Cost-effectiveness analysis of a multiple health behaviour change intervention in people aged between 45 and 75 years: a cluster randomized controlled trial in primary care (EIRA s... by Ignacio Aznar-Lou, Edurne Zabaleta-Del-Olmo, Marc Casajuana-Closas, Alba Sánchez-Viñas, Elizabeth Parody-Rúa, Bonaventura Bolíbar, Montserrat Iracheta-Todó, Oana Bulilete, Tomàs López-Jiménez, Haizea Pombo-Ramos, María Victoria Martín Miguel, Rosa Magallón-Botaya, Jose Ángel Maderuelo-Fernández, Emma Motrico, Juan Bellón, Ruth Martí-Lluch, Maria Rubio-Valera, Antoni Serrano-Blanco

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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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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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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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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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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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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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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 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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Generative Adversarial Network for Real‐Time Flash Drought Monitoring: A Deep Learning Study by Ehsan Foroumandi, Keyhan Gavahi, Hamid Moradkhani

“…Comparative assessments reveal the proposed GAN's superior ability to replicate SSI values over U‐Net and Naïve models. Evaluation metrics further underscore that the developed GAN successfully identifies both fine‐ and coarse‐scale spatial drought patterns and abrupt changes in the SSI temporal patterns that is important for flash drought identification.…”
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Efficient IDS for IoT Networks Using Host-Based Data Aggregation and Multi-Entropy Analysis by Yusei Katsura, Arata Endo, Ismail Arai, Kazutoshi Fujikawa

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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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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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Detection of Steel Reinforcement in Concrete Using Active Microwave Thermography and Neural Network-Based Analysis by Barbara Szymanik, Maja Kocoń, Sam Ang Keo, Franck Brachelet, Didier Defer

“…These images served as training data for a deep neural network designed to identify and localize rebar positions based on thermal patterns. …”
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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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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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Identifying the effectiveness of face mask in a large population with a network-based fluid model. by Akshay Anand, Kourosh Shoele

“…It is argued that subtle variations in facial features, especially the zygomatic arch, significantly influence leakage patterns, emphasizing the importance of customized mask designs. …”
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