AdditiveLLM: Large language models predict defects in metals additive manufacturing by Peter Pak, Amir Barati Farimani

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UNVEILING FAKE NEWS DETECTION MODELS AND THEIR INTEGRATION INTO WEB SYSTEMS: AN EXTENSIVE INVESTIGATION by Vimal Gaur, Naman Veerwal, Ujjwal Chaudhary, Sparsh Kadian

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Road Roughness Recognition: Feature Extraction and Speed-Adaptive Classification Based on Simulation and Real-Vehicle Tests by Jie Xing, Zhun Cheng, Shuai Ye, Songwei Liu, Jiawei Lin

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Analysis of the state of geometrization development and digital modeling in open-pit mining enterprises by M.S. Kunytska, D.S. Polishchyk, O.V. Shapochnikov

“…The article is devoted to the study of modern technologies of geometrization and digital modeling at open-pit mining enterprises. The relevance of the work is due to the requirements for optimizing mining processes, ensuring labor safety and increasing economic efficiency in the exploitation of deposits. …”
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Transient Stability Assessment Model With Sample Selection Method Based on Spatial Distribution by Yongbin Li, Yiting Wang, Jian Li, Huanbei Zhao, Huaiyuan Wang, Litao Hu

“…Sample selection aims to optimize the training set to speed up the training process while improving the preference of the TSA model. …”
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Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language Models by Saurabh Pahune, Zahid Akhtar

“…LLMOps is a subcategory of the broader concept of MLOps (Machine Learning Operations), which is the practice of automating and managing the lifecycle of ML models. …”
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Enhancing Strategic Partnerships in Higher Education: Developing and Implementing a Predictive Model by Georgios Eftychiou

“…This digital platform is designed to evolve, incorporating future advancements in machine learning and data analysis to further optimize partnership evaluations. …”
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Leveraging large language models for comprehensive locomotion control in humanoid robots design by Shilong Sun, Chiyao Li, Zida Zhao, Haodong Huang, Wenfu Xu

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Well Production Forecasting in Volve Field Using Kolmogorov–Arnold Networks by Xingyu Lu, Jing Cao, Jian Zou

“…However, traditional methods often struggle to capture the complex dynamics of reservoirs, and existing machine learning models rely on large parameter sets, resulting in high computational costs and limited scalability. …”
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A Real Time Cardiomyopathy Detection Tool Using Ml Ensemble Models by Salvador de Haro, Esteban Becerra, Pilar González-Férez, José M. García, Gregorio Bernabé

“…Key innovations include multithreading to optimize model loading times and ensemble methods to enhance segmentation consistency across MRI slices. …”
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Chemical Nose-Based Non-Invasive Detection of Breast Cancer Using Exhaled Breath by Yosef Matana, Shai Libson, Barak Amihood, Zvi Boger, David Lieberman, Offer Zeiri, Yehuda Zeiri

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Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVM by Qin Bo, Sun Guodong, Zhang Liqiang, Liu Yongliang, Zhang Chao, Wang Jianguo

“…Secondly,the IPSO algorithm is used to optimize the penalty coefficient and the structural parameters of SVM to set up the rolling bearing fault classification model. …”
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Deep‐HH: A deep learning‐based high school student hidden hunger risk prediction system by Yang Yang, Zheng Zhang, Huake Cao, Yuchen Zhang, Minao Wang, Ning Zhang

“…After quality control, we designated 632 students from Xuancheng City as the external test cohort and used the remaining 6477 students as the training cohort to develop predictive models. We used six ML algorithms (i.e., deep‐learning neural network [DNN], random forest, support vector machine, extreme gradient boosting, gradient boosting decision tree, and k‐nearest neighbor) to fit the training set using five‐fold cross‐validation, with hyperparameter tuning performed via Bayesian optimization. …”
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The establishment of constitutive model in the cutting simulation of tool design for cast iron material by Xian Wu, Zhiqin Su, Chao Zhang, Xuefeng Zhao, Hongfei Yao, Yuan Li, Feng Jiang

“…The cutting simulation is the most cost-effective and widely used method to obtain the optimal tool geometric design. The constitutive model of workpiece material is the foundation of the cutting simulation for tool design. …”
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Research on Hybrid Collaborative Development Model Based on Multi-Dimensional Behavioral Information by Shuanliang Gao, Wei Liao, Tao Shu, Zhuoning Zhao, Yaqiang Wang

“…This paper aims to propose a hybrid collaborative development model based on multi-dimensional behavioral information (HCDMB) to deal with systemic problems in modern software engineering, such as the low efficiency of cross-stage collaboration, the fragmentation of the intelligent tool chain, and the imperfect human–machine collaboration mechanism. …”
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An Empirical Study of Self-Supervised Learning with Wasserstein Distance by Makoto Yamada, Yuki Takezawa, Guillaume Houry, Kira Michaela Düsterwald, Deborah Sulem, Han Zhao, Yao-Hung Tsai

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Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

“…The novelty of this research lies in the application of a data aggregation technique to address class imbalance, significantly improving machine learning model performance and optimizing training time. …”
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Classifying kidney disease using a dense layers deep learning model by Amal Al-Rasheed, Sheikh Muhammad Saqib, Muhammad Zubair Asghar, Tehseen Mazhar, Asim Seedahmed Ali Osman, Mohammad Shahid, Muhammad Iqbal, Muhammad Amir Khan

“…The experiment used a publicly accessible dataset with 24 independent fields and independent values as chronic or not-chronic classes, building dense-layered deep neural networks based on an optimized architecture. The outcomes demonstrated that, when compared to the other models, the proposed model was the most accurate.…”
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A hybrid approach for pattern recognition and interpretation in age-related false memory by Noorbakhsh Amiri Golilarz, Elias Hossain, Shahram Rahimi, Hossein Karimi

“…This knowledge gap is partly due to limitations in current methodologies used to examine age-related cognitive changes and their origins.MethodsIn the present study, a hybrid approach was developed that combines optimized machine learning techniques with large-scale transformer-based language models to identify behavioral patterns distinguishing true from false memories in both younger and older adults. …”
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Active learning for advanced biodegradable film design by Yi Gao, Chengcheng Liu, Yang Zhao, Peng Zhao, Feihui Chen, Qinghai Li, Yanguo Zhang, Bin Yang, Hui Zhou

“…Through six iterations with 186 data points for three-target optimization and seven iterations with 166 data points for two-target modeling, a data-driven platform was established to accurately predict CMC film properties based on composition and autonomously identify optimal formulations for specific performance targets. …”
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