An HADOOP File Systems Based Data Analysis of Flood Prediction

2020 ◽  
Vol 17 (8) ◽  
pp. 3543-3547
Author(s):  
A. Thinesshar Sachin ◽  
R. Monish Chandran ◽  
S. Dhamodaran ◽  
J. Refonaa ◽  
S. L. Jany Shabu
Keyword(s):  
Feature Extraction ◽  
File Systems ◽  
Weather Condition ◽  
Extraction Methods ◽  
Machine Learning Techniques ◽  
Flood Prediction ◽  
Characteristic Extraction ◽  
Digital Era ◽  
Learning Techniques ◽  
Suggested Item

One of the most well-known uses of Artificial Intelligence which has noticed an enormous development within the digital era is actually Machine Learning Techniques in which the method scientific studies and also increases the overall performance of its via progressive learning with no explicit programming. It is popular within many programs certainly one of them becoming a weather condition prediction. Image distinction, as well as feature extraction, are regarded as to become essentially the most popularly pre-owned techniques finished utilizing machine mastering procedure. With this proposed method, a hybrid model is developed for predicting rainfall by using feature extraction methods that have been proposed by us. The unit was created in such a manner it fetches a sequence of pictures originating from a data source as well as different info regarding earlier rainfalls wearing a particular region. The pictures are actually pre-processed as well as additional segmented for option extraction. The segmented pictures are then categorized via the Random Forest algorithm in which the sequence of pictures is actually validated frame by frame. The effectiveness of the suggested design is actually evaluated and it is kept in a sent out HADOOP File Systems (HDFS) for faster retrieval of information. It’s found that this suggested model provides greater results. The functionality of this unit tends to be more precise because the unit has an iterative method for characteristic extraction inside classifying pictures. The suggested item is actually incorporated by using an aware process to be able to attain a warning or an alert to the individuals in a space properly prior to a flood really hits.

scholarly journals Weather Prediction using Advanced Machine Learning Techniques

2021 ◽  
Vol 2089 (1) ◽  
pp. 012059
Author(s):  
G. Hemalatha ◽  
K. Srinivasa Rao ◽  
D. Arun Kumar
Keyword(s):  
Linear Relationship ◽  
Weather Prediction ◽  
Weather Condition ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Weather Data ◽  
Learning Techniques ◽  
Proposed Model ◽  
Non Linear ◽  
The Relationship

Abstract Prediction of weather condition is important to take efficient decisions. In general, the relationship between the input weather parameters and the output weather condition is non linear and predicting the weather conditions in non linear relationship posses challenging task. The traditional methods of weather prediction sometimes deviate in predicting the weather conditions due to non linear relationship between the input features and output condition. Motivated with this factor, we propose a neural networks based model for weather prediction. The superiority of the proposed model is tested with the weather data collected from Indian metrological Department (IMD). The performance of model is tested with various metrics..

Case-Based Reasoning

2020 ◽  
pp. 353-374
Author(s):  
Carlos Hernán Hernán Fajardo-Toro ◽  
Andrés Lopez Astudillo ◽  
Paloma María Teresa Martínez Sánchez ◽  
Paola Andrea Sánchez Sánchez ◽  
Alvaro José Fajardo-Toro
Keyword(s):  
Planning Process ◽  
The Other ◽  
Machine Learning Techniques ◽  
Case Based Reasoning ◽  
Digital Era ◽  
Learning Techniques ◽  
Forecast Models ◽  
High Flexibility ◽  
High Uncertainty ◽  
Case Based

Companies must deal with a high uncertainty caused by the characteristics of the markets and the economic, political, and social environment in which they offer their products and services. These characteristics are defined by the preferences of the consumers, which have a high variety coupled with the digital era. On the other hand, there is the necessity to implement measures that align the companies with the sustainability concepts, because of both legislations as well as the image that the customer could have of them. Due to this context, the organizations must find a way to optimize process and structures that require high flexibility given the need of combining perfect innovation, customization, standardization, and sustainability. Part of this planning process is the construction of forecast models that allows predicting with high precisión. In this chapter, a theoretical exposition is done and a literature revision of machine learning techniques is applied to try to solve the forecasting problem with special emphasis in neural networks and Case-Based Reasoning - CBR.

scholarly journals Flood prediction in a compound channel using machine learning techniques

2018 ◽  
Author(s):  
Vasilis Bellos ◽  
Juan Pablo Carbajal ◽  
Joao Leitao
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Compound Channel ◽  
Flood Prediction ◽  
Learning Techniques

Flood prediction in a synthetic trapezoidal, com-pound channel is made, using an emulator of thecomputationally demanding 2D hydrodynamicmodel FLOW-R2D.

scholarly journals DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images

2018 ◽  
Author(s):  
Sibel Çimen ◽  
Abdulkerim Çapar ◽  
Dursun Ali Ekinci ◽  
Umut Engin Ayten ◽  
Bilal Ersen Kerman ◽  
...  
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Deep Learning ◽  
Neurological Diseases ◽  
Binary Classification ◽  
Signal Transmission ◽  
Machine Learning Techniques ◽  
Neuronal Communication ◽  
Learning Classifier ◽  
Learning Techniques

AbstractOligodendrocytes wrap around the axons and form the myelin. Myelin facilitates rapid neural signal transmission. Any damage to myelin disrupts neuronal communication leading to neurological diseases such as multiple sclerosis (MS). There is no cure for MS. This is, in part, due to lack of an efficient method for myelin quantification during drug screening. In this study, an image analysis based myelin sheath detection method, DeepMQ, is developed. The method consists of a feature extraction step followed by a deep learning based binary classification module. The images, which were acquired on a confocal microscope contain three channels and multiple z-sections. Each channel represents either oligodendroyctes, neurons, or nuclei. During feature extraction, 26-neighbours of each voxel is mapped onto a 2D feature image. This image is, then, fed to the deep learning classifier, in order to detect myelin. Results indicate that 93.38% accuracy is achieved in a set of fluorescence microscope images of mouse stem cell-derived oligodendroyctes and neurons. To the best of authors’ knowledge, this is the first study utilizing image analysis along with machine learning techniques to quantify myelination.

scholarly journals Classification of Macronutrient Deficiencies in Maize Plant Using Machine Learning

2018 ◽  
Vol 8 (6) ◽  
pp. 4197
Author(s):  
Leena N ◽  
K. K. Saju
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Nutrient Management ◽  
Management Strategies ◽  
Nutrient Deficiency ◽  
Crop Productivity ◽  
Machine Learning Techniques ◽  
Nutritional Deficiencies ◽  
Feature Sets ◽  
Learning Techniques

<p>Detection of nutritional deficiencies in plants is vital for improving crop productivity. Timely identification of nutrient deficiency through visual symptoms in the plants can help farmers take quick corrective action by appropriate nutrient management strategies. The application of computer vision and machine learning techniques offers new prospects in non-destructive field-based analysis for nutrient deficiency. Color and shape are important parameters in feature extraction. In this work, two different techniques are used for image segmentation and feature extraction to generate two different feature sets from the same image sets. These are then used for classification using different machine learning techniques. The experimental results are analyzed and compared in terms of classification accuracy to find the best algorithm for the two feature sets.</p>

scholarly journals Statistical Feature Extraction and Classification using Machine Learning Techniques in Brain-Computer Interface

2020 ◽  
Vol 9 (3) ◽  
pp. 1754-1758
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Brain Computer Interface ◽  
Essential Feature ◽  
Average Energy ◽  
Machine Learning Techniques ◽  
Computer Interface ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Techniques

Brain Computer Interface is a paralyzed system. This system is used for direct communication between brain nerves and computer devices. BCI is an imagery movement of the patients who are all unable to communicate with the people. In EEG signals feature extraction plays an important role. Statistical based features are essential feature being used in machine learning applications. Researchers mainly focus on the filters and feature extraction techniques. In this paper data are collected from the BCI Competition III dataset 1a. Statistical features like minimum, maximum, standard deviation, variance, skewnesss, kurtosis, root mean square, average, energy, contrast, correlation and Homogeneity are extracted. Classification is done using machine learning techniques such as Support Vector Machine, Artificial Neural Network and K-Nearest Neighbor. In the proposed system 90.6% accuracy is achieved

scholarly journals Convolutional Neural Network Computation for Autonomous Vehicle

2020 ◽  
pp. 368-372
Author(s):  
Aires Da Conceicao ◽  
Sheshang D. Degadwala
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Autonomous Vehicle ◽  
Human Interaction ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Robust Feature Extraction

Self-driving vehicle is a vehicle that can drive by itself it means without human interaction. This system shows how the computer can learn and the over the art of driving using machine learning techniques. This technique includes line lane tracker, robust feature extraction and convolutional neural network.

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