Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 236-247
Author(s):  
Divya Srivastava ◽  
Rajitha B. ◽  
Suneeta Agarwal
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agricultural Production ◽  
Bacterial Blight ◽  
Early Stage ◽  
Second Phase ◽  
Computationally Efficient ◽  
Stage Of Disease ◽  
Accurate Detection ◽  
Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

MACHINE LEARNING APPLICATION: IMAGE PROCESSING

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Ayush Gupta
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Early Stage ◽  
Human Beings ◽  
Significant Information ◽  
Helpful Tool ◽  
Early Stages ◽  
Literature Reviews ◽  
Current Scenario

-In the current scenario of the data world, the data holds significant information if processed correctly. The data can be in the form of images which can prove to be a boon in deriving the useful insights from it in order to get the knowledge of things at an early stage itself. But the matter of concern is deriving the information from the images will be a tedious task for human beings and would incur a heavy cost and time. So, an easy and cheaper technique is to teach a machine efficiently to do the task for us. The concept of using Machines to do human tasks is known as Machine Learning. In this paper, I present various literature reviews regarding image processing in Machine learning and how image processing has helped in identifying the issues at early stages so that they can be resolved easily without causing much harm. Also, image processing has been a helpful tool in computer vision.

Cotton Leaf Disease Detection by Feature Extraction

2016 ◽  
pp. 89-104
Author(s):  
Savita N. Ghaiwat ◽  
Parul Arora
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Feature Extraction ◽  
Early Stage ◽  
Cotton Leaf ◽  
Control Methods ◽  
The Neural Network ◽  
The World ◽  
Leaf Image

Cotton leaf diseases have occurred all over the world, including India. They adversely affect cotton quality and yield. Technology can help in identifying disease in early stage so that effective treatment can be given immediately. Now, the control methods rely mainly on artificial means. This paper propose application of image processing and machine learning in identifying three cotton leaf diseases through feature extraction. Using image processing, 12 types of features are extracted from cotton leaf image then the pattern was learned using BP Neural Network method in machine learning process. Three diseases have been diagnosed, namely Powdery mildew, Downy mildew and leafminer. The Neural Network classification performs well and could successfully detect and classify the tested disease.

scholarly journals Analysis of Machine Learning Algorithms for Opinion Mining in Different Domains

2018 ◽  
Vol 1 (1) ◽  
pp. 224-234 ◽  
Author(s):  
Donia Gamal ◽  
Marco Alfonse ◽  
El-Sayed M. El-Horbaty ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Second Phase ◽  
Adaptive Boosting ◽  
Two Phases ◽  
Textual Content

Sentiment classification (SC) is a reference to the task of sentiment analysis (SA), which is a subfield of natural language processing (NLP) and is used to decide whether textual content implies a positive or negative review. This research focuses on the various machine learning (ML) algorithms which are utilized in the analyzation of sentiments and in the mining of reviews in different datasets. Overall, an SC task consists of two phases. The first phase deals with feature extraction (FE). Three different FE algorithms are applied in this research. The second phase covers the classification of the reviews by using various ML algorithms. These are Naïve Bayes (NB), Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Passive Aggressive (PA), Maximum Entropy (ME), Adaptive Boosting (AdaBoost), Multinomial NB (MNB), Bernoulli NB (BNB), Ridge Regression (RR) and Logistic Regression (LR). The performance of PA with a unigram is the best among other algorithms for all used datasets (IMDB, Cornell Movies, Amazon and Twitter) and provides values that range from 87% to 99.96% for all evaluation metrics.

scholarly journals Hybrid Manta Ray Foraging Optimization for Novel Brain Tumor Detection

2020 ◽  
Vol 2 (3) ◽  
pp. 175-185
Author(s):  
Karrupusamy P.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Feature Selection ◽  
Brain Tumor ◽  
Medical Image ◽  
Early Stage ◽  
Medical Image Processing ◽  
Human Effort ◽  
Manta Ray

In medical image processing, segmentation and extraction of tumor portion from brain MRI is a complex task. It consumes more time and human effort to differentiate the normal and abnormal tissue. Clinical experts need more time to provide accurate results, recent technology developments in image processing reduces the human effort and provides more accurate results which reduces time and death rates by identifying the issues in early stage itself. Machine learning based algorithms occupies a major role in bio medical image processing applications. The performance of machine learning models is in satisfactory levels, but it could be improved by introducing optimization in feature selection stage itself. The research work provides a hybrid manta ray foraging optimization for feature selection from brain tumor MRI images. Convolution neural network is used to test the optimized features and detects the early stage brain tumors. The experimental model is compared with existing artificial neural network, particle swarm optimization algorithm and acquires a better detection and classification accuracy.

scholarly journals Adaptive Multi-level Backward Tracking for Sequential Feature Selection

2021 ◽  
Vol 15 (1) ◽  
pp. 1-20
Author(s):  
Knitchepon Chotchantarakun ◽  
Ohm Sornil
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Nearest Neighbor ◽  
Second Phase ◽  
K Nearest Neighbor ◽  
Backward Tracking ◽  
Sequential Feature Selection ◽  
Multi Level ◽  
A New Technique ◽  
Two Phases

In the past few decades, the large amount of available data has become a major challenge in data mining and machine learning. Feature selection is a significant preprocessing step for selecting the most informative features by removing irrelevant and redundant features, especially for large datasets. These selected features play an important role in information searching and enhancing the performance of machine learning models. In this research, we propose a new technique called One-level Forward Multi-level Backward Selection (OFMB). The proposed algorithm consists of two phases. The first phase aims to create preliminarily selected subsets. The second phase provides an improvement on the previous result by an adaptive multi-level backward searching technique. Hence, the idea is to apply an improvement step during the feature addition and an adaptive search method on the backtracking step. We have tested our algorithm on twelve standard UCI datasets based on k-nearest neighbor and naive Bayes classifiers. Their accuracy was then compared with some popular methods. OFMB showed better results than the other sequential forward searching techniques for most of the tested datasets.

A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques

2021 ◽  
pp. 267-285
Author(s):  
Ankit Kumar Jain ◽  
Sumit Kumar Yadav ◽  
Neelam Choudhary
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Second Phase ◽  
Short Message ◽  
Machine Learning Classifiers ◽  
Novel Approach ◽  
Learning Techniques ◽  
Message Service ◽  
Novel Method ◽  
Two Phases

Smishing attack is generally performed by sending a fake short message service (SMS) that contains a link of the malicious webpage or application. Smishing messages are the subclass of spam SMS and these are more harmful compared to spam messages. There are various solutions available to detect the spam messages. However, no existing solution, filters the smishing message from the spam message. Therefore, this article presents a novel method to filter smishing message from spam message. The proposed approach is divided into two phases. The first phase filters the spam messages and ham messages. The second phase filters smishing messages from spam messages. The performance of the proposed method is evaluated on various machine learning classifiers using the dataset of ham and spam messages. The simulation results indicate that the proposed approach can detect spam messages with the accuracy of 94.9% and it can filter smishing messages with the accuracy of 96% on neural network classifier.

scholarly journals Efficient Processing of Image Processing Applications on CPU/GPU

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Najia Naz ◽  
Abdul Haseeb Malik ◽  
Abu Bakar Khurshid ◽  
Furqan Aziz ◽  
Bader Alouffi ◽  
...  
Keyword(s):  
Image Processing ◽  
Heterogeneous Systems ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Phase Image ◽  
Second Phase ◽  
Healthcare Applications ◽  
Big Visual Data ◽  
Workload Distribution ◽  
Two Phases

Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.

A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques

2020 ◽  
Vol 12 (1) ◽  
pp. 21-38 ◽  
Author(s):  
Ankit Kumar Jain ◽  
Sumit Kumar Yadav ◽  
Neelam Choudhary
Keyword(s):  
Machine Learning ◽  
Short Message Service ◽  
Machine Learning Techniques ◽  
Second Phase ◽  
Short Message ◽  
Novel Approach ◽  
Learning Techniques ◽  
Message Service ◽  
Novel Method ◽  
Two Phases

Smishing attack is generally performed by sending a fake short message service (SMS) that contains a link of the malicious webpage or application. Smishing messages are the subclass of spam SMS and these are more harmful compared to spam messages. There are various solutions available to detect the spam messages. However, no existing solution, filters the smishing message from the spam message. Therefore, this article presents a novel method to filter smishing message from spam message. The proposed approach is divided into two phases. The first phase filters the spam messages and ham messages. The second phase filters smishing messages from spam messages. The performance of the proposed method is evaluated on various machine learning classifiers using the dataset of ham and spam messages. The simulation results indicate that the proposed approach can detect spam messages with the accuracy of 94.9% and it can filter smishing messages with the accuracy of 96% on neural network classifier.

scholarly journals A Machine Learning Perspective on Causes of Suicides and identification of Vulnerable Categories using Multiple Algorithms

2021 ◽  
Author(s):  
Jitendra Shreemali ◽  
Prasun Chakrabarti ◽  
Tulika Chakrabarti ◽  
Sandeep Poddar ◽  
Daniel Sipple ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Trees ◽  
Second Phase ◽  
Numeric Model ◽  
The Family ◽  
Specific Factors ◽  
Two Phases ◽  
Long Term Impact ◽  
Country Specific

AbstractBackgroundSuicides represent a social tragedy with long term impact for the family. Given the growing incidence of suicides, a better understanding of factors causing it and addressing them has emerged as a social imperative.Material and MethodsThis study analyzed suicide data for three decades (1987-2016) and was carried out in two phases. Machine Learning Models run after pre-processing the suicide data included Neural network, Regression, Random Forest, XG Boost Tree, CHAID, Generalized Linear, Random Trees, Tree-AS and Auto Numeric Model.Results and ConclusionAnalysis of findings suggested that the key predictors for suicide are Age, Gender, and Country. In the second phase, data from happiness reports were merged with suicide data to check if Country-specific factors impact the list or order of key predictors. While the key predictors remain the same, Country-specific factors like Generosity, Health and Trust impact the suicide rate in the Country.

AN INFEASIBLE ELITIST BASED PARTICLE SWARM OPTIMIZATION FOR CONSTRAINED MULTIOBJECTIVE OPTIMIZATION AND ITS CONVERGENCE

2010 ◽  
Vol 24 (03) ◽  
pp. 381-400 ◽  
Author(s):  
JINGXUAN WEI ◽  
YUPING WANG
Keyword(s):  
Particle Swarm Optimization ◽  
Pareto Front ◽  
Optimization Problems ◽  
Early Stage ◽  
Particle Swarm ◽  
Threshold Value ◽  
Second Phase ◽  
Total Force ◽  
Swarm Optimization ◽  
Two Phases

In this paper, an infeasible elitist based particle swarm optimization is proposed for solving constrained optimization problems. Firstly, an infeasible elitist preservation strategy is proposed, which keeps some infeasible solutions with smaller rank values at the early stage of evolution regardless of how large the constraint violations are, and keep some infeasible solutions with smaller constraint violations and rank values at the later stage of evolution. In this manner, the true Pareto front will be found easier. Secondly, in order to find a set of diversity and uniformly distributed Pareto optimal solutions, a new crowding distance function is designed. It can assign large function values not only for the particles located in the sparse regions of the objective space but also for the crowded particles located near to the boundary of the Pareto front as well. Thirdly, a new mutation operator with two phases is proposed. In the first phase, the particles whose constraint violations are less than the threshold value will be used to compute the total force, then the force will be used as a mutation direction, being helpful to find the better solutions along this direction. In order to guarantee the convergence of the algorithm, the second phase of mutation is proposed. Finally, the convergence of the algorithm is proved. The comparative study shows that the proposed algorithm can generate widespread and uniformly distributed Pareto fronts and outperforms those compared algorithms.

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