Novel energy efficient integration of chimney ventilation, liquid desiccant dehumidification, and evaporative cooling for humid climates by Omar Allahham, Kamel Ghali, Nesreen Ghaddar

“…The system was sized, and its operation was optimized using an advanced machine learning-genetic algorithm model for a typical office space in Beirut. …”
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A Comprehensive Review of Cryptographic Techniques in Federated Learning for Secure Data Sharing and Applications by Anik Sen, Swee-Huay Heng, Shing-Chiang Tan

“…Federated Learning (FL) introduces a decentralised machine learning paradigm whereby models can be trained over distributed nodes without sharing data. …”
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Evaluasi Kepuasan Penggemar Sepak Bola Terhadap Pemilihan Pelatih Timnas Indonesia Di Media Sosial X Dengan Metode K-Means Clustering by Nasywa Al Afif Harahap, Abdul Halim Hasugian

“…Evaluasi performa menghasilkan akurasi model sebesar 53,59%, dengan performa terbaik pada klaster sentimen positif. …”
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Generative deep learning for predicting ultrahigh lattice thermal conductivity materials by Liben Guo, Yuanbin Liu, Zekun Chen, Hongao Yang, Davide Donadio, Bingyang Cao

“…The recent development of generative models and machine learning (ML) holds great promise for predicting new functional materials. …”
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Soft computing approaches of direct torque control for DFIM Motor's by Zakariae Sakhri, El-Houssine Bekkour, Badre Bossoufi, Nicu Bizon, Mishari Metab Almalki, Thamer A.H. Alghamdi, Mohammed Alenezi

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A Design Methodology for Exploring and Communicating System Values and Assumptions by Daniel Carter

“…I first analyze an existing tool, the Versioning Machine, as a way of focusing the design of a prototype that reimagines several aspects of that original—specifically, I argue that the Versioning Machine creates an environment that to some extent assumes that TEI documents are created by one editor and intended for one instantiation. …”
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A Short-Term Risk Prediction Method Based on In-Vehicle Perception Data by Xinpeng Yao, Nengchao Lyu, Mengfei Liu

“…The method incorporates Monte Carlo simulation for threshold calibration, Boruta-based feature selection, and multiple machine learning models, including the light gradient-boosting machine (LGBM), with performance interpretation via SHAP analysis. …”
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Magnetic particle grinding and finishing test of mixed particle size abrasives by Bingyang LIU, Yunlong DING, Wenjie SHAO, Bing HAN, Yan CHEN

“…When the workpiece is machined under the optimal process parameters, the surface roughness Ra of the workpiece decreases from the original value of 0.244 μm to the test value of 0.036 μm, and the absolute value of relative error between the two is 5.26%.ConclusionsThe experimental results show that the established model is effective, and the process parameters that affect the surface roughness Ra of the workpiece are in the order of spindle speed, followed by abrasive mass ratio and abrasive particle size ratio. …”
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Performance of MRI-based radiomics for prediction of residual disease status in patients with nasopharyngeal carcinoma after radical radiotherapy by Qinqin Wu, Weiguang Qiang, Liang Pan, Tingting Cha, Qilin Li, Yang Gao, Kaiyang Qiu, Wei Xing

“…SVM models were constructed by combining the optimal radiomic features from each sequences with clinical data. …”
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Systematic Literature Review: Identifying Key Variables and Measuring Maximum Loan Limits by Algies Rifkha Fadillah, Mohamad Nurkamal Fauzan

“…Machine Learning models, such as Random Forest and Gradient Boosting, often surpass traditional methods in handling large, unstructured datasets due to their capacity for modeling complex, non-linear relationships. …”
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Enhance triangular fuzzy parametric framework for solid multi objective transportation problem with split decision variables by Vishwas Deep Joshi, Medha Sharma, Lenka Čepová, Huda Alsaud, Kanak Kalita

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Determination of rational modes for applying nanofibrous covering to a water-soluble film by electrospinning by Mikhail S. Karnilov, Dzmitry B. Ryklin

“…Three types of substrates were used to fix the film on the machine collector. Regression models were obtained that describe the effect of the emitter and collector potentials and the distance between them on the consumption of the spinning solution at which the electrospinning process was stable. …”
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Non-Destructive Determination of Starch Gelatinization, Head Rice Yield, and Aroma Components in Parboiled Rice by Raman and NIR Spectroscopy by Ebrahim Taghinezhad, Antoni Szumny, Adam Figiel, Ehsan Sheidaee, Sylwester Mazurek, Meysam Latifi-Amoghin, Hossein Bagherpour, Natalia Pachura, Jose Blasco

“…Hotelling’s T² analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 °C soaking for 180 min, followed by drying at 70 °C. …”
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Estimation of Amorphophallus Konjac Above-Ground Biomass by Integrating Spectral and Texture Information from Unmanned Aerial Vehicle-Based RGB Images by Ziyi Yang, Hongjuan Qi, Kunrong Hu, Weili Kou, Weiheng Xu, Huan Wang, Ning Lu

“…The results indicate that the integration (PCA_(PCA_VIs+PCA_TFs)) of PCA-based VIs and PCA-based TFs using PCA achieved the best prediction accuracy (R² = 0.96, RMSE = 0.08 t/hm², MAE = 0.06 t/hm²) with SVR. In contrast, the DL model derived from AlexNet, combined with RGB imagery, yielded moderate predictive accuracy (R² = 0.72, RMSE = 0.21 t/hm², MAE = 0.17 t/hm²) compared with the optimal ML model. …”
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Effect of Periodic Fluctuations of Cutting Mode Parameters on the Temperature of the Front Face of a Turning Tool by E. V. Fominov, V. E. Gvindjiliya, A. A. Marchenko, C. G. Shuchev

“…The results of the conducted research emphasize the importance of analyzing the effect of periodic disturbances on pulse changes in contact temperature in the processing zone. The presented model of the relationship between tool vibrations and temperature in the cutting zone can be used to optimize turning modes. …”
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Ensemble deep learning and anomaly detection framework for automatic audio classification: Insights into deer vocalizations by Salem Ibrahim Salem, Sakae Shirayama, Sho Shimazaki, Kazuo Oki

“…Subsequently, the isolated clips were classified into deer and non-deer categories using machine learning models. Our investigation assessed three state-of-the-art deep learning models, ResNet50, MobileNetV2, and EfficientNet-B2, considering various hyperparameter configurations to optimize the performance. …”
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Prediction of porosity, hardness and surface roughness in additive manufactured AlSi10Mg samples. by Fatma Alamri, Imad Barsoum, Shrinivas Bojanampati, Maher Maalouf

“…These models are evaluated based on the coefficient of determination and the mean squared error. …”
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Lentil plant disease and quality assessment: A detailed dataset of high-resolution images for deep learning researchMendeley Data by Eram Mahamud, Md Assaduzzaman, Shayla Sharmin

“…The dataset aims to support the development of machine-learning models for precise disease detection and quality assessment in lentil cultivation. …”
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Investigation of the relationship between the design and technological parameters of vibration and oscillating rollers by Shishkin E.A., Ivanchenko S.N., Sidorkov V.V., Mamaev L.A., Smolyakov A.A.

“…Simulation is an important step in the design of new road roller models. In the process of modeling, it is possible to exclude options that do not allow obtaining the optimal structure or parameters of the designed machine. …”
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Uncovering abnormal gray and white matter connectivity patterns in Alzheimer’s disease spectrum: a dynamic graph theory analysis for early detection by Juanjuan Jiang, Tao Kang, Ronghua Ling, Yingqian Liu, Jiuai Sun, Yiming Li, Xiaoou Li, Hui Yang, Bingcang Huang, the Alzheimer’s Disease Neuroimaging Initiative

“…DFC metrics (standard deviation of Fisher z-transformed correlations) were used to identify group differences and classify AD spectrum stages. Support vector machine (SVM) models were trained to differentiate CN/SMC/CI, with subgroup analyses in Aβ + and APOE E4 + populations.ResultsDFC with short sliding windows (20–50 TRs, 98% overlap) demonstrated greater sensitivity than SFC in detecting early functional disruptions in gray-white matter networks, identifying 34 CN-SMC [p < 0.05, e.g., ventral attention network (VAN)-white matter 2 (WM2) via Gau20-DFC], 44 CN-CI (p < 0.001), and 49 SMC-CI (p < 0.01) differential connections. …”
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