An Image caption generator system implies the detection of the image as well as producing the caption with natural language processing by the computer. This is a tedious job. Image caption generator systems can solve various problems, such as self-driving cars, aiding the blind, etc.

The recent research at the Department of Computer Science and Engineering proposes a model to generate the captions for an image using ResNet and Long Short-Term Memory. Assistant Professors Dr Morampudi Mahesh Kumar and Dr V Dinesh Reddy have published the paper Image Description Generator using Residual Neural Network and Long-Short-Term Memory in the Computer Science Journal of Moldova with an impact factor of 0.43.

The captions or descriptions for an image are generated from an inverse dictionary formed during the model’s training. Automatic image description generation is helpful in various fields like picture cataloguing, blind persons, social media, and various natural language processing applications.

Despite the numerous enhancements in image description generators, there is always a scope for development. Taking advantage of the larger unsupervised data or weakly supervised methods is a challenge to explore in this area, and this is already there among the future plan of the researchers. Another major challenge could be generating summaries or descriptions for short videos. This research work can also be extended to other sets of natural languages apart from English.

Abstract

Human beings can describe scenarios and objects in a picture through vision easily, whereas performing the same task with a computer is a complicated one. Generating captions for the objects of an image helps everyone to understand the scenario of the image in a better way. Instinctively describing the content of an image requires the apprehension of computer vision as well as natural language processing. This task has gained huge popularity in the field of technology, and there is a lot of research work being carried out. Recent works have been successful in identifying objects in the image but are facing many challenges in generating captions to the given image accurately by understanding the scenario. To address this challenge, we propose a model to generate the caption for an image. Residual Neural Network (ResNet) is used to extract the features from an image. These features are converted into a vector of size 2048. The caption generation for the image is obtained with Long Short-Term Memory (LSTM). The proposed model was experimented with on the Flickr8K dataset and obtained an accuracy of 88.4%. The experimental results indicate that our model produces appropriate captions compared to the state of art models.

Intelligent transport systems embedded with latest technology is the future of automotive industry and can revolutionise the very infrastructure and architecture of transportation. The principles of intelligent transportation systems have manifested in the development of Internet of vehicles (IoV), a novel technological network that enables autonomous and connected mobile commutation facility. Dr Sriramulu Bojjagani, Assistant Professor, Department of Computer Science and Engineering, has published a paper on this seminal topic titled “Secure Authentication and Key Management Protocol for Deployment of Internet of Vehicles (IoV) Concerning Intelligent Transport Systems” in the prestigious journal, IEEE Transactions on Intelligent Transport Systems (Q1 journal) having an Impact factor of 9.551. The paper highlights the development and significance of the pioneering AKAP-IoV system in successfully tackling cyber threats and security vulnerabilities.

Abstract

Intelligent transport systems amalgamated with advanced technologies are an important element of the automotive industry, including critical infrastructure and transportation. Internet of Vehicles (IoV) is the modern technological framework designed for intelligent transportation. IoV creates a network of information relations among vehicles, thus contributing to reduced congestion, roadside infrastructure, driver/traveller safety, and traffic efficiency through wireless communication and sensing technology. However, a significant challenge in IoV applications is security, as criminals could potentially exploit these applications. Despite increasing industry awareness, the potential danger posed by security vulnerabilities and cyber threats remains high. In this study, we have designed a new system called AKAP-IoV, which supports secure communication, mutual authentication, and key management among vehicles, roadside units, and fog and cloud servers. AKAP-IoV was tested and verified using Scyther and Tamarin to ensure its resistance to cyber threats. Furthermore, we conducted a formal security analysis using the Real-or-Random (RoR) oracle model to assess security properties logically. In addition, a detailed, comprehensive comparative study was considered to evaluate the performance, functionality, efficiency, and security features supported by AKAP-IoV compared to those of recently developed schemes.

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