DeviPriya Rangasamy

Image captioning intends to automatically produce relevant and descriptive text for a specified image, integrating Natural Language Processing (NLP) and Computer Vision (CV) to understand visual content and express it in words. Existing image captioning methods suffer from difficulty in generating accurate and contextually rich captions, which results in captions that lack descriptive quality and alignment with visual content. The objective of this study is to develop an efficient image captioning framework capable of producing accurate and semantically rich captions from images. In this research, a hybrid Attention-reinforced transformer with contrastive learning, Serval-Frigatebird Optimization, Gaussian Error Linear Unit-Long-Short Term Memory (ArCO-SerFO-GLSTM)-based Generative Adversarial Image Captioning model is introduced for performing image captioning from a given dataset. The proposed model consists of the ArCO-SerFO generator, the Reinforcement Learning (RL Generator) and a discriminator. At first, in the ArCO-SerFO generator, the input image is passed through an image encoder to extract visual features and then fed to the caption decoder to generate a sample caption. The generated caption is compared with the ground-truth caption using contrastive loss, which improves the alignment between image features and the caption. In this case, the ArCO model is tuned exploiting Serval-Frigatebird Optimization (SerFO). The system then uses an RL generator, where an image encoder and a multi-attention mechanism guide a language decoder to generate refined captions. Then, the Reinforcement Learning (RL loss) loss updates the model based on the reward metrics. Finally, both generated captions and ground-truth captions are fed into a GELU-LSTM discriminator, which distinguishes real captions from generated captions. The GELU-LSTM is developed by incorporating a GELU into an LSTM. The developed ArCO-SerFO-GLSTM acquired Recall-Oriented Understudy for Gisting Evaluation-L (Rouge-L) of 60.19%, Mean Average Precision (mAP) of 80.13%, Bilingual Evaluation Understudy (BLEU) of 84.23%, Metric for Evaluation of Translation with Explicit Ordering (METEOR) of 31.99%, Semantic Propositional Image Caption Evaluation (SPICE) of 25.99% and Consensus-based Image Description Evaluation (CIDEr) of 123.3 with the Flickr Image dataset.

Background: Segmentation of medical images plays a key role in the correct identification and management of different diseases. In this study, we present a new segmentation method that meets the difficulties posed by sophisticated organ shapes in computed tomography (CT) images, particularly targeting lung, breast, and gastric cancers Method: Our suggested methods, Resio-Inception U-Net and Deep Cluster Recognition (RIUDCR), use a Residual Inception Architecture, which combines the power of residual connections and inception blocks to achieve cutting-edge segmentation performance while reducing the risk of overfitting. Result: We present mathematical equations and functions that describe the design, including the encoding and decoding steps within the UC-Net system. Furthermore, we provide strong testing results that show the effectiveness of our method. Through thorough testing on varied datasets, our method regularly beats current techniques, achieving amazing precision and stability in organ task segmentation. These results show the promise of our residual inception architecture in better medical picture analysis. Conclusion: In summary, our research not only shows a state-of-the-art segment methodology but also reinforces its usefulness through thorough testing. The inclusion of residual inception architecture in medical picture segmentation offers good possibilities for improving the identification and management of disease planning.

Tamil character recognition is an important field of research in pattern recognition and it is a technical challenge than other languages due to similarity and complexity of characters. Stone inscriptions reveal the details of lavishness, lifestyle, economic conditions, culture, and also of the managerial regulations followed by various rulers and dynasties particular to those regions. However, due to the long history of ancient stone inscription, natural erosion and lack of early protection measures, there are lot of noise in the existing ancient stone inscriptions, which create adverse effects on reading these stone inscriptions and their aesthetic appreciation. The research challenge in recognizing Tamil characters is mainly because of the characters with a number of holes, loops and curves. The number of letters in Tamil language is higher when compared to other languages. Even though there are various approaches provided by the researchers, challenges and issues still prevail in recognition of tamil text in stone inscriptions. In the existing systems, detection algorithms fail to produce desired accuracy and hence stone inscription recognition using transfer learning, a promising method is proposed in this paper. Lion Optimization Algorithm (LOA) is applied to optimize brightness and contrast and then stone inscription images are pre-processed for noise removal and then each character is separated by identifying contours. Characters are recognized using Transfer Learning (TL), a Deep Convolution Neural Network-based multi classification approach. The proposed hybrid model Self-Adaptive Lion Optimization Algorithm with Transfer Learning (SLOA-TL) when implemented in images of stone inscriptions achieves better accuracy and speed than other existing methods. It serves as an efficient design for recognition of tamil characters in stone inscriptions and preserving tamil traditional knowledge.

Background: Microarray gene expression datasets usually contain a large number of genes that complicate further operations like classification, clustering and other kinds of analysis. During the classification process, the identification of salient genes is a brainstorming task and needs a careful selection. Methods: The classification of multi-class datasets is more critical when compared with binary classification. When there are multiple class labels, chances are more likely that the datasets are imbalanced. Large variations can be seen in the number of samples belonging to each class, and hence the classification process may go biased with incorrect samples chosen for training. There is no sufficient research work available to address all these three scenarios together in microarray datasets. Results and Discussion: The paper fills this gap with the following contributions: i) Selects salient genes for classification using multiSURF algorithm ii) Identifies right instances from imbalanced datasets using Retained Tomek Link algorithm and iii) Performs gene selection for multi-class classification using Dynamic Length Particle Swarm Optimization (DPSO). Conclusion: The proposed method is implemented on multi-class imbalanced microarray datasets, and the final classification performance is seen to be encouraging and better than other compared methods.