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   Articles with "activation functions" as a keyword



Parametric RSigELU: a new trainable activation function for deep learning

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recommendations! Published in 2024 at "Neural Computing and Applications"

DOI: 10.1007/s00521-024-09538-9

Abstract: Activation functions are used to extract meaningful relationships from real-world problems with the help of deep learning models. Thus, the development of activation functions which affect deep learning models’ performances is of great interest to… read more here.

Keywords: deep learning; activation; activation functions; activation function ... See more keywords

Adaptive activation functions for predictive modeling with sparse experimental data

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recommendations! Published in 2024 at "Neural Computing and Applications"

DOI: 10.1007/s00521-024-10156-8

Abstract: A pivotal aspect in the design of neural networks lies in selecting activation functions, crucial for introducing nonlinear structures that capture intricate input–output patterns. While the effectiveness of adaptive or trainable activation functions has been… read more here.

Keywords: functions predictive; modeling sparse; predictive modeling; activation functions ... See more keywords
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A framework for automatic detection of heart diseases using dynamic deep neural activation functions

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recommendations! Published in 2020 at "Journal of Ambient Intelligence and Humanized Computing"

DOI: 10.1007/s12652-020-01883-6

Abstract: Availability of the information related to the medical tests are wide and thus the demand for advanced analysis is continuously growing. In the recent past, a number of automatic disease detection algorithms are generated. The… read more here.

Keywords: detection; disease; activation functions; activation ... See more keywords

Global exponential stability of delayed complex-valued neural networks with discontinuous activation functions

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recommendations! Published in 2020 at "Neurocomputing"

DOI: 10.1016/j.neucom.2020.02.006

Abstract: Abstract This paper studies the global exponential stability of delayed complex-valued neural networks with discontinuous activation functions. By introducing the complex-valued Filippov differential inclusion, we construct the framework of studying the dynamical behaviors of complex-valued… read more here.

Keywords: valued neural; neural networks; activation functions; networks discontinuous ... See more keywords

Multistability and instability of competitive neural networks with non-monotonic piecewise linear activation functions

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recommendations! Published in 2019 at "Nonlinear Analysis: Real World Applications"

DOI: 10.1016/j.nonrwa.2018.08.005

Abstract: Abstract This paper addresses the issue of multistability for competitive neural networks. First, a general class of continuous non-monotonic piecewise linear activation functions is introduced. Then, based on the fixed point theorem, the contraction mapping… read more here.

Keywords: competitive neural; activation; non monotonic; neural networks ... See more keywords

M-estimation activation functions for high-performance extreme learning machine ensemble classification

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recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-16798-5

Abstract: Machine learning plays a pivotal role in addressing real-world challenges across domains such as cybersecurity, where AI-driven methods, especially in Software-Defined Networking, enhance traffic monitoring and anomaly detection. Contemporary networks often employ models like Random… read more here.

Keywords: activation functions; estimation; machine; extreme learning ... See more keywords

Convolutional Neural Network-Based Skin Lesion Classification With Variable Nonlinear Activation Functions

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recommendations! Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3196911

Abstract: One of the worst forms of skin cancer is melanoma which can be curable if it is diagnosed at an early stage. The earlier the cancer is diagnosed, the better is the outcome. The risk… read more here.

Keywords: activation functions; neural network; lesion; nonlinear activation ... See more keywords

Developing Novel Activation Functions Based Deep Learning LSTM for Classification

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recommendations! Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3205774

Abstract: This study proposes novel Long Short-Term Memory (LSTM)-based classifiers through developing the internal structure of LSTM neural networks using 26 state activation functions as alternatives to the traditional hyperbolic tangent (tanh) activation function. The LSTM… read more here.

Keywords: activation functions; functions based; classification; deep learning ... See more keywords

NeuroSCA: Evolving Activation Functions for Side-Channel Analysis

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recommendations! Published in 2023 at "IEEE Access"

DOI: 10.1109/access.2022.3232064

Abstract: The choice of activation functions can significantly impact the performance of neural networks. Due to an ever-increasing number of new activation functions being proposed in the literature, selecting the appropriate activation function becomes even more… read more here.

Keywords: activation functions; channel analysis; functions side; side channel ... See more keywords

Defending CNN Against FGSM Attacks Using Beta-Based Personalized Activation Functions and Adversarial Training

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recommendations! Published in 2024 at "IEEE Access"

DOI: 10.1109/access.2024.3432773

Abstract: Machine learning algorithms based on deep neural networks have been widely used in many fields especially in computer vision, with impressive results. However, these models are vulnerable to different types of attacks like adversarial ones,… read more here.

Keywords: based personalized; using beta; activation; activation functions ... See more keywords

Performance Evaluation of Activation Functions in Deep Residual Networks for Short-Term Load Forecasting

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recommendations! Published in 2025 at "IEEE Access"

DOI: 10.1109/access.2025.3565798

Abstract: Short-Term Load Forecasting (STLF) is essential for ensuring efficient and reliable power system operations, requiring accurate predictions of electricity demand. Deep Residual Networks (DRNs), with their ability to mitigate gradient vanishing and model complex nonlinear… read more here.

Keywords: activation; load forecasting; activation functions; performance ... See more keywords