The primary objective of our research work is to enhance the prediction of the quality of a component-based software system and to develop an artificial neural network (ANN) model for the system reliability optimization problem. In this paper, we introduced the ANN-supported Teaching-Learning Optimization by transforming constraints to objective functions. Artificial neural network techniques are found to be powerful in the modeling software package quality metrics compared with the ancient statistical techniques. Therefore, by using the neural network, the quality characteristics of software components of the proposed work are predicted. A nonlinear differentiable transfer function of ANN used in the proposed approach is hyperbolic tangent sigmoid. A new efficient optimization methodology referred to as the Teaching-Learning-based Optimization is proposed in this paper to optimize reliability and different cost functions. The weight values of the network are then adjusted consistent with a proposed optimization rule, therefore minimizing the network error. The proposed work is implemented in MATLAB by using the Neural Network Toolbox. The proposed work provides improved performance in terms of sensitivity, precision, specificity, negative predictive value, fall-out or false positive rate, false discovery rate, accuracy, Matthews correlation coefficient, and rate of convergence. KEYWORDS artificial neural network, bounded interface complexity metric, interface surface consistency, self-completeness of component's parameter, self-completeness of component's return value, Teaching-Learning-based Optimization INTRODUCTIONComponent-based software engineering (CBSE) has been described by 2 evolution processes, which are the evolution of components for reuse and the evolution of component-based software systems (CBSSs) with reuse by combining components that have been expanded individually. 1 Development of CBSSs is a promising result for the evolution of large-scale and complex systems. Instead of CBSSs, regular development methods for software development are suspected to have low productivity, high development cost, uncontrollable software quality, and high risk to move to new machinery. 896

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Summary In drug review sentimental analysis (SA), users can share their experiences after consuming the drugs, which provides an accurate decision about the safety of the drug and public health. Patient‐written medical and health‐care reviews are among the most valuable and informative textual content on social media, but researchers in the areas of natural language processing (NLP) and data mining have not researched them thoroughly. These reviews provide insight into patients' interactions with doctors, treatment, and satisfaction or dissatisfaction with health services. The existing approaches have some problems like exploding/vanishing gradients and do not have sequential modeling. When learning long reviews, the exploding and vanishing gradient problems occurs. This problem makes it hard to tune parameters and learn in the network. The existing methods do not have sequential modeling because they fail to extract long dependencies for long reviews in both backward and forward directions. To overcome these issues, we proposed a Modular Lexicon Generation and a Fusion of Bidirectional threshold weighted mapping CNN‐RNN (MLBTWCR) for classifying drug reviews based on users opinions. The Aspect based Modular Lexicon generation using the Advanced Dragon Fly Algorithm (AMLDA) is used to generate the score values for the lexicon and labels based on aspect. The Bidirectional Dropout Long and Short‐Term Memory (Bi‐DLSTM) and Bidirectional Gated Recurrent Unit (Bi‐GRU) used for extracting long dependencies and for performing the sequence of arbitrary length in both backward and forward directions. The experimental results are evaluated using http://drugslib.com and http://drugs.com datasets. Based on evaluation result, the proposed MLBTWCR gives accuracy of 93.02%, recall of 88.72%, error rate of 11.2, false positive rate (FPR) of 11.3, false negative rate (FNR) of 13.6, running time of 15 s, and convergence speed of 0.2 and F‐measure of 92.64%. Hence, our method performs well for the drug reviews classification based on aspects.

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MANETs consists of mobile nodes that dynamically form a network temporarily without any support of central administration. MANET is a self organized and self configurable network. Moreover, each node in MANET moves arbitrarily, making routing a crucial issue. This paper presents the performance evaluation of Dynamic Source Routing (DSR) and Destination-Sequenced Distance Vector (DSDV) routing protocols based on metrics such as throughput, packet delivery fraction, dropped packets, packet loss, normalized routing load and average end-to-end delay by using the NS2 simulator. DSDV is a Proactive routing algorithm where the mobile node periodically broadcasts an advertisement message which is transmitted after expiration of the timer. DSR is a Reactive routing algorithm where a mobile node of MANET connects or advertises the message only when it is needed.