Evaluation of Normalization in Fake Fingerprint Detection with Heterogeneous Sensor

Nuraisha, Safira; Shidik, Guruh Fajar

doi:10.1109/isemantic.2018.8549742

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Data from each sensor were preprocessed to address issues such as missing values and inconsistencies. The thermal images along with sensor reading values were normalized using min-max normalization [24] to ensure uniformity in scales across all images and readings before they are used in the model for sensor measurement forecasting.…”

Section: Data Preprocessingmentioning

confidence: 99%

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Kempelis,

Polaka,

Romanovs

et al. 2024

Future Internet

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Urban agriculture presents unique challenges, particularly in the context of microclimate monitoring, which is increasingly important in food production. This paper explores the application of convolutional neural networks (CNNs) to forecast key sensor measurements from thermal images within this context. This research focuses on using thermal images to forecast sensor measurements of relative air humidity, soil moisture, and light intensity, which are integral to plant health and productivity in urban farming environments. The results indicate a higher accuracy in forecasting relative air humidity and soil moisture levels, with Mean Absolute Percentage Errors (MAPEs) within the range of 10–12%. These findings correlate with the strong dependency of these parameters on thermal patterns, which are effectively extracted by the CNNs. In contrast, the forecasting of light intensity proved to be more challenging, yielding lower accuracy. The reduced performance is likely due to the more complex and variable factors that affect light in urban environments. The insights gained from the higher predictive accuracy for relative air humidity and soil moisture may inform targeted interventions for urban farming practices, while the lower accuracy in light intensity forecasting highlights the need for further research into the integration of additional data sources or hybrid modeling approaches. The conclusion suggests that the integration of these technologies can significantly enhance the predictive maintenance of plant health, leading to more sustainable and efficient urban farming practices. However, the study also acknowledges the challenges in implementing these technologies in urban agricultural models.

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Section: Data Preprocessingmentioning

confidence: 99%

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Kempelis,

Polaka,

Romanovs

et al. 2024

Future Internet

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“…In [18], a detailed skeleton of CNN-based deep learning framework for crime scene detection is presented, which uses the data sets that contain several photographs that are incomplete, and the method extracts the minutiae and classifies them according to the CNN available. In [19], a fake print analysis approach is presented, which analyzes different images obtained from different sensors. The images from optical and thermal sensors are obtained, and by using the Min-max approach, the normalization is performed.…”

Section: Plos Onementioning

confidence: 99%

Multi-region minutiae depth value-based efficient forged finger print analysis

Baskar,

Rajagopal,

B. V. V. S.

et al. 2023

PLoS ONE

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The application of biometrics has expanded the wings to many domains of application. However, various biometric features are being used in different security systems; the fingerprints have their own merits as it is more distinct. A different algorithm has been discussed earlier to improve the security and analysis of fingerprints to find forged ones, but it has a deficiency in expected performance. A multi-region minutiae depth value (MRMDV) based finger analysis algorithm has been presented to solve this issue. The image that is considered as input has been can be converted into noisy free with the help of median and Gabor filters. Further, the quality of the image is improved by sharpening the image. Second, the preprocessed image has been divided into many tiny images representing various regions. From the regional images, the features of ridge ends, ridge bifurcation, ridge enclosure, ridge dot, and ridge island. The multi-region minutiae depth value (MRMDV) has been computed based on the features which are extracted. The test image which has a similarity to the test image is estimated around MRMDV value towards forgery detection. The MRMDV approach produced noticeable results on forged fingerprint detection accuracy up to 98% with the least time complexity of 12 seconds.

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“…By utilizing the development of information technology, the process of implementing classification methods can simplify human activities. The implementation of classification methods such as the maturity classification of tomatoes [2], classification of textual information [3], fingerprint classification [4] has been proposed to help increase human productivity. Moreover, classification techniques also can be used for medical purposes, such as cancer identification.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Neural Network using Nelder Mead in Breast Cancer Classification

Kusuma¹,

Shidik²,

Pramunendar³

2020

IJIES

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Classification is one of the data mining techniques which considered as supervised learning. Classification technique such as Backpropagation Neural Network (BPNN) has been utilized in several fields to increase human productivity. BPNN can give better results (more natural) compared with other statistical techniques. However, the learning process of BPNN could give an inefficient synapse weight of each hidden layer. This ineffective weight can affect the performance of the network. In this research, BPNN optimization using Nelder Mead to identifying the appearance of breast cancer is proposed. The datasets used are Breast Cancer Coimbra Dataset (BCCD), and Wisconsin Breast Cancer Dataset (WBCD). The testing result using accuracy and k-fold validation presents better performance compared with the original BPNN. Best average performance can be seen in the fifth fold of BCCD with 76.5217% of accuracy. Moreover, the highest average result of WBCD presented in the fourth fold with 91.1765% of average accuracy.

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Evaluation of Normalization in Fake Fingerprint Detection with Heterogeneous Sensor

Cited by 7 publications

References 13 publications

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Multi-region minutiae depth value-based efficient forged finger print analysis

Optimization of Neural Network using Nelder Mead in Breast Cancer Classification

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