Computational Intelligence
Kaushal Kishore Rao Mangalore; Nikhitha Pradeep; Bhawesh Rajpal; Nitin Prasad; Ravi Shastri
Abstract
The move to standardize Indian Sign Language has created an opportunity for researchers to focus on solving local problems, to increase its reach. In this paper, a survey and assessment of the techniques applied to the recognition and conversion of Indian Sign Language are performed. An overview of the ...
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The move to standardize Indian Sign Language has created an opportunity for researchers to focus on solving local problems, to increase its reach. In this paper, a survey and assessment of the techniques applied to the recognition and conversion of Indian Sign Language are performed. An overview of the techniques used in sign language recognition for Indian Sign Language is provided to understand the status of research in this field. Following this, a comparison of techniques aimed at rendering a more detailed picture of the research results is presented. The challenges faced by researchers, the limitations of current techniques, and the need for improved research in this area are highlighted. With the intent of spurring more in-depth research, key areas within the approaches and techniques in need of improvement are summarized.
Computational Intelligence
Vishakha Arora; Afnas T
Abstract
Face Recognition has received a great deal of attention over the past few years and has become one of the most researched and spoken topics. It is a kind of automated biometric distinguishing approach that recognizes an individual based on their facial characteristics. The main aim of face reorganization ...
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Face Recognition has received a great deal of attention over the past few years and has become one of the most researched and spoken topics. It is a kind of automated biometric distinguishing approach that recognizes an individual based on their facial characteristics. The main aim of face reorganization is to implement the system for a particular face and distinguish it from a large number of stored faces with some real-time variations as well. Face recognition is in trend these days, the main reason being its efficiency and vast applications in day to day life. *Most* all the Telecom companies provide an option to unlock the phones by recognizing the face, which is a time saver and gives protection from theft as well. There are many more such applications of this technique that will be discussed in this paper along with the methods used for face recognition.
Computational Intelligence
Divya Aggarwal; Baishali Singh; K. Shweta Ranjan
Abstract
The recognition of pathways and identification of cars was seen with a prospective camera, which recognizes trajectories and predicts control points. The aim is to propose the location of the path. In this paper, lane detection algorithm Steering Assistance System (SAS) is introduced. Guiding helps to ...
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The recognition of pathways and identification of cars was seen with a prospective camera, which recognizes trajectories and predicts control points. The aim is to propose the location of the path. In this paper, lane detection algorithm Steering Assistance System (SAS) is introduced. Guiding helps to learn driving and anticipates the control points and defines the direction that makes it easy to learn in a potential way and a lane keeping assistance system which warns the driver on unintended lane departures. Path keeping is an important element for self-driving cars. This article describes the beginning to end adapting the approach to holding the car in the right direction.
Computational Intelligence
Bhawesh Rajpal; Nitin Prasad; Kaushal Kishore Rao Mangalore; Nikhitha Pradeep; Ravi Shastri
Abstract
This paper consists of analysis of an algorithm dealing with facial expressions recognition. The algorithm has three major steps, initially image is processed, then the facial features are extracted and finally facial expression is recognized. In the initial processing stage the facial region is identified ...
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This paper consists of analysis of an algorithm dealing with facial expressions recognition. The algorithm has three major steps, initially image is processed, then the facial features are extracted and finally facial expression is recognized. In the initial processing stage the facial region is identified using a Haar cascade classifier. This facial region is passed on to the model trained by a CNN where facial features are matched with the features specified in the model. In the final step on the basis of comparison in the previous step the image is labelled and results are displayed. By the experiment results it is clear that the method specified in the paper can detect facial expressions very well.
Computational Intelligence
Lenin Kanagasabai
Abstract
In this work, Greenland Wolf Optimization (GW) algorithm has been applied for real power loss reduction. Natural actions of the Greenland wolf have been mimicked to design the GW algorithm. Greenland wolf found in North West of green land and typical size of the pack is three. Arctic hares, musk oxen, ...
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In this work, Greenland Wolf Optimization (GW) algorithm has been applied for real power loss reduction. Natural actions of the Greenland wolf have been mimicked to design the GW algorithm. Greenland wolf found in North West of green land and typical size of the pack is three. Arctic hares, musk oxen, and lemmings are main prey for green land wolf and they migrate with respect to availability of food resources. Through flag vector, position, and velocity updating property Exploration, Exploitation capability of the algorithm has been enhanced. Proposed GW algorithm has been tested in standard IEEE 118 bus test system and results show the best performance of the GW algorithm in reducing the real power loss efficiently.
Computational Intelligence
Hossein Abbasimehr; Mohammad Khodizadeh Nahari
Abstract
Demand forecasting is a vital task for firms to manage the optimum quantity of raw material and products. The demand forecasting task can be formulated as a time series forecasting problem by measuring historical demand data at equal intervals. Demand time series usually exhibit a seasonal pattern. The ...
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Demand forecasting is a vital task for firms to manage the optimum quantity of raw material and products. The demand forecasting task can be formulated as a time series forecasting problem by measuring historical demand data at equal intervals. Demand time series usually exhibit a seasonal pattern. The principle idea of this study is to propose a method that predicts the demand for every different season using a specialized forecaster. In this study, we test our proposal using the Long Short-Term Memory (LSTM) which is a deep learning technique for time series forecasting. Specifically, the proposed method instead of learning an LSTM model using the whole demand data builds a specialized LSTM model corresponding to each season. The proposed method is evaluated using different topologies of the LSTM model. The results of experiments indicated that the proposed method outperforms the regular method considering the performance measures. The proposed method can be used in other domains for demand forecasting.
Computational Intelligence
Mohammed A. El-Shorbagy; Abd Allah A. Mousa; Hanaa ALoraby; Taghreed Abo-Kila
Abstract
An Improved Genetic Algorithm (I-GA) for solving multi-objective Fuzzy Multi–Index Multi-objective Transportation Problem (FM-MOTP) is presented. Firstly, we introduce a new structure for the individual to be able to represent all possible feasible solutions. In addition, in order to keep the feasibility ...
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An Improved Genetic Algorithm (I-GA) for solving multi-objective Fuzzy Multi–Index Multi-objective Transportation Problem (FM-MOTP) is presented. Firstly, we introduce a new structure for the individual to be able to represent all possible feasible solutions. In addition, in order to keep the feasibility of the chromosome, a criterion of the feasibility was designed. Based on this criterion, the crossover and mutation were modified and implemented to generate feasible chromosomes. Secondly, an external archive of Pareto optimal solutions is used, which best conform a Pareto front. For avoiding an overwhelming number of solutions, the algorithm has a finite-sized archive of non-dominated solutions, which is updated iteratively at the presence of new solutions. Finally, the computational studies using two numerical problems, taken from the literature, demonstrate the effectiveness of the proposed algorithm to solve FM-MOTP Problem under fuzziness.
