Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

The recent developments in Multimedia Internet of Things (MIoT) devices, empowered with Natural Language Processing (NLP) model, seem to be a promising future smart devices. It plays an important role industrial models such as speech understanding, emotion detection, home automation, and so on. If image needs captioned, then the objects that image, its actions connections, any silent feature remains under-projected or missing from images should identified. aim captioning process is generate caption for image. In next step, provided one most significant detailed descriptions syntactically well semantically correct. this scenario, computer vision model used identify NLP approaches are followed describe current study develops Optimal Deep Learning Enabled Intelligent Image Captioning System (NLPODL-IICS). presented NLPODL-IICS produce proper description input To attain this, proposed follows two stages encoding decoding processes. Initially, at side, makes use Hunger Games Search (HGS) Neural Architecture Network (NASNet) model. This represents data appropriately by inserting it into predefined length vector. Besides, during phase, Chimp Optimization Algorithm (COA) deeper Long Short Term Memory (LSTM) approach concatenate sentences produced method. application HGS COA algorithms helps accomplishing parameter tuning NASNet LSTM respectively. was experimentally validated help benchmark datasets. A widespread comparative analysis confirmed superior performance over other models.