Application of Group Mobility Model for Ad Hoc Network

Dewangan, Kailash P.; Bonde, Padma; Raja, Rohit

doi:10.1007/978-981-15-1483-8_31

“…The data collection results were recorded and incorporated into the research testing process. Recognition of Handwritten Digits The proposed handwritten digit recognition scheme is focused on the input picture, pre-processing the image, segmenting the image, extracting the attribute of the image, and classifying the digit based on the extracted feature [34], [35]. Following is a brief description of the feature generation process, classifiers, and digit recognition technique.…”

Section: Methodsmentioning

confidence: 99%

Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

Raja

¹

,

Gupta

²

,

Miri

³

2021

EJENG

4

0

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The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.

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An efficient node localization and failure node detection in the MANET environment

Dewangan,

Bonde,

Raja

et al. 2024

Telecommun Syst

1

0

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Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

Raja

¹

,

Gupta

²

,

Miri

³

2021

EJERS

View full text Add to dashboard Cite

The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.

show abstract

Application of Group Mobility Model for Ad Hoc Network

Cited by 3 publications

References 16 publications

Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

An efficient node localization and failure node detection in the MANET environment

Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

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