A Headway to QoS on Traffic Prediction over VANETs using RRSCM Statistical Classifier

In this paper, a novel throughput measurement forecast model is recommended for VANETs. The model is based on a statistical technique adopted and deployed over a high speed IP network traffic. Network traffic would always experience more QoS (Quality of Service) issues such as jitter, delay, packet loss and degradation due to very low bit rate codification too. Despite of all such dictated issues the traffic throughput is to be predicted with at most accuracy using a proposed multivariate analysis scheme represented as a RRSCM (Refined Regression Statistical Classifier Model) that optimizes parting parameters. Henceforth, the focus is towards the measurement methodology that estimates the traffic parameters that triggers to predict the accurate traffic and extemporize the QoS for the end-users. Finally, the proposed RRSCM classification model’s end-results are compared with the ANN (Artificial Neural Network) classification model to showcase its better act on the projected model

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Sleep-related breathing biomarkers as a predictor of vital functions

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2017-0003 ◽

2017 ◽

Vol 13 (1) ◽

Cited By ~ 1

Author(s):

Klaudia Proniewska ◽

Agnieszka Pregowska ◽

Krzysztof Piotr Malinowski

Keyword(s):

High Sensitivity ◽

Information Criterion ◽

Roc Curves ◽

Classification Model ◽

Sleep Related Breathing Disorders ◽

Vital Functions ◽

Initial Assumption ◽

Analysis Scheme ◽

Logistic Regressions

AbstractBecause an average human spends one third of his life asleep, it is apparent that the quality of sleep has an important impact on the overall quality of life. To properly understand the influence of sleep, it is important to know how to detect its disorders such as snoring, wheezing, or sleep apnea. The aim of this study is to investigate the predictive capability of a dual-modality analysis scheme for methods of sleep-related breathing disorders (SRBDs) using biosignals captured during sleep. Two logistic regressions constructed using backward stepwise regression to minimize the Akaike information criterion were extensively considered. To evaluate classification correctness, receiver operating characteristic (ROC) curves were used. The proposed classification methodology was validated with constructed Random Forests methodology. Breathing sounds and electrocardiograms of 15 study subjects with different degrees of SRBD were captured and analyzed. Our results show that the proposed classification model based on selected parameters for both logistic regressions determine the different types of acoustic events during sleep. The ROC curve indicates that selected parameters can distinguish normal versus abnormal events during sleep with high sensitivity and specificity. The percentage of prediction for defined SRBDs is very high. The initial assumption was that the quality of result is growing with the number of parameters included in the model. The best recognition reached is more than 89% of good predictions. Thus, sleep monitoring of breath leads to the diagnosis of vital function disorders. The proposed methodology helps find a way of snoring rehabilitation, makes decisions concerning future treatment, and has an influence on the sleep quality.

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Automation of selected brazes' dynamic properties in high-temperatures' measurement process

Image Processing & Communications ◽

10.2478/v10248-012-0077-0 ◽

2013 ◽

Vol 18 (2-3) ◽

pp. 33-41

Author(s):

Dominik Sankowski ◽

Marcin Bakala ◽

Rafał Wojciechowski

Keyword(s):

Surface Tension ◽

High Temperatures ◽

Dynamic Properties ◽

Automatic Measurement ◽

Measurement Methodology ◽

Joining Process ◽

Wetting Force ◽

Research Grant

Abstract The good quality of several manufactured components frequently depends on solidliquid interactions existing during processing. Nowadays, the research in material engineering focuses also on modern, automatic measurement methods of joining process properties, i.a. wetting force and surface tension, which allows for quantitative determination of above mentioned parameters. In the paper, the brazes’ dynamic properties in high-temperatures’ measurement methodology and the stand for automatic determination of braze’s properties, constructed and implmented within the research grant nr KBN N N519 441 839 - An integrated platform for automatic measurement of wettability and surface tension of solders at high temperatures, are widely described

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Faktor-Faktor yang Berpengaruh dalam Mendapatkan Pekerjaan bagi Lulusan Statistika IPB dengan Menggunakan Metode CHAID

Xplore Journal of Statistics ◽

10.29244/xplore.v8i1.156 ◽

2019 ◽

Vol 8 (1) ◽

Author(s):

Aulia Dwi Oktavia ◽

Aam Alamudi ◽

Budi Susetyo

Keyword(s):

College Graduates ◽

Influential Factors ◽

Classification Model ◽

Statistical Software ◽

Waiting Period ◽

Level Of Education ◽

Economic Problems ◽

Factors Influencing

Unemployment is one of the economic problems in Indonesia. Judging from the level of education that was completed there were unemployment from the level of college graduates. This encourages the level of competition in getting jobs to be more stringent, so that college graduates (bachelor of Statistics in IPB) must have the preparation of various factors to maintain the quality of their graduates. The quality of college graduates can be seen from the length of time waiting to get a job. This study aims to determine the influential factors in getting a job for graduates of the IPB Statistics degree, so that the CHAID method can be used in this study. The results of CHAID's analysis in this study in the form of tree diagrams using α = 10% explained that the factors influencing the waiting period variables were sex, internship, and the ability to master statistical software, where the accuracy value generated by the classification model was 79.3 %.

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Reception Quality of Terrestrial Digital Broadcasting Signals while Traveling at High Speed

The Journal of The Institute of Image Information and Television Engineers ◽

10.3169/itej.59.111 ◽

2005 ◽

Vol 59 (1) ◽

pp. 111-114 ◽

Cited By ~ 1

Author(s):

Kenji Ito ◽

Katsushi Sanda ◽

Nobuo Itoh

Keyword(s):

High Speed ◽

Digital Broadcasting

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An Examination of the Quality of Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) Extracted Wind and Temperature Data over Oklahoma Using the Computer-Assisted Artillery Meteorology BattleScale Forecast Model (CAAM BFM)

10.21236/ada381091 ◽

2000 ◽

Author(s):

Stephen F. Kirby

Keyword(s):

Forecast Model ◽

Temperature Data ◽

Computer Assisted ◽

Infrared Observation

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AI-based localization and classification of skin disease with erythema

Scientific Reports ◽

10.1038/s41598-021-84593-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ha Min Son ◽

Wooho Jeon ◽

Jinhyun Kim ◽

Chan Yeong Heo ◽

Hye Jin Yoon ◽

...

Keyword(s):

Neural Network ◽

Skin Diseases ◽

Classification Model ◽

Screening Tests ◽

Sensitivity Score ◽

Common Skin ◽

Novel Method ◽

Improved Performance ◽

High Level

AbstractAlthough computer-aided diagnosis (CAD) is used to improve the quality of diagnosis in various medical fields such as mammography and colonography, it is not used in dermatology, where noninvasive screening tests are performed only with the naked eye, and avoidable inaccuracies may exist. This study shows that CAD may also be a viable option in dermatology by presenting a novel method to sequentially combine accurate segmentation and classification models. Given an image of the skin, we decompose the image to normalize and extract high-level features. Using a neural network-based segmentation model to create a segmented map of the image, we then cluster sections of abnormal skin and pass this information to a classification model. We classify each cluster into different common skin diseases using another neural network model. Our segmentation model achieves better performance compared to previous studies, and also achieves a near-perfect sensitivity score in unfavorable conditions. Our classification model is more accurate than a baseline model trained without segmentation, while also being able to classify multiple diseases within a single image. This improved performance may be sufficient to use CAD in the field of dermatology.

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Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach

Sensors ◽

10.3390/s21093279 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3279

Author(s):

Maria Habib ◽

Mohammad Faris ◽

Raneem Qaddoura ◽

Manal Alomari ◽

Alaa Alomari ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Quality Assessment ◽

Transcript Level ◽

Assessment Process ◽

Classification Model ◽

Systematic Evaluation ◽

Transcript Levels ◽

Perceptual Evaluation

Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often handled based on a human auditory-perceptual evaluation. Typically, trained experts are needed for such tasks, as they follow systematic evaluation criteria. However, the daily rapid increase of consultations makes the evaluation process inefficient and impractical. This paper investigates the automation of the quality assessment process of patient–doctor voice-based conversations in a telehealth service using a deep-learning-based classification model. For this, the data consist of audio recordings obtained from Altibbi. Altibbi is a digital health platform that provides telemedicine and telehealth services in the Middle East and North Africa (MENA). The objective is to assist Altibbi’s operations team in the evaluation of the provided consultations in an automated manner. The proposed model is developed using three sets of features: features extracted from the signal level, the transcript level, and the signal and transcript levels. At the signal level, various statistical and spectral information is calculated to characterize the spectral envelope of the speech recordings. At the transcript level, a pre-trained embedding model is utilized to encompass the semantic and contextual features of the textual information. Additionally, the hybrid of the signal and transcript levels is explored and analyzed. The designed classification model relies on stacked layers of deep neural networks and convolutional neural networks. Evaluation results show that the model achieved a higher level of precision when compared with the manual evaluation approach followed by Altibbi’s operations team.

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High-Speed Imaging to Study an Auto-Oscillating Vocal Fold Replica for Different Initial Conditions

International Journal of Applied Mechanics ◽

10.1142/s1758825117500648 ◽

2017 ◽

Vol 09 (05) ◽

pp. 1750064 ◽

Cited By ~ 2

Author(s):

A. Van Hirtum ◽

X. Pelorson

Keyword(s):

Vocal Fold ◽

High Speed ◽

Initial Conditions ◽

Vocal Folds ◽

High Speed Imaging ◽

Human Voice ◽

Manual Intervention ◽

Geometrical Features ◽

Upstream Pressure

Experiments on mechanical deformable vocal folds replicas are important in physical studies of human voice production to understand the underlying fluid–structure interaction. At current date, most experiments are performed for constant initial conditions with respect to structural as well as geometrical features. Varying those conditions requires manual intervention, which might affect reproducibility and hence the quality of experimental results. In this work, a setup is described which allows setting elastic and geometrical initial conditions in an automated way for a deformable vocal fold replica. High-speed imaging is integrated in the setup in order to decorrelate elastic and geometrical features. This way, reproducible, accurate and systematic measurements can be performed for prescribed initial conditions of glottal area, mean upstream pressure and vocal fold elasticity. Moreover, quantification of geometrical features during auto-oscillation is shown to contribute to the experimental characterization and understanding.

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Machining quality of high speed helical milling of carbon fiber reinforced plastics

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406221996736 ◽

2021 ◽

pp. 095440622199673

Author(s):

Adel Abidi ◽

Sahbi Ben Salem ◽

Mohamed Athmane Yallese

Keyword(s):

Surface Roughness ◽

Carbon Fiber ◽

High Speed ◽

Hole Diameter ◽

Feed Speed ◽

Carbon Fiber Reinforced Plastics ◽

Reinforced Plastics ◽

Machining Quality ◽

Fiber Reinforced Plastics

Among advanced cutting methods, High Speed Milling (HSM) is often recommended to improve the productivity and to reduce the costs of machining parts. As every cutting process, HSM is characterized by some defects like surface roughness and delamination are the main defects generated in composite materials. The aim of this experimental work is the studying of the machining quality of woven Carbon fiber reinforced plastics (CFRP) using the HSM technology. Experiments were done using different machining parameters combinations to make opened holes in CFRP laminates. This study investigated the effect of cutting speed, orbital feed speed, hole diameter on the delamination defect and surface roughness responses generated in the drilled holes. The design of experimental tests was generated using the approach of Central Composite Design (CCD). The characterization of these responses was treated with the Analysis of variance (ANOVA) and Response surface methodology (RSM). Results showed that the surface roughness is highly affected by the orbital feed speed (F) with contribution of 22.45%. The delamination factor at entry and exit of holes is strongly influenced by the hole diameter D (25.97% and 57.43%) respectively. The developed model equations gave a good correlation between the empirical and predicted results. The optimization of the milling parameters was treated using desirability function to minimize the surface roughness (Ra) and the delamination factor simultaneously.

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