scholarly journals Introducing SPIDA-Web: Wavelets, Neural Networks and Internet Accessibility in an Image-Based Automated Identification System

2007 ◽  
pp. 149-170
Keyword(s):  
Neural Networks ◽  
Identification System ◽  
Automated Identification

scholarly journals Marine Network Protocols and Security Risks

2021 ◽  
Vol 1 (2) ◽  
pp. 239-251
Author(s):  
Ky Tran ◽  
Sid Keene ◽  
Erik Fretheim ◽  
Michail Tsikerdekis
Keyword(s):  
Network Protocols ◽  
Network Protocol ◽  
Identification System ◽  
Automated Identification ◽  
Security Risks ◽  
Domain Specific ◽  
Protocol Security ◽  
Challenges And Opportunities

Marine network protocols are domain-specific network protocols that aim to incorporate particular features within the specialized marine context that devices are implemented in. Devices implemented in such vessels involve critical equipment; however, limited research exists for marine network protocol security. In this paper, we provide an analysis of several marine network protocols used in today’s vessels and provide a classification of attack risks. Several protocols involve known security limitations, such as Automated Identification System (AIS) and National Marine Electronic Association (NMEA) 0183, while newer protocols, such as OneNet provide more security hardiness. We further identify several challenges and opportunities for future implementations of such protocols.

Hand Gesture Identification System Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Siddhartha Arjaria ◽  
Riya Sahu ◽  
Sejal Agrawal ◽  
Suyash Khare ◽  
Yashi Agarwal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hand Gesture ◽  
Identification System

scholarly journals Listerial meningitis: Atypical strain and misidentification by the automated identification system (Vitek2)

2019 ◽  
Vol 75 (1) ◽  
pp. 99-101
Author(s):  
Gurpreet Singh Bhalla ◽  
Mahadevan Kumar ◽  
Pooja Mahajan ◽  
Kavita Sahai
Keyword(s):  
Identification System ◽  
Automated Identification

scholarly journals Design and Implementation of an Efficient Rose Leaf Disease Detection using K-Nearest Neighbours

2020 ◽  
Vol 9 (3) ◽  
pp. 21-27
Keyword(s):  
Plant Disease ◽  
Spot Size ◽  
Extraction Process ◽  
Disease Classification ◽  
Identification System ◽  
Automated Identification ◽  
Accurate Identification ◽  
Leaf Disease ◽  
Key Factor ◽  
Agriculture Products

Plants are prone to different diseases caused by multiple reasons like environmental conditions, light, bacteria, and fungus. These diseases always have some physical characteristics on the leaves, stems, and fruit, such as changes in natural appearance, spot, size, etc. Due to similar patterns, distinguishing and identifying category of plant disease is the most challenging task. Therefore, efficient and flawless mechanisms should be discovered earlier so that accurate identification and prevention can be performed to avoid several losses of the entire plant. Therefore, an automated identification system can be a key factor in preventing loss in the cultivation and maintaining high quality of agriculture products. This paper introduces modeling of rose plant leaf disease classification technique using feature extraction process and supervised learning mechanism. The outcome of the proposed study justifies the scope of the proposed system in terms of accuracy towards the classification of different kind of rose plant disease.

Mining navigable water current information from ship-based big automated identification system data

2021 ◽  
Vol 242 ◽  
pp. 110076
Author(s):  
ZhengWei He ◽  
Shu Li ◽  
SenJie Wang ◽  
Zhong Zhuang
Keyword(s):  
Water Current ◽  
Identification System ◽  
Automated Identification ◽  
Current Information ◽  
System Data

Automated identification and grading system of diabetic retinopathy using deep neural networks

2019 ◽  
Vol 175 ◽  
pp. 12-25 ◽  
Author(s):  
Wei Zhang ◽  
Jie Zhong ◽  
Shijun Yang ◽  
Zhentao Gao ◽  
Junjie Hu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Deep Neural Networks ◽  
Grading System ◽  
Automated Identification

Cognitive spectroscopy for wood species identification: near infrared hyperspectral imaging combined with convolutional neural networks

The Analyst ◽  
2019 ◽  
Vol 144 (21) ◽  
pp. 6438-6446
Author(s):  
Hideaki Kanayama ◽  
Te Ma ◽  
Satoru Tsuchikawa ◽  
Tetsuya Inagaki
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Hyperspectral Imaging ◽  
Convolutional Neural Networks ◽  
Wood Species ◽  
Near Infrared ◽  
Wood Properties ◽  
Identification System ◽  
Illegal Logging ◽  
System P

From the viewpoint of combating illegal logging and examining wood properties, there is a contemporary demand for a wood species identification system.

scholarly journals Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3782 ◽  
Author(s):  
Julius Venskus ◽  
Povilas Treigys ◽  
Jolita Bernatavičienė ◽  
Gintautas Tamulevičius ◽  
Viktor Medvedev
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Processing Time ◽  
Heavy Traffic ◽  
Sensor Data ◽  
Identification System ◽  
Automated Identification ◽  
Maritime Traffic ◽  
Abnormal Movements ◽  
The Neural Network

The automated identification system of vessel movements receives a huge amount of multivariate, heterogeneous sensor data, which should be analyzed to make a proper and timely decision on vessel movements. The large number of vessels makes it difficult and time-consuming to detect abnormalities, thus rapid response algorithms should be developed for a decision support system to identify abnormal movements of vessels in areas of heavy traffic. This paper extends the previous study on a self-organizing map application for processing of sensor stream data received by the maritime automated identification system. The more data about the vessel’s movement is registered and submitted to the algorithm, the higher the accuracy of the algorithm should be. However, the task cannot be guaranteed without using an effective retraining strategy with respect to precision and data processing time. In addition, retraining ensures the integration of the latest vessel movement data, which reflects the actual conditions and context. With a view to maintaining the quality of the results of the algorithm, data batching strategies for the neural network retraining to detect anomalies in streaming maritime traffic data were investigated. The effectiveness of strategies in terms of modeling precision and the data processing time were estimated on real sensor data. The obtained results show that the neural network retraining time can be shortened by half while the sensitivity and precision only change slightly.

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