scholarly journals Drone-Based Water Level Detection in Flood Disasters

2021 ◽  
Vol 19 (1) ◽  
pp. 237
Author(s):  
Hamada Rizk ◽  
Yukako Nishimur ◽  
Hirozumi Yamaguchi ◽  
Teruo Higashino
Keyword(s):  
Water Level ◽  
Data Augmentation ◽  
Detection System ◽  
Ground Level ◽  
Flood Damage ◽  
Detection Accuracy ◽  
Data Set ◽  
Field Of Vision ◽  
Feasible Alternative ◽  
Art Research

Japan was hit by typhoon Hagibis, which came with torrential rains submerging almost eight-thousand buildings. For fast alleviation of and recovery from flood damage, a quick, broad, and accurate assessment of the damage situation is required. Image analysis provides a much more feasible alternative than on-site sensors due to their installation and maintenance costs. Nevertheless, most state-of-art research relies on only ground-level images that are inevitably limited in their field of vision. This paper presents a water level detection system based on aerial drone-based image recognition. The system applies the R-CNN learning model together with a novel labeling method on the reference objects, including houses and cars. The proposed system tackles the challenges of the limited and wild data set of flood images from the top view with data augmentation and transfer-learning overlaying Mask R-CNN for the object recognition model. Additionally, the VGG16 network is employed for water level detection purposes. We evaluated the proposed system on realistic images captured at disaster time. Preliminary results show that the system can achieve a detection accuracy of submerged objects of 73.42% with as low as only 21.43 cm error in estimating the water level.

Semi supervised inspection algorithm of automatic packaging curve based on deep learning

2021 ◽  
pp. 1-10
Author(s):  
Yong He
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Optimization Algorithm ◽  
Posterior Probability ◽  
Detection Method ◽  
Detection System ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Data Set ◽  
Packaging Process

The current automatic packaging process is complex, requires high professional knowledge, poor universality, and difficult to apply in multi-objective and complex background. In view of this problem, automatic packaging optimization algorithm has been widely paid attention to. However, the traditional automatic packaging detection accuracy is low, the practicability is poor. Therefore, a semi-supervised detection method of automatic packaging curve based on deep learning and semi-supervised learning is proposed. Deep learning is used to extract features and posterior probability to classify unlabeled data. KDD CUP99 data set was used to verify the accuracy of the algorithm. Experimental results show that this method can effectively improve the performance of automatic packaging curve semi-supervised detection system.

scholarly journals Robust Self-Adaptation Fall-Detection System Based on Camera Height

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3768 ◽  
Author(s):  
Kong ◽  
Chen ◽  
Wang ◽  
Chen ◽  
Meng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Fall Detection ◽  
Daily Activities ◽  
Detection Methods ◽  
Depth Camera ◽  
Detection Accuracy ◽  
Data Set ◽  
Human Segmentation ◽  
Self Adaptation

Vision-based fall-detection methods have been previously studied but many have limitations in terms of practicality. Due to differences in rooms, users do not set the camera or sensors at the same height. However, few studies have taken this into consideration. Moreover, some fall-detection methods are lacking in terms of practicality because only standing, sitting and falling are taken into account. Hence, this study constructs a data set consisting of various daily activities and fall events and studies the effect of camera/sensor height on fall-detection accuracy. Each activity in the data set is carried out by eight participants in eight directions and taken with the depth camera at five different heights. Many related studies heavily depended on human segmentation by using Kinect SDK but this is not reliable enough. To address this issue, this study proposes Enhanced Tracking and Denoising Alex-Net (ETDA-Net) to improve tracking and denoising performance and classify fall and non-fall events. Experimental results indicate that fall-detection accuracy is affected by camera height, against which ETDA-Net is robust, outperforming traditional deep learning based fall-detection methods.

scholarly journals Efficient Anomaly Detection for Smart Hospital IoT Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1026
Author(s):  
Abdel Mlak Said ◽  
Aymen Yahyaoui ◽  
Takoua Abdellatif
Keyword(s):  
Anomaly Detection ◽  
Event Detection ◽  
Emergency Services ◽  
Detection System ◽  
Detection Accuracy ◽  
Negative Effects ◽  
Data Set ◽  
Network Intrusions ◽  
Smart Hospital ◽  
Health Related

In critical Internet of Things (IoT) application domains, such as the Defense Industry and Healthcare, false alerts have many negative effects, such as fear, disruption of emergency services, and waste of resources. Therefore, an alert must only be sent if triggered by a correct event. Nevertheless, IoT networks are exposed to intrusions, which affects event detection accuracy. In this paper, an Anomaly Detection System (ADS) is proposed in a smart hospital IoT system for detecting events of interest about patients’ health and environment and, at the same time, for network intrusions. Providing a single system for network infrastructure supervision and e-health monitoring has been shown to optimize resources and enforce the system reliability. Consequently, decisions regarding patients’ care and their environments’ adaptation are more accurate. The low latency is ensured, thanks to a deployment on the edge to allow for a processing close to data sources. The proposed ADS is implemented and evaluated while using Contiki Cooja simulator and the e-health event detection is based on a realistic data-set analysis. The results show a high detection accuracy for both e-health related events and IoT network intrusions.

scholarly journals A Residual Learning-Based Network Intrusion Detection System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jiarui Man ◽  
Guozi Sun
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Class Imbalance Problem ◽  
Data Set ◽  
Residual Learning ◽  
Network Intrusion

Neural networks have been proved to perform well in network intrusion detection. In order to acquire better features of network traffic, more learning layers are necessarily required. However, according to the results of the previous research, adding layers to the neural networks might fail to improve the classification results. In fact, after the number of layers has reached a certain threshold, performance of the model tends to degrade. In this paper, we propose a network intrusion detection model based on residual learning. After transforming the UNSW-NB15 data set into images, deeper convolutional neural networks with residual blocks are built to learn more critical features. Instead of the cross-entropy loss function, the modified focal loss is calculated to address the class imbalance problem in the training set and identify minor attacks in the testing set. Batch normalization and global average pooling are used to avoid overfitting and enhance the model. Experimental results show that the proposed model can improve attack detection accuracy compared with existing models.

scholarly journals Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 576
Author(s):  
Kaihua Zhang ◽  
Haikuo Shen
Keyword(s):  
Solder Joint ◽  
Defect Detection ◽  
Data Augmentation ◽  
Detection Method ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Set ◽  
Electronic Products ◽  
Original Algorithm

The miniaturization and high integration of electronic products have higher and higher requirements for welding of internal components of electronic products. A welding quality detection method has always been one of the important research contents in the industry, among which, the research on solder joint defect detection of a connector has gradually attracted people’s attention with the development of image detection algorithm. The traditional solder joint detection method of connector adopts manual detection or automatic detection methods, which is inefficient and not safe enough. With the development of deep learning, the application of a deep convolutional neural network to target detection has become a research hotspot. In this paper, a data set of connector solder joint samples was made and the number of image samples was expanded to more than 3 times of the original by using data augmentation. Clustering generates anchor boxes and transfer learning with ResNet-101 were fused, so an improved faster region-based convolutional neural networks (Faster RCNN) algorithm was proposed. The experiment verified that the improved algorithm proposed in this paper had a great improvement in all aspects compared with the original algorithm. The average detection accuracy of this method can reach 94%, and the detection rate of some defects can even reach 100%, which can completely meet the industrial requirements.

scholarly journals Intrusion detection system based on machine learning techniques

2021 ◽  
Vol 23 (2) ◽  
pp. 953
Author(s):  
Musaab Riyadh ◽  
Dina Riadh Alshibani
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Special Kind ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Malicious Activity

Recently, the data flow over the internet has exponentially increased due to the massive growth of computer networks connected to it. Some of these data can be classified as a malicious activity which cannot be captured by firewalls and anti-malwares. Due to this, the intrusion detection systems are urgent need in order to recognize malicious activity to keep data integrity and availability. In this study, an intrusion detection system based on cluster feature concepts and KNN classifier has been suggested to handle the various challenges issues in data such as in complete data, mixed-type and noise data. To streng then the proposed system a special kind of patterns similarity measures are supported to deal with these types of challenges. The experimental results show that the classification accuracy of the suggested system is better than K-nearest neighbor (KNN) and support vector machine classifiers when processing incomplete data set, inspite of droping down the overall detection accuracy.

scholarly journals Intrusion Detection Based on Gray-Level Co-Occurrence Matrix and 2D Dispersion Entropy

2021 ◽  
Vol 11 (12) ◽  
pp. 5567
Author(s):  
Gianmarco Baldini ◽  
Jose Luis Hernandez Ramos ◽  
Irene Amerini
Keyword(s):  
Intrusion Detection ◽  
Network Flows ◽  
Learning Algorithm ◽  
Detection System ◽  
Detection Algorithm ◽  
Entropy Measure ◽  
Detection Accuracy ◽  
Gray Level ◽  
Data Set ◽  
Occurrence Matrix

The Intrusion Detection System (IDS) is an important tool to mitigate cybersecurity threats in an Information and Communication Technology (ICT) infrastructure. The function of the IDS is to detect an intrusion to an ICT system or network so that adequate countermeasures can be adopted. Desirable features of IDS are computing efficiency and high intrusion detection accuracy. This paper proposes a new anomaly detection algorithm for IDS, where a machine learning algorithm is applied to detect deviations from legitimate traffic, which may indicate an intrusion. To improve computing efficiency, a sliding window approach is applied where the analysis is applied on large sequences of network flows statistics. This paper proposes a novel approach based on the transformation of the network flows statistics to gray images on which Gray level Co-occurrence Matrix (GLCM) are applied together with an entropy measure recently proposed in literature: the 2D Dispersion Entropy. This approach is applied to the recently public IDS data set CIC-IDS2017. The results show that the proposed approach is competitive in comparison to other approaches proposed in literature on the same data set. The approach is applied to two attacks of the CIC-IDS2017 data set: DDoS and Port Scan achieving respectively an Error Rate of 0.0016 and 0.0048.

scholarly journals Software Defined Network Enabled Fog-to-Things Hybrid Deep Learning Driven Cyber Threat Detection System

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ihtisham Ullah ◽  
Basit Raza ◽  
Sikandar Ali ◽  
Irshad Ahmed Abbasi ◽  
Samad Baseer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Detection System ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Detection Accuracy ◽  
Software Defined Network ◽  
Centralized Control ◽  
Data Set ◽  
Iot Devices

Software Defined Network (SDN) is a next-generation networking architecture and its power lies in centralized control intelligence. The control plane of SDN can be extended to many underlying networks such as fog to Internet of Things (IoT). The fog-to-IoT is currently a promising architecture to manage a real-time large amount of data. However, most of the fog-to-IoT devices are resource-constrained and devices are widespread that can be potentially targeted with cyber-attacks. The evolving cyber-attacks are still an arresting challenge in the fog-to-IoT environment such as Denial of Service (DoS), Distributed Denial of Service (DDoS), Infiltration, malware, and botnets attacks. They can target varied fog-to-IoT agents and the whole network of organizations. The authors propose a deep learning (DL) driven SDN-enabled architecture for sophisticated cyber-attacks detection in fog-to-IoT environment to identify new attacks targeting IoT devices as well as other threats. The extensive simulations have been carried out using various DL algorithms and current state-of-the-art Coburg Intrusion Detection Data Set (CIDDS-001) flow-based dataset. For better analysis five DL models are compared including constructed hybrid DL models to distinguish the DL model with the best performance. The results show that proposed Long Short-Term Memory (LSTM) hybrid model outperforms other DL models in terms of detection accuracy and response time. To show unbiased results 10-fold cross-validation is performed. The proposed framework is so effective that it can detect several types of cyber-attacks with 99.92% accuracy rate in multiclass classification.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

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