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Network Architecture in Best Project Center

Now, we describe the neural network architecture used in this paper. CNNs have shown to be useful in computer vision. Recently, they are applied to problems of natural language processing (NLP) domain also. Proposed a neural network architecture which can be applied to many NLP tasks such as named entity recognition, parsing, part-of-speech tagging, and chunking used CNN for sentence classification. The best project center in nagercoil, layers present in our CNN architecture are: input layer, convolution layer, pooling layer, hidden layer, and an output layer. Each tweet is comprised group of words. We use dense representation of the words in our work.

 Dense representations of the words can be obtained in many different ways. Some of the common ways are: the word vectors which are randomly initialized, pretrained distributional word embeddings of word2vec, or global vector (Glove), fast Text embeddings, or dependence-based embeddings. The tweet vector is formed by concatenating the individual word vectors of the tweet. If the dimension of word vector is d and the length of the tweet is l then the dimension of tweet matrix is l × d. This tweet matrix is input to the first layer of CNN.

Let a tweet be comprised the sequence of words: fiterm1, term2, term3, . . . , termn. Then, the tweet vector is represented as

Tv = w1 ◦ w2 ◦ w3 ◦ . . . ◦ wn (1)

where wi is the word embedding vector of termi, and ◦ is the concatenation operator. Each wi ∈ Rd is associated with its corresponding pretrained word vector. Next layer is the convolution layer. Activation functions like tanh, relu, and sigmoid are used to get the convolution feature maps. Single filter or multiple filters can be applied depending on the task. Filter length can be 1, 3, 5, and so on.

If filter length is one, then the context of the words in the sentence is ignored, and target word feature map is calculated. If filter length is three, then target word feature map is calculated by considering target word, one word left and one word right to the target word. Here, context is preserved. Similarly, if filter length is five, then target word, two words left, and two words right of target word are considered. After finding convolution feature maps, most important activation should be selected.

This is done by pooling layer. Normally max pooling is used in NLP tasks whereas mean pooling and min pooling are also used in computer vision. We apply max pooling on the convolution layer. Next layer is fully connected hidden dense layer. Finally, sigmoid activation function is applied to classify the given tweet. We used l2 regularization to avoid overfitting. The parameters used in our method are as follows: Number of filters: 250, dropout: 0.2, batch size: 32, optimizer: Adam, and loss function: binary cross entropy.

Word Embeddings: We now describe the details of the word embeddings used in our work. Word embeddings are the distributional representation of words in lower dimensional space. Best project center in Tirunelveli, these word embeddings can be obtained by using word2vec model. There are two methods to train word embeddings in word2vec: continuous bag of words (CBOWs), and skip-gram. In CBOW, target word is predicted using the context whereas in skip-gram context is predicted using target word. CBOW is faster than skip-gram. However, skip-gram performance is better than CBOW. Have created word embeddings of Google news corpus by using word2vec model. This corpus contains 3 million words and phrases with 300 dimensions.

Glove for word representation is described the model is created using two methods, global matrix factorization and local context window. Pretrained Twitter specific glove embeddings are available. Twitter word embeddings are created using 2 Billion tweets. It contains 27 Billion tokens, 1.2 Million vocabulary uncased and several variations of dimensions (25, 50, 100, and 200). We have created the word embeddings of HSpam14 using word2vec model with the skip-gram method and 200 dimensions. We have also used Edinburgh corpus Twitter word embeddings which are trained on 10 million tweets with 100 dimensions and 400 dimensions.

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Best Project Center in Existing System

The problem addressed can be defined as follows: given a tweet t, classify whether it is a spam or not. In this section, we first discuss our proposed CNN architecture using various word embedding’s with different dimensions to detect the spam at tweet level. Next, we discuss the traditional feature-based model which uses user-based, content-based, and n-gram features for the same problem. Finally, we discuss our neural network-based ensemble architecture to detect whether the given tweet is spam tweet or non-spam tweet.

Convolutional Neural Networks

For better working of any neural network algorithm, there are two major decisions to take. One is feature representation, and another one is network architecture. Here, we describe about these two aspects in detail.

Feature Representation

The main features of tweet come from the words contained in it. Each word in the corpus acts as a feature. There are various ways to represent the features such as one-hot vector representation and dense representation. In one-hot vector feature representation, all entries of the vector are 0s except for the entries in which feature is present as best project center in nagercoil.

The value of that entry is 1. For example, assume our vocabulary has six words: actor, actress, cricketer, politician, student, and teacher. The one-hot vector for the word “student” could be: 000010. This is a natural representation to start with, although a poor one. One major drawback with one-hot vector representation is that it is high dimensional.

The dimensionality of the vector depends on the size of the vocabulary. If the vocabulary size is of |V | then a window of k words correspond to an input vector of at least |V |.k units (vectors are concatenated). This feature representation makes no assumption about the word similarity. All words are equally different from each other, and it is difficult to capture the semantics. For example, “apple,” “mango,” and “king” are equally distant in the feature space, despite “apple” should be closer to “mango” than “king” in semantic view.

In dense representation, the features (words) are represented in low dimension. The main advantage of dense vector representation is its generalization power. Similar features can have similar vectors in dense representations. However, this behavior is not there in one-hot vector representation.

Another advantage is its computational speed because of low dimensions. In cases where there is less number of features and no correlation between the features then one-hot vector representation can be used otherwise dense representation is better.

[abtechnologies]

Best Project Center Offers Spammer Detection in Social Network

This algorithm used to propagate post is trustee system. The prime goal is to detect the misinformation to assure user to receive true news and information. The paper has explored the application of principles of cognitive psychology in evaluating the spread of misinformation in online social networks. We have proposed an effective Naïve Bayes algorithm for speedy detection of spread of misinformation in online social networks taking Twitter as an example.

The aim was to propose an algorithm which would use the social media as a filter to separate misinformation from accurate information. Peoples were also interested only in misinformation which was likely to spread to a large section of the social network. The proposed algorithm is simple and effective in limiting the computation required to identify the users involved in spread of misinformation and estimate the level of acceptance of the tweets.

In future work, best project center in Tirunelveli plan to design more sophisticated rumor blocking algorithms considering the connectivity of the social network topology and node properties. To separate the entire social network into different communities with different user interests and then analyze the rumor propagation characteristics among communities.

Addressing the Class Imbalance Problem in Twitter Spam Detection Using Ensemble Learning

This paper investigates the class imbalance problem in machine learning based Twitter spam detection. It has been showed that the effectiveness of detection can be severely affected by the imbalanced distribution of spam tweets and non-spam tweets, which is widely seen in real-world Twitter data sets.

An ensemble approach has been proposed to mitigate the impact of class imbalance. Engineering project center in Tirunelveli extensive experiments have been conducted using real-world Twitter data. The results show that the proposed approach can improve the spam detection performance on imbalanced Twitter data sets with a range of imbalance degrees.

Dynamic Feature Selection for Spam Detection in Twitter

In this study, in order to determine the SPAM accounts in Twitter, special dynamic features were determined for each user groups which grouping according to the similarity of characteristics and classification were performed by feeding machine learning algorithms with these features.

Due to the selection of dynamic features in the results obtained, the accuracy rate has increased by 8-10% compared to other studies. With the reduction in size of the feature set, performance enhancement has been achieved for future work on intensive data.

[abtechnologies]

BEST PROJECT CENTER OFFERS SPAMMER DETECTION IN SOCIAL NETWORK

This algorithm used to propagate post is trustee system. The prime goal is to detect the misinformation to assure user to receive true news and information. The paper has explored the application of principles of cognitive psychology in evaluating the spread of misinformation in online social networks. We have proposed an effective Naïve Bayes algorithm for speedy detection of spread of misinformation in online social networks taking Twitter as an example.

The aim was to propose an algorithm which would use the social media as a filter to separate misinformation from accurate information. Peoples were also interested only in misinformation which was likely to spread to a large section of the social network. The proposed algorithm is simple and effective in limiting the computation required to identify the users involved in spread of misinformation and estimate the level of acceptance of the tweets.

In future work, best project center in Tirunelveli plan to design more sophisticated rumor blocking algorithms considering the connectivity of the social network topology and node properties. To separate the entire social network into different communities with different user interests and then analyze the rumor propagation characteristics among communities.

ADDRESSING THE CLASS IMBALANCE PROBLEM IN TWITTER SPAM DETECTION USING ENSEMBLE LEARNING

This paper investigates the class imbalance problem in machine learning based Twitter spam detection. It has been showed that the effectiveness of detection can be severely affected by the imbalanced distribution of spam tweets and non-spam tweets, which is widely seen in real-world Twitter data sets.

An ensemble approach has been proposed to mitigate the impact of class imbalance. Best engineering project center in Tirunelveli extensive experiments have been conducted using real-world Twitter data. The results show that the proposed approach can improve the spam detection performance on imbalanced Twitter data sets with a range of imbalance degrees.

DYNAMIC FEATURE SELECTION FOR SPAM DETECTION IN TWITTER

In this study, in order to determine the SPAM accounts in Twitter, special dynamic features were determined for each user groups which grouping according to the similarity of characteristics and classification were performed by feeding machine learning algorithms with these features.

Due to the selection of dynamic features in the results obtained, the accuracy rate has increased by 8-10% compared to other studies. With the reduction in size of the feature set, performance enhancement has been achieved for future work on intensive data.

[abtechnologies]

Best Project Center Installing MySQL on Windows

The default installation on any version of Windows is now much easier than it used to be, as MySQL now comes neatly packaged with an installer. Simply download the installer package, unzip it anywhere and run the setup.exe file. Best project center in Tirunelveli, the default installer setup.exe will walk you through the trivial process and by default will install everything under C:\mysql. Test the server by firing it up from the command prompt the first time. Go to the location of the MySQL server which is probably C:\mysql\bin, and type:

Verifying MySQL Installation

After MySQL, has been successfully installed, the base tables have been initialized and the server has been started; you can verify that everything is working as it should be via some simple tests. Use the mysqladmin Utility to Obtain Server Status Use mysqladmin binary to check the server version. This binary would be available in /usr/bin on linux and in C:\mysql\bin on windows.

[root@host]# mysqladmin --version

It will produce the following result on Linux. It may vary depending on your installation:

mysqladmin Ver 8.23 Distrib 5.0.9-0, for redhat-linux-gnu on i386

If you do not get such a message, then there may be some problem in your installation and you would need some help to fix it.

Execute simple SQL commands using the MySQL Client

You can connect to your MySQL server through the MySQL client and by using the mysql command. At this moment, you do not need to give any password as by default it will be set as blank. You can just use following command

[root@host]# mysql

It should be rewarded with a mysql> prompt. Now, you are connected to the MySQL server and you can execute all the SQL commands at the mysql> prompt as follows:

mysql> SHOW DATABASES;

+----------+

| Database |

+----------+

| mysql |

| test |

+----------+

2 rows in set (0.13 sec)

Post-installation Steps

MySQL ships with a blank password for the root MySQL user, best project center in Tirunelveli. As soon as you have successfully installed the database and the client, you need to set a root password as given in the following code block:

[root@host]# mysqladmin -u root password "new_password";

Now to make a connection to your MySQL server, you would have to use the following command:

[root@host]# mysql -u root -p Enter password:*******

UNIX users will also want to put your MySQL directory in your PATH, so you won't have to keep typing out the full path every time you want to use the command-line client.