scholarly journals Exploiting Manipulated Region in an Image using Integrated Convolution Neural Network and LRW Segmentation Features

2019 ◽  
Vol 8 (3) ◽  
pp. 5488-5495
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Random Walk ◽  
Digital Images ◽  
Convolution Neural Network ◽  
The Other ◽  
Final Decision ◽  
Convolution Neural Networks ◽  
Other Hand

To locate the manipulated region in digital images, we suggest to use Convolution Neural Networks and the segmentation based analysis. A unified CNN architecture is designed with set of training procedures for sampled training patches. Tampering map can be generated for the above said Convolution Neural Networks with the help of tampering detectors. In the other hand, a segmentation using lazy random walk based method is second-hand to generate the tampering chance map, finally integrate the maps and generate the final decision map. This can help to locate the manipulated region accurately. Experiments are conducted using the various datasets to prove the efficiency of the suggest method.

scholarly journals A Unified FPGA Virtualization Framework for General-Purpose Deep Neural Networks in the Cloud

2022 ◽  
Vol 15 (3) ◽  
pp. 1-31
Author(s):  
Shulin Zeng ◽  
Guohao Dai ◽  
Hanbo Sun ◽  
Jun Liu ◽  
Shiyao Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
Deep Neural Networks ◽  
Cost Effective ◽  
General Purpose ◽  
The Other ◽  
Private Cloud ◽  
Single Task ◽  
Other Hand

INFerence-as-a-Service (INFaaS) has become a primary workload in the cloud. However, existing FPGA-based Deep Neural Network (DNN) accelerators are mainly optimized for the fastest speed of a single task, while the multi-tenancy of INFaaS has not been explored yet. As the demand for INFaaS keeps growing, simply increasing the number of FPGA-based DNN accelerators is not cost-effective, while merely sharing these single-task optimized DNN accelerators in a time-division multiplexing way could lead to poor isolation and high-performance loss for INFaaS. On the other hand, current cloud-based DNN accelerators have excessive compilation overhead, especially when scaling out to multi-FPGA systems for multi-tenant sharing, leading to unacceptable compilation costs for both offline deployment and online reconfiguration. Therefore, it is far from providing efficient and flexible FPGA virtualization for public and private cloud scenarios. Aiming to solve these problems, we propose a unified virtualization framework for general-purpose deep neural networks in the cloud, enabling multi-tenant sharing for both the Convolution Neural Network (CNN), and the Recurrent Neural Network (RNN) accelerators on a single FPGA. The isolation is enabled by introducing a two-level instruction dispatch module and a multi-core based hardware resources pool. Such designs provide isolated and runtime-programmable hardware resources, which further leads to performance isolation for multi-tenant sharing. On the other hand, to overcome the heavy re-compilation overheads, a tiling-based instruction frame package design and a two-stage static-dynamic compilation, are proposed. Only the lightweight runtime information is re-compiled with ∼1 ms overhead, thus guaranteeing the private cloud’s performance. Finally, the extensive experimental results show that the proposed virtualized solutions achieve up to 3.12× and 6.18× higher throughput in the private cloud compared with the static CNN and RNN baseline designs, respectively.

scholarly journals Driver’s Seat Belt Detection Using CNN

2021 ◽  
Vol 12 (5) ◽  
pp. 776-785
Author(s):  
DS Bhupal Naik, G Sai Lakshmi, V Ramakrishna Sajja, D Venkatesulu,J Nageswara Rao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Error Rate ◽  
High Speed ◽  
Deep Neural Network ◽  
Seat Belt ◽  
Transportation System ◽  
Convolution Neural Network ◽  
Classification Algorithms ◽  
Convolution Neural Networks

Seat belt detection is one of the necessary task which are required in transportation system to reduce accidents due to abrupt stop or high speed accident with other vehicles. In this paper, a technique is proposed to detect whether the driver wears seat belt or not by using convolution neural networks. Convolution Neural Network is nothing but deep Neural Network. ConvNet automatically collects features using filters or kernels from images without human involvement to classify the output images. Compared to different classification algorithms, preprocessing required in ConvNet is least. In this proposed method, first ConvNet is built and trained using Seatbelt dataset of both standard and non-standard. ConvNet learns the features from the images of seat belt dataset and performed better with an accuracy of 91.4% over SVM with 87.17% and an error rate of 8.55% when compared with SVM with 12.83% in case of standard dataset.

N. Belknap's Four-Valued Logic, Belknap Computer and New Proximity Functions for Comparing Discrete Objects

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Valerii Dmitrienko ◽  
Sergey Leonov ◽  
Mykola Mezentsev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
The Other ◽  
Proximity Function ◽  
Input Information ◽  
Proximity Measures ◽  
The One ◽  
True Values

The idea of ​​Belknap's four-valued logic is that modern computers should function normally not only with the true values ​​of the input information, but also under the conditions of inconsistency and incompleteness of true failures. Belknap's logic introduces four true values: T (true - true), F (false - false), N (none - nobody, nothing, none), B (both - the two, not only the one but also the other).  For ease of work with these true values, the following designations are introduced: (1, 0, n, b). Belknap's logic can be used to obtain estimates of proximity measures for discrete objects, for which the functions Jaccard and Needhem, Russel and Rao, Sokal and Michener, Hamming, etc. are used. In this case, it becomes possible to assess the proximity, recognition and classification of objects in conditions of uncertainty when the true values ​​are taken from the set (1, 0, n, b). Based on the architecture of the Hamming neural network, neural networks have been developed that allow calculating the distances between objects described using true values ​​(1, 0, n, b). Keywords: four-valued Belknap logic, Belknap computer, proximity assessment, recognition and classification, proximity function, neural network.

scholarly journals DETECT THE SIMILARITY OF DIGITAL IMAGES USING THE EIGENFACE METHOD

JURTEKSI ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 133-142
Author(s):  
Alwin Fau ◽  
Fince Tinus Waruwu
Keyword(s):  
Digital Media ◽  
Data Security ◽  
Digital Images ◽  
The Other ◽  
Other Hand ◽  
The Face ◽  
Technological Developments ◽  
Images Processing

Abstract: In today's technological developments, digital images are a medium that is often used to store a person's identity. Digital images are currently widely used for data security needs. On the other hand, images can also be used as a medium for tapping data. Today's digital media provide many things in manipulating and changing the information contained in these images. In this study, the authors conducted a study to examine similarities in digital images so that it could be seen whether the information was authentic or not. detecting image similarities can help find out information whether the image is the same as the original object or not. The method used in this research is the Eigen Face method. The face eigen method is a method that can be used to check and match the similarities of an image. With the eigenface value, Figure 1, Figure 2, Figure 3, it can be determined that with other eigenface values can be determined based on the eigenface matrix values obtained from each image. Based on the values obtained from Figures 1, 2, and 3, it can be concluded that the eigenface method is able to present facial similarities with a presentation value of 80%.            Keywords: Eigenface; Face Recognation; Images; Images Processing  Abstrak: Dalam perkembangan teknologi saat ini, gambar digital merupakan media yang sering digunakan untuk menyimpan identitas seseorang. Gambar digital saat ini banyak digunakan untuk kebutuhan keamanan data. di sisi lain, gambar juga dapat digunakan sebagai media penyadapan data. Media digital saat ini menyediakan banyak hal dalam memanipulasi dan mengubah informasi yang terdapat pada gambar tersebut. Dalam penelitian ini penulis melakukan penelitian untuk menelaah kemiripan pada citra digital sehingga dapat diketahui apakah informasi tersebut otentik atau tidak. Mendeteksi kemiripan citra dapat membantu mengetahui informasi apakah citra tersebut sama dengan objek aslinya atau tidak. Metode yang digunakan dalam penelitian ini adalah metode Eigen Face. Metode eigen wajah merupakan metode yang dapat digunakan untuk mengecek dan mencocokkan kemiripan suatu citra. Dengan nilai eigenface, Gambar 1, Gambar 2, Gambar 3, dapat ditentukan bahwa dengan nilai eigenface lainnya dapat ditentukan berdasarkan nilai matriks eigenface yang diperoleh dari masing-masing citra. Berdasarkan nilai yang diperoleh dari Gambar 1, 2, dan 3, dapat disimpulkan bahwa metode eigenface mampu menghadirkan kemiripan wajah dengan nilai presentasi 80%.. Kata kunci: Citra; Eigenface; Pengolahan Citra Digital; Pengenalan Wajah

A Neural Networks Pruning and Data Fusion Based Intrusion Detection Model

2014 ◽  
Vol 651-653 ◽  
pp. 1772-1775
Author(s):  
Wei Gong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intrusion Detection ◽  
Data Fusion ◽  
Input Data ◽  
The Other ◽  
Pruning Algorithm ◽  
Detection Model ◽  
Process Ability

The abilities of summarization, learning and self-fitting and inner-parallel computing make artificial neural networks suitable for intrusion detection. On the other hand, data fusion based IDS has been used to solve the problem of distorting rate and failing-to-report rate and improve its performance. However, multi-sensor input-data makes the IDS lose its efficiency. The research of neural network based data fusion IDS tries to combine the strong process ability of neural network with the advantages of data fusion IDS. A neural network is designed to realize the data fusion and intrusion analysis and Pruning algorithm of neural networks is used for filtering information from multi-sensors. In the process of intrusion analysis pruning algorithm of neural networks is used for filtering information from multi-sensors so as to increase its performance and save the bandwidth of networks.

scholarly journals Critical Decision Making Using Neural Networks

2018 ◽  
Vol 7 (4.10) ◽  
pp. 15 ◽  
Author(s):  
Rajat Bhati ◽  
Shubham Saraff ◽  
Chhandak Bagchi ◽  
V. Vijayarajan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Emotional Intelligence ◽  
Final Decision ◽  
Computing Systems ◽  
Intelligent Decision Making ◽  
The Neural Network ◽  
Intelligent Decision ◽  
Car Accident

Decision Making influenced by different scenarios is an important feature that needs to be integrated in the computing systems. In this paper, the system takes prompt decisions in emotionally motivated use-cases like in an unavoidable car accident. The system extracts the features from the available visual and processes it in the Neural network. In addition to that the facial recognition plays a key role in returning factors critical to the scenario and hence alter the final decision. Finally, each recognized subject is categorized into six distinct classes which is utilised by the system for intelligent decision-making. Such a system can form the basis of dynamic and intelligent decision-making systems of the future which include elements of emotional intelligence.  

Structured Pyramidal Neural Networks

2018 ◽  
Vol 28 (05) ◽  
pp. 1750021 ◽  
Author(s):  
Alessandra M. Soares ◽  
Bruno J. T. Fernandes ◽  
Carmelo J. A. Bastos-Filho
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Processing Time ◽  
Receptive Fields ◽  
Memory Capacity ◽  
The Other ◽  
Support Vector ◽  
Proposed Model ◽  
Novel Method ◽  
Original Approach

The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.

Weight to Vision Neural Network Information Processing Influence Research

2012 ◽  
Vol 605-607 ◽  
pp. 2131-2136
Author(s):  
Chun Hua Yin ◽  
Jia Wei Chen ◽  
Lei Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information Processing ◽  
The Other ◽  
Chinese Characters ◽  
Initial Weight ◽  
Initial Value ◽  
Network Information ◽  
The Neural Network ◽  
Weight Distributions

Many factors influence vision neural network information processing process, for example: Signal initial value, weight, time and number of learning. This paper discussed the importance of weight in vision neural network information processing process. Different weight values can cause different results in neural networks learning. We structure a vision neural network model with three layers based on synapse dynamics at first. Then we change the weights of the vision neural network model’s to make the three layers a neural network of learning Chinese characters. At last we change the initial weight distribution to simulate the neural network of process of the learning Chinese words. Two results are produced. One is that weight plays a very important role in vision neural networks learning, the other is that different initial weight distributions have different results in vision neural networks learning.

scholarly journals Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System

2007 ◽  
Vol 2007 ◽  
pp. 1-6 ◽  
Author(s):  
Bekir Karlık ◽  
Kemal Yüksek
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Fuzzy Clustering ◽  
Input Data ◽  
Recognition System ◽  
Neural System ◽  
The Other ◽  
Odor Recognition ◽  
Recognition Probability

The aim of this study is to develop a novel fuzzy clustering neural network (FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network.

scholarly journals Image Classification: A Survey

2020 ◽  
Vol 1 (2) ◽  
pp. 1-9
Author(s):  
Ankita Singh ◽  
◽  
Pawan Singh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Learning Ability ◽  
Final Decision

The Classification of images is a paramount topic in artificial vision systems which have drawn a notable amount of interest over the past years. This field aims to classify an image, which is an input, based on its visual content. Currently, most people relied on hand-crafted features to describe an image in a particular way. Then, using classifiers that are learnable, such as random forest, and decision tree was applied to the extract features to come to a final decision. The problem arises when large numbers of photos are concerned. It becomes a too difficult problem to find features from them. This is one of the reasons that the deep neural network model has been introduced. Owing to the existence of Deep learning, it can become feasible to represent the hierarchical nature of features using a various number of layers and corresponding weight with them. The existing image classification methods have been gradually applied in real-world problems, but then there are various problems in its application processes, such as unsatisfactory effect and extremely low classification accuracy or then and weak adaptive ability. Models using deep learning concepts have robust learning ability, which combines the feature extraction and the process of classification into a whole which then completes an image classification task, which can improve the image classification accuracy effectively. Convolutional Neural Networks are a powerful deep neural network technique. These networks preserve the spatial structure of a problem and were built for object recognition tasks such as classifying an image into respective classes. Neural networks are much known because people are getting a state-of-the-art outcome on complex computer vision and natural language processing tasks. Convolutional neural networks have been extensively used.

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