Benchmarking Deep Learning for On-Board Space Applications

Benchmarking deep learning algorithms before deploying them in hardware-constrained execution environments, such as imaging satellites, is pivotal in real-life applications. Although a thorough and consistent benchmarking procedure can allow us to estimate the expected operational abilities of the underlying deep model, this topic remains under-researched. This paper tackles this issue and presents an end-to-end benchmarking approach for quantifying the abilities of deep learning algorithms in virtually any kind of on-board space applications. The experimental validation, performed over several state-of-the-art deep models and benchmark datasets, showed that different deep learning techniques may be effectively benchmarked using the standardized approach, which delivers quantifiable performance measures and is highly configurable. We believe that such benchmarking is crucial in delivering ready-to-use on-board artificial intelligence in emerging space applications and should become a standard tool in the deployment chain.

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Representation Learning for Fine-Grained Change Detection

Sensors ◽

10.3390/s21134486 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4486

Author(s):

Niall O’Mahony ◽

Sean Campbell ◽

Lenka Krpalkova ◽

Anderson Carvalho ◽

Joseph Walsh ◽

...

Keyword(s):

Deep Learning ◽

Change Detection ◽

Model Calibration ◽

State Of The Art ◽

Representation Learning ◽

Machine Intelligence ◽

The State ◽

Sensor Data ◽

Fine Grained ◽

Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

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

Sensors ◽

10.3390/s21154953 ◽

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.

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Named Entity Recognition and Relation Extraction

ACM Computing Surveys ◽

10.1145/3445965 ◽

2021 ◽

Vol 54 (1) ◽

pp. 1-39

Author(s):

Zara Nasar ◽

Syed Waqar Jaffry ◽

Muhammad Kamran Malik

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Named Entity Recognition ◽

Relation Extraction ◽

The State ◽

Entity Recognition ◽

Joint Models ◽

Named Entity ◽

Textual Data ◽

Benchmark Datasets

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

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Deep learning algorithms for scRNAseq analysis have yielded positive results, but there are still more promising ways that need to be developed for regenerative medicine

10.31219/osf.io/dh2pt ◽

2021 ◽

Author(s):

Moataz Dowaidar

Keyword(s):

Deep Learning ◽

Regenerative Medicine ◽

Learning Algorithms ◽

Cell Types ◽

Technical Noise ◽

Expression Variability ◽

Learning Techniques ◽

Cell Expression ◽

Positive Results ◽

Gain Access

A detailed survey of the use of many deep learning algorithms on scRNAseq data for regenerative medicine was published in this article. Currently, the best deep learning algorithms for scRNAseq analysis have yielded positive results, but there are still more promising ways that need to be developed to better handle technical noise, account for cell expression variability, identify MSCs, and anticipate stem cell type. To gain access to scRNAseq data, these deep learning techniques need to be paired with scRNAseq data. The ability to identify cell types and functions accurately and fast utilizing these algorithms has not yet been made possible. In the study we conducted, we reached the conclusion that further research has to be done into how to apply deep learning algorithms to interpret scRNAseq data, which may be used to better cell therapy and regenerative medicine efforts.

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An Efficient Method for Detection of DDoS Attacks on the Web Using Deep Learning Algorithms

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/271042021 ◽

2021 ◽

Vol 10 (4) ◽

pp. 2821-2829

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

State Of The Art ◽

Ddos Attacks ◽

Problem Statement ◽

Neural Network Approach ◽

Learning Techniques ◽

Attack Data ◽

Deep Learning Neural Network

Recently, DDoS attacks is the most significant threat in network security. Both industry and academia are currently debating how to detect and protect against DDoS attacks. Many studies are provided to detect these types of attacks. Deep learning techniques are the most suitable and efficient algorithm for categorizing normal and attack data. Hence, a deep neural network approach is proposed in this study to mitigate DDoS attacks effectively. We used a deep learning neural network to identify and classify traffic as benign or one of four different DDoS attacks. We will concentrate on four different DDoS types: Slowloris, Slowhttptest, DDoS Hulk, and GoldenEye. The rest of the paper is organized as follow: Firstly, we introduce the work, Section 2 defines the related works, Section 3 presents the problem statement, Section 4 describes the proposed methodology, Section 5 illustrate the results of the proposed methodology and shows how the proposed methodology outperforms state-of-the-art work and finally Section VI concludes the paper.

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Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning

10.36227/techrxiv.14762043.v1 ◽

2021 ◽

Author(s):

Phongsathorn Kittiworapanya ◽

Kitsuchart Pasupa ◽

Peter Auer

Keyword(s):

Computer Vision ◽

Particle Size ◽

Deep Learning ◽

Waste Management ◽

State Of The Art ◽

Learning Algorithms ◽

Input Image ◽

Size Estimation ◽

Waste Particles ◽

Set Up

<div>We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.<br></div>

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DeepPurpose: a deep learning library for drug–target interaction prediction

Bioinformatics ◽

10.1093/bioinformatics/btaa1005 ◽

2020 ◽

Author(s):

Kexin Huang ◽

Tianfan Fu ◽

Lucas M Glass ◽

Marinka Zitnik ◽

Cao Xiao ◽

...

Keyword(s):

Deep Learning ◽

Drug Target ◽

Prediction Models ◽

State Of The Art ◽

Supplementary Information ◽

Target Interaction ◽

Interaction Prediction ◽

Computer Scientists ◽

Benchmark Datasets ◽

Biomedical Field

Abstract Summary Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets. Availability and implementation https://github.com/kexinhuang12345/DeepPurpose. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling

Computers ◽

10.3390/computers9020037 ◽

2020 ◽

Vol 9 (2) ◽

pp. 37 ◽

Cited By ~ 1

Author(s):

Luca Cappelletti ◽

Tommaso Fontana ◽

Guido Walter Di Donato ◽

Lorenzo Di Tucci ◽

Elena Casiraghi ◽

...

Keyword(s):

Deep Learning ◽

Missing Data ◽

State Of The Art ◽

The State ◽

Complex Data ◽

Data Imputation ◽

Genome Sequences ◽

Missing Data Imputation ◽

The Past ◽

Learning Techniques

Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieved by deep learning techniques have opened the way to their application for solving difficult problems where human skill is not able to provide a reliable solution. Not surprisingly, some deep learners, mainly exploiting encoder-decoder architectures, have also been designed and applied to the task of missing data imputation. However, most of the proposed imputation techniques have not been designed to tackle “complex data”, that is high dimensional data belonging to datasets with huge cardinality and describing complex problems. Precisely, they often need critical parameters to be manually set or exploit complex architecture and/or training phases that make their computational load impracticable. In this paper, after clustering the state-of-the-art imputation techniques into three broad categories, we briefly review the most representative methods and then describe our data imputation proposals, which exploit deep learning techniques specifically designed to handle complex data. Comparative tests on genome sequences show that our deep learning imputers outperform the state-of-the-art KNN-imputation method when filling gaps in human genome sequences.

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Predictive Analysis of Cryptocurrency Price Using Deep Learning

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.27.17889 ◽

2018 ◽

Vol 7 (3.27) ◽

pp. 258 ◽

Cited By ~ 4

Author(s):

Yecheng Yao ◽

Jungho Yi ◽

Shengjun Zhai ◽

Yuwen Lin ◽

Taekseung Kim ◽

...

Keyword(s):

Deep Learning ◽

International Relations ◽

Short Term Memory ◽

Training Data ◽

Short Term ◽

Effective Learning ◽

Learning Techniques ◽

Benchmark Datasets ◽

Novel Method ◽

Long Short Term Memory

The decentralization of cryptocurrencies has greatly reduced the level of central control over them, impacting international relations and trade. Further, wide fluctuations in cryptocurrency price indicate an urgent need for an accurate way to forecast this price. This paper proposes a novel method to predict cryptocurrency price by considering various factors such as market cap, volume, circulating supply, and maximum supply based on deep learning techniques such as the recurrent neural network (RNN) and the long short-term memory (LSTM),which are effective learning models for training data, with the LSTM being better at recognizing longer-term associations. The proposed approach is implemented in Python and validated for benchmark datasets. The results verify the applicability of the proposed approach for the accurate prediction of cryptocurrency price.

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Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features

BMC Genomics ◽

10.1186/s12864-020-07033-8 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Zhixun Zhao ◽

Xiaocai Zhang ◽

Fang Chen ◽

Liang Fang ◽

Jinyan Li

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Case Studies ◽

Learning Algorithm ◽

State Of The Art ◽

Learning Algorithms ◽

Feature Space ◽

Sequence Features ◽

Independent Test ◽

Benchmark Datasets

Abstract Background DNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem. Results The feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene. Conclusions The results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.

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