Initial quality grading of meat is generally carried out using invasive and occasionally destructive sampling for the purposes of pH testing. Precise pH and thresholds exist to allow the classification of different statuses of meat, e.g. for detection of dry, firm, and dark (DFD) (when dealing with cattle and sheep), or pale, soft exudative meat (when dealing with pork). This paper illustrates that threshold detection for pH level in beef with different freshness levels (fresh, fresh frozen-thawed, matured, and matured frozen-thawed). Use of support vector machine (SVM) analysis allowed for the classification of beef samples with a pH above 5.9, and below 5.6, with an accuracy of 91% and 99% respectively. Biochemical and physical conditions of the meat concerning the pH are discussed.
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http://dx.doi.org/10.1016/j.meatsci.2017.07.012 | DOI Listing |
Curr Med Imaging
January 2025
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan611731, China.
Background And Objective: Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating early and accurate detection methods. Our study aims to enhance lung cancer detection by integrating VGGNet-16 form of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVM) into a hybrid model (SVMVGGNet-16), leveraging the strengths of both models for high accuracy and reliability in classifying lung cancer types in different 4 classes such as adenocarcinoma (ADC), large cell carcinoma (LCC), Normal, and squamous cell carcinoma (SCC).
Methods: Using the LIDC-IDRI dataset, we pre-processed images with a median filter and histogram equalization, segmented lung tumors through thresholding and edge detection, and extracted geometric features such as area, perimeter, eccentricity, compactness, and circularity.
Front Plant Sci
December 2024
School of Information Technology (IT) and Engineering, Melbourne Institute of Technology, Melbourne, VIC, Australia.
Introduction: Cotton, being a crucial cash crop globally, faces significant challenges due to multiple diseases that adversely affect its quality and yield. To identify such diseases is very important for the implementation of effective management strategies for sustainable agriculture. Image recognition plays an important role for the timely and accurate identification of diseases in cotton plants as it allows farmers to implement effective interventions and optimize resource allocation.
View Article and Find Full Text PDFOphthalmology
January 2025
Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA; Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA.
Objective: Extracellular lipoprotein aggregation is a critical event in AMD pathogenesis. In this study, we sought to analyze associations between clinical and genetic-based factors related to lipoprotein metabolism and risk for age-related macular degeneration (AMD) in the All of Us research program.
Design: Cross-sectional retrospective data analysis.
PLoS One
December 2024
GBA Branch of Aerospace Information Research Institute, Chinese Academy of Sciences, Guangzhou, Guangdong province, China.
Multilevel thresholding image segmentation is one of the widely used image segmentation methods, and it is also an important means of medical image preprocessing. Replacing the original costly exhaustive search approach, swarm intelligent optimization algorithms are recently used to determine the optimal thresholds for medical image, and medical images tend to have higher bit depth. Aiming at the drawbacks of premature convergence of existing optimization algorithms for high-bit depth image segmentation, this paper presents a pyramid particle swarm optimization based on complementary inertia weights (CIWP-PSO), and the Kapur entropy is employed as the optimization objective.
View Article and Find Full Text PDFSci Rep
December 2024
School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
Autonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and convenient transportation. The existing works provide achievable results but lack effective solutions, as accumulation on roads can obscure lane markings and traffic signs, making it difficult for the self-driving car to navigate safely. Heavy rain, snow, fog, or dust storms can severely limit the car's sensors' ability to detect obstacles, pedestrians, and other vehicles, which pose potential safety risks.
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