Ahsan Aziz¹, Muhammad Attique¹, Usman Tariq², Yunyoung Nam^3,*, Muhammad Nazir¹, Chang-Won Jeong⁴, Reham R. Mostafa⁵, Rasha H. Sakr⁶

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2653-2670, 2021, DOI:10.32604/cmc.2021.018606

Abstract Owing to technological developments, Medical image analysis has received considerable attention in the rapid detection and classification of diseases. The brain is an essential organ in humans. Brain tumors cause loss of memory, vision, and name. In 2020, approximately 18,020 deaths occurred due to brain tumors. These cases can be minimized if a brain tumor is diagnosed at a very early stage. Computer vision researchers have introduced several techniques for brain tumor detection and classification. However, owing to many factors, this is still a challenging task. These challenges relate to the tumor size, the shape of a tumor, location of… More >

Ameer A. Badr^1,2,*, Alia K. Abdul-Hassan²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1713-1729, 2021, DOI:10.32604/cmc.2021.016732

Abstract Age estimation in short speech utterances finds many applications in daily life like human-robot interaction, custom call routing, targeted marketing, user-profiling, etc. Despite the comprehensive studies carried out to extract descriptive features, the estimation errors (i.e. years) are still high. In this study, an automatic system is proposed to estimate age in short speech utterances without depending on the text as well as the speaker. Firstly, four groups of features are extracted from each utterance frame using hybrid techniques and methods. After that, 10 statistical functionals are measured for each extracted feature dimension. Then, the extracted feature dimensions are normalized… More >

Imran Ashraf, Soojung Hur, Yousaf Bin Zikria, Yongwan Park^*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2597-2620, 2021, DOI:10.32604/cmc.2021.016214

Abstract Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors: Smartphone heterogeneity and smaller data lengths. The use of multifarious smartphones cripples the performance of such approaches owing to the variability of the magnetic field data. In the same vein, smaller lengths of magnetic field data decrease the localization accuracy substantially. The current study proposes the use of multiple neural networks like deep neural network (DNN), long short term memory network (LSTM), and gated recurrent unit network (GRN) to perform indoor localization based on the embedded magnetic sensor of the smartphone. A voting scheme… More >

Yasemin Gültepe^*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2015-2028, 2021, DOI:10.32604/cmc.2021.014631

Abstract In 2018, 1.76 million people worldwide died of lung cancer. Most of these deaths are due to late diagnosis, and early-stage diagnosis significantly increases the likelihood of a successful treatment for lung cancer. Machine learning is a branch of artificial intelligence that allows computers to quickly identify patterns within complex and large datasets by learning from existing data. Machine-learning techniques have been improving rapidly and are increasingly used by medical professionals for the successful classification and diagnosis of early-stage disease. They are widely used in cancer diagnosis. In particular, machine learning has been used in the diagnosis of lung cancer… More >

Yinjie Wang, Yongxia Zhang, Qingquan Liu, Liangqin Liu, Suzhen Huang, Haiyan Yuan^*

Phyton-International Journal of Experimental Botany, Vol.90, No.1, pp. 277-290, 2021, DOI:10.32604/phyton.2020.011545

Abstract Quantitative real-time PCR (qPCR) is an effective and widely used method to analyze expression patterns of target genes. Selection of stable reference genes is a prerequisite for accurate normalization of target gene expression by qRT-PCR. In Iris germanica L., no studies have yet been published regarding the evaluation of potential reference genes. In this study, nine candidate reference genes were assessed at different flower developmental stages and in different tissues by four different algorithms (GeNorm, NormFinder, BestKeeper, and RefFinder). The results revealed that ACT11 (Actin 11) and EF1α (Elongation factor 1 alpha) were the most stable reference genes in different… More >

B. Indira^1,*, K. Valarmathi²

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 795-805, 2020, DOI:10.32604/iasc.2020.010114

Abstract Packet classification is a major bottleneck in Software Defined Network (SDN). Each packet has to be classified based on the action specified in each rule in the given flow table. To perform classification, the system requires much of the CPU clock time. Therefore, developing an efficient packet classification algorithm is critical for high speed inter networking. Existing works make use of exact matching, range matching and longest prefix matching for classification and these techniques sometime enlarges rule databases, thus resulting in huge memory consumption and inefficient searching performance. In order to select an efficient packet classification algorithm with less memory… More >

Swathypriyadharsini P^1,∗, K.Premalatha^2,†

Computer Systems Science and Engineering, Vol.35, No.1, pp. 39-50, 2020, DOI:10.32604/csse.2020.35.039

Abstract Microarrays have reformed biotechnological research in the past decade. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks with larger volume of genes also increases the challenges of comprehending and interpretation of the resulting mass of data. Clustering addresses these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding… More >

Lu Zhu, Qiuyue Ma, Shushun Li, Jing Wen, Kunyuan Yan, Qianzhong Li^*

Phyton-International Journal of Experimental Botany, Vol.89, No.2, pp. 385-403, 2020, DOI:10.32604/phyton.2020.09259

Abstract Quantitative real-time reverse transcriptase PCR (qRT-PCR) technology has been extensively used to estimate gene expression levels, and the selection of appropriate reference genes for qRT-PCR analysis is critically important for obtaining authentic normalized data. Acer palmatum is an important colorful leaf ornamental tree species, and reference genes suitable for normalization of the qRT-PCR data obtained from this species have not been investigated. In this study, the expression stability of ten candidate reference genes, namely, Actin3, Actin6, Actin9, EF1α, PP2A, SAMDC, TIP41, TUBα, TUBβ and UBQ10, in two distinct tissues (leaves and roots) of A. palmatum under four different abiotic stress… More >

Gang Li¹, Yao Zhou², Yaqi Zhao², Yaxue Liu², Yuwei Ke², Xiaoqing Jin¹, Haoli Ma^1,2,*

Phyton-International Journal of Experimental Botany, Vol.89, No.2, pp. 329-344, 2020, DOI:10.32604/phyton.2020.08874

Abstract Quantitative real-time PCR (qRT-PCR) is widely used for investigating gene expression patterns and has many advantages, including its high sensitivity, fidelity, and specificity. Selecting a satisfactory internal reference gene is crucial for obtaining precise gene expression results in qRT-PCR analyses. In this study, the transcriptomic data of 2 potato varieties were screened for housekeeping genes with stable expression patterns. A total of 77 putative genes were selected, which were highly and stably expressed. Then, qRT-PCR analyses were performed to examine the expression levels of these 77 candidate reference genes in various potato tissues, including leaves, flowers, stolons, and tubers. Gene… More >

Jie Wu^∗,†, Yan Cai^‡, Shixiong Xu^§, Quan Long^¶, Zurong Ding^*, Cheng Dong^∗,||

Molecular & Cellular Biomechanics, Vol.9, No.2, pp. 95-126, 2012, DOI:10.3970/mcb.2012.009.095

Abstract The effects of vascular-disrupting treatments on normalization of tumor microvasculature and its microenvironmental flow were investigated, by mathematical modeling and numerical simulation of tumor vascular-disrupting and tumor haemodynamics. Four disrupting approaches were designed according to the abnormal characteristics of tumor microvasculature compared with the normal one. The results predict that the vascular-disrupting therapies could improve tumor microenvironment, eliminate drug barrier and inhibit metastasis of tumor cells to some extent. Disrupting certain types of vessels may get better effects. In this study, the flow condition on the networks with "vascular-disrupting according to flowrate" is the best comparing with the other three… More >