Hybrid Feature and Optimized Deep Learning Model Fusion for Detecting Hateful Arabic Content by Karim Gasmi, Ibtihel Ben Ltaifa, Alameen Eltoum Abdalrahman, Omer Hamid, Mohamed Othman Altaieb, Shahzad Ali, Lassaad Ben Ammar, Manel Mrabet

“…To maximize predictive performance, we develop an ensemble framework that integrates these models. …”
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Target detection of helicopter electric power inspection based on the feature embedding convolution model. by Dakun Liu, Wei Zhou, Linzhen Zhou, Wen Guan

“…This study aims to improve the helicopter electric power inspection process by using the feature embedding convolution (FEC) model to solve the problems of small scope and poor real-time inspection. …”
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Dynamic Arm Gesture Recognition Using Spherical Angle Features and Hidden Markov Models by Hyesuk Kim, Incheol Kim

“…Because Kinect’s viewpoint and the subject’s arm length can substantially affect the estimated 3D pose of each joint, it is difficult to recognize gestures reliably with these features. The proposed system performs the feature transformation that changes the 3D Cartesian coordinates of each joint into the 2D spherical angles of the corresponding arm part to obtain view-invariant and more discriminative features. …”
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Service clustering method based on description context feature words and improved GSDMM model by Qiang HU, Jiaji SHEN, Guanghui JING, Junwei DU

“…To address the problem that current service clustering methods usually faced low quality of service representation vectors, a service clustering method based on description context feature words and improved GSDMM model was proposed.Firstly, a feature word extraction method based on context weight was constructed.The words that fit well with the context of service description were extracted as the set of feature words for each service.Then, an improved GSDMM model with topic distribution probability correction factor was established to generate service representation vectors and achieve distribution probability correction for non-critical topic items.Finally, K-means++ algorithm was employed to cluster Web services based on these service representation vectors.Experiments were conducted on real Web services in Web site of Programmable Web.Experiment results show that the quality of service representation vectors generated by the proposed method is higher than of other topic models.Further, the performance of our clustering method is significantly better than other service clustering methods.…”
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Leveraging Explainable Artificial Intelligence in Solar Photovoltaic Mappings: Model Explanations and Feature Selection by Eduardo Gomes, Augusto Esteves, Hugo Morais, Lucas Pereira

“…Additionally, we propose a feature selection methodology utilizing the explanation values to produce more efficient models, by reducing data requirements while maintaining performance within a threshold of the original model. …”
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Enhancing financial product forecasting accuracy using EMD and feature selection with ensemble models by Eddy Suprihadi, Nevi Danila, Zaiton Ali

“…This study examines the impact of Empirical Mode Decomposition (EMD) and Recursive Feature Elimination (RFE) on the prediction of financial product performance employing several ensemble machine learning models, including Random Forest, XGBoost, LightGBM, AdaBoost, CatBoost, Bagging, and ExtraTrees. …”
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Enhancing Traffic Speed Prediction Accuracy: The Multialgorithmic Ensemble Model With Spatiotemporal Feature Engineering by Ali Ardestani, Hao Yang, Saiedeh Razavi

“…The methodology involves constructing a virtual graph based on road segment correlations and applying a combination of spatial and temporal feature extraction techniques. The model is further enhanced with an attention mechanism to focus on critical time intervals. …”
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TF-LIME : Interpretation Method for Time-Series Models Based on Time–Frequency Features by Jiazhan Wang, Ruifeng Zhang, Qiang Li

“…Most existing explanation methods are based on time-domain features, making it difficult to reveal how complex models focus on time–frequency information. …”
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Development of a multi-level feature fusion model for basketball player trajectory tracking by Tao Wang

“…Second, the model has excellent performance in both object detection and track tracking, which can not only provide a new solution for athletes' motion trajectory tracking, but also significantly improve the effect of motion tracking.…”
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Fish feeding behavior recognition model based on the fusion of visual and water quality features by Zheng ZHANG, Bosheng ZOU

“…To improve the behavior recognition performance of the model in high-density aquaculture, a multimodal feature fusion module (MFFM) was designed to achieve adaptive fusion of visual features and dissolve oxygen, temperature, and pH of water quality features. …”
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The Use of Momentum-Inspired Features in Pre-Game Prediction Models for the Sport of Ice Hockey by Noel Jordan T.P., Fonseca Vinicius Prado da, Soares Amilcar

“…Instead, we quantify momentum using a small sample of a team’s recent games and a linear line of best-fit to determine the trend of a team’s performances before an upcoming game. We show that with the use of SVM and logistic regression these momentum- based features have more predictive power than traditional frequency-based features in a pre-game prediction model which only uses each team’s three most recent games to assess team quality. …”
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Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model by Kerang Cao, Chang Liu, Siqi Yang, Yuxin Zhang, Lili Li, Hoekyung Jung, Shuo Zhang

“…Then, combined with two factor Pearson correlation analysis and feature importance ranking, multiple feature selection is performed, with the optimal feature subset as the feature input. …”
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Automated multi-model framework for malaria detection using deep learning and feature fusion by Osama R. Shahin, Hamoud H. Alshammari, Raed N. Alabdali, Ahmed M. Salaheldin, Neven Saleh

“…The framework employs a transfer learning approach that incorporates ResNet 50, VGG16, and DenseNet-201 for feature extraction. This is followed by feature fusion and dimensionality reduction via principal component analysis. …”
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MapSAM: adapting segment anything model for automated feature detection in historical maps by Xue Xia, Daiwei Zhang, Wenxuan Song, Wei Huang, Lorenz Hurni

“…We further enhance the positional prompt in SAM, transforming it into a higher-level positional-semantic prompt, and modify the cross-attention mechanism in the mask decoder with masked attention for more effective feature aggregation. The proposed MapSAM framework demonstrates promising performance across three distinct historical map segmentation tasks: railway, vineyard, and building block detection. …”
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A Learning Emotion Recognition Model Based on Feature Fusion of Photoplethysmography and Video Signal by Xiaoliang Zhu, Zili He, Chuanyong Wang, Zhicheng Dai, Liang Zhao

“…To address these concerns, our work mainly includes the following: (i) the development of a temporal convolutional network model incorporating channel attention to overcome PPG-based emotion recognition challenges; (ii) the introduction of a network model that integrates multi-scale spatiotemporal features to address the challenges of emotion recognition in spontaneous environmental videos; (iii) an exploration of a dual-mode fusion approach, along with an improvement of the model-level fusion scheme within a parallel connection attention aggregation network. …”
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A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition by Lianglian Gu, Guangzhi Di, Danju Lv, Yan Zhang, Yueyun Yu, Wei Li, Ziqian Wang

“…The integration of multi-scale feature extraction and fusion enables the model to better handle scale variations, thereby enhancing its adaptability across different scales. …”
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Spectral feature modeling with graph signal processing for brain connectivity in autism spectrum disorder by Ayesha Jabbar, Huang Jianjun, Muhammad Kashif Jabbar, Khalil ur Rehman, Anas Bilal

“…To overcome these limitations, we propose a Graph Signal Processing (GSP)-based framework that integrates spectral-domain features with topological descriptors to model brain connectivity more comprehensively. …”
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Multiple model visual feature embedding and selection method for an efficient ocular disease classification by Isha Kansal, Vikas Khullar, Preeti Sharma, Supreet Singh, Junainah Abd Hamid, A. Johnson Santhosh

“…Among the tested approaches, the “Combined Data” strategy utilizing features from all three models achieved the best results, with the BiLSTM classifier attaining 100% accuracy, precision, and recall on the training set, and over 98% performance on the validation set. …”
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Feature importance analysis of solar flares and prediction research with ensemble machine learning models by Yun Yang, Yun Yang

“…These are relatively high scores for model performance evaluation metrics. (2) The importance scores of each feature under different evaluation metrics and the comprehensive importance ranking can be directly obtained through the model without the need for additional feature analysis tools. …”
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Exploring the dynamics of seasonal surface features using coastal and regional ocean community model by D. Jaishree, P.T. Ravichandran, D.V. Thattai

“…This study aims to understand the seasonal variability of the Bay of Bengal’s surface circulation features using a high-resolution numerical Coastal and Regional Ocean Community simulations model. …”
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