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Found 18 papers, 5 papers with code
Anomaly detection in surveillance videos using transformer based attention model
Therefore it is important to extract better quality features from the available videos.
Anomaly Detection In Surveillance Videos
Video Anomaly Detection
Impact of the composition of feature extraction and class sampling in medicare fraud detection
Following this, the healthcare industry has seen a significant increase in fraudulent activities owing to increased insurance, and fraud has become a significant contributor to rising medical care expenses, although its impact can be mitigated using fraud detection techniques.
BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net models
To accomplish such task, the models are required to be trained using huge amount of annotated or labelled data that highlights the region of interest with a binary mask.
Impact of Attention on Adversarial Robustness of Image Classification Models
Adversarial attacks against deep learning models have gained significant attention and recent works have proposed explanations for the existence of adversarial examples and techniques to defend the models against these attacks.
White blood cell subtype detection and classification
The classification of the white blood cells plays an important part in the medical diagnosis.
RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging
The advancements in deep learning technologies have produced immense contributions to biomedical image analysis applications.
MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification
Brain tumor is the most common and deadliest disease that can be found in all age groups.
Recommending best course of treatment based on similarities of prognostic markers
With the advancement in the technology sector spanning over every field, a huge influx of information is inevitable.
Modality specific U-Net variants for biomedical image segmentation: A survey
With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical image segmentation to address the automation in identification and detection of the target regions or sub-regions.
Hate speech detection using static BERT embeddings
With increasing popularity of social media platforms hate speech is emerging as a major concern, where it expresses abusive speech that targets specific group characteristics, such as gender, religion or ethnicity to spread violence.
Ranked #1 on
Hate Speech Detection
on Ethos Binary
Machine learning equipped web based disease prediction and recommender system
Worldwide, several cases go undiagnosed due to poor healthcare support in remote areas.
BERT-Based Sentiment Analysis: A Software Engineering Perspective
The paper presents three different strategies to analyse BERT based model for sentiment analysis, where in the first strategy the BERT based pre-trained models are fine-tuned; in the second strategy an ensemble model is developed from BERT variants, and in the third strategy a compressed model (Distil BERT) is used.
CHS-Net: A Deep learning approach for hierarchical segmentation of COVID-19 infected CT images
In the present article, an automated deep learning based model, COVID-19 hierarchical segmentation network (CHS-Net) is proposed that functions as a semantic hierarchical segmenter to identify the COVID-19 infected regions from lungs contour via CT medical imaging using two cascaded residual attention inception U-Net (RAIU-Net) models.
Face Mask Detection using Transfer Learning of InceptionV3
In this paper, a transfer learning model is proposed to automate the process of identifying the people who are not wearing mask.
Fruit classification using deep feature maps in the presence of deceptive similar classes
The objective of the present research is to address the challenge of classification of deceptively similar multi-granular objects with an ensemble approach thfat utilizes activations from multiple layers of CNN (deep features).
Enhanced Behavioral Cloning Based self-driving Car Using Transfer Learning
With the growing phase of artificial intelligence and autonomous learning, the self-driving car is one of the promising area of research and emerging as a center of focus for automobile industries.
Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques
From the experimental analysis, it is observed that the YOLO v3 with Deepsort tracking scheme displayed best results with balanced mAP and FPS score to monitor the social distancing in real-time.
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks
These datasets have limited samples concerned with the positive COVID-19 cases, which raise the challenge for unbiased learning.
