SQL injection is a type of security vulnerability that occurs in database-driven web applications where an attacker injects malicious code into the application to gain unauthorized access to sensitive information. This paper aims to provide a comprehensive and systematic review of the existing methods for preventing and detecting SQL injection attacks. The review covers a range of techniques, including input validation, parameterized queries, and intrusion detection systems, as well as the advantages and disadvantages of each method. The most common prevention techniques include input validation, parameterized queries, and stored procedures, while the most common detection techniques include intrusion detection systems (IDS), honeypots, and signature-based detection. The choice of method will depend on the specific requirements of the organization and the level of security required. Still, a combination of prevention and detection methods is likely to be the most effective way to secure web applications against SQL injection attacks. The paper concludes that SQL injection attacks continue to be a significant security threat to web applications, and it is essential for organizations to implement effective prevention and detection methods to secure their web applications against SQL injection attacks.
The crystals of Mn4+-activated fluorides, such as those of the hexafluorometallate family, are widely known for their luminescence properties. The most commonly reported red phosphors are A2XF6: Mn4+ and BXF6: Mn4+ fluorides, where A represents alkali metal ions such as Li, Na, K, Rb, Cs; X=Ti, Si, Ge, Zr, Sn, B = Ba and Zn; and X = Si, Ge, Zr, Sn, and Ti. Their performance is heavily influenced by the local structure around dopant ions. Many well-known research organizations have focused their attention on this area in recent years. However, there has been no report on the effect of local structural symmetrization on the luminescence properties of red phosphors. The purpose of this research was to investigate the effect of local structural symmetrization on the polytypes of K2XF6 crystals, namely Oh-K2MnF6, C3v-K2MnF6, Oh-K2SiF6, C3v-K2SiF6, D3d-K2GeF6, and C3v-K2GeF6. These crystal formations yielded seven-atom model clusters. Discrete Variational Xα (DV-Xα) and Discrete Variational Multi Electron (DVME) were the first principles methods used to compute the Molecular orbital energies, multiplet energy levels, and Coulomb integrals of these compounds. The multiplet energies of Mn4+ doped K2XF6 crystals were qualitatively reproduced by taking lattice relaxation, Configuration Dependent Correction (CDC), and Correlation Correction (CC) into account. The 4A2g→4T2g (4F) and 4A2g→4T1g (4F) energies increased when the Mn-F bond length decreased, but the 2Eg → 4A2g energy decreased. Because of the low symmetry, the magnitude of the Coulomb integral became smaller. As a result, the decreasing trend in the R-line energy could be attributed to a decreased electron–electron repulsion.
The spinal curve that represents an abnormally rounded back is known as kyphosis, and it can be caused by stress, infectious disease, developmental abnormalities, genetic disease, and occasionally iatrogenic disease. Due to the fact that it provides technical explanations and a significant standard, the emergence of machine learning and deep learning has demonstrated that they can better characterize data. Machine Learning and Deep Learning are promising approaches that help in the prediction, diagnosing of sickness. Compared to conventional computing algorithms, deep learning algorithms are far more effective in disease detection and diagnosing. In this study, Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Deep Neural Network (DNN) models are applied to detect the presence of kyphosis in youngsters supported medicine information and compared to point out the rise in the potency of predictions associated with kyphosis. Each classification algorithm that makes use of the hyperparameter tuning and control feature enhances the algorithms' overall prediction performance, which enhances the algorithms' overall performance. The potency of the planned models were enforced and checked over medical dataset utilized from Kaggle. Overall, the DNN model performed best among the wide range of different models and achieved maximum accuracy and other performance metrics scores derived from the stratified K-Fold cross-validation. It is advised that after a patient has completed a clinical procedure, the DNN model be trained to identify and forecast kyphosis disease.
Each student has their own characteristics and way of doing 3D geometric thinking. The way of thinking that students do influences the resulting understanding of the concept of 3D geometry. Therefore, this study aims to investigate students' geometric thinking based on the level of achievement of students in completing the 3D geometric thinking ability test (3D GTA). This study uses an exploratory case study design. The participants who voluntarily participated were 33 junior high school students (14 boys, 19 girls) in one of the schools in Indramayu Regency, Indonesia. Data obtained from the process of observation, tests, interviews, and documentation were analyzed qualitatively using Atlas. ti 8 software. The findings revealed that students with low 3D GTA achievements experienced difficulties in representing and calculating the surface area and volume of 3D shapes. In addition, students with moderate 3D GTA achievements experienced difficulties in representing 3D shapes but were able to translate 2D shapes from 3D shapes. Furthermore, students with high 3D GTA achievements experienced difficulties in calculating the surface area and volume of 3D shapes, but were able to use appropriate formulas and were able to interpret the comparisons of 3D geometric shapes well. The results of this study have implications for helping teachers identify student characteristics in understanding the concept of 3D geometry and connections with 2D geometry.
Although Kyphosis, an excessive forward rounding of the upper back, can occur at any age, adolescence is the most common time for Kyphosis. Surgery is frequently performed on Kyphosis patients; however, the condition may persist after the operation. The tricky part is figuring out, based on the patient’s traits, if the Kyphosis condition will continue after the treatment. There have been numerous models employed in the past to predict the Kyphosis disease, including Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Deep Neural Network (DNN), and others. Unfortunately, the precision was overestimated. Based on the dataset received from Kaggle, we investigated how to predict Kyphosis disorders more accurately by using these models with Hyperparameter tuning. While the calculations were being performed, certain variables were modified. The accuracy was increased by optimizing the fit parameters based on Hyperparameter tuning. Accuracy, recall or sensitivity, specificity, precision, balanced accuracy score, F1 score, and AUC-ROC score of all models, including the Hyperparameter tuning, were compared. Overall, the Hyperparameter-tuned DNN models excelled over the other models. The DNN models’ accuracy was 87.72% with 5-fold cross-validation and 87.64% with 10-fold cross-validation. It is advised that when a patient has a clinical procedure, the DNN model be trained to detect and foresee Kyphosis disease. Medical experts can use this study’s findings to correctly predict if a patient will still have Kyphosis after surgery. We propose that deep learning should be adopted and utilized as a crucial and necessary tool throughout the broad range of resolving biological queries.
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