scholarly journals The Impact of Deep Learning Techniques on SMS Spam Filtering

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Wael Hassan Gomaa
Keyword(s):  
Deep Learning ◽  
Spam Filtering ◽  
Learning Techniques ◽  
The Impact

scholarly journals Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4953
Author(s):  
Sara Al-Emadi ◽  
Abdulla Al-Ali ◽  
Abdulaziz Al-Ali
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Learning Algorithms ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Networks ◽  
Detection And Identification ◽  
Learning Techniques ◽  
The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

scholarly journals Online Public Shaming Approach using Deep Learning Techniques

2021 ◽  
Vol 23 (3) ◽  
Author(s):  
Mehdi Surani ◽  
◽  
Ramchandra Mangrulkar ◽  
Keyword(s):  
Deep Learning ◽  
Hate Speech ◽  
Severe Depression ◽  
Learning System ◽  
Online Bullying ◽  
Digital World ◽  
Learning Techniques ◽  
Proposed Model ◽  
Social Media Platforms ◽  
The Impact

Public shaming on social media platforms like Twitter / Instagram / Facebook etc. have recently increased from the past years. This results in affecting an individual’s social, political, mental and financial life. The impact can range from mild bullying to severe depression. With the growing leniency on these social platforms, many people have started misusing the opportunity by turning to online bullying and hate speech. When something is posted online, it stays there forever and it becomes extremely hard taking something out of the digital world. Manually locating and categorizing such comments is a lengthy procedure and just cannot be relied upon. To solve this challenge, automation was performed to identify and classify the shamers. This has been done using the classic SVM model which worked on a given quantity of data. To identify the negative content being posted and discussed online, this paper further explores the deep learning system which can successfully classify these content pieces into proper labels. The text-based Convolution Neural Network (CNN) is the proposed model in this paper for this analysis.

scholarly journals Iterative machine learning applied to annotation of text datasets

2021 ◽  
Author(s):  
Thiago Abdo ◽  
Fabiano Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Computational Cost ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Learning Techniques ◽  
The Creation ◽  
The Impact ◽  
High Computational Cost

The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.

Survey of Deep-learning Techniques in Big-Data analytics

2021 ◽  
Author(s):  
Shubhashish Goswami ◽  
Abhimanyu Kumar
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Learning ◽  
Learning Process ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Operational Parameters ◽  
Learning Techniques ◽  
The Impact ◽  
Business Sectors

Abstract The present elaboration of Big-data research studies relying upon Deep-learning methods had revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms' operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. The present enhancements in the Deep-learning approaches in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition to having more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper elaborates the above statements stating the impact of the Deep-learning process utilizing the Big-data to operate in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing on having the impacts of operational parameters, concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors, the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

Impact of Deep Learning Techniques in IoT

2021 ◽  
pp. 196-226
Author(s):  
Chandra Vadhana ◽  
Shanthi Bala P. ◽  
Immanuel Zion Ramdinthara
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Learning Models ◽  
Related Services ◽  
Safety Critical ◽  
The Neural Network ◽  
Learning Techniques ◽  
Iot Devices ◽  
The Impact ◽  
Level Performance

Deep learning models can achieve more accuracy sometimes that exceed human-level performance. It is crucial for safety-critical applications such as driverless cars, aerospace, defence, medical research, and industrial automation. Most of the deep learning methods mimic the neural network. It has many hidden layers and creates patterns for decision making and it is a subset of machine learning that performs end-to-end learning and has the capability to learn unsupervised data and also provides very flexible, learnable framework for representing the visual and linguistic information. Deep learning has greatly changed the way and computing devices processes human-centric content such as speech, image recognition, and natural language processing. Deep learning plays a major role in IoT-related services. The amalgamation of deep learning to the IoT environment makes the complex sensing and recognition tasks easier. It helps to automatically identify patterns and detect anomalies that are generated by IoT devices. This chapter discusses the impact of deep learning in the IoT environment.

scholarly journals Assessment of Deep Learning Techniques for Land Use Land Cover Classification in Southern New Caledonia

2021 ◽  
Vol 13 (12) ◽  
pp. 2257
Author(s):  
Guillaume Rousset ◽  
Marc Despinoy ◽  
Konrad Schindler ◽  
Morgan Mangeas
Keyword(s):  
Land Use ◽  
Deep Learning ◽  
Land Cover ◽  
Satellite Data ◽  
Data Augmentation ◽  
New Caledonia ◽  
Data Set ◽  
Detection Techniques ◽  
Learning Techniques ◽  
The Impact

Land use (LU) and land cover (LC) are two complementary pieces of cartographic information used for urban planning and environmental monitoring. In the context of New Caledonia, a biodiversity hotspot, the availability of up-to-date LULC maps is essential to monitor the impact of extreme events such as cyclones and human activities on the environment. With the democratization of satellite data and the development of high-performance deep learning techniques, it is possible to create these data automatically. This work aims at determining the best current deep learning configuration (pixel-wise vs semantic labelling architectures, data augmentation, image prepossessing, …), to perform LULC mapping in a complex, subtropical environment. For this purpose, a specific data set based on SPOT6 satellite data was created and made available for the scientific community as an LULC benchmark in a tropical, complex environment using five representative areas of New Caledonia labelled by a human operator: four used as training sets, and the fifth as a test set. Several architectures were trained and the resulting classification was compared with a state-of-the-art machine learning technique: XGboost. We also assessed the relevance of popular neo-channels derived from the raw observations in the context of deep learning. The deep learning approach showed comparable results to XGboost for LC detection and over-performed it on the LU detection task (61.45% vs. 51.56% of overall accuracy). Finally, adding LC classification output of the dedicated deep learning architecture to the raw channels input significantly improved the overall accuracy of the deep learning LU classification task (63.61% of overall accuracy). All the data used in this study are available on line for the remote sensing community and for assessing other LULC detection techniques.

scholarly journals Deep learning jet modifications in heavy-ion collisions

2021 ◽  
Vol 2021 (3) ◽  
Author(s):  
Yi-Lun Du ◽  
Daniel Pablos ◽  
Konrad Tywoniuk
Keyword(s):  
Deep Learning ◽  
Transverse Momentum ◽  
Energy Loss ◽  
Heavy Ion Collisions ◽  
Heavy Ion ◽  
Loss Ratio ◽  
Ion Collisions ◽  
Learning Techniques ◽  
Jet Observables ◽  
The Impact

Abstract Jet interactions in a hot QCD medium created in heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to the proton-proton baseline. However, the steeply falling production spectrum introduces a strong bias toward small energy losses that obfuscates a direct interpretation of the impact of medium effects in the measured jet ensemble. Modern machine learning techniques offer the potential to tackle this issue on a jet-by-jet basis. In this paper, we employ a convolutional neural network (CNN) to diagnose such modifications from jet images where the training and validation is performed using the hybrid strong/weak coupling model. By analyzing measured jets in heavy-ion collisions, we extract the original jet transverse momentum, i.e., the transverse momentum of an identical jet that did not pass through a medium, in terms of an energy loss ratio. Despite many sources of fluctuations, we achieve good performance and put emphasis on the interpretability of our results. We observe that the angular distribution of soft particles in the jet cone and their relative contribution to the total jet energy contain significant discriminating power, which can be exploited to tailor observables that provide a good estimate of the energy loss ratio. With a well-predicted energy loss ratio, we study a set of jet observables to estimate their sensitivity to bias effects and reveal their medium modifications when compared to a more equivalent jet population, i.e., a set of jets with similar initial energy. Finally, we also show the potential of deep learning techniques in the analysis of the geometrical aspects of jet quenching such as the in-medium traversed length or the position of the hard scattering in the transverse plane, opening up new possibilities for tomographic studies.

scholarly journals A New Hybrid CNN-LSTM Model with Non-SoftMax Functions for Face Spoof Detection

2021 ◽  
Author(s):  
S Lokesh Kumar ◽  
Yamani Sai Asish ◽  
Sannasi Ganapathy
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Hybrid Approach ◽  
Mobile Payments ◽  
Hybrid Techniques ◽  
Video Frames ◽  
Learning Techniques ◽  
Proposed Model ◽  
Full Picture ◽  
The Impact

Abstract Recently, the emerging applications such as banking, mobile payments, face recognition technology are gradually booming and also increases the users count around the world. The extensive deployment of facial recognition systems has drawn close attention to the dependability of facial biometrics in the fight against spoof attacks, in which a picture, video or 3D mask of a real user's face may be used to access facilities or services illegitimately. While a number of anti-spoofing or liveness detection approaches (which identify whether a face is live or spoof when captured) were suggested, the problem is still unresolved because of the difficulty in discovering discriminatory and computer-cost characteristics and techniques for spoof assaults. Existing methods also utilise a full picture or video to determine luminosity. Often though, some facial areas (video frames) are redundant or relate to the confusion of the picture (video). In this paper, we propose a new hybrid deep learning technique called Hybrid Convolutional Neural Network (CNN) based architecture with Long Short-Term Memory (LSTM) units to study the impact in classification. In this technique is applied a non-softmax function for making effective decision on classification. The hybrid approach is implemented followed by a comparative analysis with existing conventional and hybrid techniques used for spoof detection. The proposed model is proved as better than the existing deep learning techniques and other hybrid models in terms of precision, recall, f-measure and accuracy.

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