User-generated multi-media content, such as images, text, videos, and speech, has recently become more popular on social media sites as a means for people to share their ideas and opinions. One of the most popular social media sites for providing public sentiment towards events that occurred during the COVID-19 period is Twitter. This is because Twitter posts are short and constantly being generated. This paper presents a deep learning approach for sentiment analysis of Twitter data related to COVID-19 reviews. The proposed algorithm is based on an LSTM-RNN-based network and enhanced featured weighting by attention layers. This algorithm uses an enhanced feature transformation framework via the attention mechanism. A total of four class labels (sad, joy, fear, and anger) from publicly available Twitter data posted in the Kaggle database were used in this study. Based on the use of attention layers with the existing LSTM-RNN approach, the proposed deep learning approach significantly improved the performance metrics, with an increase of 20% in accuracy and 10% to 12% in precision but only 12–13% in recall as compared with the current approaches. Out of a total of 179,108 COVID-19-related tweets, tweets with positive, neutral, and negative sentiments were found to account for 45%, 30%, and 25%, respectively. This shows that the proposed deep learning approach is efficient and practical and can be easily implemented for sentiment classification of COVID-19 reviews.

Identity and Access Management proposes a web service that assists in controlling the entire work through secured ways. This research study has been started to highlight the importance of IAM by discussing its roles, characteristics, advantages and disadvantages. It is a framework comprising processes, policies, and the latest technologies, allowing the organization to monitor digital identities and control exclusive access to follow information based on user data. The IAM component proposes an approach of centralized user management, account management console, authentication approaches, and so on. In this research work, roles and key components of IAM have been discussed with all types of possible challenges. Furthermore, this research will help readers and future researchers easily identify the importance of IAM in maintaining security systems within organizations.

For the past few years, the market has changed a lot and it has become dynamic and demanding which has put the market into a competitive environment. The supply chain plays a crucial role to adapt the business to the dynamic environment as it is very reliant on collaboration integration as well as flexibility. The applications related to the supply chain have gotten the attention of many business owners and to improve the flow control of the supply chain many specialized applications are implemented. One of the most important new technological applications in the supply chain is blockchain technology which has garnered the attention of many business owners as it can be quickly adapted to dynamic market conditions and in the business environment. One upon reading this will get to know about the effect of blockchain technology utilization on this field. The results of the research paper recommend that companies invest in blockchain technology so that the supply chain becomes more transparent, flexible, and secure. There is no doubt in the fact that blockchain technology plays an important role in developing trust with the stakeholder of the supply chain. In the end, the research paper has also given some considerations on the implications that are positive as well as the potential of the blockchain in the field of collaboration and integration.

Cotton is one of the most valuable non-food agricultural products in the world. However, cotton production is often hampered by the invasion of disease. In most cases, these plant diseases are a result of insect or pest infestations, which can have a significant impact on production if not addressed promptly. It is, therefore, crucial to accurately identify leaf diseases in cotton plants to prevent any negative effects on yield. This paper presents a hybrid deep learning approach based on Bidirectional Encoder Representations from Transformers with Residual network and particle swarm optimization (BERT-ResNet-PSO) for detecting cotton plant diseases. This approach starts with image pre-processing, which they pass to a BERT-like encoder after linearly embedding the image patches. It results in segregating disease regions. Then, the output of the encoded feature is passed to ResNet-based architecture for feature extraction and further optimized by PSO to increase the classification accuracy. The approach is tested on a cotton dataset from the Plant Village dataset, where the experimental results show the effectiveness of this hybrid deep learning approach, achieving an accuracy of 98.5%, precision of 98.2% and recall of 98.7% compared to the existing deep learning approaches such as ResNet50, VGG19, InceptionV3, and ResNet152V2. This study shows that the hybrid deep learning approach is capable of dealing with the cotton plant disease detection problem effectively. This study suggests that the proposed approach is beneficial to help avoid crop losses on a large scale and support effective farming management practices.

This paper presents the development of a deep learning approach for human face sentiment classification. Bidirectional Long-Short Term Memory (Bi-LSTM) and recurrent neural networks (RNNs) are used for object-based segmentation in images, and CNN-RNN model is adopted for non-linear mapping. To test the applicability of this proposed approach, we have trained several deep neural networks to recognize facial expressions in 10,000 images. Our experiments show that the proposed approach can achieve an accuracy of 99.12% in classifying human face sentiment. Moreover, results indicate that the model not only boosts the classification model but also lessen the overhead.