A Novel Approach For Automatic Identification Of Psoriasis Affected Skin Area.

Nowadays, the inner shape and economic viability of a stone block is dependent on the skill and experience of the “expert” that makes predictions based on external observations. This actual procedure is an extremely high empirical method, and when it fails, substantial work, time, and money is wasted. At present, researchers are committed to developing models to predict the stone block internal structure based on non-destructive tests. Ultrasonic tomography and electrical resistivity tomography are the tests that best fit these objectives. Trying to improve the existing procedures for collecting stone information and data exporting, a novel approach to perform both tomographies is proposed in this paper. This novel approach presents sound advantages regarding the current manual procedure: namely, (i) high accuracy due to a new automatic positioning system; (ii) no need for highly skilled operators to process measurements; (iii) measurements are much easier to derive, and results are quickly delivered. A comparison between the new automatic process and the current manual procedure shows that the manual procedure has a very low accuracy when compared to the new developed automatic system. The automatic measurements show extremely significant time savings, which is a relevant issue for the future competitiveness of the stone sector.

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Early Recognition of Suspicious Activity for Crime Prevention

Computer Vision ◽

10.4018/978-1-5225-5204-8.ch094 ◽

2018 ◽

pp. 2139-2165

Author(s):

M. Kalaiselvi Geetha ◽

J. Arunnehru ◽

A. Geetha

Keyword(s):

Crime Prevention ◽

Early Recognition ◽

Early Stage ◽

Criminal Activity ◽

Automatic Identification ◽

Early Prediction ◽

Interest Point ◽

Prediction Problem ◽

Novel Approach ◽

2D And 3D

Automatic identification and early prediction of suspicious human activities are of significant importance in video surveillance research. By recognizing and predicting a criminal activity at an early stage, regrettable incidents can be avoided. Initially, an action recognition framework is developed for identifying the suspicious actions using interest point based 2D and 3D features and transform based approaches. This is subsequently followed by a novel approach for predicting the suspicious actions for crime prevention in real-world scenario. The prediction problem is formulated probabilistically and a novel approach that employs the mixture models for prediction is introduced. The developed system yields promising results for predicting the actions in real-time.

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Bituminous Mixtures Experimental Data Modeling Using a Hyperparameters-Optimized Machine Learning Approach

Applied Sciences ◽

10.3390/app112411710 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11710

Author(s):

Matteo Miani ◽

Matteo Dunnhofer ◽

Fabio Rondinella ◽

Evangelos Manthos ◽

Jan Valentin ◽

...

Keyword(s):

Machine Learning ◽

Bayesian Optimization ◽

Automatic Identification ◽

Learning Approach ◽

Ann Model ◽

Data Set ◽

Bituminous Mixtures ◽

Novel Approach ◽

The Neural Network ◽

Machine Learning Approach

This study introduces a machine learning approach based on Artificial Neural Networks (ANNs) for the prediction of Marshall test results, stiffness modulus and air voids data of different bituminous mixtures for road pavements. A novel approach for an objective and semi-automatic identification of the optimal ANN’s structure, defined by the so-called hyperparameters, has been introduced and discussed. Mechanical and volumetric data were obtained by conducting laboratory tests on 320 Marshall specimens, and the results were used to train the neural network. The k-fold Cross Validation method has been used for partitioning the available data set, to obtain an unbiased evaluation of the model predictive error. The ANN’s hyperparameters have been optimized using the Bayesian optimization, that overcame efficiently the more costly trial-and-error procedure and automated the hyperparameters tuning. The proposed ANN model is characterized by a Pearson coefficient value of 0.868.

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Internet AIS

Journal of Navigation ◽

10.1017/s0373463305003255 ◽

2005 ◽

Vol 58 (2) ◽

pp. 197-206 ◽

Cited By ~ 5

Author(s):

Renato Filjar ◽

Sasa Desic ◽

Danijela Pokrajac ◽

Ivica Cubic

Keyword(s):

Mobile Communications ◽

Traffic Management ◽

Data Transfer ◽

Mobile Internet ◽

Automatic Identification ◽

Identification System ◽

Data Traffic ◽

Class A ◽

Development Framework ◽

Novel Approach

Automatic Identification System (AIS) has recently become the leading issue in maritime navigation and traffic management worldwide. The present AIS solution, based on a VHF data communications scheme, provides AIS functionalities for SOLAS (AIS Class A) vessels only in a limited environment defined by radio propagation properties. Here we present a novel approach in AIS development based on current mobile communication technologies. It utilises existing mobile communications equipment that the majority of targetted end-users own and are familiar with. A novel AIS concept aims to offer a transition of AIS data traffic to mobile Internet. An innovative AIS architecture supports AIS data processing, storing and transferring to authorised parties. This enhances not only the operational area, but also provides the global AIS with data transfer security and an improved aids-for-navigation service, with all legally traceable vessels (both AIS Class A and AIS Class B) included in the system. In order to provide the development framework for Internet AIS, a set of essential four use-cases, a communication protocol and the first Internet AIS prototype have been recently developed and are briefly introduced in this article.

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Early Recognition of Suspicious Activity for Crime Prevention

Emerging Technologies in Intelligent Applications for Image and Video Processing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9685-3.ch009 ◽

2016 ◽

pp. 205-231 ◽

Cited By ~ 1

Author(s):

M. Kalaiselvi Geetha ◽

J. Arunnehru ◽

A. Geetha

Keyword(s):

Crime Prevention ◽

Early Recognition ◽

Early Stage ◽

Criminal Activity ◽

Automatic Identification ◽

Early Prediction ◽

Interest Point ◽

Prediction Problem ◽

Novel Approach ◽

2D And 3D

Automatic identification and early prediction of suspicious human activities are of significant importance in video surveillance research. By recognizing and predicting a criminal activity at an early stage, regrettable incidents can be avoided. Initially, an action recognition framework is developed for identifying the suspicious actions using interest point based 2D and 3D features and transform based approaches. This is subsequently followed by a novel approach for predicting the suspicious actions for crime prevention in real-world scenario. The prediction problem is formulated probabilistically and a novel approach that employs the mixture models for prediction is introduced. The developed system yields promising results for predicting the actions in real-time.

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Copper-dependent ATP7B up-regulation drives the resistance of TMEM16A-overexpressing head-and-neck cancer models to platinum toxicity

Biochemical Journal ◽

10.1042/bcj20190591 ◽

2019 ◽

Vol 476 (24) ◽

pp. 3705-3719 ◽

Cited By ~ 2

Author(s):

Avani Vyas ◽

Umamaheswar Duvvuri ◽

Kirill Kiselyov

Keyword(s):

Oxidative Stress ◽

Head And Neck ◽

Transcriptional Activation ◽

Platinum Resistance ◽

Mrna Levels ◽

Strong Positive Correlation ◽

Platinum Compounds ◽

Copper Chelation ◽

Novel Approach ◽

Cancer Models

Platinum-containing drugs such as cisplatin and carboplatin are routinely used for the treatment of many solid tumors including squamous cell carcinoma of the head and neck (SCCHN). However, SCCHN resistance to platinum compounds is well documented. The resistance to platinum has been linked to the activity of divalent transporter ATP7B, which pumps platinum from the cytoplasm into lysosomes, decreasing its concentration in the cytoplasm. Several cancer models show increased expression of ATP7B; however, the reason for such an increase is not known. Here we show a strong positive correlation between mRNA levels of TMEM16A and ATP7B in human SCCHN tumors. TMEM16A overexpression and depletion in SCCHN cell lines caused parallel changes in the ATP7B mRNA levels. The ATP7B increase in TMEM16A-overexpressing cells was reversed by suppression of NADPH oxidase 2 (NOX2), by the antioxidant N-Acetyl-Cysteine (NAC) and by copper chelation using cuprizone and bathocuproine sulphonate (BCS). Pretreatment with either chelator significantly increased cisplatin's sensitivity, particularly in the context of TMEM16A overexpression. We propose that increased oxidative stress in TMEM16A-overexpressing cells liberates the chelated copper in the cytoplasm, leading to the transcriptional activation of ATP7B expression. This, in turn, decreases the efficacy of platinum compounds by promoting their vesicular sequestration. We think that such a new explanation of the mechanism of SCCHN tumors’ platinum resistance identifies novel approach to treating these tumors.

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Accuracy of Four Language Analysis Procedures Performed Automatically

American Journal of Speech-Language Pathology ◽

10.1044/1058-0360(2001/017) ◽

2001 ◽

Vol 10 (2) ◽

pp. 180-188 ◽

Cited By ~ 9

Author(s):

Steven H. Long ◽

Ron W. Channell

Keyword(s):

Automatic Identification ◽

Published Data ◽

Typically Developing ◽

Language Impaired ◽

Productivity Data ◽

Language Analysis ◽

Word Classes ◽

Human Coder ◽

Impaired Children ◽

Metalinguistic Skills

Most software for language analysis has relied on an interaction between the metalinguistic skills of a human coder and the calculating ability of the machine to produce reliable results. However, probabilistic parsing algorithms are now capable of highly accurate and completely automatic identification of grammatical word classes. The program Computerized Profiling combines a probabilistic parser with modules customized to produce four clinical grammatical analyses: MLU, LARSP, IPSyn, and DSS. The accuracy of these analyses was assessed on 69 language samples from typically developing, speech-impaired, and language-impaired children, 2 years 6 months to 7 years 10 months. Values obtained with human coding and by the software alone were compared. Results for all four analyses produced automatically were comparable to published data on the manual interrater reliability of these procedures. Clinical decisions based on cutoff scores and productivity data were little affected by the use of automatic rather than human-generated analyses. These findings bode well for future clinical and research use of automatic language analysis software.

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Collecting Words: A Clinical Example of a Morphology-Focused Orthographic Intervention

Language Speech and Hearing Services in Schools ◽

10.1044/2020_lshss-19-00050 ◽

2020 ◽

Vol 51 (3) ◽

pp. 544-560 ◽

Cited By ~ 3

Author(s):

Kimberly A. Murphy ◽

Emily A. Diehm

Keyword(s):

Written Language ◽

Morphological Knowledge ◽

English Orthography ◽

Word Level ◽

Novel Approach ◽

Language Pathology ◽

The One ◽

Critical Intervention ◽

Reading And Spelling ◽

Reading And Spelling Difficulties

Purpose Morphological interventions promote gains in morphological knowledge and in other oral and written language skills (e.g., phonological awareness, vocabulary, reading, and spelling), yet we have a limited understanding of critical intervention features. In this clinical focus article, we describe a relatively novel approach to teaching morphology that considers its role as the key organizing principle of English orthography. We also present a clinical example of such an intervention delivered during a summer camp at a university speech and hearing clinic. Method Graduate speech-language pathology students provided a 6-week morphology-focused orthographic intervention to children in first through fourth grade ( n = 10) who demonstrated word-level reading and spelling difficulties. The intervention focused children's attention on morphological families, teaching how morphology is interrelated with phonology and etymology in English orthography. Results Comparing pre- and posttest scores, children demonstrated improvement in reading and/or spelling abilities, with the largest gains observed in spelling affixes within polymorphemic words. Children and their caregivers reacted positively to the intervention. Therefore, data from the camp offer preliminary support for teaching morphology within the context of written words, and the intervention appears to be a feasible approach for simultaneously increasing morphological knowledge, reading, and spelling. Conclusion Children with word-level reading and spelling difficulties may benefit from a morphology-focused orthographic intervention, such as the one described here. Research on the approach is warranted, and clinicians are encouraged to explore its possible effectiveness in their practice. Supplemental Material https://doi.org/10.23641/asha.12290687

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