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 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.
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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