Accurate Crop Yield Prediction (CYP) is pivotal for ensuring food security and optimizing agricultural practices. In the face of climate change and resource limitations, precise yield forecasts can help farmers make informed decisions, enhance sustainability, and effectively allocate resources.MethodsThis study affirms the superior efficacy of Ensemble Learning (EL) models in enhancing CYP accuracy and proposes a novel Blend Model that synergizes predictions from individual base learners (Random Forest, XGBoost, AdaBoost) with established ensemble techniques (Model Averaging, Stacking, Voting Regressor).ResultsUtilizing a comprehensive dataset encompassing temperature, rainfall, and pesticide usage, this approach is evaluated against established metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), R-squared (R²), and Explained Variance. The Blend Model, designed to combine the strengths of base models, achieved an exceptional R² of 0.9899, capturing nearly 99% of the variance in crop yields with minimal errors (MSE: 72,974,685.72, MAE: 3,274.39). While AdaBoost and Stacking models demonstrated effectiveness, the Blend Model outperformed them in precision. Gradient Boosting (R²: 0.8784) and Meta-AdaBoost (R²: 0.9861) showed promise but exhibited higher errors.ConclusionThis study, for the first time, investigates Explainable Artificial Intelligence (XAI) techniques—SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Explain Like I'm 5 (ELI5)—with EL models to elucidate the critical factors influencing CYP. This research highlights the transformative potential of EL models in agricultural practices, significantly enhancing sustainability and food security. By providing detailed insights into the factors influencing CYP, this study empowers informed decision-making by farmers and policymakers, setting a new benchmark for future research in crop yield prediction

Due to the rapid utilization of cloud services, the energy consumption of cloud data centres is increasing dramatically. These cloud services are provided by Virtual Machines (VMs) through the cloud data center. Therefore, energy-aware VMs allocation and migration are essential tasks in the cloud environment. This paper proposes a Branch-and-Price based energy-efficient VMs allocation algorithm and a Multi-Dimensional Virtual Machine Migration (MDVMM) algorithm at the cloud data center. The Branch-and-Price based VMs allocation algorithm reduces energy consumption and wastage of resources by selecting the optimal number of energy-efficient PMs at the cloud data center. The proposed MDVMM algorithm saves energy consumption and avoids the Service Level Agreement (SLA) violation by performing an optimal number of VMs migrations. The experimental results demonstrate that our proposed Branch-and-Price based VMs allocation with VMs migration algorithms saves more than 31% energy consumption and improves 21.7% average resource utilization over existing state-of-the-art techniques with a 95% confidence interval. The performance of the proposed approaches outperforms in terms of SLA violation, VMs migration, and Energy SLA Violation (ESV) combined metrics over existing state-of-the-art VMs allocation and migration algorithms.

Many real world activities in computer science scenarios are linked with concurrency and security related issues and have to handle large number of processes to be executed in parallel with false safe security solutions. There are many traditional methods in programming languages to handle concurrency. Concurrency is one of the major issues that need to be addressed by most of the servers when dealing with the group communication operations. Security of the data as well as the credibility of the users are the other aspects when a group of users involve in real-time communication. Many light-weighted servers are designed to carryout elementary operations of request handling, file sharing etc. In design of such servers having large number of clients, the request service handling will be based on the individual server programs. Keeping track of individual credibility and establishing concurrency solutions in server design is challenging. The whole work describes the significance and implementation of an Erlang based XMPP server in comparison with a Python based XMPP server with a view to service the client request handling operations for sending messages, group chatting, buddy-list creation, presence identification integrated with XML messaging pattern as per the XMPP protocol. We also accomplish the security and credibility of the users using a blockchain based interface that keep track of user activities during group communication. The security analysis is also performed for blockchain based interface.

Blockchain based distributed ledger mechanism has got a wide range of applications in this era. The degree of security measurement is always a bottleneck. Since there are technologies to break it. Data sharing through cloud for smart cities, collaborative actions, remote activities based on the data at the source, etc., need to be secure and free from masquerading and tampering. In most of the cases the data is pushed into the cloud from access points, sensors, or remote access centers. Preventing the data access and identifying anonymous access to these sensors require an enhanced security mechanism that prevents the inconsistent data to be transferred to the cloud. We propose a blockchain based enhanced security system that protects the data from the access point it leaves for the cloud using a distributed ledger. The consensus mechanism ensures the trust of existing sources during the data transfer from the source to the cloud. The trust generated by the subsequent data blocks with the security hash key ensure the integrity of the data and validity of the actual source. This prevent the illegal access to the data sharing points. We have verified the degree of security offered by our proposed model using informal analysis. We found that our method has improved the security of data access.

In non-modern countries like India, the approach of allocating basic local goods to plight families is a significant approach to meeting the needs of a large number of people. The ongoing public dissemination system in Allot stores necessitates manual sum evaluation and trade record maintenance. The ongoing system has a ton of issues. One example is the IOT-based shrewd public appropriation framework project, which proposes a programmed method for getting products to verified cardholders. Similar to this, an informational index keeps track of the nuances of trades. Clients should enter their ID and mystery expression to get to their record through the High level cell. They are able to see the stock availability when they are successfully endorsed in. This structure uses a Raspberry Pi as the controller and uses a Specifics extraction-based extraordinary imprint coordinating computation, which has a higher accuracy score than previous versions. DC engines that are directly controlled by a Raspberry Pi for programmed product appropriation are used to open and close the valves. All along, one of the relatives need to enter one of a kind username and secret articulation. Right when client is supported in, he/she can see things that is open for that specific family account. The customer must provide a remarkable finger impression to the next level of confirmation in order to manage the items.

Crop failure caused by disease-causing pests is an important problem. Farmers struggle with disease management and detection due to inadequate interventions. The goal is to create an automatic system to efficiently identify plant diseases from photos while reducing crop losses and increasing productivity. Machine learning algorithms offer a faster and cheaper alternative to visual inspection by experts. The main goal is to perform image analysis for early diagnosis and effective disease control. The use of CNN architecture for plant disease classification and detection offers a promising solution for plant health monitoring and risk mitigation. Given the threats to crop productivity and global food security, reliable methods for early detection and accurate classification are essential. CNNs enable efficient analysis of vast plant image databases, enabling accurate plant disease identification with speed and accuracy. The multi-layer CNN approach extracts feature and refines the representation, facilitating accurate prediction and accurate diagnosis. Transfer learning methods accelerate system development and allow adaptation to plant disease-specific databases. Combining computer vision algorithms with CNN architecture enables real-time monitoring, early disease detection and targeted intervention, reducing yield losses and improving crop management. This approach uses AI, image analysis, and plant pathology to solve the challenges of sustainable agriculture and plant diseases. System performance is measured by various performance metrics.

The sequential pattern mining was widely used to solve various business problems, including frequent user click pattern, customer analysis of buying product, gene microarray data analysis, etc. Many studies were going on these pattern mining to extract insightful data. All the studies were mostly concentrated on high utility sequential pattern mining (HUSP) with positive values without a distributed approach. All the existing solutions are centralized which incurs greater computation and communication costs. In this paper, we introduce a novel algorithm for mining HUSPs including negative item values in support of a distributed approach. We use the Hadoop map reduce algorithms for processing the data in parallel. Various pruning techniques have been proposed to minimize the search space in a distributed environment, thus reducing the expense of processing. To our understanding, no algorithm was proposed to mine High Utility Sequential Patterns with negative item values in a distributed environment. So, we design a novel algorithm called DHUSP-N (Distributed High Utility Sequential Pattern mining with Negative values). DHUSP-N can mine high utility sequential patterns considering the negative item utilities from Bigdata.