Predicting the temporal distribution of origin-destination traffic demand using machine learning by Keyvan Pourhassan, Mojgan Pourhassan, Sekhar Somenahalli

“…Temporal distribution of travel demand provides valuable insights into the planning and operation of transport systems. As a key input to dynamic traffic assignment (DTA) models, estimation of time-dependent origin-destination (TDOD) traffic demand matrices across the modelled network gained attention in the 1980s, with significant advancements in methods and techniques since then. …”
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Bridging Disciplinary Divides: Exploring the Synergy of Punctuated Equilibrium Theory and Artificial Neural Networks in Policy Change Analysis by Łukasz Wordliczek

“… This article explores the conceptual and theoretical intersections between Punctuated Equilibrium Theory (PET) and artificial neural networks (NNs) within the context of policy change analysis. …”
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RF-SFAD: A RANDOM FOREST MODEL FOR SELECTIVE FORWARDING ATTACK DETECTION IN MOBILE WIRELESS SENSOR NETWORKS by N Usha Bhanu, Soubhagya Ranjan Mallick, Sreenivasa Rao Chappidi, K Sangeethalakshmi

“…Among these, the Selective Forwarding Attack (SFA) poses a serious threat by selectively dropping packets, thereby disrupting cooperative data transmission and reducing network reliability. This paper proposes a Random Forest (RF)-based SFA detection framework designed for the dynamic nature of clustered MWSNs. …”
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Preparation and Performance of a Grid-Based PCL/TPU@MWCNTs Nanofiber Membrane for Pressure Sensor by Ping Zhu, Qian Lan

“…By introducing a gradient grid membrane, the strain distribution and reconstruction of the conductive network can be modulated, thereby alleviating the conflict between sensitivity, response speed, and operating range. …”
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Sysmon event logs for machine learning-based malware detection by Riki Mi’roj Achmad, Dyah Putri Nariswari, Baskoro Adi Pratomo, Hudan Studiawan

“…An extensive set of experiment were conducted to look for the best approach and the most relevant features. …”
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State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms by Romel Carrera, Leonidas Quiroz, Cesar Guevara, Patricia Acosta-Vargas

“…This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. …”
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I-MPN: inductive message passing network for efficient human-in-the-loop annotation of mobile eye tracking data by Hoang H. Le, Duy M. H. Nguyen, Omair Shahzad Bhatti, László Kopácsi, Thinh P. Ngo, Binh T. Nguyen, Michael Barz, Daniel Sonntag

“…Abstract Comprehending how humans process visual information in dynamic settings is crucial for psychology and designing user-centered interactions. …”
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Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms by D. Ciani, C. Fanelli, B. Buongiorno Nardelli

“…Previous OSSEs combined low-resolution L4 satellite equivalent ADTs with high-resolution “perfectly known” SSTs to derive high-resolution sea surface dynamical features. Here, we introduce realistic SST L4 processing errors and modify the network to concurrently predict high-resolution SST and ADT from synthetic, satellite equivalent L4 products. …”
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A Federated Reinforcement Learning Framework via a Committee Mechanism for Resource Management in 5G Networks by Jaewon Jeong, Joohyung Lee

“…These results validate the framework’s effectiveness in adaptive and efficient resource management, particularly in dynamic and varied network scenarios.…”
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Temporal dynamics of chicken host’s molecular response against Fowl adenovirus serotype 8b infection via RNA-sequencing by Bahiyah Azli, Nur Farhana Salim, Mohd Hair-Bejo, Abdul Rahman Omar, Norfitriah Mohamed Sohaimi, Ugwu Chidozie Clifford, Nor Asilah Wati Abdul Hamid, Nurulfiza Mat Isa

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A temporal-spectral graph convolutional neural network model for EEG emotion recognition within and across subjects by Rui Li, Xuanwen Yang, Jun Lou, Junsong Zhang

“…To address these challenges in emotion recognition, we propose a novel neural network model named Temporal-Spectral Graph Convolutional Network (TSGCN). …”
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Design of Morlet Wavelet Neural Networks for Solving the Nonlinear Van der Pol–Mathieu–Duffing Oscillator Model by Ali Hasan Ali, Muhammad Amir, Jamshaid Ul Rahman, Ali Raza, Ghassan Ezzulddin Arif

“…We develop an MWNN-based fitness function to predict the dynamic behavior of nonlinear Vd-PM-DO differential equations. …”
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Research on the application of a model combining improved optimization algorithms and neural networks in trajectory tracking of robotic arms by Yanhui Lai, Zuobing Chen, Ya Mao

“…It illustrates the feasibility of combining optimization algorithms with neural networks, offering innovative approaches for future research.…”
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Predicting correlation relationships of entities between attack patterns and techniques based on word embedding and graph convolutional network by Weicheng QIU, Xiuzhen CHEN, Yinghua MA, Jin MA, Zhihong ZHOU

“…Threat analysis relies on knowledge bases that contain a large number of security entities.The scope and impact of security threats and risks are evaluated by modeling threat sources, attack capabilities, attack motivations, and threat paths, taking into consideration the vulnerability of assets in the system and the security measures implemented.However, the lack of entity relations between these knowledge bases hinders the security event tracking and attack path generation.To complement entity relations between CAPEC and ATT＆amp;CK techniques and enrich threat paths, an entity correlation prediction method called WGS was proposed, in which entity descriptions were analyzed based on word embedding and a graph convolution network.A Word2Vec model was trained in the proposed method for security domain to extract domain-specific semantic features and a GCN model to capture the co-occurrence between words and sentences in entity descriptions.The relationship between entities was predicted by a Siamese network that combines these two features.The inclusion of external semantic information helped address the few-shot learning problem caused by limited entity relations in the existing knowledge base.Additionally, dynamic negative sampling and regularization was applied in model training.Experiments conducted on CAPEC and ATT＆amp;CK database provided by MITRE demonstrate that WGS effectively separates related entity pairs from irrelevant ones in the sample space and accurately predicts new entity relations.The proposed method achieves higher prediction accuracy in few-shot learning and requires shorter training time and less computing resources compared to the Bert-based text similarity prediction models.It proves that word embedding and graph convolutional network based entity relation prediction method can extract new entity correlation relationships between attack patterns and techniques.This helps to abstract attack techniques and tactics from low-level vulnerabilities and weaknesses in security threat analysis.…”
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Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification. by Jaipriya D, Sriharipriya K C

“…However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). …”
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Design of a dynamic trust management and defense decision system for shared vehicle data based on blockchain and deep reinforcement learning by Jinxiang Chen, Yan Li, Jiaxing Deng, Beicheng Qin, Chengcai He, Qiangsheng Huang, Jingchun Wu

“…By combining blockchain’s decentralized storage capabilities with DRL’s dynamic optimization potential, the system demonstrates a scalable and efficient approach for distributed data analysis in complex scenarios.…”
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One size doesn’t fit all: regional dynamics in pediatric emergency visits during the SARS-CoV-2 pandemic by Michelle Seiler, Gregory Biland, Oliver Gruebner, Oliver Gruebner, Sergio Manzano, Gianluca Gualco, Marc Sidler, Ursula Laasner, Julia Dratva, Julia Dratva, Michael von Rhein

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Single-cell sequencing-based study of ferroptosis mechanisms in heat stroke: identification of key biomarkers and dynamic analysis of the immune microenvironment by Defeng Yin, Qin Guo, Hao Jiang, Yonglan Hu, Lu Liu, Xiang Li, Chenglin Wang, Shilin Li, Kaiyu Jin, Yingchun Hu

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Joint P- and S-Wave VSP Traveltime Tomography via Well Log-Guided Physics-Informed Neural Networks by Tengyu Wang, Dingding Deng, Duoming Zheng, Zhen Zhang, Wenjun Luo, Yang Liu

“…The W-PINNPStomo framework incorporates two neural networks: a travel time network dedicated to predicting P- and S-wave traveltimes, and a velocity network tasked with estimating the corresponding wave velocities. …”
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Privacy-Preserving Federated Class-Incremental Learning by Jue Xiao, Xueming Tang, Songfeng Lu

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