Classifying Imbalanced Data Sets by a Novel RE-Sample and Cost-Sensitive Stacked Generalization Method

Learning with imbalanced data sets is considered as one of the key topics in machine learning community. Stacking ensemble is an efficient algorithm for normal balance data sets. However, stacking ensemble was seldom applied in imbalance data. In this paper, we proposed a novel RE-sample and Cost-Sensitive Stacked Generalization (RECSG) method based on 2-layer learning models. The first step is Level 0 model generalization including data preprocessing and base model training. The second step is Level 1 model generalization involving cost-sensitive classifier and logistic regression algorithm. In the learning phase, preprocessing techniques can be embedded in imbalance data learning methods. In the cost-sensitive algorithm, cost matrix is combined with both data characters and algorithms. In the RECSG method, ensemble algorithm is combined with imbalance data techniques. According to the experiment results obtained with 17 public imbalanced data sets, as indicated by various evaluation metrics (AUC, GeoMean, and AGeoMean), the proposed method showed the better classification performances than other ensemble and single algorithms. The proposed method is especially more efficient when the performance of base classifier is low. All these demonstrated that the proposed method could be applied in the class imbalance problem

High-performance Architecture of Network Intrusion Prevention Systems

Software-based Network Intrusion Prevention Systems have difficulty in handling high speed links. Network processor (NP) is an emerging field of programmable processors that are optimized to implement network data. In this paper, a novel Network Intrusion Prevention scheme is designed based on a heterogeneous multi-core processing architecture where its NP devices complement genera purpose multi-core processors to improve the performance of packet processing. We use Netronome’s network processor to process network traffic at the data link (Ethernet), network (IP), and transport/control layers. A set of network-based anomaly Intrusion Detection sensors is used in processing network traffic. Experimental results show our enhancements can reduce the processing load of the Intrusion Detection sensors. The load balancing by the protocol is better then other previous work

A model of machine learning based on user preference of features

Abstract. A formal model of machine learning by considering user preference of attributes is proposed in this paper. The model seamlessly combines internal information and external information. This model can be extended to user preference of attribute sets. By using the user preference of attribute sets, user preferred reducts can be constructed.

Effect of positive emotion intervention during late pregnancy on improving colostrum secretion: a randomised control trial protocol

Introduction Though evidence has revealed the beneficial effects of cognitive improvement interventions on breastfeeding, the effect of psychological interventions has rarely been studied. This study aims to test whether promoting a positive emotion intervention, ‘Three Good Things’ intervention, during the last trimester of pregnancy can enhance early colostrum secretion and breastfeeding behaviours by modulating the hormones associated with lactation (prolactin and insulin-like growth factor I). We will attempt to promote exclusive breastfeeding by using physiological behavioural measures.Methods and analysis This study is designed as a randomised controlled trial conducted in the Women’s Hospital School of Medicine at Zhejiang University and the Wuyi First People’s Hospital. The participants will be randomly divided into two groups using stratified random grouping: the intervention group will receive ‘Three Good Things’ intervention, while the control group will write about three things that come to mind first. These interventions will be continued from enrolment until the day of delivery. Maternal blood hormone levels will be tested approaching delivery and the following day after birth. Behavioural information about breastfeeding will be collected 1 week afterwards.Ethics and dissemination The study has been approved by the Ethics Committees of the Women’s Hospital School of Medicine at Zhejiang University and the Wuyi First People’s Hospital. Results will be disseminated through peer-reviewed journals or international academic conferences.Trial registration number ChiCTR2000038849

Prevalence and genotype distribution of human papillomavirus infection among women aged 30–65 years in Xi’an, China: a population-based study of 14,655 women

Cervical cancer is the fourth most common cancer among women worldwide in terms of both incidence and mortality. Persistent infection with high-risk human papillomavirus (HPV) has been identified as a cause of cervical intraepithelial neoplasia and invasive cervical cancer. The distribution of human papillomavirus genotypes varies regionally. To acquire baseline data on the population-based prevalence and genotype distribution of HPV infection, we investigated the molecular epidemiology of HPV infection among women in Xi’an, China. The study was conducted from September 2018 to December 2020. A total of 14,655 women aged 30–65 years were screened. The overall prevalence of HPV infection was 13.5% (95% confidence interval [CI]: 13.0–14.1%), with 10.4% of participants being positive for a single HPV type and 3.1% being positive for multiple HPV types. The prevalence of high-risk HPV (HR-HPV), low-risk HPV (LR-HPV) and mixed HPV infection was 10.1% (95% CI: 9.6–10.5%), 2.2% (95% CI: 2.0–2.4%), and 1.3% (95% CI: 1.1–1.5%), respectively. The five most frequently detected HR-HPV types were types 52 (2.6%), 16 (1.9%), 53 (1.8%), 58 (1.4%), and 51 (0.9%). The most frequently detected LR-HPV type was HPV-42 (1.1%). The prevalence and HPV genotype distribution varied by region and age. Age-specific HPV prevalence peaked in the over 60 years age group (18.8%), and Beilin District had the highest HPV prevalence (18.1%). The results of this first population-based study provide a reference for HPV-based cervical cancer screening and HPV vaccination programs in Xi’an

A budget analysis of the formation of haze in Beijing

During recent winters, hazes often occurred in Beijing, causing major environmental problems. To understand the causes of this &ldquo;Beijing Haze&rdquo;, a haze episode (from Oct. 21 to Oct. 31, 2013) in Beijing was analyzed. During the episode, the daily mean concentration of fine particulate matter (PM2.5) reached a peak value of 270&nbsp;&mu;g/m3 on Oct. 28, 2013, and rapidly decreased to 50&nbsp;&mu;g/m3 the next day (Oct. 29, 2013). This strong variability provided a good opportunity to study the causes of a &ldquo;Beijing Haze&rdquo;. Two numerical models were applied for this study. The first model is a chemical/dynamical regional model (WRF-Chem). This model is mainly used to study the effects that weather conditions have on PM2.5 concentrations in the Beijing region. The results show that the presence of high air pressure in northwest Beijing (NW-High) generally produced strong northwest winds with clean upwind air. As a result, the NW-High played an important role in cleaning Beijing&#39;s PM. However, the NW-High&#39;s cleaning effect did not occur in every situation. When there was low air pressure in southeast Beijing (SE-Low) accompanied by an NW-High, an air convergent zone appeared in Beijing. The pollutants became sandwiched, producing high PM2.5 concentrations in the Beijing region. The second model used in this study is a box model, which is applied to estimate some crucial parameters associated with the budget of PM2.5 in the Beijing region. Under calm winds, the calculations show that continuous local emissions rapidly accumulate pollutants. The PM2.5 concentrations reached 150&nbsp;&mu;g/m3 and 250&nbsp;&mu;g/m3 within one (1) day and two (2) days, respectively. Without horizontal dilution, this estimate can be considered as an upper time limit (the fastest time) for the occurrences of haze events in the Beijing region. The wind speed (WSb) is calculated for the balance between the continuous emissions and atmospheric clean processes. The results show that the WSb is strongly dependent on the planetary boundary layer (PBL) height and the wind direction. Under SE-Low weather conditions, the WSb is 2&nbsp;m/s with a higher PBL height (700&nbsp;m). However, under lower PBL heights, the WSb rapidly increases, reaching 4.5&nbsp;m/s and 7.0&nbsp;m/s with PBL heights of 300&nbsp;m and 200&nbsp;m, respectively. In contrast, under NW-High weather conditions, the WSb reduces to 2.5&nbsp;m/s and 4.0&nbsp;m/s. These results suggest that when the prevailing wind in Beijing is a northwest wind (with wind speeds of &gt;4&nbsp;m/s), particulate matter (PM) begins to decrease.</p