Ayesha Bin T. Tahir¹, Muhamamd Attique Khan¹, Majed Alhaisoni², Junaid Ali Khan¹, Yunyoung Nam^3,*, Shui-Hua Wang⁴, Kashif Javed⁵

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1099-1116, 2021, DOI:10.32604/cmc.2021.015154 - 22 March 2021

Abstract Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two More >

Hamid A. Jalab¹, Ala’a R. Al-Shamasneh¹, Hadil Shaiba², Rabha W. Ibrahim^3,4,*, Dumitru Baleanu^5,6,7

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2061-2075, 2021, DOI:10.32604/cmc.2021.015170 - 05 February 2021

Abstract Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI… More >

Ch. Srinivas^1,*, K. Chandrabhushana Rao²

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1801-1817, 2021, DOI:10.32604/cmc.2021.014981 - 05 February 2021

Abstract In this paper we propose an efficient process of physiological artifact elimination methodology from brain waves (BW), which are also commonly known as electroencephalogram (EEG) signal. In a clinical environment during the acquisition of BW several artifacts contaminates the actual BW component. This leads to inaccurate and ambiguous diagnosis. As the statistical nature of the EEG signal is more non-stationery, adaptive filtering is the more promising method for the process of artifact elimination. In clinical conditions, the conventional adaptive techniques require many numbers of computational operations and leads to data samples overlapping and instability of… More >

Rabia Javed^1,2, Mohd Shafry Mohd Rahim¹, Tanzila Saba³, Suliman Mohamed Fati³, Amjad Rehman^3,*, Usman Tariq⁴

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2337-2352, 2021, DOI:10.32604/cmc.2021.014677 - 05 February 2021

Abstract Most of the melanoma cases of skin cancer are the life-threatening form of cancer. It is prevalent among the Caucasian group of people due to their light skin tone. Melanoma is the second most common cancer that hits the age group of 15–29 years. The high number of cases has increased the importance of automated systems for diagnosing. The diagnosis should be fast and accurate for the early treatment of melanoma. It should remove the need for biopsies and provide stable diagnostic results. Automation requires large quantities of images. Skin lesion datasets contain various kinds… More >

Xia Wang¹, Xing Deng^2,3, Hongming Shen^1,*, Guodong Zhang¹, Shibing Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 693-710, 2021, DOI:10.32604/cmes.2021.013186 - 21 January 2021

Abstract Speech intelligibility enhancement in noisy environments is still one of the major challenges for hearing impaired in everyday life. Recently, Machine-learning based approaches to speech enhancement have shown great promise for improving speech intelligibility. Two key issues of these approaches are acoustic features extracted from noisy signals and classifiers used for supervised learning. In this paper, features are focused. Multi-resolution power-normalized cepstral coefficients (MRPNCC) are proposed as a new feature to enhance the speech intelligibility for hearing impaired. The new feature is constructed by combining four cepstrum at different time–frequency (T–F) resolutions in order to… More >

Muhammad Attique Khan¹, Nazar Hussain¹, Abdul Majid¹, Majed Alhaisoni², Syed Ahmad Chan Bukhari³, Seifedine Kadry⁴, Yunyoung Nam^5,*, Yu-Dong Zhang⁶

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2923-2938, 2021, DOI:10.32604/cmc.2021.013191 - 28 December 2020

Abstract In medical imaging, computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis. In response to the coronavirus 2019 (COVID-19) pandemic, new testing procedures, medical treatments, and vaccines are being developed rapidly. One potential diagnostic tool is a reverse-transcription polymerase chain reaction (RT-PCR). RT-PCR, typically a time-consuming process, was less sensitive to COVID-19 recognition in the disease’s early stages. Here we introduce an optimized deep learning (DL) scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography (CT) scans. In the proposed method,… More >

Mangena Venu Madhavan¹, Dang Ngoc Hoang Thanh², Aditya Khamparia^1,*, Sagar Pande¹, Rahul Malik¹, Deepak Gupta³

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2939-2955, 2021, DOI:10.32604/cmc.2021.012466 - 28 December 2020

Abstract Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying More >

Tianyuan Li^1,#, Saif Ullah^1,#, He Liang¹, Izhar Ali¹, Quan Zhao¹, Anas Iqbal¹, Shanqing Wei¹, Tariq Shah², Yuqiong Luo¹, Ligeng Jiang^1,*

Phyton-International Journal of Experimental Botany, Vol.90, No.1, pp. 223-243, 2021, DOI:10.32604/phyton.2020.012065 - 20 November 2020

Abstract Readily available chemical fertilizers have resulted in a decline in the use of organic manure (e.g., green manures), a traditionally sustainable source of nutrients. Based on this, we applied urea at the rate of 270 kg ha⁻¹ with and without green manure in order to assess nitrogen (N) productivity in a double rice cropping system in 2017. In particular, treatment combinations were as follows: winter fallow rice-rice (WF-R-R), milk vetch rice-rice (MV-R-R), oil-seed rape rice-rice (R-R-R) and potato crop rice-rice (P-R-R). Results revealed that green manure significantly (p ≤ 0.05) improved the soil chemical properties and… More >

A.K. GHOSH¹, RUTUJA S. BHOJE², R.C. BINDAL¹

Journal of Polymer Materials, Vol.37, No.1-2, pp. 109-120, 2020, DOI:10.32381/JPM.2020.37.1-2.8

Abstract In this study, cellulose triacetate (CTA) based nanocomposite membranes were developed by incorporation of carboxylic acid functionalized multiwalled carbon-nanotube (cMWCNT) and graphene oxide (GO) which have enhancement of both flux and fouling resistance properties of the membranes. Membranes were casted at room temperature and annealed at 90o C hot water for 10 minutes. The incorporation level of both the nanomaterials is 1.5% of the CTA polymer weight in the nanocomposite membranes. Prepared membranes were characterized in terms of water contact angle, surface morphology and mechanical strength. The performance of the membranes was evaluated both in… More >

S. Anand^a, S. Suresh^a, R. Dhanuskodi^b, D. Santhosh Kumar^b,*

Frontiers in Heat and Mass Transfer, Vol.14, pp. 1-9, 2020, DOI:10.5098/hmt.14.8

Abstract The present work investigates the wall temperature prediction at supercritical pressure of water by CFD and compares the prediction of CFD and that of 11 empirical correlations available in literature. Supercritical-water heat transfer experimental data, covering a mass flux range of 400-1500 kg/m²s, heat flux range of 150-1000 kW/m², at pressure 241 bar and diameter 10 mm tube, were obtained from literature. CFD simulations have been carried out for those operating conditions and compared with experimental data. Around 362 experimental wall temperature data of both heat transfer enhancement and heat transfer deterioration region have been taken More >