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This paper illustrates the advantages that a fuzzy-based aggregation method could bring into the validation of a multiplex method for GMO detection (DualChip GMO kit, Eppendorf). Guidelines for validation of chemical, bio-chemical, pharmaceutical and genetic methods have been developed and ad hoc validation statistics are available and routinely used, for in-house and inter-laboratory testing, and decision-making. Fuzzy logic allows summarising the information obtained by independent validation statistics into one synthetic indicator of overall method performance. The microarray technology, introduced for simultaneous identification of multiple GMOs, poses specific validation issues (patterns of performance for a variety of GMOs at different concentrations). A fuzzy-based indicator for overall evaluation is illustrated in this paper, and applied to validation data for different genetically modified elements. Remarks were drawn on the analytical results. The fuzzy-logic based rules were shown to be applicable to improve interpretation of results and facilitate overall evaluation of the multiplex method.
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http://dx.doi.org/10.1007/s11248-009-9293-9 | DOI Listing |
PLoS One
September 2025
Department of Maths and Computer Science, Faculty of Science, University of Kinshasa, Kinshasa, The Democratic Republic of the Congo.
Reliable and timely fault diagnosis is critical for the safe and efficient operation of industrial systems. However, conventional diagnostic methods often struggle to handle uncertainties, vague data, and interdependent multi-criteria parameters, which can lead to incomplete or inaccurate results. Existing techniques are limited in their ability to manage hierarchical decision structures and overlapping information under real-world conditions.
View Article and Find Full Text PDFDisabil Rehabil Assist Technol
September 2025
Department of Education, Fuzhou University of International Studies and Trade, Fuzhou, China.
This study explores the integration of traditional Chinese "Fu" culture into the moral education system for students with disabilities across K-12 and higher education through artificial intelligence. By leveraging soft computing to handle cultural ambiguities, it constructs an adaptive educational framework that aligns students' cognitive characteristics with curriculum demands, thereby enhancing their identification with Chinese culture. Guided by the theory of the "Second Combination," the research employs AI-powered soft computing to analyze the semantic and cognitive dimensions of "Fu" culture.
View Article and Find Full Text PDFBiol Cybern
September 2025
Department of Electrical Engineering, Bahria University, H-11, 44000, Islamabad, Pakistan.
The dexterity of the human hand is largely due to its multiple degrees of freedom. However, coordinating the movements of the ring and little fingers independently can be challenging because of the biomechanical and neurological interdependencies between them. This research presents a cascade control system based on fuzzy logic to manage the dynamic movements of these fingers within a simulated biomechanical model of a human hand.
View Article and Find Full Text PDFNMR Biomed
October 2025
Department of Electronics and Communication Engineering, Kongunadu College of Engineering and Technology, Trichy, Tamil Nadu, India.
The abnormal or irregular growth of cells in regions of the human body that affects surrounding tissues is termed a tumor. Brain tumors are among the most dangerous and life-threatening types of tumors, arising from the abnormal growth of cells within the brain. However, existing detection methods often suffer from limitations, such as poor noise handling in MRI images, inaccurate segmentation, and low generalization across varying datasets.
View Article and Find Full Text PDFSci Rep
September 2025
Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt.
Citrus fruits, especially lemons, play a vital economic and nutritional role worldwide but are increasingly threatened by a wide range of diseases that diminish yield quality and quantity. Traditional manual and automated methods for disease detection requires domain expert, ample observation time, and is often ineffective during early infection stages. This paper presents a novel automated approach for the symptom based detection and classification of citrus leaf diseases using a nonlinear Fuzzy Rank-Based Ensemble (NL-FuRBE) methodology, enhanced by image quality improvement techniques.
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