COMP4436 Artificial Intelligence of Things - AIoT Assignment - Comparative Analysis of ML, DL and SNN Applications lgorithms in AIoT
[2024-25] COMP4436 AIoT Assignment I
Comparative Analysis of ML, DL and SNN Algorithms in AIoT Applications
Objective: The goal of this assignment is to implement and compare the performance of various machine learning (ML) and deep learning (DL) algorithms, including a spiking neural network (SNN), in the context of an Artificial Intelligence of Things (AIoT) application. By using a dataset consisting of images of cats and dogs (or a dataset of your choice), you will gain a comprehensive understanding of supervised and unsupervised learning techniques and their applications in AIoT environments.
Relation to AIoT: This assignment is closely related to AIoT as it showcases how machine learning and deep learning models can be integrated into intelligent systems that process visual data from connected devices. In AIoT, devices such as cameras, sensors, and other smart devices generate vast amounts of data. The ability to classify and analyze this data in real-time using advanced algorithms, such as CNNs and SNNs, exemplifies the intersection of AI and IoT technologies. By applying these models to image data, the assignment demonstrates how intelligent decision-making can be achieved in AIoT applications, leading to improved automation, efficiency, and user experiences in smart environments.
Data Collection: Utilize a dataset containing images of cats and dogs (or a dataset of your choice). (https://www.kaggle.com/datasets/samuelcortinhas/cats-and-dogs-image-classification)
Algorithms: You should implement 5 algorithms of your choice, selecting one from each category and using the same dataset for all algorithms: Supervised ML Algorithm, Unsupervised ML Algorithm, Supervised DL Algorithm, Unsupervised DL Algorithm, Spiking Neural Network (SNN)
Evaluation Metrics: Algorithms should be compared based on various metrics, including but not limited to:
Accuracy: Proportion of correctly classified instances, Precision: Ratio of true positives to the total predicted positives, Recall: Ratio of true positives to the total actual positives, F1-Score: Harmonic mean of precision and recall, Runtime Efficiency: Time taken for each algorithm to complete the training and evaluation process.
Visualization: Generate comparative graphs and tables to present results, facilitating easier analysis of the performance of each algorithm. This will include plots of accuracy across different algorithms.
