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Journal articles on the topic 'AMS Classification'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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1

Benyacoub, Badreddine, Souad ElBernoussi, Abdelhak Zoglat, and EL Moudden Ismail. "Classification with hidden Markov model." Applied Mathematical Sciences 8 (2014): 2483–96. http://dx.doi.org/10.12988/ams.2014.42129.

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2

Afifi, Larbi, Meriem Joundi, Najia Amimi, and Mohamed Bahadi. "Asymptotic classification of distributed linear systems." Applied Mathematical Sciences 7 (2013): 2537–54. http://dx.doi.org/10.12988/ams.2013.13229.

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3

Koceić-Bilan, Nikola, and Ivančica Mirošević. "On classification of morphisms by box-homotopy." Acta mathematica Spalatensia 1, no. 1 (2021): 97–103. http://dx.doi.org/10.32817/ams.1.1.8.

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In [1] the authors proposed a generalization of the notion of homotopy, a relation called to be box-homotopic, proven to be an equivalence relation on Top(X,Y) and well-adjusted with the composition. In this article we prove that all the mappings of Top(X,Y) are box-homotopic, that is, the classification of morphisms by the box-homotopy relation is the coarsest.

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4

Ravikumar, S., and A. Shanmugam. "WBC image segmentation and classification using RVM." Applied Mathematical Sciences 8 (2014): 2227–37. http://dx.doi.org/10.12988/ams.2014.43191.

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5

Arafi, Ayoub, Youssef Safi, Rkia Fajr, and Abdelaziz Bouroumi. "Classification of mammographic images using artificial neural networks." Applied Mathematical Sciences 7 (2013): 4415–23. http://dx.doi.org/10.12988/ams.2013.35293.

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6

Yukhymenko, Mykola, Ruslan Ostroha, and Artem Evtuhov. "Pneumatic Classification of Granular Materials in Rhomb-Shaped Apparatus." Acta Mechanica Slovaca 25, no. 3 (2021): 46–51. http://dx.doi.org/10.21496/ams.2021.032.

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7

Beltrami, Monica, and Arinei Carlos Lindbeck da Silva. "Grid-quadtree algorithm for support vector classification parameters selection." Applied Mathematical Sciences 9 (2015): 75–82. http://dx.doi.org/10.12988/ams.2015.411938.

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8

Tsitsiashvili, G. Sh, V. P. Bulgakov, and A. S. Losev. "Hierarchical classification of directed graph with cyclically equivalent nodes." Applied Mathematical Sciences 10 (2016): 2529–36. http://dx.doi.org/10.12988/ams.2016.66192.

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9

Niyonsaba, Therence, Guy Darcy Remesha, Mwetaminwa Muyengo Blaise, Dionis Nizigiyimana, and Jean de Dieu Nkurunziza. "Statistical classification of the state of newborns in Burundi." Applied Mathematical Sciences 19, no. 1 (2025): 11–20. https://doi.org/10.12988/ams.2025.919190.

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This work aims to identify the various health districts in Burundi requiring more than others specific support for improving child health while applying the exploratory method. The latter consists in calculating, for the districts surveyed as part of the development of the statistical yearbook, indicators assessing the state of health of the newborns. For this end, a statistical classification was performed. These statistical analyzes use a set of data totaling 14 variables relating to the state of health of newborns.

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10

Delhom, Christopher D., James Knowlton, Vikki B. Martin, and Cody Blake. "COTTON GINNERS HANDBOOK: The Classification of Cotton." Journal of Cotton Science 24, no. 4 (2020): 189–96. http://dx.doi.org/10.56454/eeeg9440.

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Cotton classification is the process of using official standards and standardized procedures to measure the physical attributes of raw cotton. The USDA Agricultural Marketing Service (AMS) classes essentially all cotton grown in the U.S. Samples are collected from each bale at the gin or warehouse for classing at one of 10 AMS classing offices. Automated instrumentation is used to assess fiber length, length uniformity, strength, color, micronaire, and non-lint content. Automation has allowed AMS to continue classing cotton efficiently as crop size has expanded. A human classer examines every classing sample to determine if there is any extraneous matter present that would alter the value of the bale. Classing results are matched with a permanent bale identification number that is assigned to each bale at the time of sampling. This identification number allows the bale to be identified throughout the supply chain, from packaging of the bale at the gin through consumption on the textile mill floor.

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11

Yuhong Wu, Yuhong Wu, and Xiangdong Hu Yuhong Wu. "AMS Intrusion Detection Method Based on Improved Generalized Regression Neural Network." 網際網路技術學刊 24, no. 2 (2023): 549–63. http://dx.doi.org/10.53106/160792642023032402029.

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<p>The smart grid integrates the computer network with the traditional power system and realizes the intelligentization of the power grid. The Advanced Measurement System (AMS) interconnects the power system with the user, realizes the two-way interaction of data and information between the power supplier and the user, and promotes the development of the smart grid. Therefore, the safe operation of AMS is the key to the development of the smart grid. As smart grids and computer networks become more and more closely connected, the number of cyberattacks on AMS continues to increase. Currently, AMS intrusion detection algorithms based on machine learning are constantly being proposed. Machine learning algorithms have better learning and classification capabilities for small sample data, but when faced with a large amount of high-dimensional data information, the learning ability of machine learning algorithms is reduced, and the generalization ability is reduced. To enhance the AMS intrusion detection algorithm, this paper uses a Generalized Regression Neural Network (GRNN) to identify attack behaviors. GRNN has strong non-linear mapping ability, is suitable for unstable data processing with small data characteristics, has good classification and prediction ability, and has been widely used in power grid systems. Aiming at the existing problems, this paper proposes an upgraded generalized regression neural network AMS intrusion detection method DBN-DOA-GRNN. Based on the feature extraction and dimensionality reduction of the data by DBN, GRNN is used for data with less feature information in learning classification. In addition, to improve the detection effect of the method, the Drosophila Optimization Algorithm (DOA) is used to optimize the parameters of GRNN to reduce the influence of random parameters on the detection results, improve the detection accuracy of this method on small-scale sample data, and thereby improve the detection performance of the AMS intrusion detection algorithm. The proposed method archives an accuracy of 87.61%, 3.10% false alarm rate, and 96.9 precision rate.</p> <p>&nbsp;</p>

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12

Paglione, M., A. Kiendler-Scharr, A. A. Mensah, et al. "Identification of humic-like substances (HULIS) in oxygenated organic aerosols using NMR and AMS factor analyses and liquid chromatographic techniques." Atmospheric Chemistry and Physics 14, no. 1 (2014): 25–45. http://dx.doi.org/10.5194/acp-14-25-2014.

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Abstract. The atmospheric organic aerosol composition is characterized by a great diversity of functional groups and chemical species, challenging simple classification schemes. Traditional offline chemical methods identify chemical classes based on the retention behaviour on chromatographic columns and absorbing beds. Such an approach led to the isolation of complex mixtures of compounds such as the humic-like substances (HULIS). More recently, online aerosol mass spectrometry (AMS) was employed to identify chemical classes by extracting fragmentation patterns from experimental data series using statistical methods (factor analysis), providing simplified schemes for the classification of oxygenated organic aerosols (OOAs) on the basis of the distribution of oxygen-containing functionalities. The analysis of numerous AMS data sets suggested the occurrence of very oxidized OOAs which were postulated to correspond to HULIS. However, only a few efforts were made to test the correspondence of the AMS classes of OOAs with the traditional classifications from the offline methods. In this paper, we consider a case study representative of polluted continental regional background environments. We examine the AMS factors for OOAs identified by positive matrix factorization (PMF) and compare them to chemical classes of water-soluble organic carbon (WSOC) analysed offline on a set of filters collected in parallel. WSOC fractionation was performed by means of factor analysis applied to proton nuclear magnetic resonance (NMR) spectroscopic data, and by applying an ion-exchange chromatographic method for direct quantification of HULIS. Results show that the very oxidized low-volatility OOAs from AMS correlate with the NMR factor showing HULIS features and also with true "chromatographic" HULIS. On the other hand, UV/VIS-absorbing polyacids (or HULIS {sensu stricto}) isolated on ion-exchange beds were only a fraction of the AMS and NMR organic carbon fractions showing functional groups attributable to highly substituted carboxylic acids, suggesting that unspeciated low-molecular weight organic acids contribute to HULIS in the broad sense.

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13

崔, 晓菁. "Research on Classification of Typical Development Modes of Tourism Island." Advances in Marine Sciences 08, no. 01 (2021): 23–27. http://dx.doi.org/10.12677/ams.2021.81003.

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14

Ahmad, Asmala, and Shaun Quegan. "Comparative analysis of supervised and unsupervised classification on multispectral data." Applied Mathematical Sciences 7 (2013): 3681–94. http://dx.doi.org/10.12988/ams.2013.34214.

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15

Karková, Monika. "Possibilities for Classification of Physical Factors in Water Jet Technology." Acta Mechanica Slovaca 21, no. 2 (2017): 48–52. http://dx.doi.org/10.21496/ams.2017.018.

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16

Zao, L., D. Cavalcante, and R. Coelho. "Time-Frequency Feature and AMS-GMM Mask for Acoustic Emotion Classification." IEEE Signal Processing Letters 21, no. 5 (2014): 620–24. http://dx.doi.org/10.1109/lsp.2014.2311435.

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17

Liu, Jie, Xin Cao, Pingchuan Zhang, et al. "AMS-Net: An Attention-Based Multi-Scale Network for Classification of 3D Terracotta Warrior Fragments." Remote Sensing 13, no. 18 (2021): 3713. http://dx.doi.org/10.3390/rs13183713.

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As an essential step in the restoration of Terracotta Warriors, the results of fragments classification will directly affect the performance of fragments matching and splicing. However, most of the existing methods are based on traditional technology and have low accuracy in classification. A practical and effective classification method for fragments is an urgent need. In this case, an attention-based multi-scale neural network named AMS-Net is proposed to extract significant geometric and semantic features. AMS-Net is a hierarchical structure consisting of a multi-scale set abstraction block (MS-BLOCK) and a fully connected (FC) layer. MS-BLOCK consists of a local-global layer (LGLayer) and an improved multi-layer perceptron (IMLP). With a multi-scale strategy, LGLayer can parallel extract the local and global features from different scales. IMLP can concatenate the high-level and low-level features for classification tasks. Extensive experiments on the public data set (ModelNet40/10) and the real-world Terracotta Warrior fragments data set are conducted. The accuracy results with normal can achieve 93.52% and 96.22%, respectively. For real-world data sets, the accuracy is best among the existing methods. The robustness and effectiveness of the performance on the task of 3D point cloud classification are also investigated. It proves that the proposed end-to-end learning network is more effective and suitable for the classification of the Terracotta Warrior fragments.

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18

Ahmad, Fadhilah, Nur Hafieza Ismail, and Azwa Abdul Aziz. "The prediction of students' academic performance using classification data mining techniques." Applied Mathematical Sciences 9 (2015): 6415–26. http://dx.doi.org/10.12988/ams.2015.53289.

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19

Ahmad, Asmala, and Shaun Quegan. "The effects of haze on the accuracy of maximum likelihood classification." Applied Mathematical Sciences 10 (2016): 1935–44. http://dx.doi.org/10.12988/ams.2016.64138.

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20

Ezhil, S. Shenbaga, and C. Vijayalakshmi. "Analysis of a mixed inter programming model for microarray data classification." Applied Mathematical Sciences 7 (2013): 2001–5. http://dx.doi.org/10.12988/ams.2013.13179.

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21

Okwonu, Friday Zinzendoff, and Abdul Rahman Othman. "Heteroscedastic variance covariance matrices for unbiased two groups linear classification methods." Applied Mathematical Sciences 7 (2013): 6855–65. http://dx.doi.org/10.12988/ams.2013.39486.

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22

Pan, Haizhu, Moqi Liu, Haimiao Ge, and Liguo Wang. "One-Shot Dense Network with Polarized Attention for Hyperspectral Image Classification." Remote Sensing 14, no. 9 (2022): 2265. http://dx.doi.org/10.3390/rs14092265.

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In recent years, hyperspectral image (HSI) classification has become a hot research direction in remote sensing image processing. Benefiting from the development of deep learning, convolutional neural networks (CNNs) have shown extraordinary achievements in HSI classification. Numerous methods combining CNNs and attention mechanisms (AMs) have been proposed for HSI classification. However, to fully mine the features of HSI, some of the previous methods apply dense connections to enhance the feature transfer between each convolution layer. Although dense connections allow these methods to fully extract features in a few training samples, it decreases the model efficiency and increases the computational cost. Furthermore, to balance model performance against complexity, the AMs in these methods compress a large number of channels or spatial resolutions during the training process, which results in a large amount of useful information being discarded. To tackle these issues, in this article, a novel one-shot dense network with polarized attention, namely, OSDN, was proposed for HSI classification. More precisely, since HSI contains rich spectral and spatial information, the OSDN has two independent branches to extract spectral and spatial features, respectively. Similarly, the polarized AMs contain two components: channel-only AMs and spatial-only AMs. Both polarized AMs can use a specially designed filtering method to reduce the complexity of the model while maintaining high internal resolution in both the channel and spatial dimensions. To verify the effectiveness and lightness of OSDN, extensive experiments were carried out on five benchmark HSI datasets, namely, Pavia University (PU), Kennedy Space Center (KSC), Botswana (BS), Houston 2013 (HS), and Salinas Valley (SV). Experimental results consistently showed that the OSDN can greatly reduce computational cost and parameters while maintaining high accuracy in a few training samples.

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23

Li, Zhongwei, Xingshuai Cui, Leiquan Wang, Hao Zhang, Xue Zhu, and Yajing Zhang. "Spectral and Spatial Global Context Attention for Hyperspectral Image Classification." Remote Sensing 13, no. 4 (2021): 771. http://dx.doi.org/10.3390/rs13040771.

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Recently, hyperspectral image (HSI) classification has attracted increasing attention in the remote sensing field. Plenty of CNN-based methods with diverse attention mechanisms (AMs) have been proposed for HSI classification due to AMs being able to improve the quality of feature representations. However, some of the previous AMs squeeze global spatial or channel information directly by pooling operations to yield feature descriptors, which inadequately utilize global contextual information. Besides, some AMs cannot exploit the interactions among channels or positions with the aid of nonlinear transformation well. In this article, a spectral-spatial network with channel and position global context (GC) attention (SSGCA) is proposed to capture discriminative spectral and spatial features. Firstly, a spectral-spatial network is designed to extract spectral and spatial features. Secondly, two novel GC attentions are proposed to optimize the spectral and spatial features respectively for feature enhancement. The channel GC attention is used to capture channel dependencies to emphasize informative features while the position GC attention focuses on position dependencies. Both GC attentions aggregate global contextual features of positions or channels adequately, following a nonlinear transformation. Experimental results on several public HSI datasets demonstrate that the spectral-spatial network with GC attentions outperforms other related methods.

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24

Kosheleva, Olga, and Vladik Kreinovich. "How to gauge reliability of a binary classification result: a simple case." Applied Mathematical Sciences 15, no. 2 (2021): 95–99. http://dx.doi.org/10.12988/ams.2021.914394.

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25

Ahmad, Asmala, and Shaun Quegan. "The effects of haze on the accuracy of satellite land cover classification." Applied Mathematical Sciences 9 (2015): 2433–43. http://dx.doi.org/10.12988/ams.2015.52157.

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26

Paglione, M., A. Kiendler-Scharr, A. A. Mensah, et al. "Identification of humic-like substances (HULIS) in oxygenated organic aerosols using NMR and AMS factor analyses and liquid chromatographic techniques." Atmospheric Chemistry and Physics Discussions 13, no. 6 (2013): 17197–252. http://dx.doi.org/10.5194/acpd-13-17197-2013.

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Abstract. The atmospheric organic aerosol composition is characterized by a great diversity of functional groups and chemical species challenging simple classification schemes. Traditional off-line chemical methods identified chemical classes based on the retention behavior on chromatographic columns and absorbing beds. Such approach led to the isolation of complex mixtures of compounds such as the humic-like substances (HULIS). More recently, on-line aerosol mass spectrometry (AMS) was employed to identify chemical classes by extracting fragmentation patterns from experimental data series using statistical methods (factor analysis), providing simplified schemes for oxygenated organic aerosols (OOAs) classification on the basis of the distribution of oxygen-containing functionalities. The analysis of numerous AMS datasets suggested the occurrence of very oxidized OOAs which were postulated to correspond to the HULIS. However, only a few efforts were made to test the correspondence of the AMS classes of OOAs with the traditional classification from the off-line methods. In this paper, we consider a case study representative for polluted continental regional background environments. We examine the AMS factors for OOAs identified by positive matrix factorization (PMF) and compare to chemical classes of water-soluble organic carbon (WSOC) analysed off-line on a set of filters collected in parallel. WSOC fractionation was performed by means of factor analysis applied to H-NMR spectroscopic data, and by applying an ion-exchange chromatographic method for direct quantification of HULIS. Results show that the very oxidized low-volatility OOAs from AMS correlate with the NMR factor showing HULIS features and also with true "chromatographic" HULIS. On the other hand, UV/VIS-absorbing polyacids (or HULIS sensu stricto) isolated on ion-exchange beds were only a fraction of the AMS and NMR organic carbon fractions showing functional groups attributable to highly substituted carboxylic acids, suggesting that unspeciated low-molecular weight organic acids contribute to HULIS in the broad sense.

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27

Karaca, Yeliz, and Sengul Hayta. "Application and comparison of ANN and SVM for diagnostic classification for cognitive functioning." Applied Mathematical Sciences 10 (2016): 3187–99. http://dx.doi.org/10.12988/ams.2016.610258.

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28

Orowe, Idah. "Multi state Markov model with no classification errors with application to vertical transmission." Applied Mathematical Sciences 13, no. 24 (2019): 1147–63. http://dx.doi.org/10.12988/ams.2019.910141.

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29

Damayanti, Auli, and Indah Werdiningsih. "Classification of magnetic resonance (MR) brain images using energy coefficient and neural network." Applied Mathematical Sciences 8 (2014): 517–24. http://dx.doi.org/10.12988/ams.2014.310606.

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30

Rosalsky, Andrew. "On the Limiting Behavior of a Random Walk." Calcutta Statistical Association Bulletin 43, no. 1-2 (1993): 1–11. http://dx.doi.org/10.1177/0008068319930101.

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31

Ahmad, Asmala, and Shaun Quegan. "The effects of haze on the spectral and statistical properties of land cover classification." Applied Mathematical Sciences 8 (2014): 9001–13. http://dx.doi.org/10.12988/ams.2014.411939.

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32

Whittall Garcia, Laura P., Dafna D. Gladman, Murray Urowitz, Zahi Touma, Jiandong Su, and Sindhu R. Johnson. "New EULAR/ACR 2019 SLE Classification Criteria: defining ominosity in SLE." Annals of the Rheumatic Diseases 80, no. 6 (2021): 767–74. http://dx.doi.org/10.1136/annrheumdis-2020-218670.

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Objective To determine the ominosity of the European League Against Rheumatism (EULAR)/American College of Rheumatology (ACR) Systemic Lupus Erythematosus Classification Criteria by determining its predictive role for disease severity in the first 5 years following diagnosis. Methods 867 patients with systemic lupus erythematosus (SLE) from the Toronto Lupus Clinic were included (all first 12 months after SLE diagnosis). The EULAR/ACR criteria score was calculated based on baseline information. To determine disease severity in the first 5 years after diagnosis, adjusted mean SLE Disease Activity Index 2000 (AMS), flares, remission and immunosuppressive treatment were used as outcomes. The Systemic Lupus International Collaborating Clinics (SLICC) registry comprised the validation cohort. Results Based on receiver operating characteristic analysis, a EULAR/ACR score of 20 was used as a threshold to compare outcomes between groups. In the first 5 years of disease course, patients with a score of ≥20 had higher AMS scores (p<0.001) and were more likely to ever experience a flare (p<0.001). These patients had lower probabilities of achieving remission and higher requirements for immunosuppressives. Results were confirmed in the SLICC validation cohort. Patients with a score of ≥20 had higher AMS during the first 5 years of disease (5.4 vs 3.1% and ≥20 vs <20 respectively, p≤0.001). The score correlated with AMS (r=0.43, p≤0.001) in the same time frame. Conclusion A EULAR/ACR score of ≥20 is an indicator of ominosity in SLE. Patients with a score of ≥20 were characterised by a more active disease course throughout the first 5 years. These criteria provide prognostic information regarding disease severity in the first 5 years following diagnosis.

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33

Bagui, Subhash C. "An NN Classification Rule for Multiple Observations." Calcutta Statistical Association Bulletin 43, no. 1-2 (1993): 45–56. http://dx.doi.org/10.1177/0008068319930104.

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We consider the problem of classifying multiple ( m) observations into one of two populations using a nearest neighbor ( NN) type rule. We derive the limiting risk R( m) of the proposed NN rule. For m = 2, we obtain an improved upper bound for R( m) and show that R( m) ⩽ R( m-I) for m = 2, 3. AMS (1980) Subject classification: Primary 62H30; Secondary 62F15.

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34

Elrharras, A., R. Saadane, M. Wahbi, and A. Hamdoun. "Signal detection and automatic modulation classification based spectrum sensing using PCA-ANN with real word signals." Applied Mathematical Sciences 8 (2014): 7959–77. http://dx.doi.org/10.12988/ams.2014.49736.

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35

雷, 新明. "A Checklist for the Classification and Distribution of Coralline Algae in China (Corallinophycidae: Corallinales, Hapalidiales, Sporolithales)." Advances in Marine Sciences 06, no. 02 (2019): 70–92. http://dx.doi.org/10.12677/ams.2019.62009.

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36

Xu, Yi, Xiaotian Ma, Tan Derong, Cong Guo, Dong Guo, and Jinju Shao. "Research on Classification Method of Undergraduates’ Creative Ability for Classified Teaching." Higher Education Studies 9, no. 4 (2019): 21. http://dx.doi.org/10.5539/hes.v9n4p21.

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In this paper, a quantitative system of undergraduates&#39; creative ability is proposed through analyzing characteristics of Amabile creative theory, and the objectivity and feasibility of CAT, TTCT and AMS in creative ability quantification. The academic test scores, TTCT scores and AMS scores are used as the quantitative index of professional skills, innovation skills and task motivation in the quantitative system of undergraduates&rsquo; creative ability. Classification method of undergraduates&rsquo; creative ability using distance based K-medoids clustering method is proposed based on the quantitative system to improve the pertinence of creative ability cultivation. The feasibility and effectiveness of proposed classification method are verified by instance verification and effect analysis.

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37

AlKhayyat, Salwa L. "Classification of cases of violence against women in Saudi Arabia using discriminatory analysis based on factor scores." Applied Mathematical Sciences 15, no. 12 (2021): 625–36. http://dx.doi.org/10.12988/ams.2021.914439.

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38

Ruth., H., and G. Nirmala. "A Study on Brand Resonance of Yamaha at AMS Motors Puducherry." International Journal of Trend in Scientific Research and Development 3, no. 6 (2019): 451–54. https://doi.org/10.5281/zenodo.3588015.

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Brand resonance is an important concept for marketers to develop a long term relationship with consumers through their brand and brand loyalty, brand attachment, brand community and brand engagement are the four factors which strongly builds brand resonance. The object of the study is to find the most influencing factors of brand resonance for Yamaha and to find the significant association between brand resonance and income level of the consumers and to find the significant difference between brand resonance and age wise classification of the consumers. This study has a total population of 100 and the sample size is 80, and it used simple random sampling. Through questionnaire, primary data was collected and by referring books, journals and company records, secondary data was collected. This study used various statistical tools like chi square test and ANOVA to arrive a meaningful conclusion. This study is conclude that, there is no significant association between brand resonance and income level of the respondents and there is no significant difference between brand resonance and age wise classification of the respondents and the most influencing factor of brand resonance is brand community. Ruth. H | Nirmala. G "A Study on Brand Resonance of Yamaha at AMS Motors Puducherry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29127.pdf

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39

Gunawan, Anak Agung Ngurah, Putu Suardana, Albert Sulaiman, Anak Agung Ngurah Franky Kusuma Negara, Anak Agung Ngurah Surya Mahendra, and Anak Agung Ngurah Frady Cakra Negara. "Classification of invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC) using the support vector machine (SVM) method." Applied Mathematical Sciences 16, no. 6 (2022): 261–71. http://dx.doi.org/10.12988/ams.2022.916783.

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40

Sreenivasa, Rao Sola. "IT Incident & Risk Management for Oracle AMS and Implementation Projects." International Journal of Leading Research Publication 4, no. 2 (2023): 1–14. https://doi.org/10.5281/zenodo.15259082.

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The incident and risk management are key elements of maintaining the continuity and success of Oracle-based Application Management Services (AMS) and implementation projects. This article introduces the creation of a theoretical model framework for pavement marking management systems based on lessons learned from U.S. transportation agencies and applied in Oracle AMS environments. The research emphasizes risk discovery, response measures, and settlement processes in AMS and implementation environments. By contrasting conventional and contemporary risk management methodologies, it reinforces the importance of formal procedures, real-time tracking, and predictive analysis in the prevention of probable disruptions. The research also provides best practices in the use of automated incident monitoring, determination of root causes, and Oracle environment-based risk models. The research highlights the need for effective governance mechanism, related action among stakeholders, and compliance regulation adherence to make IT service more robust. Models of incident classification, response prioritization methods, and their influence on system integrity and effectiveness in operation are further addressed in this paper. The research is a contribution as it provides actionable suggestions for risk avoidance strategy optimization and downtime reduction for Oracle AMS projects. Through real-world case examples, it captures effective plans of implementation and experiences in risk management failure.

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41

V.V., Latpate. "STUDY OF COMMON FIXED POINT RESULT OF PAIR OF SELF MAPS IN G-METRIC SPACE." International Journal of Advanced Research 11, no. 07 (2023): 590–94. http://dx.doi.org/10.21474/ijar01/17263.

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42

Alotaibi, Areej, and Duaa AlSaeed. "Skin Cancer Detection Using Transfer Learning and Deep Attention Mechanisms." Diagnostics 15, no. 1 (2025): 99. https://doi.org/10.3390/diagnostics15010099.

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Background/Objectives: Early and accurate diagnosis of skin cancer improves survival rates; however, dermatologists often struggle with lesion detection due to similar pigmentation. Deep learning and transfer learning models have shown promise in diagnosing skin cancers through image processing. Integrating attention mechanisms (AMs) with deep learning has further enhanced the accuracy of medical image classification. While significant progress has been made, further research is needed to improve the detection accuracy. Previous studies have not explored the integration of attention mechanisms with the pre-trained Xception transfer learning model for binary classification of skin cancer. This study aims to investigate the impact of various attention mechanisms on the Xception model’s performance in detecting benign and malignant skin lesions. Methods: We conducted four experiments on the HAM10000 dataset. Three models integrated self-attention (SL), hard attention (HD), and soft attention (SF) mechanisms, while the fourth model used the standard Xception without attention mechanisms. Each mechanism analyzed features from the Xception model uniquely: self-attention examined the input relationships, hard-attention selected elements sparsely, and soft-attention distributed the focus probabilistically. Results: Integrating AMs into the Xception architecture effectively enhanced its performance. The accuracy of the Xception alone was 91.05%. With AMs, the accuracy increased to 94.11% using self-attention, 93.29% with soft attention, and 92.97% with hard attention. Moreover, the proposed models outperformed previous studies in terms of the recall metrics, which are crucial for medical investigations. Conclusions: These findings suggest that AMs can enhance performance in relation to complex medical imaging tasks, potentially supporting earlier diagnosis and improving treatment outcomes.

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Anitha.N and Latha.M. "Isomorphism and Anti Isomorphism in (Q,L)-Fuzzy Translation of (Q,L)--Fuzzy Subhemirings of A Hemiring." INTERNATIONAL JOURNAL OF SCIENCE TECHNOLOGY AND HUMANITIES 2, no. 2 (2015): 51–54. http://dx.doi.org/10.26524/ijsth54.

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Santoso, Budi, Hari Wijayanto, Khairil Anwar Notodiputro, and Bagus Sartono. "K-Neighbor over-sampling with cleaning data: a new approach to improve classification performance in data sets with class imbalance." Applied Mathematical Sciences 12, no. 10 (2018): 449–60. http://dx.doi.org/10.12988/ams.2018.8231.

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Manova-Erakovik, Vesna, N. Pandeski, and Lj Nastovski. "Distribution analogue of the Tumarkin result." Bulletin: Classe des sciences mathematiques et natturalles 133, no. 31 (2006): 69–73. http://dx.doi.org/10.2298/bmat0631069m.

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We give a distribution analogue of the Tumarkin result that concerns approximation of some functions by sequence of rational functions with given poles. AMS Mathematics Subject Classification (2000): 46F20, 30E25, 32A35.

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Bandyopadhyay, Uttam, and Pritam Sarkar. "On Fixed width Confidence Interval Estimation of the Common Mean in Symmetric Multivariate Normal Distribution." Calcutta Statistical Association Bulletin 71, no. 2 (2019): 113–20. http://dx.doi.org/10.1177/0008068319886180.

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This article deals with purely and accelerated sequential sampling procedures to find fixed-width confidence interval of completely symmetric multivariate normal mean. Procedures are studied asymptotically and are evaluated numerically. AMS 2000 subject classification: 62F25 62H12

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Bishwal, J. P. N., and Arup Bose. "Speed of Convergence of the Maximum Likelihood Estimator in the Ornstein-Uhlenbeck Process." Calcutta Statistical Association Bulletin 45, no. 3-4 (1995): 245–52. http://dx.doi.org/10.1177/0008068319950310.

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Berry-Bsseen bounds with random norming and Jario deviation probabilities arc derived for the maximum likelihood estimator of the drift parameter in tho Ornstoin-Uhlenbeck proccss. AMS (1991) Subject Classification: Primary 62F12, 62M05 Secondary 60FOS, 60F10

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O.P, Gupta* &. Madhuri .Shrama. "FIXED POINT THEOREMS IN BANACH SPACE AND 2-BANACH SPACE." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES [FRTSSDS- June 2018] (June 20, 2018): 137–43. https://doi.org/10.5281/zenodo.1293831.

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We generalize the result of Goebel and Zlotkiewiez [5] and also we prove fixed point theorems in Banach and 2-Banach spaces in this paper.   <strong>Subject Classification (AMS 2000)  :</strong>  47H10, 54H25

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Muhiuddin, G., N. Sridharan, D. Al-Kadi, S. Amutha, and M. E. Elnair. "Reinforcement Number of a Graph with respect to Half-Domination." Journal of Mathematics 2021 (April 14, 2021): 1–7. http://dx.doi.org/10.1155/2021/6689816.

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In this paper, we introduce the concept of reinforcement number with respect to half-domination and initiate a study on this parameter. Furthermore, we obtain various upper bounds for this parameter. AMS subject classification: 05C38, 05C40, 05C69.

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Hasanpour Zaryabi, Erfan, Loghman Moradi, Bahareh Kalantar, Naonori Ueda, and Alfian Abdul Halin. "Unboxing the Black Box of Attention Mechanisms in Remote Sensing Big Data Using XAI." Remote Sensing 14, no. 24 (2022): 6254. http://dx.doi.org/10.3390/rs14246254.

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This paper presents exploratory work looking into the effectiveness of attention mechanisms (AMs) in improving the task of building segmentation based on convolutional neural network (CNN) backbones. Firstly, we evaluate the effectiveness of CNN-based architectures with and without AMs. Secondly, we attempt to interpret the results produced by the CNNs using explainable artificial intelligence (XAI) methods. We compare CNNs with and without (vanilla) AMs for buildings detection. Five metrics are calculated, namely F1-score, precision, recall, intersection over union (IoU) and overall accuracy (OA). For the XAI portion of this work, the methods of Layer Gradient X activation and Layer DeepLIFT are used to explore the internal AMs and their overall effects on the network. Qualitative evaluation is based on color-coded value attribution to assess how the AMs facilitate the CNNs in performing buildings classification. We look at the effects of employing five AM algorithms, namely (i) squeeze and excitation (SE), (ii) convolution attention block module (CBAM), (iii) triplet attention, (iv) shuffle attention (SA), and (v) efficient channel attention (ECA). Experimental results indicate that AMs generally and markedly improve the quantitative metrics, with the attribution visualization results of XAI methods agreeing with the quantitative metrics.

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