The study examines how well the routing system based on logic performs using both simulations and real life situations. It shows that the system is effective in improving emergency patient transportation and reducing response times. By using membership functions and fuzzy inference the system can. Direct patients to the suitable healthcare facility. It handles imprecise and unclear inputs better through variables and fuzzy rules resulting in accurate and responsive routing decisions.By incorporating logic this approach takes into account the uncertainties and complexities in emergency scenarios, such as location, medical condition severity and real time traffic conditions.Routing emergency patients is crucial for optimizing healthcare services to ensure efficient care delivery. The study assesses how well the routing system based on logic performs through simulations and real world situations demonstrating its effectiveness, in optimizing emergency patient transportation while minimizing response times.The Patient Routing Application is an innovative solution designed to streamline the process of providing emergency medical assistance to individuals based on their health conditions and geographical location. Leveraging a combination of fuzzy logic, geospatial data, and real-time mapping, the system evaluates a patient’s vital signs and recommends the nearest and most accessible hospitals.The software uses logic to evaluate how critical a patients condition is,Take into account factors, like heart rate, blood pressure and temparature. The fuzzy logic system utilizes predefined rules to categorize the severity and prescribe appropriate medical responses. Patient data, including personal information and medical history, is collected through a user-friendly graphical interface

The emerging disciplines of machine learning and deep learning have brought about an innovative period, restructuring domains such as image identification, natural language comprehension, and autonomous systems. However, there are still complex optimisation and decision-making difficulties that need to be addressed. Swarm algorithms, inspired by the coordinated movement of natural swarms, are emerging as innovative solutions in this field. This study investigates the productive convergence of machine learning, deep learning, and swarm algorithms, with the goal of unleashing their combined potential. We explore the core principles and mechanisms of swarm algorithms such as particle swarm optimisation, ant colony optimisation, bee-inspired algorithms, firefly algorithms, and bat algorithms. These decentralised algorithms, inspired by nature, are highly effective in addressing various optimisation goals by imitating the self-organizing behaviours observed in natural systems. Additionally, we explore the complex incorporation of swarm algorithms into the operations of machine learning. We aim to utilise swarm-based techniques to enhance the performance and generalizability of machine learning models by optimising hyperparameter tweaking, model selection, and feature engineering. This chapter examines the use of swarm intelligence in the emerging subject of neural architecture search, which is crucial for automating the complex process of designing deep neural networks. In order to strengthen our comprehension, we undertake a journey through captivating instances and tangible implementations in fields like robotics and healthcare. This emerging discipline combines the collective intelligence of groups of organisms with the data-driven capabilities of machine learning to create innovative solutions that can improve decision-making, optimise intricate systems, and drive progress in the field of artificial intelligence

In the ever-evolving landscape of modern education systems, the integration of technology has become ubiquitous, opening new avenues for teaching and learning. However, this increased reliance on digital platforms has also given rise to unprecedented cybersecurity challenges, necessitating advanced detection mechanisms to safeguard sensitive educational data. This book chapter explores the application of deep learning approaches for intelligent cyber threats detection in the context of the modern education system. The chapter begins by providing a comprehensive overview of the evolving cyber threat landscape within educational institutions, highlighting the diverse range of attacks targeting student records, intellectual property, and critical infrastructure. It emphasizes the need for proactive and adaptive cybersecurity measures to counteract these threats effectively. Subsequently, the chapter delves into the foundational principles of deep learning, elucidating its capacity to autonomously learn intricate patterns and anomalies from vast datasets. Various deep learning architectures, such as convolutional neural networks and recurrent neural networks are discussed in the context of their applicability to cybersecurity in education. The practical implementation of deep learning models for cyber threats detection is then explored. Case study that illustrate how these models can analyze detect malware, and identify suspicious activities, thereby fortifying the resilience of educational systems against cyber threats. In conclusion, this book chapter provides a comprehensive exploration of deep learning approaches as a potent tool for intelligent cyber threats detection in modern education systems

This chapter explores the transformative potential of integrating blockchain and artificial intelligence (AI) technologies within educational data analytics. It begins by examining blockchain's capacity to enhance data security, streamline record-keeping, and ensure transparent credential verification. Concurrently, it analyzes AI's role in enabling adaptive learning, predictive modeling, and insightful data analysis to improve student outcomes and optimize educational strategies. The chapter further evaluates the synergistic benefits of combining blockchain and AI, proposing a robust framework to address prevalent challenges in the education sector, including data privacy, security, and personalized learning. By securing student records through blockchain's immutability and enhancing personalized learning experiences via AI-driven analytics, the chapter presents a comprehensive approach to modernizing educational systems. Additionally, it addresses technical challenges such as scalability and interoperability, alongside ethical considerations like data privacy, consent, and algorithmic bias. The chapter concludes with a call for collaborative efforts among educators, technologists, and policymakers to leverage these technologies, navigate their challenges, and fully realize their potential in revolutionizing education

We present a comprehensive system model for Industrial Internet of Things (IIoT) networks empowered by Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) technologies. The network comprises essential components such as base stations, edge servers, and numerous IIoT devices characterized by limited energy and computing capacities. The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption. The system operates in discrete time slots and employs a quasi-static approach, with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT context. This study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation, particularly relevant in real-time industrial applications. Experimental results indicate that our proposed algorithmsignificantly outperforms existing approaches, reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Q. Moreover, the algorithmeffectively balances complexity and network performance, as demonstratedwhen reducing the number of devices in each group (N) from 200 to 50, resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy consumption. This research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

Food production through Agriculture plays an important role in keeping the world’s population hunger-free and nations economically secure. The continuous change in land minerals, weather situation, and pesticide usage affect the yield of the crops. Farmers can choose successful crops for the season with the help of machine learning algorithms used for crop yield prediction. In this study, we forecasted agricultural production using numerous kinds of machine learning models while considering several factors that affect crop yields, such as rainfall, temperature, and pesticide use. By merging multiple separate model predictions, ensemble machine learning models improve the performance of the machine learning models. We have worked with individual models and ensemble models like SVR, RandomForestRegressor, LinearRegressor, and DecisionTreeRegressor to predict crop yield and found an ensemble solution that combines the strengths of both the stacked generalization model and the gradient boost algorithm which can provide improved accuracy and robustness in crop yield prediction. According to the findings, the ensemble solution provided an R2 score of 98 percent, which is higher than the R2 scores of 96 percent obtained using the Decision Tree Regressor and 89 percent obtained using the Gradient Boosting Regressor.

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The Internet of Things (IoT) has revolutionized the connectivity of physical devices, leading to an exponential increase in multimedia wireless traffic and creating substantial demand for radio spectrum. Given the inherent scarcity of available spectrum, Cognitive Radio (CR)- assisted IoT emerges as a promising solution to optimize spectrum utilization through cooperation between cognitive and IoT nodes. Unlicensed IoT nodes can opportunistically access licensed spectrum bands without causing interference to licensed users. However, energy constraints may lead to reduced cooperation from IoT nodes during the search for vacant channels, as they aim to conserve battery life. To address this issue, we propose a Punishment-reward-based Cooperative Sensing and Data Forwarding (PR-CSDF) approach for IoT data transmission. Our method involves two key steps: (1) distributing sensing tasks among IoT nodes and (2) enhancing cooperation through a reward and punishment strategy. Evaluation results demonstrate that both secondary users (SUs) and IoT nodes achieve significant utility gains with the proposed mechanism, providing strong incentives for cooperative behaviour. © 2024 Sangi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The study follows a structured workflow involving thorough data collection, quality assessment, and visualization through Exploratory Data Analysis (EDA). Addressing challenges like missing values and outliers, the paper employs Investigative Data Analysis (EDA) to ensure dataset reliability. Class imbalance is tackled using Synthetic Minority Over-sampling Technique (SMOTE) and standard scaling for consistent feature normalization. In the modeling phase, classifiers including AdaBoost, Bagging, Gradient Boosting, Decision Tree, Extra Tree, K-Nearest Neighbors, and XGBoost undergo cross-validation. Hyperparameter optimization is performed through grid search for Gradient Boosting and Bagging classifiers. Results indicate that XGBClassifier achieves the highest accuracy (0.789634), followed by GradientBoosting-Classifier (0.785061), BaggingClassifier (0.786585), and XGBClassifier

The hybrid cloud is a secure alternative for enterprises to exploit the benefits of cloud computing to overcome the privacy and security concerns of data in IoT networks. However, in hybrid cloud IoT, sensitive items such as keys in the private cloud can become compromised due to internal attacks. Once these keys are compromised, the encrypted data in the public cloud are no longer secure. This work proposes a secure multilevel privacy-protection scheme based on Generative Adversarial Networks (GAN) for hybrid cloud IoT. The scheme secures sensitive information in the private cloud against internal compromises. GAN is used to generate a mask with the input of sensory data-transformation values and a trapdoor key. GAN’s effectiveness is thoroughly assessed using Peak Signal-to-Noise Ratio (PSNR), computation time, retrieval time, and storage overhead frameworks. The obtained results reveal that the security scheme being proposed is found to require a negligible storage overhead and a 4% overhead for upload/retrieval compared to the existing works.

A vehicular ad hoc IoT network (VA-IoT) plays a key role in exchanging the constrained networked vehicle information through IPv6-enabled sensor nodes. It is noteworthy to understand that vehicular IoT is interconnection of vehicular ad hoc networks with the support of constrained IoT devices. Routing protocols in VAN-IoT are designed to route the vehicular traffic in the distributed environments. In addition, VAN-IoT is designed to enhance road safety by reducing the number of road accidents through reliable data transmission. Routing in VAN-IoT has a unique dynamic topology, frequent spectrum, and node handover with restricted versatility. Hence, it is very crucial to design the hybrid reactive routing protocols to ensure the network throughput and data reliability of the VAN-IoT networks. This paper aims to propose an AI-based reactive routing protocol to enhance the performance of the network throughput, minimize the end-to-end delay with respect to node mobility, spectrum mobility, link traffic load and end-to-end network traffic load while transmitting the vehicular images. In addition, the performance of the proposed routing protocol in terms of image transmission time is being compared with the existing initiative-taking- and reactive-based routing protocols in vehicular ad hoc IoT (VA-IoT) networks.

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