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• Tri-channel fusion in neurocognitive diagnostic model.
• Attention mechanism enhances cognitive feature fusion.
• Improved accuracy in modeling high-dimensional features.

Cognitive diagnostics is a pivotal area within educational data mining, focusing on deciphering students’ cognitive status via their academic performance. Traditionally, cognitive diagnostic models (CDMs) have evolved from manually designed ...

This work presents a novel hybrid framework called DCEnt-PredictiveNet (deep convolutional neural network (DCNN) + entropy + support vector regressor (SVR)) that concatenate both deep and handcrafted features for time series data analysis and ...

• Explainable hybrid framework called DCEnt-PredictiveNet for Time Series Forecasting (TSF) is proposed.
• Proposed architecture consists of Discrete Wavelet Transform, Convolutional Neural Network, Entropy and Support Vector Regression.

Drug sensitivity significantly influences therapeutic outcomes. Recent discoveries have highlighted the pivotal role of miRNAs in regulating drug sensitivity by modulating genes associated with drug metabolism and action. As crucial regulators of ...

• We design a dual-channel feature representation for heterogeneous networks to predict miRNA-drug sensitivity associations.
• The global attention mechanism of HGGN enhanced the performance of algorithms in data-limited scenarios.
• ...

Deep learning is applicable in mechanical fault diagnosis, ensuring the secure operation of mechanical systems. However, the lack of interpretability and noise robustness in deep learning methods has been a common challenge faced by academia and ...

Early identification of egg gender during incubation is crucial for animal welfare and commercial poultry production, as nowadays day-old male chicks are often culled due to low economic value. Hyperspectral imaging (HSI) recognition presents a ...

• Hyperspectral images of Hy-Line Sonia eggs were collected on even days from day 0 to 14 for gender identification.
• Feature bands were selected by RF, PCA, SPA, CARS and then the recombined images were processed by ViT-Base16.
• ...

The advent of smart healthcare has significantly heightened the importance of computer technologies in supporting medical diagnosis and treatment. Nevertheless, the challenges of mining latent knowledge within diagnostic data and explaining ...

Identifying subgroups with similar survival outcomes is a pivotal challenge in survival analysis. Traditional clustering methods often neglect the outcome variable, potentially leading to inaccurate representation of risk profiles. To address ...

• SurvivalLVQ: Learning Vector Quantization is adapted to survival analysis.
• SurvivalLVQ groups cases by survival probability and assigns unique survival curves.
• SurvivalLVQ demonstrates strong clustering and competitive predictive ...

In order to address the expensive computation cost of deep networks, some Single Image Super-Resolution (SISR) methods tried to design the lightweight networks by means of recursion or expert prior. However, they discuss the theoretical ...

State-of-the-art (SOTA) transformer-based models in the domains of Natural Language Processing (NLP) and Information Retrieval (IR) are often characterized by their opacity in terms of decision-making processes. This limitation has given rise to ...

Interpreting and explaining complex models such as ensemble machine learning models for opinion mining is essential to increase the level of transparency fairness and reliability of positive and negative opinion prediction results. Although ensemble ...

Existing methods for filter pruning mostly rely on specific data-driven paradigms but lack the interpretability. Besides, these approaches usually assign layer-wise compression ratios automatically only under given FLOPs by neural architecture ...

Although non-interpretable (black-box) deep learning models are well known for their accuracy, interpretable deep learning models should be used for high stake decisions, such as: healthcare. In this paper, we present a novel technique of ...

• Comb-ProtoPNet introduces a novel technique to form ensemble algorithms.
• Comb-ProtoPNet combines the algorithms themselves instead of combining their outputs.
• Comb-ProtoPNet uses prototypes with rectangular and square spatial ...

The wide dissemination of false news is increasingly threatening both individuals and society. Aiming at the problem of insufficient interpretability in false news detection task, the model TCIN based on collaborative attention mechanism is proposed. The ...

Colorectal polyp is a common structural gastrointestinal (GI) anomaly, which can in certain cases turn malignant. Colonoscopic image inspection is, thereby, an important step for isolating the polyps as well as removing them if ...

• Utilization of both un-dilated and dilated filters in the encoder and decoder.
• Stretch–Relax type Attention system within encoder and decoder pipeline.
• Additional Feature-to-Mask Pipeline for feature aggregation and prediction.

Artificial intelligence (AI) is continuously evolving; however, in the last 10 years, it has gotten considerably more difficult to explain AI models. With the help of explanations, end users can understand the outcomes generated by AI models. The ...

Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to interpret and explain, culminating in black-box machine learning models. Model developers and users ...

• A novel XAI method for local, model-agnostic, post-hoc explanations is presented.

Inverted papilloma (IP) is a common sinus neoplasm with a probability of malignant transformation. Nasal polyps (NP) are the most frequent masses in the sinus. The classification of IP and NP using computed tomography (CT) is highly ...

• A deep CNN with attention mechanism composed of DenseNet and SENet, is applied for classifying the IP and NP in CT images.
• The classification performance of SE-DenseNet is evaluated, and the accuracy of our method can achieve 88.4% ...

Continuing growth of Artificial Intelligence (AI) adoption across enterprises and governments around the world has fueled the demand for trustworthy AI systems and applications. The need ranges from the so-called Explainable or Interpretable AI to ...

The COVID-19 pandemic is an ongoing pandemic and is placing additional burden on healthcare systems around the world. Timely and effectively detecting the virus can help to reduce the spread of the disease. Although, RT-PCR is still a ...

• Quasi-ProtoPNet uses the positive reasoning process.
• The positive reasoning ...