Md Nazmul Hossain Mir

IGLOBAL, MSc in Cybersecurity, Graduate Student

Daffodil International University, BSc in CSE, Undergraduate

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I am a graduate researcher at Washington University of Science and Technology and currently affiliated with the Advanced Machine Intelligence Research Lab (AMIR Lab). My core research areas include artificial intelligence, deep learning, and cybersecurity, with a particular focus on convolutional neural networks (CNNs), transformer architectures, time-series forecasting, and AI-driven applications in healthcare and energy efficiency.

I have authored multiple peer-reviewed articles in leading journals such as IEEE Access, IEEE Open Journal of the Computer Society, and the International Journal of Medical Toxicology and Legal Medicine. My research work has accumulated over 45 citations across Google Scholar and ResearchGate.

I am proficient in Python, TensorFlow, Scikit-learn, Spark, Hadoop, and AWS, with hands-on experience in implementing scalable machine learning solutions. I am also an active member of IEEE and ACM, and I am open to contributing as a reviewer in journals covering AI, deep learning, medical imaging, and cybersecurity analytics.

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EVALUATING MACHINE LEARNING ALGORITHMS FOR BREAST CANCER DETECTION: A STUDY ON ACCURACY AND PREDICTIVE PERFORMANCE

THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY, 2024

This study evaluates several machine learning algorithms—Support Vector Machine (SVM), Random For... more This study evaluates several machine learning algorithms—Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision Tree (C4.5), and k-Nearest Neighbors (KNN)—for breast cancer detection using the Breast Cancer Wisconsin Diagnostic dataset. We implemented comprehensive pre-processing and model evaluation with Scikit-learn in Python. Our findings show that SVM achieved the highest accuracy, with 99.9% on the training set and 98.50% on the testing set, indicating superior performance in handling high-dimensional data. Random Forest also performed well, with accuracies of 98.5% and 98.20%, respectively. Logistic Regression and Decision Tree models provided reliable predictions when tuned, while KNN was less effective. SVM and Random Forest are recommended for clinical decision support systems due to their high accuracy and robustness.

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Deep learning for algorithmic trading: A systematic review of predictive models and optimization strategies

Array, 2025

a a a a b c d a b c d Share Cite Previous Next Algorithmic trading has revolutionized financial m... more a a a a b c d a b c d Share Cite Previous Next Algorithmic trading has revolutionized financial markets, offering rapid and efficient trade execution. The integration of deep learning (DL) into these systems has further enhanced predictive capabilities, providing sophisticated models that capture complex, non-linear market patterns. This systematic literature review explores recent advancements in the application of DL algorithms to algorithmic trading with a focus on optimizing financial market predictions. We analyze and synthesize the key DL architectures, such as recurrent neural networks (RNN), long short-term memory (LSTM), convolutional neural networks (CNN), and hybrid models, to evaluate their performance in predicting stock prices, volatility, and market trends. The review highlights current challenges, such as data noise, overfitting, and interpretability, while discussing emerging solutions and future research directions. Our findings provide a comprehensive understanding of how DL reshapes algorithmic trading and its potential to improve decision-making processes in volatile financial environments.

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Joint Topic-Emotion Modeling in Financial Texts: A Novel Approach to Investor Sentiment and Market Trends

IEEE ACCESS, 2025

This paper presents a novel approach to financial text analysis by jointly modeling topics and em... more This paper presents a novel approach to financial text analysis by jointly modeling topics and emotions within financial news and social media discussions, thereby advancing market trend understanding and investor sentiment analysis. Leveraging the Neural Variational Document Model (NVDM) for topic modeling, we effectively capture the thematic structure across global financial texts, achieving a coherence score of 0.68 and a perplexity of-8.5. For emotion classification, a fine-tuned FinBERT model yields an accuracy of 96.3%, F1 Score of 97.8%, precision of 98.3%, and recall of 97.5%. Our integrated framework not only uncovers the latent topics driving market discourse but also associates these topics with nuanced emotional states, providing a comprehensive perspective on financial sentiment. This joint modeling approach enhances the interpretability and reliability of financial market predictions, offering a scalable solution for understanding investor emotions with significant potential for applications in market analysis, trading strategies, and economic forecasting. INDEX TERMS Financial text analysis, joint topic modeling, emotion classification, neural variational document model (NVDM), FinBERT, sentiment analysis, natural language processing (NLP), financial market prediction, social media analysis.

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Md Nazmul Hossain Mir

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