Amit Prakash Singh¹, Kajal Kaul^1,*, Anuradha Chug¹, Ravinder Kumar², Veerubommu Shanmugam²

Journal on Artificial Intelligence, Vol.7, pp. 451-468, 2025, DOI:10.32604/jai.2025.070773 - 06 November 2025

Abstract In agricultural farms in India where the staple diet for most of the households is potato, plant leaf diseases, namely Potato Early Blight (PEB) and Potato Late Blight (PLB), are quite common. The class label Plant Healthy (PH) is also used. If these diseases are not identified early, they can cause massive crop loss and thereby incur huge economic losses to the farmers in the agricultural domain and can impact the gross domestic product of the nation. This paper presents a hybrid approach for potato plant disease severity estimation and classification of diseased and healthy… More >

Ahmed Mahmoud Ismail^1,*, Eman Said Elshewy², Ayman Y. Ahmed³, Hossam M. Darrag⁴

Phyton-International Journal of Experimental Botany, Vol.94, No.10, pp. 3023-3045, 2025, DOI:10.32604/phyton.2025.067444 - 29 October 2025

Abstract Powdery mildew of cucumber (Cucumis sativus L.) is a destructive disease caused by Podosphaera xanthii (Castagne) U.Braun & Shishkoff. This study aimed to investigate the antifungal effect of extracts of Ulva lactuca, Spirulina platensis, and Nostoc muscorum against P. xanthii and to improve the physiological and morphological traits of cucumber under commercial greenhouse conditions. The chemical composition of the individual extracts from U. lactuca, S. platensis, and N. muscorum was analyzed utilizing High-performance Liquid Chromatography (HPLC) and Gas Chromatography/Mass spectrometry (GC/MS). Cucumber plants were sprayed twice with 5% of the crude extracts of U. lactuca, S. platensis, and N. muscorum and their mixture (U. lactuca, S. platensis, and N. muscorum).… More >

Nur Nabihah Ghazali¹, Noraain Mohamed Saraf^1,*, Abdul Rauf Abdul Rasam^1,*, Ainon Nisa Othman¹, Siti Aekbal Salleh¹, Nurhafiza Md Saad²

Revue Internationale de Géomatique, Vol.34, pp. 89-101, 2025, DOI:10.32604/rig.2025.057562 - 24 February 2025

Abstract Forest fires are contributing significantly to the acceleration of deforestation. Monitoring and mapping these fires are crucial, and remote sensing technology has proven effective for this purpose. This research employs remote sensing methods to evaluate the severity of a forest fire in Kampung Balai Besar, Dungun. The incident, covering a 23-hectare area, occurred on 15 June 2021. Initial data processing utilized Sentinel-2 satellite images from 14 June 2021 (pre-fire) and 19 June 2021 (post-fire). The extent and severity of the fire were assessed using the Normalized Burn Ratio (NBR) index derived from satellite images. Different… More >

Saba Awan^1,*, Zahid Mehmood^2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.047337 - 27 February 2024

Abstract Highway safety researchers focus on crash injury severity, utilizing deep learning—specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)—as the preferred method for modeling accident severity. Deep learning’s strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through More >

Muzi Li^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2465-2493, 2023, DOI:10.32604/cmes.2023.026827 - 03 August 2023

Abstract Modular construction as an innovative method aids the construction industry in transforming to off-site construction production with high efficiency and environmental friendliness. Despite the obvious advantages, the uptake of modular construction is not booming as expected. However, previous studies have investigated and summarized the barriers to the adoption of modular construction. In this research, a Large-Scale Group Decision Making (LSGDM)- based analysis is first made of the severity of barriers to modular construction adoption from the perspective of construction stakeholders. In addition, the Technology-Organization-Environment (TOE) framework is utilized to identify the barriers based on three More >

K. Ravishankar^*, C. Jothikumar

Computer Systems Science and Engineering, Vol.47, No.1, pp. 613-635, 2023, DOI:10.32604/csse.2023.038343 - 26 May 2023

Abstract Since COVID-19 infections are increasing all over the world, there is a need for developing solutions for its early and accurate diagnosis is a must. Detection methods for COVID-19 include screening methods like Chest X-rays and Computed Tomography (CT) scans. More work must be done on preprocessing the datasets, such as eliminating the diaphragm portions, enhancing the image intensity, and minimizing noise. In addition to the detection of COVID-19, the severity of the infection needs to be estimated. The HSDC model is proposed to solve these problems, which will detect and classify the severity of… More >

Kamalrulnizam Bin Abu Bakar¹, Fatima Tul Zuhra^2,*, Babangida Isyaku^1,3, Fuad A. Ghaleb¹

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 785-798, 2023, DOI:10.32604/cmc.2023.034540 - 06 February 2023

Abstract The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization… More >

M. J. Anitha^1,*, R. Hemalatha²

Computer Systems Science and Engineering, Vol.46, No.1, pp. 1183-1200, 2023, DOI:10.32604/csse.2023.031888 - 20 January 2023

Abstract Detection of cracks at the early stage is considered as very constructive since precautionary steps need to be taken to avoid the damage to the civil structures. Moreover, identifying and classifying the severity level of cracks is inevitable in order to find the stability of buildings. Hence, this paper proposes an efficient strategy to classify the cracks into fine, medium, and thick using a novel bilayer crack detection algorithm. The bilayer crack detection algorithm helps in extracting the requisite features from the crack for efficient classification. The proposed algorithm works well in the dark background… More >

W. M. Faizal^1,*, C. Y. Khor¹, Muhammad Nooramin Che Yaakob¹, N. N. N. Ghazali², M. Z. Zainon², Norliza Binti Ibrahim³, Roziana Mohd Razi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 43-61, 2023, DOI:10.32604/cmes.2023.022716 - 05 January 2023

Abstract This paper aims to investigate and present the numerical investigation of airflow characteristics using Turbulent Kinetic Energy (TKE) to characterize the upper airway with obstructive sleep apnea (OSA) under inhale and exhale breathing conditions. The importance of TKE under both breathing conditions is that it show an accurate method in expressing the severity of flow in sleep disorder. Computational fluid dynamics simulate the upper airway’s airflow via steady-state Reynolds-averaged Navier-Stokes (RANS) with k–ω shear stress transport (SST) turbulence model. The three-dimensional (3D) airway model is created based on the CT scan images of an actual More > Graphic Abstract

G. Bhargavi^*, J. Arunnehru

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1257-1272, 2023, DOI:10.32604/iasc.2023.034335 - 05 January 2023

Abstract Landslides are a natural hazard that is unpredictable, but we can prevent them. The Landslide Susceptibility Index reduces the uncertainty of living with landslides significantly. Planning and managing landslide-prone areas is critical. Using the most optimistic deep neural network techniques, the proposed work classifies and analyses the severity of the landslide. The selected experimental study area is Kerala’s Idukki district. A total of 3363 points were considered for this experiment using historic landslide points, field surveys, and literature searches. The primary triggering factors slope degree, slope aspect, elevation (altitude), normalized difference vegetation index (NDVI), and… More >