A machine learning model is introduced to recognize the severity level of the Diabetic Retinopathy (DR), a disease observed in the people suffering from diabetes for a long time and is one of the causes of vision loss and blindness. Major objective of this approach is to generate an effective feature representation of the fundus images so that the level of severity can be identified with less effort and using limited number of samples for training. Color fundus images of the retina are collected, preprocessed and deep features are extracted by feeding them to a deep Convolutional Network, Neural Architecture Search Network (NASNet) which searches for the best convolutional layer (or "cell") in NASNet search space. The representations of retinal images in deep space are given as input to the classification model to get the severity level of the disease. The proposed model is applied on the benchmark APTOS 2019 retinal fundus image dataset to evaluate the performance of the proposed model. Our experimental studies indicate that ⱱ-Support Vector Machine (ⱱ-SVM) when trained using the projected deep features leads to an improvement in accuracy compared to other machine learning models for fundus image classification. In addition, from the experimental studies we understand that deep features from NASNet give better representation compared to the handcrafted features and features obtained using other projections. We observe that deep features transformed using t-distributed stochastic neighbor embedding (t-SNE) gives more discriminative representations of retinal images and help to achieve an accuracy of 77.90%.

PurposeGene 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.Design/methodology/approachThe 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.FindingsThe 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.Originality/valueThis 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.

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Cloud has been emerging, popular and very demanding technology now a day. Cloud has got wide popularity with its sophisticated features. The primary features of cloud include internet access, more storage, easy setup, automatic updates, and low cost and resource provisioning based on "pay as you go" policy. In spite of advantages, security is considered to be more important and drew the attention of many researchers because it is not guaranteed in an open cloud. The data storage is becoming an indispensable measurement in cloud and most of the times cloud does not guarantee that data that has been stored is secured from illegitimate access. Many researchers are working to ensure data security in the cloud but unfortunately they do not provide adequate security to data. This paper is aiming to propose a secure hybrid scheme with obfuscation and cryptography to ensure the privacy of data shared in public cloud. Experimental results show that the proposed scheme yields good results.

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