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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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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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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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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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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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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Identifying the effectiveness of face mask in a large population with a network-based fluid model. by Akshay Anand, Kourosh Shoele

“…In this study, a new semi-analytical flow network model based on the Kármán-Pohlhausen technique is introduced and utilized to efficiently assess mask performance across diverse facial features that represent the observed variations inside a large population. …”
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DFANet: A Deep Feature Attention Network for Building Change Detection in Remote Sensing Imagery by Peigeng Lu, Haiyong Ding, Xiang Tian

“…Second, we introduce a GatedConv module to improve the network’s capability for building edge detection. Finally, Transformer is introduced to capture long-range dependencies across bitemporal images, enabling the network to better understand feature change patterns and the relationships between different regions and land cover categories. …”
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Spatiotemporal Evolution Mechanism and Spatial Correlation Network Effect of Resilience in Different Shrinking Cities in China by Weijun Yu, Siyu Zhang, Entao Pang, Huihui Wang, Yunsong Yang, Yuhao Zhong, Tian Jing, Hongguang Zou

“…Finally, this paper empirically examines the spatial correlation network effects of UR under various US scenarios using a social network analysis model. …”
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Dietary habits and complex food relations in Northwest China: a population-based network analysis by Xinhua Wang, Duolao Wang, Shaonong Dang, Baibing Mi, Hong Yan, Yuhong Zhang, Yijun Kang, Jianghong Dai, Jing Hui, Samuel Chacha, Huang Yan, Zongkai Li, Jiaxin Cai, Fuchang Ma

“…The staple food-related food network indicated that the intake of rice, whole grains and beans, and potatoes was positively correlated with the intake of most other foods, while intake of wheat was negatively correlated with foods of animal source of food, milk and dairy products.Conclusions Northwest China’s diet exhibits irrational patterns, highlighting the importance of assessing overall dietary patterns in nutritional evaluation.…”
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Detection of Student Engagement via Transformer-Enhanced Feature Pyramid Networks on Channel-Spatial Attention by A. Naveen, I. Jeena Jacob, Ajay Kumar Mandava

“…Traditional techniques for evaluating student engagement are often time-consuming and subjective. …”
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Hierarchical data modeling: A systematic comparison of statistical, tree-based, and neural network approaches by Marzieh Amiri Shahbazi, Nasibeh Azadeh-Fard

“…The evaluation framework integrated quantitative metrics and qualitative factors, including analyses across varying sample sizes, simplified hierarchies, and a separate intensive-care dataset. …”
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Spatiotemporal Flood Hazard Classification in Bangkok Using Graph Convolutional Network and Temporal Fusion Transformer by Pakpoom Chaimook, Nirattaya Khamsemanan, Cholwich Nattee, Alice Sharp

“…Traditional flood prediction models often fail to capture spatial correlations across districts and the temporal patterns within different types of features. To address this problem, this study proposes a hybrid deep learning framework combining Graph Convolution Network (GCN) and the Temporal Fusion Transformer (TFT) for predicting flood hazard levels in 50 Bangkok districts. …”
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GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection by Yogesh Kumar Sharma, Deepak Singh Tomar, R. K. Pateriya, Surendra Solanki

“…To model the intricate relationships between applications, an efficient Graph Neural Network (GNN) process is utilized. Incorporating transformers, sequences of system and API calls are analyzed, harnessing this ability to discern patterns indicative of malicious activities. …”
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Investigation of a transformer-based hybrid artificial neural networks for climate data prediction and analysis by Shangke Liu, Ke Liu, Zheng Wang, Yuanyuan Liu, Bin Bai, Rui Zhao

“…Firstly, the Transformer model is introduced to capture the complex patterns in cimate data time series through its powerful sequence modeling capabilities. …”
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Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning by Yunbo Xie, Jose D. Meisel, Carlos A. Meisel, Juan Jose Betancourt, Jianqi Yan, Roberto Bugiolacchi

“…In addition, behavioral theory posits that leaders can be distinguished based on their daily conduct, while social network analysis provides valuable insights into behavioral patterns. …”
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Classifying IoT Botnet Attacks With Kolmogorov-Arnold Networks: A Comparative Analysis of Architectural Variations by Phuc Hao do, Tran Duc Le, Truong Duy Dinh, van Dai Pham

“…The rapid expansion of devices on the Internet of Things (IoTs) has led to a significant rise in IoT botnet attacks, creating an urgent need for advanced detection and classification methods. This study aims to evaluate the effectiveness of Kolmogorov-Arnold Networks (KANs) and their architectural variations in classifying IoT botnet attacks, comparing their performance with traditional machine learning and deep learning models. …”
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Forecasting Residential Energy Consumption with the Use of Long Short-Term Memory Recurrent Neural Networks by Zurisaddai Severiche-Maury, Carlos Eduardo Uc-Rios, Wilson Arrubla-Hoyos, Dora Cama-Pinto, Juan Antonio Holgado-Terriza, Miguel Damas-Hermoso, Alejandro Cama-Pinto

“…This study proposes a deep learning-based approach employing Long Short-Term Memory (LSTM) neural networks to predict household energy usage based on power consumption data from common appliances, such as lamps, fans, air conditioners, televisions, and computers. …”
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Penetration Testing and Machine Learning-Driven Cybersecurity Framework for IoT and Smart City Wireless Networks by Tamara Zhukabayeva, Zulfiqar Ahmad, Aigul Adamova, Nurdaulet Karabayev, Yerik Mardenov, Dina Satybaldina

“…Anomalies were identified using an optimized Isolation Forest model, revealing patterns such as unusual activity involving the Tenda_476300 WiFi network. …”
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