Enhanced SVM-based model for predicting cyberspace vulnerabilities: Analyzing the role of user group dynamics and capital influx. by Yicheng Long

“…This method adjusts kernel parameters or switches kernel functions in real time according to the statistical characteristics of input data, thereby improving the model's generalization capability and responsiveness in complex attack scenarios. …”
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Evaluating landslide susceptibility: the impact of resolution and hybrid integration approaches by Xia Zhao, Wei Chen, Paraskevas Tsangaratos, Ioanna Ilia

“…The study reveals that finer spatial resolutions significantly enhance the accuracy of landslide predictions and that integrated models have superior performance over single models. The Frequency Ratio method identified elevation, slope, and hydrological factors as the key predictors concerning landslide occurrences. …”
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Acoustic Emission and Digital Image Correlation-Based Study for Early Damage Identification in Sandwich Structures by Aitor García de la Yedra, Igor Erro, Javier Vivas, Oier Zubiri, Xabier Zurutuza, Ryan Sommerhuber, Matthias Kettner

“…This study employs two NDT methods to analyze the initiation of damage in such structures during a fatigue test. …”
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Design and Optimization of a Robust Wide‐Area Damping Controller for Mitigating Inter‐Area Oscillations in Wind‐Integrated Power Systems by Mehrdad Ahmadi Kamarposhti, Hassan Shokouhandeh, Sun‐Kyoung Kang, Ilhami Colak, El Manaa Barhoumi

“…ABSTRACT In power networks, low‐frequency oscillations—particularly inter‐area oscillations—pose a serious threat to system stability because of disruptions like short‐circuit faults and the increasing use of renewable energy sources like wind farms. …”
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Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked Potentials by Bo Hu, Jun Xie, Huanqing Zhang, Junjie Liu, Hu Wang

“…This study proposes an asynchronous brain–computer interface (BCI) framework based on steady-state motion visual evoked potentials (SSMVEPs), designed to enhance the accuracy and robustness of control state recognition. The method integrates filter bank common spatial patterns (FBCSPs) and filter bank canonical correlation analysis (FBCCA) to extract complementary spatial and frequency domain features from EEG signals. …”
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FLSH: A Framework Leveraging Similarity Hashing for Android Malware and Variant Detection by Hassan Jalil Hadi, Alina Khalid, Faisal Bashir Hussain, Naveed Ahmad, Mohammed Ali Alshara

“…The results showed a significant improvement in precision, recall, and F-measure, achieving an overall accuracy of 96.67%, without relying on complex machine learning or deep learning methods.…”
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Feasibility of Conoscopic Holography Measurement in the Stereolithography (SLA) Process with Alumina by Lorenzo Meana, Víctor Meana, Eduardo Cuesta, Pedro Fernández, Gonzalo Valiño, Braulio J. Álvarez

“…The investigation shows that conoscopic holography does not ensure an accurate measurement method for this additive process and ceramic material, making it impossible to achieve power and frequency settings that would allow signal-to-noise ratios above 50%.…”
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Heuristic Binary Search for Modulated Predictive Control by Rafael FIgueiredo, Igor Oliani, Angelo Lunardi, Alfeu J. Sguarezi Filho

“…This paper introduces the Heuristic Binary Search, an intelligent search algorithm that efficiently explores the solution space, accommodating arbitrary cost function constraints similar to Finite Control Set, while maintaining a fixed switching frequency. Applicable to various machine and inverter topologies, the proposed method is experimentally validated on a two-level voltage source inverter driving an induction motor using Predictive Torque Control and Deadbeat Robust Predictive Control. …”
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A discrete element simulation study of paver screed and hot mix asphalt interaction by Leandro Harries, Stefan Böhm, Jia Liu

“…This study addresses these challenges by employing a full-scale simulation using a calibrated Discrete Element Method (DEM) model to analyze HMA’s flow and pre-compaction behavior. …”
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A Dual Band Slotted Patch Antenna on Dielectric Material Substrate by M. Habib Ullah, M. T. Islam, M. R. Ahsan, J. S. Mandeep, N. Misran

“…A low profile, compact dual band slotted patch antenna has been designed using finite element method-based high frequency full-wave electromagnetic simulator. …”
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PROPER: Personality Recognition Based on Public Speaking Using Electroencephalography Recordings by Muhammad Majid, Amna Rauf Butt, Imran Fareed Nizami, Aamir Arsalan, Jihyoung Ryu

“…Five feature groups are extracted from the frequency bands of EEG data of each channel. Feature selection is applied to the extracted features via the wrapper method. …”
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Bathymetry modelling of the eastern Tendrivska Bay (Ukraine) using Sentinel-2 remote sensing data by Yurii Moskalenko

“…It will also be useful as a spatial variable for modeling the distribution of aquatic organisms and waterfowl in the bay using machine learning methods. The experience of creating a bathymetric model for the eastern Tendrivska Bay showed that this method is quite simple and accessible, allowing it to be recommended for modelling the seabed topography of other shallow water bodies.…”
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An Integrated Preprocessing and Drift Detection Approach With Adaptive Windowing for Fraud Detection in Payment Systems by Hadi M. R. Al Lawati, Anazida Zainal, Bander Ali Saleh Al-Rimy, Mohammad Al-Azawi, Mohamad Nizam Kassim, Sultan Ahmed Almalki, Tami Abdulrahman Alghamdi

“…Static machine learning and rule-based traditional detection methods cannot capture all the dynamic and evolving nature of fraudulent behaviors, resulting in lower detection accuracy and a higher false positive rate. …”
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Investigating Lexical and Syntactic Differences in Written and Spoken English Corpora by Mina Rajaei Moghadam, Mosab Rezaei, Miguel Williams, Gülşat Aygen, Reva Freedman

“…Given the availability of large quantities of machine-readable data and machine learning algorithms that can handle them, it is possible to use a large number of derived features. …”
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Fault location and isolation technology for power grid automation based on intelligent algorithms by Qi Guo, Fuhe Wang, Suxia Cheng, Ke Wang, Yifan Zhang

“…Methodology The FLA algorithm uses a Support Vector Machine (SVM) classifier to predict fault locations based on key variables like voltage, current, frequency, line impedance, and meteorological conditions. …”
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A new signature associated with anoikis predicts the outcome and immune infiltration in nasopharyngeal carcinoma by Yonglin Luo, Wenyang Wei, Yaxuan Huang, Jun Li, Weiling Qin, Quanxiang Hao, Jiemei Ye, Zhe Zhang, Yushan Liang, Xue Xiao, Yonglin Cai

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Contributions of climatic factors and vegetation cover to the temporal shift in Asian dust events by Wencun Zhou, Huanjiong Wang, Quansheng Ge

“…Based on surface observation data, we explored spatiotemporal changes in three types of dust events and their driving factors in Asia by using machine learning methods. Results indicated that the frequency of moderate dust events (MDE) and severe dust events (SDE) decreased significantly from 2000 to 2022, which could be primarily attributed to a decrease in strong wind days (contribution >50%), and to a lesser extent to increases in soil moisture, precipitation, and leaf area index (LAI). …”
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Typological Differences of Natural and Neural Network-Generated Texts in a Quantitative Aspect by R. E. Telpov, S. V. Lartsina

“…The findings of this research can be useful for both educators and machine learning specialists.…”
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Risky behaviors and road safety: An exploration of age and gender influences on road accident rates. by Dakota McCarty, Hyun Woo Kim

“…Using regression-based machine learning models, our study, looking at England, UK, explores the theoretical linkages between demographics of an area and road accident frequency, finding that census data is able to explain over 28% of the variance in road accident rates per capita. …”
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EEG-Based Emotion Recognition Datasets for Virtual Environments: A Survey by Hussein Ali Hamzah, Kasim K. Abdalla

“…This paper provides a systematic review of EEG-based emotion recognition methods, in terms of feature extraction, time domain, frequency domain, and time-frequency domain, with a focus on recent datasets used in studies related to emotion classification using EEG and their investigation, and discusses its challenges. …”
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