scholarly journals Predicting the Crop Type and Region of Abundance using Machine Learning

2021 ◽  
Vol 9 (9) ◽  
pp. 1411-1416
Author(s):  
Mohd Khalid Shaikh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Rural Area ◽  
Identification System ◽  
Plant Identification ◽  
Convolution Neural Networks ◽  
Technology Students ◽  
Modern Age ◽  
Crop Type ◽  
As If

Abstract: In this modern age of science too technology, students and people in big cities ignorance of many things, such as how we get food, how things are processed, and much more. We are just it focuses on the results we get, because of this morality our knowledge diminishes, as if we did not know the crops or the goods ourselves using. As we visit the rural area when we arrive beyond some kind of plant, we can't know that, so we have identified this place to resolve the problem of students, researchers and many more people by creating a plant identification system which will predict the type of crop and the location of abundance where the harvest is planted. Keywords: Crop Identification System, Convolution Neural networks, MobilenetV2.

scholarly journals Plant leaf identification system using convolutional neural network

2021 ◽  
Vol 10 (6) ◽  
pp. 3341-3352
Author(s):  
Amiruzzaki Taslim ◽  
Sharifah Saon ◽  
Abd Kadir Mahamad ◽  
Muladi Muladi ◽  
Wahyu Nur Hidayat
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
White Background ◽  
Identification System ◽  
Plant Identification ◽  
Software Product ◽  
And Training ◽  
Random Background

This paper proposes a leaf identification system using convolutional neural network (CNN). This proposed system can identify five types of local Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By using CNN from deep learning, the network is trained from the database that acquired from leaf images captured by mobile phone for image classification. ResNet-50 was the architecture has been used for neural networks image classification and training the network for leaf identification. The recognition of photographs leaves requested several numbers of steps, starting with image pre-processing, feature extraction, plant identification, matching and testing, and finally extracting the results achieved in MATLAB. Testing sets of the system consists of 3 types of images which were white background, and noise added and random background images. Finally, interfaces for the leaf identification system have developed as the end software product using MATLAB app designer. As a result, the accuracy achieved for each training sets on five leaf classes are recorded above 98%, thus recognition process was successfully implemented.

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks

2019 ◽  
Vol 82 (6) ◽  
pp. 709-719 ◽  
Author(s):  
Chan‐Pang Kuok ◽  
Ming‐Huwi Horng ◽  
Yu‐Ming Liao ◽  
Nan‐Haw Chow ◽  
Yung‐Nien Sun
Keyword(s):  
Neural Networks ◽  
Mycobacterium Tuberculosis ◽  
Identification System ◽  
Accurate Identification ◽  
Convolution Neural Networks

Combined Evidence of MFCC and CRP Features Using Machine Learning Algorithms for Singer Identification

2020 ◽  
pp. 2158001
Author(s):  
Sangeetha Rajesh ◽  
N. J. Nalini
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Instrumental Music ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Identification Accuracy ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Identification System ◽  
Singer Identification

Singer identification is a challenging task in music information retrieval because of the combined instrumental music with the singing voice. The previous approaches focus on identification of singers based on individual features extracted from the music clips. The objective of this work is to combine Mel Frequency Cepstral Coefficients (MFCC) and Chroma DCT-reduced Pitch (CRP) features for singer identification system (SID) using machine learning techniques. The proposed system has mainly two phases. In the feature extraction phase, MFCC, [Formula: see text]MFCC, [Formula: see text]MFCC and CRP features are extracted from the music clips. In the identification phase, extracted features are trained with Bidirectional Long Short-Term Memory (BLSTM)-based Recurrent Neural Networks (RNN) and Convolution Neural Networks (CNN) and tested to identify different singer classes. The identification accuracy and Equal Error Rate (EER) are used as performance measures. Further, the experiments also demonstrate the effectiveness of score level fusion of MFCC and CRP feature in the singer identification system. Also, the experimental results are compared with the baseline system using support vector machines (SVM).

scholarly journals Parking Assist using Convolution Neural Networks

2020 ◽  
Vol 9 (6) ◽  
pp. 248-252
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Transfer Learning ◽  
Waiting Time ◽  
Urban Areas ◽  
Parking Space ◽  
Convolution Neural Networks ◽  
Machine Learning Model ◽  
Parking System

Parking vehicles are one of the most frustrating tasks that people face these days. Locating an available parking space is a huge headache especially in urban areas. This paper aims to design one such parking system which, in many ways reduces the hassles of parking. The paper presents a system where a Machine Learning model, Convolution Neural Network(CNN) is used to classify parking slots in a parking space into vacant and filled slots. In order to optimize the task of classification, the method of Transfer Learning is implemented in the paper. The problem of parking stands not only limited to causing inconvenience to the drivers, but also escalates to much larger and extensive problems, affecting a lot more people the environment. Hence it is very important to have a system is used parking system in place. The model proposed in the paper sends across parking information to a driver well in advance, there by greatly reducing the waiting time for the vehicle.

scholarly journals Data -Enhanced Convolution Neural Networks for Wall Following Robot Navigation

2019 ◽  
Vol 9 (1) ◽  
pp. 4426-4430
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Robot Navigation ◽  
Classification Performance ◽  
Sensor Data ◽  
Convolution Neural Networks ◽  
Machine Learning Methods ◽  
Smart Data ◽  
Constant Distance ◽  
Wall Following

Machine learning has been used for solving the Robot Navigation Task through the wall-following control. The wall-following control involves the movement of the robot in some directed direction maintaining a constant distance from a given wall. The path of the movement of robot is measured by ultrasonic sensors. Many machine learning methods have been used for this problem, as classifiers, but Convolution Neural Networks (CNN) outperforms them all with almost 98% of accuracy. This study compared the performance of five classifiers SVC, MLR, ANN, CNN-1D, and CNN-2D, which play the part of controller in the navigation work. We have used the ultrasonic sensor data to understand the hidden pattern in the navigation work and classified the actions by robot in terms of different motions performed by robot in response to it. The classification reports of CNN-2D and CNN-1D with Artificial Neural Networks (ANN) have also been presented in this paper. The smart Data-Enhancement used in proposed method significantly improves the classification performance of all classifiers, especially CNN.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

MACHINE LEARNING FOR SOFTWARE

2019 ◽  
pp. 251-255
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Soft Computing ◽  
Computing Technology

The study machine learning for software based on Soft Computing technology. It analyzes Soft Computing components. Their use in software, their advantages and challenges are studied. Machine learning and its features are highlighted. The functions and features of neural networks are clarified, and recommendations were given.

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