Results 101 to 110 of about 25,088 (256)

Explainable machine learning methods to predict postpartum depression risk

Systems Science & Control Engineering
Postpartum depression (PPD) is a type of depression that mothers have following childbirth due to hormonal changes, psychological transition to parenting, and exhaustion. This depression strikes either during/or in the first year following childbirth. It
Susmita Shivaprasad, Krishnaraj Chadaga, Niranjana Sampathila, Srikanth Prabhu, Rajagopala Chadaga P, Swathi K S   +5 more
doaj   +1 more source

Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges

Epilepsia Open, EarlyView.
Abstract Preclinical translational epilepsy research uses animal models to better understand the mechanisms underlying epilepsy and its comorbidities, as well as to analyze and develop potential treatments that may mitigate this neurological disorder and its associated conditions. Artificial intelligence (AI) has emerged as a transformative tool across
Jesús Servando Medel‐Matus, Cesar Santana‐Gomez, Ruby G. Escalante, Dominique Duncan, Pedro F. Viana, Giulia Sofia Cereda, Naoto Kuroda, Aristea S. Galanopoulou   +7 more
wiley   +1 more source

Smart Solar Power Site Selection Through a Hybrid Computational Framework of Neuro‐Fuzzy With Genetic Algorithm Optimization

Energy Science &Engineering, EarlyView.
Solar Power plants at various locations a: Pavagada SPP, Karnataka b: Bhadla SPP, Rajasthan c: West Bengal Solar Park ABSTRACT This paper presents a new neuro‐fuzzy multi‐criteria decision‐making (MCDM) framework designed to optimize the selection of solar power plant (SPP) sites across India.
Rajkumari Malemnganbi Devi, Benjamin A. Shimray, Ingudam Chitrasen Meitei, Khelchandra Thongam, Shuma Adhikari, Anuradha Laishram, Subir Datta, Ahmed Al Mansur, Taha Selim Ustun   +8 more
wiley   +1 more source

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

IEEE Access
The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies.
Roohallah Alizadehsani, Solomon Sunday Oyelere, Sadiq Hussain, Senthil Kumar Jagatheesaperumal, Rene Ripardo Calixto, Mohamed Rahouti, Mohamad Roshanzamir, Victor Hugo C. De Albuquerque   +7 more
doaj   +1 more source

Shaping the Future of Radiography Education: Lessons From ChatGPT and Generative AI

Journal of Medical Radiation Sciences, EarlyView.
ChatGPT can provide structured guidance, support self‐assessment and scaffold learning processes that bridge classroom knowledge and clinical expectations. However, AI must be embedded in ways that uphold the core principles of radiographic practice: accuracy, reflective judgment, ethical reasoning, empathy and patient‐centred care.
Minh T. Chau, Haydn Kerr, Clare L. Singh, Bismark Ofori‐Manteaw, Elio Arruzza, Kelly Bentley‐Spuur   +5 more
wiley   +1 more source

Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence

Scientific Reports
A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for
Fahad Ahmed, Sagheer Abbas, Atifa Athar, Tariq Shahzad, Wasim Ahmad Khan, Meshal Alharbi, Muhammad Adnan Khan, Arfan Ahmed   +7 more
doaj   +1 more source

Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

Frontiers in Neuroscience, 2019
The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of ...
Jean-Marc Fellous, Jean-Marc Fellous, Guillermo Sapiro, Andrew Rossi, Helen Mayberg, Michele Ferrante, Michele Ferrante   +6 more
doaj   +1 more source

Automated Verification of Neural Networks: Advances, Challenges and Perspectives

, 2018
Neural networks are one of the most investigated and widely used techniques in Machine Learning. In spite of their success, they still find limited application in safety- and security-related contexts, wherein assurance about networks' performances must ...
Leofante, Francesco, Narodytska, Nina, Pulina, Luca, Tacchella, Armando   +3 more
core  

Should Dermatologists Recommend Direct‐to‐Consumer App‐Based Remote Diagnostics? An Ethical Analysis

JEADV Clinical Practice, EarlyView.
ABSTRACT Background Dermatology patients still face barriers in accessing timely specialist care. As direct‐to‐consumer (DTC) apps for remote dermatological diagnostics proliferate, guidance is lacking. While promising efficiency and efficacy, their clinical—and ethical—legitimacy is not yet well established.
Sonja Mathes, Sebastian Seurig, Theresa Willem, Marta Dechant, Stephan Traidl, Mina Kang, Pedro Marques Pinto, Annette Binder, Tilo Biedermann, Ulf Darsow   +9 more
wiley   +1 more source

AI Competency and Perception of XAI Importance Versus Attitude Toward AI: Mediating Effects of Belief in XAI Availability

Advances in Human-Computer Interaction
In this research, we hypothesize that attitudes toward artificial intelligence (AI) are shaped by individuals’ perceived competence in using and managing it, as well as their assessment of the importance of AI’s understandability and transparency, often ...
M. Liebherr, A. Babiker, S. AlShakhsi, D. Al-Thani, C. Montag, R. Ali   +5 more
doaj   +1 more source