Готові списки джерел за темами / MSVM

Добірка наукової літератури з теми "MSVM"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "MSVM"

1

Indrawan, Gede, Heri Setiawan, and Aris Gunadi. "Multi-class SVM Classification Comparison for Health Service Satisfaction Survey Data in Bahasa." HighTech and Innovation Journal 3, no. 4 (December 1, 2022): 425–42. http://dx.doi.org/10.28991/hij-2022-03-04-05.

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Анотація:
This study aimed to compare the Multi-class Support Vector Machine (MSVM) classification with the One-versus-One (OvO) and One-versus-Rest (OvR) approaches using unigram and bigram features. The study used the service satisfaction survey report of Denpasar public health centers by the Center for Public Health Innovation (CPHI), Medical School, Udayana University. As Bali is known as the world's main tourism destination, it is important to know about its supporting public health service through its representative capital city, Denpasar. Moreover, this study laid the foundation for the classification process using the available methods to fit in Indonesian health service satisfaction survey data, which assists in making decisions to improve health services. Since Bali is one of the provinces in Indonesia and all of those provinces refer to the same national regulation, health service satisfaction survey data that is in the Indonesian language (Bahasa) should have the same aspects, like category, priority, word-related matters (including abbreviations, acronyms, terminology), etc. that overall make it unique and need specific processing. That work was considered a contribution since there is no such study to the best of the author's knowledge and the foundation would be useful as a part of the future vision for the integrated system of Indonesian health big data. Since in reality, satisfaction survey data tends to be unbalanced, this study also compares the developed models using unigram and bigram features without and with feature selection (FS). Those features were then processed using the OvO MSVM and OvR MSVM models. k-fold cross-validation was used to divide training data and testing data and, at the same time, validate the models. Through experiments without and with FS, the OvO MSVM and OvR MSVM models with unigram features had better performance in general than the same models with bigram features. Without FS and with unigram features, comparable differences were found where the OvO MSVM model was slightly better on accuracy and precision, while the OvR MSVM model was slightly better on recall and the F1score. Without FS and with bigram features, comparable differences were also found, where the OvR MSVM model had slightly better performance than the OvO MSVM model. With FS and with unigram and bigram features, the OvR MSVM model had better performance in general than the OvO MSVM model. Doi: 10.28991/HIJ-2022-03-04-05 Full Text: PDF
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2

Chen, Xiao Yun, Xian Fu Chen, Shao Quan Zhang, and Wen Bin Zhang. "Classification Moving Vehicle Based on Multisensor Data Using Fusion of Multi-Class SVMs Methods." Advanced Materials Research 945-949 (June 2014): 1978–81. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1978.

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Анотація:
In this paper, we propose a special Multi-class SVMs (MSVM) data fusion strategy which is applied to classify vehicle based on multiple pavement structural strain time histories. The centralized and distributed fusion strategies are applied to combine information from several data sources. In the centralized strategy, all information from several data sources is centralized and combined to construct an input space. Then a MSVM classifier is trained. In distributed schemes, the individual data sources are processed separately and modeled by using the MSVM. Then new data fusion strategies are used to combine the information from the individual MSVM to acquire the final classification outputs. Two popular Multi-class SVMs algorithms (One-against-all OAA, One-against-one OAO) are used to construct classifier based on aforementioned two fusion strategies, respectively. The results are compared between SVM-based fusion approach and single data source SVM using two MSVM algorithms, respectively. The result shows this SVM-based fusion approach significantly improves the results of classification accuracy and robustness. The proposed Multisensor data fusion methods can also be applied in other fields.
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3

Zhang, Shuai. "Quality Diagnosis in Dynamic Process Based on Multi-Feature." Advanced Materials Research 945-949 (June 2014): 1293–96. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1293.

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Анотація:
Recognition of quality abnormal patterns for a dynamic process has seen increasing demands nowadays in the real-time process fault detection and diagnosis. Based on the analysis of the quality abnormal patterns in a dynamic process, a novel method based on multi-feature of quality abnormal patterns by using a multi-SVM (MSVM) was proposed. The simulation results indicate that the recognition accuracies of the MSVM classifiers with the different features are quite different. It is shown that this MSVM model with suitable features can increase the recognition accuracy.
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4

Renukalatha, S., and K. V. Suresh. "CLASSIFICATION OF GLAUCOMA USING SIMPLIFIED-MULTICLASS SUPPORT VECTOR MACHINE." Biomedical Engineering: Applications, Basis and Communications 31, no. 05 (September 9, 2019): 1950039. http://dx.doi.org/10.4015/s101623721950039x.

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Анотація:
Detection and diagnosis of glaucoma disease of eye fundus images at early stage is very important as this disorder leads to complete loss of vision if ignored. Usually, 80–90% of glaucoma cases are analyzed manually by ophthalmologists. As the manual analysis varies from one expert to other, diagnosis cannot be effective. Hence, there is a need for automatic assessment of glaucoma disease using computer aided diagnosis (CAD). Many researchers have devised several CAD techniques for glaucoma analysis using various classification techniques. However, most of the classifiers are efficient only for two level classification to detect whether disease is glaucoma or not. But, glaucoma disease has several stages and demands multilevel approaches with high degree of classification accuracy. Among several multiclass methods, literature suggests multiclass support vector technique (MSVM) as a better performing statistical classifier. However, many MSVMS suffer from data loss during training phase. To address this issue, a robust hybrid classification approach consisting of Naïve Bayes binary classifier in the first stage and simplified multiclass support vector machine (Sim-MSVM) in the second stage is proposed in this paper.
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5

Liu, Yumin, and Haofei Zhou. "MSVM Recognition Model for Dynamic Process Abnormal Pattern Based on Multi-Kernel Functions." Journal of Systems Science and Information 2, no. 5 (October 25, 2014): 473–80. http://dx.doi.org/10.1515/jssi-2014-0473.

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Анотація:
AbstractRecognition of quality abnormal patterns for a dynamic process has seen increasing demands nowadays in the real-time process fault detection and diagnosis. As the dynamic data from a quality abnormal process is linearly inseparable, the recognition efficiency of a support vector machine (SVM) model mainly depends on the selection of the kernel functions and the optimizing of their parameters. Based on the analysis of the quality abnormal patterns in a dynamic process, this paper presents a recognition framework of quality abnormal patterns by using a multi-SVM (MSVM). For the different quality abnormal patterns, the simulation results indicate that the recognition accuracies of the MSVM classifiers with the selected kernel functions are quite different. A MSVM recognition model for quality abnormal patterns in a dynamic process is proposed by the kernel functions being of high accuracies. It is shown that this MSVM model with suitable kernel functions can increase the recognition accuracy.
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6

Kaka, Jhansi Rani, and K. Satya Prasad. "Differential Evolution and Multiclass Support Vector Machine for Alzheimer’s Classification." Security and Communication Networks 2022 (January 13, 2022): 1–13. http://dx.doi.org/10.1155/2022/7275433.

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Анотація:
Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image normalization method is applied to enhance the quality of the image and represent the features effectively. The AlexNet model is applied to the normalized images to extract the features and also applied for feature selection. The Differential Evolution method applies Pareto Optimal Front for nondominated feature selection. This helps to select the feature that represents the characteristics of the input images. The selected features are applied in the MSVM method to represent in high dimension and classify Alzheimer’s. The DE-MSVM method has accuracy of 98.13% in the axial slice, and the existing whale optimization with MSVM has 95.23% accuracy.
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7

Topannavar, Preeti Sadanand, and Dinkar M. Yadav. "An effective feature selection using improved marine predators algorithm for Alzheimer’s disease classification." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (October 1, 2023): 5126. http://dx.doi.org/10.11591/ijece.v13i5.pp5126-5134.

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Анотація:
<span lang="EN-US">Alzheimer’s disease (AD) is an irremediable neurodegenerative illness developed by the fast deterioration of brain cells. AD is mostly common in elder people and it extremely disturbs the physical and mental health of patients, therefore early detection is essential to prevent AD development. However, the precise detection of AD and mild cognitive impairment (MCI) is difficult during classification. In this paper, the Residual network i.e., ResNet-18 is used for extracting the features, and the proposed improved marine predators algorithm (IMPA) is developed for choosing the optimum features to perform an effective classification of AD. The multi-verse optimizer (MVO) used in the IMPA helps to balance exploration and exploitation, which leads to the selection of optimal relevant features. Further, the classification of AD is accomplished using the multiclass support vector machine (MSVM). Open access series of imaging studies-1 (OASIS-1) and Alzheimer disease neuroimaging initiative (ADNI) datasets are used to evaluate the IMPA-MSVM method. The performance of the IMPA-MSVM method is analyzed using accuracy, sensitivity, specificity, positive predictive value (PPV) and matthews correlation coefficient (MCC). The existing methods such as the <a name="_Hlk134426295"></a>deep learning-based segmenting method using SegNet (DLSS), mish activation function (MAF) with spatial transformer network (STN) and BrainNet2D are used to evaluate the IMPA-MSVM method. The accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which is more when compared to the DLSS and MAF-STN.</span>
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8

Zhang, Min, and Wenming Cheng. "Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/382395.

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Анотація:
Control charts have been widely utilized for monitoring process variation in numerous applications. Abnormal patterns exhibited by control charts imply certain potentially assignable causes that may deteriorate the process performance. Most of the previous studies are concerned with the recognition of single abnormal control chart patterns (CCPs). This paper introduces an intelligent hybrid model for recognizing the mixture CCPs that includes three main aspects: feature extraction, classifier, and parameters optimization. In the feature extraction, statistical and shape features of observation data are used in the data input to get the effective data for the classifier. A multiclass support vector machine (MSVM) applies for recognizing the mixture CCPs. Finally, genetic algorithm (GA) is utilized to optimize the MSVM classifier by searching the best values of the parameters of MSVM and kernel function. The performance of the hybrid approach is evaluated by simulation experiments, and simulation results demonstrate that the proposed approach is able to effectively recognize mixture CCPs.
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9

Jiang, Hong, Xi Chen, Bai Lin Liu, and Yun Qing Liu. "Breast Tumor Recognition Based on Multiple Support Vector Machine." Advanced Materials Research 490-495 (March 2012): 252–56. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.252.

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Анотація:
In order to solve unfixed size and individual difference with the breast tumor, this paper provides a method of Multi-Support Vector Machine (MSVM) for breast tumor recognition. We take Support Vector Machine (SVM) on the eight direction of bump area to generate vector classifier and select Gauss kernel function as kernel function. The breast tumor recognition accuracy can reach 97.3% when σ=30. The experiment shows that the application of MSVM in breast tumor recognition can achieve good result, and provide the reliable basis for further medical diagnosis.
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10

Liu, Yu Min, Hao Fei Zhou, and Shuai Zhang. "A MSVM Quality Pattern Recognition Model for Dynamic Process." Applied Mechanics and Materials 433-435 (October 2013): 555–61. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.555.

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Анотація:
Quality abnormal pattern recognition for dynamic process is the key problem to achieve the online quality control and diagnose of automatic production. Firstly, this paper analyzed the quality patterns of dynamic process. Secondly, we established recognition model of quality recognition in dynamic process using MSVM and compared the SVM recognition accuracy of different kernel functions for different quality patterns. Simulation experiment indicates that different SVM classifiers should choose specified kernel functions to recognition quality patterns. At last, we established MSVM recognition model of quality pattern in dynamic process using multi-kernel function according to the experiment results.
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Більше джерел

Дисертації з теми "MSVM"

1

Jannah, Najlaa. "ECG analysis and classification using CSVM, MSVM and SIMCA classifiers." Thesis, University of Reading, 2017. http://centaur.reading.ac.uk/78068/.

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Анотація:
Reliable ECG classification can potentially lead to better detection methods and increase accurate diagnosis of arrhythmia, thus improving quality of care. This thesis investigated the use of two novel classification algorithms: CSVM and SIMCA, and assessed their performance in classifying ECG beats. The project aimed to introduce a new way to interactively support patient care in and out of the hospital and develop new classification algorithms for arrhythmia detection and diagnosis. Wave (P-QRS-T) detection was performed using the WFDB Software Package and multiresolution wavelets. Fourier and PCs were selected as time-frequency features in the ECG signal; these provided the input to the classifiers in the form of DFT and PCA coefficients. ECG beat classification was performed using binary SVM. MSVM, CSVM, and SIMCA; these were subsequently used for simultaneously classifying either four or six types of cardiac conditions. Binary SVM classification with 100% accuracy was achieved when applied on feature-reduced ECG signals from well-established databases using PCA. The CSVM algorithm and MSVM were used to classify four ECG beat types: NORMAL, PVC, APC, and FUSION or PFUS; these were from the MIT-BIH arrhythmia database (precordial lead group and limb lead II). Different numbers of Fourier coefficients were considered in order to identify the optimal number of features to be presented to the classifier. SMO was used to compute hyper-plane parameters and threshold values for both MSVM and CSVM during the classifier training phase. The best classification accuracy was achieved using fifty Fourier coefficients. With the new CSVM classifier framework, accuracies of 99%, 100%, 98%, and 99% were obtained using datasets from one, two, three, and four precordial leads, respectively. In addition, using CSVM it was possible to successfully classify four types of ECG beat signals extracted from limb lead simultaneously with 97% accuracy, a significant improvement on the 83% accuracy achieved using the MSVM classification model. In addition, further analysis of the following four beat types was made: NORMAL, PVC, SVPB, and FUSION. These signals were obtained from the European ST-T Database. Accuracies between 86% and 94% were obtained for MSVM and CSVM classification, respectively, using 100 Fourier coefficients for reconstructing individual ECG beats. Further analysis presented an effective ECG arrhythmia classification scheme consisting of PCA as a feature reduction method and a SIMCA classifier to differentiate between either four or six different types of arrhythmia. In separate studies, six and four types of beats (including NORMAL, PVC, APC, RBBB, LBBB, and FUSION beats) with time domain features were extracted from the MIT-BIH arrhythmia database and the St Petersburg INCART 12-lead Arrhythmia Database (incartdb) respectively. Between 10 and 30 PCs, coefficients were selected for reconstructing individual ECG beats in the feature selection phase. The average classification accuracy of the proposed scheme was 98.61% and 97.78 % using the limb lead and precordial lead datasets, respectively. In addition, using MSVM and SIMCA classifiers with four ECG beat types achieved an average classification accuracy of 76.83% and 98.33% respectively. The effectiveness of the proposed algorithms was finally confirmed by successfully classifying both the six beat and four beat types of signal respectively with a high accuracy ratio.
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2

SINGH, BHUPINDER. "A HYBRID MSVM COVID-19 IMAGE CLASSIFICATION ENHANCED USING PARTICLE SWARM OPTIMIZATION." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18864.

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Анотація:
COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of civilians and affected millions around the world. Mostly as result, numerous technologies that enable both the rapid and accurate identification of COVID-19 illnesses will provide much assistance to healthcare practitioners. A machine learning- based approach is used for the detection of COVID-19. In general, artificial intelligence (AI) approaches have yielded positive outcomes in healthcare visual processing and analysis. CXR is the digital image processing method that plays a vital role in the analysis of Covid-19 disease. Due to the maximum accessibility of huge scale annotated image databases, excessive success has been done using multiclass support vector machines for image classification. Image classification is the main challenge to detect medical diagnosis. The existing work used CNN with a transfer learning mechanism that can give a solution by transferring information from GENETIC object recognition tasks. The DeTrac method has been used to detect the disease in CXR images. DeTrac method accuracy achieved 93.1~ 97 percent. In this proposed work, the hybridization PSO+MSVM method has worked with irregularities in the CXR images database by studying its group distances using a group or class mechanism. At the initial phase of the process, a median filter is used for the noise reduction from the image. Edge detection is an essential step in the process of COVID-19 detection. The canny edge detector is implemented for the detection of edges in the chest x-ray images. The PCA (Principal Component Analysis) method is implemented for the feature extraction phase. There are multiple features extracted through PCA and the essential features are optimized by an optimization technique known as swarm optimization is used for feature optimization. For the detection of COVID-19 through CXR images, a hybrid multi-class support vector machine technique is implemented. The PSO (particle swarm optimization) technique is used for feature optimization. The comparative analysis of various existing techniques is also depicted in this work. The proposed system has achieved an accuracy of 97.51 percent, SP of 97.49 percent, and 98.0 percent of SN. The proposed system is compared with existing systems and achieved better performance and the compared systems are DeTrac, GoogleNet, and SqueezeNet.
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3

RACIOPPI, ANTONIO. "Anomalies, U(1)' and the MSSM." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2009. http://hdl.handle.net/2108/905.

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Анотація:
Questa Tesi analizza un'estensione del MSSM tramite l'aggiunta di un multipletto vettoriale abeliano anomalo e contiene alcuni risultati originali riguar- danti la fenomenologia di uno $Z'$ anomalo. La parte di rassegna presenta un'introduzione sul MSSM e le sue caratteristiche principali, una discussione sulle anomalie chirali e come cancellarle nel Modello Standard e tramite il meccanismo di Green-Schwarz. Quindi, vengono presentati i risultati originali. Costruiamo la Lagrangiana per la ``Minimal Anomalous U(1)'Extension of the MSSM'', dove le anomalie sono cancellate con il meccanismo di Green-Scharwz e l'aggiunta dei termini di Chern-Simons, sottolineando le principali caratteristiche tra il nostro modello e il MSSM. Il vantaggio di questa scelta rispetto alla standard è che permette valori arbitrari dei numeri quantici dell'extra U(1). Come primo passo verso lo studio delle collisioni adroniche che producono 4 leptoni nello stato finale (un segnale chiaro che potrebbe essere studiato a LHC) calcoliamo quindi i decadimenti Z' o Z_0 gamma and Z' o Z_0 Z_0. Troviamo che i valori più grandi per i tassi di decadimento sono sim 10^{-4} GeV, mentre il numero previsto di eventi all'anno in LHC è al massimo dell'ordine di 10. Quindi calcoliamo la densità residua predetta dal nostro modello con un nuovo candidato di materia oscura, l'axino, che è la LSP della teoria. Notiamo che l'accordo con i dati sperimentali implica l'assunzione che la NLSP sia una particella ``bino-like'' quasi degenere in massa con l'axino.
This Thesis reviews an extension of the MSSM by the addition of an anomalous abelian vector multiplet and contains some original results concerning the phenomenology of an anomalous Z'. The review part covers an introduction of the MSSM focusing on its main features, a discussion on the chiral anomalies and how to cancel them in the Standard Model and by the Green-Schwarz mechanism. Then, the original results are presented. We build the Lagrangian for the Minimal Anomalous U(1)' Extension of the MSSM where the anomalies are cancelled by the Green-Schwarz mechanism and the addition of Chern-Simons terms, stressing the main differences between our model and the MSSM. The advantage of this choice over the standard one is that it allows for arbitrary values of the quantum numbers of the extra U(1). As a first step towards the study of hadron annihilations producing four leptons in the final state (a clean signal which might be studied at LHC) we then compute the decays Z' o Z_0 g and Z' o Z_0 Z_0. We find that the largest values of the decay rate are sim 10^{-4} GeV, while the expected number of events per year at LHC is at most of the order of 10. Then we compute the relic density predicted by our model with a new dark matter candidate, the axino, which is the LSP of the theory. We see that agreement with experimental data implies the assumption that the NLSP is a bino-like particle almost degenerate in mass to the axino.
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4

Ewerth, Thorsten. "Rare K- and B-decays in the MSSM." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973069376.

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5

Bernal, Nicolas. "Théorie et Phénoménologie du MSSM avec des Scalaires Lourds." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2008. http://tel.archives-ouvertes.fr/tel-00447004.

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Анотація:
Ce mémoire de thèse sera consacré à l'étude de quelques aspects phénoménologiques de la supersymétrie avec, comme principaux sujets, d'une part, la physique du MSSM dans le cas où les superparticules scalaires sont lourdes et les implications possibles auprès de collisionneurs à haute énergie tels que le LHC et, d'autre part, les caractéristiques des particules de matière noire et leurs détermination auprès des collisionneurs et dans des expériences d'astroparticules.
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6

Cyr-Racine, Francis-Yan. "Study of entropy perturbations in MSSM flat direction decay." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=21950.

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Анотація:
We study how the resonant decay of moduli fields from the scalar potential of the MSSM could affect large scale curvature perturbations. After introducing the theory of cosmological perturbations and of broad and stochastic resonance, we present the supergravity inputs necessary to study the MSSM moduli in a cosmological context. We find that the resonant amplification of large scale field fluctuations is allowed for a very small range of parameters and is harmless for large scale curvature modes.
Nous étudions l'effet de la désintégration des champs de module du MSSM sur les perturbations gravitationelles adiabatiques à grandes échelles. Après avoir introduit la théorie des perturbations cosmologiques et la théorie de la resonance stochastique, nous présentons les éléments de la supergravité nécessaire à l'étude des champs de module dans un contexte cosmologique. Nous trouvons que l'amplification non-perturbative des fluctuations d'ordre cosmologiques du champ scalaire n'est permise pour que pour un mince interval de paramètres. L'effet de la résonance sur les perturbations gravitationelles à grandes échelles est négligeable.
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7

Hodgkinson, Robert Neil. "Radiative Higgs Singlet YukawaCouplings in Extensions of the MSSM." Thesis, University of Manchester, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504725.

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8

Bernal, Hernández Nicolás. "Théorie et phénoménologie du MSSM avec des scalaires lourds." Paris 6, 2008. https://tel.archives-ouvertes.fr/tel-00447004v2.

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Анотація:
On a effectué une analyse du Modèle Standard Supersymétrique Minimale dans le scénario où les superpartenaires scalaires des fermions et des bosons de Higgs (à l'exception du boson de Higgs standard) sont supposés être très lourds et donc éliminés du spectre de basse énergie. Premièrement on résume la détermination du spectre de masse, dans lequel on a inclut les corrections radiatives à une boucle et on a resommé à tout les ordres les logarithmes dominants. En plus, on a étudié en détail la phénoménologie du modèle dans des scénarios où les masses des jauginos sont non universelles à l'échelle Mgut. On a discuté les contraintes données par les recherches dans les collisionneurs, les mesures de haute précision, les contraintes cosmologiques des densité relique de matière sombre et le temps de vie du gluino. Après on a analysé les désintégrations du boson de Higgs, des charginos, des neutralinos et du gluino. Dans une deuxième partie, on a analysé les possibilités de reconstruire dans les collisionneurs de haute énergie les paramètres de base du MSSM avec des scalaires lourds. On a discuté la production des charginos et des neutralinos et dans quelle mesure on peut les utiliser pour calculer les paramètres du lagrangien. En utilisant des mesures de haute précision, on a déterminé les paramètres à basse et à haute énergie. Les paramètres du modèle peuvent être reconstruits même dans le cas où il n'y a pas d'universalité dans la masse des jauginos. Finalement, on a étudié la possibilité d'identifier les propriétés de la matière sombre à partir des expériences Xénon 100kg et du satellite GLAST. On a montré que les expériences de détection directe sont très performantes pour des WIMPs légers. Pour des profils de halo de matière sombre de type NFW, GLAST est capable de confirmer et même d'augmenter la précision lors de la reconstruction de la masse des WIMPs. On a aussi étudié le taux de production de WIMPs dans l'ILC, et comparé ses performances par rapport à Xénon et à GLAST
We perform a comprehensive analysis of the Minimal Supersymmetric Standard Model in the scenario where the scalar partners of the fermions and the Higgs particles (except for the Standard-Model-like one) are assumed to be very heavy and are removed from the low-energy spectrum. We first summarize our determination of the mass spectrum, in which we include the one-loop radiative corrections and resum to all orders the leading logarithms of the large scalar masses. We then study in detail the phenomenology of the model in scenarios where the gaugino mass parameters are non-universal at the GUT scale. We discuss the constraints from collider searches and high-precision measurements, the cosmological constraints on the relic abundance of the neutralino candidate for the Dark Matter in the Universe and the gluino lifetime. We then analyze the decays of the Higgs boson, of charginos and neutralinos and of gluinos, and highlight the differences from the case of universal gaugino masses. In a second part, we analyse the prospects to reconstruct at high-energy colliders the basic parameters of the MSSM with heavy scalars. We discuss at which extent the charginos and neutralinos can be produced and the low-energy Lagrangian parameters which enter the various measurables can be precisely measured. Using the high-precision measurements, we determine the parameters of the model both at the low and high-energy scales. It is shown that model parameters can be reconstructed even in the general case where the soft-SUSY breaking gaugino mass parameters are non universal at the high scale. Finally, we study the possibility of identifying dark matter properties from XENON-like 100 kg experiments and the GLAST satellite mission. We show that whereas direct detection experiments will probe efficiently light WIMPs, given a positive detection, GLAST will be able to confirm and even increase the precision in the case of a NFW profile. We also predict the rate of production of a WIMP in the next generation of colliders (ILC), and compare their sensitivity to the WIMP mass with the XENON and GLAST projects
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9

Dickerson, Dawne D. "Effects of Medical Professionals' Communication with Men Sleeping With Men and HIV/AIDS." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7795.

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The human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) initially emerged in the 1980s and ever since, a battle has been underway with regard to understanding some of the root issues attributed to behavior and HIV/AIDS. AIDS is prevalent in the men sleeping with men (MSM) community. There is lack of studies evaluating the quality and quantity of communication between MSM and medical professionals, which can be a catalyst to help reduce HIV/AIDS within this community. The aim of this quantitative study was to evaluate the relationship between quantity and quality of medical professionals' communication and regular HIV screening and testing, as well as modifying high-risk behaviors attributing to HIV/AIDS MSM behaviors, controlled for race/ethnicity, educational level, and income. Universal precautions theory and health belief model were the theoretical framework of this study. A total of 126 MSM were evaluated via survey research. Chi-square analysis revealed that MSM who have ever visited a medical professional about HIV/AIDS were tested in significantly higher frequency compared with those that did not visit medical professional for this reason (51.5% vs. 18.5%, respectively, p < .001). Also, significantly more participants changed their sexual behavior during the last 5 years and considered that the approach of the medical professional contributed in this change, compared with those who changed their sexual behavior but did not consider this approach helpful (73.7% vs. 26.7%, p < .001). This research can provide positive social change to the MSM community as well as medical professionals, by encouraging MSM to seek more information pertaining to safe sexual health practices, prevention, and awareness.
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10

Moffett, Peter. "Characterisation of the transcriptional properties of the mSim gene products." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0031/NQ64623.pdf.

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Книги з теми "MSVM"

1

Graham, Newman, and Miller Jim, eds. MSM mathematics. Walton-on Thames: Nelson, 1992.

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2

Korjenić, Ismet. MSM oporuke. Mostar: IC štamparija, 2022.

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3

Jim, Miller, Newman Graham, and Procter Maralyn, eds. MSM mathematics. Walton-on-Thames: Nelson, 1993.

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4

Susan, Gibson-Garvey, ed. Now appearing: MSVU collection. Halifax, N.S: Art Gallery, Mount St. Vincent University, 1996.

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5

Jacob, Stanley W. The Miracle of MSM. New York: Penguin USA, Inc., 2009.

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6

United States. National Aeronautics and Space Administration., ed. New concentric electrode metal-semiconductor-metal photodetectors: A final report, NASA grant no. NCC-1-197, August 1, 1994 - October 31, 1996. Charlottesville, VA: Dept. of Electrical Engineering, University of Virginia, School of Engineering and Applied Science, 1996.

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7

Samper, Daniel Bermúdez. 4° Lat. N, 2.600 msnm: Daniel Bermúdez : arquitectura. Barcelona: Lunwerg, 2010.

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8

Mitchell, Deborah R. MSM: The natural pain relief remedy. New York: Whole Care, 1999.

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9

Resource Centre for Sexual Health and HIV/AIDS (New Delhi, India), ed. Resource pack for interventions with MSM & Hijra. [New Delhi]: Resource Centre for Sexual Health and HIV/AIDS, 2006.

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10

Ruth, Mark F. Macro System Model (MSM) user guide version 1.3. Golden, CO: National Renewable Energy Laboratory, 2011.

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Частини книг з теми "MSVM"

1

Puri, Dinesh D., and G. K. Patnaik. "Regular Expression-Based Text Classification Using mSVM and Machine Learning Techniques." In Algorithms for Intelligent Systems, 199–210. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3311-0_17.

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2

Singh, Bhupinder, and Ritu Agarwal. "A Hybrid MSVM COVID-19 Image Classification Enhanced with Swarm Feature Optimization." In Computational Intelligence in Data Mining, 635–50. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9447-9_48.

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3

Sonia and Darpan Anand. "An Improved Hand Gesture Recognition System Based on Optimized MSVM and SIFT Feature Extraction Algorithm." In Proceedings of International Conference on Big Data, Machine Learning and their Applications, 365–77. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8377-3_31.

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4

Le Thi, Hoai An, Anh Vu Le, Xuan Thanh Vo, and Ahmed Zidna. "A Filter Based Feature Selection Approach in MSVM Using DCA and Its Application in Network Intrusion Detection." In Intelligent Information and Database Systems, 403–13. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05458-2_42.

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5

Bachtis, Michail. "Supersymmetry and the MSSM." In Heavy Neutral Particle Decays to Tau Pairs, 17–22. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03257-3_2.

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6

Zhang, Nan, Meng Wang, Zhenhua Duan, Cong Tian, and Jin Cui. "Implementing MapReduce with MSVL." In Structured Object-Oriented Formal Language and Method, 148–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90104-6_10.

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7

Mo, Dapeng, Xiaobing Wang, and Zhenhua Duan. "Asynchronous Communication in MSVL." In Formal Methods and Software Engineering, 82–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24559-6_8.

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8

Shu, Xinfeng, and Zhenhua Duan. "Extending MSVL with Semaphore." In Lecture Notes in Computer Science, 599–610. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42634-1_48.

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9

Wu, B. "MSM Reference Model." In Manufacturing and Supply Systems Management, 433–52. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0753-8_10.

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10

Spallucci, Euro, Steven Duplij, Anatoly Nikitin, Alexander Galkin, Artur Sergyeyev, Steven Duplij, Omer Faruk Dayi, Omer Faruk Dayi, and Rabindra Mohapatra. "Parity Conserving Unification, beyond MSSM." In Concise Encyclopedia of Supersymmetry, 292–94. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/1-4020-4522-0_388.

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Тези доповідей конференцій з теми "MSVM"

1

Arestis-Chartampilas, Stavros, Nikolaos Gkalelis, and Vasileios Mezaris. "AKSDA-MSVM." In MM '16: ACM Multimedia Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2964284.2967263.

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2

Jocher, Glenn R., John T. Dolloff, Peter J. Doucette, Bryant M. Hottel, and Hank J. Theiss. "Minimum Separation Vector Mapping (MSVM)." In SPIE Defense + Security, edited by Matthew F. Pellechia, Kannappan Palaniappan, Shiloh L. Dockstader, Peter J. Doucette, and Donnie Self. SPIE, 2014. http://dx.doi.org/10.1117/12.2053833.

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3

Sinha, Anupa, and Snehlata Barde. "Illumination invariant face recognition using MSVM." In INNOVATIONS AND RESEARCH IN MARINE ELECTRICAL AND ELECTRONICS ENGINEERING: ICIRMEEE 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0100936.

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4

Sultana, Nasreen, and Yedukondalu Kamatham. "MSVM-based classifier for cardiac arrhythmia detection." In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016. http://dx.doi.org/10.1109/icacci.2016.7732229.

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5

M, Ganga, and Gini R. "Optimized Multi Support Vector Machine Based Approach for Fake News Detection." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/jaad9174/ngcesi23p46.

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Анотація:
Fake News creates erroneous suspense information that can be identified. This spreads dishonesty about a country’s status or overstates the expense of special functions for a government, destroying democracy in certain countries. The project proposes an Multi Support Vector Machine (MSVM) -based approach for detecting fake news. The proposed model will be used to classify or detect the news as fake or real. Principal Component Analysis (PCA) is used for Feature Extraction. Principal Component Analysis (PCA) reduces the dimension of the data set comprising many related variables and recalls the maximum change in actual data. The proposed work will select the essential features with a Firefly-Optimized Algorithm (FA). The Firefly Optimized Algorithm (FA) is one of the various Evolutionary Algorithms (EAs) with various purposes. For the classification of fake news, an Multi Support Vector Machine (MSVM) classifier algorithm is implemented.
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6

Jannah, N., and S. Hadjiloucas. "Detection of ECG arrhythmia conditions using CSVM and MSVM classifiers." In 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2015. http://dx.doi.org/10.1109/spmb.2015.7405453.

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7

Guoli Ji, Yang Lin, Qianmin Lin, Guangzao Huang, Wenbing Zhu, and Wenjie You. "Predicting DNA-binding proteins using feature fusion and MSVM-RFE." In 2016 10th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID). IEEE, 2016. http://dx.doi.org/10.1109/icasid.2016.7873928.

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8

Sudharson, S., and Priyanka Kokil. "Abnormality Detection in the Renal Ultrasound Images using Ensemble MSVM Model." In 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET). IEEE, 2019. http://dx.doi.org/10.1109/wispnet45539.2019.9032737.

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9

Yunde Jia, Xiaoxun Zhang, Mingxiang Li, and Luping An. "A miniature stereo vision machine (MSVM-III) for dense disparity mapping." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1334290.

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10

Yuan, Pingpeng, Yuqin Chen, Hai Jin, and Li Huang. "MSVM-kNN: Combining SVM and k-NN for Multi-class Text Classification." In 2008 IEEE International Workshop on Semantic Computing and Systems (WSCS). IEEE, 2008. http://dx.doi.org/10.1109/wscs.2008.36.

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Звіти організацій з теми "MSVM"

1

Hilmer, R. V. A Magnetospheric Neutral Sheet-Oriented Coordinate System for MSM and MSFM Applications. Fort Belvoir, VA: Defense Technical Information Center, July 1997. http://dx.doi.org/10.21236/ada338067.

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2

Ruth, M., V. Diakov, T. Sa, and M. Goldsby. Macro System Model (MSM) User Guide, Version 1.3. Office of Scientific and Technical Information (OSTI), September 2011. http://dx.doi.org/10.2172/1026568.

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3

Milstene, C., Marcela S. Carena, A. Freitas, A. Finch, A. Sopczak, and Hannelies Kluge. The light stop quark with small stop-neutralino difference in the MSSM. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/879117.

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4

Dias, Rita, Miguel Rocha, Lucía Manrique, Sílvia Fraga, and Paula Meireles. HIV Prevention Combination Among Men who have Sex with Men: a scoping review protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2022. http://dx.doi.org/10.37766/inplasy2022.10.0071.

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Анотація:
Review question / Objective: To map the evidence regarding the combination of prevention strategies used by MSM to manage their HIV risk, assessing how this has been studied and how HIV prevention strategies are being combined by MSM. Eligibility criteria: We will include published scientific documents found through the search expression in the Web of Science, PubMed, and Scopus databases, about HIV prevention in HIV negative MSM. Studies about non-human animals and females will be excluded, as well as non-scientific documents and reports, editorials, opinion papers, comments, revisions, or journalistic material. Documents with no original data, such as reviews and systematic reviews, will also be excluded. Randomized controlled trials and control clinical trials will also be excluded since this type of study does not answer the scoping review questioning. Both quantitative and qualitative, as well as mixed-methods studies, will be included. The specificities of these studies will be properly acknowledged.
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5

Yang, Wan-Ching. Search for MSSM Higgs Bosons in Tau Final States with the D0 Detector. Office of Scientific and Technical Information (OSTI), January 2010. http://dx.doi.org/10.2172/992645.

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6

Semendy, Fred, Greg Meissner, and Priyalal Wijewarnasuriya. Sulfur Implanted Black Silicon for Metal Semiconductor Metal (MSM) Photodetectors. Fort Belvoir, VA: Defense Technical Information Center, December 2012. http://dx.doi.org/10.21236/ada571896.

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7

Yost, Tamera, Robert Fischl, and Peter Herczfeld. Large Area, High Speed Photodiode Using Metal-Semiconductor Metal (MSM) Device. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada382992.

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8

Rizzo, Thomas G. Report of the Beyond the MSSM Subgroup for the Tevatron Run II SUSY/Higgs Workshop. Office of Scientific and Technical Information (OSTI), July 2000. http://dx.doi.org/10.2172/763821.

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9

McCallum, Bennett. On the Relationship Between Determinate and MSV Solutions in Linear RE Models. Cambridge, MA: National Bureau of Economic Research, July 2004. http://dx.doi.org/10.3386/t0297.

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10

Almenar, Cristobal Cuenca. Search for the neutral MSSM Higgs bosons in the ditau decay channels at CDF Run II. Office of Scientific and Technical Information (OSTI), April 2008. http://dx.doi.org/10.2172/953708.

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