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Information Technology Research Papers

Explore a comprehensive collection of research papers in Information Technology, covering topics from AI and cybersecurity to software engineering and data science.

Available Research Papers

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AI-driven Cybersecurity: Building Adaptive Threat Detection Systems Using Deep Learning

👥 Available: Author, Co-author 1-4 position available
🕒 Booked: No position booked so far
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Explainable Artificial Intelligence (XAI) for Healthcare Diagnostics

👥 Available: Author, Co-author 1-4 position available
🕒 Booked: No position booked so far
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Blockchain-based Secure Data Sharing for Smart Cities

👥 Available: Author, Co-author 1-4 position available
🕒 Booked: No position booked so far

We are offering 25% discount if you order 3 papers together. Here is the discount idea: Suppose you ordered 3 paper as author position. The total service charge will be 7000x3= 21000tk. But for ordering 3 papers together, we will provide you 5250tk discount and your discounted price will be 15750tk.

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Paper Abstracts

AI-driven Cybersecurity: Building Adaptive Threat Detection Systems Using Deep Learning

As cyber threats evolve, traditional security measures struggle to keep up. This paper explores the development of adaptive threat detection systems powered by deep learning algorithms. The study focuses on leveraging neural networks to analyze patterns in real-time data and identify new, previously unknown threats. By using convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the proposed system can adapt to changing cyberattack techniques, ensuring improved detection rates with reduced false positives. The potential applications of this research could revolutionize cybersecurity systems, especially in industries such as finance, healthcare, and critical infrastructure.

Explainable Artificial Intelligence (XAI) for Healthcare Diagnostics

Artificial intelligence has shown tremendous potential in revolutionizing healthcare diagnostics, yet its "black-box" nature raises concerns about its interpretability. This paper proposes an approach to make AI models more transparent and explainable for healthcare professionals. By integrating techniques like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations), the paper examines how these models can be used to provide more trustworthy and comprehensible predictions in areas such as radiology and pathology. This work aims to bridge the gap between AI capabilities and healthcare providers' ability to trust and apply AI-driven diagnostics.

Blockchain-based Secure Data Sharing for Smart Cities

The proliferation of Internet of Things (IoT) devices in smart cities has generated vast amounts of sensitive data, requiring secure and transparent systems for data sharing. This paper investigates the use of blockchain technology to create decentralized, secure platforms for data sharing between IoT devices and municipal systems. It examines how blockchain's immutable and transparent nature can ensure privacy and data integrity in applications such as smart grids, traffic management, and public health monitoring. The research aims to develop a framework for implementing blockchain in smart cities, with an emphasis on scalability, security, and privacy.