Dorsal hand veins features extraction and recognition by correlation coefficient

Rajab, Maha A.; Hashim, Kadhim M.

doi:10.12928/telkomnika.v20i4.22068

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Four primary criteria are used to assess the efficiency and accuracy of the system that is suggested, namely accuracy, precision, recall, and specificity. Accuracy pertains to the proportion of correct estimates achieved by the system as the (1) [21]. Precision measures the ratio of real positive detections to all positive detections, as the (2) [22].…”

Section: Resultsmentioning

confidence: 99%

Classification of grapevine leaves images using VGG-16 and VGG-19 deep learning nets

A. Rajab,

Abdullatif,

Sutikno

2024

TELKOMNIKA

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The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classification by adapting VGG-16 net and VGG-19 net models and subsequently identifying the optimal performer between the two nets during the classification process. A publicly available dataset comprising 500 images categorized into 5 distinct classes (100 images per class), was utilized in this work. The obtained empirical outcomes demonstrate a remarkable accuracy rate of 99.6% for the VGG-16 net model, while VGG-19 net achieves a 100% accuracy rate. Based on these findings, it can be inferred that VGG-19 net exhibits superior performance in classifying images of grapevine leaves compared to the VGG-16 net.

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Section: Resultsmentioning

confidence: 99%

Classification of grapevine leaves images using VGG-16 and VGG-19 deep learning nets

A. Rajab,

Abdullatif,

Sutikno

2024

TELKOMNIKA

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“…Citra pembuluh darah mempunyai sifat unik karena sulit dipalsukan, diubah, atau dirusak karena letaknya di kulit manusia. Penggunaan pembuluh darah di punggung tangan untuk sistem otentikasi biometrik memiliki keunggulan higienis karena dapat digunakan tanpa menyentuh permukaan sensor karena struktur pembuluh darah tidak berubah [14].…”

Section: Pendahuluanunclassified

Backpropagation Design for Authenticating Blood Vessel Patterns of the Back of the Hand Using GLRLM

Syam,

Yudono,

Sujjada

2024

SISTEMASI

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Keamanan digital menjadi aspek kritis dalam era teknologi informasi saat ini, di mana akses terhadap perangkat dan data pribadi seringkali menjadi target utama oleh pihak yang tidak bertanggung jawab. Metode identifikasi tradisional seperti kata sandi dan PIN mulai menunjukkan keterbatasan dalam mengatasi tantangan keamanan yang semakin kompleks. Pembuluh darah punggung tangan menawarkan kelebihan tertentu yang menjadikannya pilihan menarik untuk sistem pengenalan biometrik karena pola pembuluh darah punggung tangan cenderung stabil sepanjang waktu, tidak dipengaruhi oleh faktor eksternal seperti perubahan cuaca atau kebersihan. Penelitian ini bertujuan untuk mengembangkan suatu sistem yang dapat mengidentifikasi Pembuluh Darah Punggung Tangan sebagai tanda biometrik. Pendekatan yang digunakan melibatkan ekstraksi ciri GLRLM dan penerapan metode identifikasi Jaringan Syaraf Tiruan Perambatan Balik. Tujuan utamanya adalah mencapai tingkat akurasi yang lebih tinggi daripada penelitian sebelumnya dalam domain yang sama. Proses identifikasi melibatkan beberapa tahapan, mulai dari penerimaan citra, pra-pemrosesan citra, segmentasi, ekstraksi ciri, identifikasi, hingga mendapatkan citra hasil identifikasi pembuluh darah. Hasil pengujian menunjukkan bahwa sistem yang dikembangkan mencapai tingkat keberhasilan ratarata sebesar 82,52% berdasarkan lima skenario pengujian yang berbeda. Skenario keempat terbukti memberikan hasil akurasi pengujian tertinggi, yakni 87%.

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“…The correlation coefficient of a visible image with correct brightness is one, but it is much lower for a ciphered image (almost equal to zero). An encryption technique produces ciphered images with randomly dispersed pixels of various brightness and a low correlation coefficient between neighboring pixels [3], [15], [28]. The correlation coefficient of any two-pixel color values at the same place in the original and cipher pictures is calculated using ( 14) [2].…”

Section: − Correlation Coefficient Analysismentioning

confidence: 99%

Image encryption algorithm based on the density and 6D logistic map

Rashid

Hussein

2023

IJECE

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One of the most difficult issues in the history of communication technology is the transmission of secure images. On the internet, photos are used and shared by millions of individuals for both private and business reasons. Utilizing encryption methods to change the original image into an unintelligible or scrambled version is one way to achieve safe image transfer over the network. Cryptographic approaches based on chaotic logistic theory provide several new and promising options for developing secure Image encryption methods. The main aim of this paper is to build a secure system for encrypting gray and color images. The proposed system consists of two stages, the first stage is the encryption process, in which the keys are generated depending on the chaotic logistic with the image density to encrypt the gray and color images, and the second stage is the decryption, which is the opposite of the encryption process to obtain the original image. The proposed method has been tested on two standard gray and color images publicly available. The test results indicate to the highest value of peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI), number of pixel change rate (NPCR) are 7.7268, 50.2011 and 100, respectively. While the encryption and decryption speed up to 0.6319 and 0.5305 second respectively.

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Dorsal hand veins features extraction and recognition by correlation coefficient

Cited by 7 publications

References 8 publications

Classification of grapevine leaves images using VGG-16 and VGG-19 deep learning nets

Classification of grapevine leaves images using VGG-16 and VGG-19 deep learning nets

Backpropagation Design for Authenticating Blood Vessel Patterns of the Back of the Hand Using GLRLM

Image encryption algorithm based on the density and 6D logistic map

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