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A Systematic Review of Artificial Intelligence in Assistive Technology for People with Visual Impairment
Corresponding Author(s) : Liliek Triyono
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 8, No. 4, November 2023
Abstract
Recent advances in artificial intelligence (AI) have led to the development of numerous successful applications that utilize data to significantly enhance the quality of life for people with visual impairment. AI technology has the potential to further improve the lives of visually impaired individuals. However, accurately measuring the development of visual aids continues to be challenging. As an AI model is trained on larger and more diverse datasets, its performance becomes increasingly robust and applicable to a variety of scenarios. In the field of visual impairment, deep learning techniques have emerged as a solution to previous challenges associated with AI models. In this article, we provide a comprehensive and up-to-date review of recent research on the development of AI-powered visual aides tailored to the requirements of individuals with visual impairment. We adopt the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, meticulously gathering and appraising pertinent literature culled from diverse databases. A rigorous selection process was undertaken, appraising articles against precise inclusion and exclusion criteria. Our meticulous search yielded a trove of 322 articles, and after diligent scrutiny, 12 studies were deemed suitable for inclusion in the ultimate analysis. The study's primary objective is to investigate the application of AI techniques to the creation of intelligent devices that aid visually impaired individuals in their daily lives. We identified a number of potential obstacles that researchers and developers in the field of visual impairment applications might encounter. In addition, opportunities for future research and advancements in AI-driven visual aides are discussed. This review seeks to provide valuable insights into the advancements, possibilities, and challenges in the development and implementation of AI technology for people with visual impairment. By examining the current state of the field and designating areas for future research, we expect to contribute to the ongoing progress of improving the lives of visually impaired individuals through the use of AI-powered visual aids.
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- P. Argüeso, “Human ocular mucins: The endowed guardians of sight,” Adv. Drug Deliv. Rev., vol. 180, 2022. https://doi.org/10.1016/j.addr.2021.114074
- P. R. Boyce, “Light, lighting and human health,” Light. Res. Technol., vol. 54, no. 2, pp. 101–144, 2022. https://doi.org/10.1177/14771535211010267
- S. Ferrari et al., “Presence of SARS-CoV-2 RNA in human corneal tissues donated in Italy during the COVID-19 pandemic,” BMJ Open Ophthalmol., vol. 7, no. 1, pp. 5–9, 2022. http://dx.doi.org/10.1136/bmjophth-2022-000990
- S. G. Singh Gustavo; Shah, Ruchi; Kramerov, Andrei A.; Wright, Robert Emery; Spektor, Tanya M; Ljubimov, Alexander V.; Arumugaswami, Vaithilingaraja; Kumar, Ashok, “SARS-CoV-2 and its beta variant of concern infect human conjunctival epithelial cells and induce differential antiviral innate immune response.,” Ocul. Surf., vol. 23, no. NA, pp. 184–194, 2021. https://doi.org/10.1016/j.jtos.2021.09.007
- M. M. C. Schwarz Kaleigh A; Davoli, Katherine A; McMillen, Cynthia M; Albe, Joseph R; Hoehl, Ryan M; Demers, Matthew J; Ganaie, Safder S; Price, David A; Leung, Daisy W; Amarasinghe, Gaya K; McElroy, Anita K; Reed, Douglas S; Hartman, Amy L, “Rift Valley Fever Virus Infects the Posterior Segment of the Eye and Induces Inflammation in a Rat Model of Ocular Disease.,” J. Virol., vol. 96, no. 20, pp. e0111222-NA, 2022. https://doi.org/10.1128/jvi.01112-22
- https://www.who.int/publications/i/item/9789241516570.
- B. Kuriakose, R. Shrestha, and F. E. Sandnes, “Tools and technologies for blind and visually impaired navigation support: a review,” IETE Tech. Rev., 2022. https://doi.org/10.1080/02564602.2020.1819893
- E. L. Cardillo Changzhi; Caddemi, Alina, “Millimeter-Wave Radar Cane: A Blind People Aid With Moving Human Recognition Capabilities,” IEEE J. Electromagn. RF Microwaves Med. Biol., vol. 6, no. 2, pp. 204–211, 2022. https://doi.org/10.1109/JERM.2021.3117129
- A. P. Budrionis Darius; Daniušis, Povilas; Indrulionis, Audrius, “Smartphone-based computer vision travelling aids for blind and visually impaired individuals: A systematic review.,” Assist. Technol., vol. 34, no. 2, pp. 1–17, 2020. https://doi.org/10.1080/10400435.2020.1743381
- J. M. K. Nesemann Ram Prasad; Byanju, Raghunandan; Poudyal, Bimal; Bhandari, Gopal; Bhandari, Sadhan; O’Brien, Kieran S; Stevens, Valerie M; Melo, Jason S; Keenan, Jeremy D., “Association of visual impairment with disability: a population-based study.,” Eye (Lond)., vol. 36, no. 3, pp. 1–7, 2021. https://doi.org/10.1038/s41433-021-01498-x
- V. P. Yasin Peniarsih; Gozali, Ahmad; Junaedi, Ifan, “Application of expert system diagnosis of color blindness with ishihara method with microsoft vb 6.0,” Int. J. Informatics, Econ. Manag. Sci., vol. 1, no. 1, p. 13, 2022. https://doi.org/10.52362/ijiems.v1i1.678
- H. X. Xinghong Liu; Zhuming, Zhang; Menghan, Xia; Chengze, Li; Tien-Tsin, Wong, “Colorblind-shareable videos by synthesizing temporal-coherent polynomial coefficients,” ACM Trans. Graph., vol. 38, no. 6, pp. 1–12, 2019. https://doi.org/10.1145/3355089.3356534
- R. T. Alcaraz Martínez Mireia Ribera; Granollers Saltiveri, Toni, “Methodology for heuristic evaluation of the accessibility of statistical charts for people with low vision and color vision deficiency,” Univers. Access Inf. Soc., vol. 21, no. 4, pp. 863–894, 2021. https://doi.org/10.1007/s10209-021-00816-0
- M. H. S. Mahjoob Javad; Anderson, Andrew J, “The effect of mental load on psychophysical and visual evoked potential visual acuity.,” Ophthalmic Physiol. Opt., vol. 42, no. 3, pp. 586–593, 2022. https://doi.org/10.1111/opo.12955
- M. H. S. Mahjoob Javad; Anderson, Andrew J, “The effect of mental load on psychophysical and visual evoked potential visual acuity.,” Ophthalmic Physiol. Opt., vol. 42, no. 3, pp. 586–593, 2022. https://doi.org/10.1111/opo.12955
- V. C. D. De Cock Pauline; Leu-Semenescu, Smaranda; Aerts, Cécile; Castelnovo, Giovanni; Abril, Beatriz; Drapier, Sophie; Olivet, Hélène; Corbillé, Anne-Gaëlle; Leclair-Visonneau, Laurène; Sallansonnet-Froment, Magali; Lebouteux, Marie; Anheim, Mathieu; Ruppert, E, “Safety and efficacy of subcutaneous night-time only apomorphine infusion to treat insomnia in patients with Parkinson’s disease (APOMORPHEE): a multicentre, randomised, controlled, double-blind crossover study.,” Lancet. Neurol., vol. 21, no. 5, pp. 428–437, 2022. https://doi.org/10.1016/s1474-4422(22)00085-0
- F. A. Almutairi Nawaf; Ahmad, Khabir; Magliyah, Moustafa S.; Schatz, Patrik, “Congenital stationary night blindness: an update and review of the disease spectrum in Saudi Arabia.,” Acta Ophthalmol., vol. 99, no. 6, pp. 581–591, 2020. https://doi.org/10.1111/aos.14693
- B.-J. Cho, “Congenital Stationary Night Blindness,” in Inherited Retinal Disease, Singapore: Springer Nature Singapore, 2022, pp. 117–123.
- S. Y. Mochida Takeshi; Nomura, Takuhei; Hatake, Ryoma; Ohno-Matsui, Kyoko, “Association between peripheral visual field defects and focal lamina cribrosa defects in highly myopic eyes.,” Jpn. J. Ophthalmol., vol. 66, no. 3, pp. 285–295, 2022. https://doi.org/10.1007/s10384-022-00909-0
- L. E. Donaldson Arshia; Sacco, Simone; Micieli, Jonathan A.; Margolin, Edward, “Junctional Scotoma and Patterns of Visual Field Defects Produced by Lesions Involving the Optic Chiasm.,” J. Neuroophthalmol., vol. 42, no. 1, pp. e203–e208, 2021. https://doi.org/10.1097/wno.0000000000001394
- V. N. . D. Vakharia Beate; Tisdall, Martin, “Visual field defects in temporal lobe epilepsy surgery.,” Curr. Opin. Neurol., vol. 34, no. 2, pp. 188–196, 2021. https://doi.org/10.1097/wco.0000000000000905
- E. M. Ekici Sasan; Hou, Huiyuan; Proudfoot, James A.; Zangwill, Linda M.; L., Jiun; Oh, Won Hyuk; Kamalipour, Alireza; Liebmann, Jeffrey M.; De Moraes, Carlos Gustavo; Girkin, Christopher A.; El-Nimri, Nevin W.; Weinreb, Robert N., “Central Visual Field Defects in Patients with Distinct Glaucomatous Optic Disc Phenotypes,” Am. J. Ophthalmol., vol. 223, no. NA, pp. 229–240, 2020. https://doi.org/10.1016/j.ajo.2020.10.015
- B. S. Schmitz Katherine L; Wingerson, Mathew J; Walker, Gregory A; Wilson, Julie C; Howell, David R, “Double Vision and Light Sensitivity Symptoms are Associated With Return-to-School Timing After Pediatric Concussion.,” Clin. J. Sport Med., vol. Publish Ah, 2022. https://doi.org/10.1097/jsm.0000000000001106
- R. K. Bartlett Yi Xuan; Hourcade, Juan Pablo; Rector, Kyle, “Exploring the Opportunities for Technologies to Enhance Quality of Life with People who have Experienced Vision Loss,” Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, p. 191, 2019. https://doi.org/10.1145/3290605.3300421
- S. W. Bi Cong; Zhang, Jilong; Huang, Wutao; Wu, Bochun; Gong, Yi; Ni, Wei, “A Survey on Artificial Intelligence Aided Internet-of-Things Technologies in Emerging Smart Libraries.,” Sensors (Basel)., vol. 22, no. 8, p. 2991, 2022. https://doi.org/10.3390/s22082991
- L. Z. Jia Zhi; Xu, Fei; Jin, Hai, “Cost-Efficient Continuous Edge Learning for Artificial Intelligence of Things,” IEEE Internet Things J., vol. 9, no. 10, pp. 7325–7337, 2022. https://doi.org/10.1109/JIOT.2021.3104089
- S. O. Zhu Kaoru; Dong, Mianxiong, “Energy-Efficient Artificial Intelligence of Things With Intelligent Edge,” IEEE Internet Things J., vol. 9, no. 10, pp. 7525–7532, 2022. https://doi.org/10.1109/JIOT.2022.3143722
- D. N. A. Nya Hassane, “Model-Free Control Policies for Inventory Management in Supply Chain,” 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT), vol. NA, no. NA. p. NA-NA, 2022. https://doi.org/10.1109/CoDIT55151.2022.9803914
- Y. B. Mashayekhy Amir; Yuan, Xue-Ming; Xue, Anrong, “Impact of Internet of Things (IoT) on Inventory Management: A Literature Survey,” Logistics, vol. 6, no. 2, p. 33, 2022. https://doi.org/10.3390/logistics6020033
- Y.-M. H. Tang George To Sum; Lau, Yui-Yip; Tsui, Shuk-Ying, “Integrated Smart Warehouse and Manufacturing Management with Demand Forecasting in Small-Scale Cyclical Industries,” Machines, vol. 10, no. 6, p. 472, 2022. https://doi.org/10.3390/machines10060472
- M. K. Mahobe Pradeep; Jha, Shashi Shekhar, “Nature-Inspired AI Techniques in Intelligent Transportation System,” in Lecture Notes in Electrical Engineering, Springer Nature Singapore, 2022, pp. 251–263. https://doi.org/10.1007/978-981-16-8542-2_20
- D. K. Loske Matthias, “Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics,” Int. J. Prod. Econ., vol. 241, no. NA, pp. 108236-NA, 2021. https://doi.org/10.1016/j.ijpe.2021.108236
- I. Z. Lee Helen; Moore, Kate; Zhou, Xiaofei; Perret, Beatriz; Cheng, Yihong; Zheng, Ruiying; Pu, Grace, “AI Book Club,” Proceedings of the 53rd ACM Technical Symposium on Computer Science Education. ACM, 2022. https://doi.org/10.1145/3478431.3499318
- T. P. Kabudi Ilias O.; Olsen, Dag H., “AI-enabled adaptive learning systems: A systematic mapping of the literature,” Comput. Educ. Artif. Intell., vol. 2, p. 100017, 2021. https://doi.org/10.1016/j.caeai.2021.100017
- Y. A. Alsaawy Ahmad; Abi Sen, Adnan; Alshanqiti, Abdullah; Bhat, Wasim Ahmad; Bahbouh, Nour Mahmoud, “A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics,” Appl. Sci., vol. 12, no. 4, p. 2043, 2022. https://doi.org/10.3390/app12042043
- S. P. Bhupathi Nithish, Ankit Panda, Trishala Reddy, Vishwa Gohil, Ishita Kundaliya, “Self-Driving Car to Drive Autonomously using Image Processing and Deep Learning,” Irjet, vol. 9, no. 1, pp. 125–132, 2022. http://dx.doi.org/10.13140/RG.2.2.16212.88963/1
- S. K. B. M.; Gayathri, S.; Srinidhi, S.; Hemasundari, H.; Sowmiya, S.; Shavan Kumar, S., “AI-Based Motorized Appearance Acknowledgement Scheme for an Attendance Marking System,” in Advances in Social Networking and Online Communities, vol. NA, no. NA, 2022, pp. 98–109.
- M. Surve, P. Joshi, S. Jamadar, and M. M. N. Vharkate, “Automatic Attendance System using Face Recognition Technique,” Int. J. Recent Technol. Eng., vol. 9, no. 1, pp. 2134–2138, 2020. http://www.doi.org/10.35940/ijrte.A2644.059120
- Y. Himeur et al., AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives, no. 0123456789. Springer Netherlands, 2022.
- A. Y. M. Chakor Azmani; Abdellah, Azmani, “Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervision,” Appl. Comput. Syst., vol. 27, no. 1, pp. 43–54, 2022. https://doi.org/10.2478/acss-2022-0005
- S. A. Upadhyaya Anushri; Rengaraj, Venkatesh; Srinivasan, Kavitha; Casey, Paula Anne Newman; Schehlein, Emily M., “Validation of a portable, non-mydriatic fundus camera compared to gold standard dilated fundus examination using slit lamp biomicroscopy for assessing the optic disc for glaucoma.,” Eye (Lond)., vol. 36, no. 2, pp. 1–7, 2021. https://doi.org/10.1038/s41433-021-01485-2
- D. Mishra, S. Gade, K. Glover, R. Sheshala, and T. R. R. Singh, “Vitreous Humor: Composition, Characteristics and Implication on Intravitreal Drug Delivery,” Curr. Eye Res., vol. 48, no. 2, pp. 208–218, 2022. https://doi.org/10.1080/02713683.2022.2119254
- R. F. V. Spaide Philippe; Maloca, Peter M; Scholl, Hendrik P N; Otto, Tilman P; Caujolle, Sophie, “Imaging The Vitreous With A Novel Boosted Optical Coherence Tomography Technique: Vitreous Degeneration and Cisterns.,” Retina, vol. 42, no. 8, pp. 1433–1441, 2022. https://doi.org/10.1097/iae.0000000000003474.
- S. M. . R. Zekavat Vineet K.; Trinder, Mark; Ye, Yixuan; Koyama, Satoshi; Honigberg, Michael C.; Yu, Zhi; Pampana, Akhil; Urbut, Sarah; Haidermota, Sara; O’Regan, Declan P.; Zhao, Hongyu; Ellinor, Patrick T.; Segrè, Ayellet V.; Elze, Tobias; Wiggs, Janey L.; Marton, “Deep Learning of the Retina Enables Phenome- and Genome-wide Analyses of the Microvasculature.,” Circulation, vol. 145, no. 2, pp. 134–150, 2021. https://doi.org/10.1161/CIRCULATIONAHA.121.057709
- T. M. Izumi Ichiro; Kawano, Taizo; Sakaihara, Manabu; Iida, Tomohiro, “Morphological differences of choroid in central serous chorioretinopathy determined by ultra-widefield optical coherence tomography.,” Graefes Arch. Clin. Exp. Ophthalmol., vol. 260, no. 1, pp. 1–7, 2021. https://doi.org/10.1007/s00417-021-05380-0
- X. Xu et al., “Automatic Segmentation and Measurement of Choroid Layer in High Myopia for OCT Imaging Using Deep Learning,” J. Digit. Imaging, vol. 35, no. 5, pp. 1153–1163, Oct. 2022. https://doi.org/10.1007/s10278-021-00571-x
- B. G.-V. Burgos-Blasco Noemi; Vidal-Villegas, Beatriz; Martinez-de-la-Casa, Jose M.; Donate-Lopez, Juan; Martín-Sánchez, Francisco Javier; González-Armengol, Juan Jorge; Porta-Etessam, Jesús; Martin, José Luis R.; Garcia-Feijoo, Julian, “Optic nerve and macular optical coherence tomography in recovered COVID-19 patients.,” Eur. J. Ophthalmol., vol. 32, no. 1, pp. 11206721211001020–11206721211001020, 2021. https://doi.org/10.1177/11206721211001019
- V. D.-M. Biousse Helen V; Saindane, Amit M; Lamirel, Cédric; Newman, Nancy J, “Imaging of the optic nerve: technological advances and future prospects.,” Lancet. Neurol., vol. 21, no. 12, pp. 1135–1150, 2022. https://doi.org/10.1016/s1474-4422(22)00173-9
- M. J. . M. Page Joanne E.; Bossuyt, Patrick M.M.; Boutron, Isabelle; Hoffmann, Tammy; Mulrow, Cynthia D.; Shamseer, Larissa; Tetzlaff, Jennifer; Akl, Elie A.; Brennan, Sue E.; Chou, Roger; Glanville, Julie; Grimshaw, Jeremy M.; Hróbjartsson, Asbjørn; Lalu, Manoj M., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, 2021. https://doi.org/10.1136/bmj.n71
- PRISMA Endorsers, “PRISMA.”.
- M. McDonagh, K. Peterson, P. Raina, S. Chang, and P. Shekelle, “Avoiding Bias in Selecting Studies.,” Rockville (MD), 2008.
- Y. Xiao, J. Wu, Z. Lin, and X. Zhao, “A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data,” Comput. Methods Programs Biomed., vol. 166, pp. 99–105, 2018. https://doi.org/10.1016/j.cmpb.2018.10.004
- R. Tapu, B. Mocanu, and T. Zaharia, “DEEP-SEE: Joint object detection, tracking and recognition with application to visually impaired navigational assistance,” Sensors (Switzerland), vol. 17, no. 11, 2017. https://doi.org/10.3390/s17112473
- A. Nagarajan and G. M P, “Hybrid Optimization-Enabled Deep Learning for Indoor Object Detection and Distance Estimation to Assist Visually Impaired Persons,” Adv. Eng. Softw., vol. 176, no. July 2022, p. 103362, 2023. https://doi.org/10.1016/j.advengsoft.2022.103362
- J. Ganesan, A. T. Azar, S. Alsenan, N. A. Kamal, B. Qureshi, and A. E. Hassanien, “Deep Learning Reader for Visually Impaired,” Electron., vol. 11, no. 20, 2022. https://doi.org/10.3390/electronics11203335
- M. M. Islam, S. Nooruddin, F. Karray, and G. Muhammad, “Multi-level feature fusion for multimodal human activity recognition in Internet of Healthcare Things,” Inf. Fusion, vol. 94, pp. 17–31, 2023. https://doi.org/10.1016/j.inffus.2023.01.015
- M. Zounemat-Kermani and A. Mahdavi-Meymand, “Hybrid meta-heuristics artificial intelligence models in simulating discharge passing the piano key weirs,” J. Hydrol., 2019. https://doi.org/10.1016/j.jhydrol.2018.11.052
- R. O. M. Ogundokun Rytis; Damaševičius, Robertas, “Human Posture Detection Using Image Augmentation and Hyperparameter-Optimized Transfer Learning Algorithms,” Appl. Sci., vol. 12, no. 19, p. 10156, 2022. https://doi.org/10.3390/app121910156
- P. F. . G. Felzenszwalb Ross; McAllester, David; Ramanan, Deva, “Object Detection with Discriminatively Trained Part-Based Models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1627–1645, 2010. https://doi.org/10.1109/TPAMI.2009.167
- M. Ali, F. Sahin, S. Kumar, and C. Savur, “360° view camera based visual assistive technology for contextual scene information,” in 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, 2017, vol. 2017-Janua, pp. 2135–2140. https://doi.org/10.1109/SMC.2017.8122935
- Y. B. LeCun Bernhard E.; Denker, John S.; Henderson, D.; Howard, Richard; Hubbard, W.; Jackel, Lawrence D., “Backpropagation applied to handwritten zip code recognition,” Neural Comput., vol. 1, no. 4, pp. 541–551, 1989. https://doi.org/10.1162/neco.1989.1.4.541
- C. Playout, R. Duval, M. C. Boucher, and F. Cheriet, “Focused Attention in Transformers for interpretable classification of retinal images,” Med. Image Anal., vol. 82, no. July, 2022. https://doi.org/10.1016/j.media.2022.102608
- R. Fan et al., “Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization,” Ophthalmol. Sci., vol. 3, no. 1, p. 100233, 2023. https://doi.org/10.1016/j.xops.2022.100233
- F. S. Shi Wei; Duan, Huiyu; Liu, Xiaotian; Hu, Menghan; Wang, Wei; Zhai, Guangtao, “Drawing reveals hallmarks of children with autism,” Displays, vol. 67, no. NA, pp. 102000-NA, 2021. https://doi.org/10.1016/j.displa.2021.102000
- G. S. R. . D. Satyanarayana Prashant; Das, Santos Kumar, “Vehicle detection and classification with spatio-temporal information obtained from CNN,” Displays, vol. 75, no. NA, p. 102294, 2022. https://doi.org/10.1016/j.displa.2022.102294
- C. Z. Hua Baojiang; Song, Weigang; Yang, Jianyu, “Circular coding: A technique for visual localization in urban areas,” Displays, vol. 75, no. NA, p. 102299, 2022. https://doi.org/10.1016/j.displa.2022.102299
- S. S. Feuerriegel Yash Raj; von Krogh, Georg; Zhang, Ce, “Bringing artificial intelligence to business management,” Nat. Mach. Intell., vol. 4, no. 7, pp. 611–613, 2022. https://doi.org/10.1038/s42256-022-00512-5
- X. W. Wang Chen; Liu, Bing; Zhou, Xiaoqing; Zhang, Liang; Zheng, Jin; Bai, Xiao, “Multi-view stereo in the Deep Learning Era: A comprehensive revfiew,” Displays, vol. 70, no. NA, pp. 102102-NA, 2021. https://doi.org/10.1016/j.displa.2021.102102
- L. L. Mohammadpour Teck Chaw; Liew, Chee Sun; Aryanfar, Alihossein, “A Survey of CNN-Based Network Intrusion Detection,” Appl. Sci., vol. 12, no. 16, p. 8162, 2022. https://doi.org/10.3390/app12168162
- M. Mukhiddinov, A. B. Abdusalomov, and J. Cho, “Automatic Fire Detection and Notification System Based on Improved YOLOv4 for the Blind and Visually Impaired,” Sensors, vol. 22, no. 9, 2022. https://doi.org/10.3390/s22093307
- A. Mogadala, M. Kalimuthu, and D. Klakow, “Trends in integration of vision and language research: A survey of tasks, datasets, and methods,” J. Artif. Intell. …, 2021.
- H. S. Phillips Shelly; Klang, Eyal, “Oncological Applications of Deep Learning Generative Adversarial Networks.,” JAMA Oncol., vol. 8, no. 5, pp. 677-NA, 2022. https://doi.org/10.1001/jamaoncol.2021.8202
- S. Shahriar, “GAN computers generate arts? A survey on visual arts, music, and literary text generation using generative adversarial network,” Displays, vol. 73, no. NA, pp. 102237-NA, 2022. https://doi.org/10.1016/j.displa.2022.102237
- A. Iqbal, M. Sharif, M. Yasmin, M. Raza, and S. Aftab, “Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey,” Int. J. Multimed. Inf. Retr., vol. 11, no. 3, pp. 333–368, 2022. https://doi.org/10.1007/s13735-022-00240-x
- C. Qian, J. Zhu, Y. Shen, Q. Jiang, and Q. Zhang, “Deep Transfer Learning in Mechanical Intelligent Fault Diagnosis: Application and Challenge,” Neural Process. Lett., vol. 54, no. 3, pp. 2509–2531, 2022. https://doi.org/10.1007/s11063-021-10719-z
- R. O. Ogundokun, R. Maskeliūnas, and R. Damaševičius, “Human Posture Detection Using Image Augmentation and Hyperparameter-Optimized Transfer Learning Algorithms,” Applied Sciences, vol. 12, no. 19. 2022. https://doi.org/10.3390/app121910156
- A. Radford, L. Metz, and S. Chintala, “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.” 2016.
- Y. Ganin et al., “Domain-Adversarial Training of Neural Networks.” 2016.
- M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The Pascal Visual Object Classes (VOC) Challenge,” Int. J. Comput. Vis., vol. 88, no. 2, pp. 303–338, Jun. 2010. https://doi.org/10.1007/s11263-009-0275-4
- O. D. Russakovsky Jia; Su, Hao; Krause, Jonathan; Satheesh, Sanjeev; Ma, Sean; Huang, Zhiheng; Karpathy, Andrej; Khosla, Aditya; Bernstein, Michael S.; Berg, Alexander C.; Fei-Fei, Li, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015. https://doi.org/10.1007/s11263-015-0816-y
- C. L. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár and Zitnick, “Microsoft COCO: common objects in context,” in D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Computer Vision – ECCV 2014, 2014, pp. 740–755. https://doi.org/10.1007/978-3-319-10602-1_48
- A. Kuznetsova et al., “The Open Images Dataset V4,” Int. J. Comput. Vis., vol. 128, no. 7, pp. 1956–1981, Jul. 2020. https://doi.org/10.1007/s11263-020-01316-z
- A. Z. M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, “The PASCAL Visual Object Classes Challenge 2007 (VOC2007).” .
- J. W. M. Everingham, “The PASCAL visual object classes challenge 2012 (VOC2012) development kit 32.” .
- L. F.-F. J. Deng, W. Dong, R. Socher, L. Li, Kai Li, “ImageNet: a large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255. https://doi.org/10.1109/CVPR.2009.5206848
- A. Aslam and E. Curry, “A Survey on Object Detection for the Internet of Multimedia Things (IoMT) using Deep Learning and Event-based Middleware: Approaches, Challenges, and Future Directions,” Image Vis. Comput., vol. 106, p. 104095, Feb. 2021. https://doi.org/10.1016/j.imavis.2020.104095
- N. E. Ongalia Titiek; Meryana, Pauline, “COMPUTER VISION SYNDROME IN MEDICAL STUDENTS IN THE ERA OF THE COVID-19 PANDEMIC,” J. Widya Med. Jr., vol. 4, no. 5, pp. 199–204, 2022. https://doi.org/10.33508/jwmj.v4i3.4096
- H. Ö. Öztürk Bediz, “The Effects of Smartphone, Tablet and Computer Overuse on Children’s Eyes During the COVID-19 Pandemic,” J. Pediatr. Res., vol. 8, no. 4, pp. 491–497, 2021. https://doi.org/10.4274/jpr.galenos.2021.72623
- M. M. A. Zalat Soliman; Wassif, Ghada A.; Tarhouny, Shereen A. El; Mansour, Tayseer M., “Computer vision syndrome, visual ergonomics and amelioration among staff members in a Saudi medical college,” Int. J. Occup. Saf. Ergon., vol. 28, no. 2, pp. 1–9, 2021. https://doi.org/10.1080/10803548.2021.1877928
- D. Feng, C. Lu, Q. Cai, and J. Lu, “A Study on the Design of Vision Protection Products Based on Children’s Visual Fatigue under Online Learning Scenarios,” Healthcare, vol. 10, no. 4, p. 621, Mar. 2022. https://doi.org/10.3390/healthcare10040621
- K. K. . G. Weise Sarah J.; Hale, M Heath; Springer, Daniel B.; Swanson, Mark W., “Pre-participation Vision Screening and Comprehensive Eye Care in National Collegiate Athletic Association Athletes.,” Optom. Vis. Sci., vol. 98, no. 7, pp. 764–770, 2021. https://doi.org/10.1097/opx.0000000000001738
- K. K. . S. Weise Mark W.; Galt, Sarah J.; Springer, Daniel B.; Crosson, Jason N.; DeCarlo, Dawn K.; Hale, Matthew Heath; Nicholson, Joshua Ryne; Robinson, James B., “Objective Vision-related Indications for Clear and Tinted Football Helmet Visors.,” Optom. Vis. Sci., vol. 98, no. 7, pp. 833–838, 2021. https://doi.org/10.1097/opx.0000000000001730
- L. Y. Triyono Tri Raharjo; Sukamto, Sukamto; Hestinigsih, I, “VeRO: Smart home assistant for blind with voice recognition,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1108, no. 1, pp. 012016-NA, 2021. https://doi.org/10.1088/1757-899x/1108/1/012016
- N. O. Yu Ziwei; Wang, Hehe; Tao, Da; Jing, Liang, “The Effects of Smart Home Interface Touch Button Design Features on Performance among Young and Senior Users.,” Int. J. Environ. Res. Public Health, vol. 19, no. 4, p. 2391, 2022. https://doi.org/10.3390/ijerph19042391
- S. K. Feitl Julian; Götzelmann, Timo, “Accessible Electrostatic Surface Haptics: Towards an Interactive Audiotactile Map Interface for People With Visual Impairments,” The15th International Conference on PErvasive Technologies Related to Assistive Environments, vol. NA, no. NA. p. NA-NA, 2022. https://doi.org/10.1145/3529190.3534781
References
P. Argüeso, “Human ocular mucins: The endowed guardians of sight,” Adv. Drug Deliv. Rev., vol. 180, 2022. https://doi.org/10.1016/j.addr.2021.114074
P. R. Boyce, “Light, lighting and human health,” Light. Res. Technol., vol. 54, no. 2, pp. 101–144, 2022. https://doi.org/10.1177/14771535211010267
S. Ferrari et al., “Presence of SARS-CoV-2 RNA in human corneal tissues donated in Italy during the COVID-19 pandemic,” BMJ Open Ophthalmol., vol. 7, no. 1, pp. 5–9, 2022. http://dx.doi.org/10.1136/bmjophth-2022-000990
S. G. Singh Gustavo; Shah, Ruchi; Kramerov, Andrei A.; Wright, Robert Emery; Spektor, Tanya M; Ljubimov, Alexander V.; Arumugaswami, Vaithilingaraja; Kumar, Ashok, “SARS-CoV-2 and its beta variant of concern infect human conjunctival epithelial cells and induce differential antiviral innate immune response.,” Ocul. Surf., vol. 23, no. NA, pp. 184–194, 2021. https://doi.org/10.1016/j.jtos.2021.09.007
M. M. C. Schwarz Kaleigh A; Davoli, Katherine A; McMillen, Cynthia M; Albe, Joseph R; Hoehl, Ryan M; Demers, Matthew J; Ganaie, Safder S; Price, David A; Leung, Daisy W; Amarasinghe, Gaya K; McElroy, Anita K; Reed, Douglas S; Hartman, Amy L, “Rift Valley Fever Virus Infects the Posterior Segment of the Eye and Induces Inflammation in a Rat Model of Ocular Disease.,” J. Virol., vol. 96, no. 20, pp. e0111222-NA, 2022. https://doi.org/10.1128/jvi.01112-22
https://www.who.int/publications/i/item/9789241516570.
B. Kuriakose, R. Shrestha, and F. E. Sandnes, “Tools and technologies for blind and visually impaired navigation support: a review,” IETE Tech. Rev., 2022. https://doi.org/10.1080/02564602.2020.1819893
E. L. Cardillo Changzhi; Caddemi, Alina, “Millimeter-Wave Radar Cane: A Blind People Aid With Moving Human Recognition Capabilities,” IEEE J. Electromagn. RF Microwaves Med. Biol., vol. 6, no. 2, pp. 204–211, 2022. https://doi.org/10.1109/JERM.2021.3117129
A. P. Budrionis Darius; Daniušis, Povilas; Indrulionis, Audrius, “Smartphone-based computer vision travelling aids for blind and visually impaired individuals: A systematic review.,” Assist. Technol., vol. 34, no. 2, pp. 1–17, 2020. https://doi.org/10.1080/10400435.2020.1743381
J. M. K. Nesemann Ram Prasad; Byanju, Raghunandan; Poudyal, Bimal; Bhandari, Gopal; Bhandari, Sadhan; O’Brien, Kieran S; Stevens, Valerie M; Melo, Jason S; Keenan, Jeremy D., “Association of visual impairment with disability: a population-based study.,” Eye (Lond)., vol. 36, no. 3, pp. 1–7, 2021. https://doi.org/10.1038/s41433-021-01498-x
V. P. Yasin Peniarsih; Gozali, Ahmad; Junaedi, Ifan, “Application of expert system diagnosis of color blindness with ishihara method with microsoft vb 6.0,” Int. J. Informatics, Econ. Manag. Sci., vol. 1, no. 1, p. 13, 2022. https://doi.org/10.52362/ijiems.v1i1.678
H. X. Xinghong Liu; Zhuming, Zhang; Menghan, Xia; Chengze, Li; Tien-Tsin, Wong, “Colorblind-shareable videos by synthesizing temporal-coherent polynomial coefficients,” ACM Trans. Graph., vol. 38, no. 6, pp. 1–12, 2019. https://doi.org/10.1145/3355089.3356534
R. T. Alcaraz Martínez Mireia Ribera; Granollers Saltiveri, Toni, “Methodology for heuristic evaluation of the accessibility of statistical charts for people with low vision and color vision deficiency,” Univers. Access Inf. Soc., vol. 21, no. 4, pp. 863–894, 2021. https://doi.org/10.1007/s10209-021-00816-0
M. H. S. Mahjoob Javad; Anderson, Andrew J, “The effect of mental load on psychophysical and visual evoked potential visual acuity.,” Ophthalmic Physiol. Opt., vol. 42, no. 3, pp. 586–593, 2022. https://doi.org/10.1111/opo.12955
M. H. S. Mahjoob Javad; Anderson, Andrew J, “The effect of mental load on psychophysical and visual evoked potential visual acuity.,” Ophthalmic Physiol. Opt., vol. 42, no. 3, pp. 586–593, 2022. https://doi.org/10.1111/opo.12955
V. C. D. De Cock Pauline; Leu-Semenescu, Smaranda; Aerts, Cécile; Castelnovo, Giovanni; Abril, Beatriz; Drapier, Sophie; Olivet, Hélène; Corbillé, Anne-Gaëlle; Leclair-Visonneau, Laurène; Sallansonnet-Froment, Magali; Lebouteux, Marie; Anheim, Mathieu; Ruppert, E, “Safety and efficacy of subcutaneous night-time only apomorphine infusion to treat insomnia in patients with Parkinson’s disease (APOMORPHEE): a multicentre, randomised, controlled, double-blind crossover study.,” Lancet. Neurol., vol. 21, no. 5, pp. 428–437, 2022. https://doi.org/10.1016/s1474-4422(22)00085-0
F. A. Almutairi Nawaf; Ahmad, Khabir; Magliyah, Moustafa S.; Schatz, Patrik, “Congenital stationary night blindness: an update and review of the disease spectrum in Saudi Arabia.,” Acta Ophthalmol., vol. 99, no. 6, pp. 581–591, 2020. https://doi.org/10.1111/aos.14693
B.-J. Cho, “Congenital Stationary Night Blindness,” in Inherited Retinal Disease, Singapore: Springer Nature Singapore, 2022, pp. 117–123.
S. Y. Mochida Takeshi; Nomura, Takuhei; Hatake, Ryoma; Ohno-Matsui, Kyoko, “Association between peripheral visual field defects and focal lamina cribrosa defects in highly myopic eyes.,” Jpn. J. Ophthalmol., vol. 66, no. 3, pp. 285–295, 2022. https://doi.org/10.1007/s10384-022-00909-0
L. E. Donaldson Arshia; Sacco, Simone; Micieli, Jonathan A.; Margolin, Edward, “Junctional Scotoma and Patterns of Visual Field Defects Produced by Lesions Involving the Optic Chiasm.,” J. Neuroophthalmol., vol. 42, no. 1, pp. e203–e208, 2021. https://doi.org/10.1097/wno.0000000000001394
V. N. . D. Vakharia Beate; Tisdall, Martin, “Visual field defects in temporal lobe epilepsy surgery.,” Curr. Opin. Neurol., vol. 34, no. 2, pp. 188–196, 2021. https://doi.org/10.1097/wco.0000000000000905
E. M. Ekici Sasan; Hou, Huiyuan; Proudfoot, James A.; Zangwill, Linda M.; L., Jiun; Oh, Won Hyuk; Kamalipour, Alireza; Liebmann, Jeffrey M.; De Moraes, Carlos Gustavo; Girkin, Christopher A.; El-Nimri, Nevin W.; Weinreb, Robert N., “Central Visual Field Defects in Patients with Distinct Glaucomatous Optic Disc Phenotypes,” Am. J. Ophthalmol., vol. 223, no. NA, pp. 229–240, 2020. https://doi.org/10.1016/j.ajo.2020.10.015
B. S. Schmitz Katherine L; Wingerson, Mathew J; Walker, Gregory A; Wilson, Julie C; Howell, David R, “Double Vision and Light Sensitivity Symptoms are Associated With Return-to-School Timing After Pediatric Concussion.,” Clin. J. Sport Med., vol. Publish Ah, 2022. https://doi.org/10.1097/jsm.0000000000001106
R. K. Bartlett Yi Xuan; Hourcade, Juan Pablo; Rector, Kyle, “Exploring the Opportunities for Technologies to Enhance Quality of Life with People who have Experienced Vision Loss,” Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, p. 191, 2019. https://doi.org/10.1145/3290605.3300421
S. W. Bi Cong; Zhang, Jilong; Huang, Wutao; Wu, Bochun; Gong, Yi; Ni, Wei, “A Survey on Artificial Intelligence Aided Internet-of-Things Technologies in Emerging Smart Libraries.,” Sensors (Basel)., vol. 22, no. 8, p. 2991, 2022. https://doi.org/10.3390/s22082991
L. Z. Jia Zhi; Xu, Fei; Jin, Hai, “Cost-Efficient Continuous Edge Learning for Artificial Intelligence of Things,” IEEE Internet Things J., vol. 9, no. 10, pp. 7325–7337, 2022. https://doi.org/10.1109/JIOT.2021.3104089
S. O. Zhu Kaoru; Dong, Mianxiong, “Energy-Efficient Artificial Intelligence of Things With Intelligent Edge,” IEEE Internet Things J., vol. 9, no. 10, pp. 7525–7532, 2022. https://doi.org/10.1109/JIOT.2022.3143722
D. N. A. Nya Hassane, “Model-Free Control Policies for Inventory Management in Supply Chain,” 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT), vol. NA, no. NA. p. NA-NA, 2022. https://doi.org/10.1109/CoDIT55151.2022.9803914
Y. B. Mashayekhy Amir; Yuan, Xue-Ming; Xue, Anrong, “Impact of Internet of Things (IoT) on Inventory Management: A Literature Survey,” Logistics, vol. 6, no. 2, p. 33, 2022. https://doi.org/10.3390/logistics6020033
Y.-M. H. Tang George To Sum; Lau, Yui-Yip; Tsui, Shuk-Ying, “Integrated Smart Warehouse and Manufacturing Management with Demand Forecasting in Small-Scale Cyclical Industries,” Machines, vol. 10, no. 6, p. 472, 2022. https://doi.org/10.3390/machines10060472
M. K. Mahobe Pradeep; Jha, Shashi Shekhar, “Nature-Inspired AI Techniques in Intelligent Transportation System,” in Lecture Notes in Electrical Engineering, Springer Nature Singapore, 2022, pp. 251–263. https://doi.org/10.1007/978-981-16-8542-2_20
D. K. Loske Matthias, “Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics,” Int. J. Prod. Econ., vol. 241, no. NA, pp. 108236-NA, 2021. https://doi.org/10.1016/j.ijpe.2021.108236
I. Z. Lee Helen; Moore, Kate; Zhou, Xiaofei; Perret, Beatriz; Cheng, Yihong; Zheng, Ruiying; Pu, Grace, “AI Book Club,” Proceedings of the 53rd ACM Technical Symposium on Computer Science Education. ACM, 2022. https://doi.org/10.1145/3478431.3499318
T. P. Kabudi Ilias O.; Olsen, Dag H., “AI-enabled adaptive learning systems: A systematic mapping of the literature,” Comput. Educ. Artif. Intell., vol. 2, p. 100017, 2021. https://doi.org/10.1016/j.caeai.2021.100017
Y. A. Alsaawy Ahmad; Abi Sen, Adnan; Alshanqiti, Abdullah; Bhat, Wasim Ahmad; Bahbouh, Nour Mahmoud, “A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics,” Appl. Sci., vol. 12, no. 4, p. 2043, 2022. https://doi.org/10.3390/app12042043
S. P. Bhupathi Nithish, Ankit Panda, Trishala Reddy, Vishwa Gohil, Ishita Kundaliya, “Self-Driving Car to Drive Autonomously using Image Processing and Deep Learning,” Irjet, vol. 9, no. 1, pp. 125–132, 2022. http://dx.doi.org/10.13140/RG.2.2.16212.88963/1
S. K. B. M.; Gayathri, S.; Srinidhi, S.; Hemasundari, H.; Sowmiya, S.; Shavan Kumar, S., “AI-Based Motorized Appearance Acknowledgement Scheme for an Attendance Marking System,” in Advances in Social Networking and Online Communities, vol. NA, no. NA, 2022, pp. 98–109.
M. Surve, P. Joshi, S. Jamadar, and M. M. N. Vharkate, “Automatic Attendance System using Face Recognition Technique,” Int. J. Recent Technol. Eng., vol. 9, no. 1, pp. 2134–2138, 2020. http://www.doi.org/10.35940/ijrte.A2644.059120
Y. Himeur et al., AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives, no. 0123456789. Springer Netherlands, 2022.
A. Y. M. Chakor Azmani; Abdellah, Azmani, “Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervision,” Appl. Comput. Syst., vol. 27, no. 1, pp. 43–54, 2022. https://doi.org/10.2478/acss-2022-0005
S. A. Upadhyaya Anushri; Rengaraj, Venkatesh; Srinivasan, Kavitha; Casey, Paula Anne Newman; Schehlein, Emily M., “Validation of a portable, non-mydriatic fundus camera compared to gold standard dilated fundus examination using slit lamp biomicroscopy for assessing the optic disc for glaucoma.,” Eye (Lond)., vol. 36, no. 2, pp. 1–7, 2021. https://doi.org/10.1038/s41433-021-01485-2
D. Mishra, S. Gade, K. Glover, R. Sheshala, and T. R. R. Singh, “Vitreous Humor: Composition, Characteristics and Implication on Intravitreal Drug Delivery,” Curr. Eye Res., vol. 48, no. 2, pp. 208–218, 2022. https://doi.org/10.1080/02713683.2022.2119254
R. F. V. Spaide Philippe; Maloca, Peter M; Scholl, Hendrik P N; Otto, Tilman P; Caujolle, Sophie, “Imaging The Vitreous With A Novel Boosted Optical Coherence Tomography Technique: Vitreous Degeneration and Cisterns.,” Retina, vol. 42, no. 8, pp. 1433–1441, 2022. https://doi.org/10.1097/iae.0000000000003474.
S. M. . R. Zekavat Vineet K.; Trinder, Mark; Ye, Yixuan; Koyama, Satoshi; Honigberg, Michael C.; Yu, Zhi; Pampana, Akhil; Urbut, Sarah; Haidermota, Sara; O’Regan, Declan P.; Zhao, Hongyu; Ellinor, Patrick T.; Segrè, Ayellet V.; Elze, Tobias; Wiggs, Janey L.; Marton, “Deep Learning of the Retina Enables Phenome- and Genome-wide Analyses of the Microvasculature.,” Circulation, vol. 145, no. 2, pp. 134–150, 2021. https://doi.org/10.1161/CIRCULATIONAHA.121.057709
T. M. Izumi Ichiro; Kawano, Taizo; Sakaihara, Manabu; Iida, Tomohiro, “Morphological differences of choroid in central serous chorioretinopathy determined by ultra-widefield optical coherence tomography.,” Graefes Arch. Clin. Exp. Ophthalmol., vol. 260, no. 1, pp. 1–7, 2021. https://doi.org/10.1007/s00417-021-05380-0
X. Xu et al., “Automatic Segmentation and Measurement of Choroid Layer in High Myopia for OCT Imaging Using Deep Learning,” J. Digit. Imaging, vol. 35, no. 5, pp. 1153–1163, Oct. 2022. https://doi.org/10.1007/s10278-021-00571-x
B. G.-V. Burgos-Blasco Noemi; Vidal-Villegas, Beatriz; Martinez-de-la-Casa, Jose M.; Donate-Lopez, Juan; Martín-Sánchez, Francisco Javier; González-Armengol, Juan Jorge; Porta-Etessam, Jesús; Martin, José Luis R.; Garcia-Feijoo, Julian, “Optic nerve and macular optical coherence tomography in recovered COVID-19 patients.,” Eur. J. Ophthalmol., vol. 32, no. 1, pp. 11206721211001020–11206721211001020, 2021. https://doi.org/10.1177/11206721211001019
V. D.-M. Biousse Helen V; Saindane, Amit M; Lamirel, Cédric; Newman, Nancy J, “Imaging of the optic nerve: technological advances and future prospects.,” Lancet. Neurol., vol. 21, no. 12, pp. 1135–1150, 2022. https://doi.org/10.1016/s1474-4422(22)00173-9
M. J. . M. Page Joanne E.; Bossuyt, Patrick M.M.; Boutron, Isabelle; Hoffmann, Tammy; Mulrow, Cynthia D.; Shamseer, Larissa; Tetzlaff, Jennifer; Akl, Elie A.; Brennan, Sue E.; Chou, Roger; Glanville, Julie; Grimshaw, Jeremy M.; Hróbjartsson, Asbjørn; Lalu, Manoj M., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, 2021. https://doi.org/10.1136/bmj.n71
PRISMA Endorsers, “PRISMA.”.
M. McDonagh, K. Peterson, P. Raina, S. Chang, and P. Shekelle, “Avoiding Bias in Selecting Studies.,” Rockville (MD), 2008.
Y. Xiao, J. Wu, Z. Lin, and X. Zhao, “A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data,” Comput. Methods Programs Biomed., vol. 166, pp. 99–105, 2018. https://doi.org/10.1016/j.cmpb.2018.10.004
R. Tapu, B. Mocanu, and T. Zaharia, “DEEP-SEE: Joint object detection, tracking and recognition with application to visually impaired navigational assistance,” Sensors (Switzerland), vol. 17, no. 11, 2017. https://doi.org/10.3390/s17112473
A. Nagarajan and G. M P, “Hybrid Optimization-Enabled Deep Learning for Indoor Object Detection and Distance Estimation to Assist Visually Impaired Persons,” Adv. Eng. Softw., vol. 176, no. July 2022, p. 103362, 2023. https://doi.org/10.1016/j.advengsoft.2022.103362
J. Ganesan, A. T. Azar, S. Alsenan, N. A. Kamal, B. Qureshi, and A. E. Hassanien, “Deep Learning Reader for Visually Impaired,” Electron., vol. 11, no. 20, 2022. https://doi.org/10.3390/electronics11203335
M. M. Islam, S. Nooruddin, F. Karray, and G. Muhammad, “Multi-level feature fusion for multimodal human activity recognition in Internet of Healthcare Things,” Inf. Fusion, vol. 94, pp. 17–31, 2023. https://doi.org/10.1016/j.inffus.2023.01.015
M. Zounemat-Kermani and A. Mahdavi-Meymand, “Hybrid meta-heuristics artificial intelligence models in simulating discharge passing the piano key weirs,” J. Hydrol., 2019. https://doi.org/10.1016/j.jhydrol.2018.11.052
R. O. M. Ogundokun Rytis; Damaševičius, Robertas, “Human Posture Detection Using Image Augmentation and Hyperparameter-Optimized Transfer Learning Algorithms,” Appl. Sci., vol. 12, no. 19, p. 10156, 2022. https://doi.org/10.3390/app121910156
P. F. . G. Felzenszwalb Ross; McAllester, David; Ramanan, Deva, “Object Detection with Discriminatively Trained Part-Based Models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1627–1645, 2010. https://doi.org/10.1109/TPAMI.2009.167
M. Ali, F. Sahin, S. Kumar, and C. Savur, “360° view camera based visual assistive technology for contextual scene information,” in 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, 2017, vol. 2017-Janua, pp. 2135–2140. https://doi.org/10.1109/SMC.2017.8122935
Y. B. LeCun Bernhard E.; Denker, John S.; Henderson, D.; Howard, Richard; Hubbard, W.; Jackel, Lawrence D., “Backpropagation applied to handwritten zip code recognition,” Neural Comput., vol. 1, no. 4, pp. 541–551, 1989. https://doi.org/10.1162/neco.1989.1.4.541
C. Playout, R. Duval, M. C. Boucher, and F. Cheriet, “Focused Attention in Transformers for interpretable classification of retinal images,” Med. Image Anal., vol. 82, no. July, 2022. https://doi.org/10.1016/j.media.2022.102608
R. Fan et al., “Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization,” Ophthalmol. Sci., vol. 3, no. 1, p. 100233, 2023. https://doi.org/10.1016/j.xops.2022.100233
F. S. Shi Wei; Duan, Huiyu; Liu, Xiaotian; Hu, Menghan; Wang, Wei; Zhai, Guangtao, “Drawing reveals hallmarks of children with autism,” Displays, vol. 67, no. NA, pp. 102000-NA, 2021. https://doi.org/10.1016/j.displa.2021.102000
G. S. R. . D. Satyanarayana Prashant; Das, Santos Kumar, “Vehicle detection and classification with spatio-temporal information obtained from CNN,” Displays, vol. 75, no. NA, p. 102294, 2022. https://doi.org/10.1016/j.displa.2022.102294
C. Z. Hua Baojiang; Song, Weigang; Yang, Jianyu, “Circular coding: A technique for visual localization in urban areas,” Displays, vol. 75, no. NA, p. 102299, 2022. https://doi.org/10.1016/j.displa.2022.102299
S. S. Feuerriegel Yash Raj; von Krogh, Georg; Zhang, Ce, “Bringing artificial intelligence to business management,” Nat. Mach. Intell., vol. 4, no. 7, pp. 611–613, 2022. https://doi.org/10.1038/s42256-022-00512-5
X. W. Wang Chen; Liu, Bing; Zhou, Xiaoqing; Zhang, Liang; Zheng, Jin; Bai, Xiao, “Multi-view stereo in the Deep Learning Era: A comprehensive revfiew,” Displays, vol. 70, no. NA, pp. 102102-NA, 2021. https://doi.org/10.1016/j.displa.2021.102102
L. L. Mohammadpour Teck Chaw; Liew, Chee Sun; Aryanfar, Alihossein, “A Survey of CNN-Based Network Intrusion Detection,” Appl. Sci., vol. 12, no. 16, p. 8162, 2022. https://doi.org/10.3390/app12168162
M. Mukhiddinov, A. B. Abdusalomov, and J. Cho, “Automatic Fire Detection and Notification System Based on Improved YOLOv4 for the Blind and Visually Impaired,” Sensors, vol. 22, no. 9, 2022. https://doi.org/10.3390/s22093307
A. Mogadala, M. Kalimuthu, and D. Klakow, “Trends in integration of vision and language research: A survey of tasks, datasets, and methods,” J. Artif. Intell. …, 2021.
H. S. Phillips Shelly; Klang, Eyal, “Oncological Applications of Deep Learning Generative Adversarial Networks.,” JAMA Oncol., vol. 8, no. 5, pp. 677-NA, 2022. https://doi.org/10.1001/jamaoncol.2021.8202
S. Shahriar, “GAN computers generate arts? A survey on visual arts, music, and literary text generation using generative adversarial network,” Displays, vol. 73, no. NA, pp. 102237-NA, 2022. https://doi.org/10.1016/j.displa.2022.102237
A. Iqbal, M. Sharif, M. Yasmin, M. Raza, and S. Aftab, “Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey,” Int. J. Multimed. Inf. Retr., vol. 11, no. 3, pp. 333–368, 2022. https://doi.org/10.1007/s13735-022-00240-x
C. Qian, J. Zhu, Y. Shen, Q. Jiang, and Q. Zhang, “Deep Transfer Learning in Mechanical Intelligent Fault Diagnosis: Application and Challenge,” Neural Process. Lett., vol. 54, no. 3, pp. 2509–2531, 2022. https://doi.org/10.1007/s11063-021-10719-z
R. O. Ogundokun, R. Maskeliūnas, and R. Damaševičius, “Human Posture Detection Using Image Augmentation and Hyperparameter-Optimized Transfer Learning Algorithms,” Applied Sciences, vol. 12, no. 19. 2022. https://doi.org/10.3390/app121910156
A. Radford, L. Metz, and S. Chintala, “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.” 2016.
Y. Ganin et al., “Domain-Adversarial Training of Neural Networks.” 2016.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The Pascal Visual Object Classes (VOC) Challenge,” Int. J. Comput. Vis., vol. 88, no. 2, pp. 303–338, Jun. 2010. https://doi.org/10.1007/s11263-009-0275-4
O. D. Russakovsky Jia; Su, Hao; Krause, Jonathan; Satheesh, Sanjeev; Ma, Sean; Huang, Zhiheng; Karpathy, Andrej; Khosla, Aditya; Bernstein, Michael S.; Berg, Alexander C.; Fei-Fei, Li, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015. https://doi.org/10.1007/s11263-015-0816-y
C. L. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár and Zitnick, “Microsoft COCO: common objects in context,” in D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Computer Vision – ECCV 2014, 2014, pp. 740–755. https://doi.org/10.1007/978-3-319-10602-1_48
A. Kuznetsova et al., “The Open Images Dataset V4,” Int. J. Comput. Vis., vol. 128, no. 7, pp. 1956–1981, Jul. 2020. https://doi.org/10.1007/s11263-020-01316-z
A. Z. M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, “The PASCAL Visual Object Classes Challenge 2007 (VOC2007).” .
J. W. M. Everingham, “The PASCAL visual object classes challenge 2012 (VOC2012) development kit 32.” .
L. F.-F. J. Deng, W. Dong, R. Socher, L. Li, Kai Li, “ImageNet: a large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255. https://doi.org/10.1109/CVPR.2009.5206848
A. Aslam and E. Curry, “A Survey on Object Detection for the Internet of Multimedia Things (IoMT) using Deep Learning and Event-based Middleware: Approaches, Challenges, and Future Directions,” Image Vis. Comput., vol. 106, p. 104095, Feb. 2021. https://doi.org/10.1016/j.imavis.2020.104095
N. E. Ongalia Titiek; Meryana, Pauline, “COMPUTER VISION SYNDROME IN MEDICAL STUDENTS IN THE ERA OF THE COVID-19 PANDEMIC,” J. Widya Med. Jr., vol. 4, no. 5, pp. 199–204, 2022. https://doi.org/10.33508/jwmj.v4i3.4096
H. Ö. Öztürk Bediz, “The Effects of Smartphone, Tablet and Computer Overuse on Children’s Eyes During the COVID-19 Pandemic,” J. Pediatr. Res., vol. 8, no. 4, pp. 491–497, 2021. https://doi.org/10.4274/jpr.galenos.2021.72623
M. M. A. Zalat Soliman; Wassif, Ghada A.; Tarhouny, Shereen A. El; Mansour, Tayseer M., “Computer vision syndrome, visual ergonomics and amelioration among staff members in a Saudi medical college,” Int. J. Occup. Saf. Ergon., vol. 28, no. 2, pp. 1–9, 2021. https://doi.org/10.1080/10803548.2021.1877928
D. Feng, C. Lu, Q. Cai, and J. Lu, “A Study on the Design of Vision Protection Products Based on Children’s Visual Fatigue under Online Learning Scenarios,” Healthcare, vol. 10, no. 4, p. 621, Mar. 2022. https://doi.org/10.3390/healthcare10040621
K. K. . G. Weise Sarah J.; Hale, M Heath; Springer, Daniel B.; Swanson, Mark W., “Pre-participation Vision Screening and Comprehensive Eye Care in National Collegiate Athletic Association Athletes.,” Optom. Vis. Sci., vol. 98, no. 7, pp. 764–770, 2021. https://doi.org/10.1097/opx.0000000000001738
K. K. . S. Weise Mark W.; Galt, Sarah J.; Springer, Daniel B.; Crosson, Jason N.; DeCarlo, Dawn K.; Hale, Matthew Heath; Nicholson, Joshua Ryne; Robinson, James B., “Objective Vision-related Indications for Clear and Tinted Football Helmet Visors.,” Optom. Vis. Sci., vol. 98, no. 7, pp. 833–838, 2021. https://doi.org/10.1097/opx.0000000000001730
L. Y. Triyono Tri Raharjo; Sukamto, Sukamto; Hestinigsih, I, “VeRO: Smart home assistant for blind with voice recognition,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1108, no. 1, pp. 012016-NA, 2021. https://doi.org/10.1088/1757-899x/1108/1/012016
N. O. Yu Ziwei; Wang, Hehe; Tao, Da; Jing, Liang, “The Effects of Smart Home Interface Touch Button Design Features on Performance among Young and Senior Users.,” Int. J. Environ. Res. Public Health, vol. 19, no. 4, p. 2391, 2022. https://doi.org/10.3390/ijerph19042391
S. K. Feitl Julian; Götzelmann, Timo, “Accessible Electrostatic Surface Haptics: Towards an Interactive Audiotactile Map Interface for People With Visual Impairments,” The15th International Conference on PErvasive Technologies Related to Assistive Environments, vol. NA, no. NA. p. NA-NA, 2022. https://doi.org/10.1145/3529190.3534781