Homophobic/Transphobic (H/T) content includes hatred and discriminatory comments directed at Lesbian, Gay, Bisexual, Transgender, Queer (LGBTQ) individuals on social media platforms. As this unfavourable perception towards LGBTQ individuals may affect them physically and mentally, it is necessary to detect H/T content on social media. This demands automated tools to identify and address H/T content. In view of this, in this paper, we - team MUCS describe the learning models submitted to “Homophobia/Transphobia Detection in social media comments:LT-EDI@EACL 2024” shared task at European Chapter of the Association for Computational Linguistics (EACL) 2024. The learning models: i) Homo_Ensemble - an ensemble of Machine Learning (ML) algorithms trained with Term Frequency-Inverse Document Frequency (TFIDF) of syllable n-grams in the range (1, 3), ii) Homo_TL - a model based on Transfer Learning (TL) approach with Bidirectional Encoder Representations from Transformers (BERT) models, iii) Homo_probfuse - an ensemble of ML classifiers with soft voting trained using sentence embeddings (except for Hindi), and iv) Homo_FSL - Few-Shot Learning (FSL) models using Sentence Transformer (ST) (only for Tulu), are proposed to detect H/T content in the given languages. Among the models submitted to the shared task, the models that performed better for each language include: i) Homo_Ensemble model obtained macro F1 score of 0.95 securing 4th rank for Telugu language, ii) Homo_TL model obtained macro F1 scores of 0.49, 0.53, 0.45, 0.94, and 0.95 securing 2nd, 2nd, 1st, 1st, and 4th ranks for English, Marathi, Hindi, Kannada, and Gujarathi languages, respectively, iii) Homo_probfuse model obtained macro F1 scores of 0.86, 0.87, and 0.53 securing 2nd, 6th, and 2nd ranks for Tamil, Malayalam, and Spanish languages respectively, and iv) Homo_FSL model obtained a macro F1 score of 0.62 securing 2nd rank for Tulu dataset.