Results 121 to 130 of about 6,174,615 (236)
, 2016 As a follow-up to the highly cited authors list published by Thomson Reuters in June 2014, we analyzed the top 1% most frequently cited papers published between 2002 and 2012 included in the Web of Science (WoS) subject category Information Science ...
Bornmann, L., Leydesdorff, L., Bauer, J.
core +1 more source
COVID-19 Research Output from South Asia: A Scientometric Analysis of Highly Cited Papers
Apollo MedicineBackground and Aims: Highly cited papers (HCPs) can influence both research and clinical practice. They help to identify core literature and important advances in a field. This study aimed to assess the current status and prospects of South Asia COVID-19
Brij Mohan Gupta +3 more
doaj +1 more source
Economic Impact of Sustainability in Wine Sector: A Systematic Literature Review
Agribusiness, EarlyView.ABSTRACT Sustainability has become a key issue in the wine sector, raising questions about its compatibility with firms' economic performance. Despite growing attention to environmental and social practices, evidence on their economic implications remains fragmented.
Valentina Di Chiara +2 more
wiley +1 more source
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Advanced Intelligent Discovery, EarlyView.Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
AI Powered Biobanks From Static Archives to Dynamic Discovery Engines
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) provide a potential framework for transforming biobanks from static data repositories into intelligent discovery engines. By enabling unified representation and analysis of multimodal biomedical data, LLM‐based systems facilitate dynamic risk prediction, biomarker identification, and mechanistic interpretation, thereby ...
Wenzhen Yin +5 more
wiley +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
Advanced Intelligent Discovery, EarlyView.The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
Calculating the excellence shift: How efficiently do institutions produce highly cited papers? [PDF]
, 2017 The excellence shift is proposed, which shows universities’ ability to produce highly cited papers as measured against their basic academic research efficiency (ARE).
Wohlrabe, Klaus, Bornmann, Lutz
core
Large Language Model‐Based Chatbots in Higher Education
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci +4 more
wiley +1 more source
Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation
Advanced Intelligent Systems, EarlyView.Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison +4 more
wiley +1 more source
Ecoefficiency Analysis and Regression in Data Conversion for Spiking Neural Network Training
Advanced Intelligent Systems, EarlyView.The environmental footprint of spiking neural networks is quantified during dataset encoding and training for autonomous driving regression across three benchmarks. Temporal depth emerges as the dominant driver of energy consumption and CO2 emissions, while the accuracy–energy trade‐off proves dataset‐dependent. On conventional hardware, spiking models
Fernando S. Martínez +3 more
wiley +1 more source