Prediction of thrust force and torque in canal preparation process using Taguchi method and Artificial Neural Network

Root canal preparation is a vital procedure during the treatment of pulposis and periapical periodontitis. However, the improper control of thrust force and torque in root canal preparation will cause nerve damage and cell necrosis. The aim of this study was to investigate and optimize the main factors influencing thrust force and torque and to establish an efficient predictive model for root canal preparation. This study was conducted on fresh bovine bones due to the similarity of structure and density with human teeth. A novel experimental platform was first built to measure the force and torque in canal preparation of different parameters. The effect of the experimental results on thrust force and torque was investigated based on Analysis of variance (ANOVA). The results indicated that the diameter of instrument, width of root canal, and feed rate are the most significant factors influencing the thrust forces and torque ( p < 0.05). Based on the above experiments, a Radial Basis Function Neural Network (RBFN) model was established to predict the thrust force and torque in a wider range of parameters. In confirmation tests, RBFN showed an excellent predictive model for prediction of thrust force and torque (error less than 14%) in canal preparation.

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Effect of a Surfactant on the Antimicrobial Activity of Sodium Hypochlorite Solutions

Brazilian Dental Journal ◽

10.1590/0103-6440201300049 ◽

2014 ◽

Vol 25 (5) ◽

pp. 416-419 ◽

Cited By ~ 12

Author(s):

Marcos Rodolfo Bolfoni ◽

Marcelo dos Santos Ferla ◽

Otávio da Silva Sposito ◽

Luciano Giardino ◽

Rogério de Castilho Jacinto ◽

...

Keyword(s):

Antimicrobial Activity ◽

Sodium Hypochlorite ◽

Root Canal ◽

Root Canal Preparation ◽

Human Teeth ◽

Significance Level ◽

Root Canals ◽

Tukey Test ◽

Irrigation Solution ◽

Period Data

The objective of the present study was to evaluate the antimicrobial activity of sodium hypochlorite (NaOCl) associated with a surfactant. Seventy single-rooted extracted human teeth were inoculated with Enterococcus faecalis, and incubated for 21 days (37 °C). The groups were distributed according to the irrigation solution used during root canal preparation: 5%, 2.5% and 1% NaOCl; 5%, 2.5% and 1% Hypoclean(r), a solution containing a surfactant (cetrimide) associated with NaOCl. Three microbiological samples were collected from each tooth: S1 - before instrumentation; S2 - immediately after instrumentation; and S3 - after a seven-day period. Data were submitted to ANOVA and Tukey test with 5% significance level. The results showed that immediately after root canal preparation (S2), E. faecalis was eliminated in all the experimental groups. However, after 7 days (S3), only the groups in which Hypoclean was used, remained contamination-free, including Hypoclean associated with 1% NaOCl, while the root canals irrigated with 1% NaOCl only, presented the highest percentage of bacterial growth. In conclusion, the addition of surfactant increased the antimicrobial activity of 1% NaOCl to levels similar to 5% NaOCl.

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Prevalence of hypertension: predictive analytics review

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v9.i4.pp576-583 ◽

2020 ◽

Vol 9 (4) ◽

pp. 576

Author(s):

Nur Arifah Mohd Nor ◽

Azlinah Mohamed ◽

Sofianita Mutalib

Keyword(s):

Neural Network ◽

Predictive Model ◽

Predictive Analytics ◽

Communicable Disease ◽

Medical Data ◽

Support Vector ◽

To Come ◽

Future Work ◽

Non Communicable Disease ◽

Significant Factors

Hypertension is one of the non-communicable disease (NCD) that is classify as a global health risk with many critical health cases. Malaysia raise the same concern of the increasing NCD health problem. This paper aims to study the techniques used in predictive analytics namely healthcare and identify the factors of prevalence on hypertension. This review would give a better understanding of proper techniques and suggest the technique commonly used in predictive analytics especially for medical data and at the same time provide significant factors of prevalence hypertension. A total of 27 papers reviewed, several techniques on predictive analytics in healthcare are neural network, decision tree, naïve bayes, regression and support vector machine. The rise of economic growth and correlated socio-demographic have cause rise in hypertension problem over past years. The factors of hypertension depicted in this review namely gender, age, locality, family history, physically inactive and unhealthy life style not conform to any boundaries thus far. Thus, the choice on the technique and hypertension factors for predictive analytics is significant to come out with the significant predictive model. The predictive model on prevalence of hypertension may predict the severity of adult having hypertension in future work.

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Examination of Significant Factors Influencing Response Time of Customer Complaint Based on Analytics Method

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a2642.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 3415-3418

Keyword(s):

Neural Network ◽

Financial Institutions ◽

Data Cleaning ◽

Financial Protection ◽

Sigmoid Function ◽

Data Preparation ◽

Customer Complaint ◽

Factors Influencing ◽

Artificial Neural Network Ann ◽

Significant Factors

The purpose of this study is to investigate significant factors that influenced duration of solving financial institutions’ customer complaint. Using raw customer complaint dataset from Consumer Financial Protection Bureau (CFPB) website, it was found that many of sub-categories are not well organized. Thus, it is important to proceed with data cleaning and data preparation steps before any analysis been performed. In this study, Artificial Neural Network (ANN) had been chosen since it can deal with non-linear relationship by using sigmoid function. Further to this, it was found that Product, Company response and Issues are the significant factors that are more likely to be solved more than one day. The use of this analysis can be particularly beneficial for related financial party that might need to assist their customer in future.

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Scanning electron microscopy investigation of canal cleaning after canal preparation with nickel titanium files

Srpski arhiv za celokupno lekarstvo ◽

10.2298/sarh1010551b ◽

2010 ◽

Vol 138 (9-10) ◽

pp. 551-556

Author(s):

Tatjana Brkanic ◽

Ivana Stojsin ◽

Karolina Vukoje ◽

Slavoljub Zivkovic

Keyword(s):

Electron Microscopy ◽

Scanning Electron Microscopy ◽

Root Canal ◽

Smear Layer ◽

Root Canal Preparation ◽

Human Teeth ◽

Significant Difference ◽

Cleaning Efficacy ◽

Scanning Electron

Introduction. Root canal preparation is the most important phase of endodontic procedure and it consists of adequate canal space cleaning and shaping. In recent years, rotary instruments and techniques have gained importance because of the great efficacy, speed and safety of the preparation procedure. Objective. The aim of this research was to investigate the influence of different NiTi files on the canal wall cleaning quality, residual dentine debris and smear layer. Methods. The research was conducted on extracted human teeth in vitro conditions. Teeth were divided in 7 main groups depending on the kind of instruments used for root canal preparation: ProTaper, GT, ProFile, K-3, FlexMaster, hand ProTaper and hand GT. Root canal preparation was accomplished by crown-down technique. Prepared samples were assessed on scanning electron microscopy JEOL, JSM-6460 LV. The evaluation of dentine debris was done with 500x magnification, and the evaluation of smear layer with 1,000 times magnification. Quantitive assessment of dentine debris and smear layer was done according to the criteria of Hulsmann. Results. The least amount of debris and smear layer has been found in canals shaped with ProFile instruments, and the largest amount in canals shaped with FlexMaster instruments. Canal cleaning efficacy of hand GT and ProTaper files has been similar to cleaning efficacy of rotary NiTi files. Statistic analysis has shown a significant difference in amount of dentine debris and smear layer on the canal walls between sample groups shaped with different instruments. Conclusion. Completely clean canals have not been found in any tested group of instruments. The largest amount of debris and smear layer has been found in the apical third of all canals. The design and the type of endodontic instruments influence the efficacy of the canal cleaning.

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Comparative evaluation of dentin volume removal and centralization of the root canal after shaping with the ProTaper Universal, ProTaper Gold, and One-Curve instruments using micro-CT

Journal of Dental Research Dental Clinics Dental Prospects ◽

10.34172/joddd.2021.009 ◽

2021 ◽

Vol 15 (1) ◽

pp. 47-52

Author(s):

Hatice Yalniz ◽

Mehrdad Koohnavard ◽

Aysenur Oncu ◽

Berkan Celikten ◽

Ayse Isil Orhan ◽

...

Keyword(s):

Surface Area ◽

Root Canal ◽

File Systems ◽

Micro Ct ◽

Root Canal Preparation ◽

Root Tips ◽

Human Teeth ◽

Root Canals ◽

Coronal Section ◽

Canal Transportation

Background. The main goal of our study was to assess the volume of dentin removed and transportation in root canals using ProTaper Universal (PTU), ProTaper Gold (PTG) and One-Curve (OC). Ni-Ti rotary instruments in extracted human teeth using by micro-CT. Methods: Thirty human upper 1st premolar teeth with two separate root canals and sturdy, mature root tips were used in the present study. Specimens were decoronated and root length was standardized for micro CT scanning before root canal preparation done. The teeth were randomly separated into three categories (n = 10) according to the rotary NiTi system used for canal instrumentation, i.e., PTU (Dentsply, Maillefer), PTG (Dentsply, Maillefer), and OC (Micro-Mega SA). After root canal preparation, samples were scanned again on micro-CT by the same scanning parameters. Surface area, canal volume, structure model index (SMI), percentage of uninstrumented area and transportation parameters were obtained for each sample before and after micro-CT analyse. Results: No significant differences between the PTG and PTU in terms of the total volume of removed dentin, surface area and percentage of uninstrumented areas were found. However, regarding to parameters above, OC showed a lower efficacy than PTG and PTU in coronal section. Regarding canal transportation, PTG and OC showed lower mean transportation values at all levels. Conclusion: This paper demonstrated the root canal shaping abilities of the PTU, PTG, and OC NiTi file systems. The PTG and OC systems were associated less canal transportation and a better ability to preserve dentinal walls than PTU. There was no significance different between all rotary file systems for SMI values however, PTU and PTG showed greater canal volume and surface area change than OC file systems in coronal section.

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Stock Market Prediction using Data Mining Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206290 ◽

2020 ◽

pp. 310-313

Author(s):

Karunesh Makker ◽

Prince Patel ◽

Hrishikesh Roy ◽

Sonali Borse

Keyword(s):

Neural Network ◽

Stock Market ◽

Prediction Model ◽

Efficient Market Hypothesis ◽

Deterministic System ◽

Vast Number ◽

Factors Influencing ◽

Number Of Factors ◽

Using Data ◽

Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

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ACCIDENTAL SUBMUCOSAL INJECTION OF A CHEMICAL DISINFECTANT USED IN ROOT CANAL PREPARATION

10.26226/morressier.578e3b4cd462b80292381b6d ◽

2016 ◽

Author(s):

Konstantinos Katoumas

Keyword(s):

Root Canal ◽

Root Canal Preparation ◽

Submucosal Injection ◽

Chemical Disinfectant

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Kedo-S Paediatric Rotary Files for Root Canal Preparation in Primary Teeth – Case Report

JOURNAL FOR CLINICAL AND DIAGNOSTIC RESEARCH ◽

10.7860/jcdr/2017/25856.9508 ◽

2017 ◽

Cited By ~ 2

Author(s):

Ganesh Jeevanandan

Keyword(s):

Case Report ◽

Root Canal ◽

Primary Teeth ◽

Root Canal Preparation

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Trust in Artificial Intelligence: Meta-Analytic Findings

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/00187208211013988 ◽

2021 ◽

pp. 001872082110139

Author(s):

Alexandra D. Kaplan ◽

Theresa T. Kessler ◽

J. Christopher Brill ◽

P. A. Hancock

Keyword(s):

Artificial Intelligence ◽

Empirical Research ◽

Meta Analysis ◽

Automated Vehicles ◽

Factors Influencing ◽

Moderating Factors ◽

Shared Context ◽

Build Systems ◽

Significant Factors

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

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