DCT-phase statistics for forged IMEI numbers and air ticket detection

Nandanwar, Lokesh; Shivakumara, Palaiahnakote; Kanchan, Swati; Basavaraja, V.; Guru, D. S.; Pal, Umapada; Lü, Tong; Blumenstein, Michael

doi:10.1016/j.eswa.2020.114014

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…Decision trees had a higher accuracy and better specificity and sensitivity when compared with the logistic regression and random forest while using the body attribute without the body attribute [43]- [45]. We have carried out the chi-square feature selection technique on our obtained dataset.…”

Section: Resultsmentioning

confidence: 99%

Utilizing deep learning, feature ranking, and selection strategies to classify diverse information technology ticketing data effectively

Deshai

Rao

Chittineni

et al. 2023

IJ-AI

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In today's internet world, information technology (IT) ticketing services are potentially increasing across many corporations. Therefore, the automatic classification of IT tickets becomes a significant challenge. Feature selection becomes most important, particularly in data sets with several variables and features. However, enhance classification's precision and performance by stopping insignificant variables. Through our earlier research, we have categorized the unsupervised ticket dataset. As a result, we have converted the dataset into a supervised dataset. In this article, the classification of different IT tickets on Machine learning algorithms, Feature ranking, and feature selection techniques are used to improve the efficiency of machine learning algorithms. However, compared to the machine learning (ML) algorithms, the convolutional neural network (CNN) algorithm provides a better classification of the token IDs and provide better accuracy.

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Section: Resultsmentioning

confidence: 99%

Utilizing deep learning, feature ranking, and selection strategies to classify diverse information technology ticketing data effectively

Deshai

Rao

Chittineni

et al. 2023

IJ-AI

View full text Add to dashboard Cite

show abstract

“…This section exists independently in this review because it is now the subject of study within the field of FDE as examiners are called upon more and more to examine digital documents. In the period under review nine articles [ [647] , [648] , [649] , [650] , [651] , [652] , [653] , [654] , [655] ], 13 conference presentations [ [656] , [657] , [658] , [659] , [660] , [661] , [662] , [663] , [664] , [665] , [666] , [667] , [668] ], and five workshops [ [669] , [670] , [671] , [672] , [673] ] were dedicated to this subject.…”

Section: Forensic Document Examinationmentioning

confidence: 99%

“…Nandanwar et al [ 649 ] presented a new expert system for detecting forged and altered digital documents, specifically IMEI numbers and altered air ticket images. Experimental results on their dataset of forged IMEI numbers, on altered air tickets, on benchmark datasets of video caption (tampered) text, and on altered receipts of the ICPR 2018 FDC dataset, show that the proposed method was robust across different datasets.…”

Section: Forensic Document Examinationmentioning

confidence: 99%

Interpol questioned documents review 2019–2022

Deviterne-Lapeyre

Ibrahim²

2023

Forensic Science International: Synergy

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“…For disease identification, we need to extract the arecanut region from the background rather than considering the whole image. If we use the whole image for feature extraction, it can be represented as classification but not disease identification [20][21][22][23]. In this way, the disease identification is different from image classification.…”

Section: Generating Multi-gradient Directional Image (Mgd)mentioning

confidence: 99%

Multi‐gradient‐direction based deep learning model for arecanut disease identification

Mallikarjuna

Shivakumara

Khare

et al. 2022

CAAI Trans on Intel Tech

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Arecanut disease identification is a challenging problem in the field of image processing. In this work, we present a new combination of multi‐gradient‐direction and deep convolutional neural networks for arecanut disease identification, namely, rot, split and rot‐split. Due to the effect of the disease, there are chances of losing vital details in the images. To enhance the fine details in the images affected by diseases, we explore multi‐Sobel directional masks for convolving with the input image, which results in enhanced images. The proposed method extracts arecanut as foreground from the enhanced images using Otsu thresholding. Further, the features are extracted for foreground information for disease identification by exploring the ResNet architecture. The advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut images. Experimental results on the dataset of four classes (healthy, rot, split and rot‐split) show that the proposed model is superior in terms of classification rate.

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DCT-phase statistics for forged IMEI numbers and air ticket detection

Cited by 8 publications

References 12 publications

Utilizing deep learning, feature ranking, and selection strategies to classify diverse information technology ticketing data effectively

Utilizing deep learning, feature ranking, and selection strategies to classify diverse information technology ticketing data effectively

Interpol questioned documents review 2019–2022

Multi‐gradient‐direction based deep learning model for arecanut disease identification

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