A hybrid model improves bankcard response classification accuracy.
problem Bankcard response classification accuracy improvement.
method CHAID for interaction detection in logistic regression.
result The hybrid model outperforms logistic regression in classification accuracy.
A hybrid neural network improves medical diagnostics of reactive arthritis.
problem Overlapping classes in medical diagnostics data.
method Hybrid clustering-classification neural network with cosine similarity measures and fuzzy reasoning.
result Efficiency confirmed through experiments on reactive arthritis diagnostics.
Improved time series classification with GRU-FCN model.
problem Time series classification challenges.
method Hybrid LSTM-GRU model for univariate time series classification.
result GRU-FCN model outperforms state-of-the-art models.
This thesis evaluates text-based vs audio-based classification of mental health interviews.
problem Classifying psychiatric illness using text-based methods.
method Design and evaluate a text classification network on mental health interviews, using belabBERT.
result Text-based classification is a strong alternative to audio-based methods.
NSC classifies hybrid system states for time-bounded reachability, achieving high accuracy with minimal false negatives.
problem Classifying states in hybrid systems for time-bounded reachability.
method Neural State Classification using Deep Neural Networks.
result Achieved 99.25% to 99.98% accuracy with false-negative rates reduced to 0.0015 to 0 after tuning.
A hybrid K-NN and SVM technique improves classification accuracy.
problem Improving classification accuracy in pattern recognition.
method Discriminative nearest neighbour classification combined with SVM.
result The hybrid technique outperforms state-of-the-art methods.
Quantum hybrid vision transformers improve event classification in high energy physics.
problem Excessive computational resources for training and deploying vision transformer models.
method Constructed quantum hybrid vision transformers for high energy physics event classification.
result Quantum hybrid models achieve comparable performance to classical models with fewer parameters.
Unified backpropagation improves multi-objective deep learning performance.
problem Improving classification performance in hybrid neural networks.
method Linking hybrid loss functions through unified backpropagation.
result Consistent improvements in deep convolutional neural network classification performance.
Novel hybrid bilinear model improves epilepsy diagnosis accuracy.
problem Improving accuracy in epilepsy diagnosis and treatment.
method Hybrid bilinear deep learning network using sEEG and audiovisual monitoring.
result Obtained F1-scores of 97.4% and 97.2% on two seizure datasets.
Paper proposes a SIMO DNN for indoor localization using Wi-Fi fingerprints.
problem Indoor localization with high accuracy and efficiency.
method Single-input and multi-output deep neural network architecture.
result SIMO-DNN scheme outperforms existing methods in floor detection and location accuracy.
A hybrid model uses neural networks to create features for better bankcard response classification.
problem Bankcard response classification with improved accuracy and interpretability.
method Two-stage hybrid approach with neural networks for feature construction, followed by logistic regression.
result The two-stage model outperforms traditional models in accuracy, ROC curve, and KS statistic.
Improved sarcasm detection using hybrid neural network with a new dataset.
problem Predicting sarcasm in text remains challenging for machines.
method Proposed a hybrid Neural Network architecture with attention mechanism.
result Improves classification accuracy by ~ 5%.
Develops algorithms for constructing statistical industry classifications.
problem Creating accurate industry classifications for quantitative trading.
method Combines clustering algorithms with correlation analysis to improve fundamental industry classifications.
result Shows that clustering methods improve off-the-shelf industry classifications.
Hybrid model avoids forgetting across tasks and classes.
problem Challenges of continual learning in modern deep learning.
method Hybrid generative-discriminative approach using normalizing flows.
result Strong performance on various continual learning benchmarks.
Hybrid models are reinterpreted as Neuro-Symbolic AI designs to quantify uncertainty and variability.
problem Limited semantic interface for comparing hybrid models across domains.
method Reinterpret hybrid models as Neuro-Symbolic AI, translating them into explicit inference function and logic-belief decomposition.
result Metrics SVR and BD quantify uncertainty and variability in hybrid models.
Transfer learning adapted for hybrid classical-quantum neural networks.
problem Optimizing data preprocessing and feature embedding for quantum processors.
method Adapting transfer learning to hybrid networks, using a pre-trained classical network augmented by a quantum circuit.
result Demonstrated the effectiveness of quantum transfer learning for image recognition and quantum state classification.
A hybrid model reduces graph complexity for improved classification accuracy.
problem High computational complexity and large number of parameters in higher-order graph convolutional networks.
method Weight sharing mechanism and novel fusion pooling layer to reduce parameters and complexity.
result The proposed model achieves highest classification accuracy with fewer trainable parameters.
DeepBrain learns functional neural image representations for gene ontology classification.
problem Classifying gene ontology categories from neural in-situ hybridization images.
method Uses deep convolutional denoising autoencoders (CDAE) to generate compact, invariant representations.
result Improves classification accuracy by 75% to 0.98 AUC.
Deep learning classifies corn hybrids' tolerance to drought and heat.
problem Classifying corn hybrids' tolerance to drought and heat stress.
method Unsupervised deep convolutional neural networks approach.
result 121 hybrids labeled drought tolerant, 193 heat tolerant, 29 both.
Hybrid QC system for Bengali questions using smart data balancing.
problem Classifying factoid questions in Bengali.
method Two-stage approach with 1D CNN for coarse classification and SGD for fine classification.
result Effectiveness of smart data balancing technique in improving classification accuracy.
XEM improves multivariate time series classification with explainable models.
problem Multivariate time series classification challenges.
method Hybrid ensemble method combining explicit boosting-bagging and implicit divide-and-conquer.
result XEM outperforms state-of-the-art MTS classifiers on public datasets.
Various approaches to gene selection for cancer classification based on microarray data can be found in the literature and they may be grouped into two categories: univariate methods and multivariate methods. Univariate methods look at each gene in the data in isolation from others. They measure the contribution of a p…
A hybrid neural network optimizes AI deployment on edge and cloud for energy efficiency.
problem Energy and resource constraints in edge devices for deep learning models.
method Conditionally deep hybrid neural network with quantized layers at edge and full-precision layers at cloud.
result Early classification at the edge reduces energy consumption by 5.5x on CIFAR-10 dataset.
The performance of a modulation classifier is highly sensitive to channel signal-to-noise ratio (SNR). In this paper, we focus on amplitude-phase modulations and propose a modulation classification framework based on centralized data fusion using multiple radios and the hybrid maximum likelihood (ML) approach. In order…
A hybrid CNN with shared parameters learns to incorporate recurrent structures.
problem Designing efficient recurrent neural networks.
method Parameter sharing scheme combining CNN and recurrent layers.
result Substantial parameter savings with competitive accuracy.
A hybrid method improves Convolutional Neural Networks training.
problem Training Convolutional Neural Networks efficiently and avoiding local minima.
method Combines backpropagation with evolutionary strategies.
result Improves accuracy by 0.61% on CIFAR-10 image classification.
Hybrid framework prevents forgetting in continual learning.
problem Avoiding forgetting in learning new tasks without forgetting old ones.
method Hybrid continual learning framework combining architecture growth and experience replay.
result Hybrid approach effectively avoids forgetting across multiple tasks.
Hybrid regularization avoids double descent in random feature models.
problem Avoiding the double descent phenomenon in random feature models.
method Combines early stopping and weight decay, using GCV for hyperparameter selection.
result Hybrid method successfully avoids double descent and achieves comparable generalization.
Evo-NAS combines neural and evolutionary methods for efficient neural architecture search.
problem Efficiently searching for optimal neural architectures in deep learning.
method Evolutionary-Neural hybrid agents that combine the strengths of neural and evolutionary algorithms.
result Evo-NAS outperforms both neural and evolutionary agents in architecture search for various classification tasks.
Many classification problems involve data instances that are interlinked with each other, such as webpages connected by hyperlinks. Techniques for "collective classification" (CC) often increase accuracy for such data graphs, but usually require a fully-labeled training graph. In contrast, we examine how to improve the…
Paper presents J-RFDL for robust DL in compressed space, improving data representation robustness and accuracy.
problem Improving data representation robustness and accuracy in the presence of noise and outliers.
method Joint Robust Factorization and Projective Dictionary Learning (J-RFDL) in a factorized compressed space.
result Delivers superior performance in data representation and classification over state-of-the-art methods.
Microarray cancer gene expression data comprise of very high dimensions. Reducing the dimensions helps in improving the overall analysis and classification performance. We propose two hybrid techniques, Biogeography - based Optimization - Random Forests (BBO - RF) and BBO - SVM (Support Vector Machines) with gene ranki…
Automated sentiment analysis and opinion mining is a complex process concerning the extraction of useful subjective information from text. The explosion of user generated content on the Web, especially the fact that millions of users, on a daily basis, express their opinions on products and services to blogs, wikis, so…
VQC-MLPNet combines quantum and classical elements for scalable quantum machine learning.
problem Challenges in expressivity, trainability, and noise resilience of VQCs.
method Hybrid architecture with a VQC generating weights for a classical MLP during training.
result Improved expressivity, trainability, and robustness compared to standalone quantum or hybrid approaches.
Deep learning method improves gene ontology classification of neural images.
problem Classifying gene expression in neural in situ hybridization images.
method End-to-end deep learning using convolutional denoising autoencoders (CDAE).
result Significant improvement in classification accuracy (96% reduction in error rate).
Quantum machine learning model for binary classification.
problem Efficiency in high-dimensional binary classification tasks.
method Quantum-classical hybrid algorithm and quantum computer for inference.
result Quantum discriminator achieves 99% accuracy on Iris dataset.
A new hybrid Newton algorithm improves convergence in logistic regression.
problem Solving large-scale binary classification problems efficiently.
method Proposes a hybrid stochastic Newton algorithm with two weighted components in the Hessian matrix estimation.
result Proves almost sure convergence to the true parameter of logistic regression.
Efficient hybrid networks improve AI performance at the edge.
problem Achieving AI performance at the edge with minimal energy and memory usage.
method Proposed hybrid networks combining binary and full-precision layers.
result Hybrid networks achieve close to full-precision performance with up to 21.8x memory compression.
EGFC learns from streaming data to classify power quality disturbances.
problem Real-time detection and classification of power quality disturbances.
method Evolving Gaussian Fuzzy Classification (EGFC) framework with semi-supervised learning.
result Encouraging classification results from online data streams.
Hybrid framework predicts Arctic permafrost decline, risks infrastructure, and provides tools.
problem Tackles permafrost decline and infrastructure risk assessment in Arctic territories.
method Hybrid physics-machine learning framework integrating 2.9 million observations.
result Projects mean permafrost fraction decline of -20.3 pp under RCP8.5 forcing, with high-risk zones identified.
A new method for cross-view classification using divide-and-conquer.
problem Cross-view classification with nonlinear manifolds and outliers.
method Divide-and-Conquer strategy applied to three subproblems: view discrepancy, intrinsic structure, and discriminability.
result Significant improvement in classification accuracy and robustness compared to state-of-the-art methods.
Microarray is a technology to quantitatively monitor the expression of large number of genes in parallel. It has become one of the main tools for global gene expression analysis in molecular biology research in recent years. The large amount of expression data generated by this technology makes the study of certain com…
This research compares gradient and Newton boosting methods in classification and regression.
problem The distinction between gradient descent and Newton updates in boosting algorithms is not well understood.
method Presented a unified framework for gradient and Newton boosting, and compared them with tree base learners.
result Newton boosting outperforms gradient and hybrid boosting in predictive accuracy on most datasets.
Proposes a hybrid model for stock market report classification using graph neural networks.
problem Lack of unified node embeddings for heterogeneous graphs in text datasets.
method Transductive hybrid approach combining unsupervised node representation learning and supervised node classification/edge prediction.
result Demonstrates the model's ability to classify stock market technical analysis reports.
Combining automata learning and model-based testing generates sufficient training data for hybrid systems.
problem Challenges in constructing models of hybrid systems that combine physical and digital behavior.
method Exploits a combination of automata learning and model-based testing to generate sufficient training data.
result Recurrent neural networks trained with this data achieved significantly better results compared to models learned from random data.
A novel KD classifier improves character recognition performance.
problem Improving character recognition accuracy.
method Kernel-based generative classifier in distortion subspace with iterative kernel selection.
result The KD classifier outperforms existing classifiers and has unique recognition capability.
A new hybrid framework reduces quantum runtime and noise effects.
problem Challenges in deploying deep QFMs on real quantum hardware.
method Iterative Quantum Feature Maps (IQFMs) combining shallow QFMs and classical augmentation weights.
result Numerical experiments show IQFMs outperforming quantum convolutional neural networks.
A new system detects and classifies defects in semiconductor manufacturing.
problem Detecting and classifying novel defect patterns in high-resolution imagery.
method Stacked hybrid convolutional neural networks (SH-CNN) with visual attention.
result SH-CNN outperforms current approaches in automated visual inspection.