Rajasthan Technical University

About: Rajasthan Technical University is a education organization based out in Kota, Rajasthan, India. It is known for research contribution in the topics: Photovoltaic system & PID controller. The organization has 716 authors who have published 1084 publications receiving 4530 citations. The organization is also known as: RTU.

Forecasting of PM10 Using Intelligent Crow Search Algorithm Tuned Feed-Forward Neural Network

Abstract: Pollution forecast is a pioneering task and considered as a preliminary action taken by city planners as it can exactly locate the location of industrial plants and other development centers. Along with that on the basis of pollution profile, major decisions can be taken for controlling and combating it. Keeping this fact in mind, we propose a supervised structure of Feed-Forward Neural Network for predicting PM10 concentration in Jaipur city. For training the net, we employ our recently proposed algorithm based on the intelligent behavior of crow named as Intelligent Crow Search Algorithm (ICSA). It is observed that the developed ICSA yields better results when tested on the unknown samples. Comparative analysis of ICSA-based networks has been carried out with other contemporary nature-inspired algorithm tuned networks.

An Ant System Algorithm Based on Dynamic Pheromone Evaporation Rate for Solving 0/1 Knapsack Problem

Abstract: Chauhan, Ruchi Sharma, Nirmala Sharma, HarishIn this research paper, a meta-heuristic search technique of ant system algorithm based on dynamic pheromone evaporation rate (ASA-DPER) is introduced for solving 0/1 knapsack problem (0/1 KP). In ASA-DPER algorithm, the pheromone evaporation rate is dependent on the per-iteration knapsack profit produced by the algorithm. If the present-iteration knapsack profit is higher than the previous-iteration knapsack profit, the pheromone evaporation rate is “ER 1,” and if the present-iteration knapsack profit is equal to the previous-iteration knapsack profit, the pheromone evaporation rate is “ER 2.” The value of ER 1 is always greater than the value of ER 2. To validate efficiency of ASA-DPER algorithm, experiments are performed on thirty small-scale 0/1 KP instances, and results prove that the ASA-DPER improves search quality and produces feasible result converging iteration faster, with respect to the base meta-heuristic ant system algorithm based on static pheromone evaporation rate (ASA-SPER).

Forecasting of Wind Speed by Using Deep Learning for Optimal Use of the Energy Produced by Wind Farms

Abstract: Wind speed forecasting is required for predicting the power production from wind farms. Information about estimated wind power production helps in better grid management and scheduling of different types of power plants connected to the grid. This paper uses deep learning Convolutional Neural Network (CNN) for wind speed forecasting. The time series data of wind speed is separated into training, validation, and testing data and the errors in forecasting are estimated. The errors of deep learning method are also compared with that of statistical ARIMA model. It is observed that CNN makes forecasting errors comparable to that of statistical model. The wind speed dataset used in this work are of a coastal site located in Gujarat (India) and are measured by an anemometer located at 80 m height.

Facial Expressions Recognization Based on DRLBP and CNN Classification

Abstract: Facial expression appreciation typically can be a distinct one of significant investigation in the field of AI or example appreciation. The facial expression contains rich imperceptible information, which can appreciate human emotions or intentions, which extensively investigate value. Facial expression analysis is a compelling and challenging trouble that affects essential purposes in various fields such as human–computer interface and data-driven animation. Developing useful facial expressions from original face images is a necessary step in realizing face recognition. The actual evaluation is based on local statistical traits; Dominant Rotated Local Binary Patterns (DRLBP) facial expressions. Several machine learning methods have been thoroughly observed in various databases. Researchers typically use the DRLBP function, which is efficient and capable of face recognition. Cohn Canada is the current work database or programming language used in MATLAB. First, divide the face area into small areas, drag the histogram, Dominant Rotated Local Binary Patterns out of the area, and connect it to a single function vector. This feature vector outlines a well-organized face illustration and helps determine the similarity between images. It is still a severe face recognition problem to create powerful and unique features, while increasing interpersonal differences. In this article, the researchers explained how to use Dominant Rotated Local Binary Patterns (DLBP) for feature extraction and Convolutional Neural Networks (CNN) for image classification to enhance the face recognition capabilities system. The post-workout correspondence helps CNN converge faster and achieve better accuracy. Compared to other traditional methods, it has also been significantly improved to evaluate this new method's completion.