Feature selection using correlation analysis and principal component analysis for accurate breast cancer diagnosis

Breast cancer is one of the leading causes of death among women, more so than all other cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the disease, changing treatment procedures and different patient population samples. Diagnostic techniques with better performance are very important for personalized care and treatment and to reduce and control the recurrence of cancer. The main objective of this research was to select feature selection techniques using correlation analysis and variance of input features before passing these significant features to a classification method. We used an ensemble method to improve the classification of breast cancer. The proposed approach was evaluated using the public WBCD dataset (Wisconsin Breast Cancer Dataset). Correlation analysis and principal component analysis were used for dimensionality reduction. Performance was evaluated for well-known machine learning classifiers, and the best seven classifiers were chosen for the next step. Hyper-parameter tuning was performed to improve the performances of the classifiers. The best performing classification algorithms were combined with two different voting techniques. Hard voting predicts the class that gets the majority vote, whereas soft voting predicts the class based on highest probability. The proposed approach performed better than state-of-the-art work, achieving an accuracy of 98.24%, high precision (99.29%) and a recall value of 95.89%

Thirty Years of Consanguineous Marriages in Pakistan

Almost half of all marriages in Pakistan are consanguineous. Despite its high prevalence, little is known about the change over time in consanguineous unions in Pakistan. Examining the patterns of the cousin marriages is particularly important given the substantial improvement in women’s education which is often associated with the decline in consanguineous unions across the world. Our analysis, based on four waves of nationally representative Pakistan Demographic and Health Surveys - PDHS (1990-91, 2006-07, 2012-13, and 2017-18), shows that the prevalence of consanguineous unions remains stable over time. Further, women’s education is negatively associated with cousin marriages. Hypergamous (husband is more educated than her wife) unions are more prevalent, but a consistent rise in educational hypogamy (wife is more educated than her husband) is observed during this time. The results show that consanguineous marriages are more likely to be hypogamous than non-consanguineous marriages. Moreover, contraceptive use is lower among women in consanguineous unions. An inverse relationship has been found between the mean fertility and cousin marriages. Women in consanguineous marriages are likely to have fewer children than women in non-consanguineous marriages. Overall, the results show that consanguinity patterns are stable, and there is no evidence that the societal changes such as improvement in women’s education and urbanization over time have led to a decline in cousin marriages in Pakistan

Inter and Intra Class Correlation Analysis (IIcCA) for Human Action Recognition in Realistic Scenarios

This paper has been presented at : 8th International Conference of Pattern Recognition Systems (ICPRS 2017)Human action recognition in realistic scenarios is an important yet challenging task. In this paper we propose a new method, Inter and Intra class correlation analysis (IICCA), to handle inter and intra class variations observed in realistic scenarios. Our contribution includes learning a class specific visual representation that efficiently represents a particular action class and has a high discriminative power with respect to other action classes. We use statistical measures to extract visual words that are highly intra correlated and less inter correlated. We evaluated and compared our approach with state-of-the-art work using a realistic benchmark human action recognition dataset.S.A. Velastin has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 600371, the Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) the Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander

Feature Similarity and Frequency-Based Weighted Visual Words Codebook Learning Scheme for Human Action Recognition

This paper has been presented at : 8th Pacific-Rim Symposium, PSIVT 2017.Human action recognition has become a popular field for computer vision researchers in the recent decade. This paper presents a human action recognition scheme based on a textual information concept inspired by document retrieval systems. Videos are represented using a commonly used local feature representation. In addition, we formulate a new weighted class specific dictionary learning scheme to reflect the importance of visual words for a particular action class. Weighted class specific dictionary learning enriches the scheme to learn a sparse representation for a particular action class. To evaluate our scheme on realistic and complex scenarios, we have tested it on UCF Sports and UCF11 benchmark datasets. This paper reports experimental results that outperform recent state-of-the-art methods for the UCF Sports and the UCF11 dataset i.e. 98.93% and 93.88% in terms of average accuracy respectively. To the best of our knowledge, this contribution is first to apply a weighted class specific dictionary learning method on realistic human action recognition datasets.Sergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement n 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. Authors also acknowledges support from the Directorate of ASR and TD, University of Engineering and Technology Taxila, Pakistan

Breast Tumor Classification Using an Ensemble Machine Learning Method

Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to detect possible breast cancer is very important. In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms were evaluated for the Wisconsin Breast Cancer Dataset (WBCD). The three best classifiers were then selected based on their F3 score. F3 score is used to emphasize the importance of false negatives (recall) in breast cancer classification. Then, these three classifiers, simple logistic regression learning, support vector machine learning with stochastic gradient descent optimization and multilayer perceptron network, are used for ensemble classification using a voting mechanism. We also evaluated the performance of hard and soft voting mechanism. For hard voting, majority-based voting mechanism was used and for soft voting we used average of probabilities, product of probabilities, maximum of probabilities and minimum of probabilities-based voting methods. The hard voting (majority-based voting) mechanism shows better performance with 99.42%, as compared to the state-of-the-art algorithm for WBCD

Knowledge, Attitude and Practice on Oral Hygiene Among Mothers in Rural Area of Lahore

BACKGROUND: Oral health is an important element of good general health and plays a key role in the child’s lifespan. Dental-caries is one of the best significant universal oral health-problems. In most under-developing states, the stages of dental caries were low but now have a tendency to increase (Kay, 1998) METHODOLOGY: The quantitative cross-sectional study design was used with convenient sampling (n=132). Data was analyzed by using the Statistical Package for the social science (SPSS) 21. The association between knowledge attitude and practice was drawn through Chi-square test with (p=<0.05)  RESULTS: 50.76% people answer that dental carries was due to the food residual on teeth, 29.55% reported that pigments on teeth, 19.55% don’t know. 38.64% people answer the Q2 true.  33.33% answer false. 28.28% don’t know.CONCLUSION: The study determined that over-all level of knowledge amongst women was acceptable but there are need to deliver the more responsiveness about oral-hygiene. The attitude shown positive and there were satisfactory practices of females about oral-hygiene. Majority have knowledge about plaque, cavity and other dental problem, but they did not hygienic their mouth due to deficiency of awareness about the significance of oral-hygiene. Keywords: Oral hygiene, Knowledge, Attitude, practices

Neonatal sepsis and resistance pattern of isolates in Tertiary level neonatal unit: Time to evaluate the empirical antibiotics selection

Objective: To find out the most common organisms involved in neonatal sepsis origination and observe the pattern of antibiotic sensitivity and resistance of bacterial isolates.Materials and Methods: This descriptive cross-sectional study was conducted at the Department of Paediatrics Izzat Ali Shah Hospital, Wah Cantt. Out of 420 patients admitted with sepsis in NICU, 19.5% of patients with positive blood cultures were included in the study. A consecutive, non-probability sampling technique was used.Results: Out of 82 positive blood cultures gram-positive bacteria were observed in 19 patients (23.2%) and gram-negative bacteria were seen in 63 patients (76.8%). The most common gram-negative pathogens isolated were Acinetobacter (29.3%) and Klebsiella (24.4%). Staphylococcus aureus (12.2%) was the commonest gram-positive organism isolated. Gram-negative organisms showed maximum sensitivity to Tigecycline and Colistin and were resistant to Cefixime, Aztreonam, Amoxicillin, and Ceftriaxone. Gram-positive bacteria were sensitive to Teicoplanin, Linezolid, and Vancomycin while resistance was shown to penicillin and amoxicillin.Conclusion: The current study showed that gram-negative bacteria were the major contributors to sepsis in the respective setup and showed resistance to first-line antibiotics such as Penicillins and Cephalosporins. Strict infection control measures need to be implemented to avoid the emergence of resistant strains of pathogens in NICUs. This will help to reduce the incidence of neonatal sepsis leading to mortality.

Human Action Recognition using Multi-Kernel Learning for Temporal Residual Network

This paper has been presented at the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.Deep learning has led to a series of breakthrough in the human action recognition field. Given the powerful representational ability of residual networks (ResNet), performance in many computer vision tasks including human action recognition has improved. Motivated by the success of ResNet, we use the residual network and its variations to obtain feature representation. Bearing in mind the importance of appearance and motion information for action representation, our network utilizes both for feature extraction. Appearance and motion features are further fused for action classification using a multi-kernel support vector machine (SVM).We also investigate the fusion of dense trajectories with the proposed network to boost up the network performance. We evaluate our proposed methods on a benchmark dataset (HMDB-51) and results shows the multi-kernel learning shows the better performance than the fusion of classification score from deep network SoftMax layer. Our proposed method also shows good performance as compared to the recent state-of-the-art methods.Sergio A. Velastin has received funding from the Universidad Carlos III de Madrid, the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement n◦ 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander. Authors also acknowledge support from the Higher Education Commission, Pakistan