The outcome prediction method of football matches by the quantum neural network based on deep learning by Yang Sun, Hongyang Chu

“…During the model training phase, gradient descent is used to optimize weight parameters, and quantum algorithms are integrated to continuously adjust network weights to minimize prediction errors. The model is trained, parameter tuning is completed, and performance is evaluated using the training, validation, and independent test sets. …”
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Real-Time Fire Risk Classification Using Sensor Data and Digital-Twin-Enabled Deep Learning by In-Seop Na, Vani Rajasekar, Velliangiri Sarveshwaran

“…Advanced deep learning architectures such as convolutional neural networks (CNNs), deep CNNs (DCNNs), and recurrent neural networks (RNNs) are utilized to identify critical spatial and temporal patterns in the data. …”
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Self-Supervised Neural Networks for Precoding in MIMO Rate Splitting Multiple Access Systems by Dheeraj Raja Kumar, Carles Anton-Haro, Xavier Mestre

“…The intention is to explore several alternatives to conventional iterative precoding benchmarks like Weighted Minimum Mean Square Error (WMMSE) which are computationally intensive algorithms. We evaluate the different precoding policies learnt by the neural network architectures by closely studying the respective radiation patterns. …”
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Integrating Recurrent Neural Networks and Loss Function Optimization for Efficient Indoor Camera Positioning by Shamsul Alam, Farhan Mohamed, Bellal Hossain

“…This study proposed an integrated design that combines recurrent neural networks (RNNs) and a loss function modification approach to improve the accuracy of indoor camera location. …”
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Different prefrontal cortex activity patterns in bipolar and unipolar depression during verbal fluency tasks based on functional near infrared spectroscopy study by Lan Mou, Yuqi Shen, Boyuan Wu, Chengyu Zhang, Jiayun Zhu, Qian Tan, Xiaomei Zhang, Zefeng Wang, Zhongxia Shen

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The two-mode network approach to digital skills and tasks among technology park employees by Anna Ujwary-Gil, Bianka Godlewska-Dzioboń

“…Contribution & Value Added: The article shows digital skills (information management, information evaluation, communication sharing, communication building, communication networking, collaboration, critical thinking, creativity, problem solving) in connection with the tasks performed in the workplace in terms of dyads and two-mode networks (actors and digital skills; actors and tasks performed). …”
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Enhanced-Dueling Deep Q-Network for Trustworthy Physical Security of Electric Power Substations by Nawaraj Kumar Mahato, Junfeng Yang, Jiaxuan Yang, Gangjun Gong, Jianhong Hao, Jing Sun, Jinlu Liu

“…Empirical evaluation using synthetic data derived from historical incident patterns demonstrates the significant advantages of EDDQN over other standard DQN variations, yielding an average reward of 7.5, a threat prevention success rate of 91.1%, and a notably low false alarm rate of 0.5%. …”
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Multimodal lightweight neural network for Alzheimer's disease diagnosis integrating neuroimaging and cognitive scores by Bhoomi Gupta, Ganesh Kanna Jegannathan, Mohammad Shabbir Alam, Kottala Sri Yogi, Janjhyam Venkata Naga Ramesh, Vemula Jasmine Sowmya, Isa Bayhan

“…To address these challenges, we propose Light-Mo-DAD, a lightweight multimodal diagnostic neural network designed to integrate MRI, PET imaging, and neuropsychological assessment scores for enhanced AD detection. …”
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Adaptive distributed honeypot detection network for enhanced cybersecurity against DoS and DDoS attacks by V. Selva Kumar, K.R. Mohan Raj, S. Gopalakrishnan, G. Vennila, D. Dhinakaran, P. Kavitha

“…Traditional detection techniques often fall short in addressing the complexities posed by dynamic traffic patterns, diverse attack types, and real-time processing demands. …”
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Radio Spectrum Sensing Framework for Large-Scale, High-Density IoT Sensor Networks by Partemie-Marian Mutescu, Alexandru Lavric, Adrian-Ioan Petrariu, Alin-Mihai Cailean, Valentin Popa

“…The rapid growth of the Internet of Things (IoT) requires innovative solutions to address the challenges posed by constrained radio spectrum resources in Wireless Sensor Networks (WSNs). This paper presents an Artificial Intelligence (AI) based spectrum sensing framework designed to detect and classify various IoT communication technologies and generate advanced communication channel analytics, such as the technology in use, central frequency and the bandwidth of the radio channel, time-on-air parameter, channel usage patterns, and radio spectrum occupancy degree. …”
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Physical Information Neural Network-Based Seepage Behavior Analysis of Earth and Rock Dams by XUE binghan, HUANG zhenhua, LEI Jianwei, FANG Hongyuan

“…To address these limitations, this study proposes a novel seepage field solution method based on Physics-Informed Neural Networks (PINNs).MethodsThe core of the proposed method lies in transforming the free-boundary seepage problem into a fixed-boundary optimization problem. …”
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Real-time ocean wave prediction in time domain with autoregression and echo state networks by Karoline Holand, Henrik Kalisch

“…This study evaluates the potential of applying echo state networks (ESN) and autoregression (AR) for dynamic time series prediction of free surface elevation for use in wave energy converters (WECs). …”
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A Novel Multi-Step Forecasting-Based Approach for Enhanced Burst Detection in Water Distribution Systems by Xi Wan, Raziyeh Farmani, Edward Keedwell, Xiao Zhou

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Exploring the role of electrode density in capturing spatiotemporal dynamics of resting-state networks with EEG by Matheus Mangini Bertuzzo, Rodrigo P Rocha, Ricardo Spyrídes Boabaid Pimentel Gonçalves, Adair Roberto Soares Dos Santos, Odival Cezar Gasparotto

“…Using exact low-resolution electromagnetic tomography, we estimated the cortical current density in regions of interest linked to resting state networks. Point process analysis was employed to identify regions of high activity over time, revealing dynamic brain salient activity patterns, or brain maps. …”
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Triplet-Style Dynamic Graph Network With Transformer Encoder for Scam Detection in Cryptocurrency Transactions by Min-Woo Nam, Hyeon-Ju Lee, Seok-Jun Buu

“…Evaluations on a real-world Bitcoin transaction network demonstrate TD-GCN’s superior scam detection performance. …”
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Artificial intelligence and Artificial Neural Networks in toxicology: challenges, perspectives and applications (Narrative review) by Sara Karimi ZEVERDEGANI, Elham SABER, Samira BARAKAT

“…Regularized and fully connected convolutional neural networks cannot detect and detect discrete changes in toxicity related two-dimensional data patterns. …”
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Reconfiguration of brain network dynamics in bipolar disorder: a hidden Markov model approach by Xi Zhang, Lan Yang, Jiayu Lu, Yuting Yuan, Dandan Li, Hui Zhang, Rong Yao, Jie Xiang, Bin Wang

“…This study employed hidden Markov model (HMM) analysis to delve deeper into the moment-to-moment temporal patterns of brain activity in BD. We utilized resting-state functional magnetic resonance imaging (rs-fMRI) data from 43 BD patients and 51 controls to evaluate the altered dynamic spatiotemporal architecture of the whole-brain network and identify unique activation patterns in BD. …”
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Causal inference-based graph neural network method for predicting asphalt pavement performance by CHEN Kai;WANG Xiaohe;SHI Xinli;CAO Jinde

“…The local feature extraction module utilizes dilated convolutional neural networks(CNN) with various kernel sizes to extract short-term temporal patterns at different scales. …”
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Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network by Aythem Khairi Kareem, Mohammed M. AL-Ani, Ahmed Adil Nafea

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Optimal ACL Policy Placement in Hybrid SDN Networks: A Reinforcement Learning Approach by Wajid Ullah Khan, Nadir Shah, Gabriel-Miro Muntean, Haleem Farman, Moustafa M. Nasralla, Shan Ullah, Muhammad Shabir

“…The proposed algorithm optimally determines the placement of ACL policies, minimizing both the total number of policies and redundant transmissions in the network. Extensive evaluations using real-world network traces and topologies demonstrate that our approach outperforms existing state-of-the-art methods in terms of efficiency and network performance.…”
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