End-to-end feature fusion for jointly optimized speech enhancement and automatic speech recognition by Mohamed Medani, Nasir Saleem, Fethi Fkih, Manal Abdullah Alohali, Hela Elmannai, Sami Bourouis

“…This approach integrates both the enhanced features and the raw noisy features, aiming to eliminate noise signals from the enhanced target speech while simultaneously learning fine details from the noisy signals. …”
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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

“…These features are input into multiple deep learning classifiers, including CNN-BiGRU, BiLSTM, and DNN architectures. …”
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Classification of SERS spectra for agrochemical detection using a neural network with engineered features by Mateo Frausto-Avila, Monserrat Ochoa-Elias, Jose Pablo Manriquez-Amavizca, María del Carmen González-López, Gonzalo Ramírez-García, Mario Alan Quiroz-Juárez

“…Notably, we performed feature engineering to optimize the input data; specifically, we derived 20 key features from transformation functions applied to the SERS spectra. …”
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MRI-based deep learning with clinical and imaging features to differentiate medulloblastoma and ependymoma in children by Yasen Yimit, Yasen Yimit, Parhat Yasin, Yue Hao, Abudouresuli Tuersun, Abudouresuli Tuersun, Chencui Huang, Xiaoguang Zou, Xiaoguang Zou, Ya Qiu, Ya Qiu, Yunling Wang, Mayidili Nijiati, Mayidili Nijiati

“…We developed a DL classifier using a pretrained AlexNet architecture that was fine-tuned on our dataset. To mitigate class imbalance, we implemented data augmentation and employed K-fold cross-validation to enhance model generalizability. …”
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Estimating shallow water sound power levels and mitigation radii for the R/V Marcus G. Langseth using an 8 km long MCS streamer by Timothy J. Crone, Maya Tolstoy, Helene Carton

“…We establish methods to filter, clean, and process streamer data to accurately determine received power levels and confidently establish mitigation radii. We show that in shallow water measured power levels can fluctuate due to the influence of seafloor topographic features, but that the use of the streamer for the establishment of dynamic mitigation radii is feasible and should be further pursued. …”
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Intrusion Detection and Mitigation Method for the Industrial Internet of Things Using Bidirectional Convolutional Long Short-Term Memory and Deep Recurrent Convolutional Q-Networks by Zhang Yan, Piyush Kumar Shukla, Prashant Kumar Shukla, Kanika Thakur, Anurag Sinha, Saifullah Khalid

“…A deep Q-network (DQN) handles attack mitigation and uses reinforcement learning to adjust to new threats. …”
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Flood risk and mitigation calculator tool to support decision-making for enhancing community resilience: a case study of the U.S.A. Gulf Coastal region by Rubayet Bin Mostafiz, Ayat Al Assi, Arash Taghinezhad, Carol J. Friedland, Carol J. Friedland, Robert V. Rohli, Robert V. Rohli, Md Adilur Rahim, Christopher T. Emrich, Melanie Gall, Eric Johnson

“…FRAMC’s key features include a user-friendly interface and draw on novel research related to innovative mitigation strategies. …”
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Enhancing protein O-GlcNAcylation in down syndrome mice mitigates memory dysfunctions through the rescue of mitochondrial bioenergetics, stress responses and pathological markers by Chiara Lanzillotta, Francesca Prestia, Viviana Greco, Federica Iavarone, Federica Cordella, Chiara Sette, Elena Forte, Antonella Tramutola, Simona Lanzillotta, Tommaso Cassano, Silvia Di Angelantonio, Andrea Urbani, Eugenio Barone, Marzia Perluigi, Fabio Di Domenico

“…Our study highlights the pivotal role of altered protein O-GlcNAcylation in DS neuropathology and establishes the molecular basis to envision the O-GlcNAc process as a promising therapeutic target to mitigate genetic- and metabolism-driven brain alterations linked to redox imbalance, mitochondrial failure and the development of AD features.…”
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CFANet: The Cross-Modal Fusion Attention Network for Indoor RGB-D Semantic Segmentation by Long-Fei Wu, Dan Wei, Chang-An Xu

“…For RGB images, asymmetric convolution is introduced to capture features in the horizontal and vertical directions, enhance short-range information dependence, mitigate the gridding effect of dilated convolution, and introduce criss-cross attention to obtain contextual information from global dependency relationships. …”
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Dual-Branch Spatial–Spectral Transformer with Similarity Propagation for Hyperspectral Image Classification by Teng Wen, Heng Wang, Liguo Wang

“…Specifically, this model first employs a Hybrid Pooling Spatial Channel Attention (HPSCA) module to integrate global information by pooling across different dimensional directions, thereby enhancing its ability to extract salient features. Secondly, we introduce a mechanism for transferring similarity attention that aims to retain and strengthen key semantic features, thus mitigating issues associated with information degradation. …”
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Robust Hybrid Data-Level Approach for Handling Skewed Fat-Tailed Distributed Datasets and Diverse Features in Financial Credit Risk by Musara Keith R, Ranganai Edmore, Chimedza Charles, Matarise Florence, Munyira Sheunesu

“…To bridge these gaps, we proposed a hybrid innovation framework that effectively mitigates the aberrations presented by nominal features with mismatching labels and noisy instances simultaneously. …”
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Advanced cloud intrusion detection framework using graph based features transformers and contrastive learning by Vijay Govindarajan, Junaid Hussain Muzamal

“…Key contributions include an interpretable pipeline using SHAP for feature attribution, a strategy for mitigating class imbalance, and validation across datasets with detailed security and generalizability analyses. …”
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SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target Detection by Zhihui Yu, Nian Pan, Jin Zhou

“…Specifically, we design the shallow-layer-guided feature enhancement (SLGFE) module, which guides multi-scale feature fusion with shallow layer information, effectively mitigating the loss of information in deep networks. …”
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FlexiNet: An Adaptive Feature Synthesis Network for Real-Time Ego Vehicle Speed Estimation by Abdalrahaman Ibrahim, Kyandoghere Kyamakya, Wolfgang Pointner

“…FlexiNet integrates five key components, the Contextual Motion Analysis Block, Adaptive Feature Transformer, Spatial Feature Extraction Module, Motion Feature Extraction Module, and Dynamic Integration Gate, to effectively extract and fuse spatial and temporal features, thereby overcoming limitations of previous approaches by mitigating noise and capturing subtle motion dynamics. …”
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The Detection Optimization of Low-Quality Fake Face Images: Feature Enhancement and Noise Suppression Strategies by Ge Wang, Yue Han, Fangqian Xu, Yuteng Gao, Wenjie Sang

“…The proposed algorithm introduces an innovative convolution module, Adaptive Kernel Convolution (AKConv), which dynamically adjusts kernel sizes to effectively extract image features, thereby mitigating the challenges posed by low resolution, blurriness, and noise. …”
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A Self-Supervised Monocular Depth Estimation Framework Based on Detail Recovery and Feature Fusion by Shun Li, Chongzheng Huang, Xiangzhe Li, Zhengyou Liang

“…Specifically, ASAM selectively emphasizes critical features and spatial locations in images to enhance the model’s ability to capture details. …”
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Boosting feature selection efficiency with IMVO: Integrating MVO and mutation-based local search algorithms by Maryam Askari, Farid Khoshalhan, Hodjat Hamidi

“…Feature selection is crucial in machine learning and data mining, significantly impacting model performance and efficiency by reducing dimensionality, mitigating overfitting, and improving interpretability. …”
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Multiscale feature fusion and enhancement in a transformer for the fine-grained visual classification of tree species by Yanqi Dong, Zhibin Ma, Jiali Zi, Fu Xu, Feixiang Chen

“…The MFF module aims to strike a balance between global and local feature extraction. The DFE module is employed to mitigate the impact of background noise, whereas the TFE module is used to enhance the feature extraction associated with complex textures and spatial patterns. …”
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Temporal Graph Attention Network for Spatio-Temporal Feature Extraction in Research Topic Trend Prediction by Zhan Guo, Mingxin Lu, Jin Han

“…Comprehensively extracting spatio-temporal features is essential to research topic trend prediction. …”
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ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering by Maryam Shahabikargar, Amin Beheshti, Wathiq Mansoor, Xuyun Zhang, Eu Jin Foo, Alireza Jolfaei, Ambreen Hanif, Nasrin Shabani

“…Predictive and Machine Learning (ML)-based analysis, when trained with appropriate features indicative of customer behaviour and cognitive status, can be highly effective in mitigating churn. …”
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