Artificial intelligence
Research conducted by Sheeza Ehsan and colleagues at the National University of Computer and Emerging Sciences in Lahore, Pakistan, has shed light on the impact of artificial intelligence (AI) on sustainable decision-making in the construction sector of Pakistan. The study aimed to investigate how AI-driven risk management and organizational culture
Artificial intelligence
Artificial intelligence has the potential to improve diagnostic accuracy, efficiency, and patient safety in healthcare. However, its integration into clinical practice can be undermined by over-reliance on AI, which may distract doctors, diminish their confidence in their own diagnostic abilities, and lead to incorrect diagnoses. A research team from UCLA
Data centers
In the realm of modern urban planning, integrating data from various sources is crucial for making informed decisions. Researchers from the University of Naples Federico II have been exploring the concept of City Information Modeling (CIM) and its potential in enhancing decision-making processes. By combining Building Information Modeling (BIM), Geographic
Data centers
Researchers at the National Institute of Metrology in China have conducted a comprehensive study on evaluating green data centers. The study aimed to assess the perspectives and priorities of various institutions regarding evaluation indicators and to investigate the underlying reasons for these preferences. The research involved 19 domain experts from
Artificial intelligence
Research from Bahria University has introduced a novel framework for personalized medicine, leveraging the potential of blockchain-distributed ledger technology (BDLT) and explainable artificial intelligence (XAI). The study highlights the constraints on patient trust, data security, and privacy in modern healthcare. By integrating BDLT with XAI, the researchers have developed a
Artificial neural networks
Research conducted at the University of Faisalabad in Faisalabad, Pakistan has proposed a novel fuzzy neural network (FNN) framework that operates under complex Fermatean fuzzy sets to enhance multiple attribute decision-making (MADM) in uncertain environments. The model integrates Schweizer-Sklar-based aggregation operators within the FNN's computational layers to process
Artificial neural networks
Researchers at Loughborough University have made a groundbreaking discovery in the field of neural networks, revealing that behavior variability across individuals leads to substantial performance differences during cognitive tasks. The investigation, supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek, employed recurrent neural networks trained on a multisensory decision-making task to
Mental health services
Researchers from the University of California have found that early identification and intervention for psychosis-spectrum experiences can significantly improve long-term outcomes. However, treatment-seeking in the United States is often delayed due to factors such as stigma, particularly for individuals reporting psychotic-like experiences (PLEs). A survey of mental health care utilization
Mental health services
Research published in the International Journal of Law and Psychiatry has highlighted the complex dynamics involved in decision-making capacity (DMC) assessments in Norway's mental health care system. Despite amendments to the Norwegian Mental Health Care Act in 2017, rates of involuntary care and treatment continued to rise, indicating
Machine learning
Researchers from Firat University have conducted a critical study to assess seismic risk in Denizli Province, a region in Turkey known for its industrial and touristic significance. The team utilized a combination of Analytic Hierarchy Process, Random Forest, and Frequency Ratio methods to create detailed susceptibility maps and classify risk
Machine learning
Researchers at Charles Sturt University have made a breakthrough in artificial intelligence by developing a hybrid machine-learning framework that can aid decision-making in laboratory experiments. The novel approach combines Ordinary Least Squares (OLS) for global surface estimation, Gaussian Process (GP) regression for uncertainty modeling, expected improvement (EI) for active learning,
Machine learning
Research from the University of Auckland has developed a Rough Set Machine Learning (RSML) framework to optimize adsorption processes for dye removal from wastewater. This innovation addresses the challenge of establishing universally applicable rules across diverse wastewater matrices. Using hydrochar-mediated dye removal, the RSML framework achieved over 80% accuracy for
