A Collaborative Design Method for the Cylindrical Gear Paired with Skived Face Gears Driven by Contact Performances by Zhenyu Zhou, Yuanyuan Zhang, Mou Li, Yuansheng Zhou, Zhongwei Tang, Jinyuan Tang, Liang Zhou

“…The contact performance, including transmission error, contact stress, and contact pattern, is evaluated through Tooth Contact Analysis (TCA). An optimization model is developed to identify the optimal cylindrical gear tooth surface parameters, targeting improved contact performance. …”
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Adaptive Deeping Siamese Residual Network: A Novel Model for Few-Shot Bearing Fault Diagnosis by Yonghua Jiang, Maoli Lu, Zhilin Dong, Zhichao Jiang, Weidong Jiao, Chao Tang, Jianfeng Sun, Zhongyi Xuan

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Optimization of process parameters in Nano-particle mixed EDM of hardened die steel AISI H13 using RSM and GA by Tasfia Saba, AKM Nurul Amin, Sanjida Islam, Maisha Rahman Chaity, Noshin Tasnim Tuli, Adib Bin Rashid

“…Central Composite Design (CCD) was employed to develop regression models, resulting in quadratic model for SR, TWR and MRR. …”
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The Supervised Information Bottleneck by Nir Z. Weingarten, Zohar Yakhini, Moshe Butman, Ronit Bustin

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Optimising Daily Fantasy Sports Teams with Artificial Intelligence by Beal Ryan, Norman Timothy J., Ramchurn Sarvapali D.

“…To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. …”
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Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung... by FangHao Cai, Zhengjun Guo, GuoYu Wang, FuPing Luo, Yang Yang, Min Lv, JiMin He, ZhiGang Xiu, Dan Tang, XiaoHui Bao, XiaoYue Zhang, ZhenZhou Yang, Zhi Chen

“…Abstract Objectives To extract intratumoral, peritumoral, and integrated intratumoral-peritumoral CT radiomic features, develop multi-source radiomic models using various machine learning algorithms to identify the optimal model, and integrate clinical factors to establish a nomogram for predicting the therapeutic response to induction therapy(IT) in locally advanced non-small cell lung cancer. …”
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Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management by Georgios I. Barkas, Ilias E. Dimeas, Ourania S. Kotsiou

“…AI-driven platforms showed potential to reduce inappropriate antibiotic use and nephrotoxicity while optimizing outcomes. However, most models are limited by single-center data, variable interpretability, and insufficient real-world validation. …”
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STAT3/TGFBI signaling promotes the temozolomide resistance of glioblastoma through upregulating glycolysis by inducing cellular senescence by Yanbin Zhang, Xiaohua Xiao, Ge Yang, Xiaobing Jiang, Shujie Jiao, Yingli Nie, Tao Zhang

“…We developed the CSRG signature (CSRGS) using machine learning models that exhibited optimal performance. …”
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AI-powered Somatic Cancer Cell Analysis for Early Detection of Metastasis: The 62 principal Cancer Types by Sandile Buthelezi, Solly Matshonisa Seeletse, Taurai Hungwe, Vimbai Mbirimi-Hungwe

“…Results: By leveraging advanced AI algorithms, key predictors of cancer prognosis such as fraction genome alteration, primary tumor site, and smoking history, all of which significantly influence metastasis outcomes, were identified. Furthermore, the models demonstrated exceptional predictive accuracy, with XGBoost and Support Vector Machines achieving an accuracy of 0.95. …”
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SITA: Predicting site-specific immunogenicity for therapeutic antibodies by Yewei Cun, Hao Ding, Tiantian Mao, Yuan Wang, Caicui Wang, Jiajun Li, Zihao Li, Mengdie Hu, Zhiwei Cao, Tianyi Qiu

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A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting by Salaki Reynaldo Joshua, An Na Yeon, Sanguk Park, Kihyeon Kwon

“…This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. …”
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Forest age estimation using UAV-LiDAR and Sentinel-2 data with machine learning algorithms- a case study of Masson pine (Pinus massoniana) by Jinjin Chen, Xuejian Li, Zihao Huang, Jie Xuan, Chao Chen, Mengchen Hu, Cheng Tan, Yongxia Zhou, Yinyin Zhao, Jiacong Yu, Lei Huang, Meixuan Song, Huaqiang Du

“…The optimal model is used to predict the forest age and simulate the spatial age distribution. …”
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Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Pro... by Zixiang Ye, Zhangyu Lin, Enmin Xie, Chenxi Song, Rui Zhang, Hao-Yu Wang, Shanshan Shi, Lei Feng, Kefei Dou

“…Model performance was compared using multiple parameters, and the optimal algorithm was selected. …”
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MALDI-TOF mass spectrometry combined with machine learning algorithms to identify protein profiles related to malaria infection in human sera from Côte d’Ivoire by Fateneba Kone, Lucie Conrad, Jean T. Coulibaly, Kigbafori D. Silué, Sören L. Becker, Brama Kone, Issa Sy

“…Additional research is warranted for further optimization such as specific biomarkers detection or using other ML models. …”
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Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques by Sonia Val, María Pilar Lambán, Javier Lucia, Jesús Royo

“…Milling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. …”
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Alzheimer’s Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network by Raheleh Ghadami, Javad Rahebi

“…<b>Results:</b> The proposed method achieved a classification accuracy of 97.59%, sensitivity of 97.41%, and precision of 97.25%, outperforming other models, including VGG16, GLCM, and ResNet-50, in diagnosing Alzheimer’s disease. …”
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Electromagnetic design, sensitivity analysis, optimization and Multiphysics capability of rare-earth-free synchronous reluctance motor for electric trike vehicle by V Rajini, VS Nagarajan, Karunya Harikrishnan, Mohan Lal Kolhe

“…A Design of Experiments (DoE)-based statistical analysis tool is used to identify the key parameters needed for robust motor performance in the optimization step. Furthermore, an Extreme Learning Machine (ELM)-based interpolation technique is employed for estimating the performance parameters during each step of the optimization routine. …”
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Novel channel attention-based filter pruning methods for low-complexity semantic segmentation models by Md. Bipul Hossain, Na Gong, Mohamed Shaban

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Optimizing drying and storage for edible mushrooms: Study on gamma irradiation levels, drying temperatures, and packaging materials with SVM-based predictions by Ehsan Fartash Naeimi, Mohammad Hadi Khoshtaghaza, Kemal Çağatay Selvi, Mariana Ionescu, Soleiman Abbasi

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Out-of-Distribution in Image Semantic Communication: A Solution With Multimodal Large Language Models by Feifan Zhang, Yuyang Du, Kexin Chen, Yulin Shao, Soung Chang Liew

“…However, the out-of-distribution (OOD) problem, where a pre-trained machine learning (ML) model is applied to unseen tasks that are outside the distribution of its training data, may compromise the integrity of semantic compression. …”
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