Social media interaction especially the news spreading around network is a great source of information nowadays. From one's perspective, its negligible exertion, straightforward access, and quick dispersing that lead people to look out eat up from internet-based life. Twitter being standout amongst most well-known ongoing sources additionally ends dominant radiating mediums. It known cause extensive harm by bits gossip previously. Online clients are normally vulnerable will, in general,...

Early purchase prediction plays a vital role for an e-commerce website. It enables e-shoppers to enlist consumers product suggestions, offer discount and many other interventions. Several work has already been done using session log analyzing customer behavior whether he performs on the or not. In most cases, it is difficult find out make list of customers them when their ends. this paper, we propose customer's intention model where can detect purpose earlier. First, apply feature selection...

The rapid advancement of Large Language Models (LLMs) has significantly enhanced the capabilities text generators. With potential for misuse escalating, importance discerning whether texts are human-authored or generated by LLMs become paramount. Several preceding studies have ventured to address this challenge employing binary classifiers differentiate between human-written and LLM-generated text. Nevertheless, reliability these been subject question. Given that consequential decisions may...

Abstract Text classification plays a vital role in natural language processing (NLP), especially the context of rapidly expanding textual data. This paper presents an automated approach to detecting and classifying violent incidents from large corpus Bangla news articles, specifically focusing on such as Murder, Rape, Kidnap, Clash, Suicide. Our methodology employs semi-supervised learning framework, leveraging small labeled dataset train initial predictive model, which is then expanded...

Class unbalanced datasets are frequently encountered in a variety of areas including health, security, and finance. Often these create bias the supervised learning models trained for prediction task. One most successful techniques to handle imbalanced data is undersampling. Experiments demonstrate that cluster-based undersampling improves over random many cases. In this paper, we propose three new boosting approaches improve performance technique: (i) inject unlabelled into training improved...

Now a day's social networks have become crucial part and parcel in our day to life. The gradual rise of the amount information available different kind media is sole reason behind this impact. However, having scope accessing huge so easily has made it complicated find contrast between counterfeit an actual news. Some group people often grabs chance share manipulated number with intent destroying image any people, person or specific groups. This results creating such agendas which creates...

Classification of Imbalance data is one t he most vital tasks in the field machine learning because real-life datasets available have an imbalanced distribution class labels. The effect severe where predictive model trained on faces some unprecedented problems like overfitting gets biased towards majority target class. Many techniques been proposed over time to deal with caused by oversampling and undersampling isn't able match performance acquired method. One such baseline method clustering...