Recognition and Classification of Broiler Droppings Based on Deep Convolutional Neural Network

Digestive diseases are one of the common broiler diseases that significantly affect production and animal welfare in broiler breeding. Droppings examination and observation are the most precise techniques to detect the occurrence of digestive disease infections in birds. This study proposes an automated broiler digestive disease detector based on a deep Convolutional Neural Network model to classify fine-grained abnormal broiler droppings images as normal and abnormal (shape, color, water content, and shape&water). Droppings images were collected from 10,000 25-35-day-old Ross broiler birds reared in multilayer cages with automatic droppings conveyor belts. For comparative purposes, Faster R-CNN and YOLO-V3 deep Convolutional Neural Networks were developed. The performance of YOLO-V3 was improved by optimizing the anchor box. Faster R-CNN achieved 99.1% recall and 93.3% mean average precision, while YOLO-V3 achieved 88.7% recall and 84.3% mean average precision on the testing data set. The proposed detector can provide technical support for the detection of digestive diseases in broiler production by automatically and nonintrusively recognizing and classifying chicken droppings.

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Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

Jurnal ULTIMATICS ◽

10.31937/ti.v10i1.842 ◽

2018 ◽

Vol 10 (1) ◽

pp. 57-64 ◽

Cited By ~ 1

Author(s):

Rizqa Raaiqa Bintana ◽

Chastine Fatichah ◽

Diana Purwitasari

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Question Answering ◽

Mean Average Precision ◽

Average Precision ◽

Community Based ◽

Network Methods ◽

The Mean ◽

Index Terms ◽

Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

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DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

10.36227/techrxiv.12151734.v1 ◽

2020 ◽

Author(s):

Sriram Srinivasan ◽

Shashank A ◽

vinayakumar R ◽

Soman KP

Keyword(s):

Neural Network ◽

Intrusion Detection ◽

Convolutional Neural Network ◽

Intrusion Detection System ◽

Input Data ◽

Detection System ◽

Deep Convolutional Neural Network ◽

Traffic Data ◽

Data Set ◽

Research Problems

In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

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A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images

Remote Sensing ◽

10.3390/rs12061015 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1015 ◽

Cited By ~ 3

Author(s):

Kan Zeng ◽

Yixiao Wang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Oil Spill ◽

Data Augmentation ◽

Classification Performance ◽

Deep Convolutional Neural Network ◽

Convolutional Network ◽

Data Set ◽

Oil Spill Detection ◽

Processing Framework

Classification algorithms for automatically detecting sea surface oil spills from spaceborne Synthetic Aperture Radars (SARs) can usually be regarded as part of a three-step processing framework, which briefly includes image segmentation, feature extraction, and target classification. A Deep Convolutional Neural Network (DCNN), named the Oil Spill Convolutional Network (OSCNet), is proposed in this paper for SAR oil spill detection, which can do the latter two steps of the three-step processing framework. Based on VGG-16, the OSCNet is obtained by designing the architecture and adjusting hyperparameters with the data set of SAR dark patches. With the help of the big data set containing more than 20,000 SAR dark patches and data augmentation, the OSCNet can have as many as 12 weight layers. It is a relatively deep Deep Learning (DL) network for SAR oil spill detection. It is shown by the experiments based on the same data set that the classification performance of OSCNet has been significantly improved compared to that of traditional machine learning (ML). The accuracy, recall, and precision are improved from 92.50%, 81.40%, and 80.95% to 94.01%, 83.51%, and 85.70%, respectively. An important reason for this improvement is that the distinguishability of the features learned by OSCNet itself from the data set is significantly higher than that of the hand-crafted features needed by traditional ML algorithms. In addition, experiments show that data augmentation plays an important role in avoiding over-fitting and hence improves the classification performance. OSCNet has also been compared with other DL classifiers for SAR oil spill detection. Due to the huge differences in the data sets, only their similarities and differences are discussed at the principle level.

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Fine-grained traffic checkpoint motor vehicle recognition system based on deep convolutional neural network

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) ◽

10.1109/cisp-bmei.2017.8302308 ◽

2017 ◽

Author(s):

Hua Zhang ◽

Xing Zhang ◽

Yunyu Shi ◽

Jiaxun Tang ◽

Xiang Liu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Motor Vehicle ◽

Recognition System ◽

Deep Convolutional Neural Network ◽

Fine Grained ◽

Vehicle Recognition

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Optimizing Deep Convolutional Neural Network for Facial Expression Recognition

European Journal of Engineering Research and Science ◽

10.24018/ejers.2020.5.2.495 ◽

2020 ◽

Vol 5 (2) ◽

pp. 192-195

Author(s):

Umesh B. Chavan ◽

Dinesh Kulkarni

Keyword(s):

Neural Network ◽

Facial Expression ◽

Convolutional Neural Network ◽

Facial Expression Recognition ◽

Deep Convolutional Neural Network ◽

Expression Recognition ◽

Graphic Processing Units ◽

Data Set ◽

Training Time ◽

Deep Cnn

Facial expression recognition (FER) systems have attracted much research interest in the area of Machine Learning. We designed a large, deep convolutional neural network to classify 40,000 images in the data-set into one of seven categories (disgust, fear, happy, angry, sad, neutral, surprise). In this project, we have designed deep learning Convolution Neural Network (CNN) for facial expression recognition and developed model in Theano and Caffe for training process. The proposed architecture achieves 61% accuracy. This work presents results of accelerated implementation of the CNN with graphic processing units (GPUs). Optimizing Deep CNN is to reduce training time for system.

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Research and Application of Ancient Chinese Pattern Restoration Based on Deep Convolutional Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/2691346 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Xuhui Fu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Restoration ◽

Deep Convolutional Neural Network ◽

Deep Convolutional Neural Networks ◽

Multilayer Network ◽

Restoration Method ◽

Restoration Effect

In recent years, deep learning, as a very popular artificial intelligence method, can be said to be a small area in the field of image recognition. It is a type of machine learning, actually derived from artificial neural networks, and is a method used to learn the characteristics of sample data. It is a multilayer network, which can learn the information from the bottom to the top of the image through the multilayer network, so as to extract the characteristics of the sample, and then perform identification and classification. The purpose of deep learning is to make the machine have the same analytical and learning capabilities as the human brain. The ability of deep learning in data processing (including images) is unmatched by other methods, and its achievements in recent years have left other methods behind. This article comprehensively reviews the application research progress of deep convolutional neural networks in ancient Chinese pattern restoration and mainly focuses on the research based on deep convolutional neural networks. The main tasks are as follows: (1) a detailed and comprehensive introduction to the basic knowledge of deep convolutional neural and a summary of related algorithms along the three directions of text preprocessing, learning, and neural networks are provided. This article focuses on the related mechanism of traditional pattern repair based on deep convolutional neural network and analyzes the key structure and principle. (2) Research on image restoration models based on deep convolutional networks and adversarial neural networks is carried out. The model is mainly composed of four parts, namely, information masking, feature extraction, generating network, and discriminant network. The main functions of each part are independent and interdependent. (3) The method based on the deep convolutional neural network and the other two methods are tested on the same part of the Qinghai traditional embroidery image data set. From the final evaluation index of the experiment, the method in this paper has better evaluation index than the traditional image restoration method based on samples and the image restoration method based on deep learning. In addition, from the actual image restoration effect, the method in this paper has a better image restoration effect than the other two methods, and the restoration results produced are more in line with the habit of human observation with the naked eye.

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