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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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A Leaf Disease Classification Model in Betel Vine Using machine learning Techniques

A Leaf Disease Classification Model in Betel Vine Using Mach...

引用

international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Md Zahid Hasan Nahid Zeba Md. Abdul Malek Sanjida Sultana Reya Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Betel vine leaves diseases caused by regular endangerment to bacteria which causes a huge yield loss globally. machine learning, the latest breakthrough in computer vision, is encouraging for fine-grained disease classification, as the method uses SVM classifier and Gaussian mixture model for image segmentation. Disease detection and classifications are considered as the two hardest works to the recognition of Betel vine disease. Two types of betel vine diseases are focused on the paper, Bacterial Leaf Spot and Stem Leaf. Pictures are taken using a phone camera or any kind of portable device and the dataset consists of almost 1275 images where each class contains 636 images. The proposed system reaches 83.69% accuracy in classification which appears to be good and promising in comparison to other relevant papers.

关键词： Support vector machines Training Microorganisms Computational modeling machine learning Diseases Testing

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A Review of Implementation and Obstacles in Predictive machine learning Model at Educational Institutions 2

A Review of Implementation and Obstacles in Predictive Machi...

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2nd international conference on Computer, Science, Engineering, and Technology, ICComSET 2019

作者： Ranggadara, Indra Sari, Yunita Sartika Dwiasnati, Saruni Prihandi, Ifan Sfenrianto Computer Science Department Mercu Buana University Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

machine learning is a system that can learn by itself by using training data and testing data testing. In a variety of machine learning research has various obstacles in implementation especially in education institution, in its application, many things need to considered for more information to enrich research in the area of machine learning by way of comparing various obstacles in implementation in similar research. So that in this study is to find similarities and anticipations made in the initial stages of pre-processing to visualisation, as well as the contribution that can give in this study explains the best alternative for researching following the experience of several researchers in conducting machine learning research. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： machine learning

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Cardiovascular disease prediction using machine learning tools 2nd

Cardiovascular disease prediction using machine learning too...

引用

2nd international conference on machine Intelligence and signal processing, MISP 2019

作者： Kumar, Ashutosh Gyawali, Rahul Agarwal, Sonali Indian Institute of Information Technology Allahabad India

ISBN: (纸本)9789811513657

Cardiovascular disease deals with the disarray of blood vessels and heart. It is one of the leading causes of death globally. A doctor cannot be equally skilled in each and every domain and their availability is limited in most parts, especially in a country like India. Since the inception of machine learning, healthcare has been one of the most rewarding fields for its application. In this paper, we compared the accuracy of different machine learning algorithms like multilayer perceptron, k-nearest neighbour, logistic regression and support vector machine in approximating the severity level of cardiovascular disease in a person considering various physiological parameters. The heart disease dataset is taken from the UCI machine learning repository which is publicly available and is the most widely used dataset for heart disease prediction. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Forecasting

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Business Forecasting System using machine learning Approach

Business Forecasting System using Machine Learning Approach

引用

international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Md. Anisur Rahman Mia Mohammad Abu Yousuf Rupon Ghosh Institute of Information Technology Jahangirnagar University Dhaka Bangladesh Khulna University of Engineering and Technology Khulna Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Forecasting system can help any business to predict its future sale, profit and loss. So it is very useful while launching a new product or manufacturing existing products. In this work, a business forecasting system is designed and implemented to forecast the amount of future sale of products using machine learning (ML). ML algorithms build a pattern from input variables then make decision. Use of big data technology in researches also increasing. By combining big data and ML together powerful predictive systems can be designed. So, big data processing technology has been used in this work to prepare data for training purpose of the proposed system. Prediction accuracy of the proposed system varied 99% to 75% for different products and mean absolute percentage error (MAPE) is 7.32%. So the proposed system is very much efficient to predict future sale.

关键词： Training Backpropagation Training data machine learning Big Data Forecasting Business

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An Extensive Survey on Some Deep-learning Applications 2nd

An Extensive Survey on Some Deep-Learning Applications

引用

2nd international conference on Computing, Communications and Data Engineering (CCODE)

作者： Sultana, Jabeen Rani, M. Usha Farquad, M. A. H. SPMVV Dept Comp Sci Tirupati Andhra Pradesh India InI Labs Inc Waterloo ON Canada

ISBN: (纸本)9789811501357;9789811501340

Deep learning prospered as a distinct era of research and fragment of a wider family of machine learning, based on a set of algorithms that strengthen to model high-level abstractions in data. It tries to imitate the human intellect and learns from complicated input data and resolve different types of difficult and complex tasks. Because of Deep learning, it was successful to deal with different input data types such as text, sound, and images in various fields. Improvement in deep-learning research has already influenced the search for speech recognition, automatic navigation systems, parallel computations, image processing, ImageNet, natural language processing, representation learning, Google translate, etc. Here, we present a review of DL and its applications including the recent development in natural language processing (NLP).

关键词： Deep learning (DL) Natural language processing (NLP) Image processing (IP) Speech recognition Parallel computations

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Application analysis of machine learning in fault diagnosis: A bibliometric analysis 2

Application analysis of machine learning in fault diagnosis:...

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2020 2nd international conference on Applied machine learning and Data Science, ICAMLDS2020

作者： Tu, Wenyan Fang, Ji He, Yihua Huang, Jin Huazhong Univ Sci and Technol Lib Wuhan Hubei China

This article to analyze the application of machine learning in fault diagnosis by using bibliometrics, co-citation network analysis and cluster analysis methods. The analysis found that in the application research of machine learning in fault diagnosis, China has the largest number of published papers and cited papers, among which IEEE access is the most published journal, MECHANICAL SYSTEMS And signal processing is the most cited journal,followed by the United States and India. Using document co-citation relationship analysis, a set of key documents in this field was identified., Currently, the important algorithms fault diagnosis include support vector data description method, transfer learning algorithm, convolutional neural network algorithm, natural inspired optimization algorithm, bayesian network, wavelet packet decomposition algorithm, fuzzy logic algorithm and so on. All of them are clustered according to keywords, and their application fields include acoustic emission and fault diagnosis of doubly-fed induction generators. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Failure analysis

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Prediction of Apple Leaf Diseases Using Multiclass Support Vector machine

Prediction of Apple Leaf Diseases Using Multiclass Support V...

引用

international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Soarov Chakraborty Shourav Paul Md. Rahat-uz-Zaman Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Every year apple yield has been affected by Black rot and Cedar apple rust. It has a significant effect on both the apple industry and the country's economy. Here, we recommend a system to detect diseases from the infected apple leaves by combining machine learning and image processing principles. This approach can classify both infected and non-infected apple leaves efficiently. The identification is started by preprocessing the image using several image processing techniques, including the Otsu thresholding algorithm and histogram equalization. Using the image segmentation region of the infected part separates, and a Multiclass SVM recognizes the disease type from the original leaf image among 500 images with 96% accuracy. It also demonstrates the percentage of the total infected area of that diseased apple leaf image.

关键词： Support vector machines Industries Image segmentation Image recognition Thresholding (Imaging) signal processing algorithms Diseases

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An Intelligent Direction Finding Method with Deep Neural Network 2

An Intelligent Direction Finding Method with Deep Neural Net...

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2nd IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Xu, Yajun Guo, Hesong Fan, Rong Si, Chengke Ding, Xueke Civil Aviat Fight Univ China Aviat Engn Inst Guanghan Peoples R China Tongfang Elect Sci & Technol Co Ltd Elect Countermeasure Inst Jiujiang Peoples R China

ISBN: (纸本)9781728199481

Different from the model-driven direction finding (DF) methods, the data-driven DF methods have many advantages, such as not relying on the array geometry, no need for a special channel calibration module, and better adaptability to the DF system error. In this paper, an intelligent DF method based on deep neural network (DNN) is developed. It is composed of auto-encoder network and residual neural network. Numerical simulation experiments have demonstrated the superiority of the proposed method in direction of arrival (DOA) estimation precision especially when the signal-to-noise ratio (SNR) is low.

关键词： intelligent direction finding direction of arrival estimation deep neural network array signal processing

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A Web Based Four-Tier Architecture using Reduced Feature Based Neural Network Approach for Prediction of Student Performance

A Web Based Four-Tier Architecture using Reduced Feature Bas...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Md. Anwar Hossen Rakib Bin Alamgir Arman Ul Alam Fatema Siddika Shah Fahad Hossain Md. Shohel Arman Daffodil International University Dhaka Bangladesh Jagannath University Dhaka Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Enhancing student's performance is a significant part of developing quality education in any educational institute. It is very difficult to get promising student performance without student categorization according to their academic performance as there are different standardized students. In this paper, our aim is to determine the performance of the students. For this purpose, a survey has been conducted on students in our university in order to collect data and to analyze and predict the student category based on their performance. Apart from this, another purpose of this study is to examine the effect of the reduced features on the classification model using state-of-art machine learning algorithms. Here, we propose a workflow of web-based four-tier architecture for the student performance prediction that will define the student's category in order to help them exactly pinpoint their learning capabilities. Hence, we used multiple supervised learning-based machine learning techniques for the prediction of student performance. Each of the student category categorized by considering on the top features. The analysis results indicate that we got the highest performance that is 88.00% by using the Artificial Neural Network (ANN) among the classifiers by showing its superiority to the existing model.

关键词： Supervised learning Artificial neural networks machine learning Predictive models signal processing Service-oriented architecture Robots

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A Comparative Study on Heart Disease Prediction Using Data Mining Techniques and Feature Selection

A Comparative Study on Heart Disease Prediction Using Data M...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Farzana Tasnim Sultana Umme Habiba International Islamic University Chittagong Kumira Chittagong Bangladesh Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

The world health organization shows us that cardiovascular disease is one of the noteworthy reasons for death in the world. In this paper, data mining classification techniques i.e. Naive Bayes (NB), Support Vector machine (SVM), k-nearest neighbors' (k-NN), Decision Tree (DT), Neural Network (NN), Logistic Regression (LR), Random Forest (RF), Gradient Boosting are proposed to predict the probability of the coronary heart disease. In the present world, researchers are trying heart and soul to make advancements in the smart health care system. An automated system predicting the risk of heart disease may be added as a great achievement. This work of predicting heart disease is evaluated using the dataset from the UCI machine learning repository. The feature selection method enhances the performance of traditional machine learning algorithms. Among the classification algorithms, Random Forest (RF) algorithm with PCA has given the best accuracy of 92.85% for heart disease classification.

关键词： Heart Support vector machines Radio frequency machine learning algorithms Feature extraction Random forests Principal component analysis

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