Zero-shot Learning is a machine learning technique that allows categorizing unseen objects without labeled instances. This work proposes a new approach of zero-shot learning for 3D object classification using common sense knowledge graphs. The proposed approach utilizes graph convolutional networks to build class representations and captures the knowledge within the graph. A generalized zero- shot learning framework is introduced that combines the embedding and feature generation-based model. The method includes a contrastive embedding that enables the use of instance-wise supervision to improve generalized zero-shot learning performance. The use of explicit knowledge or knowledge graphs provides an unexplored and alternative paradigm for zero-shot learning in 3D object classification. The algorithm utilizes self-supervised contrastive learning to enforce image-text correspondence and relational reasoning to extract knowledge from external sources. The effectiveness of the proposed approach has been demonstrated through experiments conducted on four state-of-the-art on all 3D datasets for both inductive zero-shot learning (ZSL) and inductive generalized zero-shot learning (GZSL). We have also proposed strong baselines for this task, which were outperformed by our method. The introduction of the knowledge graph and contrastive module have been shown to be effective in enhancing the performance of our approach.

During the COVID-19 pandemic, we realized the importance and necessity of automation in hospitals and healthcare facilities. Robotics has already established itself as anecessity in the medical field, ranging from automatic diagnostic systems to assisting nurses in health care facilities, thus reducing the tedious strain of physicians or nurses and increasing diagnostic accuracy. Our project utilizes the state-of-the-art advancements in vision-based action recognition, human robot interaction, artificial intelligence, and deep learning to build an autonomous, feature-rich hospital and clinic aid robot. The proposed Intelligent Hospital Assistance Robot (IHABOT) is equipped with a number of autonomous features. First of all, it can map its surroundings, determine the best route to take to its destination, and hence navigate by itself in a real-world environment. Second, it has a variety of sensors to collect physiological data from the patient, includ- ing temperature, systolic and diastolic blood pressure, oxygen saturation, and pulse rate. IHABOT uses artificial intelligence (AI) to automatically evaluate these physiological measures and detect patients who are deteriorating early, allowing for prompt treatment and a reduction in significant adverse events. Thirdly, in the absence of a doctor, this medical robot can keep track of and assess patients’ performance on exercises through- out post-stroke therapy. The robot delivers a performance score that aids in both patient self-evaluation of performance as well as medical professionals’ evaluation of patient development and prescription of required actions. Last but not the least, IHABOT diagnoses COVID-19 from radiography images and CT scans using a novel few-shot learning-based method. Often, the conventional diagnostic techniques with high accu- racy have the setback of being expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. Therefore, medical robots like IHABOT, which do not require direct human intervention, can be used in hospitals for automated diagnosis and to lessen the likelihood of infection spreading through reduced human-to-human contact.

In today’s digital world, automated sentiment analysis from online reviews can contribute to a wide variety of decision-making processes. One example is examining typical perceptions of a product based on customer feedbacks to have a better understanding of consumer expectations, which can help enhance everything from customer service to product offerings. Online review comments, on the other hand, frequently mix different languages, use non-native scripts and do not adhere to strict grammar norms. For a low-resource language like Bangla, the lack of annotated code-mixed data makes automated sentiment analysis more challenging. To address this, we collect online reviews of different products and construct an annotated Bangla-English code mix (BE-CM) dataset. On our sentiment corpus, we also compare several alternative models from the existing literature. We present a simple but effective data augmentation method that can be utilized with existing word embedding algorithms without the need for a parallel corpus to improve cross-lingual contextual understanding. Our experimental results suggest that training word embedding models (e.g., Word2vec, FastText) with our data augmentation strategy can help the model in capturing the cross-lingual relationship for code-mixed sentences, thereby improving the overall performance of existing classifiers in both supervised learning and zero-shot cross-lingual adaptability. With extensive experimentations, we found that XGBoost with Fasttext embedding trained on our proposed data augmentation method outperforms other alternative models in automated sentiment analysis on code-mixed Bangla-English dataset, with a weighted F1 score of 87%. This project is a collaboration with Centre for Advanced Research in Strategic Human Resource Management[CARSHRM], University of Dhaka.

Health professionals often prescribe patients to perform specific exercises for re- habilitation of several diseases (e.g., stroke, Parkinson, backpain). When patients perform those exercises in the absence of an expert (e.g., physicians/therapists), they cannot assess the correctness of the performance. Automatic assessment of physical rehabilitation exercises aims to assign a quality score given a RGBD or RGB video of the body movement as input. Recent deep learning approaches address this problem by extracting CNN features from co-ordinate grids of skele- ton data (body-joints) obtained from videos. However, they could not extract rich spatio-temporal features from variable-length inputs. To address this issue, we investigate Graph Convolutional Networks (GCNs) for this task. We adapt spatio-temporal GCN to predict continuous scores(assessment) instead of discrete class labels. Our model can process variable-length inputs so that users can per- form any number of repetitions of the prescribed exercise. Moreover, our novel design also provides self-attention of body-joints, indicating their role in predict- ing assessment scores. It guides the user to achieve a better score in future trials by matching the same attention weights of expert users. Our model successfully outperforms existing exercise assessment methods on KIMORE and UI-PRMD datasets.

This project focuses on the development and evaluation of an Artificial Intelligence (AI) agent for optimized stock trading. Utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm and other relevant techniques, the agent's primary objective is to formulate an optimal policy that maximizes profits from its actions and corresponding positions in the stock market. To validate the agent's effectiveness and versatility, comprehensive testing has been conducted using datasets from two distinct stock markets: the S&P 500 and the Dhaka Stock Exchange (DSE). The results of this research endeavor promise to offer valuable insights into AI-driven stock trading strategies applicable across diverse financial markets. This project is a collaboration with Centre for Advanced Research in Strategic Human Resource Management[CARSHRM], University of Dhaka.