scholarly journals CNN based efficient image classification system for smartphone device

2021 ◽  
Author(s):  
Mayank Mishra ◽  
Tanupriya Choudhury ◽  
Tanmay Sarkar
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification System ◽  
Text Messaging ◽  
Motivational Messages ◽  
Social Media Text ◽  
Social Media Platforms ◽  
Optimum Model

Abstract In our work, we look to classify images that make their way into our smartphone devices through various social-media text-messaging platforms. We aim at classifying images into three broad categories: document-based images, quote-based images, and photographs. People, especially students, share many document-based images that include snapshots of essential emails, handwritten notes, articles, etc. Quote based images, consisting of birthday wishes, motivational messages, festival greetings, etc., are among the highly shared images on social media platforms. A significant share of images constitutes photographs of people, including group photographs, selfies, portraits, etc. We train various convolutional neural network (CNN) based models on our self-made dataset and compare their results to find our task’s optimum model.

Spam Detection on Social Media Using Semantic Convolutional Neural Network

2018 ◽  
Vol 8 (1) ◽  
pp. 12-26 ◽  
Author(s):  
Gauri Jain ◽  
Manisha Sharma ◽  
Basant Agarwal
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Spam Detection ◽  
Learning Technology ◽  
The Social ◽  
Social Media Text ◽  
Current Article ◽  
Semantic Layer

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.

Spam Detection on Social Media Using Semantic Convolutional Neural Network

2020 ◽  
pp. 704-719
Author(s):  
Gauri Jain ◽  
Manisha Sharma ◽  
Basant Agarwal
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Spam Detection ◽  
Learning Technology ◽  
The Social ◽  
Social Media Text ◽  
Current Article ◽  
Semantic Layer

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.

scholarly journals Using Machine Learning–Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions

10.2196/26478 ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. e26478
Author(s):  
Jingcheng Du ◽  
Sharice Preston ◽  
Hanxiao Sun ◽  
Ross Shegog ◽  
Rachel Cunningham ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Social Media ◽  
Human Papillomavirus ◽  
Convolutional Neural Network ◽  
Hpv Vaccine ◽  
Support Vector ◽  
Social Media Platforms ◽  
Vaccine Promotion

Background The rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information, thus creating obstacles for vaccine promotion. Objective The aim of this study is to develop and evaluate an intelligent automated protocol for identifying and classifying human papillomavirus (HPV) vaccine misinformation on social media using machine learning (ML)–based methods. Methods Reddit posts (from 2007 to 2017, N=28,121) that contained keywords related to HPV vaccination were compiled. A random subset (2200/28,121, 7.82%) was manually labeled for misinformation and served as the gold standard corpus for evaluation. A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation. Results A convolutional neural network model achieved the highest area under the receiver operating characteristic curve of 0.7943. Of the 28,121 Reddit posts, 7207 (25.63%) were classified as vaccine misinformation, with discussions about general safety issues identified as the leading type of misinformed posts (2666/7207, 36.99%). Conclusions ML-based approaches are effective in the identification and classification of HPV vaccine misinformation on Reddit and may be generalizable to other social media platforms. ML-based methods may provide the capacity and utility to meet the challenge involved in intelligent automated monitoring and classification of public health misinformation on social media platforms. The timely identification of vaccine misinformation on the internet is the first step in misinformation correction and vaccine promotion.

scholarly journals Weeds Detection in Agricultural Fields using Convolutional Neural Network

2019 ◽  
Vol 8 (11) ◽  
pp. 292-296
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Weed Control ◽  
Classification System ◽  
Agricultural Fields ◽  
Use Of Technology ◽  
Neural Network Algorithm

Weeds are very annoying for farmers and also not very good for the crops. Its existence might damage the growth of the crops. Therefore, weed control is very important for farmers. Farmers need to ensure their agricultural fields are free from weeds for at least once a week, whether they need to spray weeds herbicides to their plantation or remove it using tools or manually. The aim of this research is to build an automated weed control robot using the Lego Mindstorm EV3 which connected to a computer. The robot consists of motors, servo motors and a camera which we use to capture the image of the crops and weeds. An automated image classification system has been designed to differentiate between weeds and crops. The robot will spray the weed herbicides directly to the area that have been detected weeds near or at it. For the image classification method, we employ the convolutional neural network algorithm to process the image of the object. Therefore, by the use of technology especially in artificial intelligence, farmers can reduce the amount of workload and workforce they need to monitor their plantation. In addition, this technology also can improve the quality of the crops.

scholarly journals Identification of Fake vs. Real Identities on Social Media using Random Forest and Deep Convolutional Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 7347-7351
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Deep Convolutional Neural Network ◽  
Social Media Platforms

Identity detection is very essential in social media platforms, various platform has facing fake accounts influence since couple of years in current eras. Many researchers has introduces approach for identify the fake profiles, but still system cant able to solve such issues. As these fake identities are being used by offenders for various malicious purposes, it has become necessity of time to identify them. The fake identities are categorized into two main types’ i.e. fake identities by bots and fake identities by humans. This system removes fake identities by bots during preprocessing and focuses mainly on identification of fake identities by humans as very little research has been made till now on the fake identities by humans. For classification we test for two different algorithms i.e. Random Forest (RF) and Recurrent Neural Network (RNN). The classification is based on various features such as user name, location, friends count, followers count and so on. Here, dataset used is that of Twitter.

scholarly journals A new corpus-based convolutional neural network for big data text analytics

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Wedjdane Nahilia ◽  
Kahled Rezega ◽  
Okba Kazara
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Easy Access ◽  
Data Sets ◽  
Accuracy Score ◽  
Social Media Platforms

Companies market their services and products on social media platforms with today's easy access to the internet. As result, they receive feedback and reviews from their users directly on their social media sites. Reading every text is time-consuming and resourcedemanding. With access to technology-based solutions, analyzing the sentiment of all these texts gives companies an overview of how positive or negative users are on specific subjects will minimize losses. In this paper, we propose a deep learning approach to perform sentiment analysis on reviews using a convolutional neural network model, because that they have proven remarkable results for text classification. We validate our convolutional neural network model using large-scale data sets: IMDB movie reviews and Reuters data sets with a final accuracy score of ~86% for both data sets.

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