Quality assessment of chicken using machine learning and electronic nose by Hassan Anwar, Talha Anwar

“…Data was collected from chicken samples over a period of 15 days. To evaluate the performance of the machine learning algorithms, different data splitting approaches were tested to understand their impact on model accuracy. …”
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Boosting Barlow Twins Reduced Order Modeling for Machine Learning‐Based Surrogate Models in Multiphase Flow Problems by T. Kadeethum, V. L. S. Silva, P. Salinas, C. C. Pain, H. Yoon

“…To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. …”
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Implementation of the internet of things network for monitoring audio information on a microprocessor and controller by V. A. Vishniakou, B. H. Shaya

“…A diagram of the first IoT structure for assessing the sound level based on the MP and controller is given.The algorithm of IoT network functioning for the analysis of voice information is detailed. …”
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Application of machine learning for predicting the incubation period of water droplet erosion in metals by Khaled AlHammad, Mamoun Medraj, Moussa Tembely

“…The performance of various ML algorithms is evaluated while investigating the effect of data transformation techniques on prediction accuracy. …”
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A hybrid ensemble framework with particle swarm optimization for network anomaly detection by Narinder Verma, Neerendra Kumar, Gourav Kumar, Kuljeet Singh

“…Our approach leverages the NSL-KDD and CICIDS datasets to ensure the IDS is trained and evaluated on data reflecting current network behaviours and threat landscapes. …”
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Stacked ensemble model for NBA game outcome prediction analysis by Guangsen He, Hyun Soo Choi

“…To improve the model’s interpretability and transparency, SHAP was used to clarify its decision-making process. The model was trained and evaluated using publicly available NBA datasets from 2021–2022,2022–2023, and 2023–2024. …”
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Pillar-X: Integrating Self-Learned Image Features to Improve 3D Object Detection by Mihaly Csontho, Andras Rovid

“…Unlike approaches that rely on pre-trained networks, Pillar-X efficiently learns and fuses features from an image directly within an end-to-end pipeline. …”
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Transfer learning based feature selection for feedforward neural network for speech emotion classifier by D. V. Krasnoproshin, M. I. Vashkevich

“…Proposed transfer learning approach consist in employing the backward-step selection algorithm for feature selection using statistical learning classifiers, the obtained subset of features than subsequently used to train feedforward neural networks. …”
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Machine-Learning-Based Optimal Feed Rate Determination in Machining: Integrating GA-Calibrated Cutting Force Modeling and Vibration Analysis by Yu-Peng Yeh, Han-Hao Tsai, Jen-Yuan Chang

“…The trained model achieves an R² score of 0.7887, indicating strong prediction accuracy. …”
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Fine-Tuning Models for Histopathological Classification of Colorectal Cancer by Houda Saif ALGhafri, Chia S. Lim

“…<b>Background/Objectives:</b> This study aims to design and evaluate transfer learning strategies that fine-tune multiple pre-trained convolutional neural network architectures based on their characteristics to improve the accuracy and generalizability of colorectal cancer histopathological image classification. …”
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Improving the resolution of solar energy potential maps derived from global DSMs for rooftop solar panel placement using deep learning by Maryam Hosseini, Hossein Bagheri

“…Subsequently, deep learning algorithms were trained to improve the resolution of the LiDAR-derived ASM. …”
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Decision-making method for residual support force of hydraulic supports during pressurized moving under fragmented roof conditions in ultra-thin coal seams by ZHANG Chuanwei, ZHANG Gangqiang, LU Zhengxiong, LI Linyue, HE Zhengwei, GONG Lingxiao, HUANG Junfeng

“…A residual support force decision-making dataset was subsequently constructed, and the IDBO-DHKELM model was trained and evaluated. Experimental results demonstrate that the proposed IDBO-DHKELM model achieves high decision-making accuracy, with a root mean square error (RMSE) of 0.143, a mean absolute error (MAE) of 0.119, and a coefficient of determination (R2) of 0.971.…”
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CloudSense: A model for cloud type identification using machine learning from radar data by Mehzooz Nizar, Jha K. Ambuj, Manmeet Singh, S.B. Vaisakh, G. Pandithurai

“…CloudSense generated results are also compared against conventional radar algorithms and we find that CloudSense performs better than radar algorithms. …”
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Monitoring Gypsiferous Soils by Leveraging Advanced Spaceborne Hyperspectral Imagery via Spectral Indices and a Machine Learning Approach by Najmeh Rasooli, Saham Mirzaei, Stefano Pignatti

“…Enhancing the spatial resolution of gypsiferous soil detection, as a valuable baseline information layer, is beneficial for investigating agroecological processes and tackling land degradation in semi-arid environments. This study evaluates the performance of PRISMA (PRecursore IperSpettrale della Missione Applicativa) and EnMAP (Environmental Mapping and Analysis Program) satellites in estimating soil gypsum content and compares models trained on satellite imagery versus lab data. …”
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Machine learning for clustering and classification of early knee osteoarthritis using single-leg standing kinematics by Ui-Jae Hwang, Kyu Sung Chung, Sung-Min Ha

“…K-means clustering was used for unsupervised learning, whereas six supervised machine learning algorithms were trained and validated for EOA classification. …”
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A Reinforcement Learning Model for Optimal Treatment Strategies in Intensive Care: Assessment of the Role of Cardiorespiratory Features by Cristian Drudi, Maximiliano Mollura, Li-wei H. Lehman, Riccardo Barbieri

“…A Markov Decision Process (MDP) was built on the extracted discrete time-series. A policy iteration algorithm was used to obtain the optimal AI policy for the MDP. …”
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ZWDX: a global zenith wet delay forecasting model using XGBoost by Laura Crocetti, Matthias Schartner, Marcus Franz Wareyka-Glaner, Konrad Schindler, Benedikt Soja

“…ZWDX is based on the XGBoost algorithm and uses ZWDs measured at over 19,000 GNSS stations as reference. …”
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GenAI-Based Jamming and Spoofing Attacks on UAVs by Burcu Sonmez Sarikaya, Serif Bahtiyar

“…Specifically, jamming and spoofing attacks on UAVs are generated to fool intrusion detection systems that may be implemented on UAVs. Experimental evaluations show that synthetically generated attack data reduces the accuracy of intrusion detections if the system was trained with inadequate attack data. …”
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Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum by Ramneet Kaur, Mudita Uppal, Deepali Gupta, Sapna Juneja, Syed Yasser Arafat, Junaid Rashid, Jungeun Kim, Roobaea Alroobaea

“…In this work, GRU qualifies as the best algorithm for developing a cryptocurrency price prediction model. …”
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Examining peptide–gold nanoparticle interactions through explainable machine learning by Malak Gamal Abdelmeguid, Jose Isagani B. Janairo, Nishanth G. Chemmangattuvalappil

“…The model was trained and evaluated on a dataset composed of 1720 peptides and their experimentally determined binding affinity for gold nanoparticles. …”
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