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Found 20 papers, 9 papers with code
SOS-1K: A Fine-grained Suicide Risk Classification Dataset for Chinese Social Media Analysis
Seven pre-trained models were evaluated in two tasks: high and low suicide risk, and fine-grained suicide risk classification on a level of 0 to 10.
AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts
Cognitive Behavioral Therapy (CBT) is an effective technique for addressing the irrational thoughts stemming from mental illnesses, but it necessitates precise identification of cognitive pathways to be successfully implemented in patient care.
Chinese MentalBERT: Domain-Adaptive Pre-training on Social Media for Chinese Mental Health Text Analysis
To address this, we have collected a huge dataset from Chinese social media platforms and enriched it with publicly available datasets to create a comprehensive database encompassing 3. 36 million text entries.
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification
Our focus was on understanding the impact of the freezing mechanism on performance.
Shift-ConvNets: Small Convolutional Kernel with Large Kernel Effects
Experimental results show that our shift-wise operator significantly improves the accuracy of a regular CNN while markedly reducing computational requirements.
Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects
The complexity of psychological principles underscore a significant societal challenge, given the vast social implications of psychological problems.
CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation
Medical dialogue generation relies on natural language generation techniques to enable online medical consultations.
Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation
This innovative framework is specifically designed for weakly supervised lesion segmentation in early-stage breast ultrasound images.
Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media
In response, we introduce two novel annotated datasets from Chinese social media, focused on cognitive distortions and suicidal risk classification.
Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals
In the contemporary landscape of social media, an alarming number of users express negative emotions, some of which manifest as strong suicidal intentions.
DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction
Then, we use GATs to dynamically extract the structural information of non-Euclidean data, and learn general features of the label dataset with contrastive learning by constructing positive and negative example samples.
RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models
The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world.
The Devil is in the Crack Orientation: A New Perspective for Crack Detection
However, the vanilla adaptation of the existing oriented object detection methods to the crack detection tasks will result in limited performance, due to the boundary discontinuity issue and the ambiguities in sub-crack orientation.
A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization
This paper begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs.
Geometry-Aware Guided Loss for Deep Crack Recognition
Despite the substantial progress of deep models for crack recognition, due to the inconsistent cracks in varying sizes, shapes, and noisy background textures, there still lacks the discriminative power of the deeply learned features when supervised by the cross-entropy loss.
Class-Incremental Learning for Wireless Device Identification in IoT
The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services.
Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT
Abnormality detection is essential to the performance of safety-critical and latency-constrained systems.
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey
In this paper, we provide a comprehensive survey on machine learning technologies for the identification of IoT devices along with the detection of compromised or falsified ones from the viewpoint of passive surveillance agents or network operators.
Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices
In this paper, we propose an enhanced deep learning framework for IoT device identification using physical layer signals.
Interpretation of Electrocardiogram (ECG) Rhythm by Combined CNN and BiLSTM
In this paper, we proposed a combined network of convolutional neural network (CNN) and Recurrent Neural Network (RNN), designed for the classification of ECG heart signals for diagnostic purpose.
