Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed idea is applied to three different datasets (hourly, monthly, yearly) gathered from the National Renewable Energy Laboratory (NREL) transforming energy database. The forecasting results show that the proposed research can accurately predict wind power using a span ranging from hours to years. A comparison is made with popular state of the art algorithms and it is demonstrated that the proposed research yields better predictions results.

Blockchain technology is widely adopted in the Internet of Medical Things (IoMT) for information storage and retrieval. The integration of blockchain with IoMT systems enhances security; however, it raises privacy and security in data searching and storage. This study proposes a novel Binary Spring Search (BSS) technique based on group theory and integrated with a hybrid deep neural network approach to enhance the security and trustworthiness of IoMT. The proposed method incorporates secure key revocation and dynamic policy updates. The proposed framework leverages blockchain technology for immutable and decentralized data management, Artificial Intelligence (AI) for dynamic data analysis and threat detection, and advanced searchable encryption techniques to facilitate secure and efficient data queries. The proposed patient-centered data access model that combines blockchain technology with trust chains makes our method safer and more efficient and demonstrates a return on investment. Furthermore, our blockchain-based architecture ensures the integrity and immutability of medical data generated by IoMT devices, allowing for decentralized and tamper-proof storage. We used the hyper-ledger fabric tool, known as OrigionLab, for simulations in a blockchain context. We claim that the suggested framework provides a more searchable and secure solution to the healthcare system when compared to the other methods given through our findings. The simulation results show that our algorithm significantly reduces transaction time while maintaining high levels of security, making it a robust solution for managing Patient Health Records (PHR) in a decentralized manner.

The rapid digitization of healthcare systems has led to a vast accumulation of electronic medical records (EMRs), offering an invaluable source of patient data that can significantly advance medical research and improve patient care. However, sharing EMRs for research purposes presents challenges, particularly concerning data privacy, security, and the limitations of traditional centralized data-sharing models. This paper introduces a novel approach that leverages blockchain technology to facilitate federated learning with EMRs, thereby addressing these challenges. Federated learning enables multiple institutions to collaboratively train a robust machine learning model without sharing raw data, preserving privacy and security. By integrating blockchain, this framework enhances data integrity, immutability, and trust, all in a decentralized environment. The blockchain serves as a transparent and secure ledger, recording model updates and aggregating them through a consensus-based mechanism. Smart contracts further enforce data usage policies, allowing only authorized access and maintaining control over data ownership and sharing. This approach empowers medical researchers and institutions to collaborate more effectively, accelerating the discovery of treatments, advancements in personalized medicine, and insights into rare diseases. It also enables patients to contribute to medical research while retaining control over their personal data, fostering a patient-centered approach to healthcare innovation. Experimental results confirm the efficacy and efficiency of this blockchain-enabled federated learning framework, highlighting its potential to transform medical research and adhere to stringent privacy and security standards. This study emphasizes the pivotal role of blockchain in enhancing big data analytics within healthcare, paving the way for improved collaboration, innovation, and patient outcomes.

Wind power is a sustainable green energy source. Power forecasting via deep learning is essential due to diverse wind behavior and uncertainty in geological and climatic conditions. However, the volatile, nonlinear and intermittent behavior of wind makes it difficult to design reliable forecasting models. This paper introduces a new approach using variational auto-encoding and hybrid transfer learning to forecast wind power for large-scale regional windfarms. Transfer learning is applied to windfarm data collections to boost model training. However, multiregional windfarms consist of different wind and weather conditions, which makes it difficult to apply transfer learning. Therefore, we propose a hybrid transfer learning method consisting of two feature spaces; the first was obtained from an already trained model, while the second, small feature set was obtained from a current windfarm for retraining. Finally, the hybrid transferred neural networks were fine-tuned for different windfarms to achieve precise power forecasting. A comparison with other state-of-the-art approaches revealed that the proposed method outperforms previous techniques, achieving a lower mean absolute error (MAE), i.e., between 0.010 to 0.044, and a lowest root mean square error (RMSE), i.e., between 0.085 to 0.159. The normalized MAE and RMSE was 0.020, and the accuracy losses were less than 5%. The overall performance showed that the proposed hybrid model offers maximum wind power forecasting accuracy with minimal error.

Agricultural systems using unmanned aerial vehicles (UAVs) for communication face significant challenges in channel modeling and resource management, particularly in dynamic environments. Traditional frameworks overlook factors such as terrain variability, vegetation growth, and atmospheric conditions, leading to unreliable signal propagation and inefficient network performance. Moreover, UAV operations are constrained by limited energy, and conventional optimization methods fail to address the adaptive allocation of resources in heterogeneous networks (HetNets). This paper proposes a novel framework for UAV channel modeling in agriculture communication systems, applying the Internet of UAV Things (IoUT) and collaborative cloud computing to improve communication reliability and efficiency. The framework dynamically models signal propagation by using machine learning-based predictive algorithms, incorporating terrain features, vegetation density, and weather conditions. A genetic algorithm-long short-term memory network (GA-LSTM) optimization technique further enhances UAV operations, balancing coverage, energy efficiency, and system reliability for smarter and sustainable agricultural networks. The mathematical derivations are performed for channel modeling, UAV mobility, coverage, and IoUT-based cloud computing. Next, the virtual scenario is designed to enhance the accuracy of IoUT-based agriculture communication.