Mainstreaming LSENs in a Regular Classroom: A Scoping Review

This study aims to generate thorough and comprehensive review of the teacher’s perspective and hands-on experience in mainstreaming LSENs in a regular classroom, including teachers’ attitudes and perceptions, challenges encountered, and teaching approach in handling mainstreamed classrooms. A scoping review framework by Arksey and O’Malley’s (2005) systematically analyzed the data of the different articles conducted by various scholars. Through scrupulous and through selection of related studies, 10 articles were included in the review from 6 different countries across the globe. The articles included were conducted from 6 countries and various databases. The study highlighted that: 1) teachers have positive and negative attitudes towards mainstreaming, 2) teachers experienced various challenges in handling a mainstream classroom, and 3) learner-centered approach to learning is used in the classroom. Mainstreaming LSENs in a regular classroom has pros and cons among teachers, regular students, and the LSENs themselves. Hence, a daunting responsibility for the teachers. Nevertheless, it is imperative to support teachers by giving seminars and training, especially to those non-special education majors, to be fully equipped to handle mainstreamed classrooms.

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.