Graph Energy Value Of IEEE-14 Bus System Paulraj Jayasimman, I; Jenitha, G; Gnanavel Chinnaraj, C ...
Journal of physics. Conference series,
05/2021, Volume:
1850, Issue:
1
Journal Article
Peer reviewed
Open access
Abstract
In this Research article the authors investigate minimum domination, neighbor-hood and Laplacian energy value of the line graph of IEEE-14 bus system in electric power networks. This paper ...presents a graph theoretic approach to solving the problem of placement of observations for the estimation of power system status. Estimation parameters for the IEEE 14 bus system. In this paper, a novel technique based on iterative stability-restricted optimum flow of power is suggested.
In this paper we are discussing the QSPR analysis of certain theocratic graphic matrices and their corresponding energy. Our study reveals some important results that help define the useful ...mathematical parameter based on their power of predicting. Our analysis shows some significant findings that aid in characterizing the valuable Mathematical Parameter based on their predictive energy.
Mathematical Parameters are used in QSAR / QSPR analysis to estimate bioactivity of chemical compounds. Throughout this paper we are discussing the QSPR analysis of certain theocratic graph matrices ...and their corresponding energy. Our analysis reveals some important findings which help define the useful Mathematical Parameter based on their predictive energy.
The Energy Graph for Minimum Majority Domination Jayasimman, Paulraj; G, Jenitha; A, Kumaravel
International journal of engineering & technology (Dubai),
10/2018, Volume:
7, Issue:
4.10
Journal Article
Open access
In this article we have introduced Minimum majority domination energy graph. A set is called a majority dominating set if at least half of the vertices either in or adjacent to the vertices . That ...is , . The minimum cardinality of a majority dominating set is called majority domination number . We defined majority dominating matrix and its energy values for some classes of graphs. Also some boundaries of Energy value of graph G are obtained.
In this study, we propose a new hybrid approach for time series prediction based on the efficient capabilities of fuzzy cognitive maps (FCMs) with structure optimization algorithms and artificial ...neural networks (ANNs). The proposed structure optimization genetic algorithm (SOGA) for automatic construction of FCM is used for modeling complexity based on historical time series, and artificial neural networks (ANNs) which are used at the final process for making time series prediction. The suggested SOGA-FCM method is used for selecting the most important nodes (attributes) and interconnections among them which in the next stage are used as the input data to ANN used for time series prediction after training. The FCM with proficient learning calculations and ANN have been as of now demonstrated as adequate strategies for setting aside a few minutes arrangement anticipating. The execution of the proposed approach is exhibited through the examination of genuine information of every day water request and the comparing expectation. The multivariate examination of recorded information is held for nine factors, season, month, day or week, occasion, mean and high temperature, rain normal, touristic action and water request. The entire approach was actualized in a clever programming device at first sent for FCM forecast. Through the exploratory investigation, the value of the new mixture approach in water request forecast is illustrated, by computing the mean outright blunder (as one of the outstanding expectation measures). The outcomes are promising for future work to this bearing.
Engineering components are often subjected to cyclic load excursions beyond elastic limit and hence cyclic plastic deformation of engineering materials becomes inevitable. Since the resultant ...elastic-plastic stress-strain response of the material plays a pivotal role in analysis, design and failure of the component, it becomes important to understand the cyclic plastic deformation behaviour of engineering materials. Also, cyclic hardening parameters are required in the design of structural components subjected to large plastic deformation. Constitutive equations were proposed by Prager, Armstrong and Frederick, Chaboche, and Ohno-Wang based on the stabilized strain-controlled hysteresis curve to evaluate the hardening parameters. In the present study, cyclic hardening parameters for SA 312 Type 304LN stainless steel have been determined based on the results of constant amplitude strain-controlled fatigue tests carried out earlier at CSIR-SERC under five different strain amplitude values, viz, 0.20%, 0.35%, 0.65%, 0.80% and 0.95%. It is observed that in isotropic hardening, the values of Q decreased with increase in strain amplitude. In kinematic hardening, the values of C1 and γ1 are constant for all values of strain amplitude.
Data mining is the process of extracting knowledge from the huge amount of data. The data can be stored in databases and information repositories. Data mining task can be divided into two models ...descriptive and predictive model. In Predictive model we can predict the values from different set of sample data, they are classified into three types such as classification, regression and time series. Descriptive model enables us to determine patterns in a sample data and sub-divided into clustering, summarization and association rules. Clustering creates group of classes based on the patterns and relationship between the data. There are different types of clustering algorithms partition, density based algorithm. In this paper we are analysing and comparing the various clustering algorithm by using WEKA tool to find out which algorithm will be more comfortable for the users.
Conventional machine models of water trash collection is here enhanced with the integration of sprinkler system. This enhanced model for water trash collection combines conventional methods ...integrated with a sprinkler system. The incorporation of the sprinkler system enhances water quality by agitating the surface, elevating oxygen levels, and facilitating the decomposition of organic substances. Moreover, the sprinkler system allows for the introduction of medications and necessary supplements to further improve water conditions. As a primary focus, the collection of debris is carried out with the help of a conveyor belt which Operates with the help of a motor which is connected to the power supply obtained from solar panels. The energy obtained from the solar panel is harnessed and stored for usage in times of less or no sunlight. With the optimized usage of sensors and imaging cameras, the target trash is identified. The direction of movement of the mechanism is instructed by the condition generated on analyzing the objects ahead to be identified as obstacles. The overall features of the mechanism are effective in energy efficiency and data-driven decision contributing to a holistic approach to the majority of the problems faced during the expected phases of operation.
One of the most common forms of illness in the world is a skin ailment. Although it's rather common, it might be hard to diagnose due to factors including skin color, hair color, and the presence of ...moles. Because skin malignancies are so common, advances in computer-aided diagnosis will provide dermatologists with a potent new tool for making accurate diagnoses. Visual inspection and clinical screening are the first steps in diagnosing skin illnesses; further testing, such as dermoscopy, biopsy, and histopathology, may be necessary in some circumstances. Fine-grained differences in lesions make automatic categorization of dermatoscopic pictures difficult. This research proposes a hybrid deep learning pre-trained MobileNet model and -based automated system for the identification of measles skin disorders using clinical photos and patient data. The Boosting Machine for Gradients. Using the Kaggle dataset, the suggested technique has achieved a higher rate of accuracy (above 97%) than any of the competing methods. There was a 98.91 percent rate of accuracy, a 97.76 percent rate of recall, an F1 score of 97.25%, and a kappa score of 0.96.
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