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.
A novel encryption scheme, BCAES, intertwines Blockchain and Arnold's cat map encryption to fortify medical data storage and transmission in the cloud. By combining chaos theory-based encryption with blockchain's tamper-resistant nature, BCAES ensures data integrity, authenticity, and confidentiality, outperforming traditional methods and offering a promising avenue for secure healthcare data management.
Researchers from South Korea and China present a pioneering approach in Scientific Reports, showcasing how deep learning techniques, coupled with Bayesian regularization and graphical analysis, revolutionize urban planning and smart city development. By integrating advanced computational methods, their study offers insights into traffic prediction, urban infrastructure optimization, data privacy, and safety and security, paving the way for more efficient, sustainable, and livable urban environments.
Researchers introduce a hierarchical federated learning framework tailored for large-scale AIoT systems in smart cities. By integrating cloud, edge, and fog computing layers and leveraging the MQTT protocol, the framework addresses data privacy and communication latency challenges, demonstrating enhanced scalability and efficiency. Experimental validation in Docker environments confirms the framework's feasibility and performance improvements, laying the foundation for future optimizations.
Researchers addressed challenges in Federated Learning (FL) within Space-Air-Ground Information Networks (SAGIN) by introducing the LCNSFL algorithm. LCNSFL, based on a Double Deep Q Network (DDQN), strategically selects nodes to minimize time and energy costs. Simulation results demonstrate LCNSFL's superiority over traditional methods, offering efficient convergence and resource utilization in dynamic network environments, essential for practical applications in SAGIN.
Researchers present a groundbreaking Federated Learning (FL) model for passenger demand forecasting in Smart Cities, focusing on the context of Autonomous Taxis (ATs). The FL approach ensures data privacy by allowing ATs in different regions to collaboratively enhance their demand forecasting models without directly sharing sensitive passenger information. The proposed model outperforms traditional methods, showcasing superior accuracy while addressing privacy concerns in the era of smart and autonomous transportation systems.
Researchers from the USA leverage Large Language Models (LLMs) to automatically extract social determinants of health (SDoH) from clinical narratives, addressing challenges in healthcare data. Their innovative approach, combining Flan-T5 models and synthetic data augmentation, showcases remarkable efficiency, emphasizing the potential to bridge gaps in understanding and addressing crucial factors influencing patients' well-being.
This article explores the integration of artificial intelligence (AI), blockchain, and the Internet of Things (IoT) to enhance the safety of power equipment. The innovative wireless temperature monitoring system, incorporating real-time monitoring and intelligent anomaly detection, showcases the potential for proactive preventive measures, minimizing the risk of fire hazards in electric power engineering.
This study introduces a Digital Twin (DT)-centered Fire Safety Management (FSM) framework for smart buildings. Harnessing technologies like AI, IoT, AR, and BIM, the framework enhances decision-making, real-time information access, and FSM efficiency. Evaluation by Facility Management professionals affirms its effectiveness, with a majority expressing confidence in its clarity, data security, and utility for fire evacuation planning and Fire Safety Equipment (FSE) maintenance.
Researchers present a groundbreaking privacy-preserving dialogue model framework, integrating Fully Homomorphic Encryption (FHE) with dynamic sparse attention (DSA). This innovative approach enhances efficiency and accuracy in dialogue systems while prioritizing user privacy. Experimental analyses demonstrate significant improvements in precision, recall, accuracy, and latency, positioning the proposed framework as a powerful solution for secure natural language processing tasks in the information era.
Researchers propose leveraging a Quality Management System (QMS) tailored to healthcare AI as a systematic solution to bridge the translation gap from research to clinical application. The QMS, aligned with ISO 13485 and risk-based approaches, addresses key components enabling healthcare organizations to navigate regulatory complexities, minimize redundancy, and optimize the ethical deployment of AI in patient care.
Researchers introduced Rainbow, an open-source Voice User Interface (VUI) designed for scientific laboratories, addressing the limitations of conventional assistants in recognizing specialized scientific vocabulary. Rainbow achieved a remarkable 91.3% speech recognition accuracy, outperforming commercial counterparts and demonstrating its potential in enhancing laboratory processes through intuitive voice control.
Researchers introduced Relay Learning, a novel deep-learning framework designed to ensure the physical isolation of clinical data from external intruders. This secure multi-site deep learning approach, Relay Learning, significantly enhances data privacy and security while demonstrating superior performance in various multi-site clinical settings, setting a new standard for AI-aided medical solutions and cross-site data sharing in the healthcare domain.
Researchers explored the application of distributed learning, particularly Federated Learning (FL), for Internet of Things (IoT) services in the context of emerging 6G networks. They discussed the advantages and challenges of distributed learning in IoT domains, emphasizing its potential for enhancing IoT services while addressing privacy concerns and the need for ongoing research in areas such as security and communication efficiency.
This research delves into the growing influence of artificial intelligence (AI) and machine learning (ML) on financial markets. Through a mixed-methods approach, it examines AI's applications in trading, risk management, and financial operations, highlighting adoption trends, challenges, and ethical considerations.
Researchers propose a novel framework integrating federated learning and edge computing to revolutionize air quality monitoring systems. This review highlights the potential of these technologies in creating scalable, privacy-preserving, and collaborative monitoring systems while emphasizing the need for further research and interdisciplinary efforts to bridge theory and practice in managing urban environmental conditions.
This paper explores how artificial intelligence (AI) is revolutionizing regenerative medicine by advancing drug discovery, disease modeling, predictive modeling, personalized medicine, tissue engineering, clinical trials, patient monitoring, patient education, and regulatory compliance.
A recent review explores the potential of artificial intelligence (AI) in revolutionizing the screening, diagnosis, and monitoring of body iron levels. The review reveals AI's promise in improving the management of iron deficiency and overload, although challenges such as data limitations and ethical concerns must be addressed for its full potential to be realized.
This research presents FL-LoRaMAC, a cutting-edge framework that combines federated learning and LoRaWAN technology to optimize IoT anomaly detection in wearable sensor data while preserving data privacy and minimizing communication costs. The results demonstrate that FL-LoRaMAC significantly reduces data volume and computational overhead compared to traditional centralized ML methods.
Researchers have developed a groundbreaking framework for training privacy-preserving models that anonymize license plates and faces captured on fisheye camera images used in autonomous vehicles. This innovation addresses growing data privacy concerns and ensures compliance with data protection regulations while improving the adaptability of models for fisheye data.
Researchers introduce a deep learning-based approach for long-distance face recognition, essential for security applications in smart cities. They evaluated the system's performance across various commercial image sensors, achieving accuracy rates exceeding 99 percent, offering valuable insights into sensor selection for enhanced security in smart city surveillance systems.
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