Electronic Health Record (EHR) systems are a valuable and effective tool for exchanging medical information about patients between hospitals and other significant healthcare sector stakeholders in order to improve patient diagnosis and treatment around the world. Nevertheless, the majority of the hospital infrastructures that are now in place lack the proper security, trusted access control, and management of privacy and confidentiality concerns that the current EHR systems are supposed to provide. Goal. For various EHR systems, this research proposes a Blockchain-enabled Hyperledger Fabric Architecture as a solution to this delicate issue. The three steps of the suggested system are the secure upload phase, the secure download phase, and authentication. Patient registration, login, and verification make up the authentication step. The administrator grants authorization to read, edit, delete, or revoke the files following user details verification. In the secure upload phase, feature extraction is carried out first, and then a hashed access policy is created from the extracted feature. Next, the hash value is stored in an IoT-based Hyperledger blockchain. The uploaded EHR files are additionally encrypted before being stored on the cloud server. In the secure download step, the physician uses a hashed access policy to send the request to the cloud and decrypts the corresponding files. The experimental findings demonstrate that the system outperformed cutting-edge techniques. The proposed Modified Key Policy Attribute-Based Encryption performs better for the remaining 10 to 25 mb file sizes. This IoT framework compares MKP-ABE with certain efficiency indicators, such as encryption, decryption period, protection level analysis and encrypted memory use, resource use on decryption, upload time, and transfer time, which are present in the KP-ABE, the ECC, RSA, and AES. Here, the IoT device suggested requires 4008 ms for data encryption and 4138 ms for the data decryption.

Forests are essential natural resources that directly impact the ecosystem. However, the rising frequency of forest fires due to natural and artificial climate change has become a critical issue. A revolutionary municipal application proposes deploying an artificial intelligence‐based forest fire warning system to prevent major disasters. This work aims to present an overview of vision‐based methods for detecting and categorizing forest fires. The study employs a forest fire detection dataset to address the classification difficulty of discriminating between photos with and without fire. This method is based on convolutional neural network transfer learning with Inception‐v3. Thus, automatic identification of current forest fires (including burning biomass) is a critical field of research for reducing negative repercussions. Early fire detection can also assist decision‐makers in developing mitigation and extinguishment strategies. Radial basis function Networks (RBFNs) with rapid and accurate image super resolution (RAISR) is a deep learning framework trained on an input dataset to detect active fires and burning biomass. The proposed RBFN‐RAISR model’s performance in recognizing fires and nonfires was compared to earlier CNN models using several performance criteria. The water wave optimization technique is used for image feature selection, noise and blurring reduction, image improvement and restoration, and image enhancement and restoration. When classifying fire and no‐fire photos, the proposed RBFN‐RAISR fire detection approach achieves 97.55% accuracy, 93.33% F‐Score, 96.44% recall, 94.19% precision, and an error rate of 24.89. Given the one‐of‐a‐kind forest fire detection dataset, the suggested method achieves promising results for the forest fire categorization problem.