The complication of economic load dispatch (ELD) of an electrical power system can be considered as an optimization problem with constraints to minimize the cost and emission concurrently. The optimization problems with various objectives gain significant momentum and priority with the advancements in renewable energy sources (RESs). Many techniques have been put forward to dispense this issue, but it still remains a challenging one. The exis-

In recent days, the growth of digital data has been increased explosively due to data-intensive applications like healthcare, education and electronic commerce. Due to the huge volume of data created and the utilization of data in emerging applications, a local storage device has to be deployed. Cloud storage services allow both the individuals and commercial users for the outsourcing of data to the cloud server and to access the data remotely with the help of the internet, so the demand for cloud storage has been raised. However, one of the critical problems of cloud data storage is security. Some researchers have used third party auditor for verifying the data stored in the cloud. This paper introduces a novel method for cloud storage and for protecting the organizations’ data. The proposed system strengthens the level of authentication with the help of AES and time-stamping algorithms. The experimental results demonstrate the efficiency of the proposed method when auditing the shared data integrity

INTRODUCTION: Preservation of fruits by drying is one of the general and important traditional technique followed by the process industries. An accurate controller of relative humidity and temperature is required for the fruit drying control system, which determines the quality of the dried fruits. OBJECTIVES: To design optimal Propositional-Integral-Derivative (PID) controller based on the Particle Swarm Optimization and Radial Basis Functional Neural Network (PSO-RBFNN) for pineapple drying system. METHODS: A Propositional-Integral-Derivative (PID) controller based on the Particle Swarm Optimization and Radial Basis Functional Neural Network (PSO-RBFNN) was proposed in this paper for pineapple drying system. Also, the coupling relationship of relative humidity and temperature is more complicated due to the fluctuations and non-linearity in the drying system. An intelligent Adaptive Neuro Fuzzy Inference System (ANFIS) coupling model is utilized in this paper to access the coupling relationship between relative humidity and temperature. RESULTS: The proposed control system has been implemented in the MATLAB and results are compared with PID controller, Fuzzy Logic Controller (FLC) and Fuzzy PID controller for the performance constraints such as settling time, peak over shoot and steady state error. CONCLUSION: The proposed PSO-RBFNN based PID controller gives better control performance with the highly minimized settling time (42 sec for humidity and 40 sec for temperature) and completely eliminated steady state error and Peak overshoot. Finally, the PSO-RBFNN algorithm based PID controller is concluded as the effective system.

Summary An energy management system that uses the Internet of Things structure using hybrid system is proposed. The proposed hybrid technique is the joint execution of both Giza pyramids construction and human urbanization algorithm and is commonly named as combined Giza Pyramids Construction, Human urbanization algorithm (GPCHUA) technique. The major aim of the proposed technique is to optimally accomplish the power and resources of the distribution system. Moreover, the proposed scheme is in charge to fulfil the general supply and energy demand. At last, the efficiency of proposed scheme is portrayed on MATLAB/Simulink and evaluated with various existing techniques like Deer hunting optimization, bacterial foraging optimization, and crow search optimization. The comparison outcomes prove the superiority of the GPCHUA system and confirm their potential to solve the problem.

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