Non-invasive detection of Parkinson’s disease based on speech analysis and interpretable machine learning by Huanqing Xu, Wei Xie, Mingzhen Pang, Ya Li, Luhua Jin, Fangliang Huang, Xian Shao

“…SHAp values highlighted the importance of fundamental frequency variation and harmonic-to-noise ratio in distinguishing PD patients from healthy individuals.ConclusionThe developed machine learning model accurately predicts Parkinson’s disease using speech recordings, with Random Forest and Gradient Boosting algorithms demonstrating superior performance. …”
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XSShield: Defending Against Stored XSS Attacks Using LLM-Based Semantic Understanding by Yuan Zhou, Enze Wang, Wantong Yang, Wenlin Ge, Siyi Yang, Yibo Zhang, Wei Qu, Wei Xie

“…Experimental evaluation shows that XSShield achieves 93% accuracy and an F1 score of 0.9266 on the GPT-4 model, improving accuracy by an average of 88.8% compared to existing solutions. …”
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A General Framework for CFAR Detection in PolSAR Imagery Based on Quadratic Statistics by Ziyuan Yang, Liguo Liu, Xiaoyang Hou, Yinghui Quan, Xian Zhang, Tao Liu

“…In the field of target detection in polarimetric synthetic aperture Radar (PolSAR) imagery, the constant false alarm rate (CFAR) algorithm is renowned for its operability and high interpretability. …”
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Blind Recognition of Convolutional Codes Based on the ConvLSTM Temporal Feature Network by Lu Xu, Yixin Ma, Rui Shi, Juanjuan Li, Yijia Zhang

“…Our method effectively distinguishes diverse coding features, surpassing existing models and establishing a new benchmark for channel-coding recognition.…”
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Systems Biology of Human Microbiome for the Prediction of Personal Glycaemic Response by Nikhil Kirtipal, Youngchang Seo, Jangwon Son, Sunjae Lee

“…We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. …”
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STATE-OF-THE-ART OF MACHINE LEARNING IN NEURO DEVELOPMENT DISORDER: A SYSTEMATIC REVIEW by Lilian Yen Wei Lee, Ag Asri Ag Ibrahim, Rayner Alfred

“…While existing reviews often lack detailed discussions on the specific ML algorithms, datasets, and performance metrics employed in NDD prediction and detection, this study aims to address this gap by examining two primary aspects: prediction and detection. …”
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Carbonate Seismic Facies Analysis in Reservoir Characterization: A Machine Learning Approach with Integration of Reservoir Mineralogy and Porosity by Papa Owusu, Abdelmoneam Raef, Essam Sharaf

“…Amid increasing interest in enhanced oil recovery and carbon geological sequestration programs, improved static reservoir lithofacies models are emerging as a requirement for well-guided project management. …”
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Numerical Investigation on Influential Factors for Quality of Smooth Blasting in Rock Tunnels by Baoping Zou, Zhipeng Xu, Jianxiu Wang, Zhanyou Luo, Lisheng Hu

“…Proposed 3-dimensional blasting modelling was based on LS-DYNA to simulate the occurrence of smooth blasting in rock masses, and the erosion algorithm was also employed to determine the fracturing of jointed rocks. …”
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Incorporating Wave-ViT for Breast Cancer Diagnosis Using MRI Imaging by Sahil Mahey, Hamid Usefi

“…Machine learning (ML) algorithms offer a transformative solution by automating this process, improving efficiency, and enhancing diagnostic accuracy. …”
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Multiple imputation integrated to machine learning: predicting post-stroke recovery of ambulation after intensive inpatient rehabilitation by Alice Finocchi, Silvia Campagnini, Andrea Mannini, Stefano Doronzio, Marco Baccini, Bahia Hakiki, Donata Bardi, Antonello Grippo, Claudio Macchi, Jorge Navarro Solano, Michela Baccini, Francesca Cecchi

“…After handling missingness using MImp, ML models were optimised, cross-validated, and tested. …”
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Distribution and Moments of the Idle Period and Interarrival Time in the G/M/1 Queueing System by Felipe A. Cruz-Perez, Sandra Lirio Castellanos-Lopez, Genaro Hernandez-Valdez, Mario Eduardo Rivero-Angeles

“…Additionally, the accuracy of the derived distribution and moments of the idle period when the LN interarrival time is approximated by HE distributions of different orders (using the Expectation-Maximization algorithm) is investigated. Numerical results show a good fit accuracy between idle period distributions obtained under the LN and the m-th order HE interarrival time models. …”
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Failure mechanism-driven multi-adversarial domain transfer learning for rolling bearing fault diagnosis by Zhihui Zhang, Zhidan Zhong, Zhe Li, Wentao Mao, Yunhao Cui

“…To address these challenges, this paper proposes a Failure Mechanism-Driven Multi-Adversarial Domain Transfer Learning algorithm. The core of this method is the deep integration of physical prior knowledge with data-driven models. …”
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Self-Shielding Treatment to Perform Cell Calculation for Seed Furl In Th/U Pwr Using Dragon Code by Ahmed Amin El Said Abd El Hameed, Mohamed Nagy, Hanaa Abou-Gabal

“…There are mainly two resonance self-shielding models commonly applied: models based on equivalence and dilution and models based on subgroup approach. …”
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Revealing long-term dynamics and spatiotemporal drivers of anthropogenic nutrients inputs in China: The effects of dietary and socioeconomic factors by Jia Liu, Wei Gao, Fen Guo, Yuan Zhang, Yanpeng Cai

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Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks by Wei Lin, Meitao Zou, Mingrui Zhao, Jiaqi Chang, Xiongyao Xie

“…By combining both types of data, this framework enhances the generalisability and prediction accuracy of trained models. The results of the data experiments demonstrate that the multi-fidelity framework outperforms models trained solely on low- or high-fidelity data, achieving a coefficient of determination of 0.980 and a root mean square error of 0.078 m. …”
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Leveraging advanced technologies for early detection and diagnosis of oral cancer: Warning alarm by Saantosh Saravanan, N. Aravindha Babu, Lakshmi T, Mukesh Kumar Dharmalingam Jothinathan

“…Specialized algorithms such as the Recombination-Based Improved Population Optimization Parallel Covariance Matrix Adaptation Evolution Strategy (RB-IPOP CMA-ES) allow for better accuracy of deep learning models, as this enhances the performance of developing models for early diagnosis of oral cancer. …”
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Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique by Moritz Schneider, Kevin Seeser-Reich, Armin Fiedler, Udo Frese

“…By using kinematic data from real near-fall incidents that occurred in physically demanding work environments, this study overcomes this limitation and improves the ecological validity of fall detection algorithms. …”
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Research on Recognition of Green Sichuan Pepper Clusters and Cutting-Point Localization in Complex Environments by Qi Niu, Wenjun Ma, Rongxiang Diao, Wei Yu, Chunlei Wang, Hui Li, Lihong Wang, Chengsong Li, Pei Wang

“…Comparative experiments on YOLOv5s, YOLOv8s, and YOLOv11s models revealed that YOLOv11s achieved a recall of 0.91 in leaf-occluded environments, marking a 21.3% improvement over YOLOv5s, with a detection speed of 28 Frames Per Second(FPS). …”
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Exploring interconnections among atoms, brain, society, and cosmos with network science and explainable machine learning by Daniele Caligiore, Daniele Caligiore, Anna Monreale, Anna Monreale, Giulio Rossetti, Angela Bongiorno, Giuseppe Fisicaro

“…A key benefit could be the possibility of using transfer learning, that is XML models trained in one domain might be adapted for use in another with limited data. …”
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A Systematic Mapping Study on State Estimation Techniques for Lithium-Ion Batteries in Electric Vehicles by Carolina Tripp-Barba, José Alfonso Aguilar-Calderón, Luis Urquiza-Aguiar, Aníbal Zaldívar-Colado, Alan Ramírez-Noriega

“…RUL prediction sees advancements through deep learning techniques, especially LSTM and gated recurrent units (GRUs), improved using algorithms such as Harris Hawks Optimization (HHO) and Adaptive Levy Flight (ALF). …”
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