Human Reactions to Incorrect Answers from Robots

As robots grow more and more integrated into numerous industries, it is critical to comprehend how humans respond to their failures. This paper systematically studies how trust dynamics and system design are affected by human responses to robot failures. The three-stage survey used in the study provides a thorough understanding of human-robot interactions. While the second stage concentrates on interaction details, such as robot precision and error acknowledgment, the first stage collects demographic data and initial levels of trust. In the last phase, participants' perceptions are examined after the encounter, and trust dynamics, forgiveness, and propensity to suggest robotic technologies are evaluated. Results show that participants' trust in robotic technologies increased significantly when robots acknowledged their errors or limitations to participants and their willingness to suggest robots for activities in the future points to a favorable change in perception, emphasizing the role that direct engagement has in influencing trust dynamics. By providing useful advice for creating more sympathetic, responsive, and reliable robotic systems, the study advances the science of human-robot interaction and promotes a wider adoption of robotic technologies.

Local Rose Breeds Detection System Using Transfer Learning Techniques

Flower breed detection and giving details of that breed with the suggestion of cultivation processes and the way of taking care is important for flower cultivation, breed invention, and the flower business. Among all the local flowers in Bangladesh, the rose is one of the most popular and demanded flowers. Roses are the most desirable flower not only in Bangladesh but also throughout the world. Roses can be used for many other purposes apart from decoration. As roses have a great demand in the flower business so rose breed detection will be very essential. However, there is no remarkable work for breed detection of a particular flower unlike the classification of different flowers. In this research, we have proposed a model to detect rose breeds from images using transfer learning techniques. For such work in flowers, resources are not enough in image processing and classification, so we needed a large dataset of the massive number of images to train our model. we have used 1939 raw images of five different breeds and we have generated 9306 images for the training dataset and 388 images for the testing dataset to validate the model using augmentation. We have applied four transfer learning models in this research, which are Inception V3, ResNet50, Xception, and VGG16. Among these four models, VGG16 achieved the highest accuracy of 99%, which is an excellent outcome. Breed detection of a rose by using transfer learning methods is the first work on breed detection of a particular flower that is publicly available according to the study.

Carrot Cure: A CNN based Application to Detect Carrot Disease

Carrot is a famous nutritional vegetable and developed all over the world. Different diseases of Carrot has become a massive issue in the carrot production circle which leads to a tremendous effect on the economic growth in the agricultural sector. An automatic carrot disease detection system can help to identify malicious carrots and can provide a guide to cure carrot disease in an earlier stage, resulting in a less economical loss in the carrot production system. The proposed research study has developed a web application Carrot Cure based on Convolutional Neural Network (CNN), which can identify a defective carrot and provide a proper curative solution. Images of carrots affected by cavity spot and leaf bright as well as healthy images were collected. Further, this research work has employed Convolutional Neural Network to include birth neural purposes and a Fully Convolutional Neural Network model (FCNN) for infection order. Different avenues regarding different convolutional models with colorful layers are explored and the proposed Convolutional model has achieved the perfection of 99.8%, which will be useful for the drovers to distinguish carrot illness and boost their advantage.