Progression in machine learning and statistical inference are facilitating the advancement of domains like computer vision, natural language processing (NLP), automation & robotics, and so on. Among the different persuasive improvements in NLP, word embedding is one of the most used and revolutionary techniques. In this paper, we manifest an open-source library for Bangla word extraction systems named BnVec which expects to furnish the Bangla NLP research community by the utilization of some incredible word embedding techniques. The BnVec is splitted up into two parts, the first one is the Bangla suitable defined class to embed words with access to the six most popular word embedding schemes (CountVectorizer, TF-IDF, Hash Vectorizer, Word2vec, fastText, and Glove). The other one is based on the pre-trained distributed word embedding system of Word2vec, fastText, and GloVe. The pre-trained models have been built by collecting content from the newspaper, social media, and Bangla wiki articles. The total number of tokens used to build the models exceeds 395,289,960. The paper additionally depicts the performance of these models by various hyper-parameter tuning and then analyzes the results.

Fish identification and classification tasks allow scientists greater data on the sustainability and diversity of fisheries. Advances in computer vision, particularly convolutional neural networks (CNNs), have enabled automated fish detection at scale; however, increased model depth may not justify the additional time and computational cost. This study examines the ResNet family of models for binary fish identification to assess whether deeper networks provide meaningful performance advantages over simpler configurations. We compare ResNet-18, ResNet-50, ResNet-101, and ResNet-152 using both non-pretrained and pretrained initializations. We introduce an Energy-Weighted Score (EWS) to enable comparison of computational resource usage using cost-based weighting. A reliable fish versus no-fish classification can be achieved with as few as 18 layers, yielding 99.975% accuracy which improved to 100% accuracy following threshold optimization. For binary fish identification, increasing ResNet depth provides increased EWS scores with little impact on accuracy returns over shallower models. Overall, models with fewer layers outperformed deeper models with more parameters, and additional depth and tuning techniques were unable to outperform simpler configurations while delivering higher EWS scores.