A Novel Approach Towards Fake News Detection: Deep Learning Augmented with Textual Entailment Features

In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text.

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A Novel Approach for Detection of Fake News using Long Short Term Memory (LSTM)

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/201052021 ◽

2021 ◽

Vol 10 (5) ◽

pp. 3062-3066

Keyword(s):

Neural Network ◽

Deep Learning ◽

Text Mining ◽

Short Term Memory ◽

Online Media ◽

Fake News ◽

Short Term ◽

Term Memory ◽

Novel Approach ◽

Long Short Term Memory

Online media for news consumption has doubtful advantages. From one perspective, it has minimal expense, simple access, and fast dispersal of data which leads individuals to search out and devour news from online media. On the other hand, it increases the wide spread of "counterfeit news", i.e., inferior quality news with purposefully bogus data. The broad spread of fake news contrarily affects people and society. Hence, fake news detection in social media has become an emerging research topic that is drawing attention from various researchers. In past, many creators proposed the utilization of text mining procedures and AI strategies to examine textual data and helps to foresee the believability of news. With more computational capacities and to deal with enormous datasets, deep learning models present a better presentation over customary text mining strategies and AI methods. Normally deep learning model, for example, LSTM model can identify complex patterns in the data. Long short term memory is a tree organized recurrent neural network (RNN) used to examine variable length sequential information. In our proposed framework we set up a fake news identification model dependent on LSTM neural network. Openly accessible unstructured news datasets are utilized to evaluate the exhibition of the model. The outcome shows the prevalence and exactness of LSTM model over the customary techniques specifically CNN for fake news recognition.

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Deep Learning through Convolutional Neural Networks for Classification of Image A Novel Approach Using Hyper Filter

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i6.164168 ◽

2019 ◽

Vol 7 (6) ◽

pp. 164-168

Author(s):

Kshitij Tripathi ◽

Rajendra G. Vyas ◽

Anil K. Gupta

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Novel Approach

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Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽

10.3390/s21061962 ◽

2021 ◽

Vol 21 (6) ◽

pp. 1962

Author(s):

Enrico Buratto ◽

Adriano Simonetto ◽

Gianluca Agresti ◽

Henrik Schäfer ◽

Pietro Zanuttigh

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Light Response ◽

Real Data ◽

Depth Image ◽

Learning Approach ◽

Multiple Reflections ◽

Noisy Input ◽

Novel Approach ◽

Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

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BerConvoNet: A deep learning framework for fake news classification

Applied Soft Computing ◽

10.1016/j.asoc.2021.107614 ◽

2021 ◽

pp. 107614

Author(s):

Monika Choudhary ◽

Satyendra Singh Chouhan ◽

Emmanuel S. Pilli ◽

Santosh Kumar Vipparthi

Keyword(s):

Deep Learning ◽

Fake News ◽

Learning Framework

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Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

Sensors ◽

10.3390/s21082834 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2834

Author(s):

Billur Kazaz ◽

Subhadipto Poddar ◽

Saeed Arabi ◽

Michael A. Perez ◽

Anuj Sharma ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Pollution Prevention ◽

Aerial Images ◽

Unmanned Aerial Systems ◽

Construction Sites ◽

State Regulations ◽

Novel Approach ◽

Model Training ◽

Aerial Systems

Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices.

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A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Scientific Reports ◽

10.1038/s41598-021-85254-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Carlo Donadio ◽

Massimo Brescia ◽

Alessia Riccardo ◽

Giuseppe Angora ◽

Michele Delli Veneri ◽

...

Keyword(s):

Deep Learning ◽

Morphological Variations ◽

Control Factors ◽

Novel Approach ◽

Drainage Networks ◽

Key Characteristics ◽

Intrinsic Complexity ◽

Solar System Bodies ◽

Manual Extraction

AbstractSeveral approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.

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A Novel Approach for Churn Prediction Using Deep Learning

2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) ◽

10.1109/iccic.2017.8524551 ◽

2017 ◽

Cited By ~ 3

Author(s):

Abinash Mishra ◽

U. Srinivasulu Reddy

Keyword(s):

Deep Learning ◽

Churn Prediction ◽

Novel Approach

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DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping

10.1101/2020.09.04.283234 ◽

2020 ◽

Author(s):

Geoffrey Schau ◽

Erik Burlingame ◽

Young Hwan Chang

Keyword(s):

Deep Learning ◽

Complete Information ◽

Specific Information ◽

Multimodal Integration ◽

Specific Data ◽

Domain Specific ◽

Cross Domain ◽

Input Feature ◽

Novel Approach ◽

Latent Representations

AbstractDeep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domainspecific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non-sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.

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