Mavericks at NADI 2023 Shared Task: Unravelling Regional Nuances through Dialect Identification using Transformer-based Approach

In this paper, we present our approach for the "Nuanced Arabic Dialect Identification (NADI) Shared Task 2023". We highlight our methodology for subtask 1 which deals with country-level dialect identification. Recognizing dialects plays an instrumental role in enhancing the performance of various downstream NLP tasks such as speech recognition and translation. The task uses the Twitter dataset (TWT-2023) that encompasses 18 dialects for the multi-class classification problem. Numerous transformer-based models, pre-trained on Arabic language, are employed for identifying country-level dialects. We fine-tune these state-of-the-art models on the provided dataset. The ensembling method is leveraged to yield improved performance of the system. We achieved an F1-score of 76.65 (11th rank on the leaderboard) on the test dataset.Comment: 5 pages, 1 figure, accepted at the NADI ArabicNLP Workshop, EMNLP 202

Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space -- Transformer Ensemble Models Tackling Deception and Persuasion

In this paper, we highlight our approach for the "Arabic AI Tasks Evaluation (ArAiEval) Shared Task 2023". We present our approaches for task 1-A and task 2-A of the shared task which focus on persuasion technique detection and disinformation detection respectively. Detection of persuasion techniques and disinformation has become imperative to avoid distortion of authentic information. The tasks use multigenre snippets of tweets and news articles for the given binary classification problem. We experiment with several transformer-based models that were pre-trained on the Arabic language. We fine-tune these state-of-the-art models on the provided dataset. Ensembling is employed to enhance the performance of the systems. We achieved a micro F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task 2-A (7th rank on the leaderboard) respectively.Comment: 6 pages, 1 figure, accepted at the ArAIEval ArabicNLP workshop, EMNLP conference 202

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery

A Robotic Process Automation for Stock Selection Process and Price Prediction Model using Machine Learning Techniques

Among these last few years, we have seen a tremendous increase in the participation in financial markets as well as there are more robotic process automation jobs emerging in recent years. We can clearly see the scope and increased requirement in both these domains. In the stock market, predicting the stock prices/direction and making profits is the main goal whereas in rpa, tasks which are done on a regular basis are converted into automated or semi-automated form. In this paper we have tried to apply both things into the picture such as developing a price prediction model using machine learning techniques and automating the stock selecting process through technical screeners depending on user requirements. Stacked LSTM and Bi-directional LSTM ML techniques are used and for automation part powerful rpa tool Automation Anywhere has been used. Factors such as evaluation metrics and graph plots are compared for models and advantages, and disadvantages are discussed for using systems with RPA and without RPA practices. Price prediction plots have been analyzed for stocks of different sectors with highest market capitalization and results/analysis and inferences have been stated.     &nbsp