Deep Hog: A Hybrid Model to Classify Bangla Isolated Alpha-Numerical Symbols

Sharif, S. M. A.; Mahboob, Mahdin

doi:10.14311/nnw.2019.29.009

Cited by 6 publications

(6 citation statements)

References 28 publications

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“…It therefore differs from various modern approaches recently used e.g. in the Neural Network World journal [4,7,21,9,8,17].…”

Section: Introductionmentioning

confidence: 87%

Pre-crash control strategy of driver assistance system

Kovanda¹,

Rulc²

2021

NNW

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The aim of the article is the optimisation process of the ADAS (Advanced Driver Assistance Systems) control. The methodology is based on the classification of ADAS systems according to the situations of unavoidable accidents. The evaluation of expected consequences uses injury biomechanics, which represents the extended definition of HMI (Human-Machine Interaction). The evaluation of injury mechanism and the machine intervention enables to control this process with the target to minimise the consequent injuries. Then the decision making takes new inputs to the control process and it enriches the multiparametric control of the system with the target to minimise the consequences.

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“…It therefore differs from various modern approaches recently used e.g. in the Neural Network World journal [4,7,21,9,8,17].…”

Section: Introductionmentioning

confidence: 87%

Pre-crash control strategy of driver assistance system

Kovanda¹,

Rulc²

2021

NNW

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“…The necessity of the individual handcrafted features in this model also has been demonstrated. The results of the experiment show that the proposed deep hog model can surpass the existing sophisticated taxonomy models in Bengali handwritten alpha numerical image classification (Sharif & Mahboob, 2019). Das et al (2018) introduced a Bengali handwritten recognition scheme for vowels in English characters which is quite fast and it does not need training data to work and based on the structural anatomy of the characters.…”

Section: Literature Reviewmentioning

confidence: 97%

“…Deep hog is a hybrid model that classify the isolated alphanumeric symbols of the Bangla language. Sharif & Mahboob (2019) proposed a mixed model that classifies all possible classes of the handwritten image and unify existing reference data sets.…”

Section: Literature Reviewmentioning

confidence: 99%

Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

Hossain

Abadin

et al. 2021

Eng. int. (Dhaka)

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The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

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“…The final layer of the proposed DAMN is a fully connected layer that learns to calculate the probability of being a malware (class). In addition to that, we utilize the dropout layers in our network to reduce overfitting [32,70,71].…”

Section: Model Architecturementioning

confidence: 99%

Securing Edge Devices: Malware Classification with Dual-Attention Deep Network

Alandjani

2024

Applied Sciences

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Detecting malware is a crucial defense mechanism against potential cyber-attacks. However, current methods illustrate significant limitations in achieving high performance while maintaining faster inference on edge devices. This study proposes a novel deep network with dual-attention feature refinement on a two-branch deep network to learn real-time malware detection on edge platforms. The proposed method introduces lightweight spatial-asymmetric attention for refining the extracted features of its backbone and multi-head attention to correlate learned features from the network branches. The experimental results show that the proposed method can significantly outperform existing methods in quantitative evaluation. In addition, this study also illustrates the practicability of a lightweight deep network on edge devices by optimizing and deploying the model directly on the actual edge hardware. The proposed optimization strategy achieves a frame rate of over 545 per second on low-power edge devices.

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Deep Hog: A Hybrid Model to Classify Bangla Isolated Alpha-Numerical Symbols

Cited by 6 publications

References 28 publications

Pre-crash control strategy of driver assistance system

Pre-crash control strategy of driver assistance system

Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

Securing Edge Devices: Malware Classification with Dual-Attention Deep Network

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