Relevant bibliographies by topics / Signal scoring

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Signal scoring'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Signal scoring"

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Truong, Khuong, Jack Boenders, Zofia Maciorowski, Philippe Vielh, Bernard Dutrillaux, Bernard Malfoy, and Claire A. Bourgeois. "Signal Amplification of FISH for Automated Detection Using Image Cytometry." Analytical Cellular Pathology 13, no. 3 (1997): 137–46. http://dx.doi.org/10.1155/1997/297216.

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The purpose of this study was to improve the detection of FISH signals, in order that spot counting by a fully automated image cytometer be comparable to that obtained visually under the microscope. Two systems of spot scoring, visual and automated counting, were investigated in parallel on stimulated human lymphocytes with FISH using a biotinylated centromeric probe for chromosome 3. Signal characteristics were first analyzed on images recorded with a coupled charge device (CCD) camera. Number of spots per nucleus were scored visually on these recorded images versus automatically with a DISCOVERY image analyzer. Several fluochromes, amplification systems and pretreatments were tested. Our results for both visual and automated scoring show that the tyramide amplification system (TSA) gives the best amplification of signal if pepsin treatment is applied prior to FISH. Accuracy of the automated scoring, however, remained low (58% of nuclei containing two spots) compared to the visual scoring because of the high intranuclear variation between FISH spots.
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Young, K. L., and E. R. Delay. "Comparison of standard scoring with signal detection theory scoring of the seashore rhythmus test." Archives of Clinical Neuropsychology 6, no. 3 (January 1, 1991): 237. http://dx.doi.org/10.1093/arclin/6.3.237.

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Senny, Frederic, Gisele Maury, Laurent Cambron, Amandine Leroux, Jacques Destiné, and Robert Poirrier. "The sleep/wake state scoring from mandible movement signal." Sleep and Breathing 16, no. 2 (June 11, 2011): 535–42. http://dx.doi.org/10.1007/s11325-011-0539-4.

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Bharath Siva Varma, P., Adimulam Yesubabu, and K. Subrahmanyam. "Identify virtual ligand hits using consensus scoring approach for drug target S. Aureus." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 84. http://dx.doi.org/10.14419/ijet.v7i2.7.10265.

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The associations amongst protein and ligand can be assessed for scoring functions based on the binding modes of ligand obtained from search algorithms. Several scoring functions have been proposed and every method has their own strengths and weaknesses, hence a multiple scoring analysis referred as consensus scoring increases the overall signal noise ratio. Therefore, top ten ligands obtained and were subjected to re-scoring using mcule, AutoDock and X-score functions. A computational consensus scoring analysis was taken up for a dataset of top ten PubChem compounds which are identified to exhibit better inhibitory properties against phosphotransacetylase, a putative drug target for S. aureus.
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Macaš, Martin, Nela Grimová, Václav Gerla, and Lenka Lhotská. "Semi-Automated Sleep EEG Scoring with Active Learning and HMM-Based Deletion of Ambiguous Instances." Proceedings 31, no. 1 (November 20, 2019): 46. http://dx.doi.org/10.3390/proceedings2019031046.

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Sleep scoring is an important tool for physicians. Assigning of segments of long biomedical signal into sleep stages is, however, a very time consuming, tedious and expensive task which is performed by an expert. Automatic sleep scoring is not well accepted in clinical practice because of low interactivity and unacceptable error, which is often caused by inter-patient variability. This is solved by proposing a semi-automatic approach, where parts of the signal are selected for manual labeling by active learning and the resulting classifier is used for automatic labeling of the remaining signal. The active learning is disturbed by noisy ambiguous data instances caused by continuous character of the sleep stage transitions and a removal of such transitional instances from the training set prior to active learning can improve the efficiency of the method. This paper proposes to use the hidden Markov model for the detection of the transitional instances. It shows experimentally on 35 sleep EEG recordings that such a method significantly improves the semi-automatic method. A complete methodology for semi-automatic sleep scoring is proposed and evaluated, which can be better accepted as a decision support tool for sleep scoring experts.
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Zhang, Wei-Xun, Xiaoyong Pan, and Hong-Bin Shen. "Signal-3L 3.0: Improving Signal Peptide Prediction through Combining Attention Deep Learning with Window-Based Scoring." Journal of Chemical Information and Modeling 60, no. 7 (June 5, 2020): 3679–86. http://dx.doi.org/10.1021/acs.jcim.0c00401.

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Procházka, Aleš, Jiří Kuchyňka, Oldřich Vyšata, Martin Schätz, Mohammadreza Yadollahi, Saeid Sanei, and Martin Vališ. "Sleep scoring using polysomnography data features." Signal, Image and Video Processing 12, no. 6 (February 10, 2018): 1043–51. http://dx.doi.org/10.1007/s11760-018-1252-6.

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Damjanov, Nemanja, Vera Milic, Juan Carlos Nieto-González, Iustina Janta, Lina Martínez-Estupiñan, Belén Serrano, Carmen Mata, et al. "Multiobserver Reliability of Ultrasound Assessment of Salivary Glands in Patients with Established Primary Sjögren Syndrome." Journal of Rheumatology 43, no. 10 (August 15, 2016): 1858–63. http://dx.doi.org/10.3899/jrheum.151220.

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Objective.To evaluate the multiobserver reliability of salivary gland ultrasonography (SGUS) for scoring greyscale (GS) parenchymal inhomogeneity and parenchymal color Doppler (CD) signal in patients with established primary Sjögren syndrome (pSS).Methods.The study comprised 2 multiobserver reliability assessments in patients with pSS in 2 European centers. The first reliability exercise was performed on 24 patients with pSS and 8 controls who were independently evaluated with GS and CD US by 5 observers at the Institute of Rheumatology, Belgrade, Serbia. The second reliability exercise was carried out on 10 patients with pSS who were independently assessed with GS and CD US by 8 observers at the Hospital G.U. Gregorio Marañón, Madrid, Spain. SGUS parenchymal inhomogeneity and parenchymal CD signal were semiquantitatively scored using a 4-grade scoring system. The multiobserver agreement was calculated by the overall agreement and Light’s κ statistics.Results.A total of 640 SGUS examinations were performed in the first reliability exercise and a total of 320 examinations in the second reliability exercise. Multiobserver reliability was good (κ = 0.71–0.79) to excellent (κ = 0.81–0.82) for GS parenchymal inhomogeneity in both exercises. There was a moderate (κ = 0.53–0.58) to good (κ = 0.70) multiobserver reliability for parenchymal CD signal in the first exercise. However, there was no agreement or only a fair agreement (κ = 0.03–0.29) for parenchymal CD signal in the second exercise.Conclusion.US may be a reliable technique in the multiobserver scoring of GS parenchymal inhomogeneity of major SG in patients with established pSS. CD scoring of SG needs further standardization to be used in multicenter studies.
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Farooq, Hemu, Anuj Jain, and V. K. Sharma. "A Proposal for Sleep Scoring Analysis Designed by Computer Assisted using Physiological Signals." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 230–35. http://dx.doi.org/10.35940/ijeat.e2609.0610521.

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Sleep is utterly regarded as compulsory component for a person’s prosperity and is an exceedingly important element for wellbeing of a healthy person. It is a condition in which an individual is physically and mentally at rest. The conception of sleep is considered extremely peculiar and is a topic of discussion and researchers all over the world has been attracted by this concept. Sleep analysis and its stages is analyzed to be useful in sleep research and sleep medicine area. By properly analyzing the sleep scoring system and its different stages has proven helpful for diagnosing sleep disorders. As it’s seen,sleep stage classification by manual process is a hectic procedure as it takes sufficient time for sleep experts to perform data analysis. Besides, mistakes and irregularities in between classification of same data can be recurrent. Therefore, theuse of automatic scoring system in order to support reliable classification is highly in greater use. The scheduled work provides an insight to use the automatic scheme which is based on real time EMG signals and Artificial neural network. EMG is an electro neurological diagnostic tool which evaluates and records the electrical activity generated by muscle cells. The sleep scoring analysis can be applied by recording Electroencephalogram (EEG), Electromyogram (EMG), and Electrooculogram (EOG) based on epoch and this method is termed as PSG test or polysomnography test. The epoch measured has length segments for a period of 30 seconds. The standard database of EMG records was gathered from various hospitals in sleep laboratory which gives the different stages of sleep. These are Waking, Non-REM1 (stage-1), NonREM2 (stage-2), Non-REM3 (stage-3), REM. The collection of datawas done for the period of 30 second known as epoch, for seven hours. The dataset obtained from the biological signal was managedso that necessary data is to be extracted from degenerated signal utilized for the purpose of study. As a matter of fact, it is known electrical signals are distributed throughout the body and is needed to be removed. These unwanted signals are termed as artifacts and they are removed with the help of filters. In this proposed work, the signal is filtered by making use of low-pass filter called Butterworth. The withdrawn characteristics were instructed and categorized by utilizing Artificial Neural Network (ANN). ANN, on the other hand is highly complicated network and utilizing same in the field of biomedical when contracted with electrical signals,acquired from human body is itself a novel. The precision obtainedby the help of the procedure was discovered to be satisfactory and hencethe processis very useful in clinics of sleep, especiallyhelpful for neuro-scientists for discovering the disturbance in sleep.
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Lweesy, K., N. Khasawneh, M. Fraiwan, H. Wenz, H. Dickhaus, and L. Fraiwan. "Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA." Methods of Information in Medicine 49, no. 03 (2010): 230–37. http://dx.doi.org/10.3414/me09-01-0054.

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Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.
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Dissertations / Theses on the topic "Signal scoring"

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Sanders, Teresa H. "Multimodal assessment of Parkinson's disease using electrophysiology and automated motor scoring." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51970.

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A suite of signal processing algorithms designed for extracting information from brain electrophysiology and movement signals, along with new insights gained by applying these tools to understanding parkinsonism, were presented in this dissertation. The approach taken does not assume any particular stimulus, underlying activity, or synchronizing event, nor does it assume any particular encoding scheme. Instead, novel signal processing applications of complex continuous wavelet transforms, cross-frequency-coupling, feature selection, and canonical correlation were developed to discover the most significant electrophysiologic changes in the basal ganglia and cortex of parkinsonian rhesus monkeys and how these changes are related to the motor signs of parkinsonism. The resulting algorithms effectively characterize the severity of parkinsonism and, when combined with motor signal decoding algorithms, allow technology-assisted multi-modal grading of the primary pathological signs. Based on these results, parallel data collection algorithms were implemented in real-time embedded software and off-the-shelf hardware to develop a new system to facilitate monitoring of the severity of Parkinson's disease signs and symptoms in human patients. Off -line analysis of data collected with the system was subsequently shown to allow discrimination between normal and simulated parkinsonian conditions. The main contributions of the work were in three areas: 1) Evidence of the importance of optimally selecting multiple, non-redundant features for understanding neural information, 2) Discovery of signi ficant correlations between certain pathological motor signs and brain electrophysiology in different brain regions, and 3) Implementation and human subject testing of multi-modal monitoring technology.
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Sohrabi, Hossein, and Enes Rahic. "Detection of wave movements." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2363.

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The aim of the thesis has been to study methods to minimize the slosh when moving liquid-filled packages in packaging machines. An automatic method for generation of the movement of a package in a packaging machine is of growing importance. The main reason is that reduced slosh leads to increased production rate. Progress within measurement technology creates possibilities for new solutions. One purpose has been to find methods and equipment to detect the height of the wave, perhaps at several places or alternatively the entire liquid surface shape. When suitable equipment for detection of the wave movements was found, collected measurements were analyzed and criteria for describing improvements of the slosh properties have been formulated.

Initially a sensor specification was written in order to simplify the search for suitable equipment. Sources of information have mainly been catalogues and Internet. The search resulted in that a number of sensors were borrowed for tests. The results of the tests supported the choice of the most suitable sensor, in this case a laser sensor. The main reason is that the sensors detection ability is good compared to its price. An analysis of the sensors most important properties confirmed the choice of the laser sensor. To be able to compare waves, criteria for what is considered to be good wave properties have been formulated and evaluated.

The work has confirmed that it is difficult to find a simple and cheap solution for wave detection given that the solution should have good detection ability. It has also been difficult to formulate simple but working criteria for wave performance, and this has led to a compromise between the complexity of the criterion functions and the result of the wave score. Ideas about how an automatic method, based on the chosen sensor and the criterion functions, can be implemented, have been introduced. During the work, some interesting discoveries have been made. These have led to better understanding of how some parameters should be chosen, to better understanding of wave movements and to better choice of future work.

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Vanbuis, Jade. "Analyse automatique des stades du sommeil à partir des voies électrophysiologiques et cardiorespiratoires." Thesis, Le Mans, 2021. http://cyberdoc-int.univ-lemans.fr/Theses/2021/2021LEMA1004.pdf.

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Le diagnostic des troubles du sommeil repose sur l'analyse de différents signaux enregistrés lors d'un examen du sommeil. Cette analyse est réalisée par un spécialiste du sommeil qui étudie la ventilation et, selon l'outil de diagnostic, la succession des stades de sommeil. Cette dernière tâche est particulièrement chronophage et complexe. Trois algorithmes d'aide au diagnostic et dédiés à cette tâche sont présentés.Le premier permet la classification éveil/sommeil lors de l'utilisation d'un nouvel outil de diagnostic. Il en découle la possibilité pour le médecin de diagnostiquer précisément le syndrome d'apnées du sommeil et à moindre coût.Le deuxième, fondé sur les voies électrophysiologiques, permet d'obtenir une classification de tous les stades de sommeil à partir de l'outil de diagnostic le plus complet. Il a été implémenté en considérant les limitations à l'utilisation d'un tel algorithme en routine clinique. L'architecture de cet algorithme reproduit ainsi le processus de classification réalisé manuellement par les médecins. Une fonction de seuillage auto-adaptatif a aussi été mise en place afin de fournir une classification patient-dépendante. Les résultats obtenus sont comparables avec ceux des médecins.Le troisième algorithme, fondé sur les voies cardio-respiratoires, permet de classifier les stades de sommeil à partir d'un outil de diagnostic très utilisé mais pour lequel il n'est normalement pas possible d'étudier les stades de sommeil. La tâche est complexe, mais les résultats obtenus sont satisfaisants vis-à-vis de la littérature.Les trois algorithmes, destinés aux différents outils de diagnostic, permettront d'aider les spécialistes à analyser le sommeil
The diagnostic of sleep-disordered breathing requires the analysis of various signals obtained while recording sleep. The analysis is carried by a sleep specialist, which studies the patient's ventilation and, depending on the diagnostic tool used for the record, sleep stages. Sleep stage scoring is a complex and time-consuming task. Three diagnosis support algorithms dedicated to this task are presented in this thesis.The first one provides a wakefulness versus sleep classification, designed for a new diagnostic tool. It results in the ability to make a precise diagnosis of sleep apnea syndrome, at low cost.The second algorithm, based on electrophysiological channels, provides a full sleep stage classification while using the most complete diagnosis tool. It was implemented considering the known limitations for the use of algorithms in clinical practice. Its architecture thus reproduces the manual scoring process. A self-adaptative thresholding function was also implemented to provide a patient-dependent classification. The obtained results are comparable with the ones from sleep experts.The third algorithm, based on cardio-respiratory channels, provides a sleep stage classification while using a diagnostic tool that is insufficient for a manual sleep scoring, yet still highly used. The task is challenging but the obtained results are satisfying compared to literature.All three algorithms, which were designed for various diagnostic tools, will help sleep experts analyzing sleep
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Park, Yoon Soo. "Rater Drift in Constructed Response Scoring via Latent Class Signal Detection Theory and Item Response Theory." Thesis, 2011. https://doi.org/10.7916/D8445TGR.

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The use of constructed response (CR) items or performance tasks to assess test takers' ability has grown tremendously over the past decade. Examples of CR items in psychological and educational measurement range from essays, works of art, and admissions interviews. However, unlike multiple-choice (MC) items that have predetermined options, CR items require test takers to construct their own answer. As such, they require the judgment of multiple raters that are subject to differences in perception and prior knowledge of the material being evaluated. As with any scoring procedure, the scores assigned by raters must be comparable over time and over different test administrations and forms; in other words, scores must be reliable and valid for all test takers, regardless of when an individual takes the test. This study examines how longitudinal patterns or changes in rater behavior affect model-based classification accuracy. Rater drift refers to changes in rater behavior across different test administrations. Prior research has found evidence of drift. Rater behavior in CR scoring is examined using two measurement models - latent class signal detection theory (SDT) and item response theory (IRT) models. Rater effects (e.g., leniency and strictness) are partly examined with simulations, where the ability of different models to capture changes in rater behavior is studied. Drift is also examined in two real-world large scale tests: teacher certification test and high school writing test. These tests use the same set of raters for long periods of time, where each rater's scoring is examined on a monthly basis. Results from the empirical analysis showed that rater models were effective to detect changes in rater behavior over testing administrations in real-world data. However, there were differences in rater discrimination between the latent class SDT and IRT models. Simulations were used to examine the effect of rater drift on classification accuracy and on differences between the latent class SDT and IRT models. Changes in rater severity had only a minimal effect on classification. Rater discrimination had a greater effect on classification accuracy. This study also found that IRT models detected changes in rater severity and in rater discrimination even when data were generated from the latent class SDT model. However, when data were non-normal, IRT models underestimated rater discrimination, which may lead to incorrect inferences on the precision of raters. These findings provide new and important insights into CR scoring and issues that emerge in practice, including methods to improve rater training.
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Wang, Zijian Gerald. "On the Use of Covariates in a Latent Class Signal Detection Model, with Applications to Constructed Response Scoring." Thesis, 2012. https://doi.org/10.7916/D8DB87ZP.

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A latent class signal detection (SDT) model was recently introduced as an alternative to traditional item response theory (IRT) methods in the analysis of constructed response data. This class of models can be represented as restricted latent class models and differ from the IRT approach in the way the latent construct is conceptualized. One appeal of the signal detection approach is that it provides an intuitive framework from which psychological processes governing rater behavior can be better understood. The present study developed an extension of the latent class SDT model to include covariates and examined the performance of the resulting model. Covariates can be incorporated into the latent class SDT model in three ways: 1) to affect latent class membership, 2) conditional response probabilities and 3) both latent class membership and conditional response probabilities. In each case, simulations were conducted to investigate both parameter recovery and classification accuracy of the extended model under two competing rater designs; in addition, implications of ignoring covariate effects and covariate misspecification were explored. Here, the ability of information criteria, namely the AIC, small sample adjusted AIC and BIC, in recovering the true model with respect to how covariates are introduced was also examined. Results indicate that parameters were generally well recovered in fully-crossed designs; to obtain similar levels of estimation precision in incomplete designs, sample size requirements were comparatively higher and depend on the number of indicators used. When covariate effects were not accounted for or misspecified, results show that parameter estimates tend to be severely biased, which in turn reduced classification accuracy. With respect to model recovery, the BIC performed the most consistently amongst the information criteria considered. In light of these findings, recommendations were made with regard to sample size requirements and model building strategies when implementing the extended latent class SDT model.
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Kim, Sunhee. "Dealing with Sparse Rater Scoring of Constructed Responses within a Framework of a Latent Class Signal Detection Model." Thesis, 2013. https://doi.org/10.7916/D8T4419T.

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In many assessment situations that use a constructed-response (CR) item, an examinee's response is evaluated by only one rater, which is called a single rater design. For example, in a classroom assessment practice, only one teacher grades each student's performance. While single rater designs are the most cost-effective method among all rater designs, the lack of a second rater causes difficulties with respect to how the scores should be used and evaluated. For example, one cannot assess rater reliability or rater effects when there is only one rater. The present study explores possible solutions for the issues that arise in sparse rater designs within the context of a latent class version of signal detection theory (LC-SDT) that has been previously used for rater scoring. This approach provides a model for rater cognition in CR scoring (DeCarlo, 2005; 2008; 2010) and offers measures of rater reliability and various rater effects. The following potential solutions to rater sparseness were examined: 1) the use of parameter restrictions to yield an identified model, 2) the use of informative priors in a Bayesian approach, and 3) the use of back readings (e.g., partially available 2nd rater observations), which are available in some large scale assessments. Simulations and analyses of real-world data are conducted to examine the performance of these approaches. Simulation results showed that using parameter constraints allows one to detect various rater effects that are of concern in practice. The Bayesian approach also gave useful results, although estimation of some of the parameters was poor and the standard deviations of the parameter posteriors were large, except when the sample size was large. Using back-reading scores gave an identified model and simulations showed that the results were generally acceptable, in terms of parameter estimation, except for small sample sizes. The paper also examines the utility of the approaches as applicable to the PIRLS USA reliability data. The results show some similarities and differences between parameter estimates obtained with posterior mode estimation and with Bayesian estimation. Sensitivity analyses revealed that rater parameter estimates are sensitive to the specification of the priors, as also found in the simulation results with smaller sample sizes.
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Books on the topic "Signal scoring"

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Patterson, Caroline, and Derek Bell. Causes and diagnosis of chest pain. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0144.

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Differentiating life-threatening from benign causes of chest pain in the critical care setting is a challenge when the symptoms and signs overlap, and patients are unable to communicate fully. A high index of suspicion is required for occult disease. Once the clinician has ensured the patient is haemodynamically stable, it is imperative to rule out myocardial infarction in the first instance. Where possible, a thorough history and a full examination should be undertaken. Electrocardiogram, chest X-ray, and routine observations are often diagnostic. Targeted investigation such as computed tomography, or transthoracic or transoesophageal ultrasonography may be required to confirm these diagnoses. Timely intervention optimizes survival benefit. Treatment may be necessary prior to confirmation of diagnosis, based on high clinical suspicion and risk scoring. Other causes of chest pain should be considered once the immediately life-threatening conditions are excluded.
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Pirani, Tasneem, and Tony Rahman. Diagnosis and management of upper gastrointestinal haemorrhage in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0177.

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Upper gastrointestinal haemorrhage is a medical emergency that may present with haematemesis and/or melena. An exhaustive history and careful examination aids in identifying the cause of bleeding and directing appropriate management. Validated scoring systems exist to guide the urgency of endoscopic therapy, although these should not be used in isolation, but in conjunction with complete patient assessment. The initial priority should be to resuscitate and stabilize the patient using the airway, breathing, circulation, and disability framework. Resuscitation should be guided by clinical and physiological parameters. Patients should be managed in an environment where vital signs such as heart rate, blood pressure, respiratory rate, conscious level, and urine output are monitored at least hourly. Attempts should be made to correct coagulopathy. Specialist advice should be sought from haematologists for guidance on the most appropriate use of packed red cells and blood products. Over-transfusion should be avoided. Initiation of pre-endoscopy proton pump inhibitor therapy, in particular to avoid definitive endoscopic therapy, is not recommended. Diagnostic endoscopy and therapy should be conducted within 24 hours of presentation. Numerous endoscopic therapies exist—when epinephrine is used for local tamponade and vasoconstriction, application of dual modality treatment is recommended. In cases where endoscopic therapy fails or is not possible, radiological diagnosis, and embolization may become necessary. Occasionally, surgery is required for definitive treatment—close liaison with surgeons is therefore necessary, especially where initial endoscopy is considered suboptimal or re-bleeding occurs.
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Woollings, Tim. Jet Stream. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828518.001.0001.

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A number of extreme weather events have struck the Northern Hemisphere in recent years, from scorching heatwaves to desperately cold winters and from floods and storms to droughts and wildfires. Is this the emerging signal of climate change, and should we expect more of this? Media reports vary widely, but one mysterious agent has risen to prominence in many cases: the jet stream. The story begins on a windswept beach in Barbados, from where we follow the ascent of a weather balloon that will travel all around the world, following the jet stream. From this viewpoint we can observe the effect of the jet in influencing human life around the hemisphere, and witness startling changes emerging. What is the jet stream and how well do we understand it? How does it affect our weather and is it changing? These are the main questions tackled in this book. We learn about how our view of the wind has developed from Aristotle’s early theories up to today’s understanding. The jet is shown to be intimately connected with dramatic contrasts between climate zones and to have played a key historical role in determining patterns of trade. We learn about the basic physics underlying the jet and how this knowledge is incorporated into computer models which predict both tomorrow’s weather and the climate of future decades. We discuss how climate change is expected to affect the jet, and introduce the urgent scientific debate over whether these changes have contributed to recent extreme weather events.
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Book chapters on the topic "Signal scoring"

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Munkhdalai, Lkhagvadorj, Khishigsuren Davagdorj, Van-Huy Pham, and Keun Ho Ryu. "Adaptive Softmax Regression for Credit Scoring." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 409–17. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6757-9_51.

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Chuluunsaikhan, Tserenpurev, Kwan-Hee Yoo, HyungChul Rah, and Aziz Nasridinov. "Pork Price Prediction Using Topic Modeling and Feature Scoring Method." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 277–82. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6757-9_35.

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Munkhdalai, Lkhagvadorj, Jong Yun Lee, and Keun Ho Ryu. "A Hybrid Credit Scoring Model Using Neural Networks and Logistic Regression." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 251–58. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9714-1_27.

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Yang, Zhihua, Qian Zhang, Feng Zhou, and Lihua Yang. "A New Credit Scoring Model Based on Prediction of Signal on Graph." In Pattern Recognition and Artificial Intelligence, 228–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_20.

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Liu, Shutang, Zhen Wang, Chuansheng Wang, Junxian Zheng, and Fuquan Zhang. "Preliminary Design and Application Prospect of Single Chinese Character Calligraphy Image Scoring Algorithm." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 443–50. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9710-3_46.

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Rajalakshmi, S., and R. Venkatesan. "Exploring Cepstral Coefficient Based Sleep Stage Scoring Method for Single-Channel EEG Signal Using Machine Learning Technique." In Advances in Intelligent Systems and Computing, 24–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67934-1_3.

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Kagan, Vadim, Edward Rossini, and Demetrios Sapounas. "Scoring Engine." In Sentiment Analysis for PTSD Signals, 33–49. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-3097-1_5.

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Smith, Joyce, and Rachel Roberts. "Early Warning Scoring Tools." In Vital Signs for Nurses, 138–55. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781119139119.ch8.

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Czabanski, R., M. Jezewski, J. Wrobel, J. Jezewski, and K. Horoba. "Classification of the Fetal Monitoring Signals Using Fuzzy Scoring System." In V Latin American Congress on Biomedical Engineering CLAIB 2011 May 16-21, 2011, Habana, Cuba, 850–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-21198-0_216.

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Borms, Samuel, Kris Boudt, Frederiek Van Holle, and Joeri Willems. "Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies." In Data Science for Economics and Finance, 217–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_10.

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Abstract:
AbstractWe present a general monitoring methodology to summarize news about predefined entities and topics into tractable time-varying indices. The approach embeds text mining techniques to transform news data into numerical data, which entails the querying and selection of relevant news articles and the construction of frequency- and sentiment-based indicators. Word embeddings are used to achieve maximally informative news selection and scoring. We apply the methodology from the viewpoint of a sustainable asset manager wanting to actively follow news covering environmental, social, and governance (ESG) aspects. In an empirical analysis, using a Dutch-written news corpus, we create news-based ESG signals for a large list of companies and compare these to scores from an external data provider. We find preliminary evidence of abnormal news dynamics leading up to downward score adjustments and of efficient portfolio screening.
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Conference papers on the topic "Signal scoring"

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Bozkurt, Ferda, Muhammed Kursad Ucar, Recep Bozkurt Mehmet, Serhat Tuna, and Cahit Bilgin. "Respiratory scoring using photoplethysmography signal and nearest neighbor algorithm." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404528.

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Canak, Busra, and Mustafa E. Kamasak. "Automatic scoring of wrinkles on the forehead." In 2017 25th Signal Processing and Communications Applications Conference (SIU). IEEE, 2017. http://dx.doi.org/10.1109/siu.2017.7960234.

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Isgum, I., B. van Ginneken, A. Rutten, and M. Prokop. "Automated coronary calcification detection and scoring." In Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. IEEE, 2005. http://dx.doi.org/10.1109/ispa.2005.195396.

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Dong, Wenwei, Yanlu Xie, and Binghuai Lin. "Unsupervised Pronunciation Fluency Scoring by infoGan." In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2019. http://dx.doi.org/10.1109/apsipaasc47483.2019.9023010.

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Duchateau, Demuynck, and Wambacq. "Confidence scoring based on backward language models." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005716.

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Lee, Tracey K. M., K. H. Leo, Saeid Sanei, and Effie Chew. "Automated scoring of rehabilitative tests with singular spectrum analysis." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362849.

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Darabi, Kaveh, and Gheorghita Ghinea. "Personalized video summarization based on group scoring." In 2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP). IEEE, 2014. http://dx.doi.org/10.1109/chinasip.2014.6889254.

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Yang, Chao-Lung, Shang-Che Hsu, Kai-Lung Hua, and Wen-Huang Cheng. "Fuzzy Personalized Scoring Model for Recommendation System." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682809.

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Zohrevand, Zahra, and Uwe Glasser. "Dynamic Attack Scoring Using Distributed Local Detectors." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054264.

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Arslan, Emre, and Ulisses M. Braga-Neto. "A Bayesian approach to Top-Scoring Pairs classification." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952280.

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