Sentiment Analysis using Improved Novel Convolutional Neural Network (SNCNN)

Sentiment Analysis is an important method in which many researchers are working on the automated approach for extraction and analysis of huge volumes of user achieved data, which are accessible on social networking websites. This approach helps in analyzing the direct falls under the domain of SA. SA comprises the vast field of effective classification of user-initiated text under defined polarities. The proposed work includes four major steps for solving these issues: the first step is preprocessing which holds tokenization, stop word removal, stemming, cleaning up of unwanted text information like removing of Ads from Web pages, Text normalization for converting binary format. Secondly, the Feature extraction is based on the Bag words, Word2Vec and TF-ID which is a Term Frequency-Inverse Document Frequency. Thirdly, this feature selection includes the procedure for examining semantic gaps along with source features using teaching models and this involves target task characteristic application for Improved Novel Convolutional Neural Network (INCNN). The Feature Selection accompanies the procedure of Information Gain (IG) and PCC which is a Pearson Correlation Coefficient. Finally, the classification step INCNN gives out sentiment posts and responses for the user-based post aspects which helps in enhancing the system performance. The experimental outcome proposes the INCNN algorithm and provides higher performance rather than the existing approach. The proposed INCNN classifier results in highest accuracy

Network Intrusion Detection Method Using Stacked BILSTM Elastic Regression Classifier with Aquila Optimizer Algorithm for Internet of Things (IoT)

Globally, over the past ten years, computer networks and Internet of Things (IoT) networks have grown significantly due to the increasing amount of data that has been collected, ranging from zettabytes to petabytes. As a result, as the network has expanded, security problems have also emerged. The large data sets involved in these types of attacks can make detection difficult. The developing networks are being used for a multitude of sophisticated purposes, such as smart homes, cities, grids, gadgets, and objects, as well as e-commerce, e-banking, and e-government. As a result of the development of numerous intrusion detection systems (IDS), computer networks are now protected from security and privacy threats. Data confidentiality, integrity, and availability will suffer if IDS prevention efforts fail. Complex attacks can't be handled by traditional methods.  There has been a growing interest in advanced deep learning techniques for detecting intrusions and identifying abnormal behavior in networks. This research aims to propose a novel network namely stacked BiLSTM elastic regression classifier (Stack_BiLSTM-ERC) with Aquila optimizer algorithm for feature selection. This optimization method computes use of a cutting-edge transition function that enables it to be transformed into a binary form of the Aquila optimizer. A better solution could be secured once number of possible solutions are found from diverse regions of the search space utilizing the Aquila optimizer method. NSL-KDD and UNSW-NB15 are two datasets that enable learning characteristics from the raw data in order to detect harmful prerequisites characteristics and effective framework patterns. The proposed Stack_BiLSTM-ERC achieves 98.l3% of accuracy, 95.1% of precision, 94.3% of recall and 95.4 of F1-score for NSL-KDD dataset. Moreover, 98.6% of accuracy, 97.2% of precision, 98.5 of recall and 97.5% of F1-score

Multi-agent based DRL with federated learning for data transmission in mobile sensor networks

There has been a flurry of activity in the field of wireless sensor networks, or WSNs, as of late. Because packets need to be transported from source nodes to the destination nodes as quickly and energy effectively as feasible in various application areas, packet routing is one of core difficulties in WSNs. A plethora of routing options have been suggested to tackle this problem. The proposed method distributed and designed to run on a network of interconnected routers. Different from most of its competitors, the proposed results frame the routing problem as a reinforcement learning problem with several agents. To optimize more complicated cost functions, such as the time it takes for bags to be delivered and the amount of energy used in a baggage handling system, it is possible to model every router as a deep neural network. The proposed MA-DRL attains latency of 2.41, energy consumption of 26J has superior efficiency compared to the existing methods. However, the MA-DRL has minimized latency and lower energy consumption. This way, each router may take into consideration different types of data about its surroundings. Based on four metrics latency, and energy consumption the simulation results show that this architecture performs well

Sentiment Analysis using Improved Novel Convolutional Neural Network (SNCNN)

Sentiment Analysis is an important method in which many researchers are working on the automated approach for extraction and analysis of huge volumes of user achieved data, which are accessible on social networking websites. This approach helps in analyzing the direct falls under the domain of SA. SA comprises the vast field of effective classification of user-initiated text under defined polarities. The proposed work includes four major steps for solving these issues: the first step is preprocessing which holds tokenization, stop word removal, stemming, cleaning up of unwanted text information like removing of Ads from Web pages, Text normalization for converting binary format. Secondly, the Feature extraction is based on the Bag words, Word2Vec and TF-ID which is a Term Frequency-Inverse Document Frequency. Thirdly, this feature selection includes the procedure for examining semantic gaps along with source features using teaching models and this involves target task characteristic application for Improved Novel Convolutional Neural Network (INCNN). The Feature Selection accompanies the procedure of Information Gain (IG) and PCC which is a Pearson Correlation Coefficient. Finally, the classification step INCNN gives out sentiment posts and responses for the user-based post aspects which helps in enhancing the system performance. The experimental outcome proposes the INCNN algorithm and provides higher performance rather than the existing approach. The proposed INCNN classifier results in highest accuracy.</jats:p

Face recognition using CNN and siamese network

Facial recognition is no longer a cutting-edge technology; it is now a part of everyday life. It has been used for various security and profiling applications around the world. Early face detection models were developed during the 1960s and were used to just classify photos of people. In past decades, the face recognition models were optimized and reengineered to identify all the people in each frame of real-time, high-resolution video input. It still has a wide variety of applications to be implemented and can be further optimized for high precision using different approaches. In this study, we have implemented two different approaches for facial detection. The first is a CNN-based approach that extracts keypoints from an image and classifies it using a KNN algorithm. The next approach uses a Siamese network to classify the input image. The initial part focuses primarily on data collection and training. The following part clearly explains the implementation of both approaches. The performance of these approaches was also evaluated and illustrated optimally