The California Civil Rights Department has long been considering adopting regulations which regulate the ability of California employers to use artificial intelligence, automated decision-making sy…

In the space between scheduled mammograms, some breast cancers can silently grow and spread. These are known as interval breast cancers, or IBCs. Unlike canc...

In today’s rapidly evolving digital landscape, Artificial Intelligence (AI) is reshaping how businesses optimize their cloud-native infrastructure. This transformative shift, especially in managing APIs (Application Programming Interfaces), has led to remarkable innovations in performance, security, and system reliability. The role of AI in optimizing cloud-native API architectures is both complex and essential. The author, Vamsi […]

While answering how much information is in DNA may seem straightforward, it actually requires a wild odyssey through information theory and molecular biology.

AI-augmented database migration is more than just a technical enhancement; it represents a shift in how organizations approach data modernization

Machine learning identifies key biomarkers predicting hydroxyurea resistance in polycythemia vera, enhancing early treatment strategies and patient outcomes.

Researchers have developed a machine learning model that helps interpret optical spectroscopy data.

Identifying the risk factors for low APGAR scores at birth is critical for improving neonatal outcomes and guiding clinical interventions. This study aimed to develop a machine-learning model that predicts low APGAR scores by incorporating maternal, fetal, and perinatal factors in Wad Medani, Sudan. Using a Random Forest Classifier, we performed hyper-parameter optimization through Grid Search cross-validation (CV) to identify the best-performing model configuration. The optimized model achieved excellent predictive performance, as evidenced by high F1 scores, accuracy, and balanced precision-recall metrics on the test set. In addition to prediction, feature importance analysis was conducted to identify the most influential risk factors contributing to low APGAR scores. Key predictors included gestational age, maternal BMI, mode of delivery, and history of previous complications such as stillbirth or abortion. Using 5-fold cross-validation (CV), the random forest model performance scored accuracy at 96%, precision at 98%, recall at 97%, and F1-score at 97% when classifying infants with APGAR score. This study underscores the importance of incorporating machine learning approaches in obstetric care to understand better and mitigate the risk factors associated with adverse neonatal outcomes, particularly low APGAR scores. The results provide a foundation for developing targeted interventions and improving prenatal care practices.

The Expert Opinion on the ethical challenge of using Artificial Intelligence in cybersecurity.

What began as a childhood fascination with stars has turned into one of the most striking discoveries in recent astronomy. Matteo Paz, a self-taught high

In a world first, the United Arab Emirates (UAE) has decided to integrate artificial intelligence into its legislative process. The idea is to modernise the process of drafting, revising and updating legislation, drawing on the advanced analytical capabilities of AI.

Apple is exploring AI search engines like ChatGPT for Safari, signaling a shift from Google’s $20B deal. How will this reshape search competition?

The recent detection of a massive Chinese nuclear fusion facility by US surveillance satellites has sent ripples through the international scientific and defense communities. This groundbreaking discovery reveals China's ambitious push toward mastering fusion technology, potentially revolutionizing global energy production while raising serious concerns about its military applications and international…

In an era where social media platforms are the driving force behind data generation, optimizing the data ingestion process has become crucial for machine learni

With the help of artificial intelligence, a man was 'brought back to life' at his killer's sentencing to deliver a victim's statement himself.

Microsoft says that it's embracing Google's recently launched open protocol, Agent2Agent, for allowing AI 'agents' to communicate with each other.

The hum of the AI co-pilot has become a familiar soundtrack in the world of software development. These intelligent tools, promising increased efficiency and code generation prowess, have been embraced with open arms by many. But what happens when this reliance morphs into over-dependence? What are the potential pitfalls of blindly trusting algorithms we don’t fully comprehend, especially when they occasionally – or even frequently – get it wrong? And perhaps most worryingly, what becomes of the core skills that define a truly capable software developer?

With modern tools and APIs, it no longer takes a massive budget or dedicated AI team to get results.

Clinical trials, which are the backbone of medical research, have become increasingly complex, generating massive amounts of data from diverse sources. In a wor

In this rapidly growing digital era, artificial intelligence (AI) is not just enhancing convenience but fundamentally transforming industries. One such domain e

This study proposes a semi-weakly supervised learning approach for pulmonary embolism (PE) detection on CT pulmonary angiography (CTPA) to alleviate the resource-intensive burden of exhaustive medical image annotation. Attention-based CNN-RNN models were trained on the RSNA pulmonary embolism CT dataset and externally validated on a pooled dataset (Aida and FUMPE). Three configurations included weak (examination-level labels only), strong (all examination and slice-level labels), and semi-weak (examination-level labels plus a limited subset of slice-level labels). The proportion of slice-level labels varying from 0 to 100%. Notably, semi-weakly supervised models using approximately one-quarter of the total slice-level labels achieved an AUC of 0.928, closely matching the strongly supervised model’s AUC of 0.932. External validation yielded AUCs of 0.999 for the semi-weak and 1.000 for the strong model. By reducing labeling requirements without sacrificing diagnostic accuracy, this method streamlines model development, accelerates the integration of models into clinical practice, and enhances patient care.