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Artificial neural networks in medical diagnosis

Filippo Amato, Alberto Botana López, Eladia María Peña‐Méndez, Petr Vaňhara et al. • Journal of Applied Biomedicine (2013)

An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that mu...

OPENALEX Open Access DOI: https://doi.org/10.2478/v10136-012-0031-x

Overcoming catastrophic forgetting in neural networks

James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness et al. • Proceedings of the National Academy of Sciences (2017)

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitabl...

OPENALEX Open Access DOI: https://doi.org/10.1073/pnas.1611835114 PMC: 5380101

Neural Networks for Chemists

Qingyang Xiao, Kaiyuan Liu, Yuhui Hong, Haixu Tang • ACS in focus (2024)

In recent years, breakthroughs in artificial intelligence (AI) have dramatically transformed many fields. Developments in neural networks primarily drive the advances. These powerful models have demonstrated unprecedented capabilities in le...

OPENALEX DOI: https://doi.org/10.1021/acsinfocus.7e8012

An Introduction to Convolutional Neural Networks

Keiron O’Shea, Ryan Nash • arXiv (Cornell University) (2015)

The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models are able to far exceed the performance of previous forms of art...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1511.08458

Deep Learning in Image Cytometry: A Review

Anindya Gupta, Philip John Harrison, Håkan Wieslander, Nicolas Pielawski et al. • Cytometry Part A (2018)

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and ho...

OPENALEX cc-by-nc DOI: https://doi.org/10.1002/cyto.a.23701 PMC: 6590257

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

Ji Su Hwang, Seok Gi Kim, Tae Hwan Shin, Yong Eun Jang et al. • Pharmaceutics (2022)

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cance...

DOAJ Open Access (DOAJ) DOI: 10.3390/pharmaceutics14050997

Evaluating artificial intelligence for a focal nodular hyperplasia diagnosis using magnetic resonance imaging: preliminary findings

Mecit Kantarcı, Volkan Kızılgöz, Ramazan Terzi, Ahmet Enes Kılıç et al. • Diagnostic and Interventional Radiology (2025)

PURPOSE: This study aimed to evaluate the effectiveness of artificial intelligence (AI) in diagnosing focal nodular hyperplasia (FNH) of the liver using magnetic resonance imaging (MRI) and compare its performance with that of radiologists....

DOAJ Open Access (DOAJ) DOI: 10.4274/dir.2025.243095

Detection of short-term peaks in VLF/LF signals around the time of gamma-ray burst detections using deep learning

Sarnovský M., Nina A., Popović L.Č., Srećković V. et al. • Serbian Astronomical Journal (2026)

Gamma-ray bursts (GRBs) are recognized as the most energetic and luminous astrophysical phenomena whose high-energy photons can ionize atmospheric particles and thus affect electrical conductivity. Consequently, electromagnetic waves ...

DOAJ Open Access (DOAJ) DOI: 10.2298/SAJ2612107S

A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence

Ali Raza, Mohammad Rustom Al Nasar, Essam Said Hanandeh, Raed Abu Zitar et al. • Technologies (2023)

Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily li...

DOAJ Open Access (DOAJ) DOI: 10.3390/technologies11020055

SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron

Milad Mozafari, Milad Mozafari, Mohammad Ganjtabesh, Abbas Nowzari-Dalini et al. • Frontiers in Neuroscience (2019)

Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient. Unlike the non-spiking counterparts, most of...

DOAJ Open Access (DOAJ) DOI: 10.3389/fnins.2019.00625

Fundamentals of Deep Learning

P. V. Chandrika, Sandeep Kelkar • Advances in medical diagnosis, treatment, and care (AMDTC) book series (2024)

This chapter deals with the basic understanding of the deep learning model. It starts with a view of artificial intelligence, machine learning, and deep learning. It also represents how these three concepts are related to each other. The en...

OPENALEX DOI: https://doi.org/10.4018/979-8-3693-1281-0.ch001

An open dataset and machine learning algorithms for Niacin Skin-Flushing Response based screening of psychiatric disorders

Xuening Lyu, Rimsa Goperma, Dandan Wang, Chunling Wan et al. • BMC Psychiatry (2025)

Abstract Background Niacin Skin-Flushing Response (NSR) has emerged as a promising objective biomarker for the precise diagnosis of mental disorders. However, its diagnostic potential has been constrained by the limitations of traditional s...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12888-025-07196-2

Quantum Leap in Healthcare: A Comparative Analysis of AI/ML and Quantum Computing for Healthcare opportunities of the new era

salma mostafa ahmed helmy, Salsabil Amin, Abeer mahmoud, El-Sayed El-Horabty • International Journal of Intelligent Computing and Information Sciences (2026)

The healthcare sector is undergoing rapid transformation driven by advances in artificial intelligence and machine learning, with quantum computing emerging as the next major breakthrough. This paper presents a comparative analysis of class...

DOAJ Open Access (DOAJ) DOI: 10.21608/ijicis.2026.442636.1439

Condensing AI-Based Attitude Control Using Kolmogorov–Arnold Networks for Memory Efficiency

Kirill Djebko, Patrick Schurk, Tom Baumann, Frank Puppe et al. • Aerospace (2025)

Artificial Intelligence (AI) is rapidly transforming engineering fields, from robotics to aerospace, with applications in control systems for UAVs and satellites. This work builds on a previously developed AI attitude controller for the Inn...

DOAJ Open Access (DOAJ) DOI: 10.3390/aerospace12121039

Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An In vitro Study

R. S. Basavanna, Ishaan Adhaulia, N. M. Dhanyakumar, Jyoti Joshi • Contemporary Clinical Dentistry (2025)

Background: The integration of artificial intelligence in dentistry has seen remarkable advancements, especially in diagnostic imaging. This study evaluates and compares the accuracy of deep learning models with that of dental postgraduate ...

DOAJ Open Access (DOAJ) DOI: 10.4103/ccd.ccd_274_24