Search

In recent times, neural networks and deep learning have made significant contributions in various research domains. In the present work, we report automatic caption generation of an image using these techniques. Automatic image caption generation is an artificial intelligence problem that receives attention from both computer vision and natural language processing researchers. Most of the caption generation tasks exist in the English language and no work has been reported yet in Assamese to the best of our knowledge. Assamese is an Indo-European language spoken by 14 million speakers in the North-East region of India. This paper reports the image caption generation on the Assamese news domain. A quality image captioning system requires an annotated training corpus. However, there is no such standard dataset available for this resource-constrained language. Therefore, we built a dataset of 13000 images collected from various online local Assamese e-newspapers. We employ two different architectures for generating the news image caption. The first model is based on CNN-LSTM and the second model is based on the attention mechanism. These models are evaluated both qualitatively and quantitatively. Qualitative analysis of the generated captions is carried out in terms of fluency and adequacy scores based on a standard rating scale. The quantitative result is evaluated using the BLEU and CIDEr evaluation metrics. We observe that the attention mechanism-based model outperforms the CNN-LSTM based model for our task. [ABSTRACT FROM AUTHOR]

The world's population has reached 8 billion and is projected to reach 9.7 billion by 2050, increasing the demand for food production. Artificial intelligence (AI) technologies that optimize resources and increase productivity are vital in an environment that has tensions in the supply chain and increasingly frequent weather events. This study performed a systemic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology on artificial intelligence technologies applied to agriculture. It retrieved 906 relevant studies from five electronic databases and selected 176 studies for bibliometric analysis. The quality appraisal step selected 17 studies for the analysis of the benefits, challenges, and trends of AI technologies used in agriculture. This work showed an evolution in the area with increased publications over the last five years, with more than 20 different AI techniques applied in the 176 studies analyzed, with machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision being the most used technologies. Considering a worldwide scope, the countries highlighted were India, China, and the USA. Agricultural sectors included crop management and prediction and disease and pest management. Finally, it presented challenges and trends that are promising when considering the future directions in AI for agriculture. [ABSTRACT FROM AUTHOR]

As everyone knows that in today's time Artificial Intelligence, Machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we are also seeing that the second wave of corona has wreaked havoc in India. More than 4 lakh cases are coming in 24 h. In the meantime, news came that a new deadly fungus has come, which doctors have named Mucormycosis (Black fungus). This fungus also spread rapidly in many states, due to which states have declared this disease as an epidemic. It has become very important to find a cure for this life-threatening fungus by taking the help of our today's devices and technology such as artificial intelligence, data learning. It was found that the CT-Scan has much more adequate information and delivers greater evaluation validity than the chest X-Ray. After that the steps of Image processing such as pre-processing, segmentation, all these were surveyed in which it was found that accuracy score for the deep features retrieved from the ResNet50 model and SVM classifier using the Linear kernel function was 94.7%, which was the highest of all the findings. Also studied about Deep Belief Network (DBN) that how easy it can be to diagnose a life-threatening infection like fungus. Then a survey explained how computer vision helped in the corona era, in the same way it would help in epidemics like Mucormycosis. [ABSTRACT FROM AUTHOR]