Automatic fire pixel detection using image processing: a comparative analysis of rule-based and machine learning-based methods

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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Human Activity Classification for Surveillance using Machine Learning and Image Processing Algorithm

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i6.343346 ◽

2019 ◽

Vol 7 (6) ◽

pp. 343-346

Author(s):

Arvind Malge ◽

Mallikarjuna Shastry P.M

Keyword(s):

Machine Learning ◽

Image Processing ◽

Human Activity ◽

Processing Algorithm ◽

Image Processing Algorithm ◽

Activity Classification

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Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

International Journal of Computational Physics Series ◽

10.29167/a1i1p236-247 ◽

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.

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A Database for Counterfeit Electronics and Automatic Defect Detection Based on Image Processing and Machine Learning

ISTFA 2016: Conference Proceedings from the 42nd International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2016p0580 ◽

2016 ◽

Author(s):

Navid Asadizanjani ◽

Sachin Gattigowda ◽

Mark Tehranipoor ◽

Domenic Forte ◽

Nathan Dunn

Keyword(s):

Machine Learning ◽

Image Processing ◽

Integrated Circuits ◽

Web Application ◽

The United States ◽

Global Market ◽

Online Database ◽

Ongoing Effort ◽

Time Period ◽

Physical Defects

Abstract Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit.

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Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200904164539 ◽

2020 ◽

Vol 13 ◽

Author(s):

Ritu Khandelwal ◽

Hemlata Goyal ◽

Rajveer Singh Shekhawat

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Data Science ◽

Training Data ◽

Machine Learning Techniques ◽

Future Trends ◽

Data Set ◽

Learning Stage ◽

Learning Techniques ◽

Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

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