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Depression is one of the most prominent mental health issues, characterized by a depressed low mood and an absence of enthusiasm in activities. In terms of early detection, accurate diagnosis, and effective treatment, doctors face a serious challenge from depression, which is a serious global health issue. For patients with this mental disease to receive prompt medical attention and improve their general well-being, early identification is essential. For the purpose of detecting various psychological illnesses including depression, anxiety, and post-traumatic stress disorder, medical audio consultations along with survey responses have been used. A depressed individual displays a range of subtle signs that may be more easily identified by combining the results of multiple modalities. Multimodality involves extracting maximum information from data by using multiple modes, so that the deep learning model can be trained efficiently to give better results. Given that each modality functions differently, combining various modalities is not easy, and each origin of a modality takes on a different form. It is clear from the literature that is currently significant in the area that, combining the modalities yields positive outcomes. A trustworthy approach to identify depression is thus urgently needed because it continues to be a problem for many individuals in today’s society. In this work, textual and audio features are incorporated related to the identification of depression, and a novel multimodal approach using an optimized Bi-directional Long Short -Term Memory model that recognizes premature depression is suggested for medical intervention before it develops further.

The ability of music to spread joy and excitement across lives, makes it widely acknowledged as the human race's universal language. The phrase "music genre" is frequently used to group several musical styles together as following a shared custom or set of guidelines. According to their unique preferences, people now make playlists based on particular musical genres. Due to the determination and extraction of appropriate audio elements, music genre identification is regarded as a challenging task. Music information retrieval, which extracts meaningful information from music, is one of several real - world applications of machine learning. The objective of this paper is to efficiently categorise songs into various genres based on their attributes using various machine learning approaches. To enhance the outcomes, appropriate feature engineering and data pre-processing techniques have been performed. Finally, using suitable performance assessment measures, the output from each model has been compared. Compared to other machine learning algorithms, Random Forest along with efficient feature selection and hyperparameter tuning has produced better results in classifying music genres.

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