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Objectives: The main objective of this study is to compare the performance evaluation of ensemble based methods and neural network learning on various combinations of unigram, bigram, and trigram feature vector along with feature selection (IG) and feature reduction (PCA) for sentiment classification of movie reviews. Methods: Bagging and Adaboost are the techniques used in ensemble learning to learn the sentiment classifier to get better classification accuracy, using SVM, NB as a core learner for different models of attribute vectors. The classification results of the ensemble approach are compared with neural network learning for classification of movie reviews. Among the ensemble methods, AdaBoost with base learner SVM outperforms in classifying attribute vectors for model m-iii. The backpropagation algorithm is used to improve classification accuracy in the neural network learning and IG and PCA are used in sentiment classification to reduce the feature length and training time. Findings:The classification results of ensemble based approach are compared with neural network learning. Between the two ensemble based methods, Adaboost + SVM outperform in classifying the sentiment of movie reviews for m-iii feature vector. IG and PCA are used in sentiment classification in order to reduce the feature length. Between the IG and PCA methods, IG performs better than PCA. Among IG+Adaboost+SVM and neural network learning methods, IG+Adaboost+SVM performs better than neural network learning. Improvement: In our application, we are using the ensemble based methods and neural network learning, these methods are compared and analyzed the performance for various levels of feature vectors. A classification algorithm may be designed to analyze the performance with other neural network methods.

Irretrievable loss of vision is the predominant result of Glaucoma in the retina. Recently, multiple approaches have paid attention to the automatic detection of glaucoma on fundus images. Due to the interlace of blood vessels and the herculean task involved in glaucoma detection, the exactly affected site of the optic disc of whether small or big size cup, is deemed challenging. Spatially Based Ellipse Fitting Curve Model (SBEFCM) classification is suggested based on the Ensemble for a reliable diagnosis of Glaucoma in the Optic Cup (OC) and Optic Disc (OD) boundary correspondingly. This research deploys the Ensemble Convolutional Neural Network (CNN) classification for classifying Glaucoma or Diabetes Retinopathy (DR). The detection of the boundary between the OC and the OD is performed by the SBEFCM, which is the latest weighted ellipse fitting model. The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here. There is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels. The ascertaining of OC and OD boundary, which characterized many output factors for glaucoma detection, has been developed by Ensemble CNN classification, which includes detecting sensitivity, specificity, precision, and Area Under the receiver operating characteristic Curve (AUC) values accurately by an innovative SBEFCM. In terms of contrast, the proposed Ensemble CNN significantly outperformed the current methods.

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Recently, an innovative trend like cloud computing has progressed quickly in Information Technology. For a background of distributed networks, the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service model. The cloud computing environment extracts the execution details of services and systems from end-users and developers. Additionally, through the system's virtualization accomplished using resource pooling, cloud computing resources become more accessible. The attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this paper. With the help of multi-agents, we attempt to represent Open-Identity (ID) design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the cloud. This research aims to determine how authentication and authorization services were provided in an agreeable and preventive manner. Based on attack-oriented threat model security, the evaluation works. By considering security for both authentication and authorization systems, possible security threats are analyzed by the proposed security systems.