Therapod development of information technology has transformed the way organizations, businesses, educational institutions, and individuals store, process, and utilize information. Two major technologies driving this transformation are Artificial Intelligence (AI) and Cloud Computing. The combination of AI and cloud co...
M.Jeswanth, K.Sai Santhiya, K.Mariammal et al.· International Journal of Sci...· 0 citations
Remote patient monitoring (RPM) systems face two critical unsolved challenges: (1) Bluetooth Low Energy (BLE) data continuity failure when progressive web applications (PWAs) enter background execution, causing silent, irrecoverable loss of patient biometric data; and (2) large language model (LLM) hallucination during...
Jublee Raju Chagantipati· Zenodo (CERN European Organi...· 0 citations
The study proposes a smart food supply chain monitoring system using Internet of Things (IoT) to detect food adulteration in real time, predict the freshness of food and ensure the food safety in a distributed food supply chain. The framework relies on the multimodal IoT sensing, edge computing, hybrid CNN–LSTM deep le...
Vandana Ahuja, P. Nagarathna, Ansuman Samal et al.· Advances in computational in...· 0 citations
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The automotive electrical/electronic (E/E) architectureis undergoing a fundamental transformation from looselycoupled, function-specific electronic control units (ECUs)toward domain-centralized, zonal, and fully centralizedcomputing architectures, forming the foundation of emerg-ing software-defined vehicles (SDVs). In...
Shirshendu Roy· Zenodo (CERN European Organi...· 0 citations
Intelligent Internet of Things (IoT) and assistive technologies are revolutionizing neurocognitive healthcare, with the ability to continuously monitor, use early diagnostics, assess disease progression and deliver personalized interventions. In this chapter, a conceptual framework concerning the integration of wearabl...
Berlin Magthalin R., Mizpah Queeny R.· Advances in computational in...· 0 citations
The integration of computer vision and machine learning in livestock farming has revolutionized precision agriculture, enabling real-time monitoring, health assessment, and behavioral analysis of animals. Among the most promising tools, image-based systems, powered by deep learning architectures such as YOLO, have demo...
Precision dairy farming (PDF) is an innovative approach that utilizes advanced technologies and methods to individually monitor and manage dairy animals and their environment with the aim of improving milk production and quality. This real-time monitoring ensures traceability and sustainability throughout the productio...
Özdal Gökdal, António Manuel Cardoso Monteiro· 0 citations
Sonic biometrics and pattern recognition represent a cutting-edge frontier in eco-acoustic intelligence, leveraging signal processing and machine learning methodologies to identify, classify, and authenticate biological and environmental sound patterns. This chapter delves into the technical architecture and interdisci...
Roopesh Ramesh, S. Priyanka, N. Kalyan· Eco‐Acoustic Intelligence· 0 citations
INTRODUCTION: Under Industry 5.0, discrete manufacturing faces a fundamental dilemma: it must balance human-centric flexibility with sustainable and resilient operations. Resolving this dilemma requires the Industrial Internet of Things (IIoT) to coordinate and integrate the three types of heterogeneous resources—sensi...
Feng Wang, Ning Wu· ICST Transactions on Scalabl...· 0 citations
Abstract— Berberine, a quaternary protoberberine alkaloid of Berberis and Coptis species, showsreproducible anti-proliferative activity in breast cancer models, but the experimental literature isfragmented across unrelated pathways and no prioritised target map exists. This study converts thatscattered evidence into a...
Dr. R. SUNDARARAJAN, S.G. RAMAN, P. RAJAKUMAR, A. RAJKUMAR, RASHID.R, J. SATHISH KUMAR, M.YOKESHWARAN· Zenodo (CERN European Organi...· 0 citations
Paper III of the Guarino Infrastructure Dependency Metric (GIDM). Abstract. Papers I and II of this series derived the buffer group ΠB = Ta/Tx on every edge of an infrastructure dependency graph and tested it on the February 2021 Texas chain, leaving two things open: the water edge below the tap, which Paper I could va...
Brian Guarino· Zenodo (CERN European Organi...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
