Tanvir Ahmed

Swastika Kundu, Autoshi Ibrahim, Mithila Rahman et al.

Sentiment analysis for regional dialects of Bangla remains an underexplored area due to linguistic diversity and limited annotated data. This paper introduces ANUBHUTI, a comprehensive dataset consisting of 2000 sentences manually translated from standard Bangla into four major regional dialects Mymensingh, Noakhali, Sylhet, and Chittagong. The dataset predominantly features political and religious content, reflecting the contemporary socio political landscape of Bangladesh, alongside neutral texts to maintain balance. Each sentence is annotated using a dual annotation scheme: multiclass thematic labeling categorizes sentences as Political, Religious, or Neutral, and multilabel emotion annotation assigns one or more emotions from Anger, Contempt, Disgust, Enjoyment, Fear, Sadness, and Surprise. Expert native translators conducted the translation and annotation, with quality assurance performed via Cohens Kappa inter annotator agreement, achieving strong consistency across dialects. The dataset was further refined through systematic checks for missing data, anomalies, and inconsistencies. ANUBHUTI fills a critical gap in resources for sentiment analysis in low resource Bangla dialects, enabling more accurate and context aware natural language processing.

Kazi Fuad Bin Akhter, Sadia Mobasshira, Saief Nowaz Haque et al.

The precise forecasting of electricity demand also referred to as load forecasting, is essential for both planning and managing a power system. It is crucial for many tasks, including choosing which power units to commit to, making plans for future power generation capacity, enhancing the power network, and controlling electricity consumption. As Bangladesh is a developing country, the electricity infrastructure is critical for economic growth and employment in this country. Accurate forecasting of electricity demand is crucial for ensuring that this country has a reliable and sustainable electricity supply to meet the needs of its growing population and economy. The complex and nonlinear behavior of such energy systems inhibits the creation of precise algorithms. Within this context, this paper aims to propose a hybrid model of Convolutional Neural Network (CNN) and stacked Bidirectional Long-short Term Memory (BiLSTM) architecture to perform an accurate short-term forecast of the electricity demand of Dhaka city. Short-term forecasting is ordinarily done to anticipate load for the following few hours to a few weeks. Normalization techniques have been also investigated because of the sensitivity of these models towards the input range. The proposed approach produced the best prediction results in comparison to the other benchmark models (LSTM, CNN- BiLSTM and CNN-LSTM) used in the study, with MAPE 1.64%, MSE 0.015, RMSE 0.122 and MAE 0.092. The result of the proposed model also outperformed some of the existing works on load-forecasting.

Ruizhe Zhao, Brian Vogel, Tanvir Ahmed

Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy compared to the existing state-of-the-art MPT and single-precision floating point

Khalid Ibnal Asad, Tanvir Ahmed, Md. Saiedur Rahman

Abundance of movie data across the internet makes it an obvious candidate for machine learning and knowledge discovery. But most researches are directed towards bi-polar classification of movie or generation of a movie recommendation system based on reviews given by viewers on various internet sites. Classification of movie popularity based solely on attributes of a movie i.e. actor, actress, director rating, language, country and budget etc. has been less highlighted due to large number of attributes that are associated with each movie and their differences in dimensions. In this paper, we propose classification scheme of pre-release movie popularity based on inherent attributes using C4.5 and PART classifier algorithm and define the relation between attributes of post release movies using correlation coefficient.