Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient’s internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance.

A descriptive study was undertaken to assess the Relationship between Quality of Sleep and Academic Performance among Nursing Students in selected College of Nursing, Ludhiana, Punjab. The objectives of the study were to assess the quality of sleep among nursing students, to identify academic performance of nursing students, to assess the relationship between quality of sleep and academic performance, to find out the relationship of quality of sleep and academic performance with selected variables like age, gender, type of course, type of family, residence, academic year, duration of sleep, use of technology, attendance (Clinical & Theory), Day nap. Quantitative Research Approach and non-experimental descriptive research design was used to accomplish the stated objectives.The conceptual framework was based on King's Conceptual System of Goal Attainment Theory (1981). Data was collected with the help of standardised tool named Pittsburgh Sleep Quality Index (PSQI) which was found reliable (r=0.76). 105 Subjects were chosen by simple random sampling technique. The data collected was analysed using descriptive and inferential statistics. The study revealed that nursing students had good quality of sleep and good academic performance whereas there was no significant relationship between quality of sleep and academic performance. Demographic variables i.e. sleep duration had a significant impact on quality of sleep whereas gender, academic year, attendance and day nap had a significant impact on the academic performance of nursing students. Based on findings of the study guidelines were prepared on quality of sleep & distributed among nursing students.

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