In the era of evolving artificial intelligence, machines are increasingly emulating human-like capabilities, including visual perception and linguistic expression. Image captioning stands at the intersection of these domains, enabling machines to interpret visual content and generate descriptive text. This paper provides a thorough review of image captioning techniques, catering to individuals entering the field of machine learning who seek a comprehensive understanding of available options, from foundational methods to state-of-the-art approaches. Beginning with an exploration of primitive architectures, the review traces the evolution of image captioning models to the latest cutting-edge solutions. By dissecting the components of these architectures, readers gain insights into the underlying mechanisms and can select suitable approaches tailored to specific problem requirements without duplicating efforts. The paper also delves into the application of image captioning in the medical domain, illuminating its significance in various real-world scenarios. Furthermore, the review offers guidance on evaluating the performance of image captioning systems, highlighting key metrics for assessment. By synthesizing theoretical concepts with practical application, this paper equips readers with the knowledge needed to navigate the complex landscape of image captioning and harness its potential for diverse applications in machine learning and beyond.
@misc{singh2024pixelsproseunderstandingart,title={Pixels to Prose: Understanding the art of Image Captioning},author={Singh, Hrishikesh},year={2024},eprint={2408.15714},archiveprefix={arXiv},primaryclass={cs.CV},dimensions={true},google_scholar_id={u5HHmVD_uO8C},doi={https://doi.org/10.48550/arXiv.2408.15714},url={https://arxiv.org/abs/2408.15714},}
RL
Financial Context-Aware Multiagent-Based Reinforcement Learning Approach for Indian Stock Portfolio Optimization
Crypto currencies are considered as the next model of economics and monetary exchange. In recent years, popular cryptocurrency such as Bitcoin and Ethereum witness an exponential growth in economic sphere. In this paper empirical testing of four conventional machine learning methods is performed to predict the bitcoin prices using last eight years of transactional data. Linear and polynomial regression is implemented using all the features individually. Polynomial regression, Support Vector regression and KNN regression are hyper tuned with grid search logic. Results depicted that KNN regression outperformed others models.
@article{IC3,title={Empirical Analysis of Bitcoin Market Volatility Using Supervised Learning Approach},author={Singh, Hrishikesh},journal={International Conference on Contemporary Computing (IC3)},volume={47},issue={10},pages={1--5},numpages={5},year={2018},month=aug,publisher={IEEE},doi={10.1109/IC3.2018.8530636},url={https://ieeexplore.ieee.org/document/8530636},dimensions={true},google_scholar_id={u5HHmVD_uO8C},}
Financial Context-Aware Multiagent-Based Reinforcement Learning Approach for Indian Stock Portfolio Optimization2024
