Predictive analysis of heart disease using quantum-assisted machine learning by Mehroush Banday, Sherin Zafar, Parul Agarwal, M. Afshar Alam, Siddhartha Sankar Biswas, Imran Hussain, K. M. Abubeker

“…The proposed research is developed with a hybrid approach that combines different machine learning algorithms, such as KNN + RF, DT + RF, LR + RF, and Adaboost + RF, for diagnosing coronary illness with higher accuracy through feature selection. The proposed system performance obtained an accuracy of 99%, utilising 20000 datasets with 14 attributes from various datasets collected from the Local Pathology Lab in the Muzaffarnagar District of Uttar Pradesh, India. …”
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Importance-Aware Resource Allocations for MIMO Semantic Communication by Yue Cao, Youlong Wu, Lixiang Lian, Meixia Tao

“…Systematic experiments validate CIBA’s effectiveness in deriving importance scores that bridge learning tasks and communication optimization. Comparisons of feature perturbation schemes confirm the necessity of importance-aware resource allocation, with the proposed allocation strategy outperforming conventional methods in task performance metrics. …”
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License Plate Recognition for Smart Construction Sites Based on GMH-YOLO by Ming Li, Ze-Quan Wang, Yu-Hang Zhao, Qiang Li

“…To enhance detection performance in complex environments, this paper proposes the GMH-YOLO model, which integrates an innovative Gated Multi-Head Attention mechanism into the C3k2 module of YOLO11. …”
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Estimating Self-Confidence in Video-Based Learning Using Eye-Tracking and Deep Neural Networks by Ankur Bhatt, Ko Watanabe, Jayasankar Santhosh, Andreas Dengel, Shoya Ishimaru

“…Our results underscore the superior performance of the deep-learning model in estimating self-confidence in video-based learning contexts compared to hand-crafted feature-based methods. …”
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Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms by Ashrafe Alam, Victor R. Prybutok

“…It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies. …”
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Experimental and analytical study on axial behaviour of square corrugated concrete filled single and double skin tube stub columns by Aya Mohsen Handousa, Mohamed Abdellatief, Fikry Abdo Salem, Nabil Mahmoud, Mohamed Ghannam

“…The findings indicated that the GPR model outperformed the ANN model in predicting the bearing capacity of square CFDST columns. …”
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Exploring EFL Teachers’ Behavioral Intentions to Integrate GenAI Applications: Insights From PLS-SEM and fsQCA by Muhammed Parviz, Francis Arthur

“…A structural model featuring 20 hypotheses based on the “UTAUT” and “AI-TPACK” was proposed. …”
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Machine learning approaches for predicting the link of the global trade network of liquefied natural gas. by Pei Zhao, Hao Song, Guang Ling

“…The results indicate robust predictive performance. This study demonstrates that machine learning algorithms-specifically random forest and decision tree-outperform in predicting links within the global LNG trade network based on local information proximity, while the graph attention network, a deep learning model, exhibits stable optimization and effective feature learning. …”
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Numerical and artificial intelligent analysis of double pipe heat exchanger exposed to solar irradiation for sustainable energy solutions by Sohail Ahmad, Hessa A. Alsalmah

“…On the other hand, the RBFNN model exhibits excellent predictive capability with high accuracy and minimal error.…”
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EDGE-BASED VIDEO RECOGNITION: ADVANCING DEEP LEARNING FOR EFFICIENT VISUAL by Sai Babu Veesam, Aravapalli Rama Satish

“…Our approach involves monitoring video streams, identifying specific events, and logging their timestamps using a compact Recurrent Neural Network (RNN). We benchmark the performance of our RNN against well-established deep learning models like Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs). …”
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Research on Long-Distance Snow Depth Measurement Method Based on Improved YOLOv8 by Jia-Wen Wang, Yu Cao, Zong-Kai Guo, Cheng Xu

“…Second, the introduction of the variable kernel convolution (AKConv) module improves the adaptability of convolutional operations, boosting the model’s performance in snow depth detection. …”
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Field Grading of Longan SSC via Vis-NIR and Improved BP Neural Network by Jun Li, Meiqi Zhang, Kaixuan Wu, Hengxu Chen, Zhe Ma, Juan Xia, Guangwen Huang

“…Subsequently, the BP network structure was refined, and the competitive adaptive reweighted sampling (CARS) algorithm was employed for feature wavelength extraction. The results show that the improved Brix-BPNN model, integrated with the CARS, achieves the highest prediction performance, with a 2.84% increase in classification accuracy relative to the original BPNN model. …”
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Filter Learning-Based Partial Least Squares Regression and Its Application in Infrared Spectral Analysis by Yi Mou, Long Zhou, Weizhen Chen, Jianguo Liu, Teng Li

“…Furthermore, we have derived bounds for the Root Mean Squared Error of Prediction (RMSEP) and the Cosine Similarity (CS) to evaluate model performance. Experimental results using spectral datasets from Corn, Octane, Mango, and Soil Nitrogen show that the FPLS model outperforms PLS, OSCPLS, VCPLS, PoPLS, LoPLS, DOSC, OPLS, MSC, SNV, SGFilter, and Lasso in terms of prediction accuracy. …”
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Cross-modal knowledge distillation for enhanced depression detection by Huang Huang, Xinhui Li, Minchao Wu, Zhao Lv, Yong Peng

“…Through knowledge distillation, the rich knowledge encapsulated in the teacher model was transferred to the student model, thereby enhancing its performance. …”
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A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background by Jianyong Chai, Limin Jia, Jianfeng Liu, Enguang Hou, Zhe Chen

“…The model’s performance is evaluated using metrics such as root mean square error (RMSE), mean absolute error (MAE), and weighted mean absolute percentage error (WMAPE) and is compared against other models including LSTM, recurrent neural network (RNN), gated recurrent unit (GRU), residual LSTM (ResLSTM), and a variant of GCN-LSTM. …”
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Named Entity Recognition in Aviation Products Domain Based on BERT by Mingye Yang, Bernadin Namoano, Maryam Farsi, John Ahmet Erkoyuncu

“…Given the high degree of specialisation of aviation product text data and the wide span of contextual information, existing models often perform poorly in entity extraction. …”
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Methodology for Removing Striping Artifacts Encountered in Planet SuperDove Ocean-Color Products by Brittney Slocum, Sherwin Ladner, Adam Lawson, Mark David Lewis, Sean McCarthy

“…It was determined that the striping artifacts could be removed from these products by filtering the top of the atmosphere radiance in the red and NIR bands prior to selecting the aerosol models, without sacrificing high-resolution features in the imagery. …”
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Investigating the landslide susceptibility assessment methods for multi-scale slope units based on SDGSAT-1 and Graph Neural Networks by Xiangqi Lei, Hanhu Liu, Zhe Chen, Shaoda Li, Hang Chen, Shuai Zeng, Xiao Wang, Wenqian Bai, Wei Li, Lorenzo Picco

“…Our analysis compared LANDSAT with similar resolutions from multiple perspectives and found that SDGSAT-1 has substantial advantages. (2) The landslide susceptibility assessment method proposed in this work, based on optimal-scale slope units and GNN, demonstrated superior performance, with various evaluation metrics, such as AUC, Accuracy, and Precision far exceeding those of other machine learning models.…”
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Enhancing clariid catfish species classification: A deep learning framework utilizing cranial morphology and explainable AI by Kriengsak Treeprapin, Rakdee Bandatang, Thitipong Panthum, Worapong Singchat, Jiraboon Prasanpan, Kornsorn Srikulnath, Suchin Trirongjitmoah

“…Future work could explore hybrid convolutional neural network (CNN) and vision transformers (ViT) models to optimize performance and efficiency.…”
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Forecasting Achievement of Inactive Disease in Juvenile Idiopathic Arthritis with Artificial Intelligence by Ana I. Rebollo-Giménez, Francesca Ridella, Silvia Maria Orsi, Elena Aldera, Marco Burrone, Valentina Natoli, Silvia Rosina, Alessandro Consolaro, Esperanza Naredo, Angelo Ravelli, Davide Cangelosi

“…In the testing cohort, the same ML model confirmed a high forecasting performance (MCC = 0.65). …”
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