@article{ingle_jasper_2025, title={A review of the evolution and concepts of deep learning and AI in the textile industry}, volume={1}, ISSN={0040-5175 1746-7748}, url={http://dx.doi.org/10.1177/00405175241310632}, DOI={10.1177/00405175241310632}, abstractNote={Machine learning (ML) and deep learning (DL) are transforming the textile industry by integrating advanced technologies into various processes. Textiles, once seen as passive materials, are now essential components of complex systems due to automation and innovative materials. This review focuses on articles that utilized AI, ML, or DL in textile research and industry. The review presents bibliometric analysis of AI methods in textiles. Later, the review is structured into sections that examine the effect of ML and DL across the textile sector. We outline key ML and DL methods applied in textiles, discussing their main uses and potential applications. This overview aims to clarify the working principles behind these methods, which are explored in greater detail. The methods analyzed range from basic linear regression to ensemble techniques such as XGBoost. DL techniques include convolutional neural networks for image analysis and long short-term memory networks for time-series analysis. In addition, a bibliometric review identifies trends and gaps in the literature, highlighting areas for future research. We also provide a detailed examination of how these methods are implemented in textiles.}, journal={Textile Research Journal}, publisher={SAGE Publications}, author={Ingle, Nilesh and Jasper, Warren J.}, year={2025}, month={Jan} }
 @article{ingle_jasper_2024, title={A review of deep learning and artificial intelligence in dyeing, printing and finishing}, volume={9}, ISSN={0040-5175 1746-7748}, url={http://dx.doi.org/10.1177/00405175241268619}, DOI={10.1177/00405175241268619}, abstractNote={This review focuses on the transformative applications of deep learning and artificial intelligence in textile dyeing, printing, and finishing. In textile dyeing, the topics span color prediction, color-based classification, dyeing recipe prediction, dyeing pattern recognition, and the nuanced domain of color fabric defect detection. In textile printing, applications of artificial intelligence and machine learning center around pattern detection in printed fabrics, the generation of novel patterns, and the critical task of detecting defects in printed textiles. In textile finishing the prediction of fabric thermosetting parameters is discussed. Artificial neural networks, diverse convolutional neural network variations like AlexNet, traditional machine learning approaches including support vector regression, principal component analysis, XGBoost, and generative artificial intelligence such as generative adversarial networks, as well as genetic algorithms all find application in this multifaceted exploration. At its core, the interest to use these methodologies is because of the need to minimize repetitive and time-consuming manual tasks, curtail prototyping costs, and promote process automation. The review unravels a plethora of innovative architectures and frameworks, each tailored to address specific challenges. However, a persistent hurdle looms – the scarcity of data, which remains a significant impediment. While unveiling a collection of research findings, the review also spotlights the inherent challenges in implementing artificial intelligence solutions in the textile dyeing and printing domain.}, journal={Textile Research Journal}, publisher={SAGE Publications}, author={Ingle, Nilesh and Jasper, Warren J}, year={2024}, month={Sep} }
 @article{ingle_jasper_2024, title={A review of deep learning within the framework of artificial intelligence for enhanced fiber and yarn quality}, volume={9}, ISSN={0040-5175 1746-7748}, url={http://dx.doi.org/10.1177/00405175241265510}, DOI={10.1177/00405175241265510}, abstractNote={In the textile production chain, fibers serve as the foundational units for yarn, and yarn, in turn, acts as a fundamental component for woven or knitted fabrics. The quality control of fabrics is intricately tied to the management of fibers and yarns. Traditional laboratory methods have been utilized to assess their quality, but the advent of machine learning and deep learning introduces a transformative approach. This review explores the application of machine learning methods such as principal component analysis, support vector machine, and deep learning methods such as artificial neural networks, convolutional neural networks, you look only once, and genetic algorithms to predict various properties of fibers and yarns. In the context of fibers, the review delves into topics such as cotton fiber grading based on color, characterization of jute fiber, and the identification of medullation in alpaca fibers. For yarns, the focus shifts to predicting parameters such as yarn tenacity, evenness, abrasion index of spun yarns, inspection of false twist textured yarn packages, breaking elongation of ring-spun cotton yarns, tensile properties of cotton/spandex yarns, yarn thickness, and yarn hairiness. The review also provides insights into the advantages and limitations of the discussed studies. Despite the comprehensiveness of this review, it is acknowledged that there might be additional relevant work not covered. The review encourages the sharing of data to expedite the integration of these technologies in future applications within the field.}, journal={Textile Research Journal}, publisher={SAGE Publications}, author={Ingle, Nilesh and Jasper, Warren J}, year={2024}, month={Sep} }
 @article{ingle_2022, place={Nagpur, India}, title={Artificial Intelligence in Agriculture: A perspective in 2022}, volume={18}, journal={International Journal of Extension Education}, publisher={College of Agriculture}, author={Ingle, N.}, year={2022}, pages={1–15} }
 @article{ingle_hexum_reineke_2020, title={Polyplexes Are Endocytosed by and Trafficked within Filopodia}, volume={21}, ISSN={1525-7797 1526-4602}, url={http://dx.doi.org/10.1021/acs.biomac.9b01610}, DOI={10.1021/acs.biomac.9b01610}, abstractNote={The improvement of nonviral gene therapies relies to a large extent on understanding many fundamental physical and biological properties of these systems. This includes interactions of synthetic delivery systems with the cell and mechanisms of trafficking delivery vehicles, which remain poorly understood on both the extra- and intracellular levels. In this study, the mechanisms of cellular internalization and trafficking of polymer-based nanoparticle complexes consisting of polycations and nucleic acids, termed polyplexes, have been observed in detail at the cellular level. For the first time evidence has been obtained that filopodia, actin projections that radiate out from the surface of cells, serve as a route for the direct endocytosis of polyplexes. Confocal microscopy images demonstrated that filopodia on HeLa cells detect external polyplexes and extend into the extracellular milieu to internalize these particles. Polyplexes are observed to be internalized into membrane-bound vesicles (i.e., clathrin-coated pits and caveolae) directly within filopodial projections and are subsequently transported along actin to the main cell body for potential delivery of the nucleic acids to the nucleus. The kinetics and speed of polyplex trafficking have also been measured. The polyplex-loaded vesicles were also discovered to traffic between two cells within filopodial bridges. These findings provide novel insight into the early events of cellular contact with polyplexes through filopodial-based interactions in addition to endocytic vesicle trafficking—an important fundamental discovery to enable advancement of nonviral gene editing, nucleic acid therapies, and biomedical materials.}, number={4}, journal={Biomacromolecules}, publisher={American Chemical Society (ACS)}, author={Ingle, Nilesh P. and Hexum, Joseph K. and Reineke, Theresa M.}, year={2020}, month={Mar}, pages={1379–1392} }