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Technological improvements have increased the number of people who use online social networking sites, resulting in an increase in cyberbullying. Bullies can attack victims through a large network of online social networking platforms. Cyberbullying is an umbrella term encompassing a wide range of online abuse, including but not limited to harassment, doxing, and reputation attacks. These attacks frequently leave the victim(s) with persistent mental scars, leading to desperate measures such as depression, self-harm, and suicidal thoughts. Given the effects of cyberbullying, there is an urgent need to prosecute and prevent such crimes. This paper gives a comprehensive review as well the empirical analysis of the machine learning, ensemble based and transformer-based models for the cyberbullying detection. This paper proposes two architectures to efficiently detect cyberbullying pattern. The proposed ensemble model makes use of CNN to extract the relevant features and the classification is performed by the SVM. Another proposed architecture utilizes the pre-trained model BERT to detect cyberbullying behavior on online platforms. Both the proposed models were tested on two separate datasets and achieved maximum accuracy of 96.88 and 97.34% for ensemble and BERT models, respectively. This paper provides a thorough examination of the various methodologies used for cyberbullying detection and conducts an empirical and comparative analysis of the presented models with traditional and current algorithms. [ABSTRACT FROM AUTHOR]

This study was undertaken to compare ophthalmic artery blood flow in eyes having primary open angle glaucoma (POAG) with age matched non glaucomatous eyes using the technique of Color Doppler imaging in Indian patients. One hundred patients of either sex over 40 years of age were divided into two groups of 50 patients each. Group 1 included 50 patients diagnosed with POAG whereas group 2 included patients who did not have POAG. Tests like visual fields and OCT RNFL were conducted and a radiologist assessed the ophthalmic artery blood flow using Color Doppler imaging. Hemodynamic calculations of ocular blood flow were done using the parameters of resistivity index (RI) and pulsatility index (PI). Statistically significant increase in the values of RI and PI were noted in patients with POAG as compared to those who did not have POAG. [ABSTRACT FROM AUTHOR]