Development and validation of an integrative 54 biomarker-based risk identification model for multi-cancer in 42,666 individuals: a population-based prospective study to guide adva... by Renjia Zhao, Huangbo Yuan, Yanfeng Jiang, Zhenqiu Liu, Ruilin Chen, Shuo Wang, Linyao Lu, Ziyu Yuan, Zhixi Su, Qiye He, Kelin Xu, Tiejun Zhang, Li Jin, Ming Lu, Weimin Ye, Rui Liu, Chen Suo, Xingdong Chen

“…However, developing a robust, practical multi-cancer risk prediction model that integrates diverse, multi-scale data and with proper validation remains a significant challenge. …”
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An improved hybrid approach involving deep learning for urban greening tree species classification with Pléiades Neo 4 imagery—A case study from Nanjing, Eastern China by Min Sun, Stephane G.P. Debulois, Zhengnan Zhang, Xiaolei Cui, Zhili Chen, Mingshi Li

“…We developed an ensemble model consisting of machine learning (ML) and DL approaches with two enhancement strategies, namely an attention mechanism and a fixed category weighting scheme (i.e., a weighted dictionary), which reweights classifier outputs based on the classification accuracy of each category. …”
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Confirmation of driving principle by weight analysis of Integration Neural Network and extension of deductive approximator by Yoshiharu IWATA, Hidefumi WAKAMATSU

“…Simulation-based optimization often requires many simulations and can be difficult to adapt due to time constraints. …”
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Advantages of Friction Welding of Fittings with Small Diameter Conical Contact Form by Yu. V. Poletaev, V. V. Shchepkin

“…The solution to this problem is possible on the basis of research and development of welding technology with optimal heat input instead of MAW. Existing fusion welding technologies do not allow for optimal regulated heat input. …”
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Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health Forecasting by Sahar Qaadan, Aiman Alshare, Rami Alazrai, Alexander Popp, Benedikt Schmuelling

“…The proposed framework is rigorously validated using two open-source datasets: the Samsung INR21700-50E and the Forklift Battery Degradation datasets. The CPIHL model demonstrates exceptional performance, achieving an R2 score of 0.9994, a mean absolute error of 0.0007, and a root mean square error of 0.0025, outperforming all baseline machine learning and deep learning models, including Random Forest, Artificial Neural Networks, Long Short-Term Memory, and Gated Recurrent Units. …”
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Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears by Jhonathan Sora-Cardenas, Wendy M. Fong-Amaris, Cesar A. Salazar-Centeno, Alejandro Castañeda, Oscar D. Martínez-Bernal, Daniel R. Suárez, Carol Martínez

“…Using a dataset of 1000 clinically diagnosed images, we applied feature extraction techniques, including histogram bins and texture analysis with the gray level co-occurrence matrix (GLCM), alongside support vector machines (SVMs), for image quality assessment. Leukocyte detection employed optimal thresholding segmentation utility (OTSU) thresholding, binary masking, and erosion, followed by the connected components algorithm. …”
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Artificial Intelligence Empowering Dynamic Spectrum Access in Advanced Wireless Communications: A Comprehensive Overview by Abiodun Gbenga-Ilori, Agbotiname Lucky Imoize, Kinzah Noor, Paul Oluwadara Adebolu-Ololade

“…Case studies show how ML can help self-optimize networks, reducing energy consumption while improving performance. …”
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Analyzing the performance of biomedical time-series segmentation with electrophysiology data by Richard Redina, Jakub Hejc, Marina Filipenska, Zdenek Starek

“…Notably, Faster R-CNN has never been applied to 1D signals segmentation before. Each model underwent Bayesian optimization to minimize hyperparameter bias. …”
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BACAD: AI-based framework for detecting vertical broken access control attacks by Ahmed Anas, Ayman A. Alhelbawy, Salwa El Gamal, Basheer Youssef

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Simulation and Research Methodology for the Process of Conveying Bulk Materials by Screw Conveyors by Andrey V. Butovchenko, Anastasia P. Kopeikina, Daria E. Bastrykina, Mikhail A. Chebotarev, Fedor Yu. Zhigailov

“…Increase in the performance of modern agricultural machines is not possible without the use of more powerful conveying working tools. …”
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Improving internet of vehicles research: A systematic preprocessing framework for the VeReMi datasetZenodo by Aparup Roy, Debotosh Bhattacharjee, Ondrej Krejcar

“…However, its large size (∼7 GB) and inherent class imbalance pose significant challenges for machine learning model development. This paper presents a preprocessing framework to enhance VeReMi’s usability and relevance. …”
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Seismic Wave Propagation Simulation With Quantum Computing by Xixiang Wen, Yanfei Wang

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Artificial intelligence in acoustic ecology: Soundscape classification in the Cerrado by Bruno Daleffi da Silva, Linilson Rodrigues Padovese

“…Additionally, the study proposes a multiple classification methodology by majority voting for frequently observed events, enabling reliable classifications through models with moderate performance. The conclusion is that it is possible to classify different Cerrado formations through their acoustic landscape, and the choice of the optimal model for classification should consider a balance between accuracy, operational complexity, and efficiency. …”
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Equivalent method for DFIG wind farms based on modified LightGBM considering voltage deep drop faults by Xuecheng Liu, Peixiao Fan, Jun Yang, Song Ke, Binyu Ma, Yangzhou Pei, Jian Xu

“…Second, based on simulations to obtain sample data of wind turbine operating states under different operating scenarios in wind farms, a classification model based on mLightGBM is established. Different weights are assigned to various samples and hyperparameter optimization is conducted to enhance the model’s classification accuracy. …”
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DDoS Attack Detection in SDN-Assisted Federated Learning Environment Based on Contrastive Learning by Minghong Fan, Jinghua Lan, Yiyi Zhou, Mengshuang Pan, Junrong Li, Daqiang Zhang

“…In the SDN-assisted FL environment, the FL network requires the interaction of model parameters among multiple participants. During this process, DDoS attacks may target the SDN control plane, disrupt its normal operation, and thus affect the transmission of model parameters in FL. …”
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Causes of Multi-Mechanism Abnormal Formation Pressure in Offshore Oil and Gas Wells by Yang Xu, Jin Yang, Zhiqiang Hu, Quanmin Zhao, Lei Li, Qishuai Yin

“…This paper introduces an innovative analytical framework that combines hierarchical clustering algorithms with the LightGBM model. Further refined by the application of Bayesian optimization, the model intelligently adjusts hyperparameters to enhance predictive accuracy. …”
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A reactive hybrid product-driven system for rescheduling in a manufacturing planning by Patricio Sáez Bustos, Victor Parada Daza

“…Practical implications: The PDS-SBH model offers a robust approach for real-time schedule adjustments, maintaining operational continuity, and optimizing resource use. …”
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Protein sequence classification using natural language processing techniques by Huma Perveen, Julie Weeds

“…Abstract Purpose This study aimed to enhance protein sequence classification using natural language processing (NLP) techniques while addressing the impact of sequence similarity on model performance. We compared various machine learning and deep learning models under two different data-splitting strategies: random splitting and ECOD family-based splitting, which ensures evolutionary-related sequences are grouped together. …”
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Enhancing flood susceptibility mapping in Sana’a, Yemen with random forest and eXtreme gradient boosting algorithms by Yahia Alwathaf, Ahmed M. Al-Areeq, Yousef A. Al-Masnay, Ali R. Al-Aizari, Nabil M. Al-Areeq

“…Both models demonstrated high accuracy in predicting flood-prone areas, with RF achieving an accuracy of 92% and XGBoost slightly outperforming it with an accuracy of 94%. …”
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Comprehensive evaluation of modern combine harvester performance in Southern Russia by M. E. Chaplygin, E. V. Zhalnin

“…Given the diverse range of machines used on farms (including different brands, models, and manufacturers, etc.), the absence of regulatory guidelines often results in violations of optimality and harmony within the equipment fleet. …”
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