Annotated Dataset Research Articles

Symmetry-induced ambiguity in orientation estimation from RGB images

The estimation of object orientation from RGB images is a core component in many modern computer vision pipelines. Traditional techniques mostly predict a single orientation per image, learning a one-to-one mapping between images and rotations. However, when objects exhibit rotational symmetries, they can appear identical from multiple viewpoints. This induces ambiguity in the estimation problem, making images map to rotations in a one-to-many fashion. In this paper, we explore several ways of addressing this problem. In doing so, we specifically consider algorithms that can map an image to a range of multiple rotation estimates, accounting for symmetry-induced ambiguity. Our contributions are threefold. Firstly, we create a data set with annotated symmetry information that covers symmetries induced through self-occlusion. Secondly, we compare and evaluate various learning strategies for multiple-hypothesis prediction models applied to orientation estimation. Finally, we propose to model orientation estimation as a binary classification problem. To this end, based on existing work from the field of shape reconstruction, we design a neural network that can be sampled to reconstruct the full range of ambiguous rotations for a given image. Quantitative evaluation on our annotated data set demonstrates its performance and motivates our design choices.

Mapping Pituitary Neuroendocrine Tumors: An Annotated MRI Dataset Profiling Tumor and Carotid Characteristics

Pituitary neuroendocrine tumors remain one of the most common intracranial tumors. While radiomic research related to pituitary tumors is progressing, public data sets for external validation remain scarce. We introduce an open dataset comprising high-resolution T1 contrast-enhanced MR scans of 136 patients with pituitary tumors, annotated for tumor segmentation and accompanied by clinical, radiological and pathological metadata. This diverse dataset captures variations in tumor size, location, and pathological activity, essential for understanding this complex condition. Expert annotations of both the tumor and adjacent carotid arteries ensure precise delineation, facilitating the development of automated segmentation algorithms. Our initiative addresses the need for standardized data in pituitary oncology, fosters collaboration and innovation, and enables the development and benchmarking of workflows that utilize pituitary radiomics for treatment planning and outcome prediction.

Pneumothorax Detection System in Thoracic Radiography Images Using CNN Method

Purpose: This research aims to develop an automatic pneumothorax detection system using Convolutional Neural Networks (CNN) to classify thoracic radiography images. By leveraging CNN's effectiveness in identifying medical abnormalities, the system seeks to enhance diagnostic accuracy, reduce evaluation time, and minimize subjective interpretation errors. The output will provide a predicted label of "pneumothorax" or "non-pneumothorax," facilitating faster clinical treatment and improving diagnostic services while supporting radiologists in making more accurate and efficient decisions for this critical condition. Methods: This research employs an experimental deep learning approach using Convolutional Neural Networks (CNN) to detect pneumothorax in thoracic radiography images. The CNN model is trained on an annotated dataset with preprocessing steps, including zooming, brightness adjustment, flipping and format adjustment, followed by performance evaluation using accuracy, precision, recall, and F1 score metrics. Result: The results showed that the CNN model detected pneumothorax with 79.59% accuracy, a loss of 1.3056, and 1,092 correct predictions out of 1,372 test data. Precision was 51.12%, recall 78.62%, and F1 score 61.96%, confirming the system's potential, though further optimization is needed. Novelty: The novelty of this research lies in developing an automated pneumothorax detection system using a CNN architecture, improving diagnostic accuracy and efficiency. Despite high accuracy, precision and recall can be improved. Future research can focus on optimizing the model and applying data augmentation techniques.

Facilitating automated fact-checking: a machine learning based weighted ensemble technique for claim detection

The rapid digitization of media, driven by technological advancements, has accelerated the spread of information through online platforms. This has heightened the need for robust fact-checking mechanisms to counter misinformation. The prevalence of misinformation necessitates the development of automated claim detection systems to support efficient automated or semi-automated fact-checking processes. Existing claim detection systems predominantly focus on the English language, with limited resources available for other regional languages like Bangla. This paper proposes a novel ensemble machine learning framework for the effective detection of claims in a low-resource language like Bangla, a critical initial step in the automated fact-checking process. The proposed weighted ensemble technique combines Support Vector Machines, Bernoulli Naive Bayes, and Decision Trees as base classifiers to effectively detect claims. An annotated text dataset comprising 5010 sentences sourced from various online platforms, including several online fact-checking sites, was developed. To determine the optimal model and feature representation for claim detection, various machine learning algorithms were evaluated using BoW, TF-IDF, Word2Vec, and FastText features. The efficacy of ensemble models was examined by investigating both averaging and weighting strategies. Evaluation metrics showcased that the proposed weighted ensemble approach outperformed all baseline models, achieving a maximum F1 score of 0.87. To the best of our knowledge, this study is the first and only approach to claim detection in the Bangla language, with the potential for extension to other resource-constrained languages. Our work aspires to serve as a crucial tool in the fight against misinformation by advancing the accuracy and transparency of information.

TransCAE: Enhancing cell type annotation in single-cell RNA-seq data with transfer learning and convolutional autoencoder.

Single-cell RNA sequencing (scRNA-seq) analysis offers tremendous potential for addressing various biological questions, with one key application being the annotation of query datasets with unknown cell types using well-annotated external reference datasets. However, the performance of existing supervised or semi-supervised methods largely depends on the quality of source data. Furthermore, these methods often struggle with the batch effects arising from different platforms when handling multiple reference or query datasets, making precise annotation challenging. We developed transCAE, a robust transfer learning-based algorithm for single-cell annotation that integrates unsupervised dimensionality reduction with supervised cell type classification. This approach fully leverages information from both reference and query datasets to achieve precise cell classification within the query data. Extensive evaluations show that transCAE significantly enhances classification accuracy and efficiently mitigates batch effects. Compared to other state-of-the-art methods, transCAE demonstrates superior performance in experiments involving multiple reference or query datasets. These strengths position transCAE as an optimal annotation method for scRNA-seq datasets.

Diagnosing Helicobacter pylori using autoencoders and limited annotations through anomalous staining patterns in IHC whole slide images.

This work addresses the detection of Helicobacter pylori (H. pylori) in histological images with immunohistochemical staining. This analysis is a time-demanding task, currently done by an expert pathologist that visually inspects the samples. Given the effort required to localize the pathogen in images, a limited number of annotations might be available in an initial setting. Our goal is to design an approach that, using a limited set of annotations, is capable of obtaining results good enough to be used as a support tool. We propose to use autoencoders to learn the latent patterns of healthy patches and formulate a specific measure of the reconstruction error of the image in HSV space. ROC analysis is used to set the optimal threshold of this measure and the percentage of positive patches in a sample that determines the presence of H. pylori. Our method has been tested on an own database of 245 whole slide images (WSI) having 117 cases without H. pylori and different density of the bacteria in the remaining ones. The database has 1211 annotated patches, with only 163 positive patches. This dataset of positive annotations was used to train a baseline thresholding and an SVM using the features of a pre-trained RedNet-18 and ViT models. A 10-fold cross-validation shows that our method has better performance with 91% accuracy, 86% sensitivity, 96% specificity and 0.97 AUC in the diagnosis of H. pylori . Unlike classification approaches, our shallow autoencoder with threshold adaptation for the detection of anomalous staining is able to achieve competitive results with a limited set of annotated data. This initial approach is good enough to be used as a guide for fast annotation of infected patches.

SProtFP: a machine learning-based method for functional classification of small ORFs in prokaryotes.

Small proteins (≤100 amino acids) play important roles across all life forms, ranging from unicellular bacteria to higher organisms. In this study, we have developed SProtFP which is a machine learning-based method for functional annotation of prokaryotic small proteins into selected functional categories. SProtFP uses independent artificial neural networks (ANNs) trained using a combination of physicochemical descriptors for classifying small proteins into antitoxin type 2, bacteriocin, DNA-binding, metal-binding, ribosomal protein, RNA-binding, type 1 toxin and type 2 toxin proteins. We have also trained a model for identification of small open reading frame (smORF)-encoded antimicrobial peptides (AMPs). Comprehensive benchmarking of SProtFP revealed an average area under the receiver operator curve (ROC-AUC) of 0.92 during 10-fold cross-validation and an ROC-AUC of 0.94 and 0.93 on held-out balanced and imbalanced test sets. Utilizing our method to annotate bacterial isolates from the human gut microbiome, we could identify thousands of remote homologs of known small protein families and assign putative functions to uncharacterized proteins. This highlights the utility of SProtFP for large-scale functional annotation of microbiome datasets, especially in cases where sequence homology is low. SProtFP is freely available at http://www.nii.ac.in/sprotfp.html and can be combined with genome annotation tools such as ProsmORF-pred to uncover the functional repertoire of novel small proteins in bacteria.

An Improved Method for Human Activity Detection with High-Resolution Images by Fusing Pooling Enhancement and Multi-Task Learning

Deep learning has garnered increasing attention in human activity detection due to its advantages, such as not relying on expert knowledge and automatic feature extraction. However, the existing deep learning-based approaches are primarily confined to recognizing specific types of human activities, which hinders scientific decision-making and comprehensive environmental protection. Therefore, there is an urgent need to develop a deep learning model to address multiple-type human activity detection with finer-resolution images. In this study, we proposed a new multi-task learning model (named PE-MLNet) to simultaneously achieve change detection and land use classification in GF-6 bitemporal images. Meanwhile, we also designed a pooling enhancement module (PEM) to accurately capture multi-scale change details from the bitemporal feature maps through combining differencing and concatenating branches. An independent annotated dataset at Yellow River Delta was taken to examine the effectiveness of PE-MLNet. The results showed that PE-MLNet exhibited obvious improvements in both detection accuracy and detail handling compared with other existing methods. Further analysis uncovered that the areas of buildings, roads, and oil depots has obviously increased, while the farmland and wetland areas largely decreased over the five years, indicating an expansion of human activities and their increased impacts on natural environments.

Emerging Trends in Deep Learning for Early Alzheimer's Disease Diagnosis and Classification: A Comprehensive Review

Alzheimer's Disease (AD), a progressive neurodegenerative disorder, manifests as cognitive decline and memory loss, significantly impacting individuals' lives and healthcare systems globally. Early diagnosis and intervention are crucial for improving patient outcomes and managing the disease effectively. Recent advancements in deep learning (DL) have shown substantial promise in medical image classification for early AD diagnosis. This survey evaluates state-of-the-art DL techniques, including hybrid models, Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), applied across imaging modalities such as computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI). It emphasizes their performance, accuracy, and computational efficiency while addressing critical challenges like the need for large annotated datasets, overfitting, and model interpretability. Furthermore, the survey explores how DL could revolutionize AD diagnosis and identifies future research directions to bridge existing gaps, aiming to improve early detection and personalized diagnostic approaches for individuals with AD.

Privacy-ensuring Open-weights Large Language Models Are Competitive with Closed-weights GPT-4o in Extracting Chest Radiography Findings from Free-Text Reports.

Background Large-scale secondary use of clinical databases requires automated tools for retrospective extraction of structured content from free-text radiology reports. Purpose To share data and insights on the application of privacy-preserving open-weights large language models (LLMs) for reporting content extraction with comparison to standard rule-based systems and the closed-weights LLMs from OpenAI. Materials and Methods In this retrospective exploratory study conducted between May 2024 and September 2024, zero-shot prompting of 17 open-weights LLMs was preformed. These LLMs with model weights released under open licenses were compared with rule-based annotation and with OpenAI's GPT-4o, GPT-4o-mini, GPT-4-turbo, and GPT-3.5-turbo on a manually annotated public English chest radiography dataset (Indiana University, 3927 patients and reports). An annotated nonpublic German chest radiography dataset (18 500 reports, 16 844 patients [10 340 male; mean age, 62.6 years ± 21.5 {SD}]) was used to compare local fine-tuning of all open-weights LLMs via low-rank adaptation and 4-bit quantization to bidirectional encoder representations from transformers (BERT) with different subsets of reports (from 10 to 14 580). Nonoverlapping 95% CIs of macro-averaged F1 scores were defined as relevant differences. Results For the English reports, the highest zero-shot macro-averaged F1 score was observed for GPT-4o (92.4% [95% CI: 87.9, 95.9]); GPT-4o outperformed the rule-based CheXpert [Stanford University] (73.1% [95% CI: 65.1, 79.7]) but was comparable in performance to several open-weights LLMs (top three: Mistral-Large [Mistral AI], 92.6% [95% CI: 88.2, 96.0]; Llama-3.1-70b [Meta AI], 92.2% [95% CI: 87.1, 95.8]; and Llama-3.1-405b [Meta AI]: 90.3% [95% CI: 84.6, 94.5]). For the German reports, Mistral-Large (91.6% [95% CI: 90.5, 92.7]) had the highest zero-shot macro-averaged F1 score among the six other open-weights LLMs and outperformed the rule-based annotation (74.8% [95% CI: 73.3, 76.1]). Using 1000 reports for fine-tuning, all LLMs (top three: Mistral-Large, 94.3% [95% CI: 93.5, 95.2]; OpenBioLLM-70b [Saama]: 93.9% [95% CI: 92.9, 94.8]; and Mixtral-8×22b [Mistral AI]: 93.8% [95% CI: 92.8, 94.7]) achieved significantly higher macro-averaged F1 score than did BERT (86.7% [95% CI: 85.0, 88.3]); however, the differences were not relevant when 2000 or more reports were used for fine-tuning. Conclusion LLMs have the potential to outperform rule-based systems for zero-shot "out-of-the-box" structuring of report databases, with privacy-ensuring open-weights LLMs being competitive with closed-weights GPT-4o. Additionally, the open-weights LLM outperformed BERT when moderate numbers of reports were used for fine-tuning. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Gee and Yao in this issue.

Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotation

Background:Suicide is a leading cause of death worldwide, making early identification of suicidal behaviors crucial for clinicians. Current Natural Language Processing (NLP) approaches for identifying suicidal behaviors in Electronic Health Records (EHRs) rely on keyword searches, rule-based methods, and binary classification, which may not fully capture the complexity and spectrum of suicidal behaviors. This study aims to create a multi-class labeled dataset with annotation guidelines and develop a novel NLP approach for fine-grained, multi-label classification of suicidal behaviors, improving the efficiency of the annotation process and accuracy of the NLP methods. Methods:We develop a multi-class labeling system based on guidelines from FDA, CDC, and WHO, distinguishing between six categories of suicidal behaviors and allowing for multiple labels per data sample. To efficiently create an annotated dataset, we use an MPNet-based semantic retrieval framework to extract relevant sentences from a large EHR dataset, reducing annotation space while capturing diverse expressions. Experts annotate the extracted sentences using the multi-class system. We then formulate the task as a multi-label classification problem and fine-tune transformer-based models on the curated dataset to accurately classify suicidal behaviors in EHRs. Results:Lexical analysis revealed key themes in assessing suicide risk, considering an individual’s history, mental health, substance use, and family background. Fine-tuned transformer-based models effectively identified suicidal behaviors from EHRs, with Bio_ClinicalBERT, BioBERT, and XLNet achieving the F1 scores (0.81), outperforming BERT and RoBERTa. The proposed approach, based on a multi-label classification system, captures the complexity of suicidal behaviors effectively particularly “Suicide Attempt” and “Family History” instances. The proposed approach, using task-specific NLP models and a multi-label classification system, captures the complexity of suicidal behaviors more effectively than traditional binary classification. However, direct comparisons with existing studies are difficult due to varying metrics and label definitions. Conclusion:This study presents a robust NLP framework for detecting suicidal behaviors in EHRs, leveraging task-specific fine-tuning of transformer-based models and a semi-automated pipeline. Despite limitations, the approach demonstrates the potential of advanced NLP techniques in enhancing the identification of suicidal behaviors. Future work should focus on model expansion and integration to further improve patient care and clinical decision-making.

Understanding Privacy Norms through Web Forms

Web forms are one of the primary ways to collect personal information online, yet they are relatively under-studied. Unlike web tracking, data collection through web forms is explicit and contextualized. Users (i) are asked to input specific personal information types, and (ii) know the specific context (i.e., on which website and for what purpose). For web forms to be trusted by users, they must meet the common sense standards of appropriate data collection practices within a particular context (i.e., privacy norms). In this paper, we extract the privacy norms embedded within web forms through a measurement study. First, we build a specialized crawler to discover web forms on websites. We run it on 11,500 popular websites, and we create a dataset of 293K web forms. Second, to process data of this scale, we develop a cost-efficient way to annotate web forms with form types and personal information types, using text classifiers trained with assistance of large language models (LLMs). Third, by analyzing the annotated dataset, we reveal common patterns of data collection practices. We find that (i) these patterns are explained by functional necessities and legal obligations, thus reflecting privacy norms, and that (ii) deviations from the observed norms often signal unnecessary data collection. In addition, we analyze the privacy policies that accompany web forms. We show that, despite their wide adoption and use, there is a disconnect between privacy policy disclosures and the observed privacy norms.

Resnet for Blood Sample Detection: A Study on Improving Diagnostic Accuracy

Automated blood cell analysis plays a crucial role in medical diagnostics, enabling rapid and accurate assessment of a patient's health status. In this paper, we provide a unique technique for detecting and classifying WBCs,RBCs, and platelets inside blood smear pictures using ResNet (Residual Neural Network), a deep learning architecture. Because of its capacity to efficiently train very deep neural networks while minimizing the vanishing gradient problem, the ResNet architecture has exhibited excellent performance in a variety of image recognition applications. Leveraging the power of ResNet, we developed a multi-class classification model capable of distinguishing between WBCs, RBCs, and platelets within microscopic images of blood smears. Our methodology involved preprocessing the blood smear images to enhance contrast and remove noise, followed by image segmentation to isolate individual blood cells and platelets. The segmented images were then used to train and fine-tune a ResNet model, utilizing a large annotated dataset of labeled blood cell images. The trained model exhibited remarkable accuracy in identifying and classifying different blood cell types, even in the presence of overlapping cells or artifacts. We extensively tested our suggested technique, on a range of blood smear images to evaluate its performance. The findings demonstrated that ResNet effectively identifies and categorizes WBCs, (RBCs) and platelets. When compared to methods our approach showcased superior accuracy, robustness and generalization capabilities. After training the model with the Resnet algorithm we got 92% of Accuracy

Novel Framework for Artificial Bubble Image Generation and Boundary Detection Using Superformula Regression and Computer Vision Techniques

Bubble multiphase systems are crucial in industries such as biotechnology, medicine, oil and gas, and water treatment. Optical data analysis provides critical insights into bubble characteristics, such as the shape and size, complementing physical sensor data. Existing detection techniques rely on classical computer vision algorithms and neural network models. While neural networks achieve a higher accuracy, they require extensive annotated datasets, and classical methods often struggle with complex systems due to their lower accuracy. This study proposes a novel framework to address these limitations. Using Superformula parameter regression, we introduce an advanced border detection method for accurately identifying gas inclusions and complex-shaped objects in multiphase environments. The framework also includes a new approach for generating realistic artificial bubble images based on physical flow conditions, leveraging the Superformula to create extensive, labeled datasets without manual annotation. Tested on real bubble flows in mass transfer equipment, the algorithms enable bubble classification by shape and size, enhance detection accuracy, and reduce development time for neural network solutions. This work provides a robust method for object detection and dataset generation in multiphase systems, paving the way for more precise modeling and analysis.

SmartSeg: machine learning infrastructure software for accelerating medical image segmentation in patient-specific applications

ABSTRACT Patient-specific applications in biomechanics and orthopaedics call for segmentation of volumetric medical images. This task has historically been performed manually or semi-automatically, which entails significant effort by trained experts. Computer vision algorithms based on machine learning have emerged as an effective tool for accelerating medical image processing tasks through automation. The current work describes fully no-code software which supports development of custom machine learning models for automatic segmentation of volumetric medical images. This cloud-integrated desktop application allows researchers and biomedical engineers to define segmentation algorithms, configure annotated datasets, and train personalised machine learning algorithms with cloud compute in just a few clicks. A presented case study demonstrates an ideal workflow in the software using femoral cartilage segmentation from magnetic resonance imaging as a representative use case. Models developed in the software demonstrated mean dice similarity coefficients of up to 0.886 on a test cohort, which is competitive with previously reported methods developed with significantly larger data sets. The mean surface difference between digitised models reconstructed from ground truth and predicted segmentation was 0.33 mm. Results suggest the described software enables creation of accurate machine learning models with limited engineering effort.

Real-time lane detection for autonomous vehicles using YOLOV5 Segmentation Model

ABSTRACT The increasing prominence of autonomous vehicles underscores the critical need for precise and prompt lane detection to ensure safe and reliable transportation. This paper aims to enhance autonomous vehicle performance by developing an advanced real-time lane detection system using the YOLOV5 Segmentation Large Model. The methodology utilises LabelMe for dataset annotation and employs rigorous pre-processing techniques such as resizing, augmentation, noise addition, greyscale conversion, enhanced exploration size, and image rotation. YOLOv5 architecture is extensively employed for in-lane instance segmentation. The proposed model achieves high performance with a pixel accuracy of 89%, a processing speed of 48 frames per second, and mAP@0.5 scores of 0.885 for the bounding box and 0.731 for the mask. These results demonstrate the model’s effectiveness in various conditions. This paper introduces an innovative approach to autonomous vehicle lane detection, leveraging the YOLOV5 Segmentation Large Model for unparalleled precision and recall rates. This paper makes a significant contribution to the advancement of autonomous vehicle technology, emphasising the critical role of reliable and timely lane detection in strengthening the dependability and safety of transportation systems.

Revolutionizing MRI Brain Tumor Detection Through GAN Augmentation and Smart Algorithms.

The accurate detection and classification of brain tumors in Magnetic Resonance Technology is critical to accurate diagnosis and therapy planning. However, the absence of annotated MR imaging datasets presents a significant challenge for advanced machine learning models, which require a range of data to achieve high accuracy and generalizability. This study provides a novel data augmentation method to enhance MR images for improved tumor identification by combining noise-to-image and image-to-image Generative Adversarial Networks (GANs). Noise-to-image GANs generate synthetic MR images from random noise, expanding the dataset with a range of anatomical variations, whereas image-to-image GANs refine and enhance existing MR images, highlighting important features relevant to tumor detection. The enlarged set of input data was taken to teach CNN, which was then correlated to a baseline model built solely on the original dataset. The GAN-augmented model beat the baseline cancer sorting and division tasks, as measured by parameters such as accuracy, recall, F1-score, and Dice coefficient. An ablation study verified the independent contributions of noise-to-image and image-to-image GANs, demonstrating that both types of augmentation operate together to improve model performance. This work illustrates the promise of GAN-based augmentation in medical imaging by giving a realistic solution to data restrictions and improving the robustness of deep learning models in brain tumor detection. This technology provides the way for more accurate and reliable based on artificial intelligence tools for diagnosis that can help medical practitioners make clinical decisions by integrating alternative GAN techniques. Key Words: GAN-based augmentation, Deep learning, Image augmentation techniques, Generative adversarial networks (GANs)

Enhancing Image Quality With Deep Learning: Techniques And Applications

The emergence of deep learning has transformed numerous fields, particularly image processing, where it has substantially enhanced image quality. This paper provides a structured overview of the objectives, methods, results, and conclusions of deep learning techniques for image enhancement. It examines deep learning methodologies and their applications in improving image quality across diverse domains. The discussion includes state-of-the-art algorithms such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Autoencoders, highlighting their applications in medical imaging, photography, and remote sensing. These methods have demonstrated notable impacts, including noise reduction, resolution enhancement, and contrast improvement. Despite its significant promise, deep learning faces challenges such as computational complexity and the need for large annotated datasets. outlines future research directions to overcome these limitations and further advance deep learning's potential in image enhancement.

Corrosion Detection and Grading Method for Hydraulic Metal Structures Based on an Improved YOLOv10 Sequential Architecture

Herein, we present a method for detecting and determining the corrosion level of hydraulic metal structure surfaces through images while reducing the difficulty of dataset annotation. To achieve accurate detection of corrosion targets, the MobileViTv3 block is integrated into YOLOv10, resulting in the proposed YOLOv10-vit for corrosion target detection. Based on YOLOv10-vit, the YOLOv10-vit-cls classification network is introduced for corrosion level determination. This network leverages the pre-trained parameters of YOLOv10-vit to more quickly learn the features of different corrosion levels. To avoid subjective factors in the corrosion level annotation process and reduce annotation difficulty, a cascaded corrosion detection architecture combining YOLOv10-vit and YOLOv10-vit-cls is proposed. Finally, based on the proposed corrosion detection architecture, we achieve accurate corrosion detection and level determination for hydraulic metal structures.

Computational humor recognition: a systematic literature review

Computational humor recognition is considered to be one of the hardest tasks in natural language processing (NLP) since humor is such a particularly complex emotion. There are very few recent studies that offer an analysis of certain aspects of computational humor. However, there has been no attempt to study the empirical evidence on computational humor recognition in a systematic way. The aim of this research is to examine computational humor detection from three aspects: datasets, features and algorithms. Therefore, a Systematic Literature Review (SLR) was carried out to present in detail the computational techniques for humor identification under these aspects. After posing some research questions, a total of 106 primary papers were identified as relevant to the objectives of these questions and further detailed analysis was conducted. The study revealed that there are a great number of publicly available annotated humor datasets with many different types of humor instances. Twenty-one (21) humor features have been carefully studied, and research evidence of their use in humor computational detection is presented. Additionally, a classification of the humor detection approaches was performed, and the results are presented. Finally, the challenges of applying these techniques to humor recognition as well as promising future research directions are discussed.