Comprehensive Framework for Collaborative Decision-Making in Evaluating Computer Network Security Using Interval Neutrosophic Information by Wenbao Qiao

“…Through this evaluation, potential threats and vulnerabilities within the network can be systematically identified, effectively preventing possible cyber-attacks and data breaches. …”
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Short-Term Load Forecasting for Electrical Power Distribution Systems Using Enhanced Deep Neural Networks by Shewit Tsegaye, Sanjeevikumar Padmanaban, Lina Bertling Tjernberg, Kinde Anlay Fante

“…The rationale for using enhanced Deep Neural Networks (DNNs) in the power distribution system for short-term load forecasting (STLF) originates from a thorough analysis of current trends, the emergence of the state-of-the-art use cases and approaches. …”
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Review and performance evaluation of FIFO, PQ, CQ, FQ, and WFQ algorithms in multimedia wireless sensor networks by Hani Attar, Mohammad R Khosravi, Shmatkov Sergiy Igorovich, Kuchuk Nina Georgievan, Mohammad Alhihi

“…The best service mechanism in multimedia wireless sensor networks can be achieved based on the multimedia traffic flow by developing a proper simulation algorithm process model, to be a trustable indication for real implementations, which is proposed in this article, together with the algorithm model outcome analysis. …”
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A Blockchain-Based Cross-Domain Authentication Scheme for Unmanned Aerial Vehicle-Assisted Vehicular Networks by Wenming Wang, Shumin Zhang, Guijiang Liu, Yue Zhao

“…With the rapid increase in the number of vehicles and the growing demand for low-latency and reliable communication, traditional vehicular network architectures face numerous challenges. Unmanned Aerial Vehicle (UAV)-assisted vehicular networks provide an innovative solution for real-time data transmission and efficient cross-domain communication, significantly enhancing resource allocation efficiency and traffic safety. …”
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LightCardiacNet: light-weight deep ensemble network with attention mechanism for cardiac sound classification by Suma K. V., Deepali B. Koppad, Dharini Raghavan, Manjunath P. R.

“…An ensemble of the two networks is constructed by employing a weighted average approach that combines the two light-weight attention Bi-GRU networks trained on different datasets, which outperforms several state-of-the-art networks achieving an accuracy of 99.8%, specificity of 99.6%, sensitivity of 95.2%, ROC-AUC of 0.974 and inference time of 17 ms on the PASCAL dataset, accuracy of 98.5%, specificity of 95.1%, sensitivity of 90.9%, ROC-AUC of 0.961 and inference time of 18 ms on the CirCor dataset, and an accuracy of 96.21%, sensitivity of 92.78%, specificity of 93.16%, ROC-AUC of 0.913 and inference time of 17.5 ms on real-world data. …”
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A drug response prediction method for single-cell tumors combining attention networks and transfer learning by BingWei Zhou, BingWei Zhou, SiLin Sun, SiLin Sun, ShengZheng Liu, ShengZheng Liu, HaiXia Long, HaiXia Long, YuChun Li, YuChun Li

“…The model utilizes transfer learning and attention networks to predict drug responses in single-cell tumor data, after pre-training on bulk cell gene expression data. …”
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Cultural Heritage Color Regeneration: Interactive Genetic Algorithm Optimization Based on Color Network and Harmony Models by Zhonghua Jiang, Qianlong Xia, Zhizhou Wang, Kaiwei Zhu, Qianyu Su, Jiajun Wang, Yirui Huang, Bo Wu, Yan Hong

“…First, the role of the color network model in providing color genes for subsequent design is emphasized. …”
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Impact of Load Factor on Distinct Feeders of 132/11 kV Grid Station in Distribution Network by Mohsin Ali Koondhar

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Prediction method of TBM excavation axis deviation for small turning tunnels based on LSTM neural network by Lijie DU, Hongda HAO, Qingwei LI, Yalei YANG, Weidong ZHANG, Jiayi LIU, Hongchao FENG

“…Firstly, the data is preprocessed, including binary discriminant function and other methods, and the 24 dimensional tunneling parameters are selected by Pearson analysis method as the input of prediction model. …”
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Neural Network-Based Lower Limb Prostheses Control Using Super Twisting Sliding Mode Control by Adisu Tadese Demora, Chala Merga Abdissa

“…Simulation results show that training the neural network with processed data increases regression value and decreases trajectory tracking mean squared error (MSE). …”
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Research on Deep Integration of the “Belt and Road” Aviation Network Based on Community Structure from the Perspective of China by Hongguang Yao, Huihui Xiao

“…Based on the data of international routes between 522 airports along the “Belt and Road” in 2019, this paper establishes an aviation network model and calculates the degree, average path, clustering coefficient, and centrality index of the “Belt and Road” aviation network. …”
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Physics-integrated neural networks for improved mineral volumes and porosity estimation from geophysical well logs by Prasad Pothana, Kegang Ling

“…To address this, we present a new approach using Physics-Integrated Neural Networks (PINNs), that combines data-driven learning with domain-specific physical constraints, embedding petrophysical relationships directly into the neural network architecture. …”
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Examining changes in social care referrals during the 2021 Winter Storm Uri in the Unite Texas network by Zackery White, Amanda Terry, Gillian Feldmeth, Alyssa Altstaedter, Halima Ahmadi-Montecalvo

“…Methods A retrospective analysis of Unite Texas social care data from January 12 to March 23, 2021, assessed the distribution of social needs and examined daily case counts, 30-day resolution rates, and closure times before, during, and after the storm. …”
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Functional near⁃infrared spectroscopy of brain network functional connectivity in patients with post⁃stroke cognitive impairment by WANG Xinyu*，LIU Yanling，LIU Jiaqi，GAO Yunhan，JIN Yan，ZHU Chongtian

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Efficient Interference Management Design for NTN/TN Co-Existence in HAP-Based 6G Networks by Muhammet Kirik, Aseel Alkana'Neh, Liza Afeef, Huseyin Arslan

“…Mathematical analysis and numerical results validate the proposed method, demonstrating significant reductions in CCI and inter-carrier interference (ICI), maintained similar level of peak-to-average power ratio (PAPR), and enhanced signal-to-interference-plus-noise ratio (SINR), making it a scalable and efficient solution for non-terrestrial network (NTN)/terrestrial network (TN) co-existence in 6G networks.…”
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Stochastic and Percolating Models of Blocking Computer Networks Dynamics during Distribution of Epidemics of Evolutionary Computer Viruses by S. A. Lesko, A. S. Alyoshkin, V. V. Filatov

“…On one hand, this model is based on the use of percolation theory methods, which makes it possible to determine such structural-information characteristics of networks as the dependence of the percolation threshold on the average number of connections per one node (network density). …”
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Forecasting of Inflation Based on Univariate and Multivariate Time Series Models: An Empirical Application by Hasnain Iftikhar, Faridoon Khan, Paulo Canas Rodrigues, Abdulmajeed Atiah Alharbi, Jeza Allohibi

“…The study considers autoregressive models, autoregressive neural networks, autoregressive moving average models, and other nonparametric autoregressive models within the univariate category. …”
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DCASAM: advancing aspect-based sentiment analysis through a deep context-aware sentiment analysis model by Xiangkui Jiang, Binglong Ren, Qing Wu, Wuwei Wang, Hong Li

“…To address these challenges, we propose the Deep Context-Aware Sentiment Analysis Model (DCASAM). This model integrates the capabilities of Deep Bidirectional Long Short-Term Memory Network (DBiLSTM) and Densely Connected Graph Convolutional Network (DGCN), enhancing the ability to capture long-distance dependencies and subtle contextual variations.The DBiLSTM component effectively captures sequential dependencies, while the DGCN component leverages densely connected structures to model intricate relationships within the data. …”
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Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence by Mehak Arshad, Muhammad Attique Khan, Nouf Abdullah Almujally, Areej Alasiry, Mehrez Marzougui, Yunyoung Nam

“…Feature extraction was performed with a global average pooling layer, and shallow neural networks were used for final classification. …”
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Optimization Strategies for Urban Waterlogging Warning in Complex Environments: Based on Particle Swarm Optimization and Deep Neural Networks by Xiande Hu, Fenfei Gu

“…First, the influencing factors of urban waterlogging were analyzed in the article, and the PSO algorithm was used to determine the influencing factors of urban waterlogging in this study; then, the selected influencing factors were used as input data to design a backpropagation (BP) neural network (NN) structure; several representative waterlogging points can be selected to construct a BP NN model and perform fitting analysis. …”
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