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ANALYSIS OF ARTIFICIAL INTELLIGENCE APPLICATIONS USING MICROSOFT LOBE: A LITERATURE REVIEW

Pratik M, Pooja S, Niveda ., Nethravathi B, Sandhyarani ., Abhigna K Prasad, Aadrika Srivastava

Abstract


Microsoft Lobe represents a significant advancement in democratizing artificial intelligence, enabling individuals with diverse technical backgrounds to effortlessly build and deploy custom machine learning models. Developed by Microsoft, Lobe offers an innovative platform that simplifies the complexities of model creation without requiring extensive coding expertise. With its user-friendly visual interface, Lobe facilitates tasks such as image classification, object detection, and data recognition through a straightforward drag-and-drop mechanism. This abstract highlights Lobe's pivotal role in making advanced Artificial Intelligence capabilities accessible to a broader audience. Beyond its technical features, Lobe fosters inclusivity by removing traditional barriers, catering to students, hobbyists, professionals, and businesses. Whether leveraging pre-built models or creating custom ones, Lobe's versatility extends across industries such as healthcare, education, and retail, promising transformative applications.


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References


Moldoveanu, R. A. (2015). FACIAL EMOTION EXPRESSIONS RECOGNITION WITH BRAIN ACTIVITES USING KINECT SENSOR V2.

Noel, G. P. (2023). Evaluating AI‐powered text‐to‐image generators for anatomical illustration: A comparative study. Anatomical Sciences Education.

Bhaumik, U., Chatterjee, S., & Kumar Singh, K. (2021). Suryanamaskar Pose Identification and Estimation Using No Code Computer Vision. In Machine Vision and Augmented Intelligence—Theory and Applications: Select Proceedings of MAI 2021 (pp. 85-90). Springer Singapore.

Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., &Fernández-Leal, Á. (2023). Human-in-the-loop machine learning: A state of the art. Artificial Intelligence Review, 56(4), 3005-3054.

Kırık, F., Demirkıran, B., Aslanoğlu, C. E., Koytak, A., &Özdemir, H. (2023). Detection and Classification of Diabetic Macular Edema with a Desktop-Based Code-Free Machine Learning Tool. Turkish Journal of Ophthalmology, 53(5), 301.

L. A. A. Mahmuddah, S. A. Wibowo, and G. Budiman, “AnalisisPerformasipadaPengambilan URL Berbasis Web Crawling denganMenggunakanTeknologiPengenalanWajah YOLOv3,” e-Proceeding Eng., vol. 8, no. 5, pp. 4756–4767, 2021.

A. S.P, P. S, and K. G, “Facial Expression Analysis of Students in Classroom using Machine Learning Technique,” Int. J. Curr. Adv. Res., vol. 8, no. 07, pp. 1–5, 2019.

F. D. Sukma and R. Mukhaiyar, “AlatPendeteksiEkspresiWajahpadaPengendaraBerbasis Image Processing,” JTEIN J. Tek. ElektroIndones., vol. 3, no. 2, pp. 364–373, 2022

Uchida, S. (2013). Image processing and recognition for biological images. Development, growth & differentiation, 55(4), 523-549.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).


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