Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.

Correlation analysis of human papillomavirus E6/E7 mRNA detection with diagnosis,prognosis and recurrence risk in patients with cervical epithelioma

BACKGROUND Cervical intraepithelial neoplasia(CIN)is an important precursor of cervical cancer.Early detection and treatment can reduce the incidence of cervical cancer.AIM To investigate the detection rate of human papillomavirus(HPV)E6/E7 mRNA in cervical tissue of patients with different types of epithelial cell neoplasia(CIN)and its relationship with CIN progression and diagnosis.METHODS One hundred women with HPV infection detected by cervical exfoliation cytology between January 2022 and January 2023 were retrospectively selected.These patients were graded CIN based on colposcopy and cervical pathology.The positive expression rates of HPV E6/E7 mRNA and HPV[polymerase chain reaction(PCR)-reverse dot crossing]were compared among all groups.Patients with HPV E6/E7 mRNA expression in the grade 1 CIN group were followed up for 1 yr.The relationship between atypical squamous epithelium and high malignant epithelial neoplasia was investigated by univariate and multivariate analysis.RESULTS The diagnostic sensitivity,specificity,and sensitivity of PCR-reverse point hybrid ization technology for secondary CIN were 70.41%,70.66%,and 0.714,respectively.Sensitivity and specificity for secondary CIN were 752%and 7853%,respectively,the area under the curve value was 0.789.Logistic Multifactorial model analysis revealed that the HPV positive rates and the HPV E6/E7 mRNA positive rates were independent risk factors of CIN grade I(P<0.05).In CIN grade I patients with positive for HPV E6/E7 mRNA,in its orientation to grade CIN patients,in its orientation to grade CIN patients,at 69.2%,compared with patients negative for HPV E6/E7 mRNA(30.8%),significant difference(P<0.05).CONCLUSION HPV E6/E7 mRNA and HPV(PCR-reverse dot hybrid)positive expression have a close relationship with CINgrade disease progression and is an independent risk factor for high-grade CIN lesions.

Correlation Analysis Between Changes of D-Dimer Level and Rheumatoid Arthritis Complicated with Interstitial Lung Disease

Objective:To explore the correlation between the change of D-dimer level and rheumatoid arthritis complicated with interstitial lung disease.Methods:From January 2022 to February 2024,20 rheumatoid arthritis patients complicated with interstitial lung disease(interstitial lung disease group),20 rheumatoid arthritis patients without interstitial lung disease(without interstitial lung disease group),and 20 healthy people(control group)in Xijing Hospital were selected for this study.The fasting venous blood of the three groups of subjects was collected and their D-dimer,C-reactive protein(CRP),rheumatoid factor(RF),and erythrocyte sedimentation rate(ESR)were detected.Subsequently,the correlation between each index and rheumatoid arthritis complicated with interstitial lung disease was analyzed.Results:The D-dimer level of the interstitial lung disease group was significantly higher than the other two groups(P<0.05).The D-dimer level of the group without interstitial lung disease was significantly higher than the control group(P<0.05).CRP levels in the interstitial lung disease group and the group without interstitial lung disease were significantly higher than those of the control group(P<0.05).The ESR and RF levels of the interstitial lung disease group were significantly higher than the other two groups(P<0.05).The levels of ESR and RF levels of the group without interstitial lung disease were significantly higher than the control group(P<0.05).Conclusion:D-dimer levels of rheumatoid arthritis patients are higher than those of healthy individuals,and those complicated with interstitial lung disease present even higher levels.This finding shows that there is a correlation between D-dimer levels and rheumatoid arthritis with interstitial lung disease,which may facilitate the evaluation and diagnosis of this disease.

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

Correlation and variability analysis of yield and quality related traits in different peanut varieties across various ecological zones of China

Peanut cultivation in China spans various ecological zones, each with unique environmental conditions. Identifying suitable peanut varieties for these regions has been challenging due to significant phenotypic variations observed across environments. This study, based on a comprehensive analysis of 256 peanut varieties, selected nine representative varieties(Huayu23, Yuanza9102, Silihong, Wanhua2, Zhonghua6, Zhonghua16, Zhonghua21,Zhonghua215, Zhonghua24) for cultivation in five distinct ecological zones including Chengdu, Hefei, Nanjing,Shijiazhuang, and Wuhan. The yield and quality related phenotypic traits of these varieties were thoroughly assessed, revealing a complex interplay between genetic and environmental factors. Principal component analysis(PCA) effectively distinguished varieties based on yield and quality traits. Strong correlations were observed between specific traits, such as seed size and quality components. The G × E interaction was evident, as some varieties consistently performed better in certain environments. Varieties with lower coefficient of variation(CV)values exhibited stable trait expression, making them reliable choices for broad cultivation. In contrast, varieties with higher CV values displayed greater sensitivity to environmental fluctuations, potentially due to specific genetic factors. Two high oleic acid varieties, Zhonghua24 and Zhonghua215, demonstrated remarkable stability in oleic acid content across diverse environments, suggesting the presence of genetic mechanisms that buffer against environmental variations. Overall, this study underscores the importance of selecting peanut varieties based on their adaptability and performance in specific ecological zones. These findings provide valuable insights for peanut breeders and farmers, facilitating informed decisions for improved crop production and quality.

Correlation Study on Grain Size Characteristics and Geotechnical Properties of Surface Sediments in Qingdao Offshore Area

The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study delineated the sedimentary environment zoning in the northern sea area of Qingdao through cluster analysis of grain size parameters derived from 123 surface sediment samples.The study analyzed the correlation between sediment geotechnical indices and grain size parameters across diverse sedimentary environments.A correlation equation was established for samples exhibiting a strong correlation.The study found four distinct sedimentary environments in the study area:coastal,transitional,shallow sea,and residual.Within the same sedimentary environment,the average grain size and sorting coefficient exhibit significant correlations with geotechnical indices such as water content,density,shear strength,plastic limit,liquid limit,and plastic index.However,notable disparities in the correlation between grain size parameters and geotechnical indices emerge across different sedimentary environments.

The Correlation between Free Triiodothyronine, Free Thyroxine and Hypertension in Depression Patients with Hypothyroidism and Its Clinical Guiding Value

Background: Cardiovascular diseases, such as hypertension and coronary heart disease, are often accompanied by thyroid and mental diseases, the harm of which poses great threats to patients’ health. Objective: To explore the correlation between free triiodothyronine (FT3), free thyroxine (FT4) and hypertension in depression patients with hypothyroidism and its clinical guiding value. Methods: A total of 548 patients diagnosed with hypothyroidism in Wuxue First People’s Hospital of Hubei Province from January 2018 to September 2022 were enrolled. According to whether complicated with depression, they were divided into hypothyroidism without depression group (group A) and hypothyroidism with depression group (group B). The gender, age, comorbidities (such as depression, hypertension, diabetes, dyslipidemia, acute myocardial infarction), FT3, FT4, and thyroid stimulating hormone (TSH) levels were recorded. Spearman rank correlation was used to analyze hypertensive patients with hypothyroidism. Multivariate binary Logistic regression was used to analyze the influencing factors of hypertension in patients with hypothyroidism. Results: The TSH level, the number of hypertension, coronary heart disease and hyperlipidemia in group B were statistically significantly higher than those in group A (P 3 level in group B was statistically significantly lower than that in group A (P s = 0.092), coronary heart disease (rs = 0.000), hyperlipidemia (rs = 0.000), diabetes (rs = 0.000), and age (rs = 0.000), and negatively correlated with FT3 (rs = 0.000) (P 3 and FT4 were the influencing factors of hypertension. The risk of hypertension in patients with coronary heart disease and hyperlipidemia significantly increased by 3.425 and 1.761 times (P 3, the risk of hypertension increased (P 4, the risk of hypertension significantly increased (P 3 and FT4 are the influencing factors of hypertension. The lower the FT3 level, the higher the FT4 level, the higher the risk of hypertension. FT3 and FT4 may be potential biomarkers of depression in hypertensive patients. Thyroid function assessment is recommended in patients with hypertension.

A CSMA/CA based MAC protocol for hybrid Power-line/Visible-light communication networks:Design and analysis

Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency.Current research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)devices.So designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network CC.To solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 standards.Firstly,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,respectively.Based on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC MAC.In the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into consideration.Moreover,we prove the proposed analytical model has the solvability.Finally,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.

Correlation analysis between fault frequency and service time of underground fluid instruments

In this paper, we make a statistical analysis of the fault information of the underground fluid instruments of 12 models in China from January 2021 to May 2022 based on the Pearson correlation coefficient, and compare the fault statistics of the meteorological three-element instruments of 3 models during the study period. The results show that:(1) The numbers of faults of the underground fluid instruments of 12models with different service times are basically positively correlated with the numbers of the corresponding instruments, with good consistency. Moreover, the automatic observation instruments(8models) with more than 30 units are significantly correlated at a 0.05 significance level(95% confidence level). Even at a 0.01 significance level(99% confidence level), there are 7 models(7/8) with significant correlation.(2) The positive and negative correlations between the monthly average number of faults and the corresponding service times of the underground fluid instruments of 12 models with different service times are random, and there are 9 models(75%) with no significant correlation at a 0.05 significance level(95% confidence level), while 12 models(100%) with no significant correlation at a 0.01significance level(99% confidence level).(3) The monthly average numbers of faults of the underground fluid instruments of 12 models are basically 0.02-0.05 times/(unit·month), and the overall fault frequency is low.(4) The fault statistics results of the meteorological three-element instruments of 3 models are consistent with the characteristics of the underground fluid instruments of 12 models. In general,there is no significant correlation between the fault frequency and the service time of underground fluid instruments.(5) The results of this paper demonstrate that the service time of underground fluid instruments cannot be taken as the main reason for whether to update the instruments. Similarly, the fault frequency of the instruments cannot be taken as the main reason for the service life of the instruments in the process of formulating the service life standards of underground fluid instruments.

Correlation analysis of mental health conditions and personality of patients with alcohol addiction

BACKGROUND Alcohol addiction,or alcohol dependence,refers to a psychological state of strong craving for alcohol caused by drinking when both the drinking times and alcohol consumption reach a certain level.Alcohol addiction can cause irreversible damage,leading to mental illness or mental disorders,negative changes in their original personality,and a tendency to safety incidents such as committing suicide or violent attacks on others.Significant attention needs to be given to the mental health of alcohol addicts,which could reflect their abnormal personality traits.However,only a few papers on this issue have been reported in China.AIM To investigate the correlation between mental health and personality in patients with alcohol addiction.METHODS In this single-center observational study,we selected 80 patients with alcohol addiction as the research subjects,according to the criteria of the K10 scale to evaluate the mental health of patients with alcohol addiction,and divided these patients into four groups based on the evaluation results:Good,average,relatively poor and bad.And then analyzed the correlation between mental health conditions and personality characteristics from these four groups of patients.RESULTS The average score of the K10 scale(Kessler 10 Simple Psychological Status Assessment Scale)in 80 patients with alcohol addiction was 25.45 points,the median score was 25 points,the highest score was 50 points,and the lowest score was 11 points.Pearson's analysis showed that the K10 score was positively correlated with the scores of these two subscales,such as the P-subscale and the N-subscale(P<0.05).In contrast,the K10 score had no significant correlation with the scores from the E-subscale and the L-subscale(P>0.05).CONCLUSION The mental health conditions of patients with alcohol addiction are positively correlated with their personality characteristics.

Correlation Analysis of Turbidity and Total Phosphorus in Water Quality Monitoring Data

At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.

Prediction of land subsidence caused by groundwater exploitation in Hanoi, Vietnam, using multifactorial correlation analysis

Multifactorial correlation analysis is a new method used to predict the land subsidence caused by groundwater exploitation This article introduces and applies the method to establish the function of the surface settlement rate （Vs） and the function of the time-dependent surface settlement （St） caused by groundwater exploitation, based on data acquired at three land subsidence monitoring stations in the Hanoi area of Vietnam. Comparison with actual monitoring data indicates that the prediction results are relatively close to the monitoring data. From this, we conclude that multifactorial correlation analysis is a reliable method and can be used to predict future land subsidence caused by groundwater exploitation in Hanoi.

Reliability Distribution of Numerical Control Lathe Based on Correlation Analysis

Combined Reliability distribution with correlation analysis,a new method has been proposed to make Reliability distribution where considering the elements about structure correlation and failure correlation of subsystems.Firstly,we make a sequence for subsystems by means of TOPSIS which comprehends the considerations of Reliability allocation,and introducing a Copula connecting function to set up a distribution model based on structure correlation,failure correlation and target correlation,and then acquiring reliability target area of all subsystems by Matlab.In this method,not only the traditional distribution considerations are concerned,but also correlation influences are involved,to achieve supplementing information and optimizing distribution.

Correlation analysis of the North Equatorial Current bifurcation and the Indonesian Throughflow

Based on monthly mean Simple Ocean Data Assimilation （SODA） products from 1958 to 2007, this study analyzes the seasonal and interannual variability of the North Equatorial Current （NEC） bifurcation latitude and the Indonesian Throughflow （ITF） volume transport. Further, Empirical Mode Decomposition （EMD） method and lag-correlation analysis are employed to reveal the relationships between the NEC bifurcation location, NEC and ITF volume transport and ENSO events. The analysis results of the seasonal variability show that the annual mean location of NEC bifurcation in upper layer occurs at 14.33°N and ITF volume transport has a maximum value in summer, a minimum value in winter and an annual mean transport of 7.75×10^6 m^3/s. The interannual variability analysis indicates that the variability of NEC bifurcation location can be treated as a precursor of El Nino. The correlation coefficient between the two reaches the maximum of 0.53 with a time lag of 2 months. The ITF volume transport is positively related with E1 Nifio events with a maximum coefficient of 0.60 by 3 months. The NEC bifurcation location is positively correlated with the ITF volume transport with a correlation coefficient of 0.43.

The Grey Correlation Analysis on the Relationship between the Rural Finance and the Rural Economical Growth in Sichuan Province

This paper analyzes the relationship between rural finance and the rural economy of Sichuan Province from 1999 to 2009 by using the sequential growth rate of the gross value of farming, forestry, animal husbandry and fishery and the sequential growth rate of the per capital total income of rural households as the indicators of rural economic development; and taking the volume of deposit, volume of credit, volume of agricultural credit and the credit volume of township enterprises as the indicators of rural financial development; as well as the method of grey correlation analysis. The results show that there is an obvious positive correlation between them, and the development of country finance has the closest connection with the sequential growth rate of farming, forestry, animal husbandry, sideline production and fishery. The loan scale of township enterprises has the biggest influence on the increase of the rural economy. The countermeasures are put forward, covering optimizing investment structure; supporting the development of township enterprises; encouraging loan; actively lightening the financial difficulties in the process of developing rural economy; innovating and exploring; and promoting the diversified development of rural finance.

Detection of Mechanical Deformation in Old Aged Power Transformer Using Cross Correlation Co-Efficient Analysis Method

Detection of minor faults in power transformer active part is essential because minor faults may develop and lead to major faults and finally irretrievable damages occur. Sweep Frequency Response Analysis (SFRA) is an effective low-voltage, off-line diagnostic tool used for finding out any possible winding displacement or mechanical deterioration inside the Transformer, due to large electromechanical forces occurring from the fault currents or due to Transformer transportation and relocation. In this method, the frequency response of a transformer is taken both at manufacturing industry and concern site. Then both the response is compared to predict the fault taken place in active part. But in old aged transformers, the primary reference response is unavailable. So Cross Correlation Co-Efficient (CCF) measurement technique can be a vital process for fault detection in these transformers. In this paper, theoretical background of SFRA technique has been elaborated and through several case studies, the effectiveness of CCF parameter for fault detection has been represented.

Study on soil water characteristics of tobacco fields based on canonical correlation analysis

In order to identify the principal factors influencing soil water characteristics （SWC） and evaluate SWC effectively, the multivariate-statistical canonical correlation analysis （CCA） method was used to study and analyze the correlation between SWC and soil physical and chemical properties. Twenty-two soil samples were taken from 11 main tobacco-growing areas in Guizhou Province in China and the soil water characteristic curves （SWCC） and basic physical and chemical properties of the soil samples were determined. The results show that： （1） The soil bulk density, soil total porosity and soil capillary porosity have significant effects on SWC of tobacco fiels. Bulk density and total porosity are positively correlated with soil water retention characteristics （SWRC）, and soil capillary porosity is positively correlated with soil water supply characteristics （SWSC）. （2） Soil samples from different soil layers at the same soil sampling point show similarity or consistency in SWC. Inadequate soil water supply capability and imbalance between SWRC and SWSC are problems of tobacco soil. （3） The SWC of loamy clay are generally superior to those of silty clay loam.

Novel DDoS Feature Representation Model Combining Deep Belief Network and Canonical Correlation Analysis

Distributed denial of service(DDoS)attacks launch more and more frequently and are more destructive.Feature representation as an important part of DDoS defense technology directly affects the efficiency of defense.Most DDoS feature extraction methods cannot fully utilize the information of the original data,resulting in the extracted features losing useful features.In this paper,a DDoS feature representation method based on deep belief network(DBN)is proposed.We quantify the original data by the size of the network flows,the distribution of IP addresses and ports,and the diversity of packet sizes of different protocols and train the DBN in an unsupervised manner by these quantified values.Two feedforward neural networks(FFNN)are initialized by the trained deep belief network,and one of the feedforward neural networks continues to be trained in a supervised manner.The canonical correlation analysis(CCA)method is used to fuse the features extracted by two feedforward neural networks per layer.Experiments show that compared with other methods,the proposed method can extract better features.

Feature Fusion Multi-View Hashing Based on Random Kernel Canonical Correlation Analysis

Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system,making it more and more widely used in image retrieval.Multi-view data describes image information more comprehensively than traditional methods using a single-view.How to use hashing to combine multi-view data for image retrieval is still a challenge.In this paper,a multi-view fusion hashing method based on RKCCA(Random Kernel Canonical Correlation Analysis)is proposed.In order to describe image content more accurately,we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT(Bag-of-Words model+SIFT feature)feature.This algorithm uses RKCCA method to fuse multi-view features to construct association features and apply them to image retrieval.The algorithm generates binary hash code with minimal distortion error by designing quantization regularization terms.A large number of experiments on benchmark datasets show that this method is superior to other multi-view hashing methods.

LOCAL CORRELATION DISCRIMINANT ANALYSIS AND ITS SEMI-SUPERVISED EXTENSION

Considering limitations of Linear Discriminant Analysis (LDA) and Marginal Fisher Analysis (MFA), a novel discriminant analysis called Local Correlation Discriminant Analysis (LCDA) is proposed in this paper. The main idea behind LCDA is to use more robust similarity measure, correlation metric, to measure the local similarity between image data. This results in better classifi-cation performance. In addition, to further improve the discriminant power of LCDA, we extend LCDA to semi-supervised case, which can make use of both labeled and unlabeled data to perform dis-criminant analysis. Extensive experimental results on ORL and AR face databases demonstrate that the proposed LCDA and its semi-supervised version are superior to Principal Component Analysis (PCA), LDA, CEA, and MFA.