Enhancing Urban Parking Efficiency Through Machine Learning Model Integration by Sai Sneha Channamallu, Sharareh Kermanshachi, Jay Michael Rosenberger, Apurva Pamidimukkala

“…It employs a dataset from a college campus garage collected from January 2022 to June 2023 and analyzes the performance of random forest, decision tree, linear regression, and support vector models by comparing them, using multiple evaluation metrics. …”
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Biomechanical study athletes’ movement techniques in the hurdles (on example of phase of flight) by V.M. Adashevskiy, S.S. Iermakov, N.V. Korzh, Muszkieta Radosław, Krzysztof Prusik, Mirosława Cieślicka

“…Shows the calculated and actual performance movement of the athlete. Conclusions : The developed model provides a good theoretical understanding of the interactions of individual elements of movement and the ability to simulate different situations and to determine the optimal values of the kinematic and dynamic characteristics of the movement of the athlete. …”
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Enhancing Structured Pathology Report Generation With Foundation Model and Modular Design by Kyung A Kim, Sungman Hong, Sehwan Yoo, Yousun Kang, Hyo Sup Shim

“…In the first stage, using 752 high resolution WSIs from bladder tumor tissue samples collected at Korea University Medical Center, we leveraged foundation models to extract robust features. Then, we built a multi-label classification based on key diagnostic elements to achieve high performance with an Attention-based Multi-Instance Learning (MIL) model and a Transformer-based MIL with Knowledge Distillation. …”
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The Modified Weibull–Fréchet Mixture distribution for enhanced econometric data modelling by I. O. Akalagboro, C. O. Aronu

“…The objectives of the study include to: derive the probability density function (PDF) and cumulative density function (CDF) of the MWFM distribution to establish its mathematical foundation; determine the statistical properties of the MWFM distribution, including moments, skewness, and kurtosis; ascertain the reliability characteristics of the MWFM distribution, providing insights into its performance in modelling reliability and survival data; and compare, on a theoretical basis, the statistical properties of the MWFM distribution with existing distributions to highlight its unique features. …”
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Credit Scoring Prediction Using Deep Learning Models in the Financial Sector by Xi Shi, Dingfen Tang, Yike Yu

“…We confront the issue of class imbalance by applying dynamic re-sampling and weight adjustment techniques, ensuring balanced model performance across varied data segments. Extensive evaluations on standard datasets validate the effectiveness of our proposed model, revealing enhanced prediction accuracy and model interpretability when compared to more traditional techniques. …”
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Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model by Hanlin Liu, Yule Hu, Zhenkun Wu

“…However, traditional mechanical analysis methods have high computational complexity, and the existing data-driven models cannot fully integrate non-sequential features such as stratum lithology. …”
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YOLO-ALW: An Enhanced High-Precision Model for Chili Maturity Detection by Yi Wang, Cheng Ouyang, Hao Peng, Jingtao Deng, Lin Yang, Hailin Chen, Yahui Luo, Ping Jiang

“…The model introduces the AKConv (Alterable Kernel Convolution) module in the head section, which adaptively adjusts the convolution kernel shape and size based on the target and scene, improving detection performance under occlusion and dense environments. …”
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Lightweight human activity recognition method based on the MobileHARC model by Xingyu Gong, Xinyang Zhang, Na Li

“…Recently, there have been proposals based on Transformer and its variant models. However, due to the fact that these models have sequential network structures and are unable to simultaneously focus on local and global features, thus, resulting in a reduction in recognition performance. …”
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Fair and Transparent Student Admission Prediction Using Machine Learning Models by George Raftopoulos, Gregory Davrazos, Sotiris Kotsiantis

“…We employ a diverse set of algorithms, including Logistic Regression, Decision Trees, and ensemble methods, to forecast admission outcomes based on academic, demographic, and extracurricular features. Experimental results on real-world datasets highlight the effectiveness of the proposed models in achieving competitive predictive performance while adhering to fairness metrics such as demographic parity and equalized odds. …”
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Ultra-wideband digital channel modeling based on generative adversarial network by ZHUGE Bin, WANG Zhengxian, WANG Ying, CAI Xiaodan, DONG Ligang, ZHANG Zitian, JIANG Xian, LI Hua, XU Yueqian

“…During the model training phase, reconstructed channel data representing global features were used, while actual channel data experiencing wireless fading were employed during the testing phase. …”
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A diagnostic model for polycystic ovary syndrome based on machine learning by Cheng Tong, Yue Wu, Zhenchao Zhuang, Ying Yu

“…Combining the evaluation metrics of each model performance, we concluded that the logistic classification model had the best performance capability in our study. …”
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Deep Learning Model for Precipitation Nowcasting Based on Residual and Attention Mechanisms by Zhan Zhang, Qingping Song, Minzheng Duan, Hailei Liu, Juan Huo, Congzheng Han

“…Meanwhile, depthwise separable convolutions are employed to replace conventional convolutions, significantly improving computational efficiency while preserving model performance. To evaluate the model’s performance, experiments were conducted using 6 min resolution radar echo data from China in 2024, with comparisons made against the optical flow (OF) method and the U-Net model. …”
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Signal Processing-Free Intelligent Model for Power Quality Disturbances Identification by Mohammed F. Al-Mashdali, Asif Islam, Abdulbasit Hassan, Md Shafiullah, Mujahed Al-Dhaifallah, Khalid Al Fuwail

“…By directly examining PQD images generated from signal data, these models reduce the requirement for human-crafted features. …”
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Advancing BCI with a transformer-based model for motor imagery classification by Wangdan Liao, Hongyun Liu, Weidong Wang

“…Additionally, we use multiple parallel structures to enhance the model’s performance. When tested on the BCI Competition IV-2a dataset, our proposed model achieved an average accuracy of 86.46% for subject dependent and average 74.48% for subject independent.…”
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Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes by Alex Otten, Michael Francis, Anna Bulysheva

“…ML models have been used previously to predict ablation performance in irreversible electroporation procedures and single-cell electroporation platforms. …”
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Metabolic reprogramming and prognostic modeling in pancreatic cancer: insights from WGCNA by Zhuo Song, Zhijia Sun, Yupeng Di, Xu Liu, Xiaoli Kang, Gang Ren, Yingjie Wang

“…The model was verified efficacy and demonstrated with good performance through analysis of Kaplan-Meier plotter, receiver operating characteristic curves, comparing with reported models, application in predicting drug sensitivity and constructing a nomogram model. …”
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When Remote Sensing Meets Foundation Model: A Survey and Beyond by Chunlei Huo, Keming Chen, Shuaihao Zhang, Zeyu Wang, Heyu Yan, Jing Shen, Yuyang Hong, Geqi Qi, Hongmei Fang, Zihan Wang

“…Recently, foundation models (FMs) have been presented to learn richer features from multi-modal data. …”
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Towards precision agriculture: metaheuristic model compression for enhanced pest recognition by Sana Parez, Norah Saleh Alghamdi, Tahir Mahmood, Waseem Ullah, Muhammad Attique Khan, Taha Houda, Naqqash Dilshad

“…While deep learning (DL) models have improved performance over conventional approaches, they typically suffer from high computational demands and large model sizes, limiting their real-world applicability. …”
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Chemometric and computational modeling of polysaccharide coated drugs for colonic drug delivery by Ahmad Khaleel AlOmari, Khaled Almansour

“…Model hyperparameters were tuned using the Slime Mould Algorithm (SMA), and each model’s performance was evaluated through k-fold cross-validation (k = 3). …”
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Subgrouping autism and ADHD based on structural MRI population modelling centiles by Clara Pecci-Terroba, Meng-Chuan Lai, Michael V. Lombardo, Bhismadev Chakrabarti, Amber N. V. Ruigrok, John Suckling, Evdokia Anagnostou, Jason P. Lerch, Margot J. Taylor, Rob Nicolson, Stelios Georgiades, Jennifer Crosbie, Russell Schachar, Elizabeth Kelley, Jessica Jones, Paul D. Arnold, Jakob Seidlitz, Aaron F. Alexander-Bloch, Edward T. Bullmore, Simon Baron-Cohen, Saashi A. Bedford, Richard A. I. Bethlehem

“…Methods Here, we use population modelling to cluster a multi-site dataset based on global and regional centile scores of cortical thickness, surface area and grey matter volume. …”
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