Pooja Jain

We have witnessed a big surge in the use of online social media platforms in the past few years with the widespread availability of mobile phones and cheaper data rates. This has also led to an increase in misuse of these platforms to spread hatred in the society. The stats show that most of the cyberbullying happens on social media platforms like Facebook, WhatsApp, etc. The Hatred speech was used for spreading hatred against specific persons, communities, etc. It is necessary to check for the hate speech on social media platforms before it can lead to damaging the peace situation. Automating the task of hate speech detection is a very important step in mitigating this issue as such contents can be quickly acted upon before it spreads hatred. In this paper, we are proposing an Automated Hate speech identification system. The datasets are obtained from HASOC which stands for Hate Speech and Offensive Content Identification in Indo-European Languages, for sub-task b to classify into one of the four categories, Hate, Profane, Offensive, or None for English and Hindi languages. The experiments are carried out to compare the performance of Bert-based architecture with conventional deep learning architectures including CNN (Convolution Neural Network) architecture for English subtask and for Hindi Subtask we fine-tuned the Bert model which gave better results than conventional solutions. We obtained 75.41% accuracy with BERT-based fine tuning better than baseline methods LSTM (Long Short-Term Memory) with 41% accuracy for Hindi dataset and for English dataset, we achieved an accuracy of 82.92% compared to 81.57% with baseline models.

This research paper presents a hand gesture recognition mobile application designed to assist specially abled children. The application aims to help children with limited mobility to interact with mobile devices using hand gestures as an alternative to touch or keyboard input. The application uses machine learning algorithms to recognize different hand gestures and translate them into specific actions or commands. The research study involved the development and testing of the application with a group of children with various physical limitations. We aim to assist children facing difficulties in their daily lives due to conditions like cerebral palsy and Down syndrome. The results show that the application has the potential to improve the interaction experience for children with physical limitations and enhance their ability to communicate with the world around them. The research paper discusses the application's design, development process, and testing methodology, and concludes with recommendations for future improvements and possible extensions to the application's functionality.

. Epochs present in the voiced speech are defined as time instants of significant excitation of the vocal tract system during the production of speech. Nonstationary nature of excitation source and vocal tract system makes accurate identification of epochs a difficult task. Most of the existing methods for epoch detection require prior knowledge of voiced regions and a rough estimation of pitch frequency. In this paper, we propose a novel method that relies on time-order representation based on short-time Fourier–Bessel series expansion which can be employed on entire speech signal to detect epochs without any prior information. The proposed method automatically detects voiced regions in the speech signal by computing the marginal energy density with respect to time in the low frequency range from the energy distribution in the time-frequency plane. An estimate of pitch frequency for each detected voiced region is then obtained by computing the marginal energy density with respect to frequency in the LFR from the energy distribution in the time-frequency plane. Epochs are located for each detected voiced region as peaks in the derivative of the low pass filtered signal corresponding to falling edges of peak negative cycles in the LPF signal synthesized from TOR coefficients corresponding to LFR. Experimental results obtained by the proposed method on speech signals taken from the CMU-Arctic database are found to be promising. The proposed method detects epochs with high accuracy and reliability.