2 research outputs found

    Clinical evaluation of marketed and non-marketed orthodontic products: are researchers now ahead of the times? A meta-epidemiological study.

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    BACKGROUND The advertisement and adoption of untested orthodontic products is common. This study aimed to provide an update regarding the prevalence of clinical trials in orthodontics evaluating commercially marketed products. Associations between marketed/non-marketed products and study characteristics such as direction of effect, declaration of conflict of interest and industry sponsorship were evaluated. In addition, within the marketed products associations between direction of effect and study characteristics were explored. MATERIAL AND METHODS Electronic searching of a single database (Medline via PubMed) was undertaken to identify Randomized controlled trials (RCTs) published over a 5-year period (1st January 2017 to 31st December 2021). Descriptive statistics and associations between trial characteristics were explored. RESULTS 196 RCTs were analysed. RCTs were frequently published in Angle Orthodontist (18.4%), American Journal of Orthodontics and Dentofacial Orthopedics (14.8%) and European Journal of Orthodontics (13.3%). 65.3% (128/196) of trials assessed marketed products after their introduction. The majority of trials assessed interventions to improve treatment efficiency (33.7%). Growth modification appliances were typically analysed in non-marketed compared to marketed products. An association between the type of product (marketed vs non-marketed) and both the declaration of conflict of interest and industry sponsorship was detected. For individual RCTs assessing marketed products either a positive effect (45.3%) or equivalence between interventions or between intervention and untreated control (47.7%) was evident. In 27% of these trials either no conflict of interest or industry funding was not clearly declared. Within the marketed products, no association between the direction of the effect and conflict of interest or funding was detected. CONCLUSIONS The analysis of marketed orthodontic products after their introduction is still common practice. To reduce research waste, collaboration prior to the licensing and marketing of orthodontic products between researchers, industry and manufacturers is recommended

    Homoglyph Attack Detection Model Using Machine Learning and Hash Function

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    Phishing is still a major security threat in cyberspace. In phishing, attackers steal critical information from victims by presenting a spoofing/fake site that appears to be a visual clone of a legitimate site. Several Unicode characters are visually identical to ASCII characters. This similarity in characters is generally known as homoglyphs. Malicious adversaries utilize homoglyphs in URLs and DNS domains to target organizations. To reduce the risks caused by phishing attacks, effective ways of detecting phishing websites are urgently required. This paper proposes a homoglyph attack detection model that combines a hash function and machine learning. There are two phases to the model approach. The machine was being trained during the development phase. The deployment phase involved deploying the model with a Java interface and testing the outcomes through actual user interaction. The results are more accurate when the URL is hashed, as any little changes to the URL can be recognized. The homoglyph detector can be developed as a stand-alone software that is used as the initial step in requesting a webpage as it enhances browser security and protects websites from phishing attempts. To verify the effectiveness, we compared the proposed model on several criteria to existing phishing detection methods. By using the hash function, the proposed security features increase the overall security of the homoglyph attack detection in terms of accuracy, integrity, and availability. The experiment results showed that the model can detect phishing sites with an accuracy of 99.8% using Random Forest, and the hash function improves the accuracy of homoglyph attack detection