A self-supervised learning approach for high throughput and high content cell segmentation by Van K. Lam, Jeff M. Byers, Michael C. Robitaille, Logan Kaler, Joseph A. Christodoulides, Marc P. Raphael

“…Moreover, our SSL algorithm is capable of identifying complex cellular structures and organelles, which are otherwise easily missed, thereby broadening the machine learning applications to high-content imaging. …”
Get full text

Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning by YANG Yinghe, WEI Handi, FAN Dixia, LI Ang

“…In order to overcome the complexity and variability of underwater working environments, as well as the difficulty of controlling the flapping motion due to the significant nonlinear characteristics and numerous variables involved, a direct exploration approach is proposed to search for the optimal flapping foil propulsion parameters in the environment. The Latin hypercube sampling technique is utilized to obtain the samples of multi-dimensional flapping parameters in actual water pool data, and a Gaussian process regression (GPR) machine learning model is established based on these samples to generalize the working environment. …”
Get full text

Investigation on Pressure Drop Characteristics During Refrigerants Condensation Inside Internally Threaded Tubes by Xiangrui Meng, Jian Wang, Qian Sun, Xinling Ma

“…This study investigates the influence of geometric parameters of internally threaded tubes on heat transfer and resistance characteristics. …”
Get full text

A Hybrid Nondominant-Based Genetic Algorithm (NSGA-II) for Multiobjective Optimization to Minimize Vibration Amplitude in the End Milling Process by Mahesh Gopal, Endalkachew Mosisa Gutema, Hirpa G. Lemu, Jaleta Sori

“…At the same time, in machining, tremendous efforts are taken to keep friction and chatter to a minimum and to attain better quality and perfect output, and also more attention is required while selecting the machining process parameters. …”
Get full text

Improvement and Experimental Explore on Coordinated Control of Kinematic Mechanism of FDM 3D Printer by Guozhu Li

“…The defects of small size, slow printing speed, and low precision are deeply studied, and the machine structure is optimized according to the structural strength analysis. …”
Get full text

Light-GBM based minority oversampling model using biomedical data analysis for breast cancer classification by Mukesh Soni, Mohammed Wasim Bhatt, Paul Ofori-Amanfo

“…A model for the identification of breast cancer based on the lightweight gradient boosting machine (GBM) algorithm. To address the problem of skewed diagnostic data for breast cancer, the Borderline-SMOTE method is used. …”
Get full text

Research on False Alarm Rate Reducing Strategies for Subway Train Fire Alarm System by ZHANG Qian, XU Shaohong, XUE Hongquan

“…The false alarm rate can be reduced by hardware optimization methods such as adding dust filter devices, adopting dual light source detectors, multi-parameter gas detectors and pyrolysis particle detectors; the rate can also be reduced by optimizing software algorithms (optimization modeling method, automatic adjustment of alarm thresholds, machine self-learning algorithm, etc.). …”
Get full text

An analysis of compensation for radial thermal errors of a turning center with a three axis feed system by Zhao Haitao, Guo Jingfeng, Tang Yongbo, Zhang Shuixiang, Yi Xiaoxi

“…The whole compensation process is demonstrated using a turning center equipped with this feed system, and the needed data is mainly from simulation. The effects of the parameter l of this feed system on the two radial thermal error components are analyzed. …”
Get full text

Additive manufacturing of metal matrix composites by Mohan Sai Kiran Kumar Yadav Nartu, Priyanshi Agrawal

“…A detailed comparison of microstructural evolution and process parameter optimization, including feedstock preparation methods and the role of machine learning and modeling among the different AM processes, is also presented. …”
Get full text

Advances in the Additive Manufacturing of Superalloys by Antonio del Bosque, Pablo Fernández-Arias, Diego Vergara

“…Additionally, emerging trends point to the integration of machine learning and artificial intelligence for real-time quality monitoring and manufacturing parameter optimization. …”
Get full text

Modeling Algal Toxin Dynamics and Integrated Web Framework for Lakes by Özlem Baydaroğlu, Serhan Yeşilköy, Anchit Dave, Marc Linderman, Ibrahim Demir

“…From this point of view, we employed the sparse identification nonlinear dynamics (SINDy) technique to model microcystin, an algal toxin, utilizing dissolved oxygen as a water quality metric and evaporation as a meteorological parameter. SINDy is a novel approach that combines a sparse regression and machine learning method to reconstruct the analytical representation of a dynamical system. …”
Get full text

Rainfall Prediction in Khorasan Razavi Stations Using a Hybrid Neural Network and Genetic Algorithm Approach by Mahdi Naseri, Mahsa Mardani

“…This study proposes a novel hybrid approach, combining the Non-linear Auto Regressive with eXogenous inputs (NARX) neural network with a Genetic Algorithm (GA) for parameter optimization, aiming to improve daily rainfall prediction in Khorasan Razavi province, Iran. …”
Get full text

Potential of hydrogen production from intermittent renewable energy resources in different locations of Nigeria: Technical, economic and environmental perspective by Richard Oladayo Olarewaju, Ayodeji Samson Olatunji Ogunjuyigbe, Temitope Raphael Ayodele, Samson Oladayo Ayanlade, Yuming Feng, Chaoran Liu

Get full text

Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm by Qiyuan Cui, Jianhong Pu, Wei Li, Yun Zheng, Jiaxi Lin, Lu Liu, Peng Xue, Jinzhou Zhu, Mingqing He

“…The cohort was randomly divided into a training dataset and a validation dataset in a ratio of 8:2. The machine learning algorithms used in this study include Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), Naive Bayes, Decision Trees (DT), and traditional Logistic Regression (LR). …”
Get full text

Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations by Snir Pardo, Dovi Poznanski, Steve Croft, Andrew P. V. Siemion, Matthew Lebofsky

“…We implement a machine learning algorithm to search for extraterrestrial technosignatures in radio observations of several hundred nearby stars, obtained with the Parkes and Green Bank Telescopes by the Breakthrough Listen collaboration. …”
Get full text

Comparison of sample preparation methods for higher heating values in various sugarcane varieties using near-infrared spectroscopy by Kantisa Phoomwarin, Khwantri Saengprachatanarug, Jetsada Posom, Seree Wongpichet, Kittipong Laloon, Arthit Phuphaphud

“…This study developed an efficient system for measuring the energy characteristics of energy canes in breeding programs using near-infrared spectroscopy with the aim of significantly improving the accuracy of selecting high-performing sugarcane clones. A key parameter for evaluating energy potential is the heating value, which is typically determined using bomb calorimetry. …”
Get full text

Measuring and Improving the Efficiency of Python Code Generated by LLMs Using CoT Prompting and Fine-Tuning by Ramya Jonnala, Jeong Yang, Young Lee, Gongbo Liang, Zechun Cao

“…Leveraging the EffiBench benchmark datasets within the Google Vertex AI Workbench environment, across a spectrum of machine configurations, the study implemented a consistent seed parameter to ensure experimental reproducibility. …”
Get full text

Keandalan Sistem Lintasan Produksi Pembuatan Pipa by Taufik Taufik, Wirli Hidayanti

“…In addition, mean time between failure (MTBF) was calculated to the pipe manufacturing machine. The machine reliability was obtained based on a formula and parameters. …”
Get full text

Algorithm for Cloud Particle Phase Identification Based on Bayesian Random Forest Method by Fu Tao, Yang Zhipeng, Tao Fa, Hu Shuzhen, Lu Yuxiang, Fu Changqing

“…It is regarded as a crucial parameter in cloud microphysics. The study of cloud particle phase states is regarded as crucial for a deep understanding of the impact of clouds on the earth's climate, environment, and sustainable development, providing a scientific foundation for addressing climate change and improving environmental quality. …”
Get full text

Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection by Md. Najmul Mowla, Davood Asadi, Shamsul Masum, Khaled Rabie

“…On the Fire Luminosity Airborne-based Machine Learning Evaluation (FLAME) dataset, the model attained accuracy rates of 99.83%, 99.10%, and 99.32%, with corresponding cKappa values of 99.66%, 98.20%, and 98.65%. …”
Get full text