Offline Signature Verification using Deep Learning Convolutional Neural Network (CNN) Architectures GoogLeNet Inception-v1 and Inception-v3

Jahandad,; Sam, Suriani Mohd; Kamardin, Kamilia; Sjarif, Nilam Nur Amir; Mohamed, Norliza

doi:10.1016/j.procs.2019.11.147

Cited by 119 publications

(42 citation statements)

References 3 publications

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“…[228] The smart sensor system can potentially replace medical practitioners in the diagnosis of diseases such as the smart sensor system created by Esteva et al [126] which identifies skin cancers from images, including keratinocyte carcinomas, benign seborrheic keratoses, malignant melanomas, and benign nevi. The system implements a smart model based on a CNN that is trained with a technique called transfer learning method, [229] in which a pretrained model (GoogleNet Inception v3) [230] forms the foundation of the skin cancer classification model. This method significantly reduces the training time, and classification errors as the GoogleNet model has previously learned how to separate 1000 object categories from 1.28 million images that are not related to skin cancer.…”

Section: Medical Image Diagnosis and Analysismentioning

confidence: 99%

Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

124

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Recent advancements and major breakthroughs in machine learning (ML) technologies in the past decade have made it possible to collect, analyze, and interpret an unprecedented amount of sensory information. A new era for “smart” sensor systems is emerging that changes the way that conventional sensor systems are used to understand the world. Smart sensor systems have taken advantage of classic and emerging ML algorithms and modern computer hardware to create sophisticated “smart” models that are tailored specifically for sensing applications and fusing diverse sensing modalities to gain a more holistic appreciation of the system being monitored. Herein, a review of the recent sensing applications, which harness ML enabled smart sensor systems, is presented. First well‐known ML algorithms implemented in smart sensor systems for practical sensing applications are discussed. Subsequent sections summarize the practical sensing applications under two major categories: physical and chemical sensing and visual imaging sensing describing how the sensor technologies are coupled with ML “smart” models and how these systems achieve practical benefits. Finally, an outlook on the current trajectory and challenges that will be faced by future smart sensing systems and the opportunities that may be unlocked is provided.

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Section: Medical Image Diagnosis and Analysismentioning

confidence: 99%

Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

124

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“…Simonyan and Zisserman [43] adopted an architecture of very small (3 × 3) convolution filters to conduct a comprehensive evaluation of networks with increasing depth and the two best-performing ConvNet models were available publicly to facilitate the further research in this field. By increasing the depth and width of the network while keeping the computational budget constant, Szegedy et al [44] introduced a deep convolutional neural network architecture named "Inception" in which the utilization of the computing resources can be improved significantly, and Jahandad et al [45] worked on 2 convolutional neural network architectures (Inception-v1 and Inception-v3) based on "Inception" and proved that these 2 models performed better than others, and Inception-v1 with 22-layer-deep network performed better than 42-layer-deep Inception-v3 network when facing low-resolution input images and 2D images of signatures; however, Inception-v3 outperformed in ImageNet challenge. e general trend of neural networks is to increase the depth of the network and the width of layer.…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition of Students Based on Facial Expressions in Online Education Based on the Perspective of Computer Simulation

Wang

Niu

et al. 2020

Complexity

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Online education has developed rapidly due to its irreplaceable convenience. Under the severe circumstances caused by COVID-19 recently, many schools around the world have delayed opening and adopted online education as one of the main teaching methods. However, the efficiency of online classes has long been questioned. Compared with traditional face-to-face classes, there is a lack of direct, timely, and effective communication and feedback between teachers and students in the online courses. Previous studies have shown that there is a close and stable relationship between a person’s facial expressions and emotions generally. From the perspective of computer simulation, a framework combining a face expression recognition (FER) algorithm with online courses platforms is proposed in this work. The cameras in the devices are used to collect students’ face images, and the facial expressions are analyzed and classified into 8 kinds of emotions by the FER algorithm. An online course containing 27 students conducted on Tencent Meeting is used to test the proposed method, and the result proved that this method performs robustly in different environments. This framework can also be applied to other similar scenarios such as online meetings.

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“…The main objective of GoogLeNet network is Inception network framework while GoogLeNet technique is called Inception networks [19]. It encompasses maximal GoogLeNet version and is classified as distinct versions such as Inception v1, Inception v2, Inception v3, Inception v4, and Inception-ResNet.…”

Section: Activation Functionmentioning

confidence: 99%

Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

Almasoud¹,

Eisa²,

Duhayyim³

et al. 2022

Computers, Materials &Amp; Continua

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In agriculture, rice plant disease diagnosis has become a challenging issue, and early identification of this disease can avoid huge loss incurred from less crop productivity. Some of the recently-developed computer vision and Deep Learning (DL) approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes. With this motivation, the current research work devises an Efficient Deep Learning based Fusion Model for Rice Plant Disease (EDLFM-RPD) detection and classification. The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient manner. In addition, EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected portions. The study also used a fusion of handcrafted Gray Level Co-occurrence Matrix (GLCM) and Inception-based deep features to derive the features. Finally, Salp Swarm Optimization with Fuzzy Support Vector Machine (FSVM) model is utilized for classification. In order to validate the enhanced outcomes of EDLFM-RPD technique, a series of simulations was conducted. The results were assessed under different measures. The obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

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Offline Signature Verification using Deep Learning Convolutional Neural Network (CNN) Architectures GoogLeNet Inception-v1 and Inception-v3

Cited by 119 publications

References 3 publications

Machine Learning‐Enabled Smart Sensor Systems

Machine Learning‐Enabled Smart Sensor Systems

Emotion Recognition of Students Based on Facial Expressions in Online Education Based on the Perspective of Computer Simulation

Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

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