Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk

This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.

Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An Innovative Multilingual Approach for Social Media Reviews

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

Intelligent Diagnosis of Highway Bridge Technical Condition Based on Defect Information

In the bridge technical condition assessment standards,the evaluation of bridge conditions primarily relies on the defects identified through manual inspections,which are determined using the comprehensive hierarchical analysis method.However,the relationship between the defects and the technical condition of the bridges warrants further exploration.To address this situation,this paper proposes a machine learning-based intelligent diagnosis model for the technical condition of highway bridges.Firstly,collect the inspection records of highway bridges in a certain region of China,then standardize the severity of diverse defects in accordance with relevant specifications.Secondly,in order to enhance the independence between the defects,the key defect indicators were screened using Principal Component Analysis(PCA)in combination with the weights of the building blocks.Based on this,an enhanced Naive Bayesian Classification(NBC)algorithm is established for the intelligent diagnosis of technical conditions of highway bridges,juxtaposed with four other algorithms for comparison.Finally,key defect variables that affect changes in bridge grades are discussed.The results showed that the technical condition level of the superstructure had the highest correlation with cracks;the PCA-NBC algorithm achieved an accuracy of 93.50%of the predicted values,which was the highest improvement of 19.43%over other methods.The purpose of this paper is to provide inspectors with a convenient and predictive information-rich method to intelligently diagnose the technical condition of bridges based on bridge defects.The results of this research can help bridge inspectors and even non-specialists to better understand the condition of bridge defects.

Within-Project and Cross-Project Software Defect Prediction Based on Improved Transfer Naive Bayes Algorithm

With the continuous expansion of software scale,software update and maintenance have become more and more important.However,frequent software code updates will make the software more likely to introduce new defects.So how to predict the defects quickly and accurately on the software change has become an important problem for software developers.Current defect prediction methods often cannot reflect the feature information of the defect comprehensively,and the detection effect is not ideal enough.Therefore,we propose a novel defect prediction model named ITNB(Improved Transfer Naive Bayes)based on improved transfer Naive Bayesian algorithm in this paper,which mainly considers the following two aspects:(1)Considering that the edge data of the test set may affect the similarity calculation and final prediction result,we remove the edge data of the test set when calculating the data similarity between the training set and the test set;(2)Considering that each feature dimension has different effects on defect prediction,we construct the calculation formula of training data weight based on feature dimension weight and data gravity,and then calculate the prior probability and the conditional probability of training data from the weight information,so as to construct the weighted bayesian classifier for software defect prediction.To evaluate the performance of the ITNB model,we use six datasets from large open source projects,namely Bugzilla,Columba,Mozilla,JDT,Platform and PostgreSQL.We compare the ITNB model with the transfer Naive Bayesian(TNB)model.The experimental results show that our ITNB model can achieve better results than the TNB model in terms of accurary,precision and pd for within-project and cross-project defect prediction.

Accuracies and Training Times of Data Mining Classification Algorithms:An Empirical Comparative Study

Two important performance indicators for data mining algorithms are accuracy of classification/ prediction and time taken for training. These indicators are useful for selecting best algorithms for classification/prediction tasks in data mining. Empirical studies on these performance indicators in data mining are few. Therefore, this study was designed to determine how data mining classification algorithm perform with increase in input data sizes. Three data mining classification algorithms—Decision Tree, Multi-Layer Perceptron (MLP) Neural Network and Naïve Bayes— were subjected to varying simulated data sizes. The time taken by the algorithms for trainings and accuracies of their classifications were analyzed for the different data sizes. Results show that Naïve Bayes takes least time to train data but with least accuracy as compared to MLP and Decision Tree algorithms.

Best early-onset Parkinson dementia predictor using ensemble learning among Parkinson's symptoms,rapid eye movement sleep disorder,and neuropsychological profile

BACKGROUND Despite the frequent progression from Parkinson’s disease(PD)to Parkinson’s disease dementia(PDD),the basis to diagnose early-onset Parkinson dementia(EOPD)in the early stage is still insufficient.AIM To explore the prediction accuracy of sociodemographic factors,Parkinson's motor symptoms,Parkinson’s non-motor symptoms,and rapid eye movement sleep disorder for diagnosing EOPD using PD multicenter registry data.METHODS This study analyzed 342 Parkinson patients(66 EOPD patients and 276 PD patients with normal cognition),younger than 65 years.An EOPD prediction model was developed using a random forest algorithm and the accuracy of the developed model was compared with the naive Bayesian model and discriminant analysis.RESULTS The overall accuracy of the random forest was 89.5%,and was higher than that of discriminant analysis(78.3%)and that of the naive Bayesian model(85.8%).In the random forest model,the Korean Mini Mental State Examination(K-MMSE)score,Korean Montreal Cognitive Assessment(K-MoCA),sum of boxes in Clinical Dementia Rating(CDR),global score of CDR,motor score of Untitled Parkinson’s Disease Rating(UPDRS),and Korean Instrumental Activities of Daily Living(KIADL)score were confirmed as the major variables with high weight for EOPD prediction.Among them,the K-MMSE score was the most important factor in the final model.CONCLUSION It was found that Parkinson-related motor symptoms(e.g.,motor score of UPDRS)and instrumental daily performance(e.g.,K-IADL score)in addition to cognitive screening indicators(e.g.,K-MMSE score and K-MoCA score)were predictors with high accuracy in EOPD prediction.

Analysis and Prediction of New Media Information Dissemination of Police Microblog

This paper aims to analyze the microblog data published by the official account in a certain province of China,and finds out the rule of Weibo that is easier to be forwarded in the new police media perspective.In this paper,a new topic-based model is proposed.Firstly,the LDA topic clustering algorithm is used to extract the topic categories with forwarding heat from the microblogs with high forwarding numbers,then the Naive Bayesian algorithm is used to topic categories.The sample data is processed to predict the type of microblog forwarding.In order to evaluate this method,a large number of microblog online data is used to analysis.The experimental results show that the proposed method can accurately predict the forwarding of Weibo.