Chien-Ming Chen¹, Zhenzhou Zhang¹, Jimmy Ming-Tai Wu¹, Kuruva Lakshmanna^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 733-759, 2023, DOI:10.32604/cmes.2023.027463

Abstract Periodic pattern mining has become a popular research subject in recent years; this approach involves the discovery of frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pattern mining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodic patterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequences is more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences is important. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To address existing problems, three… More >

Ghofran Kh. Shraida¹, Hameed A. Younis¹, Taief Alaa Al-Amiedy², Mohammed Anbar^2,*, Hussain A. Younis^3,4, Iznan H. Hasbullah²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2641-2654, 2023, DOI:10.32604/cmc.2023.035793

Abstract Nowadays, high-resolution images pose several challenges in the context of image encryption. The encryption of huge images’ file sizes requires high computational resources. Traditional encryption techniques like, Data Encryption Standard (DES), and Advanced Encryption Standard (AES) are not only inefficient, but also less secure. Due to characteristics of chaos theory, such as periodicity, sensitivity to initial conditions and control parameters, and unpredictability. Hence, the characteristics of deoxyribonucleic acid (DNA), such as vast parallelism and large storage capacity, make it a promising field. This paper presents an efficient color image encryption method utilizing DNA encoding with two types of hyper-chaotic maps.… More >

Yuan-Nong Ye^1,2,3,*, Ding-Fa Liang², Abraham Alemayehu Labena⁴, Zhu Zeng^2,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2731-2741, 2023, DOI:10.32604/iasc.2023.026761

Abstract Essential ncRNA is a type of ncRNA which is indispensable for the survival of organisms. Although essential ncRNAs cannot encode proteins, they are as important as essential coding genes in biology. They have got wide variety of applications such as antimicrobial target discovery, minimal genome construction and evolution analysis. At present, the number of species required for the determination of essential ncRNAs in the whole genome scale is still very few due to the traditional methods are time-consuming, laborious and costly. In addition, traditional experimental methods are limited by the organisms as less than 1% of bacteria can be cultured… More >

S. Kayalvizhi^*, S. Malarvizhi

Computer Systems Science and Engineering, Vol.46, No.1, pp. 1201-1214, 2023, DOI:10.32604/csse.2023.032381

Abstract In the Digital World scenario, the confidentiality of information in video transmission plays an important role. Chaotic systems have been shown to be effective for video signal encryption. To improve video transmission secrecy, compressive encryption method is proposed to accomplish compression and encryption based on fractional order hyper chaotic system that incorporates Compressive Sensing (CS), pixel level, bit level scrambling and nucleotide Sequences operations. The measurement matrix generates by the fractional order hyper chaotic system strengthens the efficiency of the encryption process. To avoid plain text attack, the CS measurement is scrambled to its pixel level, bit level scrambling decreases… More >

Bao Rong Chang¹, Hsiu-Fen Tsai^2,*, Han-Lin Chou¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 107-134, 2023, DOI:10.32604/cmes.2023.024018

Abstract Our previous work has introduced the newly generated program using the code transformation model GPT-2, verifying the generated programming codes through simhash (SH) and longest common subsequence (LCS) algorithms. However, the entire code transformation process has encountered a time-consuming problem. Therefore, the objective of this study is to speed up the code transformation process significantly. This paper has proposed deep learning approaches for modifying SH using a variational simhash (VSH) algorithm and replacing LCS with a piecewise longest common subsequence (PLCS) algorithm to faster the verification process in the test phase. Besides the code transformation model GPT-2, this study has… More > Graphic Abstract

XIAOJUN ZHOU^1,*, MENGXUE LIU¹, LINLIN SONG²

BIOCELL, Vol.47, No.3, pp. 657-668, 2023, DOI:10.32604/biocell.2023.026781

Abstract Azalea is a general designation of Rhododendron in the Ericaceae family. Rhododendron not only has high ornamental value but also has application value in ecological protection, medicine, and scientific research. In this study, we used Illumina and PacBio sequencing to assemble and annotate the entire chloroplast genomes (cp genomes) of four Rhododendron species. The chloroplast genomes of R. concinnum, R. henanense subsp. lingbaoense, R. micranthum, and R. simsii were assembled into 207,236, 208,015, 207,233, and 206,912 bp, respectively. All chloroplast genomes contain eight rRNA genes, with either 88 or 89 protein-coding genes. The four cp genomes were compared and analyzed… More >

Umair Muneer Butt^1,2,*, Hadiqa Aman Ullah², Sukumar Letchmunan¹, Iqra Tariq², Fadratul Hafinaz Hassan¹, Tieng Wei Koh³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5017-5033, 2023, DOI:10.32604/cmc.2023.035512

Abstract Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models… More >

Ahmed Zohair Ibrahim^1,*, P. Prakash², V. Sakthivel², P. Prabu³

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2447-2460, 2023, DOI:10.32604/csse.2023.030134

Abstract In order to research brain problems using MRI, PET, and CT neuroimaging, a correct understanding of brainfunction is required. This has been considered in earlier times with the support of traditional algorithms. Deep learning process has also been widely considered in these genomics data processing system. In this research, brain disorder illness incliding Alzheimer’s disease, Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods. Moeover, deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks (DBN). Images are stored in… More >

Adel A. Rezk^1,2,*, Hala A. Amin²

Phyton-International Journal of Experimental Botany, Vol.92, No.3, pp. 691-705, 2023, DOI:10.32604/phyton.2022.024208

Abstract Citrus Tristeza Virus (CTV), usually occurs in nature as a mixture of genotypes. Six naturally infected citrus (Citrus sinensis) trees grafted on sour orange rootstock were collected from three citrus growing governorates in Egypt (Sharqia, Qalyubia and Garbia). In this study, RT-PCR, Single-Strand Conformation Polymorphism (SSCP) and nucleotide sequence analysis were used for four independent CTV genomic regions (p65, p18, p20, and p23) to detect and assess the sequence and genetic variabilities among CTV Egyptian isolates. RTPCR products (650 bp) for the CTV p23 gene obtained from the selected isolates were used for the SSCP analysis and DNA sequencing. SSCP… More >

A. Jameer Basha^1,*, M. Ramya Devi², S. Lokesh¹, P. Sivaranjani³, D. Mansoor Hussain⁴, Venkat Padhy⁵

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1483-1493, 2023, DOI:10.32604/csse.2023.021540

Abstract Data transmission through a wireless network has faced various signal problems in the past decades. The orthogonal frequency division multiplexing (OFDM) technique is widely accepted in multiple data transfer patterns at various frequency bands. A recent wireless communication network uses OFDM in long-term evolution (LTE) and 5G, among others. The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network. This transmission loss is called peak-to-average power ratio (PAPR). This wireless signal distortion can be reduced using various techniques. This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.… More >