Information Technology

Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM.

Artificial Intelligence (AI) is being integrated into increasingly more domains of everyday activities. Whereas AI has countless benefits, its convoluted and sometimes vague internal operations can establish difficulties. Nowadays, AI is significantly employed for evaluations in cybersecurity that find it challenging to justify their proceedings; this absence of accountability is alarming. Additio...

Cyber security Enhancements with reinforcement learning: A zero-day vulnerabilityu identification perspective.

A zero-day vulnerability is a critical security weakness of software or hardware that has not yet been found and, for that reason, neither the vendor nor the users are informed about it. These vulnerabilities may be taken advantage of by malicious people to execute cyber-attacks leading to severe effects on organizations and individuals. Given that nobody knows and is aware of these weaknesses, it...

Reducing diagnostic delays in acute hepatic porphyria using health records data and machine learning.

BACKGROUND: Acute hepatic porphyria (AHP) is a group of rare but treatable conditions associated with diagnostic delays of 15 years on average. The ad...

Mini-mental status examination phenotyping for Alzheimer's disease patients using both structured and narrative electronic health record features.

OBJECTIVE: This study aims to automate the prediction of Mini-Mental State Examination (MMSE) scores, a widely adopted standard for cognitive assessme...

Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.

Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care...

Uncovering Important Diagnostic Features for Alzheimer's, Parkinson's and Other Dementias Using Interpretable Association Mining Methods.

Alzheimer's Disease and Related Dementias (ADRD) afflict almost 7 million people in the USA alone. The majority of research in ADRD is conducted using...

A Dynamic Model for Early Prediction of Alzheimer's Disease by Leveraging Graph Convolutional Networks and Tensor Algebra.

Alzheimer's disease (AD) is a neurocognitive disorder that deteriorates memory and impairs cognitive functions. Mild Cognitive Impairment (MCI) is gen...

Researching public health datasets in the era of deep learning: a systematic literature review.

Explore deep learning applications in predictive analytics for public health data, identify challenges and trends, and then understand the current la...

Artificial Intelligence-Assisted Matching of Human Postmortem Donors to Ocular Research Projects.

The scarcity of human ocular samples with short postmortem intervals (PMIs) is a significant issue in ophthalmic research and drug discovery. A contri...

Digital transformation in healthcare management: from Artificial Intelligence to blockchain.

The digital transformation of healthcare is revolutionizing the management of medical institutions, improving operational efficiency, patient outcomes...

HCAP: Hybrid cyber attack prediction model for securing healthcare applications.

The rapid development and integration of interconnected healthcare devices and communication networks within the Internet of Medical Things (IoMT) hav...

SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction

Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis prediction and identification allow for timely ...

The Future of IPTV: Security, AI Integration, 5G, and Next-Gen Streaming

The evolution of Internet Protocol Television (IPTV) has transformed the landscape of digital broadcasting by leveraging high-speed internet connect...

SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation

Survival analysis (SA) models have been widely studied in mining electronic health records (EHRs), particularly in forecasting the risk of critical ...

scReader: Prompting Large Language Models to Interpret scRNA-seq Data

Large language models (LLMs) have demonstrated remarkable advancements, primarily due to their capabilities in modeling the hidden relationships wit...

Automated CVE Analysis: Harnessing Machine Learning In Designing Question-Answering Models For Cybersecurity Information Extraction

The vast majority of cybersecurity information is unstructured text, including critical data within databases such as CVE, NVD, CWE, CAPEC, and the ...

A Machine Learning Approach for Emergency Detection in Medical Scenarios Using Large Language Models

The rapid identification of medical emergencies through digital communication channels remains a critical challenge in modern healthcare delivery, p...

Transversal PACS Browser API: Addressing Interoperability Challenges in Medical Imaging Systems

Advances in imaging technologies have revolutionised the medical imaging and healthcare sectors, leading to the widespread adoption of PACS for the ...

PT: A Plain Transformer is Good Hospital Readmission Predictor

Hospital readmission prediction is critical for clinical decision support, aiming to identify patients at risk of returning within 30 days post-disc...

BioBridge: Unified Bio-Embedding with Bridging Modality in Code-Switched EMR

Pediatric Emergency Department (PED) overcrowding presents a significant global challenge, prompting the need for efficient solutions. This paper in...