Feature Graph Construction With Static Features for Malware Detection by Binghui Zou, Chunjie Cao, Longjuan Wang, Yinan Cheng, Chenxi Dang, Ying Liu, Jingzhang Sun

“…Malware can greatly compromise the integrity and trustworthiness of information and is in a constant state of evolution. Existing feature fusion-based detection methods generally overlook the correlation between features. …”
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Simulation-Based Electrothermal Feature Extraction and FCN–GBM Hybrid Model for Lithium-ion Battery Temperature Prediction by Luyan WANG, Hongliang HAO, Zhongkang ZHOU, Huimin MA, Jin ZHAO, Zeyang LIU, Qiangqiang LIAO

“…This study proposes a hybrid temperature prediction model that integrates Fully Connected Networks (FCN) and Gradient Boosting Machines (GBM) to capture temperature evolution under varying discharge rates. …”
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AI-based tool wear prediction with feature selection from sound signal analysis by Viet Q. Vu, Tien-Ninh Bui, Minh-Quang Tran

“…Finally, an artificial neural network (ANN) model is designed to estimate tool wear levels. …”
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Spatiotemporal pattern evolution and quantitative prediction of electrical carbon emissions from a demand-side perspective in urban areas by Ying Tian, Hui Cao, Dapeng Yan, Jinmei Chen, Yayan Hua

“…Utilizing high-frequency monitoring data from  3000 distribution network stations (May–Sept 2018), it creates an integrated ’spatiotemporal evolution-data driven prediction’ framework to reveal emission dynamics and enhance forecast accuracy. …”
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Multi-channel spatio-temporal graph attention contrastive network for brain disease diagnosis by Chaojun Li, Kai Ma, Shengrong Li, Xiangshui Meng, Ran Wang, Daoqiang Zhang, Qi Zhu

“…Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. …”
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Financial Evolution and Interdisciplinary Research by Ana Njegovanović

“…This paper (summary of the second chapter of the manuscript "quantum dance") talks about the multidimensionality of finance through evolution, philosophy with interdisciplinary features (interweaving of neuroscience, mathematics, quantum physics, biology and artificial intelligence). …”
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Flow Field Reconstruction and Prediction of Powder Fuel Transport Based on Scattering Images and Deep Learning by Hongyuan Du, Zhen Cao, Yingjie Song, Jiangbo Peng, Chaobo Yang, Xin Yu

“…Based on the acquired scattering images, a prediction and reconstruction method was developed using a deep network framework composed of a Stacked Autoencoder (SAE), a Backpropagation Neural Network (BP), and a Long Short-Term Memory (LSTM) model. …”
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Study on evolution of NR duplex operation by Ting KE, Xinghua SONG, Fei WANG, Zhiheng GUO, Tuo YANG, Chunxia GUO, Chengcheng HAN, Yunfeng LIU, Zhenyu LI

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Study on evolution of NR duplex operation by Ting KE, Xinghua SONG, Fei WANG, Zhiheng GUO, Tuo YANG, Chunxia GUO, Chengcheng HAN, Yunfeng LIU, Zhenyu LI

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Hybrid Big Bang-Big crunch with cuckoo search for feature selection in credit card fraud detection by Mohd Shukri Ab Yajid, Nilesh Bhosle, Gadug Sudhamsu, Ali Khatibi, Sahil Sharma, Rubal Jeet, R. Sivaranjani, A. Bhowmik, A. Johnson Santhosh

“…Here, the CS algorithm uses the Levy flight attribute to help the BB-BC agents escape from stagnation and premature convergence. After feature selection, classification is performed using Deep Convolutional Neural Networks (DCNN) and Enhanced DCNN (EDCNN) to improve detection accuracy. …”
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Multiscale Attention Feature Fusion Based on Improved Transformer for Hyperspectral Image and LiDAR Data Classification by Aili Wang, Guilong Lei, Shiyu Dai, Haibin Wu, Yuji Iwahori

“…However, the complexity of urban areas and their surrounding structures makes it extremely difficult to capture correlations between features. This article proposes a novel multiscale attention feature fusion network, composed of hierarchical convolutional neural networks and transformer to enhance joint classification accuracy of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. …”
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Deriving structure from evolution: metazoan segmentation by Paul François, Vincent Hakim, Eric D Siggia

“…Abstract Segmentation is a common feature of disparate clades of metazoans, and its evolution is a central problem of evolutionary developmental biology. …”
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Overview of detection techniques for malicious social bots by Rong LIU, Bo CHEN, Ling YU, Ya-shang LIU, Si-yuan CHEN

“…The attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly,development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides,main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection,the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method,approaches from the previous research based on features,machine learning,graph and crowd sourcing were summarized.Also,the limitation of these methods in detection accuracy,computation cost and so on was dissected.At last,a framework based on parallelizing machine learning methods to detect malicious social bots was proposed.…”
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Overview of detection techniques for malicious social bots by Rong LIU, Bo CHEN, Ling YU, Ya-shang LIU, Si-yuan CHEN

“…The attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly,development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides,main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection,the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method,approaches from the previous research based on features,machine learning,graph and crowd sourcing were summarized.Also,the limitation of these methods in detection accuracy,computation cost and so on was dissected.At last,a framework based on parallelizing machine learning methods to detect malicious social bots was proposed.…”
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Modeling Upscaled Mass Discharge From Complex DNAPL Source Zones Using a Bayesian Neural Network: Prediction Accuracy, Uncertainty Quantification and Source Zone Feature Importance by Xueyuan Kang, Amalia Kokkinaki, Xiaoqing Shi, Jonghyun Lee, Zhilin Guo, Lingling Ni, Jichun Wu

“…Instead, the BNN model chooses three physically meaningful SZ quantities related to mass discharge as input features. Then, we use the expected gradients method to identify the feature importance for mass‐discharge prediction. …”
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Modélisation du changement d’échelles en télédétection par une méthode neuronale : application a l’étude de l’évolution de l’occupation hivernale des sols en Bretagne... by Thomas Houet, Laurence Hubert-Moy, Grégoire Mercier

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Artificial Intelligence-Powered Insights into Polyclonality and Tumor Evolution by Hong Zhao, Trey Ideker, Stephen T. C. Wong

“…Recent studies have revealed that polyclonality—where multiple distinct subclones cooperate during early tumor development—is a critical feature of tumor evolution, as demonstrated by Sadien et al. and Lu et al. in Nature (October 2024). …”
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Evolution of the activities of states as reflected in legal and political Teachings by A. A. Klishin

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Research on Multi-Step Prediction of Pipeline Corrosion Rate Based on Adaptive MTGNN Spatio-Temporal Correlation Analysis by Mingyang Sun, Shiwei Qin

“…In order to comprehensively investigate the spatio-temporal dynamics of corrosion evolution under complex pipeline environments and improve the corrosion rate prediction accuracy, a novel framework for corrosion rate prediction based on adaptive multivariate time series graph neural network (MTGNN) multi-feature spatio-temporal correlation analysis is proposed. …”
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The role of learned song in the evolution and speciation of Eastern and Spotted towhees. by Ximena León Du'Mottuchi, Nicole Creanza

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