Automating Dermatological Diagnosis - A Comparative Analysis Aleen Johnson, Vishnupriya Arora, Lakshita Aggarwal, Puneet Goswami Proceedings IEEE 2023 5th International Conference on Advances in Computing Communication Control and Networking Icac3n 2023, 2023 Evolving and adapting healthcare along with growing technology is an urgent need of an hour using machine learning is an addition to classify health data for the large aspect of identifying dermatological illness. Convolution Neural Network (CNN) can accurately classify a variety of skin illnesses by extracting complex information from images. CNN do away with the requirement for manual feature engineering because they are able to learn from large amounts of data. They are resistant to noise and changes in lighting, ensuring accurate analysis every time. CNN help in early detection, increasing the efficacy of treatment, and lowering the possibility of a false positive. Their combination has the potential to greatly improve patient care and change dermatological procedures. Objective: The goal of this study is to determine the best effective algorithm, whether it comes from deep learning or machine learning, based on a thorough comparative analysis that considers elements like accuracy, interpret ability, and scalability. Methodology: This study uses a comparative analysis methodology to decide which machine learning and deep learning algorithms are the most effective. It entails a review of the literature, the gathering of data, the design and execution of experiments, statistical analysis, and result interpretation. Findings: This study’s findings shows that deep learning approaches are more effective than machine learning ones. In contrast to machine learning, which depends on manual, subjective techniques, deep learning allows for automatic and objective feature extraction. Deep learning achieves greater prediction accuracy and generalization ability despite requiring more training time and computational resources, demonstrating its superior learning powers.
Dermcare-Skin Disease Classification from Image Vishnupriya Arora, Aleen Johnson, Lakshita Aggarwal, Puneet Goswami, Sana Alam Proceedings 4th IEEE 2023 International Conference on Computing Communication and Intelligent Systems Icccis 2023, 2023
Patient-mediated Health Data Exchange using Blockchain Arun Kumar, Nancy Aggarwal, Urvi Vasisht, Lakshita Aggarwal, Sana Alam, Puneet Goswami Proceedings IEEE 2023 5th International Conference on Advances in Computing Communication Control and Networking Icac3n 2023, 2023
Crypto currency portfolio allocation Using Machine Learning Pradhyumna Rao, Nishit Bhasin, Puneet Goswami, Lakshita Aggarwal Proceedings 2021 3rd International Conference on Advances in Computing Communication Control and Networking Icac3n 2021, 2021
Brain emulation machine model for communication International Journal of Scientific and Technology Research, 2019
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IoIT: Integrating Artificial Intelligence With IoT to Solve Pervasive IoT Issues L Aggarwal Artificial Intelligence to Solve Pervasive Internet of Things Issues, 251-267 , 2020 2020.0 Citations: 10
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