This chapter highlights the challenges faced by agricultural enterprises in safeguarding their crops from pests, diseases, and environmental stressors. By examining the evolution of plant protection methods, the chapter emphasizes the need for innovative solutions. It provides an overview of advanced technologies such as drones, remote sensing, artificial intelligence, precision agriculture, biotechnology, and integrated pest management systems. The role of these technologies in enhancing plant protection is discussed, including their applications in aerial surveillance, data collection, disease detection, pest management, and resource optimization. The chapter also presents case studies and success stories showcasing the positive impact of advanced technologies on crop yields, sustainability, and profitability. Future perspectives and challenges are explored, highlighting the potential advancements and trends in plant protection technologies and the need for widespread adoption.

Intelligent Transport Systems (ITS) are gradually progressing to practical application because of the rapid growth in network and information technology. Currently, the low-latency ITS requirements are hard to achieve in the conventional cloud-based Internet of Vehicles (IoV) infrastructure. In the context of IoV, Vehicular Fog Computing (VFC) has become recognized as an inventive and viable architecture that can effectively decrease the time required for the computation of diverse vehicular application activities. Vehicles receive rapid task execution services from VFC. The benefits of fog computing and vehicular cloud computing are combined in a novel concept called fog-based Vehicular Ad Hoc Networks (VANETs). These networks depend on a movable power source, so they have specific limitations. Cost-effective routing and load distribution in VANETs provide additional difficulties. In this work, a novel method is developed in vehicular applications to solve the difficulty of allocating limited fog resources and minimizing the service latency by using parked vehicles. Here, the improved heuristic algorithm called Revised Fitness-based Binary Battle Royale Optimizer (RF-BinBRO) is proposed to solve the problems of vehicular networks effectively. Additionally, the combination of Deep Adaptive Reinforcement Learning (DARL) and the improved BinBRO algorithm effectively analyzes resource allocation, vehicle parking, and movement status. Here, the parameters are tuned using the RF-BinBRO to achieve better transportation performance. To assess the performance of the proposed algorithm, simulations are carried out. The results defined that the developed VFC resource allocation model attains maximum service satisfaction compared to the traditional methods for resource allocation.

This research compares federated and centralized learning paradigms to discover the best machine learning privacy-model accuracy balance. Federated learning allows model training across devices or clients without data centralization. It's innovative distributed machine learning. Keeping data on individual devices reduces the hazards of centralized data storage, improving user privacy and security. However, centralized learning concentrates data on a server, which raises privacy and security problems. It evaluates two learning approaches using simulated data in a simple regression problem framework. Federated learning seems to be as accurate as centralized learning while protecting privacy. The paper also shows how federated learning works in popular machine learning frameworks like TensorFlow Federated. This research shows that federated learning protects privacy while producing accurate machine learning models. It challenges the idea that machine learning must constantly choose between privacy and accuracy. Empirical facts and theoretical ideas from this study advance machine learning methodology discussions. In the digital era, it promotes privacy-conscious, dispersed learning frameworks.

Introduction: the Internet of Things (IoT) is gaining popularity in several industries owing to the autonomous and low-cost functioning of its sensors. In medical and healthcare usage, IoT gadgets provide an environment to detect patients' medical problems, such as blood volume, oxygen concentration, pulse, temperatures, etc. and take emergency action as necessary. The problem of imbalanced energy usage across biosensor nodes slows down the transmission of patient data to distant centres and has a detrimental effect on the health industry. In addition, the patient's sensitive information is sent through the insecure Internet and is exposed to potential threats. For clinical uses, information privacy and stability against hostile traffic constitute a further research challenge. Methods: this article proposes a Secured and Energy-Efficient System (SEES-IoMT) e-healthcare utilizing the Internet of Medical Things (IoMT) monitoring, the main goal of which is to reduce the connectivity cost and energy usage between sensing devices while feasibly forwarding the medical data. SEES-IoMT also guarantees the clinical data of the patients against unverified and malevolent nodes to enhance the privacy and security of the system.Result and Discussion: in consideration of the memory and power limitations of healthcare IoT gadgets, this approach is designed to be very lightweight. A thorough examination of this system's safety is performed to demonstrate its reliability.Conclusion: in terms of computing speed and security, the research compares SEES-IoMT to relevant methods in the IoT medical environment to demonstrate its applicability and resilience

Drug Recommendation systems have the capability to recommend drugs. On daily basis, a huge amount of data is being generated by the patients. All the valuable data can be properly utilized for creating a reliable drug recommendation system. In this paper, the researchers aimed to propose a system for drug recommendations. The main scope of the system is to predict the correct medication based on reviews and ratings. The proposed system uses Natural Language Processing techniques (NLP) and Recurrent Neural Network (RNN). The researchers also considered various metrices such as precision, recall, accuracy, f1 Score, roc curve as the measures of the system performance. Natural Language Processing techniques were used for gathering useful information from patients data, and RNN is a machine learning methodology, that works really well when it comes to analysing textual data. The system considers various patient data attributes like age, gender, dosage, medical history, symptom in order to make appropriate predictions. As the result, the proposed system has the potential to help medical professionals in making informed drug recommendations.