Nowadays, the Extreme learning machine (ELM) is playing a key role in machine intelligence and big data analytics due to its various advantages such as fast training rate, universal classification/regression and the capability of approximation. The standard ELM uses the Moore-Penrose generalized pseudo-inverse for solving the hidden layer activation matrix and also it identifies the output weights. Because of that, the standard ELM takes more time to train the features from the dataset. In ELM, scalability also considered as a one of the major concern while processing the large dataset. In order to overcome this concern, the Automotive Rank based ELM (AR-ELM) is proposed to obtain an effective tensor decomposition for diminishing the training time. Besides, the Bayesian approach is considered in this AR-ELM to remove the redundancy from the decomposed samples of the tensor. The major objective of this proposed AR-ELM is to process the large amount of dataset without depending on memory capacities. The recognition accuracy is improved by eliminating redundant information. The key idea of the AR-ELM is to reduce the training time while processing the huge dataset. The implementation and simulation of the AR-ELM is done in Spark Python 3.7. The performance of the AR-ELM is analysed in terms of accuracy, precision, recall and training time. The proposed methodology is compared with three existing methodologies such as basic ELM, ELM-TUCKER and ELM-PARAFAC. The recognition accuracy of the AR-ELM methodology with Hardlims activation function is 0.8879 for letter recognition dataset, it is high when compared to the basic ELM, ELM-TUCKER and ELM-PARAFAC that are 0.8102, 0.8375 and 0.834 respectively.

Sentiment analysis based on aspects seeks to anticipate the polarities of sentiment in specified targets related to the text data. Several studies have shown a strong interest in using an attention network to represent the target as well as context on generating an efficient representation of features used for tasks while sentiment classification. Still, the attention score computation of the target using an average vector for context is unequal. While the interaction mechanism is simplistic, it needs to be overhauled. Therefore, this paper intends to introduce a novel aspect-based sentiment analysis with three phases: (i) Preprocessing, (ii) Aspect Sentiment Extraction, (iii) Classification. Initially, the input data is given to the preprocessing phase, in which the tokenization, lemmatization, and stop word removal are performed. From the preprocessed data, the weighted implicit and weighted explicit extraction is determined in the Aspect Sentiment Extraction. Moreover, the weighted implicit aspect extraction is done by Stanford Dependency Passer (SDP) method, and the weighted explicit extraction is done through proposed Association Rule Mining (ARM). Subsequently, the extracted features are provided to the classification phase in which the Optimized Bi-LSTM is utilized. For making the classification more accurate and precise, it is planned to tune the weights of Bi-LSTM optimally. For this purpose, an Opposition Learning Cat and Mouse-Based Optimization (OLCMBO) Algorithm will be introduced in this work. In the end, the outcomes of the presented approach are calculated to the extant approaches with respect to different measures like F1-measure, specificity, Negative Predictive Value (NPV), accuracy, False Negative Rate (FNR), sensitivity, precision, False Positive Rate(FPR), and Matthew’s correlation coefficient, respectively.

In this paper, we have addressed a scenario, where a group of people are in need to access the common files of enormous size through mails. However, the space constraint observed in the mailbox of an individual, be in the public domain or in the private domain, either due to insufficient remaining space or the allocated space, the considerably large files cannot be communicated. We have proposed a simplex mail account, meant for people within an organization, academic institution, government, etc., wherein the bigger files can easily be stored and accessed through mail. The simplex mail account is a single mail account and can be viewed by number of people with two sets of privileges. Though a single mailbox is accessed with number of people simultaneously, the security issues are as good as the ones being offered by the other mailing systems already available.

The sentiment data provides vital information about the feedback of the user’s opinion, attitude and emotions. The business of product development and digital marketing teams entirely depends upon the outcome of these sentiments and they apply various Data Mining techniques, Machine Learning and Deep Learning approaches to analyse the depth of the dataset. The Sentiment Analysis provides the automatic data mining of reviews, comments, opinions and suggestions, received from various input methods, including text, audio notes, images and emoticons, through Natural Language Processing. The analysis assists in the classification of reviewer feedback in terms of positive, negative and neutral categories. In this study, the opinions shared by individuals over various social networking sites in the case of any big event, the release of any new product or show and political events were analysed. Machine Learning and Deep Learning techniques are discussed and used dominantly to illustrate the outcome of opinions and events. The accurate analysis of vast information shared by individuals free of cost and without any influence can provide vital information for organisations and management authorities. This review analyses various techniques in the field of Aspect-Based Sentiment Analysis along with their features and research scopes and thus, it helps researchers to focus on more precise works in the future. Among the machine learning algorithms, Random Forest performed much better as compared to other methods, and among the Deep Learning approaches, Multichannel CNN outperformed with the highest accuracy of 96.23%. The paper includes the comparative study of multiple Machine Learning and Deep Learning techniques for the evaluation of sentiment data and concludes with the challenges and scope of Sentiment Analysis.