Knowledge and Awareness about Nikah Nama Reforms and its Clauses in Lahore

The present research was carried out to investigate the knowledge and awareness regarding Nikah Nama reforms and its clauses among the unmarried males and females of Lahore. The objectives of this study were to explore knowledge and level of awareness about Nikah Nama reforms and to explore the reasons for not reading the Nikah Nama before marriage. A total of 400 adults were sampled, including 200 males and 200 females. A self-developed questionnaire was used; which included both closed and open ended questions. The results of this study revealed that most of the people were not aware about Nikah Nama reforms and women supportive clauses. It was also revealed that three broad factors contributed to not reading the Nikah Nama before marriage: (i) Patriarchal culture, (ii) Lack of awareness; and (iii) Insecurity and fear. It is recommended that awareness about marriage rights should be part of the formal and informal education system and there must be some accountability against people who break laws and strike out the Nikah Nama clauses or prevent the reading of the contract.

Breast Cancer Detection Based on Hybrid Features Using Machine Learning Classification Techniques

The breast cancer in the women is most commonly diagnosed type of cancer. The mortality rate can be reduced if proper and early breast cancer treatment can be made. Masses and microcalcification contain very important diagnostic information in breast cancer. There is great variation in masses and micro-calcifications so, radiologists face difficulties in proper diagnosis of the breast cancer. Researchers in the past developed efficient systems based on computer aided diagnostic (CAD) systems. Moreover, relevant feature extraction plays a vital role in proper diagnostic and prognostic. Based on the diverse nature and variations in the breast cancer mammograms, we propose hybrid feature extraction approach including morphological, entropybased features, elliptic Fourier descriptors (EFDs), texture and scale invariant feature transform (SIFT). For improving the detection accuracy based on the extracted features, we applied machine learning classifiers including Support vector machine (SVM) alongwith its kernels such as Gaussian, radial base function (RBF), polynomial; Na?ve Bayes and Decision tree (DT). The validation was measured using 10-fold cross validation (CV) system. For a performance evaluation, we computed different metrics including specificity, sensitivity, false positive rate (FPR), negative predictive value (NPV), positive predictive value (PPV), and area under the receiver operating curve (AUC). Both combination and single feature are used as an input for classifiers. The results reveal that both single and combination of features provides higher detection results. Thus, the new feature extracting approach is more robust in early detection of breast cancer.