{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T03:45:45Z","timestamp":1775187945886,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Lossless compression of medical images allows for rapid image data exchange and faithful recovery of the compressed data for medical image assessment. There are many useful telemedicine applications, for example in diagnosing conditions such as malaria in resource-limited regions. This paper presents a novel machine learning-based approach where lossless compression of malaria-infected erythrocyte images is assisted by cutting-edge classifiers. To this end, we first use a Vision Transformer to classify images into two categories: those cells that are infected with malaria and those that are not. We then employ distinct deep autoencoders for each category, which not only reduces the dimensions of the image data but also preserves crucial diagnostic information. To ensure no loss in reconstructed image quality, we further compress the residuals produced by these autoencoders using the Huffman code. Simulation results show that the proposed method achieves lower overall bit rates and thus higher compression ratios than traditional compression schemes such as JPEG 2000, JPEG-LS, and CALIC. This strategy holds significant potential for effective telemedicine applications and can improve diagnostic capabilities in regions impacted by malaria.<\/jats:p>","DOI":"10.3390\/computers14040127","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T05:29:19Z","timestamp":1743485359000},"page":"127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Lossless Compression of Malaria-Infected Erythrocyte Images Using Vision Transformer and Deep Autoencoders"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7007-0411","authenticated-orcid":false,"given":"Md Firoz","family":"Mahmud","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA"}]},{"given":"Zerin","family":"Nusrat","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7265-2188","authenticated-orcid":false,"given":"W. David","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hoyos, K., and Hoyos, W. (2024). Supporting Malaria Diagnosis Using Deep Learning and Data Augmentation. Diagnostics, 14.","DOI":"10.3390\/diagnostics14070690"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.cell.2016.07.055","article-title":"Malaria: Biology and disease","volume":"167","author":"Cowman","year":"2016","journal-title":"Cell"},{"key":"ref_3","unstructured":"World Health Organization (2024). World Malaria Report 2024: Addressing Inequity in the Global Malaria Response, WHO."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s12098-017-2332-2","article-title":"Malaria: An update","volume":"84","author":"Basu","year":"2017","journal-title":"Indian J. Pediatr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"63593","DOI":"10.1007\/s11042-023-17866-6","article-title":"Dlrfnet: Deep learning with random forest network for classification and detection of malaria parasite in blood smear","volume":"83","author":"Murmu","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2221728","DOI":"10.1155\/2022\/2221728","article-title":"Deep learning and transfer learning for malaria detection","volume":"2022","author":"Jameela","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Grignaffini, F., Simeoni, P., Alisi, A., and Frezza, F. (2024). Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review. Electronics, 13.","DOI":"10.3390\/electronics13163174"},{"key":"ref_8","unstructured":"World Health Organization (2010). Basic Malaria Microscopy, World Health Organization."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.cviu.2009.08.003","article-title":"Parasite detection and identification for automated thin blood film malaria diagnosis","volume":"114","author":"Tek","year":"2010","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.micron.2012.11.002","article-title":"Machine learning approach for automated screening of malaria parasite using light microscopic images","volume":"45","author":"Das","year":"2013","journal-title":"Micron"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Linder, N., Turkki, R., Walliander, M., M\u00e5rtensson, A., Diwan, V., Rahtu, E., Pietik\u00e4inen, M., Lundin, M., and Lundin, J. (2014). A malaria diagnostic tool based on computer vision screening and visualization of Plasmodium falciparum candidate areas in digitized blood smears. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0104855"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s11517-006-0044-2","article-title":"Automated image processing method for the diagnosis and classification of malaria on thin blood smears","volume":"44","author":"Ross","year":"2006","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_13","unstructured":"Sharma, R.K., Sharma, M., Sharma, P., and Aparjeeta, J. (2024). Efficient Medicinal Image Transmission and Resolution Enhancement via GAN. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cheng, Y., and Yu, W. (2024, January 21\u201323). Research on ResNet34 Improved Model. Proceedings of the 2024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan.","DOI":"10.1109\/ICIIBMS62405.2024.10792749"},{"key":"ref_15","first-page":"66","article-title":"Image classification using deep neural networks for malaria disease detection","volume":"10","author":"Lydia","year":"2019","journal-title":"Int. J. Emerg. Technol."},{"key":"ref_16","first-page":"5889","article-title":"A novel convolutional neural network model for malaria cell images classification","volume":"72","author":"Hassan","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15945","DOI":"10.1109\/ACCESS.2023.3245025","article-title":"Malaria Disease Cell Classification with Highlighting Small Infected Regions","volume":"11","author":"Yebasse","year":"2023","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"012110","DOI":"10.1088\/1742-6596\/1634\/1\/012110","article-title":"Image classification based on RESNET","volume":"1634","author":"Liang","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_19","first-page":"7602384","article-title":"A novel image classification approach via dense-MobileNet models","volume":"2020","author":"Wang","year":"2020","journal-title":"Mob. Inf. Syst."},{"key":"ref_20","first-page":"382","article-title":"Image classification of malaria using hybrid algorithms: Convolutional neural network and method to find appropriate K for K-Nearest neighbor","volume":"16","author":"Lumchanow","year":"2019","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liang, Z., Powell, A., Ersoy, I., Poostchi, M., Silamut, K., Palaniappan, K., Guo, P., Hossain, M.A., Sameer, A., and Maude, R.J. (2016, January 15\u201318). CNN-based image analysis for malaria diagnosis. Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China.","DOI":"10.1109\/BIBM.2016.7822567"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Saravan, P.D., M, S.S., Mahesh, S.V., and Singh, T. (2024, January 24\u201328). Malaria Cell Image Classification using Deep Learning. Proceedings of the 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Mandi, India.","DOI":"10.1109\/ICCCNT61001.2024.10724659"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gopakumar, G.P., Swetha, M., Sai Siva, G., and Sai Subrahmanyam, G.R.K. (2018). Convolutional neural network-based malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner. J. Biophotonics, 11.","DOI":"10.1002\/jbio.201700003"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"15297","DOI":"10.1007\/s11042-019-7162-y","article-title":"Deep learning approach to detect malaria from microscopic images","volume":"79","author":"Vijayalakshmi","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_25","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pisner, D.A., and Schnyer, D.M. (2020). Support vector machine. Machine learning, Elsevier.","DOI":"10.1016\/B978-0-12-815739-8.00006-7"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, E.H., Amer, H., and Jiang, Y. (2021). Compression helps deep learning in image classification. Entropy, 23.","DOI":"10.3390\/e23070881"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jo, Y.Y., Choi, Y.S., Park, H.W., Lee, J.H., Jung, H., Kim, H.E., Ko, K., Lee, C.W., Cha, H.S., and Hwangbo, Y. (2021). Impact of image compression on deep learning-based mammogram classification. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-86726-w"},{"key":"ref_29","unstructured":"Dejean-Servi\u00e8res, M., Desnos, K., Abdelouahab, K., Hamidouche, W., Morin, L., and Pelcat, M. (2017). Study of the Impact of Standard Image Compression Techniques on Performance of Image Classification with a Convolutional Neural Network. [Ph.D. Thesis, INSA Rennes, Univ Rennes, IETR, Institut Pascal]."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ozah, N., and Kolokolova, A. (2019, January 28\u201331). Compression improves image classification accuracy. Proceedings of the Advances in Artificial Intelligence: 32nd Canadian Conference on Artificial Intelligence, Canadian AI 2019, Kingston, ON, Canada. Proceedings 32.","DOI":"10.1007\/978-3-030-18305-9_55"},{"key":"ref_31","unstructured":"Mohsen, A., and Tiwari, M. (2021). Image compression and classification using qubits and quantum deep learning. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jha, C.K., and Kolekar, M.H. (2018). Classification and compression of ECG signal for holter device. Biomedical Signal and Image Processing in Patient Care, IGI Global.","DOI":"10.4018\/978-1-5225-2829-6.ch004"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ayoobkhan, M.U.A., Chikkannan, E., Ramakrishnan, K., and Balasubramanian, S.B. Prediction-based lossless image compression. Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB).","DOI":"10.1007\/978-3-030-00665-5_161"},{"key":"ref_34","unstructured":"(2025, January 14). PEIR-VM. Available online: https:\/\/peir-vm.path.uab.edu\/about.php."},{"key":"ref_35","unstructured":"(2025, January 14). UAB Dataset. Available online: http:\/\/www.ece.uah.edu\/~dwpan\/malaria_dataset\/."},{"key":"ref_36","unstructured":"(2025, March 15). NIH Dataset, Available online: https:\/\/lhncbc.nlm.nih.gov\/LHC-research\/LHC-projects\/image-processing\/malaria-datasheet.html."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s13735-021-00218-1","article-title":"A review on deep learning in medical image analysis","volume":"11","author":"Suganyadevi","year":"2022","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, H., Zendehdel, N., Leu, M.C., Moniruzzaman, M., Yin, Z., and Hajmohammadi, S. (2024, January 21\u201324). Repetitive Action Counting Through Joint Angle Analysis and Video Transformer Techniques. Proceedings of the International Symposium on Flexible Automation. American Society of Mechanical Engineers, Seattle, WA, USA.","DOI":"10.1115\/ISFA2024-140665"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103111","DOI":"10.1016\/j.jnca.2021.103111","article-title":"Towards secure intrusion detection systems using deep learning techniques: Comprehensive analysis and review","volume":"187","author":"Lee","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1007\/s42979-022-01166-1","article-title":"Survey of supervised learning for medical image processing","volume":"3","author":"Aljuaid","year":"2022","journal-title":"SN Comput. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhou, T., Ye, X., Lu, H., Zheng, X., Qiu, S., and Liu, Y. (2022). Dense convolutional network and its application in medical image analysis. BioMed Res. Int., 2022.","DOI":"10.1155\/2022\/2384830"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gogoi, M., and Begum, S.A. (2017, January 14\u201316). Image classification using deep autoencoders. Proceedings of the 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India.","DOI":"10.1109\/ICCIC.2017.8524276"},{"key":"ref_43","first-page":"18","article-title":"Image classification based on CNN: A survey","volume":"6","author":"Elngar","year":"2021","journal-title":"J. Cybersecur. Inf. Manag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"49","DOI":"10.4236\/jcc.2024.124005","article-title":"Image Classification Based on Vision Transformer","volume":"12","author":"Omer","year":"2024","journal-title":"J. Comput. Commun."},{"key":"ref_45","unstructured":"Alexey, D. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Michelucci, U. (2022). An Introduction to Autoencoders. arXiv.","DOI":"10.1007\/978-1-4842-8020-1_9"},{"key":"ref_47","unstructured":"Zhang, Y. (2025, January 01). A Better Autoencoder for Image: Convolutional Autoencoder. Available online: https:\/\/theory.sinp.msu.ru\/lib\/exe\/fetch.php\/archive\/dlcp\/kryukov22\/biblio\/abcs2018_paper_58-conv_ae.pdf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"100314","DOI":"10.1016\/j.array.2023.100314","article-title":"A novel lossy image compression algorithm using multi-models stacked AutoEncoders","volume":"19","author":"Fraihat","year":"2023","journal-title":"Array"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Erdal, E., and Erg\u00fczen, A. (2019). An efficient encoding algorithm using local path on huffman encoding algorithm for compression. Appl. Sci., 9.","DOI":"10.3390\/app9040782"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1109\/JRPROC.1952.273898","article-title":"A method for the construction of minimum-redundancy codes","volume":"40","author":"Huffman","year":"1952","journal-title":"Proc. IRE"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yin, J., Lei, M., Zheng, H., Yang, Y., Li, Y., and Xu, M. (2019). The average coding length of Huffman coding based signal processing and its application in fault severity recognition. Appl. Sci., 9.","DOI":"10.3390\/app9235051"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"28509","DOI":"10.1007\/s11042-022-12846-8","article-title":"Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression","volume":"81","author":"Otair","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_53","unstructured":"Yang, M., and Bourbakis, N. (2005, January 7\u201310). An overview of lossless digital image compression techniques. Proceedings of the 48th Midwest Symposium on Circuits and Systems, 2005, Covington, KY, USA."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.neucom.2018.02.094","article-title":"Image compression techniques: A survey in lossless and lossy algorithms","volume":"300","author":"Hussain","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4781","DOI":"10.1007\/s11042-021-11017-5","article-title":"An improved lossless image compression algorithm based on Huffman coding","volume":"81","author":"Liu","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R., and Gool, L.V. (2019, January 15\u201320). Practical full resolution learned lossless image compression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01088"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"110632","DOI":"10.1016\/j.patcog.2024.110632","article-title":"Hybrid-context-based multi-prior entropy modeling for learned lossless image compression","volume":"155","author":"Fu","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Rahman, M.A., Hamada, M., and Shin, J. (2021). The impact of state-of-the-art techniques for lossless still image compression. Electronics, 10.","DOI":"10.3390\/electronics10030360"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Urbaniak, I.A. (2024). Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review. Appl. Sci., 14.","DOI":"10.3390\/app142210524"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Madsen, A.B., Faurskov, R., and Sahafi, A. (2023, January 17\u201319). JPEG-LS Compression on FPGA: A Solution for Wireless Capsule Endoscopy. Proceedings of the 2023 IEEE International Conference on Imaging Systems and Techniques (IST), Copenhagen, Denmark.","DOI":"10.1109\/IST59124.2023.10355693"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"5393","DOI":"10.1109\/ACCESS.2024.3350344","article-title":"Adaptive Pipeline Hardware Architecture Design and Implementation for Image Lossless Compression\/Decompression Based on JPEG-LS","volume":"12","author":"Liu","year":"2024","journal-title":"IEEE Access"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/83.855427","article-title":"The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS","volume":"9","author":"Weinberger","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"24173","DOI":"10.1007\/s11042-021-10796-1","article-title":"A novel lossless compression framework for facial depth images in expression recognition","volume":"80","author":"Fan","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhang, M., Tong, X., Wang, Z., and Chen, P. (2021). Joint lossless image compression and encryption scheme based on CALIC and hyperchaotic system. Entropy, 23.","DOI":"10.3390\/e23081096"},{"key":"ref_65","unstructured":"Li, D., Bai, Y., Wang, K., Jiang, J., Liu, X., and Gao, W. (2024). CALLIC: Content Adaptive Learning for Lossless Image Compression. arXiv."},{"key":"ref_66","unstructured":"MathWorks (2025, January 10). Train Vision Transformer Network for Image Classification. Available online: https:\/\/www.mathworks.com\/help\/deeplearning\/ug\/train-vision-transformer-network-for-image-classification.html."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Bazi, Y., Bashmal, L., Rahhal, M.M.A., Dayil, R.A., and Ajlan, N.A. (2021). Vision transformers for remote sensing image classification. Remote. Sens., 13.","DOI":"10.3390\/rs13030516"},{"key":"ref_68","unstructured":"Pan, Y., and Li, Y. (2023). Toward understanding why adam converges faster than sgd for transformers. arXiv."},{"key":"ref_69","unstructured":"Diederik, P.K. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_70","unstructured":"Patro, B.N., and Agneeswaran, V.S. (2023). Efficiency 360: Efficient vision transformers. arXiv."},{"key":"ref_71","unstructured":"Masters, D., and Luschi, C. (2018). Revisiting small batch training for deep neural network. arXiv."},{"key":"ref_72","first-page":"7795","article-title":"Learning rates as a function of batch size: A random matrix theory approach to neural network training","volume":"23","author":"Granziol","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref_73","unstructured":"Pykes, K. (2024, September 15). Cross-Entropy Loss Function in Machine Learning: Enhancing Model Accuracy. Available online: https:\/\/www.datacamp.com\/tutorial\/the-cross-entropy-loss-function-in-machine-learning."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"108238","DOI":"10.1016\/j.compeleceng.2022.108238","article-title":"A deep autoencoder approach for detection of brain tumor images","volume":"102","author":"Nayak","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Shen, H., Pan, W.D., Dong, Y., and Alim, M. (2016, January 4\u20137). Lossless compression of curated erythrocyte images using deep autoencoders for malaria infection diagnosis. Proceedings of the 2016 Picture Coding Symposium (PCS), Nuremberg, Germany.","DOI":"10.1109\/PCS.2016.7906393"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Shen, H., Pan, W.D., and Wang, Y. (2015, January 9\u201312). A novel method for lossless compression of arbitrarily shaped regions of interest in hyperspectral imagery. Proceedings of the SoutheastCon 2015, Fort Lauderdale, FL, USA.","DOI":"10.1109\/SECON.2015.7132982"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/4\/127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:07:31Z","timestamp":1760029651000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/4\/127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,1]]},"references-count":76,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["computers14040127"],"URL":"https:\/\/doi.org\/10.3390\/computers14040127","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,1]]}}}