Chirag Chandrashekar¹, K. P. Vijayakumar^1,*, K. Pradeep¹, A. Balasundaram^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.047697

Abstract The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. This model employs the strength… More >

Zahid Farooq Khan¹, Muhammad Ramzan^1,*, Mudassar Raza¹, Muhammad Attique Khan^2,3, Khalid Iqbal⁴, Taerang Kim⁵, Jae-Hyuk Cha⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1207-1225, 2024, DOI:10.32604/cmc.2023.045491

Abstract Accurate detection and classification of artifacts within the gastrointestinal (GI) tract frames remain a significant challenge in medical image processing. Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases. Key to this is the development of robust algorithms for image classification and detection, crucial in designing sophisticated systems for diagnosis and treatment. This study makes a small contribution to endoscopic image classification. The proposed approach involves multiple operations, including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and Xception. Additionally, feature optimization utilizes the binary dragonfly algorithm… More >

Shaik Thaseentaj, S. Sudhakar Ilango^*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3593-3618, 2023, DOI:10.32604/cmc.2023.042496

Abstract The South Indian mango industry is confronting severe threats due to various leaf diseases, which significantly impact the yield and quality of the crop. The management and prevention of these diseases depend mainly on their early identification and accurate classification. The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks (CNNs) as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees. Our study collected a rich dataset of leaf images representing different disease classes, including Anthracnose, Powdery Mildew, and Leaf Blight. To… More >

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 >

Huanhua Liu, Wei Wang^*, Hanyu Liu, Shuheng Yi, Yonghao Yu, Xunwen Yao

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 459-472, 2024, DOI:10.32604/cmes.2023.029084

Abstract Deep Convolutional Neural Networks (CNNs) have achieved high accuracy in image classification tasks, however, most existing models are trained on high-quality images that are not subject to image degradation. In practice, images are often affected by various types of degradation which can significantly impact the performance of CNNs. In this work, we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model (DTA-ICM) to improve the existing CNNs’ classification accuracy on degraded images. The proposed DTA-ICM comprises two key components: a Degradation Type Predictor (DTP) and a Degradation Type… More >

Ali Ahmadi Shahrakht¹, Parisa Hajirahimi², Omid Rostami³, Diego Martín^4,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3059-3081, 2023, DOI:10.32604/iasc.2023.040502

Abstract As the internet of things (IoT) continues to expand rapidly, the significance of its security concerns has grown in recent years. To address these concerns, physical unclonable functions (PUFs) have emerged as valuable tools for enhancing IoT security. PUFs leverage the inherent randomness found in the embedded hardware of IoT devices. However, it has been shown that some PUFs can be modeled by attackers using machine-learning-based approaches. In this paper, a new deep learning (DL)-based modeling attack is introduced to break the resistance of complex XAPUFs. Because training DL models is a problem that falls under the category of NP-hard… More >

Amjed Al Fahoum^*, Ala’a Zyout, Hiam Alquran, Isam Abu-Qasmieh

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1173-1193, 2023, DOI:10.32604/cmc.2023.038304

Abstract Complex proteins are needed for many biological activities. Folding amino acid chains reveals their properties and functions. They support healthy tissue structure, physiology, and homeostasis. Precision medicine and treatments require quantitative protein identification and function. Despite technical advances and protein sequence data exploration, bioinformatics’ “basic structure” problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved. Protein function inference from amino acid sequences is the main biological data challenge. This study analyzes whether raw sequencing can characterize biological facts. A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.… More >

Shuang Liu^1,2,3, Xiao Liu¹, Shilong Chang¹, Yufeng Sun⁴, Kaiyuan Li¹, Ya Hou¹, Shiwei Wang¹, Jie Meng⁵, Qingliang Zhao⁶, Sibei Wu¹, Kun Yang^1,2,3,*, Linyan Xue^1,2,3,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5837-5852, 2023, DOI:10.32604/cmc.2023.034720

Abstract Achieving accurate classification of colorectal polyps during colonoscopy can avoid unnecessary endoscopic biopsy or resection. This study aimed to develop a deep learning model that can automatically classify colorectal polyps histologically on white-light and narrow-band imaging (NBI) colonoscopy images based on World Health Organization (WHO) and Workgroup serrAted polypS and Polyposis (WASP) classification criteria for colorectal polyps. White-light and NBI colonoscopy images of colorectal polyps exhibiting pathological results were firstly collected and classified into four categories: conventional adenoma, hyperplastic polyp, sessile serrated adenoma/polyp (SSAP) and normal, among which conventional adenoma could be further divided into three sub-categories of tubular adenoma,… More >

S. Kiruthika Devi¹, Amani Abdulrahman Albraikan², Fahd N. Al-Wesabi³, Mohamed K. Nour⁴, Ahmed Ashour⁵, Anwer Mustafa Hilal^6,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3191-3207, 2023, DOI:10.32604/csse.2023.036980

Abstract Artificial Intelligence (AI) and Computer Vision (CV) advancements have led to many useful methodologies in recent years, particularly to help visually-challenged people. Object detection includes a variety of challenges, for example, handling multiple class images, images that get augmented when captured by a camera and so on. The test images include all these variants as well. These detection models alert them about their surroundings when they want to walk independently. This study compares four CNN-based pre-trained models: Residual Network (ResNet-50), Inception v3, Dense Convolutional Network (DenseNet-121), and SqueezeNet, predominantly used in image recognition applications. Based on the analysis performed on… More >

Isam Abu-Qasmieh, Amjed Al Fahoum^*, Hiam Alquran, Ala’a Zyout

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3971-3991, 2023, DOI:10.32604/cmc.2023.037431

Abstract Proteins are essential for many biological functions. For example, folding amino acid chains reveals their functionalities by maintaining tissue structure, physiology, and homeostasis. Note that quantifiable protein characteristics are vital for improving therapies and precision medicine. The automatic inference of a protein’s properties from its amino acid sequence is called “basic structure”. Nevertheless, it remains a critical unsolved challenge in bioinformatics, although with recent technological advances and the investigation of protein sequence data. Inferring protein function from amino acid sequences is crucial in biology. This study considers using raw sequencing to explain biological facts using a large corpus of protein… More >