Nuzhat Noor Islam Prova

Nuzhat Prova

Recent advances in natural language processing (NLP) may enable artificial intelligence (AI) models to generate writing that is identical to human written form in the future. This might have profound ethical, legal, and social repercussions. This study aims to address this problem by offering an accurate AI detector model that can differentiate between electronically produced text and human-written text. Our approach includes machine learning methods such as XGB Classifier, SVM, BERT architecture deep learning models. Furthermore, our results show that the BERT performs better than previous models in identifying information generated by AI from information provided by humans. Provide a comprehensive analysis of the current state of AI-generated text identification in our assessment of pertinent studies. Our testing yielded positive findings, showing that our strategy is successful, with the BERT emerging as the most probable answer. We analyze the research's societal implications, highlighting the possible advantages for various industries while addressing sustainability issues pertaining to morality and the environment. The XGB classifier and SVM give 0.84 and 0.81 accuracy in this article, respectively. The greatest accuracy in this research is provided by the BERT model, which provides 0.93% accuracy.

Aurora Lithe Roy, Md Kamrul Siam, Nuzhat Noor Islam Prova et al.

Diabetes, particularly Type 2 diabetes (T2D), poses a substantial global health burden, compounded by its associated complications such as cardiovascular diseases, kidney failure, and vision impairment. Early detection of T2D is critical for improving healthcare outcomes and optimizing resource allocation. In this study, we address the gap in early T2D detection by leveraging machine learning (ML) techniques on gene expression data obtained from T2D patients. Our primary objective was to enhance the accuracy of early T2D detection through advanced ML methodologies and increase the model's trustworthiness using the explainable artificial intelligence (XAI) technique. Analyzing the biological mechanisms underlying T2D through gene expression datasets represents a novel research frontier, relatively less explored in previous studies. While numerous investigations have focused on utilizing clinical and demographic data for T2D prediction, the integration of molecular insights from gene expression datasets offers a unique and promising avenue for understanding the pathophysiology of the disease. By employing six ML classifiers on data sourced from NCBI's Gene Expression Omnibus (GEO), we observed promising performance across all models. Notably, the XGBoost classifier exhibited the highest accuracy, achieving 97%. Our study addresses a notable gap in early T2D detection methodologies, emphasizing the importance of leveraging gene expression data and advanced ML techniques.