scholarly journals Evaluation of the impact of fly ash on infiltration characteristics using different soft computing techniques

2018 ◽  
Vol 8 (6) ◽  
Author(s):  
Parveen Sihag ◽  
Balraj Singh ◽  
Saurabh Gautam ◽  
Sourav Debnath
Keyword(s):  
Fly Ash ◽  
Soft Computing ◽  
Soft Computing Techniques ◽  
The Impact

An Intelligent Location Prediction System for Wind Farms Using Belief Networks

2020 ◽  
Vol 17 (8) ◽  
pp. 3412-3415
Author(s):  
P. Sardar Maran ◽  
K. Ashokkumar ◽  
J. Refonaa ◽  
Jany Shabu ◽  
A. Jesudoss ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Energy ◽  
Soft Computing ◽  
Alternative Energy ◽  
Machine Language ◽  
Energy Output ◽  
Energy Sources ◽  
Soft Computing Techniques ◽  
Ecological Parameters ◽  
The Impact

An energy crisis is a major problem India is facing today. Alternative energy sources are very important today to overcome the impact of rising oil prices. The main alternative energy source is the power from wind energy sources having 10% in India’s total energy consumption. The wind farm location planning to achieve the best energy output generation is in need of a finding solution. In this article, together with the belief network, a challenge is made exactly to prospects the energy from wind resources with and applied a statistical approach. Soft Computing techniques play a major role in the research applications especially in the multi-disciplinary areas. According to the technological development various soft computing techniques can be used in many areas to analyze the problem including Artificial Neural Network, Fuzzy logic, Adaptive Neuro and machine language. In this study the potential location of wind energy is identified by the belief neural network technique. The basic concept of Bayesian uncertainty treatment is that this analysis analyzes the conditional probability of occurrence. In addition, a particular region’s ecological parameters and environmental issues are also important. The ecological parameters and environmental factors also involved in the wind velocity of a particular region.

scholarly journals Analysis of Harassment Complaints to Detect Witness Intervention by Machine Learning and Soft Computing Techniques

2021 ◽  
Vol 11 (17) ◽  
pp. 8007
Author(s):  
Marina Alonso-Parra ◽  
Cristina Puente ◽  
Ana Laguna ◽  
Rafael Palacios
Keyword(s):  
Machine Learning ◽  
Soft Computing ◽  
Language Processing ◽  
Public Awareness ◽  
Free Text ◽  
Soft Computing Techniques ◽  
Awareness Raising ◽  
Processing Techniques ◽  
The Impact ◽  
The City

This research is aimed to analyze textual descriptions of harassment situations collected anonymously by the Hollaback! project. Hollaback! is an international movement created to end harassment in all of its forms. Its goal is to collect stories of harassment through the web and a free app all around the world to elevate victims’ individual voices to find a societal solution. Hollaback! pretends to analyze the impact of a bystander during a harassment in order to launch a public awareness-raising campaign to equip everyday people with tools to undo harassment. Thus, the analysis presented in this paper is a first step in Hollaback!’s purpose: the automatic detection of a witness intervention inferred from the victim’s own report. In a first step, natural language processing techniques were used to analyze the victim’s free-text descriptions. For this part, we used the whole dataset with all its countries and locations. In addition, classification models, based on machine learning and soft computing techniques, were developed in the second part of this study to classify the descriptions into those that have bystander presence and those that do not. For this machine learning part, we selected the city of Madrid as an example, in order to establish a criterion of the witness behavior procedure.

One-step models for soft computing techniques. Industrial application to image processing in quality assurance process

2015 ◽  
Vol 81 (5-8) ◽  
pp. 771-778 ◽  
Author(s):  
Pascual Noradino Montes Dorantes ◽  
Marco Aurelio Jiménez Gómez ◽  
Gerardo Maximiliano Méndez ◽  
Juan Pablo Nieto González ◽  
Jesús de la Rosa Elizondo
Keyword(s):  
Image Processing ◽  
Quality Assurance ◽  
Soft Computing ◽  
Industrial Application ◽  
Quality Assurance Process ◽  
Soft Computing Techniques ◽  
One Step ◽  
Assurance Process

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

scholarly journals Forecasting rainfall using soft computing techniques – A case study using India rainfall data

2019 ◽  
Vol 561 ◽  
pp. 012119
Author(s):  
Binoy B Nair ◽  
S Silamparasu ◽  
R Mohnish ◽  
T S Deepak ◽  
M Rahul
Keyword(s):  
Soft Computing ◽  
Rainfall Data ◽  
Soft Computing Techniques
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