Sensors are now used by farmers and agronomists to help them improve their operations. They use sensor data transmitted via IoT to remotely monitor their crops. Farmers today manage crops in a controlled environment to increase yields in the name of modern farming. Crop productivity, on the other hand, is influenced by the severity of the weather and disease variations. The primary objective of this paper is to present a novel Multisensor Machine-Learning Approach (MMLA) for classifying multisensor data. The fusion strategy supports high-quality data analysis in agricultural contexts for cultivation recommendations. Based on the proposed recommendation system, eight crops were classified: cotton, gram, groundnut, maize, moong, paddy, sugarcane, and wheat. Crop species were classified using three machine learning algorithms: J48 Decision Tree, Hoeffding Tree, and Random Forest. To evaluate the performance of the proposed multi-text classifier, only the top eight classes were investigated. The classifier’s performance is measured in terms of precision, recall, F-measure, MCC, ROC Area, and PRC Area class, and the results are compared with the state-of-the-art classifiers. The Random forest algorithm has the lowest error measure of RMSE at 13%, RAE at 38.67%, and RRSE at 44.21%, demonstrating effectiveness in classifying the agriculture text. Thus, the use of a multisensor data fusion approach based on crop recommendation provides greater precision in prediction, resulting in a significant increase in crop yield while also creating awareness in the condition-based environmental monitoring system.

Utilizing Unmanned Aerial Vehicles (UAVs) as flying edge nodes to support task offloading from terminal devices has recently attracted significant research attention. However, the literature lacks a systematic perspective on this emerging topic. The goals are to understand the volume and trends of research, identify use case scenarios and proposed architectures, classify the core topics addressed, explore group techniques explored, recognize task types considered, and summarize open issues needing further work. Publications are mapped by type and source from 2019 to 2023 to assess the maturity and activity level in this field over time. Various use case scenarios for UAV-enabled Mobile Edge Computing (MEC) task offloading are identified and categorized, and different proposed architectures for offloading between UAV-MEC platforms are summarized. Techniques for offloading decision-making and performance enhancement are grouped to identify popular and less explored methods. The literature is also mapped based on the types of tasks considered for offloading to UAV-enabled MEC platforms to recognize the focus areas. Open issues that are briefly discussed across papers but require additional research are summarized on basis of the gaps identified. This systematic perspective consolidates existing research in an organized manner to guide future works and establish a coherent taxonomy to organize future studies and reviews. Overall, mapping trends helps characterize research maturity, guiding its continued development.

Mobile ad-hoc networks are functional for usage, which require inter-node contact, including disaster relief, protection, and combat spotting. These networks face critical challenges, including rapid energy consumption in battery-limited devices and random topological changes resulting from node mobility, which lead to communication instability and packet loss. These factors reduce network efficiency and shorten lifespan, particularly in mission-critical applications such as environmental monitoring or military operations. This research addresses the imbalance in energy consumption caused by a node failure that acts as a relay node. It presents the Replacing Relay Nodes to Prolong Lifetime and improve Energy Efficiency (RePLEE) model. Comprehensive simulations conducted in NS-2 reveal that RePLEE significantly improves performance compared to standard AODV and enhanced EEAODV protocols. Specifically, RePLEE achieves a 13% reduction in energy consumption, a 61% decrease in packet loss, and a 26% reduction in end-to-end delay relative to AODV. Additionally, it extends network lifetime by 49% over AODV and by 26% over EEAODV. RePLEE also demonstrates superior scalability by maintaining high packet delivery ratios and lower control overhead across varying node densities. These results confirm the effectiveness of RePLEE in addressing the limitations of traditional protocols by enhancing energy efficiency, prolonging operational lifespan, and ensuring stable connectivity in MANET environments.

Customer churn, the phenomenon of customers discontinuing their relationship with a company, poses significant challenges for businesses in various industries, including the banking sector. In this paper, we address the problem of customer churn in the banking sector by selecting and applying four machine learning algorithms on the dataset of the U.S Bank in the case of Imbalance and Balance to predict the churn of customers from financial institutions such as banks and the reasons that drive them to do so. The algorithms were Logistics Regression, SVM, Neural network and Random Forest. The CRISP model was followed for the necessary processing and analysis, as it became clear after that the Random Forest algorithm provided better results in all cases. Consequently, this paper attempted to provide a better improvement to the results obtained from the default Random-Forest algorithm by understanding its working mechanism and adjusting its hyperparameters. Therefore, a mathematical model was proposed and built for the default Random Forest algorithm as a new version of it called ERFA. The final results of ERFA were positive through an increase in prediction accuracy from 85% to 86% in the case of the imbalanced dataset and from 89% to 91% in the case of balanced dataset. The outcomes of this study can help and motivate bank managers in general and in Yemen in particular to adopt this type of smart strategies to maintain their customers and provide them with a better experience. Furthermore, the mechanism established by this paper may help researchers who are interested in the field of developing smart business models.

Recently, Network Function Virtualization (NFV) and Software Defined Networking (SDN) have attracted many mobile operators. For the flexible deployment of Network Functions (NFs) in an NFV environment, NF decompositions and control/user plane separation have been introduced in the literature. That is to map traditional functions into their corresponding Virtual Network Functions (VNFs). This mapping requires the NFV Resource Allocation (NFV-RA) for multi-path service graphs with a high number of virtual nodes and links, which is a complex NP-hard problem that inherited its complexity from the Virtual Network Embedding (VNE). This paper proposes a new path mapping approach to solving the NFV-RA problem for decomposed Network Service Chains (NSCs). The proposed solution has symmetrically considered optimizing an average embedding cost with an enhancement on average execution time. The proposed approach has been compared to two other existing schemes using 6 and 16 scenarios of short and long simulation runs, respectively. The impact of the number of nodes, links and paths of the service requests on the proposed scheme has been studied by solving more than 122,000 service requests. The proposed Integer Linear Programming (ILP) and heuristic schemes have reduced the execution time up to 39.58% and 6.42% compared to existing ILP and heuristic schemes, respectively. Moreover, the proposed schemes have also reduced the average embedding cost and increased the profit for the service providers.