Article
Information Science & Library Science
Arpan Kumar Kar, Spyros Angelopoulos, H. Raghav Rao
Summary: While data availability and access used to be challenging for information systems research, the growth and ease of access to large datasets and data analysis tools has increased interest in using such resources for publishing. However, these publications often lack strong theoretical contributions and focus only on descriptive analysis of big data. This article addresses the need for theory building with Big Data by providing guidelines for both inductive and deductive approaches, as well as highlighting common pitfalls to avoid.
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
(2023)
Article
Agricultural Engineering
Chao-Tung Yang, Endah Kristiani, Yoong Kit Leong, Jo-Shu Chang
Summary: This paper examines and summarizes the literature related to artificial intelligence (AI) in the bioprocessing field, aiming to explore the potential of machine learning algorithms in revolutionizing bioengineering. By employing natural language processing (NLP), papers from 2013 to 2022 with specific keywords of bioprocessing using AI were collected and analyzed. The results show that in the past five years, 50% of the publications focused on topics such as hybrid models, artificial neural networks (ANN), biopharmaceutical manufacturing, and biorefinery. The summarization and analysis indicate that implementing AI can improve the design and process engineering strategies in bioprocessing fields.
BIORESOURCE TECHNOLOGY
(2023)
Review
Clinical Neurology
Yuzhe Liu, Yuan Luo, Andrew M. Naidech
Summary: Significant advances in medical data accumulation, computational techniques, and management have been made in the last decade. Big data and computational methods can address gaps in patient selection, complications prediction, and outcome understanding. Automated neuroimaging analysis can help triage patients, and data-intensive techniques enable accurate risk calculations for timely prediction of adverse events.
Article
Energy & Fuels
Vinicius B. F. Costa, Lucas Scianni, Rafael C. Miranda, Benedito Bonatto
Summary: The deployment of photovoltaic distributed generation (PV DG) is increasing substantially in Brazil. This paper evaluates the status and trends of PV DG in Brazil using a robust dataset provided by the Brazilian Electricity Regulatory Agency (ANEEL). The results provide essential insights for PV DG in Brazil.
Review
Medicine, General & Internal
Ljiljana Trtica Majnaric, Frantisek Babic, Shane O'Sullivan, Andreas Holzinger
Summary: Multimorbidity, the coexistence of two or more chronic diseases in a person, presents unique care needs that current healthcare systems struggle to address due to their focus on single diseases. To improve patient care in these cases, a radical change in medical research and treatment approaches is required, with a shift towards interactive research supported by artificial intelligence and big data analytics.
JOURNAL OF CLINICAL MEDICINE
(2021)
Editorial Material
Environmental Studies
Sarah Barns
Summary: This commentary discusses how routine urban behaviors are now being replicated computationally, emphasizing the significant role of big data in the city. The emerging urban systems of learned intelligence are described as both radical and routine, with attention also being drawn to the generative design principles of data-driven models of urban behavior.
Review
Clinical Neurology
Bharath Raju, Fareed Jumah, Omar Ashraf, Vinayak Narayan, Gaurav Gupta, Hai Sun, Patrick Hilden, Anil Nanda
Summary: The application and limitations of big data in the healthcare sector are influenced by factors such as lack of technical knowledge, technological limitations in data acquisition and analysis, and improper governance of healthcare big data. Despite these limitations, much of the medical literature is filled with articles related to big data, many of which are limited to neurosurgical registries, leading to misconceptions about big data.
JOURNAL OF NEUROSURGERY
(2021)
Article
Environmental Sciences
Victor O. K. Li, Jacqueline C. K. Lam, Jiahuan Cui
Summary: This article discusses the role and challenges of AI and big data technologies in environmental decision-making, raises a series of important questions, and summarizes the significance and innovation of the articles included in the special issue. It also highlights the important principles of AI for social good.
ENVIRONMENTAL SCIENCE & POLICY
(2021)
Review
Computer Science, Interdisciplinary Applications
Juli Kumari, Ela Kumar, Deepak Kumar
Summary: Machine learning and deep learning, combined with big data analytics, are playing an increasingly important role in the healthcare sector worldwide. This study comprehensively analyzes the trends in the adoption of these approaches in healthcare, using various strategies and statistical analysis methods. It aims to help academics, researchers, decision-makers, and healthcare professionals understand and direct research in order to revolutionize healthcare.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Mario Ibanez, Manuel Luna, Jose Luis Bosque, Ramon Beivide
Summary: This paper introduces the problem of high-performance computing in enterprise software using artificial intelligent techniques to solve big data and business intelligence issues. It demonstrates the improvement of a aquaculture business intelligence tool called AquiAID, which utilizes parallel programming. Through parallelization, the computation time is reduced by 60 times and energy efficiency is increased by 600 times, resulting in improved fish farming management in the aquaculture industry.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Amir Masoud Rahmani, Elham Azhir, Saqib Ali, Mokhtar Mohammadi, Omed Hassan Ahmed, Marwan Yassin Ghafour, Sarkar Hasan Ahmed, Mehdi Hosseinzadeh
Summary: This survey investigates the research on big data analytics using artificial intelligence techniques, selecting related research papers using the Systematic Literature Review method. The study focuses on four groups of mechanisms - machine learning, knowledge-based and reasoning methods, decision-making algorithms, and search methods and optimization theory, discussing and comparing the selected AI-driven big data analytics techniques in terms of scalability, efficiency, precision, and privacy. Additionally, it provides important areas for enhancing big data analytics mechanisms in the future.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Hardware & Architecture
Claudio A. Ardagna, Valerio Bellandi, Ernesto Damiani, Michele Bezzi, Cedric Hebert
Summary: The importance of fully utilizing data through advanced analytics, machine learning, and artificial intelligence for businesses becomes crucial in global market competition. However, it also introduces new risks and threats, especially in domains like Smart Grid in the Energy sector which face challenges related to managing intelligent devices securely and protecting data.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Review
Energy & Fuels
Gema Hernandez-Moral, Sofia Mulero-Palencia, Victor Ivan Serna-Gonzalez, Carla Rodriguez-Alonso, Roberto Sanz-Jimeno, Vangelis Marinakis, Nikos Dimitropoulos, Zoi Mylona, Daniele Antonucci, Haris Doukas
Summary: This paper discusses the necessity of managing energy demand in buildings in the context of current climate change threats and increasing CO2 emissions, and proposes paving the way for smart and energy-aware buildings by combining new technologies. It specifically analyzes the importance and challenges of the big data value chain in the built environment in Europe.
Review
Computer Science, Information Systems
Vittorio Palmieri, Andrea Montisci, Maria Teresa Vietri, Paolo C. Colombo, Silvia Sala, Ciro Maiello, Enrico Coscioni, Francesco Donatelli, Claudio Napoli
Summary: This study explores the opportunities and limitations of using artificial intelligence (AI) and big data in the field of heart transplantation. The results show that AI performs well in predicting prognosis and diagnosing heart transplantation, but there are risks of bias, lack of external validation, and limited applicability. Therefore, more unbiased research and high-quality data are needed to support the use of medical AI in clinical decision-making.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2023)
Article
Computer Science, Theory & Methods
Liang Zhao
Summary: Events are crucial occurrences with significant impacts on society or nature, such as earthquakes, system failures, and pandemics. Anticipating events in advance can help mitigate social upheaval, and event prediction is now more feasible in the big data era due to advancements in AI and high-performance computing. Existing methods are often domain-specific but share generalizable techniques and evaluation procedures, though cross-referencing techniques across domains remains a challenge.
ACM COMPUTING SURVEYS
(2021)