Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

Sentiment analysis(AS)is one of the basic research directions in natural language processing(NLP),it is widely adopted for news,product review,and politics.Aspect-based sentiment analysis(ABSA)aims at identifying the sentiment polarity of a given target context,previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low accuracy.Hence this research work develops a deep-semantic and contextual knowledge networks(DSCNet).DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given aspects.At first temporal relationships are established then deep semantic knowledge and contextual knowledge are introduced.Further,a deep integration layer is introduced to measure the importance of features for efficient extraction of different dimensions.Novelty of DSCNet model lies in introducing the deep contextual.DSCNet is evaluated on three datasets i.e.,Restaurant,Laptop,and Twitter dataset considering different deep learning(DL)metrics like precision,recall,accuracy,and Macro-F1 score.Also,comparative analysis is carried out with different baselinemethods in terms of accuracy andMacro-F1 score.DSCNet achieves 92.59%of accuracy on restaurant dataset,86.99%of accuracy on laptop dataset and 78.76%of accuracy on Twitter dataset.

Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities

Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy management.In smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart grids.In complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research challenge.These challenges can be resolved through various machine learning techniques and data analytics.In this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart grid.The proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution companies.In the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm optimization.The simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.

Wave solutions and numerical validation for the coupled reaction-advection-diffusion dynamical model in a porous medium

The current study examines the special class of a generalized reaction-advection-diffusion dynamical model that is called the system of coupled Burger’s equations.This system plays a vital role in the essential areas of physics,including fluid dynamics and acoustics.Moreover,two promising analytical integration schemes are employed for the study;in addition to the deployment of an efficient variant of the eminent Adomian decomposition method.Three sets of analytical wave solutions are revealed,including exponential,periodic,and dark-singular wave solutions;while an amazed rapidly convergent approximate solution is acquired on the other hand.At the end,certain graphical illustrations and tables are provided to support the reported analytical and numerical results.No doubt,the present study is set to bridge the existing gap between the analytical and numerical approaches with regard to the solution validity of various models of mathematical physics.