Pseudo-outbreak of Enterococcus durans infections and colonization associated with introduction of an automated identification system software update.

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.

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

Medical Journal Armed Forces India ◽

10.1016/j.mjafi.2018.07.003 ◽

2019 ◽

Vol 75 (1) ◽

pp. 99-101

Author(s):

Gurpreet Singh Bhalla ◽

Mahadevan Kumar ◽

Pooja Mahajan ◽

Kavita Sahai

Keyword(s):

Identification System ◽

Automated Identification

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

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4213.099320 ◽

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.

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

Ocean Engineering ◽

10.1016/j.oceaneng.2021.110076 ◽

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

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

Sensors ◽

10.3390/s19173782 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3782 ◽

Cited By ~ 3

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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Automated Identification of Cephalometric Landmarks: Part 2- Might It Be Better Than human?

The Angle Orthodontist ◽

10.2319/022019-129.1 ◽

2019 ◽

Vol 90 (1) ◽

pp. 69-76 ◽

Cited By ~ 7

Author(s):

Hye-Won Hwang ◽

Ji-Hoon Park ◽

Jun-Ho Moon ◽

Youngsung Yu ◽

Hansuk Kim ◽

...

Keyword(s):

Reference Points ◽

Identification System ◽

Learning Method ◽

Detection Error ◽

Hard Tissue ◽

Automated Identification ◽

Viable Option ◽

The Mean ◽

Clinically Significant ◽

Better Than

ABSTRACT Objectives To compare detection patterns of 80 cephalometric landmarks identified by an automated identification system (AI) based on a recently proposed deep-learning method, the You-Only-Look-Once version 3 (YOLOv3), with those identified by human examiners. Materials and Methods The YOLOv3 algorithm was implemented with custom modifications and trained on 1028 cephalograms. A total of 80 landmarks comprising two vertical reference points and 46 hard tissue and 32 soft tissue landmarks were identified. On the 283 test images, the same 80 landmarks were identified by AI and human examiners twice. Statistical analyses were conducted to detect whether any significant differences between AI and human examiners existed. Influence of image factors on those differences was also investigated. Results Upon repeated trials, AI always detected identical positions on each landmark, while the human intraexaminer variability of repeated manual detections demonstrated a detection error of 0.97 ± 1.03 mm. The mean detection error between AI and human was 1.46 ± 2.97 mm. The mean difference between human examiners was 1.50 ± 1.48 mm. In general, comparisons in the detection errors between AI and human examiners were less than 0.9 mm, which did not seem to be clinically significant. Conclusions AI showed as accurate an identification of cephalometric landmarks as did human examiners. AI might be a viable option for repeatedly identifying multiple cephalometric landmarks.

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