Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and a hybrid Long Short-Term Memory network (LSTM) model. The feature extraction process combines a novel feature selection method called Elite Term Score (ETS) and Word2Vec to extract the syntactic and semantic information respectively. First, the ETS method leverages the document level, class level, and corpus level probabilities for computing the weightage/score of the terms. Then, the ideal and pertinent set of features with a high ETS score is selected, and the Word2vec model is trained to generate the intense feature vector representation for the set of selected terms. Finally, the resultant word vector obtained is called EliteVec, which is fed to the hybrid LSTM model based on Honey Badger optimizer with population reduction technique (PHB) which predicts whether the input textual content is depressive or not. The PHB algorithm is integrated to explore and exploit the optimal hyperparameters for strengthening the performance of the LSTM network. The comprehensive experiments are carried out with two different Twitter depression corpus based on accuracy and Root Mean Square Error (RMSE) metrics. The results demonstrated that the proposed EliteVec+LSTM+PHB model outperforms the state-of-art models with 98.1% accuracy and 0.0559 RMSE.

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The proposed work deals with finding related reviews posted on various online Forums. Conventional methods for matching related documents compute the content similarity over the entire review instead of partitioning into segments revealing different intentions. In this work, intention-based similarity clustering is introduced to find the relatedness of two documents. This method forms the document clusters based on the similarity of the segments with similar intentions. The segmentation points are identified using a number of text features which can express when the segmentation should be done. Finally, the document clusters are formed by grouping the segments with similar intentions in same cluster and then the similarities among the segments with the same intention are computed. The proposed model is trained on TripAdvisor and Yelp Open Review datasets to evaluate the performance of the model, and the evaluation results show that the model produces more precise results in mining documents related to the user’s interest.

Big data computing in clouds is a new paradigm for next-generation analytics development. It enables large-scale data organizations to share and explore large quantities of ever-increasing data types using cloud computing technology as a back-end. Knowledge exploration and decision-making from this rapidly increasing volume of data encourage data organization, access, and timely processing, an evolving trend known as big data computing. This modern paradigm incorporates large-scale computing, new data-intensive techniques, and mathematical models to create data analytics for intrinsic information extraction. Cloud computing emerged as a service-oriented computing model to deliver infrastructure, platform, and applications as services from the providers to the consumers meeting the QoS parameters by enabling the archival and processing of large volumes of rapidly growing data faster economy models.

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Various unimaginable opportunities and applications can be attained by the development of internet-connected automation. The network system with numerous wired or wireless smart sensors is called as IoT. It is showing various enhancement for past few years. Without proper security protection, various attacks and threats like cyberattacks threat causes serious disaster to IoT from the day it was introduced. Hence, IoT security system is improvised by various security and the management techniques. There are six sections in security management of IoT works. IoT security requirement is described intensively. The proposed layered of security management architecture is being defined and explained. Thus, this proposed architecture shows the security management system for IoT network tight security management for a network of the IoT which is elaborately explained with examples and about GDPR. In information security, intrusion recognizable proof is the showing of placing exercises that attempt to deal the protection, respectability, or availability of a benefit.