Global Normalization for Fingerprint Image Enhancement

Patel, M. P.; Parikh, Satyen M.; Patel, Ashok R.

doi:10.1007/978-3-030-37218-7_111

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Minimum-maximum normalization sets all dynamic data ranges to a scale from zero to one and decreases the overall standard deviation. However, this normalization may exclude outliers, which can bring important information to the analysis of the dataset [37].…”

Section: Data Normalizationmentioning

confidence: 99%

Diesel Engine Fault Prediction Using Artificial Intelligence Regression Methods

et al. 2023

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Predictive maintenance has been employed to reduce maintenance costs and production losses and to prevent any failure before it occurs. The framework proposed in this work performs diesel engine prognosis by evaluating the absolute value of the failure severity using random forest (RF) and multilayer perceptron (MLP) neural networks. A database was implemented with 3500 failure scenarios to overcome the problem of inducing destructive failures in diesel engines. Diesel engine failure signals were developed with the zero-dimensional thermodynamic model inside a cylinder coupled with the crankshaft torsional vibration model. Artificial neural networks and random forest regression models were employed for classifying and quantifying failures. The methodology was applied alongside an engine simulator to assess effectiveness and accuracy. The best-fitting performance was obtained with the random forest regressor with an RMSE value of 0.10 ± 0.03%.

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Section: Data Normalizationmentioning

confidence: 99%

Diesel Engine Fault Prediction Using Artificial Intelligence Regression Methods

et al. 2023

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“…Normalization is typically used in the preprocessing process. In image processing, normalization changes the pixel intensity values range [24]. Normalization changes the dimension of a gray image 𝐼: { 𝕏 ⊆ ℝ 𝑛 } → {𝑀𝑖𝑛, .…”

Section: Image Processing (Normalization)mentioning

confidence: 99%

Classification SARS-CoV-2 Disease based on CT-Scan Image Using Convolutional Neural Network

Kohsasih¹,

Hayadi²

2022

SJI

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Purpose: Convolutional Neural Network (CNN) is one of the most popular and widely used deep learning algorithms. These algorithms are commonly used in various applications, including image processing in medical and digital forensics, speech recognition, and other academic disciplines. SARS-CoV-2 (COVID-19) is a disease that first appeared in Wuhan, China, and has symptoms similar to pneumonia. This study aims to classify the covid-19 virus by proposing a deep learning model to prevent infection rates.Methods: The dataset used in this study is a public dataset originating from a hospital in Sao Paulo, Brazil. The data images consisted of 1252 infected with covid and 1230 data classified as non-covid but have other lung diseases. The classification method proposed in this research is a CNN model based on Resnet 50.Result: The experimental results show that the proposed Resnet 50-based convolutional neural network model works well in classifying SARS-CoV-2 disease using CT-Scan images. Our proposed model obtains 95% accuracy, precision, recall, and f1 values on the Epoch 500.Novelty: In this experiment, we utilized the Resnet50-based CNN model to classify the SARS-CoV-2 (COVID-19) disease using CT-Scan images and got good performance.

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“…• In past, our works were done on Fingerprint Recognition. Many different algorithms proposed for Pre-Processing [35][36][37][38][39][40][41] and Post-Processing [42][43][44] and improved the accuracy for recognition. Now, in future work applied those algorithms to different phases of handwritten character recognition and check the feasibility of improving the accuracy.…”

Section: Future Workmentioning

confidence: 99%

Comparative Study of Handwritten Character Recognition System for Indian Languages

Patel

Parikh

Patel

2021

ICT With Intelligent Applications

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Handwritten Character Recognition is the emerging domain for the research in image processing and pattern recognition because a huge amount of handwritten and OCR documents need to be processed in daily needs of different systems. Based on surveys of Human Development very less amount of Indian population can write and read English language. It specifies the need for research in Indian script for OCR technology. The research in this area is actually needed for developing countries such as India and remains beneficial to inspire the human-machine interface system. HCR makes it ease the automatic processing of tax and admission forms, bank cheques, addresses and postal codes. In the comparison of printed character recognition, handwritten character recognition is difficult because it can be different from person to person based on the style of writing, curve, size, strokes and thickness of character. Sufficient research work has been performed in foreign handwritten characters such as Arabic, Chinese and Japanese but very less research work has been performed in Indian scripts. This paper provides a comparative study of famous and well-known Tamil, Telugu, Kannada and Gujarati Indian scripts. This paper provides a detailed study based on the used methodology, dataset and accuracy.

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Global Normalization for Fingerprint Image Enhancement

Cited by 7 publications

References 9 publications

Diesel Engine Fault Prediction Using Artificial Intelligence Regression Methods

Diesel Engine Fault Prediction Using Artificial Intelligence Regression Methods

Classification SARS-CoV-2 Disease based on CT-Scan Image Using Convolutional Neural Network

Comparative Study of Handwritten Character Recognition System for Indian Languages

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