Manmohan Singh

Today’s smart farming has ushered in a new era of agriculture, one where information and communication technologies seamlessly integrate with the latest equipment in machinery and sensors based in high-tech farming systems. This transformative shift is driven by the latest technologies notably the IoT and cloud computing which are poised to revolutionize farming practices by introducing robots using artificial intelligence into the agricultural landscape. While these advancements hold immense promise, they also bring forth a set of formidable challenges. To investigate the profound impact, of innovative farming underpinned by the IOT on advancing sustainable agriculture. Through an in-depth exploration of empirical evidence and scholarly research, it rigorously examines how IoT technologies optimize resource allocation, foster precision farming practices effectively address environmental concerns in agriculture. That empowers farmers to optimize their practices, improve crop yields navigate the complex interplay of technology and tradition in modern farming.

Bayesian ranking-based drug-target relationship prediction has achieved good results, but it ignores the relationship between drugs of the same target. A new method is proposed for drug-target relationship prediction based on groups by Appling Bayesian. According to the reality that drugs interacting with a specific target have similarities, a grouping strategy was introduced to make these similar drugs interact. A theoretical model based on the grouping strategy is derived in this study. The method is compared with five typical methods on five publicly available datasets and produces superior results to the compared methods. The impact of grouping interaction on the Bayesian ranking approach is examined in this study to create a grouped medication set; comparable pharmaceuticals that interact with the same target are first grouped based on this reality. Then, based on the grouped drug set, new hypotheses were put forth and the conceptual approach of grouped Bayesian ranking was constructed. Finally, to predict novel medications and targets, the article also includes neighbor information. The associated studies demonstrate that the strategy presented in this study outperforms the conventional performance techniques. Plans for further performance improvement through the creation of new comparable grouping objectives are included in future work.

COVID-19 has greatly disturbed life in many ways and has changed the way we live. Various surveys have been conducted in different fields, and the teaching-learning process has been affected to a great extent. During this pandemic, various online tools and technologies have been available for guiding students without attending school. Many governments, corporations, and research fields have officially ordered to use of online media for the teaching-learning process. Platforms such as Google Meet, Microsoft Team, and Web-e-X have allowed and arranged for online video conferencing mediums to achieve the goal of the teaching-learning process. However, as mentioned above, there are some serious issues with the online teaching-learning process. These include problems with continuous network bandwidth during sessions, physical and mental presence in the class, difficulties handling mathematics classes, and the potential for non-sense activities that may disturb the entire class. In order to discover knowledge, I am using a new approach to data mining technology called CRISP-DM. This study addresses the effectiveness of online teaching mode and learning and the challenges faced by students and teachers who are taking online classes during COVID-19. According to this study, 88.2% of students did not have proper internet or technology facilities, 58.30% of students were not satisfied with online learning, 85.3% of students complained about eyesight issues from taking online classes on devices, and 50.01% of students were unable to manage university affairs.

Worldwide, organizations are migrating their IT infrastructure to public clouds. One of the biggest challenges during this transition is the implementation of appropriate security techniques to withstand cyber attacks. Unfortunately, this process is still ambiguous for many organizations and data are exposed to different threats and vulnerabilities during this migration.To address this, there are several approaches applied worldwide to strengthen security of the stored data on cloud computing. The two-fold DNA Cryptography is one such data security techniques.The existing technique focuses only on the ASCII character set, ignoring the non-English user of the cloud computing. Thus, proposing two-fold DNA Cryptography using the Unicode characters so that it can be used for non-English users also.

A new algorithm for practical Byzantine fault tolerance (PBFT), called score-PBFT or S-PBFT, is proposed to solve the problems of high communication overhead and low algorithm efficiency. This algorithm is based on the characteristics of the consortium chain. The scoring mechanism for nodes is added. All the nodes are broken up into consensus nodes, candidate nodes, and early nodes. To make sure the consensus nodes are as reliable as possible, the nodes are changed dynamically based on how each node is behaving. Improved: the election method for the controller node has been changed. The node’s score and behaviour are used as the election basis to make the algorithm more stable. In this paper, we want to improve the consensus protocol’s execution process, cut down on how many nodes are involved in the consensus process, simplify it, and make it more efficient. Results show that, when compared with the PBFT algorithm, the S-PBFT algorithm has a shorter consensus delay, less communication overhead and throughput, and better consensus node reliability.