scholarly journals Deep Learning Network for Classifying Target of Same Shape using RCS Time Series

2021 ◽  
Author(s):  
Rashmi Narasimhamurthy ◽  
Osamah Ibrahim Khalaf
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Target Prediction ◽  
Critical Element ◽  
Radar Target ◽  
Radar Signature ◽  
Radar Target Recognition ◽  
Different Shapes ◽  
Deep Learning Network

The main intension of this work is to find the warhead and decoy classification and identification. Classification of radar target is one of the utmost imperatives and hardest practical problems in finding out the missile. Detection of target in the pool of decoys and debris is one of the major radas technologies widely used in practice. In this study we mainly focus on the radar target recognition in different shapes like cone, cylinder and sphere based on radar cross section (RCS). RCS is a critical element of the radar signature that is used in this work to identify the target. The concept is to focus on new technique of ML for analyzing the input data and to attain a better accuracy. Machine learning has had a significant impact on the entire industry as a result of its high computational competency for target prediction with precise data analysis. We investigated various machine learning classifiers methods to categorize available radar target data. This chapter summarizes conventional and deep learning technique used for classification of radar target.

scholarly journals DETECTION AND CLASSIFICATION OF SYMBOLS IN PRINCIPLE SKETCHES USING DEEP LEARNING

2021 ◽  
Vol 1 ◽  
pp. 1183-1192
Author(s):  
Sebastian Bickel ◽  
Benjamin Schleich ◽  
Sandro Wartzack
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Product Development Process ◽  
Data Set ◽  
Deep Learning Network ◽  
The Cost ◽  
Early Phases

AbstractData-driven methods from the field of Artificial Intelligence or Machine Learning are increasingly applied in mechanical engineering. This refers to the development of digital engineering in recent years, which aims to bring these methods into practice in order to realize cost and time savings. However, a necessary step towards the implementation of such methods is the utilization of existing data. This problem is essential because the mere availability of data does not automatically imply data usability. Therefore, this paper presents a method to automatically recognize symbols from principle sketches, which allows the generation of training data for machine learning algorithms. In this approach, the symbols are created randomly and their illustration varies with each generation. . A deep learning network from the field of computer vision is used to test the generated data set and thus to recognize symbols on principle sketches. This type of drawing is especially interesting because the cost-saving potential is very high due to the application in the early phases of the product development process.

scholarly journals Machine Learning and Deep Learning Techniques, Features and Obstacles in the Cataract Diagnosis

2020 ◽  
Vol 9 (3) ◽  
pp. 87-93
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Congenital Cataract ◽  
Low Cost ◽  
Current Situation ◽  
Learning Techniques ◽  
Research Questions ◽  
New Research

Cataract is a degenerative condition that, according to estimations, will rise globally. Even though there are various proposals about its diagnosis, there are remaining problems to be solved. This paper aims to identify the current situation of the recent investigations on cataract diagnosis using a framework to conduct the literature review with the intention of answering the following research questions: RQ1) Which are the existing methods for cataract diagnosis? RQ2) Which are the features considered for the diagnosis of cataracts? RQ3) Which is the existing classification when diagnosing cataracts? RQ4) And Which obstacles arise when diagnosing cataracts? Additionally, a cross-analysis of the results was made. The results showed that new research is required in: (1) the classification of “congenital cataract” and, (2) portable solutions, which are necessary to make cataract diagnoses easily and at a low cost.

Deep Learning Network

2020 ◽  
pp. 1-30
Author(s):  
Bhanu Chander
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Great Power ◽  
Human Being ◽  
Technology Improvement ◽  
Deep Learning Network ◽  
High Level ◽  
Human Brains ◽  
Near Future

Artificial intelligence (AI) is defined as a machine that can do everything a human being can do and produce better results. Means AI enlightening that data can produce a solution for its own results. Inside the AI ellipsoidal, Machine learning (ML) has a wide variety of algorithms produce more accurate results. As a result of technology, improvement increasing amounts of data are available. But with ML and AI, it is very difficult to extract such high-level, abstract features from raw data, moreover hard to know what feature should be extracted. Finally, we now have deep learning; these algorithms are modeled based on how human brains process the data. Deep learning is a particular kind of machine learning that provides flexibility and great power, with its attempts to learn in multiple levels of representation with the operations of multiple layers. Deep learning brief overview, platforms, Models, Autoencoders, CNN, RNN, and Appliances are described appropriately. Deep learning will have many more successes in the near future because it requires very little engineering by hand.

scholarly journals Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1302 ◽  
Author(s):  
Reem Ibrahim Hasan ◽  
Suhaila Mohd Yusuf ◽  
Laith Alzubaidi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Vital Role ◽  
Considerable Improvement ◽  
Plant Diseases ◽  
Recent Emergence

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

Classification of pilots’ mental states using a multimodal deep learning network

2020 ◽  
Vol 40 (1) ◽  
pp. 324-336 ◽  
Author(s):  
Soo-Yeon Han ◽  
No-Sang Kwak ◽  
Taegeun Oh ◽  
Seong-Whan Lee
Keyword(s):  
Deep Learning ◽  
Mental States ◽  
Learning Network ◽  
Deep Learning Network

scholarly journals Comparison of deep learning and conventional machine learning methods for classification of colon polyp types

2021 ◽  
Vol 5 (1) ◽  
pp. 34-42
Author(s):  
Refika Sultan Doğan ◽  
Bülent Yılmaz
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
White Light ◽  
Narrow Band ◽  
Optical Biopsy ◽  
Colon Polyps ◽  
Tissue Biopsy ◽  
Conventional Machine ◽  
White Light Imaging

AbstractDetermination of polyp types requires tissue biopsy during colonoscopy and then histopathological examination of the microscopic images which tremendously time-consuming and costly. The first aim of this study was to design a computer-aided diagnosis system to classify polyp types using colonoscopy images (optical biopsy) without the need for tissue biopsy. For this purpose, two different approaches were designed based on conventional machine learning (ML) and deep learning. Firstly, classification was performed using random forest approach by means of the features obtained from the histogram of gradients descriptor. Secondly, simple convolutional neural networks (CNN) based architecture was built to train with the colonoscopy images containing colon polyps. The performances of these approaches on two (adenoma & serrated vs. hyperplastic) or three (adenoma vs. hyperplastic vs. serrated) category classifications were investigated. Furthermore, the effect of imaging modality on the classification was also examined using white-light and narrow band imaging systems. The performance of these approaches was compared with the results obtained by 3 novice and 4 expert doctors. Two-category classification results showed that conventional ML approach achieved significantly better than the simple CNN based approach did in both narrow band and white-light imaging modalities. The accuracy reached almost 95% for white-light imaging. This performance surpassed the correct classification rate of all 7 doctors. Additionally, the second task (three-category) results indicated that the simple CNN architecture outperformed both conventional ML based approaches and the doctors. This study shows the feasibility of using conventional machine learning or deep learning based approaches in automatic classification of colon types on colonoscopy images.

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