UAV On-Board Emergency Safe Landing Spot Detection System Combining Classical and Deep Learning-Based Segmentation Methods

2021 ◽  
pp. 467-478
Author(s):  
Marcin Paszkuta ◽  
Jakub Rosner ◽  
Damian Pęszor ◽  
Marcin Szender ◽  
Marzena Wojciechowska ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Spot Detection ◽  
Segmentation Methods ◽  
Landing Spot

scholarly journals Design of photovoltaic hot spot detection system based on deep learning

2020 ◽  
Vol 1693 ◽  
pp. 012075
Author(s):  
Yifeng Ren ◽  
Yongjun Yu ◽  
Jing Li ◽  
Wenhua Zhang
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Hot Spot ◽  
Spot Detection

Data Leakage Detection System based on Deep Learning

2016 ◽  
Vol 4 (2) ◽  
pp. 13-18
Author(s):  
Min-Ji Seo ◽  
◽  
Myung-Ho Kim ◽  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Leakage Detection

scholarly journals Deep learning for vision‐based fall detection system: Enhanced optical dynamic flow

2020 ◽  
Author(s):  
Sagar Chhetri ◽  
Abeer Alsadoon ◽  
Thair Al‐Dala'in ◽  
P. W. C. Prasad ◽  
Tarik A. Rashid ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Fall Detection ◽  
Dynamic Flow

scholarly journals An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 344
Author(s):  
Jeyaprakash Hemalatha ◽  
S. Abijah Roseline ◽  
Subbiah Geetha ◽  
Seifedine Kadry ◽  
Robertas Damaševičius
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Malware Detection ◽  
Class Imbalance ◽  
Learning Model ◽  
Computational Time ◽  
Learning Approaches ◽  
Security Threat ◽  
Static And Dynamic Analysis ◽  
Deep Learning Model

Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

scholarly journals IoT Botnet Anomaly Detection Using Unsupervised Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1876
Author(s):  
Ioana Apostol ◽  
Marius Preda ◽  
Constantin Nila ◽  
Ion Bica
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
False Positive Rate ◽  
Empirical Evaluation ◽  
Learning Approaches ◽  
Promising Alternative ◽  
Positive Rate ◽  
Unsupervised Deep Learning ◽  
Attack Surface ◽  
Iot Devices

The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.

scholarly journals Acceptance of Transportation Technologies by Aging Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 555-555
Author(s):  
Neil Charness ◽  
Dustin Souders ◽  
Ryan Best ◽  
Nelson Roque ◽  
JongSung Yoon ◽  
...  
Keyword(s):  
Older Adults ◽  
Older Adult ◽  
Detection System ◽  
Blind Spot ◽  
Driving Performance ◽  
Older Drivers ◽  
Crash Risk ◽  
Warning Systems ◽  
Risk Of Death ◽  
Spot Detection

Abstract Older adults are at greater risk of death and serious injury in transportation crashes which have been increasing in older adult cohorts relative to younger cohorts. Can technology provide a safer road environment? Even if technology can mitigate crash risk, is it acceptable to older road users? We outline the results from several studies that tested 1) whether advanced driver assistance systems (ADAS) can improve older adult driving performance, 2) older adults’ acceptance of ADAS and Autonomous Vehicle (AV) systems, and 3) perceptions of value for ADAS systems, particularly for blind-spot detection systems. We found that collision avoidance warning systems improved older adult simulator driving performance, but not lane departure warning systems. In a young to middle-aged sample the factor “concern with AV” showed age effects with older drivers less favorable. Older drivers, however, valued an active blind spot detection system more than younger drivers.

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