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Facial expression recognition (FER) is the process of identifying human expressions. People vary in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively important research area. Various works have been conducted on automating the recognition of facial expressions. The main intent of this paper is to plan for the FER model with the aid of intelligent techniques. The proposed models consist of steps like data collection, face detection, optimized feature extraction and emotion recognition. Initially, the standard benchmark facial emotion dataset is collected, and it is subjected to face detection. The optimized scale-invariant feature transform (OSIFT) is adopted for feature extraction, in which the key points that are giving unique information are optimized by the hybrid meta-heuristic algorithm. Two meta-heuristic algorithms like spotted hyena optimization and beetle swarm optimization (BSO) are merged to form the proposed spotted hyena-based BSO (SH-BSO). Also, the local tri-directional pattern is extracted, which is further combined with optimized SIFT. Here, the proposed SH-BSO is utilized for optimizing the number of hidden neurons of both deep neural network and convolutional neural network in such a way that the recognition accuracy could attain maximum.

In today’s global scenario, with the evolution of new technologies and robust ideas, the world gets more involved and embed the advancements of wireless communication with information technology. An ongoing Gartner report assesses that, by 2021, there will be 25.1 billion web associated gadgets, developing at a pace of 32% every year. Bounties of automation are minimizing the human assistance, intervention and reduced risk factor in industry. Here Industrial Automation is used to control systems or things such as computers or robots or machines or sensors with the help of Internet protocol and cloud computing. In this paper six parameters viz., vibration, temperature, humidity, air quality, sound rate and pressure are monitored and controlled remotely using cloud computing. The system performance automatically changes on the basis of sensor data being collected at regular intervals with a feedback mechanism, thereby allowing the system to control or monitor various devices using internet protocols. The threshold values for all the sensors are set as per the industrial standards. These automation techniques find extensive applications in various control mechanisms to operate the equipment under production processes like boilers and heat-treating ovens, steering and stabilization, pressure exerted by ideal gases in confined containers, vibrations by machinery, air pollution released from chemical composites etc.,

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