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@article{164735,
        author = {Duvvuru Manoj Kumar and Tiriveedi Hari Kiran and Annavarapu Ganesh and Appajodu Rama krishna and Muhammed Swalih and Mirsah vt},
        title = {Recipe Recognition for Indian Cuisine: Deep Learning vs Machine Learning },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {1607-1615},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164735},
        abstract = {In this research, we compared the Machine Learning and Deep Learning Algorithms on Indian Dataset. We Collected Indian food Images of more than 20 categories and applied Machine learning and Deep learning algorithms which includes MobileNet, KNN, CNN, and Random Forest, to the Indian food dataset and to train the algorithms and test them on that data. We measured the performance of each algorithm in the terms of accuracy, precision, recall and F1 score, while taking into account Identification of Indian food was the main priority. In contrast, to compare and analyze the computational efficiency and resource requirements for different algorithms, taking into account the model complexity, and the training model. Via thorough evaluation using the test data, each algorithm's accuracy in the food Recognition model is Comprehensively examined. impressively, MobileNet turned up as the top performer, achieving an impressive accuracy score of  92.1%, followed by CNN at 79.1%. On the other hand, Machine learning algorithms which are KNN and Randomforest display relatively lower accuracies of 25.5% and 33.3%, respectively. These results provide valuable insights into the efficacy of diverse machine learning and Deep learning techniques in food Recognition.},
        keywords = {Image Classification, Food Recognition, CNN, MobileNet, KNN, Random Forest, Machine learning, Deep Learning},
        month = {},
        }