www.bing.com/ck/a?!&&p=174a554ef376e3562e63e2a95b4610366619e4d7ca6cc86dd5e9f0c34299370bJmltdHM9MTc3MjU4MjQwMA&ptn=3&ver=2&hsh=4&fclid=21c6c41a-c9d9-6626-2632-d308c85e6786&u=a1aHR0cHM6Ly93b3JsZGRlZXIub3JnL2FuaW1hbHMvcm9kZW50cy8&ntb=1
This classification demonstrates the remarkable variety within the rodent family, ranging from tiny gerbils to massive capybaras, each playing a unique role in their ecosystems.
github.com/dipanjanS/nlp_workshop_odsc_europe20
Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation…
arxiv.org/abs/2105.05496v2
Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this problem, publicly available themat...
arxiv.org/abs/2012.10715v6
The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The MLC methods based on convolutional neural networks (CNNs) have shown strong performance gai...
github.com/lanzen/CREST
CREST - Classification Resources for Environmnetal Sequence Tags, is a collection of software and databases for taxonomic classification of environmental marker genes from sequencing-based community profiling studies. (⭐ 12)
arxiv.org/abs/2310.18589v1
We present ProtoConcepts, a method for interpretable image classification combining deep learning and case-based reasoning using prototypical parts. Existing work in prototype-based image classification uses a ``this looks like that'' reasoning proce...
arxiv.org/abs/2408.16881v1
Published research highlights the presence of demographic bias in automated facial attribute classification algorithms, particularly impacting women and individuals with darker skin tones. Existing bias mitigation techniques typically require demogra...
arxiv.org/abs/2001.02372v1
In the last years, image classification processes like neural networks in the area of art-history and Heritage Informatics have experienced a broad distribution (Lang and Ommer 2018). These methods face several challenges, including the handling of c...
arxiv.org/abs/2403.04882v1
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains. To tackle this challenge effectively, it is imperative that the state-of-the-art attention model is s...
arxiv.org/abs/2503.12206v2
Contrastive Language-Image Pretraining (CLIP) has shown impressive zero-shot performance on image classification. However, state-of-the-art methods often rely on fine-tuning techniques like prompt learning and adapter-based tuning to optimize CLIP's...
arxiv.org/abs/2202.12019v3
This paper proposes a classification model for predicting the main activity of bitcoin addresses based on their balances. Since the balances are functions of time, we apply methods from functional data analysis; more specifically, the features of the...
arxiv.org/abs/2501.00356v1
Malicious URL (Uniform Resource Locator) classification is a pivotal aspect of Cybersecurity, offering defense against web-based threats. Despite deep learning's promise in this area, its advancement is hindered by two main challenges: the scarcity o...
arxiv.org/abs/2409.14306v1
Malicious URL classification represents a crucial aspect of cyber security. Although existing work comprises numerous machine learning and deep learning-based URL classification models, most suffer from generalisation and domain-adaptation issues ari...
github.com/dhruvp/wbc-classification
Classifying White Blood Cells with CNNs (⭐ 179)
arxiv.org/abs/2308.01327v1
This paper presents a fully automated approach for identifying speech anomalies from voice recordings to aid in the assessment of speech impairments. By combining Connectionist Temporal Classification (CTC) and encoder-decoder-based automatic speech...
arxiv.org/abs/2507.12602v1
Tree species classification from terrestrial LiDAR point clouds is challenging because of the complex multi-scale geometric structures in forest environments. Existing approaches using multi-scale dynamic graph convolutional neural networks (MS-DGCNN...
arxiv.org/abs/2501.13598v1
Recent approaches in hierarchical text classification (HTC) rely on the capabilities of a pre-trained transformer model and exploit the label semantics and a graph encoder for the label hierarchy. In this paper, we introduce an effective hierarchical...
arxiv.org/abs/2506.00662v1
INTRODUCTION: Alzheimer's disease (AD) is genetically complex, complicating robust classification from genomic data. METHODS: We developed a transformer-based ensemble model (TrUE-Net) using Monte Carlo Dropout for uncertainty estimation in AD classi...
github.com/semasuka/Credit-card-approval-prediction-classification
Credit risk analysis for credit card applicants (⭐ 301)
arxiv.org/abs/2102.04721v1
In the era of big data, the utilization of credit-scoring models to determine the credit risk of applicants accurately becomes a trend in the future. The conventional machine learning on credit scoring data sets tends to have poor classification for...
