A5058601659- Various Chemistry Research Topics
- Blockchain Technology Applications and Security
- Emergency and Acute Care Studies
- Network Security and Intrusion Detection
- Imbalanced Data Classification Techniques
- Human Rights and Immigration
- Internet Traffic Analysis and Secure E-voting
- Artificial Intelligence in Healthcare
- Plant and soil sciences
- Electrolyte and hormonal disorders
- Visual Attention and Saliency Detection
- Trauma and Emergency Care Studies
- Finance, Taxation, and Governance
- Computational Drug Discovery Methods
- Advanced Malware Detection Techniques
- Explainable Artificial Intelligence (XAI)
- Crime, Illicit Activities, and Governance
- Administrative Law and Governance
- CCD and CMOS Imaging Sensors
- Advanced Neural Network Applications
- Machine Learning in Materials Science
- Machine Learning in Healthcare
Fordham University
2023-2025
Deleted Institution
2013
The healthcare industry seeks to integrate AI into clinical applications, yet understanding decision making remains a challenge for practitioners as these systems often function black boxes. Our work benchmarks the Pattern Discovery and Disentanglement (PDD) system’s unsupervised learning algorithm, which provides interpretable outputs clustering results from notes aid making. Using MIMIC-IV dataset, we process free-text ICD-9 codes with Term Frequency-Inverse Document Frequency Topic...
Detecting unknown cyberattacks remains an open research problem and a significant challenge for the community security industry. This paper tackles detection of cybersecurity attacks in Internet Things (IoT) traditional networks by categorizing them into two types: entirely new classes (type-A) within already known (type-B). To address this, we propose novel multi-stage, multi-layer zero trust architecture intrusion system (IDS), uniquely designed to handle these attack types. The employs...
Given the growing prevalence of computational methods in chemistry, it is essential that undergraduate curricula introduce students to these approaches. One such area application machine learning (ML) techniques chemistry. Here we describe a new activity applies ML regression analysis common physical chemistry laboratory experiment on electronic absorption spectra cyanine dyes. In classic version this experiment, collect experimental and interpret them using Kuhn free electron model, based...
Given the growing prevalence of computational methods in chemistry, it is essential that undergraduate curricula introduce students to these approaches. One such area application machine learning (ML) techniques chemistry. Here we describe a new activity applies ML regression analysis common physical chemistry laboratory experiment on electronic absorption spectra cyanine dyes. In classic version this experiment, collect experimental and interpret them using Kuhn free electron model, based...
Given the growing prevalence of computational methods in chemistry, it is essential that undergraduate curricula introduce students to these approaches. One such area application machine learning (ML) techniques chemistry. Here we describe a new activity applies ML regression analysis common physical chemistry laboratory experiment on electronic absorption spectra cyanine dyes. In classic version this experiment, collect experimental and interpret them using Kuhn free electron model, based...
Cryptocurrencies, particularly Bitcoin, have gained considerable attention due to their decentralized nature and potential for high returns. However, they are also subject fraud-ulent activities, posing challenges security transparency. In this paper, we aim detect fraudulent Bitcoin transactions using machine learning models, including traditional models like Logistic Regression, Decision Trees, Random Forests, in addition other deep models. Our results demonstrate that when trained on the...
Inspired by the success of various visual attention techniques in computer vision, we introduce a novel method for integrating multiple mechanisms to boost model performance. Our approach involves augmenting base with Parallel Visual Attention Encoder (PVAE) branch, which concurrently employs two different modules (modified large kernel and modified convolutional block attention) capture essential features. To reduce training cost incurred these additional components, apply an encoder...