Financial Misgivings of Married Working Women in Lahore

In Pakistan, the financial issues of married working women are rarely discussed. There is an absence of literature on the subject. Nevertheless, the social sciences literature has been debating financial aspect of gender, either from the perspective of employer or employee; ignoring the working women’s reservations regarding their financial contribution in their marital life, particularly with reference to spouse and in-laws expectations. This issue becomes more acute when it comes to patriarchal conservative developing societies. This study is a delicate attempt to understand the magnitude of financial support of Pakistani married working women for spouse and in-laws, in the city of Lahore. In routine, the husband as well as the in-laws expect that a working wife should surrender her income, in entirety or partially towards the household budget, thus taking the financial responsibility of her spouse along with his extended family. Such financial misgivings create tensions and pressure for the already burdened woman having a disadvantaged status. In certain cases, the contribution is by free will of the wife, as well. This research will make an attempt through a survey with 50 married working women of different social classes, residing in Lahore. The purpose is to understand the rationale of approval or disapproval of this practice. Refusal for cooperation often create problems for the earning woman, even at the risk of separation or divorce, or at the least, tensions in the married life. At the end, the study will debate over possible adjustments and compromises, which could reduce tense situation for the married working woman, and at the same time maintaining her financial independence.

Lung Cancer Classification With Discriminant Features of Mutated Genes Using Machine Learning

Machine learning based mathematical and statistical models are employed for the development of improved classification systems. These decision based systems have the capability of automatically learning from complex sequential data. In this work, machine learning models are developed for the classification of lung cancer. The early classification of lung cancer is critical for successful cancer treatment. Genes and proteins are important in the normal functioning of the human body. The abnormal processes due to somatic mutations transform normal cells into cancer cells. The somatic mutations in genes are ultimately reflected in gene expression and proteins amino acid sequences. Influential information is extracted during the statistical analysis of gene expression and proteins amino acid sequences data. This information is transformed into discriminant feature spaces using physiochemical properties. The machine learning capability is exploited effectively using discriminant information of mutated genes in proteomic and genomic data.This study aims to develop artificial intelligent lung cancer classification systems. The development was carried out in three main phases. In the first phase, lung cancer classification system using protein amino acid sequences is developed by employing various individual learning algorithms. In the second phase, lung cancer classification system using protein amino acid sequences is developed by employing multi-gene genetic programming. This approach exploits evolutionary learning capability by optimally combining the selected discriminant features with primitive functions. The third phase is focussed on the development of improved lung cancer classification system using influential features of gene expression with the imbalanced dataset by employing rotation forest. In the thesis work, extensive experiments are conducted to evaluate the performance of various lung cancer classification systems. The proposed systems have obtained excellent accuracy values in the range of 95%99%. The comparative analysis highlights that proposed lung cancer classification systems are better than previous approaches. It is expected that research outcome would impact in the fields of diagnosis, prevention, and effective treatment of lung cancer.