K. K. Thyagharajan, I. Kiruba Raji^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2061-2076, 2021, DOI:10.32604/cmc.2021.017591

Abstract This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model (F-HOBINM) and adaptive neuro classifier (ANFIS). India exports USD 0.28-million worth of neem leaf to the UK, USA, UAE, and Europe in the form of dried leaves and powder, both of which help reduce diabetes-related issues, cardiovascular problems, and eye disorders. Diagnosing neem leaf disease is difficult through visual interpretation, owing to similarity in their color and texture patterns. The most common diseases include bacterial blight, Colletotrichum and Alternaria leaf spot, blight, damping-off, powdery mildew, Pseudocercospora leaf spot, leaf web… More >

K. Muthumayil¹, S. Manikandan², S. Srinivasan³, José Escorcia-Gutierrez^4,*, Margarita Gamarra⁵, Romany F. Mansour⁶

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2031-2044, 2021, DOI:10.32604/cmc.2021.017116

Abstract White Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten the existence of human beings. In spite of its prevalence and serious consequences, it is mostly diagnosed through manual practices. The risks of inappropriate, sub-standard and wrong or biased diagnosis are high in manual methods. So, there is a need exists for automatic diagnosis and classification method that can replace the manual process. Leukemia is mainly classified into acute and chronic types. The current research work proposed a computer-based application to classify the disease. In the feature extraction stage, we use excellent physical properties to… More >

Wei Zhang^1,2, Qihui Ling^1,2,*, Qiancheng Zhao^1,2, Hushu Wu³

Energy Engineering, Vol.118, No.5, pp. 1515-1535, 2021, DOI:10.32604/EE.2021.014162

Abstract The condition monitoring and fault diagnosis have been identified as the key to achieving higher availabilities of wind turbines. Numerous studies show that the open-circuit fault is a significant contributor to the failures of wind turbine converter. However, the multiple faults combinations and the influence of wind speed changes abruptly, grid voltage sags and noise interference have brought great challenges to fault diagnosis. Accordingly, concerning the open-circuit fault of converters in direct-driven PMSG wind turbine, a diagnostic method for multiple open-circuit faults is proposed in this paper, which is divided into two tasks: The first one is the fault detection… More >

S. Narayan^1,*, M. U. Kaisan², Shitu Abubakar², Faisal O. Mahroogi³, Vipul Gupta⁴

Sound & Vibration, Vol.55, No.3, pp. 221-234, 2021, DOI:10.32604/sv.2021.016539

Abstract The health monitoring has been studied to ensure integrity of design of engine structure by detection, quantification, and prediction of damages. Early detection of faults may allow the downtime of maintenance to be rescheduled, thus preventing sudden shutdown of machines. In cylinder pressure developed, vibrations and noise emissions data provide a rich source of information about condition of engines. Monitoring of vibrations and noise emissions are novel non-intrusive methodologies for which positioning of various transducers are important issue. The presented work shows applicability of these diagnosis methodologies adopted in case of diesel engines. The effects of changing various fuel injection… More >

Mohammed Faisal^1,*, Fahad Albogamy², Hebah ElGibreen³, Mohammed Algabri³, Syed Ahad M. Alvi¹, Mansour Alsulaiman³

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 649-667, 2021, DOI:10.32604/iasc.2021.017898

Abstract COVID-19 was first discovered in Wuhan, China, in December 2019 and has since spread worldwide. An automated and fast diagnosis system needs to be developed for early and effective COVID-19 diagnosis. Hence, we propose two- and three-classifier diagnosis systems for classifying COVID-19 cases using transfer-learning techniques. These systems can classify X-ray images into three categories: healthy, COVID-19, and pneumonia cases. We used two X-ray image datasets (DATASET-1 and DATASET-2) collected from state-of-the-art studies and train the systems using deep learning architectures, such as VGG-19, NASNet, and MobileNet2, on these datasets. According to the validation and testing results, our proposed diagnosis… More >

Yongtao Yu, Guangdong Zou^*

Oncologie, Vol.23, No.2, pp. 293-301, 2021, DOI:10.32604/Oncologie.2021.015520

Abstract Objective: The purpose was to explore the value of multi-slice spiral CT (MSCT) multiplanar reconstruction technique in the diagnosis and clinicopathological analysis of gastrointestinal lymphoma (GIL). Methods: 82 GIL patients treated in our hospital from February 2018 to February 2019 were selected as the experimental group of this study, and 82 patients with other gastrointestinal tumors diagnosed by pathology during the same period were selected as the control group. Both groups of patients were scanned by MSCT and analyzed by multiplanar reconstruction technique to compare the diagnostic results and clinicopathological indexes of the two groups. Results: The diagnostic accuracy of… More >

Xin Hu^1,2,#, Fangyu Jing^3,#, Qingjun Wang^1,4, Linyang Shi¹, Yunfeng Cao^4,5, Zhitu Zhu^1,4,*

Oncologie, Vol.23, No.2, pp. 203-217, 2021, DOI:10.32604/Oncologie.2021.016155

Abstract It is noteworthy that colon cancer is the fourth place in new cases and the fifth in mortalities according to global cancer statistics 2018. Tumorigenesis displays specific correlation with metabolic alterations. A variety of metabolites, including ornithine (Orn), are related to colon cancer according to sources of disease metabolic information retrieval in human metabolome database. The metabolic regulation of Orn pathway is a key link in the survival of cancer cells. In this study, the plasma Orn levels in colon cancer patients and healthy participants were measured by liquid chromatography tandem mass spectrometry, and the metabolic disturbances of Orn in… More >

Zheng Wang¹, Yongxia He¹, Yuwei Song¹, Yue Wang², Feng Chen^3,*

Oncologie, Vol.23, No.2, pp. 177-184, 2021, DOI:10.32604/Oncologie.2021.014773

Abstract Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality. At present, diagnostic methods such as imaging observation, serum testing and tissue biopsy, as well as treatment methods such as surgical resection, radiotherapy, and chemotherapy have certain limitations in clinical interventions for HCC due to the complex pathogenesis and drug resistance of liver cancer, which seriously affect the survival and prognosis of patients. As a large-scale cytokine, microRNA (miRNA) plays an important role in regulating various life activities of cells. Extensive evidence proved that certain miRNAs are specifically expressed in the tissues and blood of HCC patients, and… More >

Walid El-Shafai¹, Noha A. El-Hag², Ghada M. El-Banby³, Ashraf A. M. Khalaf², Naglaa F. Soliman^4,*, Abeer D. Algarni⁴, Fathi E. Abd El-Samie^1,4

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1323-1341, 2021, DOI:10.32604/cmc.2021.017385

Abstract Corona Virus Disease-2019 (COVID-19) continues to spread rapidly in the world. It has dramatically affected daily lives, public health, and the world economy. This paper presents a segmentation and classification framework of COVID-19 images based on deep learning. Firstly, the classification process is employed to discriminate between COVID-19, non-COVID, and pneumonia by Convolutional Neural Network (CNN). Then, the segmentation process is applied for COVID-19 and pneumonia CT images. Finally, the resulting segmented images are used to identify the infected region, whether COVID-19 or pneumonia. The proposed CNN consists of four Convolutional (Conv) layers, four batch normalization layers, and four Rectified… More >

Dileep Kumar^1,*, Imtiaz Hussain Kalwar², Tanweer Hussain¹, Bhawani Shankar Chowdhry¹, Sanaullah Mehran Ujjan¹, Tayab Din Memon³

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 393-408, 2021, DOI:10.32604/cmc.2021.014941

Abstract Reliability of rotating machines is highly dependent on the smooth rolling of bearings. Thus, it is very essential for reliable operation of rotating machines to monitor the working condition of bearings using suitable fault diagnosis and condition monitoring approach. In the recent past, Deep Learning (DL) has become applicable in condition monitoring of rotating machines owing to its performance. This paper proposes a novel bearing fault diagnosis method based on the processing and analysis of the vibration images. The proposed method is the UNET model that is a recent development in DL models. The model is applied to the 2D… More >