AI is employed in data privacy to enhance security measures and protect sensitive information. It utilizes techniques like machine learning, natural language processing, and anomaly detection to identify potential breaches, encrypt data, and automate privacy controls, ensuring compliance with regulations and safeguarding user privacy.
Researchers demonstrate the potential of Artificial Intelligence (AI) and Federated Learning (FL) to predict and prevent food fraud while preserving data privacy in complex supply chains. Their framework, utilizing a data-driven Bayesian Network model, effectively integrated data from various sources and improved decision-making regarding food fraud control while upholding data confidentiality.
Researchers discuss how artificial intelligence (AI) is reshaping higher education. The integration of AI in universities, known as smart universities, enhances efficiency, personalization, and student experiences. However, challenges such as job displacement and ethical considerations require careful consideration as AI's transformative potential in education unfolds.
Researchers discuss the integration of artificial intelligence (AI) and networking in 6G networks to achieve efficient connectivity and distributed intelligence. It explores the use of Transfer Learning (TL) algorithms in 6G wireless networks, demonstrating their potential in optimizing learning processes for resource-constrained IoT devices and various IoT paradigms such as Vehicular IoT, Satellite IoT, and Industrial IoT. The study emphasizes the importance of optimizing TL factors like layer selection and training data size for effective TL solutions in 6G technology's distributed intelligence networks.
https://www.sciencedirect.com/science/article/pii/S0140366423002724?via%3Dihub
The research investigates the conceptual difficulties faced by ChatGPT, an AI-powered tool, in comprehending and responding to chemistry problems related to Introduction to Material Science. The study highlights the limitations of ChatGPT's text-based capabilities and proposes the use of converters that can transform text into graphical representations to overcome these limitations.
This article discusses the need for regulatory oversight of large language models (LLMs)/generative artificial intelligence (AI) in healthcare. LLMs can be implemented in healthcare settings to summarize research papers, obtain insurance pre-authorization, and facilitate clinical documentation. LLMs can also improve research equity and scientific writing, improve personalized learning in medical education, streamline the healthcare workflow, work as a chatbot to answer patient queries and address their concerns, and assist physicians to diagnose conditions based on laboratory results and medical records.
The integration of artificial intelligence (AI) is transforming the battle against food waste and propelling the transition towards a circular economy. By leveraging AI technologies, such as advanced analytics and machine learning, various applications are being developed to optimize food manufacturing, distribution networks, and waste management processes. These AI-driven solutions enhance decision-making, enable efficient resource utilization, and support recycling and upcycling initiatives.
Researchers propose a groundbreaking solution in the form of a blockchain layer and an enhanced Dragonfly algorithm to fortify smart home networks. This innovative approach ensures reliable user authentication, safeguards data privacy, and optimizes communication while paving the way for further advancements such as 5G integration and edge computing, promising secure and efficient smart homes of the future.
The study proposes a smart system for monitoring and detecting anomalies in IoT devices by leveraging federated learning and machine learning techniques. The system analyzes system call traces to detect intrusions, achieving high accuracy in classifying benign and malicious samples while ensuring data privacy. Future research directions include incorporating deep learning techniques, implementing multi-class classification, and adapting the system to handle the scale and complexity of IoT deployments.
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