Results 281 to 290 of about 9,883,756 (332)

Deep Learning (DL) Oriented Forensic Analysis

Advances in Multidisciplinary and scientific Research Journal Publication, 2022
Cyber-attacks are now more prevalent than ever before in all aspects of our daily lives. As a result of this circumstance, both individuals and organizations are fighting cybercrime on a regular basis. Furthermore, today's hackers have advanced a step further and are capable of employing complex cyber-attack strategies, exacerbating the problem.
Herbert Cyril Dodoo
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Malware Detection using Deep Learning (DL)

Journal of Advanced Research in Applied Sciences and Engineering Technology
The attack that occurred recently involved the utilization of malicious software, commonly referred to as malware, along with advanced techniques such as machine learning, specifically deep learning, code transformation, and polymorphism. This makes it harder for cyber experts to detect malware using traditional analysis methods.
Chowdhury Sajadul Islam, Madihah Mohd Saudi, Nur Hafiza Zakaria, Muji Setiyo   +3 more
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Deep Learning (DL)-Based Channel Prediction and Hybrid Beamforming for LEO Satellite Massive MIMO System

IEEE Internet of Things Journal, 2022
Low-Earth orbit (LEO) satellites are recognized as one of the most promising infrastructures for realizing global Internet of Things (IoT) services. With the explosive growth of user terminals (UTs) and data traffic, the integration of massive multiple ...
Yun-Yang Zhang, Aijun Liu, Ping-Hui Li, Si-Hui Jiang   +3 more
semanticscholar   +1 more source

Review of: Deep Learning (DL) for Beamforming Array (BFA) Antenna

2024 21st International Multi-Conference on Systems, Signals & Devices (SSD)
In the realm of wireless communication, smart antennas are widely employed to augment system capacity, mitigate interference, and function with little complexity.
Ali A. Naji, Thamer M. Jamel, Hassan F. Khazaal   +2 more
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Auto-DL: A Platform to Generate Deep Learning Models

2021
Deep Learning (DL) model building is a tedious and taxing process. The number of prerequisites is high and a lot of time is invested. Hence, there is a scope of Automation. Code to build DL models follows a standard structure, broadly classified into four categories (Imports, Data Input, Model Creation, and Evaluation).
Aditya Srivastava, Tanvi Shinde, Raj Joshi, Sameer Ahmed Ansari, Nupur Giri   +4 more
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Deep Learning for Caries Detection: A Systematic Review: DL for Caries Detection.

E -journal of dentistry, 2022
OBJECTIVES Detecting caries lesions is challenging for dentists, and deep learning models may help practitioners to increase accuracy and reliability. We aimed to systematically review deep learning studies on caries detection.
Hossein Mohammad-Rahimi, S. Motamedian, M. H. Rohban, J. Krois, Sergio Uribe, Erfan Mahmoudi Nia, R. Rokhshad, Mohadeseh Nadimi, F. Schwendicke   +8 more
semanticscholar   +1 more source

VoC-DL: Revisiting Voice Of Customer Using Deep Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
In the field of digital marketing, understanding the voice of the customer is paramount. Mining textual content written by visitors on websites or social media can offer new dimensions to marketers and CX executives. Traditional tasks in NLP like sentiment analysis, topic modeling etc.
Susheel Suresh, Guru Rajan T. S, Vipin Gopinath   +2 more
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PPD-DL: Privacy-Preserving Decentralized Deep Learning

2019
Privacy is a fundamental challenge for the collection of massive training data in deep learning. Decentralized neural network enables clients to collaboratively learn a shared prediction model, which can protect clients’ sensitive dataset without the need to centrally store training data. But distributed training process by iteratively averaging client-
Lei Song, Chunguang Ma, Peng Wu, Yun Zhang 0009   +3 more
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A model-based deep learning (DL) approach for denoising MR images

ISMRM Annual Meeting
Motivation: The most prominent approach for MRI denoising has been the direct DL denoising methods. Robustness of such methods are not guaranteed while denoising MR images with unseen noise levels. Goal(s): Propose and evaluate a model-based DL denoising
Sudhanya Chatterjee, Marc Lebel
semanticscholar   +1 more source