An Efficient Secured PIT Management and Attack Detection Strategy Enhanced by CSOA-DCNN Algorithm in a Named Data Networking (NDN)

Named Data Networking (NDN) is a developing Internet design that utilizes a new network communication model dependent on the identity of Internet content. Its core component, the Pending Interest Table (PIT) serves an important role of recording Interest packet information. In managing PIT, the issue of flow PIT measuring has been very challenging because of the huge use of long Interest lifetime especially when there is no adaptable replacement strategy, subsequently affecting PIT performance. Named Data Networking (NDN) might experience some emerging threats such as Interest Flooding Attacks (IFA). In this paper, we focus on the IFA that can seriously devour the memory resource for the Pending Interest Table (PIT) of each included NDN router by flooding a huge amount of malicious Interests with spoofed names. To extricate the pressure of PIT attacked by IFA, we propose a methodology of efficient Secured PIT management and attack detection strategy by using a cuckoo search optimization algorithmDeep convolutional neural network (CSOA-DCNN) algorithm in Named Data Network. The CSO algorithm initially utilizes a learning technique and afterward considers improved search operators and deep convolutional neural network architecture (DCNN) for classification. The network simulation tool is utilized to design and calculate PIT management. The results of the study on a 20 Gbps gateway trace shows that the corresponding PIT contains 1.5 M entries, and the lookup, insert and delete frequencies are 1.4 M/s, 0.9 M/s and 0.9 M/s. The contribution of this study is significant for Interest packet management in NDN routing and forwarding systems.

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Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-180 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 181-1-181-7

Author(s):

Takahiro Kudo ◽

Takanori Fujisawa ◽

Takuro Yamaguchi ◽

Masaaki Ikehara

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Large Scale ◽

Blind Deconvolution ◽

Training Dataset ◽

Image Deconvolution ◽

Classic Problem ◽

Key Points ◽

Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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Analysis of Using Regularization Technique in The Convolutional Neural Network Architecture to Detect Paddy Disease for Small Dataset

Journal of Physics Conference Series ◽

10.1088/1742-6596/1726/1/012010 ◽

2021 ◽

Vol 1726 ◽

pp. 012010

Author(s):

S Mujahidin ◽

N F Azhar ◽

B Prihasto

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Neural Network Architecture ◽

Regularization Technique ◽

Small Dataset

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A novel convolutional neural network architecture of multispectral remote sensing images for automatic material classification

Signal Processing Image Communication ◽

10.1016/j.image.2021.116329 ◽

2021 ◽

pp. 116329

Author(s):

Chuen-Horng Lin ◽

Ting-You Wang

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Network Architecture ◽

Remote Sensing Images ◽

Neural Network Architecture ◽

Material Classification ◽

Multispectral Remote Sensing

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A novel one‐dimensional convolutional neural network architecture for chemical process fault diagnosis

The Canadian Journal of Chemical Engineering ◽

10.1002/cjce.24087 ◽

2021 ◽

Author(s):

Xin Niu ◽

Xia Yang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Network Architecture ◽

Chemical Process ◽

Neural Network Architecture ◽

One Dimensional ◽

Process Fault Diagnosis

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Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽

10.3390/s21082852 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2852

Author(s):

Parvathaneni Naga Srinivasu ◽

Jalluri Gnana SivaSai ◽

Muhammad Fazal Ijaz ◽

Akash Kumar Bhoi ◽

Wonjoon Kim ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Skin Disease ◽

Network Architecture ◽

Large Scale ◽

Short Term Memory ◽

Convolutional Networks ◽

Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Enhanced Convolutional-Neural-Network Architecture for Crop Classification

Applied Sciences ◽

10.3390/app11094292 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4292

Author(s):

Mónica Y. Moreno-Revelo ◽

Lorena Guachi-Guachi ◽

Juan Bernardo Gómez-Mendoza ◽

Javier Revelo-Fuelagán ◽

Diego H. Peluffo-Ordóñez

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Classification Model ◽

Classification Error ◽

Small Scale ◽

Post Processing ◽

Average Accuracy ◽

Processing Step ◽

Crop Classification

Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.

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Exploring a Deeper Convolutional Neural Network Architecture with high dropout for Motor Imagery BCI Decoding

2021 9th International Winter Conference on Brain-Computer Interface (BCI) ◽

10.1109/bci51272.2021.9385323 ◽

2021 ◽

Author(s):

Ramtin Alami ◽

Andi Partovi ◽

Farhad Goodarzy

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Network Architecture ◽

Neural Network Architecture

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BengaliNet: A Low-Cost Novel Convolutional Neural Network for Bengali Handwritten Characters Recognition

Applied Sciences ◽

10.3390/app11156845 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6845

Author(s):

Abu Sayeed ◽

Jungpil Shin ◽

Md. Al Mehedi Hasan ◽

Azmain Yakin Srizon ◽

Md. Mehedi Hasan

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Character Recognition ◽

High Performance ◽

Low Cost ◽

Traditional Learning ◽

Neural Network Architecture ◽

Handwritten Character Recognition ◽

Handwritten Character

As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the area of handwritten character recognition, Bengali has not received many noteworthy contributions in this domain because of the complex curvatures and similar writing fashions of Bengali characters. Previously, studies were conducted by using different approaches based on traditional learning, and deep learning. In this research, we proposed a low-cost novel convolutional neural network architecture for the recognition of Bengali characters with only 2.24 to 2.43 million parameters based on the number of output classes. We considered 8 different formations of CMATERdb datasets based on previous studies for the training phase. With experimental analysis, we showed that our proposed system outperformed previous works by a noteworthy margin for all 8 datasets. Moreover, we tested our trained models on other available Bengali characters datasets such as Ekush, BanglaLekha, and NumtaDB datasets. Our proposed architecture achieved 96–99% overall accuracies for these datasets as well. We believe our contributions will be beneficial for developing an automated high-performance recognition tool for Bengali handwritten characters.

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