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Lasers represent a promising method for implant decontamination, but evidence on implant surface changes and subsequent biofilm formation is limited. This study aimed to assess the effect of erbium-doped yttrium aluminum garnet (Er:YAG) laser treatment on zirconia and titanium discs, and the differences in biofilm formation as a result of surface alterations.A two-stage (in vitro and in vivo) experiment utilizing Er:YAG laser on titanium and zirconia discs was performed. In vitro, surface alterations, roughness, and elemental-material weight differences following laser treatment were assessed using scanning electron microscopy and atomic force microscopy. In vivo, four participants wore custom-made intra-oral stents, embedded with laser-treated and untreated titanium and zirconia discs overnight. Biofilm-coated discs were stained using nucleic acid fluorescence dye and visualized using multiphoton confocal laser scanning microscopy. Biofilm 3D structure, biomass, thickness, and live-to-dead bacteria ratio were assessed.Both titanium and zirconia discs treated with Er:YAG laser resulted in visual surface alterations, but showed no significant change in average surface roughness (titanium P = 0.53, zirconia P = 0.34) or elemental-material-weight (titanium, P = 0.98), (zirconia, P = 0.96). No significant differences in biofilm biomass, average thickness, and live-to-dead bacteria ratio of laser-treated titanium and zirconia discs were identified compared to untreated groups (titanium P 0.05, zirconia P 0.05). Generally, zirconia discs presented with a lower live-to-dead bacteria ratio compared to titanium discs, regardless of laser treatment.Er:YAG laser treatment of titanium and zirconia implant surfaces does not significantly affect surface roughness, elemental material weight, or early biofilm formation in the oral cavity.

Purpose This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models. Design/methodology/approach First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared. Findings The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets. Originality/value In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

While the password-based authentication used in social networks, e-mail, e-commerce, and online banking is vulnerable to hackings, biometric-based continuous authentication systems have been used successfully to handle the rise in unauthorized accesses. In this study, an empirical evaluation of online continuous authentication (CA) and anomaly detection (AD) based on mouse clickstream data analysis is presented. This research started by gathering a set of online mouse-dynamics information from 20 participants by using software developed for collecting mouse information, extracting approximately 87 features from the raw dataset. In contrast to previous work, the efficiency of CA and AD was studied using different machine learning (ML) and deep learning (DL) algorithms, namely, decision tree classifier (DT), k-nearest neighbor classifier (KNN), random forest classifier (RF), and convolutional neural network classifier (CNN). User identification was determined by using three scenarios: Scenario A, a single mouse movement action, Scenario B, a single point-and-click action, and Scenario C, a set of mouse movement and point-and-click actions. The results show that each classifier is capable of distinguishing between an authentic user and a fraudulent user with a comparatively high degree of accuracy.

During natural disaster events, social media users generate a significant amount of data, some of which are valuable for relief efforts and emergency management. In this paper, we study the nature of social media content generated during two natural disasters. We provide a system for labeling Twitter data and classifying disaster-related tweets. The focus is on Twitter data generated before, during, and after Hurricane Florence and Hurricane Michael, both of which occurred in 2018. Then we apply various machine learning models on the labeled data and provide a performance comparison between the models using features generated from TfidfVectorizer and CountVectorizer Approaches.