crossrefPeerJ Computer Science2026-07-03Cited by 0

Lya-DRL-SMC: a Lyapunov-stability-constrained deep reinforcement learning enhanced sliding mode control method for remotely operated vehicles

Remotely operated vehicles (ROVs) operating in complex marine environments are subject to multimodal disturbances, such as wave forces, ocean currents, and model uncertainties, which pose significant challenges to the robustness and stability of the control system. This article proposes a Lyapunov-stability-constrained Deep Reinforcement Learning enhanced Sliding Mode Control (Lya-DRL-SMC) framework. This framework dynamically optimizes the sliding surface parameters of the SMC via a Lyapunov-constrained Deep Reinforcement Learning (Lya-DRL) approach, achieving an optimal balance among robustness, tracking accuracy, and energy consumption. Simultaneously, a frequency-decoupled multimodal Extended State Observer (ESO) is introduced to accurately estimate and compensate for the system’s lumped disturbances. The finite-time stability of the closed-loop system is rigorously proven. Comparative simulation results against conventional SMC (CSMC) and Active Disturbance Rejection Control (ADRC) demonstrate the superior performance of the proposed Lya-DRL-SMC in terms of trajectory tracking accuracy (MAE), energy consumption, and chattering suppression.

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Systematic review of unveiling the potential of AI using machine learning and deep learning methods in neurodegenerative diseases

S. Mohanraj, Sujatha Radhakrishnan

Background Neurodegenerative diseases (NDDs) are becoming a major worldwide issue, especially for the elderly because they are incurable and permanent. It is extremely difficult to provide any medication to people suffering from NDDs. Comprehending essential processes of NDDs are…

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crossrefPeerJ Computer Science2026-06-22

A systematic review of machine learning and deep learning approaches for gastrointestinal cancer diagnosis

Vijayalakshmi D., Bharanidharan Nagarajan

Globally, one of the prominent causes of cancer-related deaths is gastrointestinal cancer. It includes the tumour in the regions of the gastrointestinal tract, such as the esophagus, stomach, liver, pancreas, and colon. Improving patient outcomes requires an early and accurate di…

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crossrefPeerJ Computer Science2026-04-22Cited by 1

A review of current imaging techniques for histopathology-based breast cancer diagnosis with comparative insights using machine learning and deep learning models and its challenges and future directions

Gokula Lakshmi G., Uma Kuppusamy

Breast cancer remains one of the principal reasons of death among women worldwide, highlighting the critical requirement for primary and exact diagnosis to expand patient persistence rates. Manual clarification of histopathological slides is frequently subjective, time-consuming,…

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crossrefPeerJ Computer Science2026-05-21

A deep learning model using convolutional neural networks and conditional generative adversarial networks with multi-head attention for stock prediction

Zhiqi Wang, Feng Gu

Stock prediction utilizing machine learning and deep learning models has attracted increasing attention in recent years. While recent research has made substantial progress in stock forecasting, many existing models perform inconsistently across markets and are sensitive to rando…

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crossrefPeerJ Computer Science2026-07-07

JackVisualNet: a fine-tuned hybrid deep learning model for jackfruit disease classification with explainable AI

Amir Sohel, Md. Hasan Imam Bijoy, Sarbajit Paul Bappy, Rittik Chandra Das Turjy, Manal Othman, Md Abdus Samad

Jackfruit, a vital agricultural crop in Bangladesh, is a key player in ensuring food security and sustaining rural communities’ livelihoods. The escalating challenges posed by plant diseases and the shortcomings of traditional manual disease detection methods underscore the press…

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crossrefPeerJ Computer Science2026-07-07

Dense121GAN: transfer learning-enhanced conditional generative adversarial network with DenseNet121 for reliable and efficient segmentation in medical and industrial imaging

Muhammed Davud

Accurate image segmentation in medical and industrial domains remains challenging due to small object sizes, complex textures, and diverse defect morphologies. To address these limitations, we propose Dense121GAN, a conditional generative adversarial network (cGAN) that integrate…

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Lya-DRL-SMC: a Lyapunov-stability-constrained deep reinforcement learning enhanced sliding mode control method for remotely operated vehicles

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Dense121GAN: transfer learning-enhanced conditional generative adversarial network with DenseNet121 for reliable and efficient segmentation in medical and industrial imaging