A Comparison of Pre-Trained Models for Pneumonia Disease Prediction Using Chest Images

Aim: As viral diseases like Corona spread from one person to another, it has great impact on the public health system and socio-economic activities all over the world. Material and method: The only way to solve the spreading of this disease is early diagnosis of this disease. Statistics and Result: Deep learning algorithms were utilized in this study for comparative analysis of pre-trained models such as VGG16, MobileNetV2 for the detection of pneumonia using different hyper parameters such as batch-size, learning rate, epochs and so on. The proposed models that are MobileNetV2 and VGG16 attains better performance

Toxic Comment Classification using Deep Learning

Online Conversation media serves as a means for individuals to engage, cooperate, and exchange ideas; however, it is also considered a platform that facilitates the spread of hateful and offensive comments, which could significantly impact one's emotional and mental health. The rapid growth of online communication makes it impractical to manually identify and filter out hateful tweets. Consequently, there is a pressing need for a method or strategy to eliminate toxic and abusive comments and ensure the safety and cleanliness of social media platforms. Utilizing LSTM, Character-level CNN, Word-level CNN, and Hybrid model (LSTM + CNN) in this toxicity analysis is to classify comments and identify the different types of toxic classes by means of a comparative analysis of various models. The neural network models utilized for this analysis take in comments extracted from online platforms, including both toxic and non-toxic comments. The results of this study can contribute towards the development of a web interface that enables the identification of toxic and hateful comments within a given sentence or phrase, and categorizes them into their respective toxicity classes