250 days of Artificial Intelligence and Machine Learning

This is the 250 days Challenge of Machine Learning, Deep Learning, AI, and Optimization (mini-projects and research papers) that I picked up at the start of January 2022. I have used various environments and Google Colab, and certain environments for this work as it required various libraries and datasets to be downloaded. The following are the problems that I tackled:

Day 1 (01/01/2022): GradCAM Implementation on Dogs v/s Cats using VGG16 pretrained models

Classification for Cat (GradCAM-based Explainability)	Classification for Dog (GradCAM-based Explainability)

Day 2 (01/02/2022): Multi-task Learning (focussed on Object Localization)

Day 3 (01/03/2022): Implementing GradCAM on Computer Vision problems
1. GradCAM for Semantic Segmentation
2. GradCAM for ObjectDetection

Computer Vision domains	CAM methods used	Detected Images	CAM-based images
Semantic Segmentation	GradCAM
Object Detection	EigenCAM
Object Detection	AblationCAM

Day 4 (01/04/2022): Deep Learning using PointNet-based Dataset
1. Classification

3D Point Clouds	Meshes Used	Sampled Meshes
Beds
Chair	TBA

Segmentation

Day 5 (01/05/2022): Graph Neural Network on YouChoose dataset

Implementing GNNs on YouChoose-Click dataset
Implementing GNNs on YouChoose-Buy dataset

Dataset	Loss Curve	Accuracy Curve
YouChoose-Click
YouChoose-Buy

Day 6 (01/06/2022): Graph neural Network for Recommnedation Systems
Day 7 (01/07/2022): Vision Transformers for efficient Image Classification

SN	Training and Validation Metrices
1
2

Day 8 (01/08/2022): Graph Neural Networks for Molecular Machine Learning

Loss Metrices

Day 9 (01/09/2022): Latent 3D Point Cloud Generation using GANs and Auto Encoders
Day 10 (01/10/2022): Deep Learning introduced on Audio Signal
Day 11 (01/11/2022): Ant-Colony Optimization

Explore Difference between Ant Colony Optimization and Genetic Algorithms for Travelling Salesman Problem.

Methods Used	Geo-locaion graph
Ant Colony Optimization
Genetic Algorithm

Day 12 (01/12/2022): Particle Swarm Optimization
Day 13 (01/13/2022): Cuckoo Search Optimization
Day 14 (01/14/2022): Physics-based Optimization algorithms Explored the contents of Physics-based optimization techniques such as:

Tug-Of-War Optimization (Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492.)
Nuclear Reaction Optimization (Wei, Z., Huang, C., Wang, X., Han, T., & Li, Y. (2019). Nuclear Reaction Optimization: A novel and powerful physics-based algorithm for global optimization. IEEE Access.)

    + So many equations and loops - take time to run on larger dimension 
    + General O (g * n * d) 
    + Good convergence curse because the used of gaussian-distribution and levy-flight trajectory
    + Use the variant of Differential Evolution

Henry Gas Solubility Optimization (Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646-667.)

    + Too much constants and variables
    + Still have some unclear point in Eq. 9 and Algorithm. 1
    + Can improve this algorithm by opposition-based and levy-flight
    + A wrong logic code in line 91 "j = id % self.n_elements" => to "j = id % self.n_clusters" can make algorithm converge faster. I don't know why?
    + Good results come from CEC 2014

Day 15 (01/15/2022): Human Activity-based Optimization algorithms Explored the contents of Human Activity-based optimization techniques such as:

Queuing Search Algorithm (Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464-490.)

Day 16 (01/16/2022): Evolutionary Optimization algorithms Explored the contents of Human Activity-based optimization techniques such as: Genetic Algorithms (Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73) Differential Evolution (Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359) Coral Reefs Optimization Algorithm (Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J. A. (2014). The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014)
Day 17 (01/17/2022): Swarm-based Optimization algorithms Explored the contents of Swarm-based optimization techniques such as:

Particle Swarm Optimization (Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). IEEE)
Cat Swarm Optimization (Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg)
Whale Optimization (Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67)
Bacterial Foraging Optimization (Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, 22(3), 52-67)
Adaptive Bacterial Foraging Optimization (Yan, X., Zhu, Y., Zhang, H., Chen, H., & Niu, B. (2012). An adaptive bacterial foraging optimization algorithm with lifecycle and social learning. Discrete Dynamics in Nature and Society, 2012)
Artificial Bee Colony (Karaboga, D., & Basturk, B. (2007, June). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In International fuzzy systems association world congress (pp. 789-798). Springer, Berlin, Heidelberg)
Pathfinder Algorithm (Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545-568)
Harris Hawks Optimization (Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872)
Sailfish Optimizer (Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20-34)

Credits (from Day 14--17): Learnt a lot due to Nguyen Van Thieu and his repository that deals with metaheuristic algorithms. Plan to use these algorithms in the problems enountered later onwards.

Day 18 (01/18/2022): Grey Wolf Optimization Algorithm
Day 19 (01/19/2022): Firefly Optimization Algorithm
Day 20 (01/20/2022): Covariance Matrix Adaptation Evolution Strategy Referenced from CMA (can be installed using pip install cma)

CMAES without bounds	CMAES with bounds

Refered from: Nikolaus Hansen, Dirk Arnold, Anne Auger. Evolution Strategies. Janusz Kacprzyk; Witold Pedrycz. Handbook of Computational Intelligence, Springer, 2015, 978-3-622-43504-5. ffhal-01155533f