Peer-reviewed journal on social and business aspects of informatics that covers the theory and application of enterprise information systems, as well as interactions between ICT and people.

Radiology report generation is a critical task for radiologists, and automating the process can significantly simplify their workload. However, creating accurate and reliable radiology reports requires radiologists to have sufficient experience and time to review medical images. Unfortunately, many radiology reports end with ambiguous conclusions, resulting in additional testing and diagnostic procedures for patients. To address this, we proposed an encoder-decoder-based deep learning framework that utilizes chest X-ray images to produce diagnostic radiology reports. In our study, we have introduced a novel text modelling and visual feature extraction strategy as part of our proposed encoder-decoder-based deep learning framework. Our approach aims to extract essential visual and textual information from chest X-ray images to generate more accurate and reliable radiology reports. Additionally, we have developed a dynamic web portal that accepts chest X-rays as input and generates a radiology report as output. We conducted an extensive analysis of our model and compared its performance with other state-of-the-art deep learning approaches. Our findings indicate significant improvement achieved by our proposed model compared to existing models, as evidenced by the higher BLEU scores (BLEU1 = 0.588, BLEU2 = 0.4325, BLEU3 = 0.4017, BLEU4 = 0.3860) attained on the Indiana University Dataset. These results underscore the potential of our deep learning framework to enhance the accuracy and reliability of radiology reports, leading to more efficient and effective medical treatment.

India is one of the fastest developing countries with significant advancement in the field of the railway network. There have been several incidents of railway accidents due to derailments caused by cracks in the railway track. These cracks generally go unnoticed due to irregular maintenance and manual track monitoring. In this work, we have proposed an solar powered autonomous vehicle called “Rail-Rakshak” for inspecting and detecting cracks in the railway line. The proposed robot moves along the path and detects the cracks. The exact location of the cracks is sent to the concerned authorities. The proposed robot leverages the concepts of Cloud Computing and Natural Language Processing (NLP) for better visualization and interaction with the robot.

With the increasing population, there is a growing need for low-cost waste management solutions. By leveraging the benefits of modern technological tools, smarter solutions for this basic problem can be provided. In this paper, a Smart Waste segregation and garbage level monitoring System is proposed, which can be remotely monitored and which is built at a very low cost. The design of the proposed system considers portability and ease of assembly as essential factors during implementation. The paper provides required details to build such a system and thus promotes a Do-It-Yourself spirit. The discussed results show the implemented system and its interaction with the user using mobile as well as a web application.