Jahanzaib Latif¹, Shanshan Tu^1,*, Chuangbai Xiao¹, Anas Bilal², Sadaqat Ur Rehman³, Zohaib Ahmad⁴

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1151-1172, 2023, DOI:10.32604/cmc.2023.040152

Abstract Glaucoma is a progressive eye disease that can lead to blindness if left untreated. Early detection is crucial to prevent vision loss, but current manual scanning methods are expensive, time-consuming, and require specialized expertise. This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine (EGWO-SVM) method. The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter (AMF) and feature extraction using the previously processed speeded-up robust feature (SURF), histogram of oriented gradients (HOG), and Global features. The enhanced Grey Wolf Optimization (GWO) technique is then employed… More >

Muhammad Rashid^1,*, Omar S. Sonbul¹, Muhammad Arif², Furqan Aziz Qureshi³, Saud. S. Alotaibi⁴, Mohammad H. Sinky¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1009-1025, 2023, DOI:10.32604/cmc.2023.039901

Abstract This work presents a flexible/unified hardware architecture of Elliptic-curve Cryptography (ECC) and PRESENT for cryptographic applications. The features of the proposed work are (i) computation of only the point multiplication operation of ECC over for a 163-bit key generation, (ii) execution of only the variant of an 80-bit PRESENT block cipher for data encryption & decryption and (iii) execution of point multiplication operation (ECC algorithm) along with the data encryption and decryption (PRESENT algorithm). To establish an area overhead for the flexible design, dedicated hardware architectures of ECC and PRESENT are implemented in the first step, and a sum of… More >

Shah Faisal¹, Kashif Javed¹, Sara Ali¹, Areej Alasiry², Mehrez Marzougui², Muhammad Attique Khan^3,*, Jae-Hyuk Cha^4,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 895-914, 2023, DOI:10.32604/cmc.2023.039781

Abstract Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy. Using… More >

Baowei Wang^1,2,*, Neng Wang¹, Yuxiao Zhang¹, Zenghui Xu¹, Junhao Zhang¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 849-859, 2023, DOI:10.32604/cmc.2023.039749

Abstract The trusted sharing of Electronic Health Records (EHRs) can realize the efficient use of medical data resources. Generally speaking, EHRs are widely used in blockchain-based medical data platforms. EHRs are valuable private assets of patients, and the ownership belongs to patients. While recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals, it does not address the challenge of record sharing when patients revisit doctors. In order to solve this problem, this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate Blockchain. This paper uses cross-chain technology to connect the… More >

Ali Haider Khan^1,2,*, Hassaan Malik², Wajeeha Khalil³, Sayyid Kamran Hussain⁴, Tayyaba Anees⁵, Muzammil Hussain²

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 133-150, 2023, DOI:10.32604/cmc.2023.039518

Abstract To prevent irreversible damage to one’s eyesight, ocular diseases (ODs) need to be recognized and treated immediately. Color fundus imaging (CFI) is a screening technology that is both effective and economical. According to CFIs, the early stages of the disease are characterized by a paucity of observable symptoms, which necessitates the prompt creation of automated and robust diagnostic algorithms. The traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of eyes. In addition, they usually only target one or a few different… More >

Tawfeeq Shawly¹, Ahmed A. Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 731-751, 2023, DOI:10.32604/cmc.2023.039433

Abstract More than 500,000 patients are diagnosed with breast cancer annually. Authorities worldwide reported a death rate of 11.6% in 2018. Breast tumors are considered a fatal disease and primarily affect middle-aged women. Various approaches to identify and classify the disease using different technologies, such as deep learning and image segmentation, have been developed. Some of these methods reach 99% accuracy. However, boosting accuracy remains highly important as patients’ lives depend on early diagnosis and specified treatment plans. This paper presents a fully computerized method to detect and categorize tumor masses in the breast using two deep-learning models and a classifier… More >

Ang Liu^1,2, Xiu-Bo Chen^1,*, Gang Xu³, Zhuo Wang⁴, Xuefen Feng⁵, Huamin Feng⁶

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 259-277, 2023, DOI:10.32604/cmc.2023.039397

Abstract The rapid advancement of quantum technology poses significant security risks to blockchain systems. However, quantum technology can also provide solutions for enhancing blockchain security. In this paper, we propose a quantum-enhanced blockchain scheme to achieve a high level of security against quantum computing attacks. We first discuss quantum computing attacks on classic blockchains, including attacks on hash functions, digital signatures, and consensus mechanisms. We then introduce quantum technologies, such as a quantum hash function (QHF), a quantum digital signature (QDS), and proof of authority (PoA) consensus mechanism, into our scheme to improve the security of the blockchain system. Our security… More >

Jeongha Lee¹, Hyokyung Bahn^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 607-629, 2023, DOI:10.32604/cmc.2023.039236

Abstract Due to the recent trend of software intelligence in the Fourth Industrial Revolution, deep learning has become a mainstream workload for modern computer systems. Since the data size of deep learning increasingly grows, managing the limited memory capacity efficiently for deep learning workloads becomes important. In this paper, we analyze memory accesses in deep learning workloads and find out some unique characteristics differentiated from traditional workloads. First, when comparing instruction and data accesses, data access accounts for 96%–99% of total memory accesses in deep learning workloads, which is quite different from traditional workloads. Second, when comparing read and write accesses,… More >

Fuan Xiao¹, Tong Lai¹, Yutong Guan¹, Jiaming Hong¹, Honglai Zhang¹, Guoyu Yang², Zhengfei Wang^1,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 35-48, 2023, DOI:10.32604/cmc.2023.038937

Abstract Traditional Chinese Medicine (TCM) is one of the most promising programs for disease prevention and treatment. Meanwhile, the quality of TCM has garnered much attention. To ensure the quality of TCM, many works are based on the blockchain scheme to design the traceability scheme of TCM to trace its origin. Although these schemes can ensure the integrity, sharability, credibility, and immutability of TCM more effectively, many problems are exposed with the rapid growth of TCM data in blockchains, such as expensive overhead, performance bottlenecks, and the traditional blockchain architecture is unsuitable for TCM data with dynamic growth. Motivated by the… More >

Deepthi K. Oommen^*, J. Arunnehru

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 793-811, 2023, DOI:10.32604/cmc.2023.038640

Abstract Alzheimer’s Disease (AD) is a progressive neurological disease. Early diagnosis of this illness using conventional methods is very challenging. Deep Learning (DL) is one of the finest solutions for improving diagnostic procedures’ performance and forecast accuracy. The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups. In light of research investigations, it is vital to consider age as one of the key criteria when choosing the subjects. The younger subjects are more susceptible to the perishable side than the older onset. The proposed investigation concentrated on the younger onset. The research used… More >