In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in the efficacy of medical decision systems. This paper presents a novel approach utilizing a deep convolutional neural network that effectively amalgamates the strengths of EfficientNetB0 and DenseNet121, and it is enhanced by a suite of attention mechanisms for refined pneumonia image classification. Leveraging pre-trained models, our network employs multi-head, self-attention modules for meticulous feature extraction from X-ray images. The model’s integration and processing efficiency are further augmented by a channel-attention-based feature fusion strategy, one that is complemented by a residual block and an attention-augmented feature enhancement and dynamic pooling strategy. Our used dataset, which comprises a comprehensive collection of chest X-ray images, represents both healthy individuals and those affected by pneumonia, and it serves as the foundation for this research. This study delves deep into the algorithms, architectural details, and operational intricacies of the proposed model. The empirical outcomes of our model are noteworthy, with an exceptional performance marked by an accuracy of 95.19%, a precision of 98.38%, a recall of 93.84%, an F1 score of 96.06%, a specificity of 97.43%, and an AUC of 0.9564 on the test dataset. These results not only affirm the model’s high diagnostic accuracy, but also highlight its promising potential for real-world clinical deployment.

In the cloud-assisted Industrial Internet of Things (IIoT), ciphertext-policy attribute-based encryption (CP-ABE) could help the data owner (DO) share his sensitive data via the cloud under self-defined access structures. Among general CP-ABE schemes, the decryption overhead, the key generation cost, and the ciphertext length increase with the number of involved attributes. Additionally, only regular attributes are taking into consideration rather than weighted attributes. In this article, we proposed a secure, efficient, and weighted access control scheme (SEWAC) for cloud-assisted IIoT applications. SEWAC enables the DO to formulate any fine-grained access structure over weighted attributes without making it more complicated. Furthermore, such weighted attributes would not add the length of ciphertext. SEWAC also supports online/offline key generation to alleviate the computational cost of the authority from answering mass key requests in the online phase, while most computational tasks are executed in the offline phase. The heavy decryption overhead is offloaded to the cloud. To ensure the cloud to honestly execute the process of outsourced decryption, we design an efficient batch verification method, which allows the user to spend only three bilinear pairing operations in checking the correctness of batch results. We also give the formal security proof of the proposed scheme. Comprehensive comparisons and implementation results indicate that SEWAC can better achieve weighted access control, compressed ciphertext length, efficient key generation, and the assurance of the outsourced decryption result.

With the advancement of cloud computing technology, the cloud-based personal health record (CB-PHR) has become an increasingly popular way for modern patients to flexibly manage and share their health records with doctors. However, the confidentiality of CB-PHR privacy is vulnerable to threats due to unauthorized users and untrusted cloud service provider (CSP). Additionally, patients and doctors may be constrained by changes in access permissions and limited device resources. To address these challenges, we propose an efficient decentralized privacy-preserving attribute-based access control scheme with verifiable policy update (DPVPU) for CB-PHR systems. DPVPU supports large attribute universe and safeguards the privacy of both the access policy and the doctor’s identity through partially hiding the access policy and employing a one-way anonymous key agreement technique. Unlike re-encrypting ciphertext, it can dynamically update the policy by fully utilizing the previous policy and outsourcing the computation of ciphertext update to the CSP. Also, we design an efficient verification algorithm enabling patients to check the correctness of updated ciphertext. For devices with limited resources, we use online/offline and outsourced decryption techniques to reduce system costs. Finally, we provide formal security proofs and performance analysis to demonstrate the security and practicality of DPVPU.