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Trends in education for library and information science are traced and the genesis of an open learning project for library and information science in the University of Madras is presented. This was the introduction of the first master′s programme of this kind in India. The planning, organisation, student enrolment, preparation of study materials and conduct of a personal contact programme are explained. The results of the first batch of students (1989/90) are analysed.

The need for continuing professional education (CPE) for librarians is discussed. The libraries and educational institutions in Tamil Nadu are briefly examined and attempts at CPE programmes in India are traced. A draft plan for CPE for librarians in Tamil Nadu is presented and the New Education Policy of the Government of India and role of the UGC and staff colleges are highlighted. Obstacles to CPE are listed.

Discusses a questionnaire survey of philosophy scholars to indicate preferences concerning indexes in philosophy books. Discusses the diverse opinions gathered and analyses the results. Considers aspects of indexes such as external guidances, scope and coverage, types and sequences, compilers, typography, etc. Suggestions arising are the basis for proposals for the improvement of indexes in philosophy books.

Gene selection is considered as the fundamental process in the bioinformatics field. The existing methodologies pertain to cancer classification are mostly clinical basis, and its diagnosis capability is limited. Nowadays, the significant problems of cancer diagnosis are solved by the utilization of gene expression data. The researchers have been introducing many possibilities to diagnose cancer appropriately and effectively. This paper aims to develop the cancer data classification using gene expression data.

The proposed classification model involves three main phases: “(1) Feature extraction, (2) Optimal Feature Selection and (3) Classification”. Initially, five benchmark gene expression datasets are collected. From the collected gene expression data, the feature extraction is performed. To diminish the length of the feature vectors, optimal feature selection is performed, for which a new meta-heuristic algorithm termed as quantum-inspired immune clone optimization algorithm (QICO) is used. Once the relevant features are selected, the classification is performed by a deep learning model called recurrent neural network (RNN). Finally, the experimental analysis reveals that the proposed QICO-based feature selection model outperforms the other heuristic-based feature selection and optimized RNN outperforms the other machine learning methods.

The proposed QICO-RNN is acquiring the best outcomes at any learning percentage. On considering the learning percentage 85, the accuracy of the proposed QICO-RNN was 3.2% excellent than RNN, 4.3% excellent than RF, 3.8% excellent than NB and 2.1% excellent than KNN for Dataset 1. For Dataset 2, at learning percentage 35, the accuracy of the proposed QICO-RNN was 13.3% exclusive than RNN, 8.9% exclusive than RF and 14.8% exclusive than NB and KNN. Hence, the developed QICO algorithm is performing well in classifying the cancer data using gene expression data accurately.

This paper introduces a new optimal feature selection model using QICO and QICO-based RNN for effective classification of cancer data using gene expression data. This is the first work that utilizes an optimal feature selection model using QICO and QICO-RNN for effective classification of cancer data using gene expression data.