FGMFN: Fine-Grained Multiscale Cross-Modal Sentiment Analysis in Advertisements by Han Wang, Peng Chen, Xiangyu Du

“…The model is designed to process multi-scale feature inputs, facilitate efficient sentiment fusion between images and text. …”
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A knowledge‐based materials descriptor for compositional dependence of phase transformation in NiTi shape memory alloys by Cheng Li, Qingkai Liang, Yumei Zhou, Dezhen Xue

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Diagnosis of depression based on facial multimodal data by Nani Jin, Renjia Ye, Peng Li

“…Through the multi-modal feature fusion, the model can effectively capture different feature patterns related to depression.ResultsWe conduct extensive experiments on the publicly available clinical dataset, the Extended Distress Analysis Interview Corpus (E-DAIC). …”
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Enhancing Cybersecurity Through Fusion of Optimization With Deep Wavelet Neural Networks on Denial of Wallet Attack Detection in Serverless Computing by P. Renukadevi, Sibi Amaran, A. Vikram, T. Prabhakara Rao, Mohamad Khairi Ishak

“…Finally, the hyperparameter tuning of the DWNN classifier is performed using the hierarchical learning-based chaotic crayfish optimizer (HLCCO) model. …”
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An advanced fire detection system for assisting visually challenged people using recurrent neural network and sea-horse optimizer algorithm by Fahd N. Al-Wesabi, Abeer A. K. Alharbi, Ishfaq Yaseen

“…To achieve this, the proposed SFDAB-ARNNSHO model performs image pre-processing by utilizing the sobel filtering (SF) model to remove noise in input data. …”
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Image Classification and Annotation Based on Hadoop Cloud Computing Platform by Jiyuan Lu, Chenghui Huang, Fang Hou, Bin Li

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Work Place Safety: Machine Learning Techniques for Assessing Workplace Incident Severity by Munayr Hasan Khalleefah Hassan, Wagdi M. S. Khalifa

“…Analyzing a comprehensive dataset of occupational injury records from Occupational Safety and Health Administration (OSHA), this research explores the efficacy of seven distinct ML algorithms AdaBoostClassifier (ABC), K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Extreme Learning Machine (ELM), Support Vector Classifier (SVC), Multi-Layer Perceptron (MLP), and Stacked Model (SM). Among these, ABC emerges as the standout performer, demonstrating superior accuracy and F1-score, Specifically 92.5% and 99.3% accuracy in predicting hospitalization and amputation severity, respectively, thus underscoring the potential of ensemble learning in refining occupational injury prediction models. …”
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A Fire Segmentation Method with Flame Detail Enhancement U-Net in Multispectral Remote Sensing Images Under Category Imbalance by Rui Zou, Zhihui Xin, Guisheng Liao, Penghui Huang, Rui Wang, Yuhu Qiao

“…Experimental results show that the FDE U-Net model exhibits robust performance in fire detection, accurately extracting flame regions even when their proportion is low and the background is complex. …”
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LPCF-YOLO: A YOLO-Based Lightweight Algorithm for Pedestrian Anomaly Detection with Parallel Cross-Fusion by Peiyi Jia, Hu Sheng, Shijie Jia

“…Furthermore, the Wise-IoU loss function is employed to enhance the model’s localization performance and generalization ability. …”
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Cognitive functions in schizophrenia: the interplay between blood viscosity, serum osmolarity, and symptom severity by Ipek Özönder Ünal, Miray Pirincci Aytac, Derya Adalı Aker, Tomris Duymaz, Tonguc Demir Berkol

“…Higher WBV, within a physiological range, may support cerebral perfusion and be associated with better executive performance. Although serum osmolarity showed a positive association with executive function in univariate analysis, it was not an independent predictor in multivariate models, and its cognitive relevance remains to be clarified. …”
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Comparative assessment of structural and tear film alterations in rabbit meibomian gland dysfunction models using chemical and electrocauterization techniques by Deeksha Prasad, Saumya Jakati, Kiran Kumar Bokara, Sayan Basu, Vivek Singh, Pragnya Rao Donthineni

“…Non-invasive assessments can provide standardized, objective metrics for studying DED in animal models and evaluating therapeutic interventions.…”
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Enhancing drought prediction through machine learning: advanced techniques combining phenotypic and agrometeorological data by Efrem Yohannes Obsie, Yongguo Liu

“…Image-based phenotypic features were extracted using a CNN-based network, resulting in 512 features, while handcrafted methods generated 48 features. …”
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Predicting Diabetic Retinopathy and Nephropathy Complications Using Machine Learning Techniques by D. R. Manjunath, J. J. Lohith, S. Selva Kumar, Abhijit Das

“…Through feature engineering, sampling strategies and hyperparameter tuning the models performed well on all the datasets. …”
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Machine learning classification meets migraine: recommendations for study evaluation by Igor Petrušić, Andrej Savić, Katarina Mitrović, Nebojša Bačanin, Gabriele Sebastianelli, Daniele Secci, Gianluca Coppola

“…This paper presents a framework of six essential recommendations for evaluating ML-based classification in migraine research: (1) group homogenization by clinical phenotype, attack frequency, comorbidity, therapy, and demographics; (2) defining adequate sample size; (3) quality control of collected and preprocessed data; (4) transparent training, testing, and performance evaluation of ML models, including strategies for data splitting, overfitting control, and feature selection; (5) interpretability of results with clinical relevance; and (6) open data and code sharing to facilitate reproducibility. …”
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A Picture Guide to Cancer Progression and Evolutionary Accumulation Models: Systematic Critique, Plausible Interpretations, and Alternative Uses by Ramon Diaz-Uriarte, Iain G. Johnston

“…This review identifies major standing issues, recommends turning inferences into actionable insights for translational impact —which requires careful consideration of empirical entities for causally-justified interpretations—, assessing method performance with entity-appropriate simulations, and should encourage the use of these methods in other areas with a better alignment between entities and model assumptions.…”
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A High-Accuracy Underwater Object Detection Algorithm for Synthetic Aperture Sonar Images by Jiahui Su, Deyin Xu, Lu Qiu, Zhiping Xu, Lixiong Lin, Jiachun Zheng

“…Experiments on the Sonar Common object Detection dataset (SCTD) demonstrate that the proposed HAUOD algorithm achieves superior performance in small object detection accuracy and multi-scenario robustness, attaining a detection accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>95.1</mn><mo>%</mo></mrow></semantics></math></inline-formula>, which is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>8.3</mn><mo>%</mo></mrow></semantics></math></inline-formula> higher than the baseline model (YOLOv8n). …”
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Affective Video Content Analysis: Decade Review and New Perspectives by Junxiao Xue, Jie Wang, Xiaozhen Liu, Qian Zhang, Xuecheng Wu

“…We summarize and compare representative methods in the following aspects: (1) unimodal AVCA models, including facial expression recognition and posture emotion recognition; (2) multimodal AVCA models, including feature fusion, decision fusion, and attention-based multimodal models; and (3) model performance evaluation standards. …”
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PCLC-Net: Parallel Connected Lateral Chain Networks for Infrared Small Target Detection by Jielei Xu, Xinheng Han, Jiacheng Wang, Xiaoxue Feng, Zhenxu Li, Feng Pan

“…The PCLC-Net preserves large-scale feature maps to prevent small target information loss, integrates causal-based retention gates (CBR Gates) within each chain for improved feature selection and fusion, and leverages the attention-based network-wide feature map aggregation (AN-FMA) output module to ensure that all feature maps abundant with small target information contribute effectively to the model’s output. …”
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Research on the Estimation Model of Electrical Parameters of Silver-Based Contacts Based on Surface Morphology by Chao Wang, Xiancheng Wang, Chengjun Guo

“…The quality of surface morphology can reflect the electrical performance of silver-based contacts. Existing research on the correlation of morphological–electrical performance is based solely on empirical models from traditional visual inspections and only considers the impact of visually observable macro-textural features on electrical performance. …”
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A YOLO-Based Method for Head Detection in Complex Scenes by Ming Xie, Xiaobing Yang, Boxu Li, Yingjie Fan

“…They significantly improve the model’s multi-scale detection capabilities, thus enhancing the detection ability of the system. …”
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