Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented.

This experiment was conducted to observe the postharvest behaviors of litchi for using low temperature (4 °C) and polypropylene (PP) bags of different thickness. The two-factor experiment was conducted in a completely randomized design with three replications. Total 8 treatments are implemented in this study. The experiment consisted of two factors: Factor A: Temperature (T1: Ambient temperature, T2: 4ºC temperature) and Factor B: Polypropylene bag (P1: unwrapped, P2: 50µ PP bag, P3: 75µ PP bag, P4: 100µ PP bag. In case of low temperature (4 °C), litchi retained its 50% color at 10th days of storage, highest shelf life (21.33 days), highest moister content (83.3%) & highest amount of vitamin C (35.61 mg/100g) were found at 3rd day of storage. In case of different thickness of polypropylene bags, litchi kept in 75µ PP bags retained its 100% color up to 4th day of storage, shelf life (16.34 days), maximum moister content (83.14%) & maximum amount of vitamin C (35.78 mg/100g) were found at 3rd day of storage. It can be possible to save a large amount of litchi fruits every year from postharvest decay by using low temperature (4 °C) and 75µ PP bag.