Damage toughness assessment method of power backbone communication network based on power big data by Xue HAN

“…The damage toughness assessment method of power backbone communication network based on power big data was studied, and the damage toughness assessment results were used to formulate protection strategies and improve the network survivability.The power backbone communication network and the attacker were set as the two sides of the game, and the Nash equilibrium strategy of both sides was determined by the minimax method.The damage probability of nodes was obtained by the Nash equilibrium strategy, and the node damage probability was transformed into the network damage form.According to the network damage pattern, the repair conditions contained in the power big data were obtained by cloud computing technology.According to the importance of each node, the repair resources and repair budget were allocated to the nodes in order.The performance time-history response curve of the power backbone communication network was constructed, and the dynamic evaluation results of the network damage toughness were output.The experimental results show that all operations using cloud computing technology to process massive power big data can not exceed 330 ms.When the repair budget and repair resources are the highest, the damage toughness of the power backbone communication network can reach 0.925 at most, indicating that this method can effectively evaluate the damage toughness of the power backbone communication network.…”
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Damage toughness assessment method of power backbone communication network based on power big data by Xue HAN

“…The damage toughness assessment method of power backbone communication network based on power big data was studied, and the damage toughness assessment results were used to formulate protection strategies and improve the network survivability.The power backbone communication network and the attacker were set as the two sides of the game, and the Nash equilibrium strategy of both sides was determined by the minimax method.The damage probability of nodes was obtained by the Nash equilibrium strategy, and the node damage probability was transformed into the network damage form.According to the network damage pattern, the repair conditions contained in the power big data were obtained by cloud computing technology.According to the importance of each node, the repair resources and repair budget were allocated to the nodes in order.The performance time-history response curve of the power backbone communication network was constructed, and the dynamic evaluation results of the network damage toughness were output.The experimental results show that all operations using cloud computing technology to process massive power big data can not exceed 330 ms.When the repair budget and repair resources are the highest, the damage toughness of the power backbone communication network can reach 0.925 at most, indicating that this method can effectively evaluate the damage toughness of the power backbone communication network.…”
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Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia by Aditya Eaturu, Krishna Prasad Vadrevu

“…Furthermore, simpler models, such as Simple Persistence and MLP, showed limitations in capturing dynamic patterns and temporal dependencies. Our findings highlight the importance of evaluating various ML and DL models before integrating them into any decision support systems (DSS) for fire management studies. …”
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Real‐time object detection for unmanned vehicles in Bangladesh: Dataset, implementation and evaluation by Muhammad Liakat Ali, Topu Biswas, Shahin Akter, Mohammed Farhan Jawad, Hadaate Ullah

“…The MS COCO (Microsoft Common Objects in Context) dataset weights are included in the YOLOv5 deep learning network for transfer learning. Finally, Python TensorBoard was used to evaluate and visualize the model's performance. …”
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Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance by M. Elhoseny, Deepak Dasaratha Rao, Bala Dhandayuthapani Veerasamy, Noha Alduaiji, J. Shreyas, Piyush Kumar Shukla

“…Here, the major objective is to locate problems in detection by analysing previous data or sequential patterns of data that cause failure. This study evaluates the use of deep learning for improved sensor data fusion in fault identification and tolerance using the KITTI dataset. …”
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Relapse Frequency and Pattern Following Adjuvant Radiotherapy for Intermediate and High-Intermediate Risk Endometrial Cancer Based on Retrospective ESGO-ESTRO-ESP Risk Classificati... by Kathryn Graham, Laura Hannington, Claire Duncanson, Andrew Wilkinson, Douglas Cartwright, Rosie Harrand, Ashleigh Kerr, Nick Reed, Azmat Sadozye

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Innovative transformer neural network for wind density function estimation at different hub heights of turbine by Amit Kumar Yadav, Vibha Yadav, Walle Tilahun

“…To compute this paper introduces an innovative Transformer Neural Network (TNN) model for WDE estimation leverage self attention mechanism to capture complex pattern. …”
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Application of a Stochastic Model for Water Demand Assessment under Water Scarcity and Intermittent Networks by Stefania Piazza, Mariacrocetta Sambito, Gabriele Freni

“…The analysis was conducted using a short-term water demand forecast model that reproduces periodic patterns observed at an annual, weekly and daily level to evaluate the adaptation response of users concerning the scarcity of water resources through a comparison between the real pattern of the network and the pattern of local tanks.…”
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Exploring Applications of Convolutional Neural Networks in Analyzing Multispectral Satellite Imagery: A Systematic Review by Antonia Ivanda, Ljiljana Šerić, Maja Braović

“…Today is possible to extract features specific to various fields of application with the application of modern machine learning techniques, such as Convolutional Neural Networks (CNN) on MultiSpectral Images (MSI). This systematic review examines the application of 1D-, 2D-, 3D-, and 4D-CNNs to MSI, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. …”
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Attention-enhanced hybrid CNN–LSTM network with self-adaptive CBAM for COVID-19 diagnosis by Fatin Nabilah Shaari, Aimi Salihah Abdul Nasir, Wan Azani Mustafa, Wan Aireene Wan Ahmed, Abdul Syafiq Abdull Sukor

“…However, baseline Convolutional Neural Network (CNN) commonly faced obstacles to fully capture the temporal dependencies present in sequential medical imaging data, limiting their diagnostic performance. …”
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Spatial–Temporal Evolution of Ecological Network Structure During 1967–2021 in Yongding River Floodplain by Junyi Su, Minghao Wu, Zhicheng Liu

“…Overall, this study advances our understanding of the spatial distribution and composition of key ecological elements within river corridor networks and offers a framework for evaluating these networks through a multidimensional optimization approach for ecological source patches. …”
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A Comprehensive Evaluation of Machine Learning and Deep Learning Models for Churn Prediction by Nabil M. AbdelAziz, Mostafa Bekheet, Ahmad Salah, Nissreen El-Saber, Wafaa T. AbdelMoneim

“…This would help conclude whether the varied patterns of the churn throughout different sectors to the level that affects the model performance and to what extent. …”
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Advanced hybrid deep learning model for enhanced evaluation of osteosarcoma histopathology images by Arezoo Borji, Arezoo Borji, Arezoo Borji, Gernot Kronreif, Bernhard Angermayr, Bernhard Angermayr, Sepideh Hatamikia, Sepideh Hatamikia

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A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification by A. M. Mutawa, Sai Sruthi

“…While earlier studies primarily relied on conventional machine learning and recurrent neural networks, this work evaluates the effectiveness of transformer-based models—an area not extensively explored for this task. …”
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AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory by V. Mahalakshmi

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Recognition and Evaluation of Architectural Heritage Value in Fujian Overseas Chinese New Villages by Jing Hu, Hanyi Wu, Fan Huo, Zhihong Chen

“…Three primary findings emerged: (1) Spatial distribution patterns revealed core-periphery clustering characteristics, with Xiamen and Zhangzhou forming high-density cores (23.5% concentration ratio) showing KDE values of 4.138–4.976, reflecting historical migration networks and policy-driven site selection logic. (2) Heritage values were categorized into seven dimensions, with historical significance (0.2904), artistic merit (0.1602), and functional utility (0.1638) identified as primary value drivers. (3) A four-tier evaluation system quantified heritage significance through weighted indices, demonstrating 53.89% dominance of intrinsic value components, with historical and cultural factors contributing 29.04% and 18.52% respectively. …”
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Metaweb approach to unravel food web structures: Exploring environmental changes and biotic interactions in Korean stream ecosystems by Da-Yeong Lee, Sagar Adhurya, Dae-Seong Lee, Young-Seuk Park

“…Metawebs enable the construction of food webs using biomonitoring data, and provide a comprehensive analysis of ecosystem interactions. Using network theory and food web metrics, we (1) applied metaweb approaches to construct and compare local food webs across multiple study sites, offering a holistic perspective on regional ecosystem dynamics; (2) examined the impacts of environmental changes on food web structures to identify patterns indicative of ecosystem status; and (3) evaluated the influence of habitat heterogeneity on food web configuration. …”
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From communication to action: using ordered network analysis to model team performance in clinical simulation by Vitaliy Popov, Lauryn R. Rochlen

“…Teams were classified as high- or low-performing based on timely dantrolene administration and appropriate MH treatment actions. Network visualizations and statistical tests compared communication patterns between groups. …”
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DPN-GAN: Inducing Periodic Activations in Generative Adversarial Networks for High-Fidelity Audio Synthesis by Zeeshan Ahmad, Shudi Bao, Meng Chen

“…In recent years, generative adversarial networks (GANs) have made significant progress in generating audio sequences. …”
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Brain Tumour Segmentation and Grading Using Local and Global Context-Aggregated Attention Network Architecture by Ahmed Abdulhakim Al-Absi, Rui Fu, Nadhem Ebrahim, Mohammed Abdulhakim Al-Absi, Dae-Ki Kang

“…The main advantage of LGCNet is its dedicated network for a specific task. The proposed model is evaluated by considering the BraTS2019 dataset with different metrics, such as the Dice score, sensitivity, specificity and Hausdorff score. …”
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