This document proposes using neural networks and data mining to support intelligent decision support systems (IDSS). It discusses how neural networks can help with knowledge learning, problem solving abilities, and real-time processing. Data mining can be used for analysis, clustering, and concept description. The paper then presents a framework for an IDSS combining neural networks, data mining, reasoning, and natural language processing. It provides an example application to evaluate using marsh gas instead of oil and natural gas in China.
1) The document discusses VLSI architecture and implementation for 3D neural network based image compression. It proposes developing new hardware architectures optimized for area, power, and speed for implementing 3D neural networks for image compression.
2) A block diagram is presented showing the overall process of image acquisition, preprocessing, compression using a 3D neural network, and encoding for transmission.
3) The proposed 3D neural network architecture uses multiple hidden layers with lower dimensions than the input and output layers to perform compression and decompression. The network is trained using backpropagation.
The document is a research paper that studies using a neural network model for fingerprint recognition. It discusses how fingerprint recognition is an important technique for security and restricting intruders. The paper proposes using an artificial neural network with backpropagation training to recognize fingerprints. It describes collecting fingerprint images, classifying them, enhancing the images, and training the neural network to match images and recognize fingerprints with high accuracy. The methodology, implementation, and results of using a backpropagation neural network for fingerprint recognition are analyzed.
International Journal of Computational Engineering Research(IJCER)ijceronline
The document discusses image compression using artificial neural networks. It begins with an introduction to image compression and the need for it. Then it reviews various existing neural network approaches for image compression, including backpropagation networks, hierarchical networks, multilayer feedforward networks, and radial basis function networks. It proposes a new approach using a multilayer perceptron with a modified Levenberg-Marquardt training algorithm to improve compression performance. Authentication and protection would be incorporated by exploiting the one-to-one mapping and one-way properties of neural networks. The proposed system is described as compressing images using neural networks trained with a modified LM algorithm to achieve high compression ratios while maintaining image quality.
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
This document proposes a privacy-preserving algorithm for backpropagation neural network learning when the training data is arbitrarily partitioned between two parties. Existing approaches only address vertically or horizontally partitioned data. The proposed algorithm keeps each party's data private during training, revealing only the final learned weights. It aims to be efficient in computation and communication overhead while providing strong privacy guarantees. The algorithm uses secure scalar product and techniques from previous work on vertically partitioned data to perform training without either party learning about the other's data.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
ON THE PERFORMANCE OF INTRUSION DETECTION SYSTEMS WITH HIDDEN MULTILAYER NEUR...IJCNCJournal
Deep learning applications, especially multilayer neural network models, result in network intrusion detection with high accuracy. This study proposes a model that combines a multilayer neural network with Dense Sparse Dense (DSD) multi-stage training to simultaneously improve the criteria related to the performance of intrusion detection systems on a comprehensive dataset UNSW-NB15. We conduct experiments on many neural network models such as Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), etc. to evaluate the combined efficiency with each model through many criteria such as accuracy, detection rate, false alarm rate, precision, and F1-Score.
Evaluation of deep neural network architectures in the identification of bone...TELKOMNIKA JOURNAL
This document evaluates the performance of three deep neural network architectures - ResNet, DenseNet, and NASNet - in identifying bone fissures in radiological images. The networks were trained on a dataset of 1000 labeled images of fissured and seamless bones. NASNet achieved the best performance with 75% accuracy, outperforming ResNet and DenseNet. While all networks reduced classification errors, NASNet did so with the fewest parameters. The document concludes NASNet is the best solution for this bone fissure identification task.
1) The document discusses VLSI architecture and implementation for 3D neural network based image compression. It proposes developing new hardware architectures optimized for area, power, and speed for implementing 3D neural networks for image compression.
2) A block diagram is presented showing the overall process of image acquisition, preprocessing, compression using a 3D neural network, and encoding for transmission.
3) The proposed 3D neural network architecture uses multiple hidden layers with lower dimensions than the input and output layers to perform compression and decompression. The network is trained using backpropagation.
The document is a research paper that studies using a neural network model for fingerprint recognition. It discusses how fingerprint recognition is an important technique for security and restricting intruders. The paper proposes using an artificial neural network with backpropagation training to recognize fingerprints. It describes collecting fingerprint images, classifying them, enhancing the images, and training the neural network to match images and recognize fingerprints with high accuracy. The methodology, implementation, and results of using a backpropagation neural network for fingerprint recognition are analyzed.
International Journal of Computational Engineering Research(IJCER)ijceronline
The document discusses image compression using artificial neural networks. It begins with an introduction to image compression and the need for it. Then it reviews various existing neural network approaches for image compression, including backpropagation networks, hierarchical networks, multilayer feedforward networks, and radial basis function networks. It proposes a new approach using a multilayer perceptron with a modified Levenberg-Marquardt training algorithm to improve compression performance. Authentication and protection would be incorporated by exploiting the one-to-one mapping and one-way properties of neural networks. The proposed system is described as compressing images using neural networks trained with a modified LM algorithm to achieve high compression ratios while maintaining image quality.
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
This document proposes a privacy-preserving algorithm for backpropagation neural network learning when the training data is arbitrarily partitioned between two parties. Existing approaches only address vertically or horizontally partitioned data. The proposed algorithm keeps each party's data private during training, revealing only the final learned weights. It aims to be efficient in computation and communication overhead while providing strong privacy guarantees. The algorithm uses secure scalar product and techniques from previous work on vertically partitioned data to perform training without either party learning about the other's data.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
ON THE PERFORMANCE OF INTRUSION DETECTION SYSTEMS WITH HIDDEN MULTILAYER NEUR...IJCNCJournal
Deep learning applications, especially multilayer neural network models, result in network intrusion detection with high accuracy. This study proposes a model that combines a multilayer neural network with Dense Sparse Dense (DSD) multi-stage training to simultaneously improve the criteria related to the performance of intrusion detection systems on a comprehensive dataset UNSW-NB15. We conduct experiments on many neural network models such as Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), etc. to evaluate the combined efficiency with each model through many criteria such as accuracy, detection rate, false alarm rate, precision, and F1-Score.
Evaluation of deep neural network architectures in the identification of bone...TELKOMNIKA JOURNAL
This document evaluates the performance of three deep neural network architectures - ResNet, DenseNet, and NASNet - in identifying bone fissures in radiological images. The networks were trained on a dataset of 1000 labeled images of fissured and seamless bones. NASNet achieved the best performance with 75% accuracy, outperforming ResNet and DenseNet. While all networks reduced classification errors, NASNet did so with the fewest parameters. The document concludes NASNet is the best solution for this bone fissure identification task.
The document is a dissertation submitted to Gujarat University in partial fulfillment of a Master's degree in Computer Application, which discusses character recognition using neural networks. It provides an index of the contents including the introduction to neural networks, their architecture and applications, an introduction to character recognition, the use of Matlab and its neural network toolbox, a literature survey, the proposed work on digit recognition, potential enhancements, and conclusions. The dissertation was submitted by Sachinkumar M. Bharadva and Dhara Solanki under the guidance of their internal guide Mr. Sandeep R. Vasant at the AES Institute of Computer Studies.
IRJET - Study on the Effects of Increase in the Depth of the Feature Extracto...IRJET Journal
This document discusses a study on the effects of increasing the depth of the feature extractor for recognizing handwritten digits in a convolutional neural network (CNN). Specifically, it analyzes the performance of a CNN model on the Modified National Institute of Standards and Technology (MNIST) dataset with variations in the number of filters used in deeper layers of the proposed model. The study finds that increasing the number of filters in the convolutional layers improves the accuracy of the model for classifying handwritten digits.
UNSUPERVISED LEARNING MODELS OF INVARIANT FEATURES IN IMAGES: RECENT DEVELOPM...ijscai
Object detection and recognition are important problems in computer vision and pattern recognition
domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on
computer based systems has proved to be a non-trivial task. In particular, despite significant research
efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition
systems in real time remain elusive. Here we present a survey of one particular approach that has proved
very promising for invariant feature recognition and which is a key initial stage of multi-stage network
architecture methods for the high level task of object recognition.
Image compression and reconstruction using a new approach by artificial neura...Hưng Đặng
This document describes a neural network approach to image compression and reconstruction. It discusses using a backpropagation neural network with three layers (input, hidden, output) to compress an image by representing it with fewer hidden units than input units, then reconstructing the image from the hidden unit values. It also covers preprocessing steps like converting images to YCbCr color space, downsampling chrominance, normalizing pixel values, and segmenting images into blocks for the neural network. The neural network weights are initially randomized and then trained using backpropagation to learn the image compression.
This document describes a neural network model for generating image captions to help visually impaired people understand images. A convolutional neural network extracts image features, which are fed into a recurrent neural network or long short-term memory network to generate natural language captions. The model achieves state-of-the-art performance on image captioning tasks and has the potential to greatly improve the lives of visually impaired individuals by allowing them to understand images through automatically generated captions.
The document discusses using deep learning approaches for handwritten Bangla digit recognition. It proposes using convolutional neural networks (CNNs) and deep belief networks (DBNs) with dropout and different filters. It finds that a CNN using Gabor features and dropout achieved the best accuracy compared to other techniques. The research is continuing to develop more advanced neural networks combining state preserving extreme learning machines to recognize Bangla numerals and characters.
Image Compression and Reconstruction Using Artificial Neural NetworkIRJET Journal
1) The document presents a neural network based method for image compression and reconstruction. An artificial neural network is used to compress image data for storage or transmission and then restore the image when desired.
2) The neural network accepts image data as input, compresses it by generating an internal representation, and then decompresses the data to reconstruct the original image.
3) The performance of the neural network method for image compression and reconstruction is evaluated using standard test images. Results show that it achieves high compression ratios and low distortion while maintaining its ability to generalize and is robust.
Comparison Between Levenberg-Marquardt And Scaled Conjugate Gradient Training...CSCJournals
The document compares the Levenberg-Marquardt and Scaled Conjugate Gradient algorithms for training a multilayer perceptron neural network for image compression. It finds that while both algorithms performed comparably in terms of accuracy and speed, the Levenberg-Marquardt algorithm achieved slightly better accuracy as measured by average training accuracy and mean squared error, while the Scaled Conjugate Gradient algorithm was faster as measured by average training iterations. The document compresses a standard test image called Lena using both algorithms and analyzes the results.
IRJET- Survey on Text Error Detection using Deep LearningIRJET Journal
This document summarizes a survey on using deep learning for text error detection. It begins with an introduction to natural language processing and deep learning. Deep learning models like convolutional neural networks, recurrent neural networks, and recursive neural networks are effective for natural language tasks. The document then discusses several deep learning networks that are relevant for text error detection, including recursive neural networks, recurrent neural networks, convolutional neural networks, and generative models. It concludes that deep learning is well-suited for modeling complex language data through multiple representation layers, but requires large labeled datasets for training.
Efficient mobilenet architecture_as_image_recognitEL Mehdi RAOUHI
1. The document discusses the MobileNet architecture for image recognition on mobile and embedded devices with limited computing resources. MobileNet uses depthwise separable convolutions to reduce computational costs compared to traditional convolutional neural networks.
2. MobileNet splits regular convolutions into depthwise convolutions followed by 1x1 pointwise convolutions. This factorization significantly reduces computations and model size while maintaining accuracy.
3. The document evaluates MobileNet on the Caltech101 dataset using a mobile device. MobileNet achieved 92.4% accuracy while drawing only 2.1 Watts of power, demonstrating its efficiency for resource-constrained environments.
IRJET- Visual Question Answering using Combination of LSTM and CNN: A SurveyIRJET Journal
This document discusses using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) for visual question answering (VQA). It proposes extracting image features from CNNs and encoding question semantics with LSTMs. A multilayer perceptron would then combine the image and question representations to predict answers. The methodology aims to reduce statistical biases in VQA datasets by focusing attention on relevant image regions. It was implemented in Keras with TensorFlow using pre-trained CNNs for images and word embeddings for questions. The proposed approach analyzes local image features and question semantics to improve VQA classification accuracy over methods relying solely on language.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONijaia
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets to help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A Hybrid Approach for Ensuring Security in Data Communication cscpconf
For a very long time, various forms of steganographic and cryptographic techniques have been used to ensure security in data communication. Whereas steganography is the art of hiding the fact that any communication is taking place, cryptography on the other hand ensures data security by changing the very form of the data being communicated by using a symmetric or an asymmetric key. But, both the methods are susceptible to being weakened by a challenger. In
steganography, there is always a possibility of detection of the presence of a message by the opponent and most of the cryptographic techniques are vulnerable to disclosure of the key. This paper proposes a hybrid approach where in image steganography and cryptography are combined to protect the sensitive data thereby ensuring improved security in data
communication. To find the impact of the same, a simulator was designed in MATLAB and corresponding time complexities were recorded. The simulation results depict that this hybrid
technique although increases the time complexity but ensures an enhanced security in data communication.
The document proposes a reversible data hiding method that embeds secret bits into a compressed thumbnail image during an image interpolation process. As the original thumbnail is scaled up to the original size, secret data is embedded by modifying pixel values based on their maximum and minimum neighboring pixel values in the original thumbnail. Experimental results show this method achieves higher embedding capacities than an existing approach.
This document proposes an efficient data steganography method called Adaptive Pixel Pair Matching (APPM) with high security. APPM hides data by substituting pixel pairs in a cover image based on a secret key. It defines an extraction function and compact neighborhood set for pixel pairs to minimize embedding distortion. APPM converts the secret message into digits of a B-ary numerical system for hiding. It calculates the optimal value of B and neighborhood set based on the image and message size. APPM generates a random embedding sequence using a key for substitution. It also provides an external password for additional security of the hidden message. The document claims this method provides better image quality and higher payload than previous pixel pair matching methods with increased security.
Deep learning is a branch of machine learning that uses artificial neural networks inspired by the human brain. These neural networks can learn complex patterns from large amounts of data without needing to be explicitly programmed. Deep learning uses neural networks that consist of interconnected layers that process data and learn hierarchical representations. Popular deep learning models include convolutional neural networks, recurrent neural networks, and deep belief networks.
This document provides an overview and summary of a student project report on simulating a feed forward artificial neural network in C++. The report includes an abstract, table of contents, list of figures, and 5 chapters that discuss the objectives of the project, provide background on artificial neural networks, describe the design and implementation of a 3-layer feed forward neural network using backpropagation, present the results, and provide references. The design section explains the backpropagation algorithm and provides pseudocode for calculating outputs at each layer. The implementation section provides pseudocode for training patterns and minimizing error.
Image compression and reconstruction using a new approach by artificial neura...Hưng Đặng
This document describes a neural network approach to image compression and reconstruction. It discusses using a backpropagation neural network with three layers (input, hidden, output) to compress an image by representing it with fewer hidden units than input units, then reconstructing the image from the hidden unit values. It also covers preprocessing steps like converting images to YCbCr color space, downsampling chrominance, normalizing pixel values, and segmenting images into blocks for the neural network. The neural network weights are initially randomized and then trained using backpropagation to learn the image compression.
A Survey on Image Processing using CNN in Deep LearningIRJET Journal
This document discusses the use of convolutional neural networks (CNNs) for image processing tasks. It provides an overview of CNNs and their application in image classification. The document then reviews several papers that have applied CNNs to tasks like image classification, object detection, and image segmentation. Some key advantages of CNNs discussed are their ability to directly take images as input without needing separate preprocessing steps. However, challenges include overfitting when training data is limited and complex images can confuse networks. The document concludes that CNN performance improves with more network layers and training data. CNNs are widely used for computer vision tasks due to their strong image feature extraction capabilities.
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
The document is a dissertation submitted to Gujarat University in partial fulfillment of a Master's degree in Computer Application, which discusses character recognition using neural networks. It provides an index of the contents including the introduction to neural networks, their architecture and applications, an introduction to character recognition, the use of Matlab and its neural network toolbox, a literature survey, the proposed work on digit recognition, potential enhancements, and conclusions. The dissertation was submitted by Sachinkumar M. Bharadva and Dhara Solanki under the guidance of their internal guide Mr. Sandeep R. Vasant at the AES Institute of Computer Studies.
IRJET - Study on the Effects of Increase in the Depth of the Feature Extracto...IRJET Journal
This document discusses a study on the effects of increasing the depth of the feature extractor for recognizing handwritten digits in a convolutional neural network (CNN). Specifically, it analyzes the performance of a CNN model on the Modified National Institute of Standards and Technology (MNIST) dataset with variations in the number of filters used in deeper layers of the proposed model. The study finds that increasing the number of filters in the convolutional layers improves the accuracy of the model for classifying handwritten digits.
UNSUPERVISED LEARNING MODELS OF INVARIANT FEATURES IN IMAGES: RECENT DEVELOPM...ijscai
Object detection and recognition are important problems in computer vision and pattern recognition
domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on
computer based systems has proved to be a non-trivial task. In particular, despite significant research
efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition
systems in real time remain elusive. Here we present a survey of one particular approach that has proved
very promising for invariant feature recognition and which is a key initial stage of multi-stage network
architecture methods for the high level task of object recognition.
Image compression and reconstruction using a new approach by artificial neura...Hưng Đặng
This document describes a neural network approach to image compression and reconstruction. It discusses using a backpropagation neural network with three layers (input, hidden, output) to compress an image by representing it with fewer hidden units than input units, then reconstructing the image from the hidden unit values. It also covers preprocessing steps like converting images to YCbCr color space, downsampling chrominance, normalizing pixel values, and segmenting images into blocks for the neural network. The neural network weights are initially randomized and then trained using backpropagation to learn the image compression.
This document describes a neural network model for generating image captions to help visually impaired people understand images. A convolutional neural network extracts image features, which are fed into a recurrent neural network or long short-term memory network to generate natural language captions. The model achieves state-of-the-art performance on image captioning tasks and has the potential to greatly improve the lives of visually impaired individuals by allowing them to understand images through automatically generated captions.
The document discusses using deep learning approaches for handwritten Bangla digit recognition. It proposes using convolutional neural networks (CNNs) and deep belief networks (DBNs) with dropout and different filters. It finds that a CNN using Gabor features and dropout achieved the best accuracy compared to other techniques. The research is continuing to develop more advanced neural networks combining state preserving extreme learning machines to recognize Bangla numerals and characters.
Image Compression and Reconstruction Using Artificial Neural NetworkIRJET Journal
1) The document presents a neural network based method for image compression and reconstruction. An artificial neural network is used to compress image data for storage or transmission and then restore the image when desired.
2) The neural network accepts image data as input, compresses it by generating an internal representation, and then decompresses the data to reconstruct the original image.
3) The performance of the neural network method for image compression and reconstruction is evaluated using standard test images. Results show that it achieves high compression ratios and low distortion while maintaining its ability to generalize and is robust.
Comparison Between Levenberg-Marquardt And Scaled Conjugate Gradient Training...CSCJournals
The document compares the Levenberg-Marquardt and Scaled Conjugate Gradient algorithms for training a multilayer perceptron neural network for image compression. It finds that while both algorithms performed comparably in terms of accuracy and speed, the Levenberg-Marquardt algorithm achieved slightly better accuracy as measured by average training accuracy and mean squared error, while the Scaled Conjugate Gradient algorithm was faster as measured by average training iterations. The document compresses a standard test image called Lena using both algorithms and analyzes the results.
IRJET- Survey on Text Error Detection using Deep LearningIRJET Journal
This document summarizes a survey on using deep learning for text error detection. It begins with an introduction to natural language processing and deep learning. Deep learning models like convolutional neural networks, recurrent neural networks, and recursive neural networks are effective for natural language tasks. The document then discusses several deep learning networks that are relevant for text error detection, including recursive neural networks, recurrent neural networks, convolutional neural networks, and generative models. It concludes that deep learning is well-suited for modeling complex language data through multiple representation layers, but requires large labeled datasets for training.
Efficient mobilenet architecture_as_image_recognitEL Mehdi RAOUHI
1. The document discusses the MobileNet architecture for image recognition on mobile and embedded devices with limited computing resources. MobileNet uses depthwise separable convolutions to reduce computational costs compared to traditional convolutional neural networks.
2. MobileNet splits regular convolutions into depthwise convolutions followed by 1x1 pointwise convolutions. This factorization significantly reduces computations and model size while maintaining accuracy.
3. The document evaluates MobileNet on the Caltech101 dataset using a mobile device. MobileNet achieved 92.4% accuracy while drawing only 2.1 Watts of power, demonstrating its efficiency for resource-constrained environments.
IRJET- Visual Question Answering using Combination of LSTM and CNN: A SurveyIRJET Journal
This document discusses using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) for visual question answering (VQA). It proposes extracting image features from CNNs and encoding question semantics with LSTMs. A multilayer perceptron would then combine the image and question representations to predict answers. The methodology aims to reduce statistical biases in VQA datasets by focusing attention on relevant image regions. It was implemented in Keras with TensorFlow using pre-trained CNNs for images and word embeddings for questions. The proposed approach analyzes local image features and question semantics to improve VQA classification accuracy over methods relying solely on language.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONijaia
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets to help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A Hybrid Approach for Ensuring Security in Data Communication cscpconf
For a very long time, various forms of steganographic and cryptographic techniques have been used to ensure security in data communication. Whereas steganography is the art of hiding the fact that any communication is taking place, cryptography on the other hand ensures data security by changing the very form of the data being communicated by using a symmetric or an asymmetric key. But, both the methods are susceptible to being weakened by a challenger. In
steganography, there is always a possibility of detection of the presence of a message by the opponent and most of the cryptographic techniques are vulnerable to disclosure of the key. This paper proposes a hybrid approach where in image steganography and cryptography are combined to protect the sensitive data thereby ensuring improved security in data
communication. To find the impact of the same, a simulator was designed in MATLAB and corresponding time complexities were recorded. The simulation results depict that this hybrid
technique although increases the time complexity but ensures an enhanced security in data communication.
The document proposes a reversible data hiding method that embeds secret bits into a compressed thumbnail image during an image interpolation process. As the original thumbnail is scaled up to the original size, secret data is embedded by modifying pixel values based on their maximum and minimum neighboring pixel values in the original thumbnail. Experimental results show this method achieves higher embedding capacities than an existing approach.
This document proposes an efficient data steganography method called Adaptive Pixel Pair Matching (APPM) with high security. APPM hides data by substituting pixel pairs in a cover image based on a secret key. It defines an extraction function and compact neighborhood set for pixel pairs to minimize embedding distortion. APPM converts the secret message into digits of a B-ary numerical system for hiding. It calculates the optimal value of B and neighborhood set based on the image and message size. APPM generates a random embedding sequence using a key for substitution. It also provides an external password for additional security of the hidden message. The document claims this method provides better image quality and higher payload than previous pixel pair matching methods with increased security.
Deep learning is a branch of machine learning that uses artificial neural networks inspired by the human brain. These neural networks can learn complex patterns from large amounts of data without needing to be explicitly programmed. Deep learning uses neural networks that consist of interconnected layers that process data and learn hierarchical representations. Popular deep learning models include convolutional neural networks, recurrent neural networks, and deep belief networks.
This document provides an overview and summary of a student project report on simulating a feed forward artificial neural network in C++. The report includes an abstract, table of contents, list of figures, and 5 chapters that discuss the objectives of the project, provide background on artificial neural networks, describe the design and implementation of a 3-layer feed forward neural network using backpropagation, present the results, and provide references. The design section explains the backpropagation algorithm and provides pseudocode for calculating outputs at each layer. The implementation section provides pseudocode for training patterns and minimizing error.
Image compression and reconstruction using a new approach by artificial neura...Hưng Đặng
This document describes a neural network approach to image compression and reconstruction. It discusses using a backpropagation neural network with three layers (input, hidden, output) to compress an image by representing it with fewer hidden units than input units, then reconstructing the image from the hidden unit values. It also covers preprocessing steps like converting images to YCbCr color space, downsampling chrominance, normalizing pixel values, and segmenting images into blocks for the neural network. The neural network weights are initially randomized and then trained using backpropagation to learn the image compression.
A Survey on Image Processing using CNN in Deep LearningIRJET Journal
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recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
Image Processing Compression and Reconstruction by Using New Approach Artific...CSCJournals
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database they will create a lot of profit for the organization. The question they are asking is how to extract
this value. The answer is data mining. There are many technologies available to data mining practitioners,
including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are
wary of Neural Networks due to their black box nature, even though they have proven themselves in many
situations. This paper is an overview of artificial neural networks and questions their position as a
preferred tool by data mining practitioners.
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Data protection based neural cryptography and deoxyribonucleic acidIJECEIAES
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2) A block diagram is presented showing the overall process of image acquisition, preprocessing, compression using a 3D neural network, and encoding for transmission.
3) The proposed 3D neural network architecture uses multiple hidden layers with lower dimensions than the input and output layers to perform the compression and decompression transformations between the image pixels and hidden layer representations.
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CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfAsst.prof M.Gokilavani
UNIT I INTRODUCTION
Neural Networks-Application Scope of Neural Networks-Artificial Neural Network: An IntroductionEvolution of Neural Networks-Basic Models of Artificial Neural Network- Important Terminologies of
ANNs-Supervised Learning Network.
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UNSUPERVISED LEARNING MODELS OF INVARIANT FEATURES IN IMAGES: RECENT DEVELOPM...ijscai
Object detection and recognition are important problems in computer vision and pattern recognition
domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on
computer based systems has proved to be a non-trivial task. In particular, despite significant research
efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition
systems in real time remain elusive. Here we present a survey of one particular approach that has proved
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Unsupervised learning models of invariant features in images: Recent developm...IJSCAI Journal
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domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on
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methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
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This document discusses Fourier series representation of periodic signals. It introduces continuous-time periodic signals and their representation as a linear combination of harmonically related complex exponentials. The coefficients in the Fourier series representation can be determined by multiplying both sides of the representation by complex exponentials and integrating over one period. The key steps are: 1) multiplying both sides by e-jω0t, 2) integrating both sides from 0 to T=2π/ω0, and 3) using the fact that the integral equals T when k=n and 0 otherwise to obtain an expression for the coefficients an. Examples are provided to illustrate these concepts.
2. 3)No Creation and self-learning of DSS. It is only of the There are three levels of CP network which are import
functions which programmed by developer. arrangement of ideas, argue unexpectedly arrangement of ideas
and output arrangement of ideas. The self-configuration
4)Bad ability of real time. reflecting neural network is composed of import arrangement
In order to solve the above problems of DSS, the bionics of and argue unexpectedly arrangement. The basic argument
neural network and the intelligence of mining system are made network is composed of argue arrangement and output
use of to explore the solutions in theory. arrangement. The argue arrangement of ideas which is between
the import arrangement of ideas and output arrangement of
III. FUNCTION OF NEURAL NETWORK TO DSS ideas reflects the statistics characteristics of import mode and
output mode. Then there is a reflecting between import mode
The function of neural network to DSS is as following by and output mode through argue arrangement of ideas. CP
means of analyzing of characteristic of neural network and network has been widely used in lots of fields such as modes
problems of DSS. The learning function, the parallel clustering, statistics analysis, data compressing and so on.
distributing processing function with large scale, non-linear
dynamics with constant time and the collectivity of neural
network are made use of to have a realization of automation of
knowledge learning, self-learning of natural language
processing system, overcoming the difficulties of “assembled
blast” and “infinite recursion”, adaptive parallel associating
reasoning, promotion of deciding ability of DSS and processing
of real time.
As shown in Fig. 2, a intelligent decision support system of
neural network is composed of knowledge, data and model. It
is of main four sub-systems, neural network, reasoning system,
data mining system of neural network and natural language
alteration system. [3]
Figure 3. Configuration Drawing of CP Network
There are two kinds of data mining which are directly data
mining and indirectly data mining. The aim of directly data
mining is that a model is built up by means of available data
and a special variable is described. The definition of indirectly
data mining is that no some real variable is described with a
model but some kind of relationship has been built up. [4]
B. Reasoning of Neural Network
Figure 2. Block Drawing of IDSS of Neural Network
A. Data Mining of Neural Network
Data mining of neural network is a data mining mode based
on neural network technology. There are five basic tasks of
data mining, relative analysis, clustering, concept description,
error monitoring and forecast. The forward feeding neural
network, for example BP network, is usually worked in concept
Figure 4. Sketch Map of Repeat Reasoning of Neural Network
description and forecast. The counter spreading (CP) neural
network can be worked in statistics analysis and clustering. As The main research of neural network system is the double
shown in Fig. 3, it is a configuration drawing of CP network directions reasoning method based on the data driving and aim
which created by American neural calculating expert Robert driving of neural network. Reasoning is the main method of
Hecht-Nielsen solutions. The course of knowledge reasoning is the process of
solutions. There are some problems such as “assembled blast”
3. and “infinite recursion” in the traditional reasoning method. A. Man-machine Alteration System
The parallel processing of neural network is the best method of The alteration structure has been established by man-
solving above problems. An explanation of reasoning course of machine alteration system which has input and output between
double directions associate memory (BAM) network is shown system and user. Man-machine alteration system is the
in the next. As shown in Fig. 4, the first arrangement of repeat important part of IDSS which is of all functions as shown in
reasoning of neural network is of no calculate function, but of Fig. 5.
fan-out function which is distributed the output to input. An
input vector A is applied up to power matrix and an output
vector B is turn out. And vector B is applied to the turn matrix
WT of power matrix W, then a new output vector A is turn
out. The course is repeated until a steady point of network
which A and B are constant. The steady point is called as
homeostasis. [5]
There is a formula of the repeat course as following.
B=F(AW) (1)
A=F(BWT) (2)
A is output vector of the first arrangement, herein, B is
output vector of the second arrangement, W is the power
matrix between the first arrangement and the second, and F is
power function. [6]
Equation (1) can be used to fulfill the reasoning of data
driving and Equation (2) can be used to fulfill the reasoning of
Figure 5. Configuration of IDSS Based on Neural Network and Data Mining
aim driving. Double directions reasoning can be realized by
homeostasis of BAM. The homeostasis is the crossing point of
data driving and aim driving of BAM. And it is the decision B. Neural Network, Data Mining, Solutions
solution. Neural network, data mining and solutions include two
modules which are solutions module and data mining module.
C. Natural Language Alteration System of Neural Network Data mining module works up in order to gain knowledge
The main research of natural language process (LS) is the needed through making use of the model of models base,
syntax analysis and meaning analysis based on neural network. method of methods base and knowledge of knowledge base.
Natural language is belonged to non-numerical valve symbol Solutions module works up in order to configure or half-
which is symbol flow with different numbers. It is of its own configure the problems through making use of the
syntax and means system and its data structure, means corresponding model of models base, method of methods base,
expression and calculation rules are rather different from knowledge of knowledge base and data of data base. Reasoning
numerical valve information. The core of natural language can be made use of for the non-configuration problems.
processing system of neural network is how to understand the
knowledge and the expression of natural language. The basic V. APPLICATION OF IDSS BASED ON NEURAL NETWORK
tasks of syntax analysis system based on neural network are (1) AND DATA MINING IN USING OF ENERGY AND PROTECTION OF
confirmation of syntax structure of input sentence, which is a RESOURCES
identification course based on neural network, (2)
standardization of syntax structure, which is a conclusion There is a lack of natural gas and oil in China. And there is
course that lots of input structures turn into a few of input a great air pollution of coal burning. There is a kind of new
structures according to some syntax exchanging relationships. energy,so far, biological energy , that is grain alcohol, for the
substitute of oil and natural gas. But grain alcohol is made from
grain. Therefore, it is not suitable to develop grain alcohol in
IV. IDSS SUPPORTED BY NEURAL NETWORK AND DATA
stead of oil and natural gas. Some experts suggest that marsh
MINING gas should be developed in order to replace the oil and natural
IDSS supported by neural network and data mining is gas.
shown in Fig. 5. It is derived from the combination of
traditional DSS with data mining technology in order to For the above problems, we make a research on whether
increase the intelligence of system. It is composed of man- marsh gas can be made use of in stead of oil and natural gas by
machine alteration system based on neural network, data means of IDSS based on neural network and data mining.。
mining, reasoning and solutions, data base management, The main researching movements are as following.
knowledge base management, methods base management and 1) Man-machine Alteration System which is of
models base management. convenience to users.
4. 2) Building up data base which is composed of oil, natural neural network technology into IDSS. And there are lots of
gas, coal, grain alcohol petrol, cost of marsh gas, using valve, problems which should be studied deeply in the future.
pollution, cost of over pollution, health cost and so on.
3) Building up knowledge base which derived from the REFERENCES
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