Convolutional Neural Network(CNN)

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3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction

3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction

In this article, we utilize a 3D convolutional neural network (CNN) to develop a prediction model which can estimate the bioaccumulation propensity of a compound characterised by the bioconcentration factor (BCF) for a number of different organic molecules. As an input, we use three-dimensional distributions of water around these molecules, obtained by 3D-RISM with Kovalenko-Hirata closure (KH) [23]. Artificial neural networks (ANNs) have been previously used for predicting biological effects of organic molecules [20, 21, 22]. However, they were combined with a very broad set of descriptors that have diverse physical meanings. Here we focus on a single descriptor; solvation shell structure in an attempt to show that this can be a universal descriptor for prediction of properties of molecules which are difficult to formalise by a theory. To determine whether the CNN-based machine learning setup is necessary, we also tested linear and Extreme Gradient Boosting (XGBoost) models and compared them with the 3D CNN approach.
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Disease Detection in the Leaves of Multiple Plants

Disease Detection in the Leaves of Multiple Plants

in the agriculture sector. Pathogens such as fungi, bacteria and viruses causes different types of infections in plants leading to its damage. An early information about the crop health and detection of diseases can control loss in production to a large extend. The deep algorithms can be made useful in plant disease diagnosis. This paper proposes a deep learning based method for the detection of diseases effected in the leaves of plants. Here the deep Convolutional Neural Network (CNN) technique is used for the detection and classification of different types of diseases effected to the plant leaves. The CNN network is trained with 5 varieties of diseases effected to different plant leaves. Data base for this work has been collected from various agricultural fields and also through online sources. The dataset containing 1222 diseased images of plant leaves were made used. The architecture used is the RESNET – 50. The proposed CNN model acquires a classification accuracy in the range above 96%, which is accurate than the conventional machine learning techniques.
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Soft Computing Techniques Based Automatic Licence Plate Recognition Systems for Indian Vehicles

Soft Computing Techniques Based Automatic Licence Plate Recognition Systems for Indian Vehicles

Lastly, the segmented characters are recognized [2][3]. A number of soft computing techniques have been implemented for various applications and these techniques are doing well also[4]. Here soft computing techniques such as Random Forest (RF), Neural Network(NN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) are suggested for Indian licence plate recognition systems. The main contribution of the paper is as follows:  The RF, NN, SVM, and CNN based ALPR systems are

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EEG-Based Emotion Classification By Using Convolutional Neural Network (CNN)

EEG-Based Emotion Classification By Using Convolutional Neural Network (CNN)

EEG is a non-invasive neuroimaging method to record the oscillation of brain electric potentials resulting from ionic current flow between brain neurons. It is ubiquitous, non-invasive and having excellent temporal resolution compare to other neuroimaging method. However, working with EEG signal poses some challenges, the spatial resolution for EEG signal is low and it contains low signal to noise ratio (SNR) which lead to complexity in traditional hand feature extraction. Therefore, a deep learning technique, Convolutional Neural Network (CNN) is introduced as a classification method in this project due to it can generalise well and able to extract important feature automatically through the operation inside.
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Learning to Answer Questions from Wikipedia Infoboxes

Learning to Answer Questions from Wikipedia Infoboxes

of interesting questions from infoboxes. We then trained a convolutional neural network model on this dataset that uses the infobox attribute as a bridge in matching the question to the answer. Our Tri-CNN model achieved the best results when compared to recent CNN and RNN-based architectures. We plan to test our model’s ability to generalize to other types of infobox-like tables on the Web. We expect our methods to achieve good results for sources such as product descriptions on shopping websites.

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Design And Develop Object Detection System For Blind People Based On CNN Image Recognition

Design And Develop Object Detection System For Blind People Based On CNN Image Recognition

Recently, visual recognition becomes a popular topic and grows drastically. There are many features that used in visual recognition in the pass such as scale-invariant feature transform (SIFT) and histogram of oriented gradients (HOG). These features are collaborated with Support Vector Machine (SVM) which implementing into multiple classes and object detection in a single image. Hence, many people are replaced by Convolutional neural network (CNN) in object detection which having significant accuracy by utilizing a deep CNN due to the poor performance of the previous models.
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Blind Navigation System using Artificial Intelligence

Blind Navigation System using Artificial Intelligence

CNNs use a multilayer perceptron’s to obtain minimal preprocessing. They are also known as space invariant artificial neural networks (SIANN), due to their shared-weight architecture and translation invariance characteristics. The deep convolutional neural network can achieve reasonable performance on hard visual recognition tasks, matching or exceeding human performance in some domains. This network that we build is a very small network that can run on a CPU and on GPU as well.0.

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Performance Evaluation Of Convolutional Neural Network (CNN) For EEG Emotion Classification

Performance Evaluation Of Convolutional Neural Network (CNN) For EEG Emotion Classification

The CNN algorithm is trained with both EEG data collected in the lab as well as an open source dataset (SEED). The categorization of emotion for both EEG data collected in the lab and SEED are based on dimensional model of emotion. There are two classes of emotion included in the EEG data collected in the lab, which is positive and negative emotions. On the other hand, a total of three classes of emotions, which is positive, neutral, and negative emotions are included in SEED dataset. The properties of datasets used in the project are summarized in Table 1.2.

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Human Face Recognition in Video using Convolutional Neural Network (CNN)

Human Face Recognition in Video using Convolutional Neural Network (CNN)

A detection of human faces in VFR task is achieved. The tracking of faces in each frame of a video is accomplished using Viola-jones algorithm, hence faces and faces in each frame are distinguished properly. The Brisk feature extraction method extracts interested keypoints in faces which helps in matching persons face in video. CNN classifier achieves better recognition performance, memory management and it’s easy to use on detecting faces in video. Video-based face recognition system help in tracking and recognizing person face with different poses and illuminance condition, that leads to improve performance of recognition.
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Human Face Recognition in Video using Convolutional Neural Network (CNN)

Human Face Recognition in Video using Convolutional Neural Network (CNN)

A detection of human faces in VFR task is achieved. The tracking of faces in each frame of a video is accomplished using Viola-jones algorithm, hence faces and faces in each frame are distinguished properly. The Brisk feature extraction method extracts interested keypoints in faces which helps in matching persons face in video. CNN classifier achieves better recognition performance, memory management and it’s easy to use on detecting faces in video. Video-based face recognition system help in tracking and recognizing person face with different poses and illuminance condition, that leads to improve performance of recognition.
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A Multi task Approach for Named Entity Recognition in Social Media Data

A Multi task Approach for Named Entity Recognition in Social Media Data

This paper describes a multi-task neural net- work that aims at generalizing the underneath rules of emerging NEs in user-generated text. In addition to the main category classification task, we employ an auxiliary but related secondary task called NE segmentation (i.e. a binary classifica- tion of whether a given token is a NE or not). We use both tasks to jointly train the network. More specifically, the model captures word shapes and some orthographic features at the character level by using a Convolutional Neural Network (CNN). For contextual and syntactical informa- tion at the word level, such as word and Part- of-Speech (POS) embeddings, the model imple- ments a Bidirectional Long-Short Term Memory (BLSTM) architecture. Finally, to cover well- known entities, the model uses a dense representa- tion of gazetteers. Once the network is trained, we use it as a feature extractor to feed a Conditional Random Fields (CRF) classifier. The CRF clas- sifier jointly predicts the most likely sequence of labels giving better results than the network itself. With respect to the participants of the shared task, our approach achieved the best results in both categories: 41.86% F1-score for entities, and 40.24% F1-score for surface forms. The data for this shared task is provided by Derczynski et al. (2017).
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Original Article Infrared thermal imaging analysis of the human abdomen based on convolution neural network optimized by a genetic algorithm

Original Article Infrared thermal imaging analysis of the human abdomen based on convolution neural network optimized by a genetic algorithm

Abstract: In order to find an efficient and accurate method to analyze infrared thermography images, this paper innovatively combines a genetic algorithm with a convolutional neural network to construct a convolution neural network model (GA-CNN), which provides the basis of a diagnostic tool for doctors, also saving valuable time for patients. Using genetic algorithm initialization, the GA-CNN model can be used to find the optimal network structure, thus avoiding the influence of random initialization of traditional neural network weights. The genetic algorithm can improve the accuracy of the network through optimization of the convolution core content, convolution core size, convolution core number, pooling mode, pooling layer size and other structural parameters of convolution neural network. Real infrared thermography images from a Chinese medicine hospital were taken as samples for the model in order to detect illness in human abdomens through classification of the infrared thermography images. The excellent performance of the model proposed in this paper offers direct improvement of the prediction accuracy to 92.96%. Therefore, the GA-CNN model proposed in this paper is a very effective infrared thermal imaging analysis method, which can provide a reliable basis for the diagnosis of abdominal diseases.
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Deep Learning Based Visual Tracking: A Review

Deep Learning Based Visual Tracking: A Review

Abstract. Visual tracking, a traditional computer vision task, has been a popular research field in recent decades. As a powerful features learning method, deep learning provides a new way for the realization of visual tracking with higher accuracy and performance. Many novel trackers that based on different network models, including auto-encoder (SAE), convolutional neural network (CNN), recurrent neural networks (RNN) deep reinforcement learning (DRL) and the fusion of them were proposed by researchers in the literatures. This paper presents a comprehensive survey on deep learning based visual tracking algorithms.
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Recognition of Handwritten Characters based on Deep Learning with Tensorflow

Recognition of Handwritten Characters based on Deep Learning with Tensorflow

Recognition of Handwritten Characters based on Deep Learning with Tensorflow gives the most accurate classification and prediction values which can be taken for further research. The training time taken by the Convolutional Neural Network (CNN) model is very less as compared with any other model. The error rate or the rate of misclassified characters are also less compared with the previous models. Convolutional Neural Network (CNN) is not a lot different from other machine learning models but it tries to find patterns in the dataset. In future, further improvement can be made in the hidden layers to avoid misclassification and more data can be used for training.
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Recognition of Handwritten Characters based on Deep Learning with Tensorflow

Recognition of Handwritten Characters based on Deep Learning with Tensorflow

Recognition of Handwritten Characters based on Deep Learning with Tensorflow gives the most accurate classification and prediction values which can be taken for further research. The training time taken by the Convolutional Neural Network (CNN) model is very less as compared with any other model. The error rate or the rate of misclassified characters are also less compared with the previous models. Convolutional Neural Network (CNN) is not a lot different from other machine learning models but it tries to find patterns in the dataset. In future, further improvement can be made in the hidden layers to avoid misclassification and more data can be used for training.
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Transcription Factor Bound Regions Prediction: Word2Vec Technique with Convolutional Neural Network

Transcription Factor Bound Regions Prediction: Word2Vec Technique with Convolutional Neural Network

After we have 200-dimensional vectors, we apply Convolutional Neural Network (CNN) in one dimension for the sequential information and to fulfill the task of binary classification by extracting more features from the input. Firstly, we use an embedding layer to map each motif with its corresponding 200-dimensional vector, and input is all types of motif patterns (244 in total). For the second layer and before the last layer, we apply dropout to reduce calculating cost and avoid overfitting. Then, to extract main features and correlations between motif pat- terns and reduce parameters in calculation, we employ one-dimensional convo- lutional layer and max pooling layer respectively for three times. We also use flatten layer as a connection to transform the data into the form of input for the dense layer, which is also known as fully-connected layer. In order to output one-bit value for the binary classification, we choose sigmoid function as the ac- tivation function of last layer, and ReLU unit in other layers (see in Figure 5).
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Recent Trends and Insight Towards Automated Identification of Plant Species

Recent Trends and Insight Towards Automated Identification of Plant Species

Present studies still mostly function on the small and non-representative datasets used in the past. Only a few studies train CNN classifiers on large plant image datasets, demonstrating their applicability in automated plant species identification systems. An analogous dataset of digital images of plant elements (e.g., leaves) does not exist widely. However, there are several opportunities that should be utilized. Firstly use of camera for large scale image capture, secondly international working group on Taxonomic Databases for Plant Sciences (TDWG) is a team offers the recording of plant distributions and thereby aim to provide a standard in which different organizations maintaining databases could adopt so that they could compare and exchange data with each other without loss of information due to incompatible geographical boundaries (28). Finally, upcoming trends in crowdsourcing offer excellent opportunities to generate and continuously update large repositories of required information. Crowdsourcing systems with community- driven forums can contribute both visual datasets of flora and assisting members in determining species names of a given visual observation (29). Crowdsourcing is a technique that aims to take contributions from a large group of people, especially an online community where each person’s contribution combines with those of others to achieve a cumulative result (30). Pl@ntNet can be considered as the most successful crowdsourcing system for plants. Another approach tackling the issue of small datasets is using data augmentation schemes, commonly
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Phishdect & Mitigator: SDN based Phishing Attack Detection

Phishdect & Mitigator: SDN based Phishing Attack Detection

---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Phishing is a social engineering attack that aims at exploiting the weakness found in system processes as caused by system users threat actors find a chance to gain access to critical information systems. It has become a widespread problem across every industry because this type of scam is extremely easy to pull off. Phishing can be done through the use of e-mail communication with an embedded hyperlink. the detection and mitigation of phishing attack was a grand challenge due to its complexity. Therefore, PhishDect and Mitigator, a new detection and mitigation approach using Software-Defined Networking (SDN) to identify adverse phishing behaviors is proposed. In order to classify phishing attack signatures, convolutional neural network (CNN) is used. Along with this, to cluster the different phishing attacks, K-means clustering algorithm is used.
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Phishdect & Mitigator: SDN based Phishing Attack Detection

Phishdect & Mitigator: SDN based Phishing Attack Detection

---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Phishing is a social engineering attack that aims at exploiting the weakness found in system processes as caused by system users threat actors find a chance to gain access to critical information systems. It has become a widespread problem across every industry because this type of scam is extremely easy to pull off. Phishing can be done through the use of e-mail communication with an embedded hyperlink. the detection and mitigation of phishing attack was a grand challenge due to its complexity. Therefore, PhishDect and Mitigator, a new detection and mitigation approach using Software-Defined Networking (SDN) to identify adverse phishing behaviors is proposed. In order to classify phishing attack signatures, convolutional neural network (CNN) is used. Along with this, to cluster the different phishing attacks, K-means clustering algorithm is used.
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Multimodal Decision level Group Sentiment Prediction of Students in Classrooms

Multimodal Decision level Group Sentiment Prediction of Students in Classrooms

There have been numerous techniques used in the past researches for analyzing the sentiments using automated machines. Neural networks stand out of the lot as it has been modeled around the human brain and the neurons are capable of learning from new data and correlate the learned information to the past experiences to subjectively decode the data. Deep learning systems have advanced software design, adaptable learning procedures, and access to computing power and training data. It has already found its place in research areas like computer vision, speech recognition and Natural Language Processing (NLP) [3] and diverse field areas like object detection, robot navigation, visual classification etc. Deep learning has been embraced today by the researchers working on sentiment analysis for its architectural sophistication, learning prowess and the ability to work with both supervised and unsupervised methods.
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