AIMC Topic: Algorithms

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Evolving and Incremental Value Iteration Schemes for Nonlinear Discrete-Time Zero-Sum Games.

IEEE transactions on cybernetics
In this article, evolving and incremental value iteration (VI) frameworks are constructed to address the discrete-time zero-sum game problem. First, the evolving scheme means that the closed-loop system is regulated by using the evolving policy pair....

Modeling Based on a Two-Step Parameter Identification Strategy for Liquid Crystal Elastomer Actuator Considering Dynamic Phase Transition Process.

IEEE transactions on cybernetics
Liquid crystal elastomer (LCE) is a promising candidate for actuation in light-driven soft robot applications. Due to the fact that LCE has complex hysteretic nonlinearities, which are highly dependent on the environment, modeling of actuators made o...

Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.

IEEE transactions on cybernetics
The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation using exoskeleton/exosuit. For system development, the conventional classificatio...

Training Novel Adaptive Fuzzy Cognitive Map by Knowledge-Guidance Learning Mechanism for Large-Scale Time-Series Forecasting.

IEEE transactions on cybernetics
A fuzzy cognitive map (FCM) is a graph-based knowledge representation model wherein the connections of the nodes (edges) represent casual relationships between the knowledge items associated with the nodes. This model has been applied to solve variou...

Learning Performance of Weighted Distributed Learning With Support Vector Machines.

IEEE transactions on cybernetics
The divide-and-conquer strategy is a very effective method of dealing with big data. Noisy samples in big data usually have a great impact on algorithmic performance. In this article, we introduce Markov sampling and different weights for distributed...

Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data.

Sensors (Basel, Switzerland)
As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our obj...

Improved Robot Path Planning Method Based on Deep Reinforcement Learning.

Sensors (Basel, Switzerland)
With the advancement of robotics, the field of path planning is currently experiencing a period of prosperity. Researchers strive to address this nonlinear problem and have achieved remarkable results through the implementation of the Deep Reinforcem...

Underwater Target Detection Utilizing Polarization Image Fusion Algorithm Based on Unsupervised Learning and Attention Mechanism.

Sensors (Basel, Switzerland)
Since light propagation in water bodies is subject to absorption and scattering effects, underwater images using only conventional intensity cameras will suffer from low brightness, blurred images, and loss of details. In this paper, a deep fusion ne...

Diagnostic ability of deep learning in detection of pancreatic tumour.

Scientific reports
Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are c...

Current trends in chromatographic prediction using artificial intelligence and machine learning.

Analytical methods : advancing methods and applications
Artificial intelligence (AI) and machine learning (ML) gained tremendous growth and are rapidly becoming popular in various fields of prediction due to their potential abilities, accuracy, and speed. Machine learning algorithms employ historical data...