An interpretable XAI deep EEG model for schizophrenia diagnosis using feature selection and attention mechanisms by Ahmad Almadhor, Stephen Ojo, Thomas I. Nathaniel, Shtwai Alsubai, Abdullah Alharthi, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…In addition to fine-tuning input dimensionality, F-test feature selection increases learning efficiency.ResultsThrough the integration of feature importance analysis and conventional performance measures, this study presents valuable insights into the discriminative neurophysiological patterns associated with Schizophrenia, advancing both diagnostic and neuroscientific expertise. …”
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AI-driven wastewater management through comparative analysis of feature selection techniques and predictive models by Faruk Dikmen, Ahmet Demir, Bestami Özkaya, Muhammad Owais Raza, Jawad Rasheed, Tunc Asuroglu, Shtwai Alsubai

“…The results demonstrate that effluent volatile suspended solids (VSS) consistently held the highest predictive importance across all feature selection methods. Ensemble models significantly outperformed Decision Trees, with Gradient Boosting achieving the best predictive accuracy for TSS and total nitrogen (Mean Absolute Error (MAE): 3.667 $$R^2$$ : 97.53), XGBoost excelling in COD prediction with MAE and $$R^2$$ of 6.251 and 83. 41%, respectively, and XGBoost showing superior performance for BOD (MAE: 1.589 $$R^2$$ :79.64%). …”
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Financial sentiment analysis for pre-trained language models incorporating dictionary knowledge and neutral features by Yongyong Sun, Haiping Yuan, Fei Xu

“…Ablation analysis reveals that dictionary knowledge embedding and neutral feature extraction contribute most significantly to model improvement.…”
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Spectrogram-Based Arrhythmia Classification Using Three-Channel Deep Learning Model with Feature Fusion by Alaa Eleyan, Fatih Bayram, Gülden Eleyan

“…To enhance performance, the extracted features are concatenated before feeding them into a gated recurrent unit (GRU) model. …”
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Linear Model and Gradient Feature Elimination Algorithm Based on Seasonal Decomposition for Time Series Forecasting by Sheng-Tzong Cheng, Ya-Jin Lyu, Yi-Hong Lin

“…An augmented feature generator is introduced to enhance predictive performance by generating features such as differences, rolling statistics, and moving averages. …”
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A model for tobacco growing area classification based on time series features of thermogravimetric analysis by Jiaxu Xia, Yunong Tian, Xianwei Hao, Yuhan Peng, Guanqun Luo, Zhihua Gan

“…This study proposes a classification model for tobacco growing areas based on time series features from thermogravimetric analysis (TGA). …”
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The Bayesian mixture expert recognition model for tobacco leaf curing stages based on feature fusion by Panzhen Zhao, Shijiang Duan, Songfeng Wang, Aihua Wang, Lingfeng Meng, Zhicheng Wang, Yingpeng Dai

“…Next, different feature fusion methods of the same model are optimized to select the best-performing model as the foundational model for ensemble learning. …”
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Data Reconstruction Methods in Multi-Feature Fusion CNN Model for Enhanced Human Activity Recognition by Jae Eun Ko, SeungHui Kim, Jae Ho Sul, Sung Min Kim

“…Methods: This study proposes a multi-input, two-dimensional CNN architecture using three distinct data reconstruction methods. By fusing features from reconstructed images, the model enhances feature extraction capabilities. …”
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A complex roadside object detection model based on multi-scale feature pyramid network by Zhihao Zheng, Jianguang Zhao, Jingjing Fan, Ruirui Bai, Jiana Zhao, Jianan Liu

“…Additionally, a novel C3FB structure (Efficient Fusion of C3 modules and FocalNextBlock structure) is introduced to replace the C2f module in the neck network of YOLOv8, aiming to reduce the parameter count while enhancing model accuracy. Combining the weighted Bi-directional Feature Pyramid Network (BCFPN) for feature fusion incorporates deep, shallow, and original features, reinforces feature integration, minimizes information loss during convolution processes, and enhances target detection accuracy. …”
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TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion by Dingming Zhang, Yangcheng Bu, Qiaohong Chen, Shengbo Cai, Yichi Zhang

“…Experiments on blood cell detection datasets such as BloodCell-Detection-Dataset (BCD) reveal that TW-YOLO outperforms other models by 2%, demonstrating excellent performance in the task of blood cell detection. …”
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Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features by Rajkumar Govindarajan, K. Thirunadanasikamani, Komal Kumar Napa, S. Sathya, J. Senthil Murugan, K. G. Chandi Priya

“…A comparative analysis of multiple classification algorithms was conducted, with the Gradient Boosting classifier yielding the best performance (accuracy: 93.9 %, F1-score: 91.8 %). To improve interpretability, SHapley Additive exPlanations (SHAP) were integrated into the workflow to quantify feature contributions at both global and individual levels. …”
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Data‐Driven Feature Decomposition Integrated Prediction Model for Dust Concentration in Open‐Pit Mines by Shuangshuang Xiao, Jin Liu, Qing Yang, Zhiguo Chang, Yonggui Zhang

“…Combining the characteristics of dust concentration data and the concept of multimodal information integration modeling, a support vector machine (SVM)‐long short‐term memory (LSTM) network was chosen to build a data feature‐driven dust concentration combination prediction model. …”
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Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study by Lambert, Charlotte, Virgili, Auriane

“…The SDM performances were inspected by statistical metrics, model composition, shapes of relationships and prediction quality. …”
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An IoT intrusion detection framework based on feature selection and large language models fine-tuning by Huan Ma, Wan Zhang, Dalong Zhang, Baozhan Chen

“…Therefore, we propose a Feature Selection and Large Language Models (LLMs)-based IoT intrusion detection framework (FSLLM). …”
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Vision Transformer Embedded Feature Fusion Model with Pre-Trained Transformers for Keratoconus Disease Classification by Md Fatin Ishrak, Md Maruf Rahman, Md Imran Kabir Joy, Anna Tamuly, Salma Akter, Dewan M. Tanim, Shahajada Jawar, Nayeem Ahmed, Md Sadekur Rahman

“…The dataset was subsequently partitioned into training, testing, and validation subsets to facilitate robust model training and performance evaluation. Eight state-of-the-art CNN architectures, including DenseNet121, EfficientNetB0, InceptionResNetV2, InceptionV3, MobileNetV2, ResNet50, VGG16, and VGG19, were utilized for feature extraction, while the ViT served as the classification head, leveraging its global attention mechanism for enhanced contextual learning, achieving near-perfect accuracy (e.g., DenseNet121+ViT: 99.28%). …”
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Prediction of Vehicle Interior Wind Noise Based on Shape Features Using the WOA-Xception Model by Yan Ma, Hongwei Yi, Long Ma, Yuwei Deng, Jifeng Wang, Yudong Wu, Yuming Peng

“…The methodology integrates vehicle shape features with a whale optimization Xception model (WOA-Xception). …”
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Between Development, Growth and Survival: Some Current Features of Iran’s Model of Socio-Economic Development by N. A. Kozhanov

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EXAMINING THE IMPACT OF FEATURE SELECTION TECHNIQUES ON MACHINE AND DEEP LEARNING MODELS FOR THE PREDICTION OF COVID-19 by Hafiza Zoya Mojahid, Jasni Mohamad Zain, Marina Yusoff, Abdul Basit, Abdul Kadir Jumaat, Mushtaq Ali

“…LASSO with SVM performed the best overall in terms of accuracy = 0.7679 and precision=0.8236, but PCA outperformed RFE with XGBoost, underscoring the importance of matching feature selection methods to model types. …”

PCCNN: A CNN classification model integrating EEG time-frequency features for stroke classification by Teng Wang, Fenglian Li, Jia Yang, Wenhui Jia, Fengyun Hu

“…Unlike single-channel data or simple multi-channel concatenation, our method processes EEG data as a channel matrix, significantly improving classification performance. We employ two complementary feature extraction techniques: discrete wavelet transform (DWT) and empirical mode decomposition (EMD). …”
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Research on deep learning model for stock prediction by integrating frequency domain and time series features by Wenjie Sun, Jianhua Mei, Shengrui Liu, Chunhong Yuan, Jiaxuan Zhao

“…The StockMixer with ATFNet model proposed in this paper integrates both time-domain and frequency-domain features. …”
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