{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T12:23:34Z","timestamp":1764332614138,"version":"3.46.0"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry for Economic Affairs and Climate Action","award":["16KN120120"],"award-info":[{"award-number":["16KN120120"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>In recent decades, data collection technologies have evolved to facilitate the monitoring and improvement of numerous activities and processes in everyday human life. Their evolution is propelled by the advancement of artificial intelligence (AI), which aims to emulate human intelligence in the execution of related tasks. The remarkable success of deep learning (DL) and computer vision (CV) on image data prompted researchers to consider its application to time series and multivariate data. In this context, time series imaging has been identified as the research field for the transformation of time series data (a one-dimensional data format) into images (a two-dimensional data format). These data can be the variables or features of a system or phenomenon under consideration. State-of-the-art techniques for time series imaging include recurrence plot (RP), Gramian angular field (GAF), and Markov transition field (MTF). This paper proposes a novel, robust, and simple technique of time series imaging using Grayscale Fingerprint Features Field Imaging (G3FI). This novel technique is distinguished by the low resolution of the resulting image and the simplicity of the transformation procedure. The efficacy of the novel and state-of-the-art techniques for enhancing the performance of CNN-based classification models on time series datasets is thoroughly examined and compared.<\/jats:p>","DOI":"10.3390\/make7040155","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T11:19:51Z","timestamp":1764328791000},"page":"155","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Time Series-to-Image Encoding for Classification Using Convolutional Neural Networks: A Novel and Robust Approach"],"prefix":"10.3390","volume":"7","author":[{"given":"Hammoud","family":"Al Joumaa","sequence":"first","affiliation":[{"name":"Cologne Laboratory for Artificial Intelligence and Smart Automation (CAISA), Institute of Product Development and Engineering Design, Faculty of Process Engineering, Energy and Mechanical Systems, TH K\u00f6ln\u2014University of Applied Sciences, Betzdorfer Street 2, 50679 Cologne, Germany"}]},{"given":"Loui","family":"Al-Shrouf","sequence":"additional","affiliation":[{"name":"Cologne Laboratory for Artificial Intelligence and Smart Automation (CAISA), Institute of Product Development and Engineering Design, Faculty of Process Engineering, Energy and Mechanical Systems, TH K\u00f6ln\u2014University of Applied Sciences, Betzdorfer Street 2, 50679 Cologne, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0347-9913","authenticated-orcid":false,"given":"Mohieddine","family":"Jelali","sequence":"additional","affiliation":[{"name":"Cologne Laboratory for Artificial Intelligence and Smart Automation (CAISA), Institute of Product Development and Engineering Design, Faculty of Process Engineering, Energy and Mechanical Systems, TH K\u00f6ln\u2014University of Applied Sciences, Betzdorfer Street 2, 50679 Cologne, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16977","DOI":"10.1109\/ACCESS.2022.3148711","article-title":"Benchmarking Transfer Learning Strategies in Time-Series Imaging: Recommendations for Analyzing Raw Sensor Data","volume":"10","author":"Gross","year":"2022","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ferencz, K., Domokos, J., and Kov\u00e1cs, L. 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