Krishna Kumar Mohbey

Modern search engines should be able to retrieve images effectively, but traditional techniques mostly rely on manually generated metadata and textual annotations, which are prone to inconsistency and incompleteness. These constraints lower the retrieval accuracy and scalability. This paper presents an image captioning system based on deep learning to produce descriptive, contextually rich captions that facilitate better human image indexing and retrieval. The semantically meaningful captions generated by the proposed system reflect the content and context of a given image. User interaction is provided through a Flask-based web interface, and MongoDB is an extensible, scalable database for storing images and their generated captions. Evaluation of the system was conducted on benchmark image captioning datasets and other image collections to assess its real-world performance. The comparative experiment was conducted against traditional metadata-based retrieval methods using the standard measurement metrics of precision, recall, and mean reciprocal rank. Findings indicate that the caption-based system is superior to traditional techniques in delivering more relevant search results and achieving a higher retrieval accuracy, especially when manual annotations are low or absent. The study shows that deep learning-based image captioning is a viable and scalable alternative to the conventional indexing methods. The system can be used to improve performance in applications such as digital libraries, multimedia archives, and content-intensive web platforms by incorporating semantic understanding directly into the retrieval pipeline, thereby reducing manual input and improving the relevance of retrieved material.

The rapid emergence of resistance in cancer chemotherapy is a major challenge in the drug discovery of cancer, restricting the action of various important classes of inhibitors against EGFR, VEGF, BRAF, alkylating agents, and DNA damaging agents. c-MET plays an important role in the development of resistance to cancer. Identifying a potent c-MET inhibitor can improve therapeutic access to existing anti-cancer agents. In the current study, we propose a novel technique for the prediction of drug activity class by using class balancing and ML classifiers. This study utilizes 3091 molecules with c-MET inhibitory concentration value (IC₅₀) publicly available from the ChEMBL Database. Using 14 descriptors and random oversampling for class balancing, we investigated seven classical ML models, i.e., decision tree (DT), Adaboost decision tree (ABDT), K-nearest neighbors (K-NN), support vector machine (SVM), Bernoulli Naïve Bayes (BNB), random forest (RF), and linear logistic regression (LLR) for activity prediction against c-MET. Of which SVM, LR, and RF were the top three models providing high predictability after applying balancing techniques and performing rigorous.hyperparameter tuning. Even though SVM, LR, and RF showed exemplary performance in terms of ROC-AUC and recall metrics, their validation on seven FDA-approved drugs demonstrated their susceptibility to high false negatives. Owing to this, we developed a consensus mechanism based on these three models. The consensus mechanism can work on a large, diverse database and screen potential inhibitors, prioritizing which molecule to be considered further for experimental testing. The consensus model proved beneficial as a drug design algorithm for c-MET inhibitor drug discovery and development.