzed. Finally, a prediction model of the result of gear modification is built, based on test parameters, by method of support regression vector machine, providing a valuable reference to determine the tooth-surface modification parameters of the transmission gear of electric vehicles. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cervical histopathology image classification is a crucial indicator in cervical biopsy results. OBJECTIVE: The objective of this study is to identify histopathology images of cervical cancer at an early stage by extracting texture and morphological features for the Support Vector Machine (SVM) classifier. METHODS: We extract three different texture features and one morphological feature of cervical histopathology images: first-order histogram, K-means clustering, Gray Level Co-occurrence Matrix (GLCM) and nucleus feature. The original dataset used in our experiment is obtained from 20 patients diagnosed with cervical cancer, including 135 whole slide images (WSIs). Given an entire WSI, the patches on its tissue region are extracted randomly. RESULTS: We finally obtain 3,000 patches, including 1,000 normal, 1,000 hysteromyoma and 1,000 cancer images. Among them, 80% of the entire data set is randomly selected as training set and the remaining 20% as test set. The accuracy of SVM classification using first-order histogram, K-means clustering, GLAM and nucleus feature for extracting features are respectively 87.4%, 90.6%, 91.6% and 93.5%. CONCLUSIONS: The classification accuracy of the SVM combining the four features is 96.8%, and the proposed nucleus feature plays a key role in the SVM classification of cervical histopathology images. [ABSTRACT FROM AUTHOR]

A capability of cloud-based IDS in identifying complicated and anonymous attacks is rising in the current era. However, unwanted delays hinder the detection rate. A malicious user might utilize vast quantities of computational power. The cloud provides to perform attacks both within and without the cloud. Furthermore, there are major challenges for intrusion detection due to the ease of the cloud and also the continual restructuring and movement of cloud resources. Intruder detection, feature extraction, and data processing are all included in the novel optimization-based Intrusion Detection System (IDS) paradigm that will be presented in this study. Data normalization is used to first pre-process the input data. Then, appropriate feature extraction is carried out, including the extraction of (a) raw features, (b) statistical features, then (c) higher-order statistical features using suggested kurtosis. The detection phase is then applied to the retrieved features. A two-stage ensemble method is suggested for finding intruders in clouds. Random forest (RF), Support Vector Machine (SVM), optimal Neural Network (NN), and RNN make up the suggested ensemble technique. The RF, SVM, and Optimized NN algorithms are directly fed the collected features. The output of these classifiers is then provided to the RNN classifier (i.e.), RF output to RNN1, SVM output to RNN2, and optimized NN output to RNN3. Then, the weighted average of RNN 1, 2, and 3 is considered as the final output. A Self Adaptive Salp Swarm Optimization optimizes the weights of NN for exact detection (SA-SSO). Finally, a test is conducted to confirm the developed model's superiority. [ABSTRACT FROM AUTHOR]

Background: The major mechanisms of dementia and cognitive impairment are vascular and neurodegenerative processes. Early diagnosis of cognitive impairment can facilitate timely interventions to mitigate progression.Objective: This study aims to develop a reliable machine learning (ML) model using socio-demographics, vascular risk factors, and structural neuroimaging markers for early diagnosis of cognitive impairment in a multi-ethnic Asian population.Methods: The study consisted of 911 participants from the Epidemiology of Dementia in Singapore study (aged 60- 88 years, 49.6% male). Three ML classifiers, logistic regression, support vector machine, and gradient boosting machine, were developed. Prediction results of independent classifiers were combined in a final ensemble model. Model performances were evaluated on test data using F1 score and area under the receiver operating curve (AUC) methods. Post modelling, SHapely Additive exPlanation (SHAP) was applied on the prediction results to identify the predictors that contribute most to the cognitive impairment prediction.Findings: The final ensemble model achieved a F1 score and AUC of 0.87 and 0.80 respectively. Accuracy (0.83), sensitivity (0.86), specificity (0.74) and predictive values (positive 0.88 negative 0.72) of the ensemble model were higher compared to the independent classifiers. Age, ethnicity, highest education attainment and neuroimaging markers were identified as important predictors of cognitive impairment.Conclusion: This study demonstrates the feasibility of using ML tools to integrate multiple domains of data for reliable diagnosis of early cognitive impairment. The ML model uses easy-to-obtain variables and is scalable for screening individuals with a high risk of developing dementia in a population-based setting. [ABSTRACT FROM AUTHOR]

Big Data is a popular research area where a vast amount of data is created, replicated, and consumed by society. The quality of the data used directly influences big data knowledge discovery. The existence of noise is the most prevalent problem influencing data quality. The following techniques were developed to reduce noise in data with a distributed setting: Homogenous Ensemble for Big Data (HME-BD) and Heterogeneous Ensemble for Big Data (HTE-BD). In this article, the performance of HTE-BD is improved further by developing Enhanced HTE-BD (EHTE-BD), which combines Logistic Regression based Support Vector Machine (LR-SVM) in conjunction with RF, LR, and KNN to reduce noisy data. Furthermore, the Multi-Objective Evolutionary Fuzzy Method for Subgroup Discovery throughout Big Data (MEFASDBD) was used to resolve the multi-objective optimization challenge, and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was utilized to handle the rising dimensionality issue through subgroup discovery. To address the NSGA-II's slow convergence rate, an Improved Multi-Objective Meta-Heuristic Fuzzy approach for discovering subgroups in big data is described, that contains a meta-heuristic method for subgroup discovery known as the Multi-Objective Differential Search Algorithm (MODSA). It selects the most relevant subgroups from vast amounts of data, reducing the data's dimensionality. The Fuzzy Deep Neural Network (FDNN) classifier assesses the main subgroups. By removing noisy data and selecting the most relevant subgroups, the performance of FDNN in classifying vast amounts of data is improved. [ABSTRACT FROM AUTHOR]

Quantitative investment can manage enterprise assets better to obtain higher revenues. This paper analyzed quantitative investment prediction using machine learning algorithms. First, the support vector machine (SVM) algorithm was introduced, and stock changes were predicted by the SVM algorithm. Then, the feature factors in stock data were extracted by maximum information coefficient (MIC) as the input of the SVM algorithm. Finally, the performance and backtest results of the SVM algorithm was analyzed. It was found that the SVM algorithm had a good performance, and its F1-score was 0.9884, which was better than C4.5 and random forest algorithms. In terms of backtesting, the portfolio built based on the prediction results of the SVM algorithm obtained a higher annualized return rate when the number of stocks was small; when the number of stocks was 10, the portfolio built based on the SVM algorithm had an annualized return rate of 83.67%, a smaller maximum retracement, and a higher Sharpe ratio than the other algorithms, which balanced the risk and return well. The results demonstrate the reliability of the SVM algorithm in predicting quantitative investment, which is beneficial to achieving the optimization of enterprise asset management. [ABSTRACT FROM AUTHOR]

PURPOSE: To investigate the value of a CT-based radiomics model in identification of Crohn's disease (CD) active phase and remission phase. METHODS: CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models. RESULTS: Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively. CONCLUSION: This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance. [ABSTRACT FROM AUTHOR]

Pancreatic tumor is the deadliest disease which needs earlier identification to reduce the mortality rate. With this motivation, this study introduces a Multi-Objective Metaheuristics with Intelligent Deep Learning Model for Pancreatic Tumor Diagnosis (MOM-IDL) model. The proposed MOM-IDL technique encompasses an adaptive Weiner filter based pre-processing technique to enhance the image quality and get rid of the noise. In addition, multi-level thresholding based segmentation using Kapur's entropy is employed where the threshold values are optimally chosen by the barnacles mating optimizer (BMO). Besides, densely connected network (DenseNet-169) is employed as a feature extractor and fuzzy support vector machine (FSVM) is utilized as a classifier. For improving the classification performance, the BMO technique was implemented for fine-tuning the parameters of the FSVM model. The design of MOBMO algorithm for threshold selection and parameter optimization processes shows the novelty of the work. A wide range of simulations take place on the benchmark dataset and the experimental results highlighted the enhanced performance of the MOM-IDL technique over the recent state of art techniques. [ABSTRACT FROM AUTHOR]

Software defect prediction is used to assist developers in finding potential defects and allocating their testing efforts as the scale of software grows. Traditional software defect prediction methods primarily concentrate on creating static code metrics that are fed into machine learning classifiers to predict defects in the code. To achieve the desired classifier performance, appropriate design decisions are required for deep neural network (DNN) and convolutional neural network (CNN) models. This is especially important when predicting software module fault proneness. When correctly identified, this could help to reduce testing costs by concentrating efforts on the modules that have been identified as fault prone. This paper proposes a CONVSDP and DNNSDP (cognitive and neural network) approach for predicting software defects. Python Programming Language with Keras and TensorFlow was used as the framework. From three NASA system datasets (CM1, KC3, and PC1) selected from PROMISE repository, a comparative analysis with machine learning algorithms (such as Random Forest (RF), Decision Trees (DT), Nave Bayes (NF), and Support Vector Machine (SVM) in terms of F-Measure (known as F1-score), Recall, Precision, Accuracy, Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) has been presented. We extract four dataset attributes from the original datasets and use them to estimate the development effort, development time, and number of errors. The number of operands, operators, branch count, and executable LOCs are among these attributes. Furthermore, a new parameter called cognitive weight (Wc) of Basic Control Structure (BCS) is proposed to make the proposed cognitive technique more effective, and a cognitive data set of 8 features for NASA system datasets (CM1, KC3, and PC1) selected from the PROMISE repository to predict software defects is created. The experimental results showed that the CONVSDP and DNNSDP models was comparable to existing classifiers in both original datasets and cognitive data sets, and that it outperformed them in most of the experiments. [ABSTRACT FROM AUTHOR]

This study consists of modeling studies for thermal separation of a Ranque Hilsch Vortex Tube (RHVT) by using four different machine learning methods. Compressed air used RHVT, the data obtained as a result of experiments with different nozzles were modeled with linear, k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM) regression methods to compare each other. Nozzle properties and inlet pressure were used as input parameters, and the total temperature gradient ΔT was examined as the output. Experiment results were handled in two groups as training and test groups at different percentages. ΔT calculated by modeling hot and cold output test data, and ΔT calculated directly with the experiments were modeled and compared. According to the obtained results, the highest percentage of accuracy value of 97.58% was obtained with the SVM method, and this value was obtained with the set in which 90%–10% of the experimental results were used as the training and test data, respectively. The accuracy ratios calculated with RF, kNN, and linear regression models under the same conditions are 93.99, 88.49, and 78.97, respectively. [ABSTRACT FROM AUTHOR]

Background: We previously introduced a machine learning-based Alzheimer's Disease Designation (MAD) framework for identifying AD-related metabolic patterns among neurodegenerative subjects.Objective: We sought to assess the efficiency of our MAD framework for tracing the longitudinal brain metabolic changes in the prodromal stage of AD.Methods: MAD produces subject scores using five different machine-learning algorithms, which include a general linear model (GLM), two different approaches of scaled subprofile modeling, and two different approaches of a support vector machine. We used our pre-trained MAD framework, which was trained based on metabolic brain features of 94 patients with AD and 111 age-matched cognitively healthy (CH) individuals. The MAD framework was applied on longitudinal independent test sets including 54 CHs, 51 stable mild cognitive impairment (sMCI), and 39 prodromal AD (pAD) patients at the time of the clinical diagnosis of AD, and two years prior.Results: The GLM showed excellent performance with area under curve (AUC) of 0.96 in distinguishing sMCI from pAD patients at two years prior to the time of the clinical diagnosis of AD while other methods showed moderate performance (AUC: 0.7-0.8). Significant annual increment of MAD scores were identified using all five algorithms in pAD especially when it got closer to the time of diagnosis (p < 0.001), but not in sMCI. The increased MAD scores were also significantly associated with cognitive decline measured by Mini-Mental State Examination in pAD (q < 0.01).Conclusion: These results suggest that MAD may be a relevant tool for monitoring disease progression in the prodromal stage of AD. [ABSTRACT FROM AUTHOR]