Social Botnet Community Detection: A Novel Approach based on Behavioral Similarity in Twitter Network using Deep Learning

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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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 Deep Learning Framework for Self-evolving Hierarchical Community Detection

10.1145/3459637.3482223 ◽

2021 ◽

Author(s):

Daizong Ding ◽

Mi Zhang ◽

Hanrui Wang ◽

Xudong Pan ◽

Min Yang ◽

...

Keyword(s):

Deep Learning ◽

Community Detection ◽

Learning Framework

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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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DISMIR: a deep learning-based cancer-detection method by integrating DNA sequence and methylation information of individual cell-free DNA reads

10.1101/2021.01.12.426440 ◽

2021 ◽

Author(s):

Jiaqi Li ◽

Lei Wei ◽

Xianglin Zhang ◽

Wei Zhang ◽

Haochen Wang ◽

...

Keyword(s):

Deep Learning ◽

Dna Sequence ◽

Cancer Detection ◽

High Throughput Sequencing ◽

Detection Method ◽

Low Cost ◽

Sequencing Data ◽

Cell Free Dna ◽

Free Dna ◽

Novel Approach

ABSTRACTDetecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel non-invasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise prediction with low-depth cfDNA sequencing data. Here we propose a novel approach named DISMIR, which can provide ultrasensitive and robust cancer detection by integrating DNA sequence and methylation information in plasma cfDNA whole genome bisulfite sequencing (WGBS) data. DISMIR introduces a new feature termed as “switching region” to define cancer-specific differentially methylated regions, which can enrich the cancer-related signal at read-resolution. DISMIR applies a deep learning model to predict the source of every single read based on its DNA sequence and methylation state, and then predicts the risk that the plasma donor is suffering from cancer. DISMIR exhibited high accuracy and robustness on hepatocellular carcinoma detection by plasma cfDNA WGBS data even at ultra-low sequencing depths. Analysis showed that DISMIR tends to be insensitive to alterations of single CpG sites’ methylation states, which suggests DISMIR could resist to technical noise of WGBS. All these results showed DISMIR with the potential to be a precise and robust method for low-cost early cancer detection.

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