⏩ Volume 19, Issue No.6, 2021 (HCAI)
Deep Learning Techniques for Speech Recognition and Natural Language Processing in Healthcare Applications

This paper explores the use of deep learning techniques for speech recognition and natural language processing (NLP) in healthcare applications. The research focuses on improving patient care by enabling AI systems to accurately understand and process medical conversations, thereby enhancing communication in clinical environments.

Jonathan Michael Peterson, Wei Jun Tan, Priya Sharma, Cheng Wei Li, Maria Elena Gomez

Paper ID: 42119601
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Quantum Computing and Its Role in Revolutionizing AI and Machine Learning for Advanced Data Analytics

This study investigates the potential of quantum computing in revolutionizing AI and machine learning techniques for advanced data analytics. The paper covers how quantum computers can outperform classical computers in solving complex data problems and enable AI algorithms to process and analyze data at unprecedented speeds.

Christopher Robert Harris, Taro Hiroshi Nakamura, Lin Fei Wu, Isabella Claire Fisher, Elena Maria Rossi

Paper ID: 42119602
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Exploring the Use of AI in Environmental Sustainability: Analyzing Big Data for Climate Change Predictions

This paper explores the applications of AI in environmental sustainability, focusing on the analysis of big data to predict and mitigate the effects of climate change. It discusses how AI-driven models can enhance data collection, processing, and decision-making in the fight against climate change.

Linda Marie Carter, Wei Jun Tan, Christopher Robert Harris, Lin Fei Wu, Maria Elena Gomez

Paper ID: 42119603
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Ethical Considerations in AI Algorithms for Autonomous Decision-Making in Critical Systems

This research focuses on the ethical implications of AI algorithms used for autonomous decision-making in critical systems. The study discusses how AI systems make decisions in high-stakes environments, such as healthcare and defense, and explores the need for ensuring fairness, accountability, and transparency in these systems.

Michael Julian Fields, Wei Li Zhang, Priya Nair, Cheng Wei Li, Isabella Claire Fisher

Paper ID: 42119604
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Reinforcement Learning for Robotic Process Automation: Enhancing Efficiency in Manufacturing Systems

This paper examines the application of reinforcement learning in robotic process automation (RPA) to enhance efficiency in manufacturing systems. It explores how AI-driven robotic systems can learn from their environment and optimize processes autonomously, leading to faster production cycles and cost reduction in manufacturing.

Jonathan Michael Peterson, Elena Maria Rossi, Wei Li Tan, Priya Sharma, Taro Hiroshi Nakamura

Paper ID: 42119605
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