A Classification Prediction Analysis of Flight Cancellation Based on Spark

This chapter is devoted to the study of 16 types of greedy algorithms for decision tree construction. The dynamic programming approach is used for construction of optimal decision trees. Optimization is performed relative to minimal values of average depth, depth, number of nodes, number of terminal nodes, and number of nonterminal nodes of decision trees. We compare average depth, depth, number of nodes, number of terminal nodes and number of nonterminal nodes of constructed trees with minimum values of the considered parameters obtained based on a dynamic programming approach. We report experiments performed on data sets from UCI ML Repository and randomly generated binary decision tables. As a result, for depth, average depth, and number of nodes we propose a number of good heuristics.

It is of paramount importance to establish an identity of citizenry to curb criminalities. Principal Component Analysis (PCA) which is one of the foremost methods for feature extraction and feature selection is adopted for identification and authentication of people. The computational time used by PCA is too much and Chinese Remainder Theorem was employed to reduce its computational time. TOAM database was setup which contained 120 facial images of 40 persons frontal faces with 3 images of each individual. 80 images were used for training while 40 were used for testing. Training time and testing time were used as performance metrics to determine the effect of CRT on PCA in terms of computational time. The experimenal results indicated an average training time of 13.5128 seconds and average testing time of 1.5475 second for PCA while PCA-CRT average training time is 13.2387 seconds and average testing time of 1.5185 seconds. Column chart was used to show the graphical relationship between PCA and PCA-CRT Training time and testing time. The research revealed that CRT reduce PCA computational time.

Introduction: Up to 60% of patients with one or more positive sentinel lymph nodes (SLNs) have no additional nodal involvement and do not benefit from completion axillary lymph node dissection (ALND). Several predictive nomograms have been developed to predict non-SLN status in patients with breast cancer and SLN metastases. The purpose of our retrospective investigation was to compare some of these nomograms and we chose 3, all of which incorporate the size of the metastases in the SLNs among their variables. Methods: Three different predictive nomograms (MD Anderson, Helsinki and Cambridge) were tested on a cohort of 80 SLN-positive patients who subsequently underwent completion ALND. The nomograms utilise 3 (Cambridge), 7 (MD Anderson) and 9 (Helsinki) variables. The predictive accuracy of the nomograms for our cohort was evaluated by assessing receiver operating characteristic (ROC) curves and areas under curves (AUC) for each nomogram. Descriptive characteristics of the patient populationTotal number of patients80 Tumor size (T)T1: 36 (45%)T2: 40 (50%)T3: 4 (5%)Tumor typeDuctal: 58 (72.5%)Lobular:17 (21.25%)Mixed:2 (2.5%) Other:3 (3.75%)Lympovascular invasionPresent: 49 (61.25%)Absent:31 (38.75%) Histological GradeG1: 9 (11.25%)G2: 54 (67.5%)G3: 17 (21.25%)Multifocality:Present:48 (60%)Absent:32 (40%) Average SLNs removed (range)3.7 (1-11) Average SLNs positive (range)1.8 (1-7) Average non-SLNs positive (range)3.5 (1-16) Max. size of metastasesAverage:5.2mmMicrometastases: 13 (16.25%)Macrometastases: 67 (83.75%)Her-2 statusPositive:10 (12.5%)Negative:70 (87.5%) Prevalence of non-sentinel node metastasis in patient series31 patients (39%) Extranodal extensionPresent:26 (32.5%)Absent:54 (67.5%) Results: In our cohort, the average number of SLNs removed was 3.7 (1-11) and the average number of positive SLNs was 1.8 (1-7). 13 of the 80 patients had micrometastases within the SLNs (16.2%). 31 of the 80 patients had at least one positive non-SLN (39%) and the average number of additional positive nodes found in the ALND was 3.5 (1-16). For our cohort, all of the nomograms had a similar predictive accuracy (MD Anderson AUC = 0.69, Helsinki AUC = 0.69, Cambridge AUC = 0.63). All nomograms fell short of an acceptable AUC value (at least 0.8). Variables included in the different predictive models testedVariablesMD AndersonHelsinkiCambridgeTumor size++ Tumor type+ Nuclear grade +Lymphovascular invasion++ Number of positive SLNs++ Number of negative SLNs + Number of SLNs+ Proportion of positive SLNs +Extranodal extension++ Max. size of SLN metastases+++Her-2 status + Prevalence of non-sentinel node metastasis in patient series + Multifocality + Conclusions: Our study found that none of the chosen nomograms could reliably predict the presence of positive non-SLNs in our cohort. One reason for this may be the fact they utilize different methods of pathological assessment of nodes and different surgical techniques to those used in our centre. In view of this it may be desirable to produce a more accurate nomogram for our population based on specific variables. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-01-16.

After radical prostatectomy (RP), one-third of patients will experience biochemical recurrence (BCR), which is associated with subsequent metastasis and cancer-specific mortality. We employed machine learning (ML) algorithms to predict BCR after RP, and compare them with traditional regression models and nomograms. Utilizing a prospective Uro-oncology registry, 18 clinicopathological parameters of 1130 consecutive patients who underwent RP (2009-2018) were recorded, yielding over 20,000 data points for analysis. The data set was split into a 70:30 ratio for training and validation. Three ML models: Naïve Bayes (NB), random forest (RF), and support vector machine (SVM) were studied, and compared with traditional regression models and nomograms (Kattan, CAPSURE, John Hopkins [JHH]) to predict BCR at 1, 3, and 5 years. Over a median follow-up of 70.0 months, 176 (15.6%) developed BCR, at a median time of 16.0 months (interquartile range [IQR]: 11.0-26.0). Multivariate analyses demonstrated strongest association of BCR with prostate-specific antigen (PSA) (p: 0.015), positive surgical margins (p < 0.001), extraprostatic extension (p: 0.002), seminal vesicle invasion (p: 0.004), and grade group (p < 0.001). The 3 ML models demonstrated good prediction of BCR at 1, 3, and 5 years, with the area under curves (AUC) of NB at 0.894, 0.876, and 0.894, RF at 0.846, 0.875, and 0.888, and SVM at 0.835, 0.850, and 0.855, respectively. All models demonstrated (1) robust accuracy (>0.82), (2) good calibration with minimal overfitting, (3) longitudinal consistency across the three time points, and (4) inter-model validity. The ML models were comparable to traditional regression analyses (AUC: 0.797, 0.848, and 0.862) and outperformed the three nomograms: Kattan (AUC: 0.815, 0.798, and 0.799), JHH (AUC: 0.820, 0.757, and 0.750) and CAPSURE nomograms (AUC: 0.706, 0.720, and 0.749) (p < 0.001). Supervised ML algorithms can deliver accurate performances and outperform nomograms in predicting BCR after RP. This may facilitate tailored care provisions by identifying high-risk patients who will benefit from multimodal therapy.

Stock Classification Prediction Based on Spark

Aiming at the problems of weak generalization ability and long training time in most fault diagnosis models based on deep learning, such as support vector machines and random forest algorithms, one intelligent diagnosis method of rolling bearing fault based on the improved convolution neural network and light gradient boosting machine is proposed. At first, the convolution layer is used to extract the features of the original signal. Second, the generalization ability of the model is improved by replacing the full connection layer with the global average pooling layer. Then, the extracted features are classified by a light gradient boosting machine. Finally, the verification experiment is carried out, and the experimental result shows that the average training and diagnosis time of the model is only 39.73 s and 0.09 s, respectively, and the average classification accuracy of the model is 99.72% and 95.62%, respectively, on the same and variable load test sets, which indicates that the diagnostic efficiency and classification accuracy of the proposed model are better than those of other comparison models.

Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh

Detection and classification of masses in mammographic images in a multi-kernel approach

Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images

The use of Machine Learning (ML) and Sentiment Analysis (SA) on data from microblogging sites has become a popular method for stock market prediction. In this work, we developed a model for predicting stock movement utilizing SA on Twitter and StockTwits data. Stock movement and sentiment data were used to evaluate this approach and validate it on Microsoft stock. We gathered tweets from Twitter and StockTwits, as well as financial data from Finance Yahoo. SA was applied to tweets, and seven ML classification models were implemented: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF) and Multilayer Perceptron (MLP). The main novelty of this work is that it integrates multiple SA and ML methods, emphasizing the retrieval of extra features from social media (i.e., public sentiment), for improving stock prediction accuracy. The best results were obtained when tweets were analyzed using Valence Aware Dictionary and sEntiment Reasoner (VADER) and SVM. The top F-score was 76.3%, while the top Area Under Curve (AUC) value was 67%.

Diversity based imbalance learning approach for software fault prediction using machine learning models

Naive Bayes (NB) classifier is a simple and efficient classifier, but the independent assumption of its attribute limits the application of the actual data. This paper presents an approach called particle swarm optimization-naive Bayes (PSO-NB) which takes advantage of combination particle swarm optimization with naive Bayes for attribute selection to improve naive Bayes classifier. This method applies PSO firstly to search out an optimal subset of attributes reduction in the original attribute space, and then constructs a naive Bayes classifier on the gotten subset of the attributes reduction. Nineteen experimental results on UCI datasets distinctly show that compared with Cfs-BestFirst algorithm, NB algorithm, Decision Tree(C4.5) algorithm, K-neighbor(KNN) algorithm, the proposed algorithm has higher classification accuracy.

Machine learning methods are widely used in automated technologies. Classification prediction is a machine learning based on data mining. Today, many technological devices can make new predictions by gaining experience from past data with machine learning methods. Machine learning is widely studied in two types, supervised and unsupervised. The limits of the objectives in supervised learning are predetermined. In unsupervised learning, there are no predetermined targets. In this learning, the machines are required to determine the targets automatically. Prediction process is one of the basic components of machine learning. Machines need to use some algorithms in order to perform the prediction process on the basis of data mining. k nearest neighbor (kNN), Naive Bayes (NB), Decision Tree (DT) and Support Vector Machine (SVM) algorithms are used mostly. k nearest neighbor (kNN), Naive Bayes (NB), Decision Tree (DT) and Support Vector Machine (SVM) algorithms are used mostly. These algorithms can be applied with the help of some tools on data sets. In this study, kNN algorithm was used to estimate Iris data set classification using Orange tool. The success of the KNN algorithm depends on using the correct attribute and changing the optimum k value. As a result of the tests, it was determined that when the k neighbor value was selected as 15, it was the most suitable k neighbor value, providing 98.67% classification prediction success in the Iris dataset.

The flow of traffic analysis can be helpful for to improving the traffic quality and protect it from the conditions to cause danger, risk, or injury. Our goal is about reducing the accidents risk which involves designing the roads and principles. The previous road designing patterns of highly accidental risk. Especially some of the circumstances of accidents may be related to the driver such as driver fault, health problems, or also vehicles such as brake failure, steering, or the road itself such as poor sidewalks, and lack of road equipment, and safety measures. In this paper, we talk about some of the best road designing techniques in the world. ‘Double crossover diamond interchange (DCD)’, is a true way to go more people reduce the vehicle striking violently against another. One more design is that a ‘roundabout’, is a circular common intersection where the vehicles can travel anticlockwise around that center part. In This Modern roundabout, there is no need for traffic signals or speed breakers. When the vehicles enter the roundabout zone, if traffic is there in the roundabout, then go to the circulating roadway area and go to their desired street. Find the exact models to predict the deaths and severe injuries caused by road accidents. This analysis needs to classify the various models of machine learning and data science algorithms for instance Linear Regression, Decision Tree, Random Forest (RF), and Naive Bayes Classification and Support Vector Machine (SVM). The decision tree algorithm has a root node and contain child nodes. But Random Forest is having multiple decision trees. Random Forest has the best performance with 79% accuracy then decision tree with 70% accuracy, SVM with 75%, Logic Regression with 72%, and Naive Bayes with 73% accuracy.

With the rapid development of China's economy, credit business has become one of the main business of financial institutions such as banks, and the evaluation of personal credit is one of the important components of loan business of financial institutions such as banks. However, how to accurately and efficiently predict the credit of borrowers has become an urgent problem for practitioners in the financial field. More and more attention has been paid to using machine learning algorithm to predict people's credit rating in the era of artificial intelligence. Using previous data, this paper uses five models of support vector machine, K-nearest neighbor, naive bayes, convolutional neural network and decision tree in machine learning algorithms to predict people's credit rating in four countries of China, Japan, Australia and Germany by modeling, using accuracy and F1score as evaluation criteria, the accuracy of five models for credit prediction is obtained, and the definition and structure of decision tree model, principle, and decision tree construction and algorithm are emphatically introduced. The results show that the accuracy of decision tree algorithm models for credit prediction is relatively high and the most stable.