Autism Spectrum Disorder (ASD) is a complicated collection of neurodevelopmental illnesses characterized by a variety of developmental defects. It is a binary classification system that cannot cope with reality. Furthermore, ASD, data label noise, high dimension, and data distribution imbalance have all hampered the existing classification algorithms. As a result, a new ASD was proposed. This strategy employs label distribution learning (LDL) to deal with label noise and uses support vector regression (SVR) to deal with sample imbalance. The experimental results show that the proposed method balances the effects of majority and minority classes on outcomes. It can effectively deal with imbalanced data in ASD diagnosis, and it can help with ASD diagnosis. This study presents a cost-sensitive approach to correct sample imbalance and uses a support vector regression (SVR)-based method to remove label noise. The label distribution learning approach overcomes high-dimensional feature classification issues by mapping samples to the feature space and then diagnosing multiclass ASD. This technique outperforms previous methods in terms of classification performance and accuracy, as well as resolving the issue of unbalanced data in ASD diagnosis.

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Patients’ medical data are extremely sensitive information during storage and transfer, and it needs the highest security level. Furthermore, these records must frequently be linked to patient medical data, and then the linked medical data are securely transmitted to the healthcare center. In this study, a Blockchain-Based Traceable Data Sharing method is proposed to securely transfer the medical data. A Paillier homomorphic encryption method is used to prevent data theft or attacks from occurring in the cloud as a result of the transfer of medical data there. It prevents intravenous third parties, which executes arithmetic operations on the ciphertext. Then the encrypted data are stored in the cloud and to remove clone nodes in the gateway, a software-defined networking technology is introduced. Then a Blockchain-Based Traceable Data Sharing is proposed to ensure data privacy and authenticity while maintaining data privacy at the point of data transmission. Data are then encrypted using a new Enhanced Cipher Text-Policy Encryption Attribute-based Encryption (E-CP-ABE). Private blockchain transfers are carried out on the chain, supporting fine-grain access control with flexible access policies and creating a private key in E-CP-ABE. The presented technique is executed in Matlab software of version R2020a. The performance parameters are encryption, and decryption time, mean square error (MSE), peak signal-to-noise ratio (PSNR), sensitivity, respectively. The encryption process function is nearly 8% superior than the existing methods and the decryption time is 14% greater than other methods. As a result, this study shows that the research approach outperformed in terms of encryption time and decryption time, as well as PSNR, MSE, and sensitivity. This technique outperforms other state-of-the-art algorithms in terms of imperceptibility and robustness against various attacks. Consequently, this approach is more reliable than previous methods for the transmission of medical data.

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