Shaozheng Zhang, Qiuyu Zhang^*, Jiahui Tang, Ruihua Xu

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2137-2158, 2025, DOI:10.32604/cmc.2024.057269 - 17 February 2025

Abstract Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a… More >

Qiu-yu Zhang^*, Yu-gui Jia, Fang-Peng Li, Le-Tian Fan

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 107-128, 2023, DOI:10.32604/cmc.2023.038911 - 08 June 2023

Abstract Existing speech retrieval systems are frequently confronted with expanding volumes of speech data. The dynamic updating strategy applied to construct the index can timely process to add or remove unnecessary speech data to meet users’ real-time retrieval requirements. This study proposes an efficient method for retrieving encryption speech, using unsupervised deep hashing and B+ tree dynamic index, which avoid privacy leakage of speech data and enhance the accuracy and efficiency of retrieval. The cloud’s encryption speech library is constructed by using the multi-threaded Dijk-Gentry-Halevi-Vaikuntanathan (DGHV) Fully Homomorphic Encryption (FHE) technique, which encrypts the original speech.… More >

Zhen Wang, Qiu-yu Zhang^*, Ling-tao Meng, Yi-lin Liu

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6161-6184, 2023, DOI:10.32604/cmc.2023.037134 - 29 April 2023

Abstract To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security, retrieval efficiency, and retrieval accuracy. This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure, searchable encryption scheme. First, a deep learning framework based on residual network and transfer learning model is designed to extract more representative image deep features. Secondly, the central similarity is used to quantify and construct the deep hash sequence of features. The Paillier homomorphic encryption encrypts the deep hash sequence… More >

Santhana Krishnan Jayapal¹, Sivakumar Poruran^2,*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1261-1275, 2023, DOI:10.32604/iasc.2023.026564 - 06 June 2022

Abstract Tea plant cultivation plays a significant role in the Indian economy. The Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea Plant. Various climatic factors and other parameters cause these diseases. In this paper, the image retrieval model is developed to identify whether the given input tea leaf image has a disease or is healthy. Automation in image retrieval is a hot topic in the industry as it doesn’t require any form of metadata related to the images for storing or retrieval. Deep Hashing with Integrated Autoencoders… More >