Xianfeng Zhou^1,2,*, Ruiju Sun¹, Zhaojie Zhang¹, Yuanyuan Song¹, Lijiao Jin¹, Lin Yuan³

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 503-519, 2025, DOI:10.32604/phyton.2025.060827 - 06 March 2025

Abstract An accurate and robust estimation of leaf chlorophyll content (LCC) is very important to better know the process of material and energy exchange between plants and the environment. Compared with traditional remote sensing methods, abundant research has made progress in agronomic parameter retrieval using different CNN frameworks. Nevertheless, limited reports have paid attention to the problems, i.e., limited measured data, hyperspectral redundancy, and model convergence issues, when concerning CNN models for parameter estimation. Therefore, the present study tried to analyze the effects of synthetic data size expansion employing a Gaussian process regression (GPR) model for… More >

Libo Han^1,2, Sha Tao^1,2, Wen Xia³, Weixin Sun³, Li Yan³, Wanlin Gao^1,2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2431-2449, 2025, DOI:10.32604/cmc.2024.057833 - 17 February 2025

Abstract At present, salient object detection (SOD) has achieved considerable progress. However, the methods that perform well still face the issue of inadequate detection accuracy. For example, sometimes there are problems of missed and false detections. Effectively optimizing features to capture key information and better integrating different levels of features to enhance their complementarity are two significant challenges in the domain of SOD. In response to these challenges, this study proposes a novel SOD method based on multi-strategy feature optimization. We propose the multi-size feature extraction module (MSFEM), which uses the attention mechanism, the multi-level feature… More >

Abdullah Alabdulatif¹, Navod Neranjan Thilakarathne^2,*, Mohamed Aashiq^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3655-3683, 2024, DOI:10.32604/cmc.2024.054610 - 12 September 2024

Abstract The increasing prevalence of Internet of Things (IoT) devices has introduced a new phase of connectivity in recent years and, concurrently, has opened the floodgates for growing cyber threats. Among the myriad of potential attacks, Denial of Service (DoS) attacks and Distributed Denial of Service (DDoS) attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic. As IoT devices often lack the inherent security measures found in more mature computing platforms, the need for robust DoS/DDoS detection systems tailored to IoT is paramount for… More >

Ejaz Khan¹, Muhammad Zia Ur Rehman², Fawad Ahmed³, Suliman A. Alsuhibany^4,*, Muhammad Zulfiqar Ali⁵, Jawad Ahmad⁶

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3799-3814, 2023, DOI:10.32604/csse.2023.037131 - 03 April 2023

Abstract In 2020, COVID-19 started spreading throughout the world. This deadly infection was identified as a virus that may affect the lungs and, in severe cases, could be the cause of death. The polymerase chain reaction (PCR) test is commonly used to detect this virus through the nasal passage or throat. However, the PCR test exposes health workers to this deadly virus. To limit human exposure while detecting COVID-19, image processing techniques using deep learning have been successfully applied. In this paper, a strategy based on deep learning is employed to classify the COVID-19 virus. To… More >

Mousa Alhajlah^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6161-6175, 2023, DOI:10.32604/cmc.2023.032505 - 28 December 2022

Abstract Automated Facial Expression Recognition (FER) serves as the backbone of patient monitoring systems, security, and surveillance systems. Real-time FER is a challenging task, due to the uncontrolled nature of the environment and poor quality of input frames. In this paper, a novel FER framework has been proposed for patient monitoring. Preprocessing is performed using contrast-limited adaptive enhancement and the dataset is balanced using augmentation. Two lightweight efficient Convolution Neural Network (CNN) models MobileNetV2 and Neural search Architecture Network Mobile (NasNetMobile) are trained, and feature vectors are extracted. The Whale Optimization Algorithm (WOA) is utilized to More >

Ayman Altameem¹, Sandeep Kumar², Ramesh Chandra Poonia³, Abdul Khader Jilani Saudagar^4,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4719-4736, 2022, DOI:10.32604/cmc.2022.022177 - 14 January 2022

Abstract Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes More >

K. Yuvaraj^*, U. S. Ragupathy

Computer Systems Science and Engineering, Vol.40, No.3, pp. 823-834, 2022, DOI:10.32604/csse.2022.018837 - 24 September 2021

Abstract This research implements a novel segmentation of mammographic mass. Three methods are proposed, namely, segmentation of mass based on iterative active contour, automatic region growing, and fully automatic mask selection-based active contour techniques. In the first method, iterative threshold is performed for manual cropped preprocessed image, and active contour is applied thereafter. To overcome manual cropping in the second method, an automatic seed selection followed by region growing is performed. Given that the result is only a few images owing to over segmentation, the third method uses a fully automatic active contour. Results of the… More >

Husam Ali Abdulmohsin^1,*, Hala Bahjat Abdul Wahab², Abdul Mohssen Jaber Abdul Hossen³

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3997-4016, 2021, DOI:10.32604/cmc.2021.014840 - 06 May 2021

Abstract

Feature selection (FS) (or feature dimensional reduction, or feature optimization) is an essential process in pattern recognition and machine learning because of its enhanced classification speed and accuracy and reduced system complexity. FS reduces the number of features extracted in the feature extraction phase by reducing highly correlated features, retaining features with high information gain, and removing features with no weights in classification. In this work, an FS filter-type statistical method is designed and implemented, utilizing a t-test to decrease the convergence between feature subsets by calculating the quality of performance value (QoPV). The approach utilizes

… More >