Applications of Deep Learning and Reinforcement Learning to Biological Data

Mahmud, Mufti; Kaiser, M. Shamim; Hussain, Amir; Vassanelli, Stefano

doi:10.1109/tnnls.2018.2790388

Cited by 734 publications

(312 citation statements)

References 179 publications

(245 reference statements)

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“…Finally, the state-of-the-art in machine learning, such as deep learning approaches, is achieving excellent results in many domains and can be also exploited in our field. 36,37 ORCID Maha Driss https://orcid.org/0000-0001-8236-8746 Wadii Boulila https://orcid.org/0000-0003-2133-0757…”

Section: Resultsmentioning

confidence: 99%

Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach

Safaei

Ismail

Chizari³

et al. 2020

Softw Pract Exp

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Summary Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time‐series‐based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well‐known algorithms using benchmark real‐life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.

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Section: Resultsmentioning

confidence: 99%

Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach

Safaei

Ismail

Chizari³

et al. 2020

Softw Pract Exp

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“…In this study, a convolutional neural network (CNN)-based deep learning technique [35][36][37][38], one of the significant machine learning algorithms [39][40][41][42] in the artificial intelligence field, is utilized to robustly detect only a human facial region from the extracted skin color distribution region. Generally, the CNN is one of the most popular structures in the deep learning research field due to its excellent image processing and pattern recognition performance.…”

Section: Detection Of Target Regionmentioning

confidence: 99%

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Jang

Lee

2020

Sustainability

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High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges as a social issue. This paper proposes an effective blocking technique that makes it possible to robustly detect target objects with exposed personal information from various types of input images with the use of deep neural computing and to effectively block the detected objects' regions. The proposed technique first utilizes the neural computing-based learning algorithm to robustly detect the target object including personal information from an image. It next generates a grid-type mosaic and lets the mosaic overlap the target object region detected in the previous step so as to effectively block the object region that includes personal information. Experimental results reveal that the proposed algorithm robustly detects the target object region with exposed personal information from a variety of input images and effectively blocks the detected region through grid-type mosaic processing. The object blocking technique proposed in this paper is expected to be applied to various application fields such as image security, sustainable anticipatory computing, object tracking, and target blocking.

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“…Finally, an LBP network for face spoofing detection is proposed in [36], where LBP features are combined with deep learning. In spite of the immense potential of deep learning methods, they are computationally expensive, they need extremely large datasets for training and their internal complexity makes it difficult in some applications to interpret the results or to understand the algorithm mechanism [37][38][39].…”

Section: Related Workmentioning

confidence: 99%

Entropy-Based Face Recognition and Spoof Detection for Security Applications

2019

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Nowadays, cyber attacks are becoming an extremely serious issue, which is particularly important to prevent in a smart city context. Among cyber attacks, spoofing is an action that is increasingly common in many areas, such as emails, geolocation services or social networks. Identity spoofing is defined as the action by which a person impersonates a third party to carry out a series of illegal activities such as committing fraud, cyberbullying, sextorsion, etc. In this work, a face recognition system is proposed, with an application to the spoofing prevention. The method is based on the Histogram of Oriented Gradients (HOG) descriptor. Since different face regions do not have the same information for the recognition process, introducing entropy would quantify the importance of each face region in the descriptor. Therefore, entropy is added to increase the robustness of the algorithm. Regarding face recognition, our approach has been tested on three well-known databases (ORL, FERET and LFW) and the experiments show that adding entropy information improves the recognition rate significantly, with an increase over 40% in some of the considered databases. Spoofing tests has been implemented on CASIA FASD and MIFS databases, having obtained again better results than similar texture descriptors approaches.

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Applications of Deep Learning and Reinforcement Learning to Biological Data

Cited by 734 publications

References 179 publications

Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach

Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Entropy-Based Face Recognition and Spoof Detection for Security Applications

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