Process mining is a part of data science concerned with analyzing event logs produced by information systems to learn about business processes. This paper addresses process mining techniques, which involve process discovery. All these are very important in organizations, especially in workflow optimization and enhancing efficiency and potential areas for improvement. One major problem in… →
Logs provide important insights that are frequently the earliest signs of system problems, making them an essential tool for program maintenance and failure diagnostics. These logs must be effectively parsed for automated log analysis tasks like anomaly identification, troubleshooting, and root cause investigation. The act of turning semi-structured log messages into structured templates is known… →
Deep generative models learn continuous data representations from a limited set of training samples, with global metrics like Fréchet Inception Distance (FID) often used to evaluate their performance. However, these models may perform inconsistently across different regions of the learned manifold, especially in foundation models like Stable Diffusion, where generation quality can vary based on… →
Physical therapy students must learn about heart transplantation. They need to know how to care for these patients’ emotions and needs. The study aimed to compare the effectiveness of a narrative photography (NP) program and a traditional learning (TL) program in physical therapy students’ knowledge, satisfaction, empathy, and moral sensitivity. A two-armed assessor-blinded randomized controlled… →
CONCLUSIONS: Ada demonstrated a higher diagnostic accuracy than Symptoma, and substantially more patients would recommend Ada and assessed Ada as easy to use. The high number of unrecognized potentially life-threatening diagnoses by both SCs and inappropriate triage advice by Ada was alarming. Overall, the trustworthiness of SC recommendations appears questionable. SC authorization should necessitate rigorous… →
CONCLUSIONS: This represents the methodological design for the first evaluation of CT-152 as an adjunct to ADT. This study protocol is methodologically robust and incorporates many aspects of conventional pivotal pharmaceutical phase 3 trial design, such as randomization and safety end points. Novel considerations included the use of a sham comparator, masking considerations for visible… →
Our aim was to find out whether speech-related temporal parameters (SRTPs) are sensitive indicators of the clinical outcome in acetylcholinesterase (AChE) inhibitor therapy with donepezil, compared to the standard cognitive Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) used in clinical trials. In this 24-week-long, naturalistic, self-control, open-labeled, prospective pilot study with 10 mg donepezil on 20… →
Data visualizations (DVs) have become a common practice in the big data era, utilized by various applications and institutions to convey insights from massive raw data. However, creating suitable DVs remains a challenging task, even for experts, as it requires visual analysis expertise and familiarity with the domain data. Also, users must master complex declarative… →
Automated design in artificial intelligence (AI) is an emerging field focusing on developing systems capable of independently generating and optimizing their components. This approach is built on the premise that machine learning can surpass the limitations of manual design, enabling the creation of more efficient, adaptable, and powerful AI systems. The aim is to allow… →