Xiaozhong Yu^1,2,*, Jinhua Zheng^1,2

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 197-225, 2024, DOI:10.32604/cmc.2023.043943

Abstract In rice production, the prevention and management of pests and diseases have always received special attention. Traditional methods require human experts, which is costly and time-consuming. Due to the complexity of the structure of rice diseases and pests, quickly and reliably recognizing and locating them is difficult. Recently, deep learning technology has been employed to detect and identify rice diseases and pests. This paper introduces common publicly available datasets; summarizes the applications on rice diseases and pests from the aspects of image recognition, object detection, image segmentation, attention mechanism, and few-shot learning methods according to the network structure differences; and… More >

Uma Ramasamy^*, Sundar Santhoshkumar

Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 1-14, 2024, DOI:10.32604/jimh.2023.046995

Abstract More than 140 autoimmune diseases have distinct autoantibodies and symptoms, and it makes it challenging to construct an appropriate model using Machine Learning (ML) for autoimmune disease. Arthritis-related autoimmunity requires special attention. Although many conventional biomarkers for arthritis have been established, more biomarkers of arthritis autoimmune diseases remain to be identified. This review focuses on the research conducted using data obtained from clinical laboratory testing of real-time arthritis patients. The collected data is labelled the Arthritis Profile Data (APD) dataset. The APD dataset is the retrospective data with many missing values. We undertook a comprehensive APD dataset study comprising four… More >

Hassen Louati^1,2, Ali Louati^3,*, Elham Kariri³, Slim Bechikh²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2519-2547, 2024, DOI:10.32604/cmes.2023.030806

Abstract Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues, particularly in the field of lung disease diagnosis. One promising avenue involves the use of chest X-Rays, which are commonly utilized in radiology. To fully exploit their potential, researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems. However, constructing and compressing these systems presents a significant challenge, as it relies heavily on the expertise of data scientists. To tackle this issue, we propose an automated approach that utilizes an evolutionary algorithm (EA) to optimize the design and compression of a convolutional neural network… More >

Deepak Kumar¹, Vinay Kukreja¹, Ayush Dogra^1,*, Bhawna Goyal², Talal Taha Ali³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2097-2121, 2023, DOI:10.32604/cmc.2023.044287

Abstract Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20% every year. The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques. The experienced evaluators take time to identify the disease which is highly laborious and too costly. If wheat rust diseases are predicted at the development stages, then fungicides are sprayed earlier which helps to increase wheat yield quality. To solve the experienced evaluator issues, a combined region extraction and cross-entropy support vector machine (CE-SVM) model is proposed for wheat rust disease identification. In the proposed… More >

AMYLLY SANUELLY DA PAZ MARTINS^1,#, MARLA DE CERQUEIRA ALVES^2,#, ORLANDO ROBERTO PIMENTEL DE ARAÚJO³, FABIANA OLIVEIRA DOS SANTOS CAMATARI⁴, MARÍLIA OLIVEIRA FONSECA GOULART^1,3,5, FABIANA ANDRÉA MOURA^2,6,*

BIOCELL, Vol.47, No.11, pp. 2547-2566, 2023, DOI:10.32604/biocell.2023.043253

Abstract

Curcumin, a natural product, has exhibited promising effects in both animal models and clinical trials, interacting with a multitude of factors linked to Inflammatory Bowel Disease (IBD). These factors encompass cytokines, oxidative stress-associated enzymes, and modulation of the intestinal microbiota. Notably, curcumin has demonstrated therapeutic potential in animal models of colitis, wherein it exerts a negative regulatory influence on pivotal signaling pathways such as PI3/Akt, JAK/STAT, and β-catenin. Moreover, it inhibits the expression of pro-inflammatory enzymes and co-stimulatory molecules (including RANKL, ICAM-1, CD205, CD256, TLR4, among others), while curbing immune cell chemotaxis, thereby attenuating the characteristic neutrophil infiltration observed in… More > Graphic Abstract

QINGPAN BU, PING LI, YUNFEI XIA, XINPEI WEI, KAI SONG^*

BIOCELL, Vol.47, No.11, pp. 2535-2546, 2023, DOI:10.32604/biocell.2023.030781

Abstract Mannose, a different isomer of the hydroxyl group at the C-2 position of glucose, shares the same transport carrier protein with glucose to enter cells and participate in the regulation of glucose metabolism. It affects cell growth, differentiation, and function and plays an active role in tumor immunity and inflammatory processes. This paper provides theoretical support for expanding the clinical applications of mannose by exploring its constitution, metabolic pathways, and role in regulating immune cell function and treating immunology-related diseases. More > Graphic Abstract

MENGYING MAO^1,2,3,#, TING DONG^1,2,3,#, YANJING LIANG^3,4, KEYONG YUAN^1,2,3, QIAOQIAO JIN^1,2,3, PENGFEI ZHANG^1,2,3, ZHENGWEI HUANG^1,2,3,*

BIOCELL, Vol.47, No.10, pp. 2187-2193, 2023, DOI:10.32604/biocell.2023.042357

Abstract The oral microbiota is a vital part of the human microbiota that functions in various physiological processes and is highly relevant to cancers and other diseases. With the alterations of host immune competence, the homeostatic balance existing between the oral microbiota and host may be disturbed and result in the development of diseases. Numerous observations have suggested that small RNAs are key regulators of bacterial pathogenesis and bacteria-host interactions. Further, bacterial small RNAs are considered to be promising biomarkers for the development of novel, and efficacious therapies for oral dysbiosis. Mechanistic insights into how oral pathogens communicate with other bacteria… More > Graphic Abstract

Fayaz Muhammad¹, Jahangir Khan¹, Asad Ullah¹, Fasee Ullah¹, Razaullah Khan², Inayat Khan², Mohammed ElAffendi³, Gauhar Ali^3,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.038748

Abstract IIF (Indirect Immune Florescence) has gained much attention recently due to its importance in medical sciences. The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases. The use of IIF for detecting autoimmune diseases is widespread in different medical areas. Nearly 80 different types of autoimmune diseases have existed in various body parts. The IIF has been used for image classification in both ways, manually and by using the Computer-Aided Detection (CAD) system. The data scientists conducted various research works using an automatic CAD system with low accuracy. The diseases in the human body… More >

Uzair Aslam Bhatti^1,*, Sibghat Ullah Bazai², Shumaila Hussain¹, Shariqa Fakhar³, Chin Soon Ku^4,*, Shah Marjan⁵, Por Lip Yee⁶, Liu Jing¹

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 681-697, 2023, DOI:10.32604/cmc.2023.037958

Abstract Crop diseases have a significant impact on plant growth and can lead to reduced yields. Traditional methods of disease detection rely on the expertise of plant protection experts, which can be subjective and dependent on individual experience and knowledge. To address this, the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification. In this paper, we propose a novel approach that utilizes a convolutional neural network (CNN) model in conjunction with Inception v3 to identify plant leaf diseases. The research focuses on developing a mobile application that leverages… More >

Mehwish Zafar¹, Javeria Amin², Muhammad Sharif¹, Muhammad Almas Anjum³, Seifedine Kadry^4,5,6, Jungeun Kim^7,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2779-2793, 2023, DOI:10.32604/cmc.2023.035860

Abstract Worldwide cotton is the most profitable cash crop. Each year the production of this crop suffers because of several diseases. At an early stage, computerized methods are used for disease detection that may reduce the loss in the production of cotton. Although several methods are proposed for the detection of cotton diseases, however, still there are limitations because of low-quality images, size, shape, variations in orientation, and complex background. Due to these factors, there is a need for novel methods for features extraction/selection for the accurate cotton disease classification. Therefore in this research, an optimized features fusion-based model is proposed,… More >