scholarly journals Dynasty recognition algorithm of an adaptive enhancement capsule network for ancient mural images

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jianfang Cao ◽  
Minmin Yan ◽  
Huiming Chen ◽  
Xiaodong Tian ◽  
Shang Ma
Keyword(s):  
Recall Rate ◽  
High Energy ◽  
Recognition Algorithm ◽  
Data Set ◽  
Mogao Grottoes ◽  
Adaptive Enhancement ◽  
Proposed Model ◽  
The Rich ◽  
High Level

AbstractIn view of the polysemy of mural images and the style difference among mural images painted in different dynasties as well as the high energy costs of the traditional manual dynasty classification method, which resorts to mural texts and historical documents, this study proposed an adaptive enhancement capsule network (AECN) for automatic dynasty identification of mural images. Based on the original capsule network, we introduced a preconvolution structure to extract the high-level features of the mural images from Mogao Grottoes, such as color and texture. Then, we added an even activation operation to the layers of the network to enhance the fitting performance of the model. Finally, we performed adaptive modifications on the capsule network to increase the gradient smoothness of the model, based on which to optimize the model and thus to increase its classification precision. With the self-constructed DH1926 data set as the study subject, the proposed model achieved an accuracy of 84.44%, an average precision of 82.36%, an average recall rate of 83.75% and a comprehensive assessment score F1 of 83.96%. Compared with modified convolution neural networks and the original capsule network, the model proposed in study increased all the considered indices by more than 3%. It has a satisfactory fitting performance, which can extract the rich features of mural images at multiple levels and well express their semantic information. Furthermore, it has a higher accuracy and better robustness in the classification of the Mogao Grottoes murals, and therefore is of certain application values and research significance.

scholarly journals Sentiment analysis of MOOC reviews via ALBERT-BiLSTM model

2021 ◽  
Vol 336 ◽  
pp. 05008
Author(s):  
Cheng Wang ◽  
Sirui Huang ◽  
Ya Zhou
Keyword(s):  
Sentiment Analysis ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Analysis Model ◽  
Accuracy Rate ◽  
Massive Open Online ◽  
Data Set ◽  
Proposed Model ◽  
Contextual Feature

The accurate exploration of the sentiment information in comments for Massive Open Online Courses (MOOC) courses plays an important role in improving its curricular quality and promoting MOOC platform’s sustainable development. At present, most of the sentiment analyses of comments for MOOC courses are actually studies in the extensive sense, while relatively less attention is paid to such intensive issues as the polysemous word and the familiar word with an upgraded significance, which results in a low accuracy rate of the sentiment analysis model that is used to identify the genuine sentiment tendency of course comments. For this reason, this paper proposed an ALBERT-BiLSTM model for sentiment analysis of comments for MOOC courses. Firstly, ALBERT was used to dynamically generate word vectors. Secondly, the contextual feature vectors were obtained through BiLSTM pre-sequence and post-sequence, and the attention mechanism that could calculate the weight of different words in a sentence was applied together. Finally, the BiLSTM output vectors were input into Softmax for the classification of sentiments and prediction of the sentimental tendency. The experiment was performed based on the genuine data set of comments for MOOC courses. It was proved in the result that the proposed model was higher in accuracy rate than the already existing models.

scholarly journals Webshell Detection Based on Executable Data Characteristics of PHP Code

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zulie Pan ◽  
Yuanchao Chen ◽  
Yu Chen ◽  
Yi Shen ◽  
Xuanzhen Guo
Keyword(s):  
State Of The Art ◽  
Recall Rate ◽  
Remote Access ◽  
Detection Accuracy ◽  
Controlled Experiments ◽  
Data Set ◽  
Detection Model ◽  
Proposed Model ◽  
Text Features ◽  
And Control

A webshell is a malicious backdoor that allows remote access and control to a web server by executing arbitrary commands. The wide use of obfuscation and encryption technologies has greatly increased the difficulty of webshell detection. To this end, we propose a novel webshell detection model leveraging the grammatical features extracted from the PHP code. The key idea is to combine the executable data characteristics of the PHP code with static text features for webshell classification. To verify the proposed model, we construct a cleaned data set of webshell consisting of 2,917 samples from 17 webshell collection projects and conduct extensive experiments. We have designed three sets of controlled experiments, the results of which show that the accuracy of the three algorithms has reached more than 99.40%, the highest reached 99.66%, the recall rate has been increased by at least 1.8%, the most increased by 6.75%, and the F1 value has increased by 2.02% on average. It not only confirms the efficiency of the grammatical features in webshell detection but also shows that our system significantly outperforms several state-of-the-art rivals in terms of detection accuracy and recall rate.

scholarly journals A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

2020 ◽  
Vol 37 (6) ◽  
pp. 1093-1101
Author(s):  
Divakar Yadav ◽  
Akanksha ◽  
Arun Kumar Yadav
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clustering Algorithm ◽  
Daily Intake ◽  
Plant Diseases ◽  
Data Set ◽  
Proposed Model ◽  
Fcm Clustering ◽  
Contrast Stretching

Plants have a great role to play in biodiversity sustenance. These natural products not only push their demand for agricultural productivity, but also for the manufacturing of medical products, cosmetics and many more. Apple is one of the fruits that is known for its excellent nutritional properties and is therefore recommended for daily intake. However, due to various diseases in apple plants, farmers have to suffer from a huge loss. This not only causes severe effects on fruit’s health, but also decreases its overall productivity, quantity, and quality. A novel convolutional neural network (CNN) based model for recognition and classification of apple leaf diseases is proposed in this paper. The proposed model applies contrast stretching based pre-processing technique and fuzzy c-means (FCM) clustering algorithm for the identification of plant diseases. These techniques help to improve the accuracy of CNN model even with lesser size of dataset. 400 image samples (200 healthy, 200 diseased) of apple leaves have been used to train and validate the performance of the proposed model. The proposed model achieved an accuracy of 98%. To achieve this accuracy, it uses lesser data-set size as compared to other existing models, without compromising with the performance, which become possible due to use of contrast stretching pre-processing combined with FCM clustering algorithm.

scholarly journals Mural classification model based on high- and low-level vision fusion

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Jianfang Cao ◽  
Hongyan Cui ◽  
Zibang Zhang ◽  
Aidi Zhao
Keyword(s):  
Network Architecture ◽  
Recall Rate ◽  
Classification Performance ◽  
Human Vision ◽  
Classification Model ◽  
Bp Network ◽  
Depth Migration ◽  
Low Level ◽  
The Rich ◽  
High Level

AbstractThe rapid classification of ancient murals is a pressing issue confronting scholars due to the rich content and information contained in images. Convolutional neural networks (CNNs) have been extensively applied in the field of computer vision because of their excellent classification performance. However, the network architecture of CNNs tends to be complex, which can lead to overfitting. To address the overfitting problem for CNNs, a classification model for ancient murals was developed in this study on the basis of a pretrained VGGNet model that integrates a depth migration model and simple low-level vision. First, we utilized a data enhancement algorithm to augment the original mural dataset. Then, transfer learning was applied to adapt a pretrained VGGNet model to the dataset, and this model was subsequently used to extract high-level visual features after readjustment. These extracted features were fused with the low-level features of the murals, such as color and texture, to form feature descriptors. Last, these descriptors were input into classifiers to obtain the final classification outcomes. The precision rate, recall rate and F1-score of the proposed model were found to be 80.64%, 78.06% and 78.63%, respectively, over the constructed mural dataset. Comparisons with AlexNet and a traditional backpropagation (BP) network illustrated the effectiveness of the proposed method for mural image classification. The generalization ability of the proposed method was proven through its application to different datasets. The algorithm proposed in this study comprehensively considers both the high- and low-level visual characteristics of murals, consistent with human vision.

scholarly journals Evaluating Unemployment through Grey Incidence Analysis Model: A Study of One Hundred Thirteen Selected Countries

2021 ◽  
Vol VI (I) ◽  
pp. 23-35
Author(s):  
Abdul Aziz Khan Niazi ◽  
Tehmina Fiaz Qazi ◽  
Abdul Basit
Keyword(s):  
Secondary Data ◽  
Analysis Model ◽  
Data Set ◽  
Development Indicators ◽  
Low Level ◽  
New Information ◽  
Grey Incidence Analysis ◽  
High Level ◽  
Incidence Analysis

The purpose of the study is to gauge the unemployment level of selected one hundred and thirteen countries. The design of the study includes a survey of the literature, extraction of relevant data and analysis. The study follows a quantitative paradigm of research that uses secondary data set taken from the website of World Development Indicators (WDI). The analysis encompasses selected countries based on the availability of data. The data has been analyzed using Grey Incidence Analysis Model, commonly known as GRA. For interpretation of the results, the methodology has been augmented with the scheme of ensigns (i.e. classification of countries into Extremely Low, Very Low, Low, Moderate, High, Very High, Extremely High) of the level of unemployment. Results show that J&APR have an extremely low level of unemployment and member countries of SADC have an extremely high level of unemployment. Pakistan fall under the ensign of very low, therefore have a low level of unemployment. It is valuable to study equally useful for governments, academia and the international community. This study provides critical new information on the phenomenon.

NetLens: Iterative Exploration of Content-Actor Network Data

2007 ◽  
Vol 6 (1) ◽  
pp. 18-31 ◽  
Author(s):  
Hyunmo Kang ◽  
Catherine Plaisant ◽  
Bongshin Lee ◽  
Benjamin B. Bederson
Keyword(s):  
Data Model ◽  
Network Data ◽  
Usability Study ◽  
Data Set ◽  
Actor Network ◽  
Complex Queries ◽  
Abstract Data ◽  
The Rich ◽  
Abstract Content

Networks have remained a challenge for information retrieval and visualization because of the rich set of tasks that users want to accomplish. This paper offers an abstract Content-Actor network data model, a classification of tasks, and a tool to support them. The NetLens interface was designed around the abstract Content-Actor network data model to allow users to pose a series of elementary queries and iteratively refine visual overviews and sorted lists. This enables the support of complex queries that are traditionally hard to specify. NetLens is general and scalable in that it applies to any data set that can be represented with our abstract data model. This paper describes the use of NetLens with a subset of the ACM Digital Library consisting of about 4000 papers from the CHI conference written by about 6000 authors, and reports on a usability study with nine participants.

scholarly journals An Efficient Model for TV broadcast Audio Classification through InceptionV3 and ResNet50

2020 ◽  
Vol 9 (5) ◽  
pp. 2234-2238
Keyword(s):  
Classification Model ◽  
Audio Classification ◽  
Development Environment ◽  
Data Set ◽  
Novel Approach ◽  
Proposed Model ◽  
Audio Data ◽  
Data Surveillance ◽  
Voice Data

In the recent advancements of applications, one of the challenging task in many gadgets are incorporated, which is based on audio classification and recognition. A set of emotion detection after post-surgical issues, classification of various voice sequence, classification of random voice data, surveillance and speaker detection audio data act as a crucial input. Most of the audio data is inherent with the environmental noise or instrumental noise. Extracting the unique features from the audio data is very important to determine the speaker effectively. Such kind of a novel idea is evaluated here. The research focus is based on classification of TV broadcast audios in which the type of audio is being class separated through a novel approach. The design evaluates, the five different categories of audio data such as advertisement, news, songs, cartoon and sports from the data collected using the TV tuner card. The proposed design associated with python as a Development environment. The audio samples are converted to images using Spectrogram and then transfer learning is applied on the pretrained models ResNet50 and Inceptionv3 to extract the deep features and to classify the audio data. Inception V3 is compared here with the ResNet50 to get greater accuracy in classification. The pre-trained models are models that was trained on the ImageNet data set for a certain task and are used here to quick train the audio classification model on training set with high accuracy. The proposed model produces accuracy of 94% for Inceptionv3 which gives greater accuracy when compared with the ResNet50 which gives 93%. accuracy.

scholarly journals AMS-Net: An Attention-Based Multi-Scale Network for Classification of 3D Terracotta Warrior Fragments

2021 ◽  
Vol 13 (18) ◽  
pp. 3713
Author(s):  
Jie Liu ◽  
Xin Cao ◽  
Pingchuan Zhang ◽  
Xueli Xu ◽  
Yangyang Liu ◽  
...  
Keyword(s):  
Real World ◽  
Data Sets ◽  
Semantic Features ◽  
Real World Data ◽  
Global Features ◽  
Data Set ◽  
Multi Scale ◽  
Public Data ◽  
High Level

As an essential step in the restoration of Terracotta Warriors, the results of fragments classification will directly affect the performance of fragments matching and splicing. However, most of the existing methods are based on traditional technology and have low accuracy in classification. A practical and effective classification method for fragments is an urgent need. In this case, an attention-based multi-scale neural network named AMS-Net is proposed to extract significant geometric and semantic features. AMS-Net is a hierarchical structure consisting of a multi-scale set abstraction block (MS-BLOCK) and a fully connected (FC) layer. MS-BLOCK consists of a local-global layer (LGLayer) and an improved multi-layer perceptron (IMLP). With a multi-scale strategy, LGLayer can parallel extract the local and global features from different scales. IMLP can concatenate the high-level and low-level features for classification tasks. Extensive experiments on the public data set (ModelNet40/10) and the real-world Terracotta Warrior fragments data set are conducted. The accuracy results with normal can achieve 93.52% and 96.22%, respectively. For real-world data sets, the accuracy is best among the existing methods. The robustness and effectiveness of the performance on the task of 3D point cloud classification are also investigated. It proves that the proposed end-to-end learning network is more effective and suitable for the classification of the Terracotta Warrior fragments.

LF-LDA

2018 ◽  
Vol 14 (2) ◽  
pp. 18-36 ◽  
Author(s):  
Yongjun Zhang ◽  
Zijian Wang ◽  
Yongtao Yu ◽  
Bolun Chen ◽  
Jialin Ma ◽  
...  
Keyword(s):  
Big Data ◽  
Topic Model ◽  
State Of The Art ◽  
Text Documents ◽  
Data Set ◽  
Proposed Model ◽  
Textual Data ◽  
Noisy Function ◽  
Major Data

This article describes how text documents are a major data structure in the era of big data. With the explosive growth of data, the number of documents with multi-labels has increased dramatically. The popular multi-label classification technology, which is usually employed to handle multinomial text documents, is sensitive to the noise terms of text documents. Therefore, there still exists a huge room for multi-label classification of text documents. This article introduces a supervised topic model, named labeled LDA with function terms (LF-LDA), to filter out the noisy function terms from text documents, which can help to improve the performance of multi-label classification of text documents. The article also shows the derivation of the Gibbs Sampling formulas in detail, which can be generalized to other similar topic models. Based on the textual data set RCV1-v2, the article compared the proposed model with other two state-of-the-art multi-label classifiers, Tuned SVM and labeled LDA, on both Macro-F1 and Micro-F1 metrics. The result shows that LF-LDA outperforms them and has the lowest variance, which indicates the robustness of the LF-LDA classifier.

scholarly journals Agricultural Machinery Abnormal Trajectory Recognition

2021 ◽  
Vol 11 (4) ◽  
pp. 291-297
Author(s):  
Hui Liu ◽  
◽  
Yujie Qiao ◽  
Guofa Zhao ◽  
Jingping Cheng ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Recall Rate ◽  
Recognition Algorithm ◽  
Agricultural Machinery ◽  
Trajectory Data ◽  
Data Set ◽  
Attribute Data ◽  
Dbscan Clustering ◽  
Spatial Distribution Characteristics ◽  
Different Types

The service system of supervision of agricultural machinery subsoiling operation enables acquisition of a large amount of agricultural machinery movement track data. These trajectories include not only farmland operation track data, but also road driving track data. Their spatial distribution characteristics and attribute data are different. In this paper, we make a study of the abnormal trajectory data in data set, and propose an abnormal trajectory recognition algorithm based on DBSCAN clustering. According to the attribute data of agricultural machinery trajectory, the trajectory is divided to form different types of motion trajectory, then to judge the spatial distribution of different types of agricultural machinery tracks. If the attribute data of the tracks are inconsistent with their spatial distribution, it will be judged as abnormal tracks. The experimental results show that both the accuracy of the algorithm and the recall rate is 98.61%, which can identify the abnormal tracks of agricultural machinery.

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