تصنيف الصور القائم باستخدام التعلم العميق لتطبيقات كشف النماذج داخل وخارج المنزل

Lv H, Shi S, Gursoy D. A look back and a leap forward: a review and synthesis of big data and artificial intelligence literature in hospitality and tourism. J Hosp Mark Manag. 2022; 31(2): 145-75. https://doi.org/10.1080/19368623.2021.1937434

Rozenwald MB, Galitsyna AA, Sapunov GV, Khrameeva EE, Gelfand MS. A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features. Peer J Comput Sci. 2020; 6(30). https://doi.org/10.7717/peerj-cs.307

Sharma N, Sharma R, Jindal N. Machine learning and deep learning applications-a vision. Glob Trans Proc. 2021; 2(1): 24-8. https://doi.org/10.1016/j.gltp.2021.01.004

Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021; 8(1): 1-74. DOI:10.1186/s40537-021-00444-8

Adeel A, Gogate M, Hussain A. Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments. Inf Fusion. 2020; 1(59): 163-70. https://doi.org/10.1016/j.inffus.2019.08.008

Tian H, Chen SC, Shyu ML. Evolutionary programming based deep learning feature selection and network construction for visual data classification. Inf Syst Front. 2020 Oct; 22(5): 1053-66. https://doi.org/10.1007/s10796-020-10023-6

Lee SB, Gui X, Manquen M, Hamilton ER. Use of training, validation, and test sets for developing automated classifiers in quantitative ethnography. Int Conf Quant Ethn. 2019; 117-127. https://doi.org/10.1007/978-3-030-33232-7_10

Vishwakarma M, Singh HP, Kumar N, Arora M. The Need of Smart Guidance Systems for Blind People in the World. Proc Int Conf Big Data Mach Learn App. 2021; 191-195. https://doi.org/10.1007/978-981-15-8377-3_17

Durgadevi S, Thirupurasundari K, Komathi C, Balaji SM. Smart machine learning system for blind assistance. Int Conf Power Energy Control Trans Syst. IEEE. 2020; 1-4. https://doi.org/10.1109/ICPECTS49113.2020.9337031

Tapu R, Mocanu B, Zaharia T. DEEP-SEE: Joint object detection, tracking and recognition with application to visually impaired navigational assistance. Sensors. 2017; 17(11): 2473. https://doi.org/10.3390/s17112473

Yadav AV, Verma SS, Singh DD. Virtual Assistant for blind people. Int J Adv Sci Res Eng Trends. 2021; 6(5):156-159. http://ijasret.com/VolumeArticles/FullTextPDF/831_36.VIRTUAL_ASSISTANT_FOR_BLIND_PEOPLE.pdf

Ephzibah EP. Assisting Blind People Using Machine Learning Algorithms. Tur. Co. Mat. 2021; 12(8): 3162-70. https://doi.org/10.17762/turcomat.v12i8.4161

Qureshi TA, Rajbhar M, Pisat Y, Bhosale V. AI Based App for Blind People. Int Res J Eng Technol. 2021; 8(03): 2883-7. https://doi.org/10.1177/02646196221131746

Mamun SA, Daud ME, Mahmud M, Kaiser MS, Rossi AL. ALO: AI for least observed people. Int Conf Appl Intell Inform. Springer. 2021; 306-317. https://www.springerprofessional.de/en/alo-ai-for-least-observed-people/19396306

Chaurasia MA, Rasool S, Afroze M, Jalal SA, Zareen R, Fatima U, et al. Automated Navigation System with Indoor Assistance for Blind. In Contactless Healthcare Facilitation and Commodity Delivery Management During COVID 19 Pandemic. Springer, Singapore. 2022; 119-128. https://doi.org/10.1007/978-981-16-5411-4_10

Mone S, Salunke N, Jadhav O, Barge A, Magar N. Machine Learning Based Computer Vision Application for Visually Disabled People. Int J Sci Res Comput Sci Eng Inf Technol. 2021; 7(3): 488-494. https://doi.org/10.32628/CSEIT2173130

Wang K, Chen CM, Hossain MS, Muhammad G, Kumar S, Kumari S. Transfer reinforcement learning-based road object detection in next generation IoT domain. Comput Netw. 2021; 193:1-12. https://doi.org/10.1016/j.comnet.2021.108078

Bouteraa Y. Design and Development of a Wearable Assistive Device Integrating a Fuzzy Decision Support System for Blind and Visually Impaired People. Micromachines. 2021; 12(9): 1082. https://doi.org/10.3390/mi12091082

Periša M, Peraković D, Cvitić I, Krstić M. Innovative ecosystem for informing visual impaired person in smart shopping environment: IoT Shop. Wirel Netw. 2022; 28(1): 469-79. https://doi.org/10.1007/s11276-021-02591-5

Dhou S, Alnabulsi A, Al-Ali AR, Arshi M, Darwish F, Almaazmi S, et al. An IoT machine learning-based mobile sensors unit for visually impaired people. Sensors. 2022; 22(14): 5202. https://doi.org/10.3390/s22145202

Zhang E, fruit recognition dataset. Kaggle. 2022. [Online]. https://www.kaggle.com/datasets/sshikamaru/fruit-recognition .

Buyukkinaci M., fruit images for object detection dataset. Kaggle. 2018. https://www.kaggle.com/datasets/mbkinaci/fruit-images-for-object-detection .

Roy P, Ghosh S, Bhattacharya S, Pal U. Natural images dataset. Kaggle, 2018. https://www.kaggle.com/datasets/prasunroy/natural-images .

Annamraju. Weapon detection dataset. Kaggle. 2019. https://www.kaggle.com/datasets/abhishek4273/gun-detection-dataset .

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. Proc. IEEE conf Comput Vis Pattern Recognit. 2015: 1-9. https://doi.org/10.48550/arXiv.1409.4842

Hub T. Convolutional neural network architectures. Principles and Labs for Deep Learning. 2021: 6: 201. https://doi.org/10.1016/C2020-0-03408-0

AL-Huseiny MS, Sajit AS. Transfer learning with GoogLeNet for detection of lung cancer. Indones J Electr Eng. 2021; 22(2): 1078-86. http://doi.org/10.11591/ijeecs.v22.i2.pp1078-1086

Sharma S, Kumar H. Detection and classification of plant diseases by Alexnet and GoogleNet deep learning architecture. Int J Sci Res Eng Trends. 2022; 8(1): 218-23. https://ijsret.com/wp-content/uploads/2022/01/IJSRET_V8_issue1_120.pdf

Zakaria N, Mohamed F, Abdelghani R, Sundaraj K. VGG16, ResNet-50, and GoogLeNet Deep Learning Architecture for Breathing Sound Classification: A Comparative Study. Int Conf Artif Intell Cyber Secur Syst. IEEE. 2021:1-6. https://doi.org/10.1109/AI-CSP52968.2021.9671124

Mayya A, Khozama S. A. Novel Medical Support Deep Learning Fusion Model for the Diagnosis of COVID-19. Int Conf Adv Trends Multidiscip Res Innov. IEEE. 2020: 1-6. https://doi.org/10.1109/ICATMRI51801.2020.9398317

Abdullah TH, Alizadeh F, Abdullah BH. COVID-19 Diagnosis System using SimpNet Deep Model. Baghdad Sci J. 2022; 19(5): 1078-1089. DOI: http://dx.doi.org/10.21123/bsj.2022.19.4.ID0000.

Zaki SM, Jaber MM, Kashmoola MA. Diagnosing COVID-19 Infection in Chest X-Ray Images Using Neural Network. Baghdad Sci J. 2022; 19(6): 1356-1361. DOI: https://dx.doi.org/10.21123/bsj.2022.5965.

Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. Mobilenetv2: Inverted residuals and linear bottlenecks. Proc IEEE conf Comput Vis Pattern Recognit. 2018: 4510-4520. https://doi.org/10.48550/arXiv.1801.04381

Feigenbaum J J. A machine learning approach to census record linking. Harvard University. 2016. https://ranabr.people.stanford.edu/sites/g/files/sbiybj26066/files/media/file/machine_learning_approach.pdf