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Journal articles on the topic 'Cinc 2020 challenge dataset'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Moqurrab, Syed Atif, Hari Mohan Rai, and Joon Yoo. "HRIDM: Hybrid Residual/Inception-Based Deeper Model for Arrhythmia Detection from Large Sets of 12-Lead ECG Recordings." Algorithms 17, no. 8 (2024): 364. http://dx.doi.org/10.3390/a17080364.

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Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons of death in the world. The timely, accurate, and effective prediction of heart diseases is crucial for saving lives. Electrocardiography (ECG) is a primary non-invasive method to identify cardiac abnormalities. However, manual interpretation of ECG recordings for heart disease diagnosis is a time-consuming and inaccurate process. For the accurate and efficient detection of heart diseases from the 12-lead ECG dataset, we have proposed a hybrid residual/inception-based deeper model (HRIDM). In this study, we

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Alhasani, Ahmed T., Zainab H. Albakaa, Shahad A. Alabidi, Osamah Qasim Abd Zaid Gburi, Ammar Kadi, and Irina Potoroko. "Heartbeat Sound Classification Using Mel-Spectrogram and CNN Optimized by Frilled Lizard Algorithm for Cardiovascular Disease Detection." Mesopotamian Journal of Artificial Intelligence in Healthcare 2025 (May 21, 2025): 96–104. https://doi.org/10.58496/mjaih/2025/010.

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Cardiovascular disease (CVD) continues to be the predominant cause of mortality globally, underscoring the critical necessity for prompt and precise diagnostic techniques. This paper introduces an innovative machine learning framework for categorizing heartbeat sounds into four classifications—normal, murmur, additional heart sound, and artifact—utilizing audio recordings from the PhysioNet/CinC Challenge 2016 dataset. The methodology employs Mel-Spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, converting raw heart sound data into comprehensive time-frequenc

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Rahman Ahad, Md Atiqur, and Israt Jahan. "A Study of Left Ventricular (LV) Segmentation on Cardiac Cine-MR Images." Jurnal Kejuruteraan 34, no. 3 (2022): 463–73. http://dx.doi.org/10.17576/jkukm-2022-34(3)-13.

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Left ventricular segmentation from cardiac images has high impact to have early diagnosis of various cardiovascular disorders. However, it is really a challenging task to segment left ventricular images from magnetic resonance image (MRI). In this paper, we explore several state-of-the-art segmentation algorithms applied on left ventricular (LV) segmentation on cardiac cine-MR images. Both adaptive and global thresholding algorithms along with region-based segmentation algorithm have been explored. Edge-based segmentation is disregard due to the absence of edge information in the employed data

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Abdessater, Elza, Paniz Balali, Jimmy Pawlowski, et al. "A Novel Method for ECG-Free Heart Sound Segmentation in Patients with Severe Aortic Valve Disease." Sensors 25, no. 11 (2025): 3360. https://doi.org/10.3390/s25113360.

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Severe aortic valve disease (AVD) cause changes in heart sounds, making phonocardiogram (PCG) analyses challenging. This study presents a novel method for segmenting heart sounds without relying on an electrocardiogram (ECG), specifically targeting patients with severe AVD. Our algorithm enhances traditional Hidden Semi-Markov Models by incorporating signal envelope calculations and statistical tests to improve the detection of the first and second heart sounds (S1 and S2). We evaluated the method on the PhysioNet/CinC 2016 Challenge dataset and a newly acquired AVD-specific dataset. The metho

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Arora, Vinay, Rohan Leekha, Raman Singh, and Inderveer Chana. "Heart sound classification using machine learning and phonocardiogram." Modern Physics Letters B 33, no. 26 (2019): 1950321. http://dx.doi.org/10.1142/s0217984919503214.

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This research pertains to classification of the heart sound using digital Phonocardiogram (PCG) signals targeted to screen for heart ailments. In this study, an existing variant of the decision tree, i.e. XgBoost has been used with unsegmented heart sound signal. The dataset provided by PhysioNet Computing in Cardiology (CinC) Challenge 2016 has been used to validate the technique proposed in this research work. The said dataset comprises six databases (A–F) having 3240 heart sound recordings in all with the duration lasting from 5–120 s. The approach proposed in this paper has been compared w

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Alotaiby, Turky N., Nuwayyir A. Alsahle, and Gaseb N. Alotibi. "Abnormal Heart Sound Detection Using Common Spatial Patterns and Random Forests." Electronics 14, no. 8 (2025): 1512. https://doi.org/10.3390/electronics14081512.

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Early and accurate diagnosis of heart conditions is pivotal for effective treatment. Phonocardiography (PCG) has become a standard diagnostic tool for evaluating and detecting cardiac abnormalities. While traditional cardiac auscultation remains widely used, its accuracy is highly dependent on the clinician’s experience and auditory skills. Consequently, there is a growing need for automated, objective methods of heart sound analysis. This study explores the efficacy of the Common Spatial Patterns (CSP) feature extraction algorithm paired with the Random Forest (RF) classifier to distinguish b

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Sridhar, Niranjan, Atiyeh Ghoreyshi, Lance Myers, and Zachary Owens. "247 Automated sleep staging using wrist-worn device and deep neural networks." Sleep 44, Supplement_2 (2021): A100. http://dx.doi.org/10.1093/sleep/zsab072.246.

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Abstract Introduction Heart rate is well-known to be modulated by sleep stages. If clinically useful sleep scoring can be performed using only cardiac rhythms, then existing medical and consumer-grade devices that can measure heart rate can enable low-cost sleep evaluations. Methods We trained a neural network which uses dilated convolutional blocks to learn both local and long range features of heart rate extracted from ECG R-wave timing to predict for every non-overlapping 30s epoch of the input the probabilities of the epoch being in one of four classes—wake, light sleep, deep sleep or REM.

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Li, Fan, Hong Tang, Shang Shang, Klaus Mathiak, and Fengyu Cong. "Classification of Heart Sounds Using Convolutional Neural Network." Applied Sciences 10, no. 11 (2020): 3956. http://dx.doi.org/10.3390/app10113956.

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Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connec

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Lee, Hyeonjeong, and Miyoung Shin. "Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs." Sensors 21, no. 13 (2021): 4331. http://dx.doi.org/10.3390/s21134331.

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Automatic detection of abnormal heart rhythms, including atrial fibrillation (AF), using signals obtained from a single-lead wearable electrocardiogram (ECG) device, is useful for daily cardiac health monitoring. In this study, we propose a novel image-based deep learning framework to classify single-lead ECG recordings of short variable length into several different rhythms associated with arrhythmias. By transforming variable-length 1D ECG signals into fixed-size 2D time-morphology representations and feeding them to the beat–interval–texture convolutional neural network (BIT-CNN) model, we

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Orozco-Reyes, Leonel, Miguel A. Alonso-Arévalo, Eloísa García-Canseco, Roilhi F. Ibarra-Hernández, and Roberto Conte-Galván. "A Deep-Learning Approach to Heart Sound Classification Based on Combined Time-Frequency Representations." Technologies 13, no. 4 (2025): 147. https://doi.org/10.3390/technologies13040147.

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Worldwide, heart disease is the leading cause of mortality. Cardiac auscultation, when conducted by a trained professional, is a non-invasive, cost-effective, and readily available method for the initial assessment of cardiac health. Automated heart sound analysis offers a promising and accessible approach to supporting cardiac diagnosis. This work introduces a novel method for classifying heart sounds as normal or abnormal by leveraging time-frequency representations. Our approach combines three distinct time-frequency representations—short-time Fourier transform (STFT), mel-scale spectrogram

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Lee, Jin-A., and Keun-Chang Kwak. "Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models." Applied Sciences 13, no. 21 (2023): 11942. http://dx.doi.org/10.3390/app132111942.

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Analyzing the condition and function of the heart is very important because cardiovascular diseases (CVDs) are responsible for high mortality rates worldwide and can lead to strokes and heart attacks; thus, early diagnosis and treatment are important. Phonocardiogram (PCG) signals can be used to analyze heart rate characteristics to detect heart health and detect heart-related diseases. In this paper, we propose a method for designing using wavelet analysis techniques and an ensemble of deep learning models from phonocardiogram (PCG) for heart sound classification. For this purpose, we use wav

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Dognin, Pierre, Igor Melnyk, Youssef Mroueh, et al. "Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge." Journal of Artificial Intelligence Research 73 (January 31, 2022): 437–59. http://dx.doi.org/10.1613/jair.1.13113.

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Image captioning has recently demonstrated impressive progress largely owing to the introduction of neural network algorithms trained on curated dataset like MS-COCO. Often work in this field is motivated by the promise of deployment of captioning systems in practical applications. However, the scarcity of data and contexts in many competition datasets renders the utility of systems trained on these datasets limited as an assistive technology in real-world settings, such as helping visually impaired people navigate and accomplish everyday tasks. This gap motivated the introduction of the novel

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Jallouli, Malika, Sabrine Arfaoui, Anouar Ben Mabrouk, and Carlo Cattani. "Clifford Wavelet Entropy for Fetal ECG Extraction." Entropy 23, no. 7 (2021): 844. http://dx.doi.org/10.3390/e23070844.

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Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The challenge lies in the extraction of the fetal ECG from the mother ECG during pregnancy. This approach has the advantage of being a reliable and non-invasive technique. In the present paper, a wavelet/multiwavelet method is proposed to perfectly extract the fetal ECG parameters from the abdominal mother ECG. In a first step

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Guven, Mesut, and Fatih Uysal. "A New Method for Heart Disease Detection: Long Short-Term Feature Extraction from Heart Sound Data." Sensors 23, no. 13 (2023): 5835. http://dx.doi.org/10.3390/s23135835.

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Heart sounds have been extensively studied for heart disease diagnosis for several decades. Traditional machine learning algorithms applied in the literature have typically partitioned heart sounds into small windows and employed feature extraction methods to classify samples. However, as there is no optimal window length that can effectively represent the entire signal, windows may not provide a sufficient representation of the underlying data. To address this issue, this study proposes a novel approach that integrates window-based features with features extracted from the entire signal, ther

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Chen, Wei-Wen, Ling Kuo, Yi-Xun Lin, et al. "A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance." International Journal of Biomedical Imaging 2024 (April 26, 2024): 1–9. http://dx.doi.org/10.1155/2024/6114826.

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A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and geneticists. This variability in the interpretation and classification of LVH leads to inconsistent diagnoses. LVH, HCM, and Fabry cardiomyopathy can be differentiated using T1 mapping on cardiac magnetic resonance imaging (MRI). However, differentiation between HCM and Fabry cardiomyopathy using ec

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Langenfeld, Peng, Lai, et al. "SHREC 2020: Multi-domain protein shape retrieval challenge." Computer & Graphics 91 (July 15, 2020): 189–98. https://doi.org/10.1016/j.cag.2020.07.013.

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Proteins are natural modular objects usually composed of several domains, each domain bearing a spe- cific function that is mediated through its surface, which is accessible to vicinal molecules. This draws attention to an understudied characteristic of protein structures: surface, that is mostly unexploited by protein structure comparison methods. In the present work, we evaluated the performance of six shape comparison methods, among which three are based on machine learning, to distinguish between 588 multi-domain proteins and to recreate the evolutionary relationships at the protein and sp

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Alsaleem, Mona N., Md Saiful Islam, Saad Al-Ahmadi, and Adel Soudani. "Multiscale Encoding of Electrocardiogram Signals with a Residual Network for the Detection of Atrial Fibrillation." Bioengineering 9, no. 9 (2022): 480. http://dx.doi.org/10.3390/bioengineering9090480.

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Atrial fibrillation (AF) is one of the most common cardiac arrhythmias, and it is an indication of high-risk factors for stroke, myocardial ischemia, and other malignant cardiovascular diseases. Most of the existing AF detection methods typically convert one-dimensional time-series electrocardiogram (ECG) signals into two-dimensional representations to train a deep and complex AF detection system, which results in heavy training computation and high implementation costs. In this paper, a multiscale signal encoding scheme is proposed to improve feature representation and detection performance w

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Palmero, Cristina, Abhishek Sharma, Karsten Behrendt, Kapil Krishnakumar, Oleg V. Komogortsev, and Sachin S. Talathi. "OpenEDS2020 Challenge on Gaze Tracking for VR: Dataset and Results." Sensors 21, no. 14 (2021): 4769. http://dx.doi.org/10.3390/s21144769.

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This paper summarizes the OpenEDS 2020 Challenge dataset, the proposed baselines, and results obtained by the top three winners of each competition: (1) Gaze prediction Challenge, with the goal of predicting the gaze vector 1 to 5 frames into the future based on a sequence of previous eye images, and (2) Sparse Temporal Semantic Segmentation Challenge, with the goal of using temporal information to propagate semantic eye labels to contiguous eye image frames. Both competitions were based on the OpenEDS2020 dataset, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under

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Zhao, Yinghua, Lianying Yang, Changqing Sun, et al. "Discovery of Urinary Proteomic Signature for Differential Diagnosis of Acute Appendicitis." BioMed Research International 2020 (April 6, 2020): 1–9. http://dx.doi.org/10.1155/2020/3896263.

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Acute appendicitis is one of the most common acute abdomens, but the confident preoperative diagnosis is still a challenge. In order to profile noninvasive urinary biomarkers that could discriminate acute appendicitis from other acute abdomens, we carried out mass spectrometric experiments on urine samples from patients with different acute abdomens and evaluated diagnostic potential of urinary proteins with various machine-learning models. Firstly, outlier protein pools of acute appendicitis and controls were constructed using the discovery dataset (32 acute appendicitis and 41 control acute

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Enrietti, Aldo, Aldo Geuna, and Pier Paolo Patrucco. "L'evoluzione dell'industria automobilistica italiana (1894-2020) attraverso il dataset AUTOITA." IMPRESE E STORIA, no. 48 (September 2024): 5–41. http://dx.doi.org/10.3280/isto2023-048001.

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The article reconstructs the historical evolution of the Italian automotive industry in the period between 1894 and 2020 through the original AUTOITA database. This database represents the first, and so far unique, dataset covering the entire historical period of the industry in Italy, as well as the first Italian database allowing international comparisons. First, the article provides a concise qualitative discussion of the evolution of the automotive industry in Italy since the foundation of Bernardi & Miari Giusti in Padua in 1894 up to the first two decades of the 21st century. Subsequ

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Song, S., and R. Qin. "OPTIMIZING MESH RECONSTRUCTION AND TEXTURE MAPPING GENERATED FROM A COMBINED SIDE-VIEW AND OVER-VIEW IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 403–9. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-403-2020.

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Abstract. Image-based 3D modelling are rather mature nowadays with well-acquired images through standard photogrammetric processing pipeline, while fusing 3D dataset generated from images with different views for surface reconstruction remains to be a challenge. Meshing algorithms for image-based 3D dataset requires visibility information for surfaces and such information can be difficult to obtain for 3D point clouds generated from images with different views, sources, resolutions and uncertainties. In this paper, we propose a novel multi-source mesh reconstruction and texture mapping pipelin

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David, Etienne, Simon Madec, Pouria Sadeghi-Tehran, et al. "Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods." Plant Phenomics 2020 (August 20, 2020): 1–12. http://dx.doi.org/10.34133/2020/3521852.

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The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge

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Jäkel, Tim. "External Peer Challenge in Local Government: The Role of Spatial Spillover and Past Performance." Statistics, Politics and Policy 12, no. 1 (2021): 195–215. http://dx.doi.org/10.1515/spp-2020-0006.

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Abstract Voluntary assessments by a team of critical friends (external peer challenges) among local governments became established as popular complement to compulsory and centralized audits and inspections. This study empirically investigates the decision of English local authorities to have a voluntary peer challenge or not by taking advantage of an original dataset about participation in the Local Government Association’s Peer Challenge Programme (CPC) 2010–2015. We find that the LGA’s CPC programme does not carry a risk of leaving behind authorities with performance shortcomings. Councils w

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Ganguly, Debasis, Gareth J. F. Jones, Procheta Sen, Manisha Verma, and Dipasree Pal. "Report on supporting and understanding of conversational dialogues workshop (SUD 2021) at WSDM 2021." ACM SIGIR Forum 55, no. 1 (2021): 1–7. http://dx.doi.org/10.1145/3476415.3476420.

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This report describes the workshop on Supporting and Understanding of (multi-party) conversational Dialogues (SUD) organized as a part of the Web Search and Data Mining conference (WSDM) 2021. The aim of SUD workshop was to encourage researchers to investigate automated methods to analyze and understand conversations. We also discuss the release of a dataset that would be useful in IR research on conversations. The dataset was constructed to support the data challenge in SUD workshop and its precursor event - the Retrieval from Conversational Dialogues (RCD) track at the Forum of Information R

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Zhang, Shaomin, Lijia Zhi, and Tao Zhou. "Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network." BioMed Research International 2020 (December 26, 2020): 1–12. http://dx.doi.org/10.1155/2020/6687733.

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Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in CBMIR systems. Features play a vital role in the accuracy and speed of the search process. In this paper, we propose a deep convolutional neural network- (CNN-) based framework to learn concise feature vector for medical image retrieval. The medical images are decomposed into five components using empirical mode decomposition (EMD). The deep CNN is

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Álvarez, Aitor, Haritz Arzelus, Iván G. Torre, and Ander González-Docasal. "Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database." Applied Sciences 12, no. 4 (2022): 1889. http://dx.doi.org/10.3390/app12041889.

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This work presents three novel speech recognition architectures evaluated on the Spanish RTVE2020 dataset, employed as the main evaluation set in the Albayzín S2T Transcription Challenge 2020. The main objective was to improve the performance of the systems previously submitted by the authors to the challenge, in which the primary system scored the second position. The novel systems are based on both DNN-HMM and E2E acoustic models, for which fully- and self-supervised learning methods were included. As a result, the new speech recognition engines clearly outperformed the performance of the in

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Okokpujie, Kennedy, Grace Chinyere Kennedy, Vingi Patrick Nzanzu, Mbasa Joaquim Molo, Emmanuel Adetiba, and Joke Badejo. "ANOMALY-BASED INTRUSION DETECTION FOR A VEHICLE CAN BUS: A CASE FOR HYUNDAI AVANTE CN7." Journal of Southwest Jiaotong University 56, no. 5 (2021): 144–56. http://dx.doi.org/10.35741/issn.0258-2724.56.5.14.

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Flooding, spoofing, replay, and fuzzing are common in various types of attacks faced by enterprises and various network systems. In-vehicle network systems are not immune to attacks and threats. Intrusion detection systems using different algorithms are proposed to enhance the security of the in-vehicle network. We use a dataset provided and collected in "Car Hacking: Attack and Defense Challenge" during 2020. This dataset has been realized by the organizers of the challenge for security researchers. With the aid of this dataset, the work aimed to develop attack and detection techniques of Con

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Elngar, Ahmed A., and Mohammed Kayed. "Vehicle Security Systems using Face Recognition based on Internet of Things." Open Computer Science 10, no. 1 (2020): 17–29. http://dx.doi.org/10.1515/comp-2020-0003.

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AbstractNowadays, the automobile sector is one of the hottest applications, where vehicles can be intelligent by using IoT technology. But unfortunately, these vehicles suffer from many crimes. Hence it has become a big challenge for the IoT to avoid such these crimes from professional thieves. This paper presents a proposal for the development of a vehicle guard and alarm system using biometric authentication based on IoT technology. Whereas, for vehicle security issues; the proposed system VSS − IoT gives only full access for authorized vehicle’s driver based on the interface of a Raspberry

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Kasieczka, Gregor, Benjamin Nachman, David Shih, et al. "The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics." Reports on Progress in Physics 84, no. 12 (2021): 124201. http://dx.doi.org/10.1088/1361-6633/ac36b9.

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Abstract A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (o

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Ishida, Yutaro, and Hakaru Tamukoh. "Semi-Automatic Dataset Generation for Object Detection and Recognition and its Evaluation on Domestic Service Robots." Journal of Robotics and Mechatronics 32, no. 1 (2020): 245–53. http://dx.doi.org/10.20965/jrm.2020.p0245.

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This paper proposes a method for the semi-automatic generation of a dataset for deep neural networks to perform end-to-end object detection and classification from images, which is expected to be applied to domestic service robots. In the proposed method, the background image of the floor or furniture is first captured. Subsequently, objects are captured from various viewpoints. Then, the background image and the object images are composited by the system (software) to generate images of the virtual scenes expected to be encountered by the robot. At this point, the annotation files, which will

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Zouinina, Sarah, Younès Bennani, Nicoleta Rogovschi, and Abdelouahid Lyhyaoui. "Data Anonymization through Collaborative Multi-view Microaggregation." Journal of Intelligent Systems 30, no. 1 (2020): 327–45. http://dx.doi.org/10.1515/jisys-2020-0026.

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Abstract The interest in data anonymization is exponentially growing, motivated by the will of the governments to open their data. The main challenge of data anonymization is to find a balance between data utility and the amount of disclosure risk. One of the most known frameworks of data anonymization is k-anonymity, this method assumes that a dataset is anonymous if and only if for each element of the dataset, there exist at least k − 1 elements identical to it. In this paper, we propose two techniques to achieve k-anonymity through microaggregation: k-CMVM and Constrained-CMVM. Both, use to

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Zhou, Shuncheng, Honghui Li, Xueliang Fu, and Yuanyuan Jiao. "A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs." Applied Sciences 14, no. 15 (2024): 6678. http://dx.doi.org/10.3390/app14156678.

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With the increasingly severe challenge of Software Supply Chain (SSC) security, the rising trend in guarding against security risks has attracted widespread attention. Existing techniques still face challenges in both accuracy and efficiency when detecting malware in SSC. To meet this challenge, this paper introduces two novel models, named the Bayesian Optimization-based Support Vector Machine (BO-SVM) and the Long Short-Term Memory–BO-SVM (LSTM-BO-SVM). The BO-SVM model is constructed on an SVM foundation, with its hyperparameters optimized by Bayesian Optimization. To further enhance its ac

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Almar, Rafael, Erwin Bergsma, Patrick Marchesiello, Rachid Benshila, and Ehouarn Simon. "WAVE CHARACTERISTICS FROM SPARSE DATA WITH IMPLICATIONS TO DERIVE COASTAL BATHYMETRY FROM REMOTE SENSING." Coastal Engineering Proceedings, no. 38 (May 29, 2025): 15. https://doi.org/10.9753/icce.v38.waves.15.

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Estimating bathymetry through remotely-sensed waves from optical imagery, lidar and radar, is a current major challenge of coastal science and engineering. Dense, abundant and regular dataset are generally required to capture wave characteristics. This seems to be the main limitation preventing the use of satellite remote sensing and other short observation bursts with space or time constraints (Almar et al., 2020). While traditional approaches privilege either space or time sampling, a great advantage can be found in the use of both.

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Jing, Yinghong, Xinghua Li, and Huanfeng Shen. "STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) for China." Earth System Science Data 14, no. 7 (2022): 3137–56. http://dx.doi.org/10.5194/essd-14-3137-2022.

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Abstract. Snow dynamics are crucial in ecosystems, affecting radiation balance, hydrological cycles, biodiversity, and human activities. Snow areas with notably diverse characteristics are extensively distributed in China, mainly including Northern Xinjiang (NX), Northeast China (NC), and the Qinghai–Tibet Plateau (QTP). Spatiotemporal continuous snow monitoring is indispensable for ecosystem maintenance. Nevertheless, the formidable challenge of cloud obscuration severely impedes data collection. In the past decades, abundant binary snow cover area (SCA) maps have been retrieved from moderate

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Qin, R., S. Song, and X. Huang. "3D DATA GENERATION USING LOW-COST CROSS-VIEW IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 157–62. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-157-2020.

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Abstract. 3D data generation often requires expensive data collection such as aerial photogrammetric or LiDAR flight. In cases such data are unavailable, for example, areas of interest inaccessible from aerial platforms, alternative sources to be considered can be quite heterogeneous and come in the form of different accuracy, resolution and views, which challenge the standard data processing workflows. Assuming only overview satellite and ground-level go-pro images are available, which we call cross-view data due to the significant view differences, this paper introduces a framework from our

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Dai, Hua, Xuelong Dai, Xiao Li, Xun Yi, Fu Xiao, and Geng Yang. "A Multibranch Search Tree-Based Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data." Security and Communication Networks 2020 (January 23, 2020): 1–15. http://dx.doi.org/10.1155/2020/7307315.

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In the interest of privacy concerns, cloud service users choose to encrypt their personal data before outsourcing them to cloud. However, it is difficult to achieve efficient search over encrypted cloud data. Therefore, how to design an efficient and accurate search scheme over large-scale encrypted cloud data is a challenge. In this paper, we integrate bisecting k-means algorithm and multibranch tree structure and propose the α-filtering tree search scheme based on bisecting k-means clusters. The novel index tree is built from bottom-up, and a greedy depth first algorithm is used for filterin

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Sogacheva, Larisa, Thomas Popp, Andrew M. Sayer, et al. "Merging regional and global aerosol optical depth records from major available satellite products." Atmospheric Chemistry and Physics 20, no. 4 (2020): 2031–56. http://dx.doi.org/10.5194/acp-20-2031-2020.

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Abstract. Satellite instruments provide a vantage point for studying aerosol loading consistently over different regions of the world. However, the typical lifetime of a single satellite platform is on the order of 5–15 years; thus, for climate studies, the use of multiple satellite sensors should be considered. Discrepancies exist between aerosol optical depth (AOD) products due to differences in their information content, spatial and temporal sampling, calibration, cloud masking, and algorithmic assumptions. Users of satellite-based AOD time-series are confronted with the challenge of choosi

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Hayati, Nur, Fauziah Fauziah, Dendi Rizka Poetra, and Dede Wandi. "Trend of the spread of COVID-19 in Indonesia using the machine learning prophet algorithm." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1780–88. https://doi.org/10.11591/ijeecs.v24.i3.pp1780-1788.

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Based on information on the BNPB website on 2 September 2020, the positive rate for coronavirus disease (COVID-19) in Indonesia reached 25.25% on 30 August 2020. This is a big challenge for the Indonesian government to reduce the positivity rate to meet the standards safe accepted by World Health Organization (WHO) is 5%. To ensure the accuracy of government policies, accurate data predictions are needed. Therefore, the prophet's machine learning algorithm can be used to see trends in the spread of COVID-19 in the next one year. This algorithm has a fairly high level of accuracy because th

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Huang, Xiaodong, Hui Zhang, Li Zhuo, Xiaoguang Li, and Jing Zhang. "TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine." Computational and Mathematical Methods in Medicine 2020 (August 7, 2020): 1–13. http://dx.doi.org/10.1155/2020/6029258.

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Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue. In this study, an automated tongue image segmentation method using enhanced fully convolutional network with encoder-decoder structure was presented. In the frame of the proposed network, the deep residual network was adopted as an encoder to obtain dense feature maps, and a Receptive Field Block was assembled behind the encoder. Receptive Field

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Wang, Wenjing, Hongwei Feng, Qirong Bu, et al. "MDU-Net: A Convolutional Network for Clavicle and Rib Segmentation from a Chest Radiograph." Journal of Healthcare Engineering 2020 (July 17, 2020): 1–9. http://dx.doi.org/10.1155/2020/2785464.

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Automatic bone segmentation from a chest radiograph is an important and challenging task in medical image analysis. However, a chest radiograph contains numerous artifacts and tissue shadows, such as trachea, blood vessels, and lung veins, which limit the accuracy of traditional segmentation methods, such as thresholding and contour-related techniques. Deep learning has recently achieved excellent segmentation of some organs, such as the pancreas and the hippocampus. However, the insufficiency of annotated datasets impedes clavicle and rib segmentation from chest X-rays. We have constructed a

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Nataraj, Lakshmanan, Michael Goebel, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, and B. S. Manjunath. "Holistic Image Manipulation Detection using Pixel Cooccurrence Matrices." Electronic Imaging 2021, no. 4 (2021): 277–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.4.mwsf-277.

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Digital image forensics aims to detect images that have been digitally manipulated. Realistic image forgeries involve a combination of splicing, resampling, region removal, smoothing and other manipulation methods. While most detection methods in literature focus on detecting a particular type of manipulation, it is challenging to identify doctored images that involve a host of manipulations. In this paper, we propose a novel approach to holistically detect tampered images using a combination of pixel co-occurrence matrices and deep learning. We extract horizontal and vertical co-occurrence ma

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Valliammal and Sathiyasekar. "AN ENHANCED BRAIN TUMOR SEGMENTATION USING AN IMPROVED 3D U-NET TOPOLOGY: SOLUTION TO BRATS 2020 CHALLENGE." Latin American Applied Research - An international journal 54, no. 3 (2024): 361–68. http://dx.doi.org/10.52292/j.laar.2024.3238.

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The process of automatically recognizing malignant brain tissues and labeling them according to tumor types is known as brain tumor segmentation. Manually segmenting a tumor from a brain MRI takes a long time and is prone to errors. Thereby, a variety of fast and accurate brain tumor segmentation approaches have been developed. Deep learning algorithms have recently exhibited outstanding segmentation performance. As a result, this research suggested U-Net architecture, which is a deep learning topology extension for segmentation. The BraTS2020 dataset is used in this study to assess the effici

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Shen, Yulin, Benoît Mercatoris, Qingzhi Liu, et al. "Use Self-Training Random Forest for Predicting Winter Wheat Yield." Remote Sensing 16, no. 24 (2024): 4723. https://doi.org/10.3390/rs16244723.

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The effectiveness of supervised ML heavily depends on having a large, accurate, and diverse annotated dataset, which poses a challenge in applying ML for yield prediction. To address this issue, we developed a self-training random forest algorithm capable of automatically expanding the annotated dataset. Specifically, we trained a random forest regressor model using a small amount of annotated data. This model was then utilized to generate new annotations, thereby automatically extending the training dataset through self-training. Our experiments involved collecting data from over 30 winter wh

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Gan, Jing, Linheng Li, Dapeng Zhang, Ziwei Yi, and Qiaojun Xiang. "An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm." Journal of Advanced Transportation 2020 (December 20, 2020): 1–13. http://dx.doi.org/10.1155/2020/1257627.

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Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models. Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the

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Hayati, Nur, Fauziah Fauziah, Dendi Rizka Poetra, and Dede Wandi. "Trend of the spread of COVID-19 in Indonesia using the machine learning prophet algorithm." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1780. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1780-1788.

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Based on information on the <span>BNPB website on 2 September 2020, the positive rate for coronavirus disease (COVID-19) in Indonesia reached 25.25% on 30 August 2020. This is a big challenge for the Indonesian government to reduce the positivity rate to meet the standards safe accepted by World Health Organization (WHO) is 5%. To ensure the accuracy of government policies, accurate data predictions are needed. Therefore, the prophet's machine learning algorithm can be used to see trends in the spread of COVID-19 in the next one year. This algorithm has a fairly high level of accuracy be

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Tregua, Marco, and Anna D'Auria. "One More Tweet: Firms Challenge a Sustainable Future." puntOorg International Journal 5, no. 2 (2020): 201–19. http://dx.doi.org/10.19245/25.05.pij.5.2.6.

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In the last decades, researchers have been provided with a huge amount of data thanks to the diffusion of online sources. Additionally, more companies are issuing reports and documents to share information with stakeholders about their sustainable approach to both strengthen and encourage people to adopt a similar approach. To support researchers in managing the increasing quantity of information, several tools have been provided for text mining studies, such as sentiment analysis, semantic analysis, and content analysis. We proposed to analyse the usefulness of automated and semi-automated te

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Asare, Sarpong Kwadwo, Fei You, and Obed Tettey Nartey. "A Semisupervised Learning Scheme with Self-Paced Learning for Classifying Breast Cancer Histopathological Images." Computational Intelligence and Neuroscience 2020 (December 8, 2020): 1–16. http://dx.doi.org/10.1155/2020/8826568.

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The unavailability of large amounts of well-labeled data poses a significant challenge in many medical imaging tasks. Even in the likelihood of having access to sufficient data, the process of accurately labeling the data is an arduous and time-consuming one, requiring expertise skills. Again, the issue of unbalanced data further compounds the abovementioned problems and presents a considerable challenge for many machine learning algorithms. In lieu of this, the ability to develop algorithms that can exploit large amounts of unlabeled data together with a small amount of labeled data, while de

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Hemalatha, Putta, and Geetha Mary Amalanathan. "FG-SMOTE: Fuzzy-based Gaussian synthetic minority oversampling with deep belief networks classifier for skewed class distribution." International Journal of Intelligent Computing and Cybernetics 14, no. 2 (2021): 270–87. http://dx.doi.org/10.1108/ijicc-12-2020-0202.

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PurposeAdequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance. The data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a dataset. This issue is known as the imbalance problem, which is one of the most common issues occurring in real-time applications. Learning of imbalanced datasets is a ubiquitous challenge in the field of data mining. Imbalanced data degrades the performance of the classifier by producing inaccurate results.Design/methodology/ap

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Nienaber, Birte, Ioana Manafi, Volha Vysotskaya, Monica Roman, and Daniela Marinescu. "Challenging Youth Unemployment Through International Mobility." Journal of Social and Economic Statistics 9, no. 1 (2020): 5–27. http://dx.doi.org/10.2478/jses-2020-0002.

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AbstractYouth unemployment is a challenge in many European countries – especially since the financial crises. Young people face difficulties in the transition from education into employment. This article focuses on young mobile Europeans from six countries (Germany, Hungary, Luxembourg, Norway, Romania and Spain). The research question is whether and to which extent international mobility has an impact on employability and therefore reduces youth unemployment. By using a cluster analysis of personal adaptability, social and human capital and career identity, the importance of mobility experien

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Dresvyanskiy, Denis, Elena Ryumina, Heysem Kaya, Maxim Markitantov, Alexey Karpov, and Wolfgang Minker. "End-to-End Modeling and Transfer Learning for Audiovisual Emotion Recognition in-the-Wild." Multimodal Technologies and Interaction 6, no. 2 (2022): 11. http://dx.doi.org/10.3390/mti6020011.

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As emotions play a central role in human communication, automatic emotion recognition has attracted increasing attention in the last two decades. While multimodal systems enjoy high performances on lab-controlled data, they are still far from providing ecological validity on non-lab-controlled, namely “in-the-wild” data. This work investigates audiovisual deep learning approaches to emotion recognition in in-the-wild problem. Inspired by the outstanding performance of end-to-end and transfer learning techniques, we explored the effectiveness of architectures in which a modality-specific Convol

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