Charul Giri

• Abeyrathna, Kuruge Darshana; Abouzeid, Ahmed Abdulrahem Othman; Bhattarai, Bimal; Giri, Charul; Glimsdal, Sondre & Granmo, Ole-Christoffer [Show all 11 contributors for this article] (2023). Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size . In Elkind, Edith (Eds.), Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. AAAI Press. ISSN 978-1-956792-03-4. p. 3395–3403. doi: 10.24963/ijcai.2023/378. Full text in Research Archive Show summary

  Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they become less interpretable. Further, longer clauses increase the switching activity of the clause logic in hardware, consuming more power. This paper introduces a novel variant of TM learning – Clause Size Constrained TMs (CSC-TMs) – where one can set a soft constraint on the clause size. As soon as a clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate CSC-TM, we conduct classifcation, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC, IMDb, and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches a single literal. We fnally analyze CSC-TM power consumption and derive new convergence properties.

• Giri, Charul; Sharma, Jivitesh & Goodwin, Morten (2022). Brain Tumour Segmentation on 3D MRI Using Attention V-Net. In Iliadis, Lazaros; Jayne, Chrisina; Tefas, Anastasios & Pimenidis, Elias (Ed.), Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science. Springer Nature. ISSN 978-3-031-08223-8. p. 336–348. doi: 10.1007/978-3-031-08223-8_28. Show summary

  Brain tumour segmentation on 3D MRI imaging is one of the most critical deep learning applications. In this paper, for the segmentation of tumour sub-regions in brain MRI images, we study some popular architecture for medical imaging segmentation. We further, inspired by them, proposed an architecture that is an end-to-end trainable, fully convolutional neural network that uses attention block to learn localization of different features of the multiple sub-regions of a tumour. We also experiment with a combination of the weighted cross-entropy loss function and dice loss function on the model’s performance and the quality of the output segmented labels. The results of the evaluation of our model are received through BraTS’19 dataset challenge. The model can achieve a dice score of 0.80 for the whole tumour segmentation and dice scores of 0.639 and 0.536 for the other two sub-regions within the tumour on the validation dataset.

• Glimsdal, Sondre; Saha, Rupsa; Bhattarai, Bimal; Giri, Charul; Sharma, Jivitesh & Tunheim, Svein Anders [Show all 7 contributors for this article] (2022). Focused Negative Sampling for Increased Discriminative Power in Tsetlin Machines. In Shafik, Rishad (Eds.), 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE conference proceedings. ISSN 978-1-6654-7116-9. p. 73–80. doi: 10.1109/ISTM54910.2022.00021. Show summary

  Tsetlin Machines learn from input data by creating patterns in propositional logical, using the literals available in the data. These patterns vote for the classes in a classification task. Despite their simplistic premise, Tsetlin machine (TM)s have been performing at with other popular machine learning methods across various benchmarks. Not only accuracy, TMs also perform well in terms of energy efficiency and learning speed. The general TM scheme works best when there is sufficient discriminatory information available between two classes. In this paper, we explore the use of focused negative sampling (FNS) to discriminate between classes which are not easily distinguishable from each other. We carry out experiments across diverse classification tasks ranging over natural language processing, image processing, reinforcement learning to show that this approach forces the TM to arrive at patterns that can successfully tell apart two classes that are correlated. Further, we show that the proposed method achieves accuracy comparable to a vanilla Tsetlin Machine approach but in approximately 42% less number of epochs on average

• Giri, Charul; Granmo, Ole-Christoffer; Van Hoof, Herke & Blakely, Christian Dallas (2022). Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine, 2022 International Joint Conference on Neural Networks (IJCNN). IEEE conference proceedings. ISSN 978-1-7281-8671-9. doi: 10.1109/IJCNN55064.2022.9892796. Show summary

  Hex is a turn-based two-player connection game with a high branching factor, making the game arbitrarily complex with increasing board sizes. As such, top-performing algorithms for playing Hex rely on accurate evaluation of board positions using neural networks. However, the limited interpretability of neural networks is problematic when the user wants to understand the reasoning behind the predictions made. In this paper, we propose to use propositional logic expressions to describe winning and losing board game positions, facilitating precise visual interpretation. We employ a Tsetlin Machine (TM) to learn these expressions from previously played games, describing where pieces must be located or not located for a board position to be strong. Extensive experiments on 6×6 boards compare our TM-based solution with popular machine learning algorithms like XGBoost, InterpretML, decision trees, and neural networks, considering various board configurations with 2 to 22 moves played. On average, the TM testing accuracy is 92.1%, outperforming all the other evaluated algorithms. We further demonstrate the global interpretation of the logical expressions, and map them down to particular board game configurations to investigate local interpretability. We believe the resulting interpretability establishes building blocks for accurate assistive AI and human-AI collaboration, also for more complex prediction tasks.

• Giri, Charul; Goodwin, Morten & Oppedal, Ketil (2020). Deep 3D Convolution Neural Network for Alzheimer’s Detection, Machine Learning, Optimization, and Data Science. Springer. ISSN 978-3-030-64582-3. p. 347–358. doi: 10.1007/978-3-030-64583-0_32.
• Sharma, Jivitesh; Giri, Charul; Granmo, Ole-Christoffer & Goodwin, Morten (2019). Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation. EURASIP Journal on Information Security. ISSN 2510-523X. 2019(1). doi: 10.1186/s13635-019-0098-y. Full text in Research Archive Show summary

  Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works in three stages. First, the ExtraTrees classifier is used to select relevant features for each type of attack individually for each (ELM). Then, an ensemble of ELMs is used to detect each type of attack separately. Finally, the results of all ELMs are combined using a softmax layer to refine the results and increase the accuracy further. The intuition behind our system is that multi-class classification is quite difficult compared to binary classification. So, we divide the multi-class problem into multiple binary classifications. We test our method on the UNSW and KDDcup99 datasets. The results clearly show that our proposed method is able to outperform all the other methods, with a high margin. Our system is able to achieve 98.24% and 99.76% accuracy for multi-class classification on the UNSW and KDDcup99 datasets, respectively. Additionally, we use the weighted extreme learning machine to alleviate the problem of imbalance in classification of attacks, which further boosts performance. Lastly, we implement the ensemble of ELMs in parallel using GPUs to perform intrusion detection in real time.

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