Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects by Changda Lei, Wenqiang Sun, Kun Wang, Ruixia Weng, Xiuji Kan, Rui Li

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LRMAHpan: a novel tool for multi-allelic HLA presentation prediction using Resnet-based and LSTM-based neural networks by Xue Mi, Shaohao Li, Zheng Ye, Zhu Dai, Bo Ding, Bo Sun, Yang Shen, Yang Shen, Zhongdang Xiao, Zhongdang Xiao

“…A major challenge remains in determining which HLA allele eluted peptides correspond to.MethodsTo address this, we present a tool for prediction of multiple allele (MA) presentation called LRMAHpan, which integrates LSTM network and ResNet_CA network for antigen processing and presentation prediction. …”
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Neuroevolutionary reinforcing learning of neural networks by Y. A. Bury, D. I. Samal

“…The article presents the results of combining 4 different types of neural network learning: evolutionary, reinforcing, deep and extrapolating. …”
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Learning Policies for Neural Network Architecture Optimization Using Reinforcement Learning by Raghav Vadhera, Manfred Huber

“…To address this and to open up the potential for transfer across tasks, this paper presents a novel approach that uses Reinforcement Learning to learn a policy for network optimization in a derived architecture embedding space that incrementally optimizes the network for the given problem. …”
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Adaptive learning on mobile network traffic data by Zhen Liu, Nathalie Japkowicz, Ruoyu Wang, Deyu Tang

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Neural network compression for reinforcement learning tasks by Dmitry A. Ivanov, Denis A. Larionov, Oleg V. Maslennikov, Vladimir V. Voevodin

“…This work presents a systematic study on the applicability limits of using pruning and quantization to optimize neural networks in RL tasks, with a perspective of deployment in hardware to reduce power consumption and latency, while increasing throughput.…”
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Hybrid Approach for WDM Network Restoration: Deep Reinforcement Learning and Graph Neural Networks by Isaac Ampratwum, Amiya Nayak

“…This article presents a hybrid framework that integrates Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to optimize WDM network restoration. …”
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Pedagogy framework design in social networked-based learning: Focus on children with learning difficulties by Samira Sadat Sajadi, Tariq M Khan

“…This paper presents an investigation on the theory of constructivism applicable for learners with learning difficulties, specifically learners with Attention Deficit Hyperactivity Disorder (ADHD). …”
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Neural networks and reinforcement learning in wind turbine control by J. E. Sierra-García, M. Santos

“…Direct pitch control based on neural networks and reinforcement learning, and some hybrid control configurations are described. …”
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Impartial competitive learning in multi-layered neural networks by Ryotaro Kamimura

“…The present paper aims to propose a new learning and interpretation method called “impartial competitive learning”, meaning that all participants in a competition should be winners. …”
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Machine Learning and Neural Networks for IT-Diagnostics of Neurological Diseases by U. A. Vishniakou, Y. W. Xia, Ch. Y. Yu

“…The article considers machine learning methods and neural networks for diagnosing neurological diseases (Alzheimer’s and Parkinson’s diseases) in patients based on voice analysis. …”
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Leveraging Graph Networks to Model Environments in Reinforcement Learning by Viswanath Chadalapaka, Volkan Ustun, Lixing Liu

“…This paper proposes leveraging graph neural networks (GNNs) to model an agent’s environment to construct superior policy networks in reinforcement learning (RL). …”
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An Overview of Deep Neural Networks for Few-Shot Learning by Juan Zhao, Lili Kong, Jiancheng Lv

“…Recent advancements in deep learning have led to significant breakthroughs across various fields. …”
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Voice Spoofing Detection Through Residual Network, Max Feature Map, and Depthwise Separable Convolution by Il-Youp Kwak, Sungsu Kwag, Junhee Lee, Youngbae Jeon, Jeonghwan Hwang, Hyo-Jung Choi, Jong-Hoon Yang, So-Yul Han, Jun Ho Huh, Choong-Hoon Lee, Ji Won Yoon

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Contrastive Learning‑based Simplified Graph Convolutional Network Recommendation by YU Yuchen, WU Siqi, ZHAO Qinghua, WU Xuhong, WANG Lei

“…[Purposes] Considering the problems of the existing Graph Convolutional Network (GCN) recommendation models, such as low model convergence efficiency, over-smoothing, and deteriorative recommendations for long-tail items caused by the effect of high-degree nodes on presentation learning, a Contrastive Learning-based Simplified Graph Convolutional Network recommendation algorithm (SGCN-CL) is presented. …”
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Zero-Touch Network Security (ZTNS): A Network Intrusion Detection System Based on Deep Learning by Emad-Ul-Haq Qazi, Tanveer Zia, Muhammad Hamza Faheem, Khurram Shahzad, Muhammad Imran, Zeeshan Ahmed

“…Our proposed approach presents a major improvement in IoT security. We have used the CICIDS-2018 benchmark dataset and propose a deep learning-based network intrusion detection System for Zero Touch Networks (DL-NIDS-ZTN). …”
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Machine Learning in Intelligent Networks: Architectures, Techniques, and Use Cases by Elias Dritsas, Maria Trigka

“…Integrating machine learning (ML) into intelligent networks (INs) has redefined the capabilities of modern communication systems by enabling real-time decision-making, adaptive optimization, and enhanced security. …”
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Hybrid feature learning framework for the classification of encrypted network traffic by S. Ramraj, G. Usha

“…Previous research has shown that deep learning methods are effective in the feature learning process, so this study uses a simple feed-forward Deep Neural Network (DNN) to improve the performance of the SVM algorithm. …”
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Benchmarking Spiking Neural Network Learning Methods With Varying Locality by Jiaqi Lin, Sen Lu, Malyaban Bal, Abhronil Sengupta

“…Spiking Neural Networks (SNNs), providing more realistic neuronal dynamics, have been shown to achieve performance comparable to Artificial Neural Networks (ANNs) in several machine learning tasks. …”
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A neural network model for the evolution of reconstructive social learning by Jacob Chisausky, Inès Marguerite Daras, Franz J. Weissing, Magdalena Kozielska

“…To represent the reconstructive nature of social learning, we present a modelling framework that incorporates the evolution of a neural network and a simple yet biologically realistic learning mechanism. …”
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