This study aimed to predict the skeletal growth maturation using convolutional neural network-based deep learning methods using cervical vertebral maturation and the lower 2nd molar calcification level so that skeletal maturation can be detected from orthopantomography using multiclass classification. About 1200 cephalometric radiographs and 1200 OPGs were selected from patients seeking treatment in dental centers. The level of skeletal maturation was detected by CNN using the multiclass classification method, and each image was identified as a cervical vertebral maturation index (CVMI); meanwhile, the chronological age was estimated from the level of the 2nd molar calcification. The model’s final result demonstrates a high degree of accuracy with which each stage and gender can be predicted. Cervical vertebral maturation reported high accuracy in males (98%), while females showed high accuracy of 2nd molar calcification. CNN multiclass classification is an accurate method to detect the level of maturation, whether from cervical maturation or the calcification of the lower 2nd molar, and the calcification level of the lower 2nd molar is a reliable method to trust in the growth level, so the traditional OPG is enough for this purpose.

Background and Objectives: Orthodontics is a field that has seen significant advancements in recent years, with technology playing a crucial role in improving diagnosis and treatment planning. The study aimed to implement artificial intelligence to predict the arch width as a preventive measure to avoid future crowding in growing patients or even in adult patients seeking orthodontic treatment as a tool for orthodontic diagnosis. Materials and Methods: Four hundred and fifty intraoral scan (IOS) images were selected from orthodontic patients seeking treatment in private orthodontic centers. Real inter-canine, inter-premolar, and inter-molar widths were measured digitally. Two of the main machine learning models were used: the Python programming language and machine learning algorithms, implementing the data on k-nearest neighbor and linear regression. Results: After the dataset had been implemented on the two ML algorithms, linear regression and k-nearest neighbor, the evaluation metric shows that KNN gives better prediction accuracy than LR does. The resulting accuracy was around 99%. Conclusions: it is possible to leverage machine learning to enhance orthodontic diagnosis and treatment planning by predicting linear dental arch measurements and preventing anterior segment malocclusion.

Hydrological modeling is one of the most complicated tasks in sustainable water resources management, particularly in terms of predicting rainfall. Predicting rainfall is critical to build a sustainable society in terms of hydropower operations, agricultural planning, and flood control. In this study, a hybrid model based on the integration of k-nearest neighbor (KNN), XGBoost (XGB), decision tree (DCT), and Random Forest (RF) has been developed and implemented for forecasting daily rainfall for the first time at Sydney airport, Australia. Daily rainfall, temperature, evaporation, and humidity have been selected as input parameters. Three statistical measurements, namely, root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and Normalized Root Mean Square Error (NRMSE) have been utilized in order to check the accuracy of the proposed model. A sensitivity analysis was conducted, and the results indicated that for the purpose of prediction, the temperature, humidity, and evaporation were highly sensitive to the rainfall data. According to the results, the developed hybrid model was capable of predicting daily rainfall with high performance for both training and testing parts with RMSE = 0.124, R2 = 0.999, MAE = 0.007, NRMSE = 0.04 and RMSE = 1.246, R2 = 0.991, MAE = 0.109, NRMSE = 0.339, respectively.

These days there are many security issues facing users while they are using the internet for exchanging information among them. And, users use technology devices, such as mobiles and computers, and they connect them to the network and internet. Therefore, users always looking for a safe way to exchange information locally and globally, when they are connecting to a network. Also, these problems lead them to many further issues such as losing privacy, hacking and detecting personal information. Although, many security techniques have been used to solve these issues by creating many different software utilities; some of them worked perfectly to some extent, while some others still did not comply with the security environment. This research paper finds a new methodology to secure text information while exchanging among permitted users over the internet. This method is a combination of cryptography and steganography with audio and imaging multimedia, which works on hiding and encrypting information before sending it over a network. As result, this technique will add additional security processes to the data exchange, and it will provide a more reliable environment for the user to connect to the network. In addition, the quality of the data will not be altered or noticed during the encryption and decryption process.

Background: A promising improvement in orthodontic diagnostics is the use of convolutional neural networks (CNNs) to predict skeletal maturity using dental radiographic data, namely the calcification phases of the lower canine. Objectives: The purpose of this study is to improve the prediction of skeletal growth maturation through the use of sophisticated deep learning techniques, particularly CNNs, in the analysis of orthopantomography developmental markers. Methods: The CNN was trained and validated using the suggested model, which enables precise assessment of skeletal maturity across data collected from patients undergoing orthodontic and dental evaluations. By using a multiclass classification framework to classify the various stages of skeletal maturation. Results: CNN model predicting the development of the lower canine from the second molar provided a test accuracy of 97.50%, the model made it possible to automatically interpret radiographic features that were previously evaluated manually. Conclusions: CNN models can be trained to correctly identify the lower canine calcification stage, this helped clinicians with treatment planning and timing, especially with regard to growth modification, implant, prosthodontics approach and its clinical applicability. It guarantees ethical imaging procedures and optimizes clinical workflows by doing away with the necessity for further imaging.