On Zagreb Energy of Certain Classes of Graphs S. Sripriya, A. Anuradha Mathematics and Statistics, 2024 Energy of the graph G is the sum of absolute values of eigenvalues of its adjacency matrix. Given a simple connected graph G, its first (second) Zagreb matrix is constructed by including the sum (product) of the degrees of each pair of adjacent vertices of G. Computation of sum of absolute eigen values of these matrices yields the corresponding Zagreb energies. In this paper, the first and second Zagreb energies of certain families of graphs have been computed and a criterion to discern the nature of graph G based on their energies is obtained. The paper focuses on the comparative analysis of first and second Zagreb energies in terms of regular graphs such as cycle graphs, bipartite and tripartite graphs. Our findings reveal that the second Zagreb energy is always greater than first Zagreb energy for all complete bipartite graphs of even order greater than or equal to 4. Also we have established that the same is the case for complete tripartite graphs too. Furthermore, we illustrate that the two Zagreb energies coincide exclusively for the complete bipartite graph with equal partite sets if and only if the graph is of order 2. Additionally, we provide a criterion leading to an infinite set of non-isomorphic Zagreb equi-energetic graphs for all r>1 within partite graphs. The computations of two Zagreb energies for graph operations like t-splitting graph and t-shadow graph are also illustrated. The first and second Zagreb energies for some specific graphs along with bounds on Zagreb energies for wheel graphs are also discussed.
SUM-CONNECTIVITY ENERGY ON GRAPH OPERATIONS S. Sripriya, A. Anuradha International Journal of Applied Mathematics, 2024 Consider a simple, finite, undirected, connected graph G on q vertices.Depending on the context, various matrix representations of G are available in literature.One such matrix representation is sum-connectivity matrix SC(G) which is of order q × q with G vertices indexing its rows and columns.The (ij) th entry of SC(G) isotherwise it is 0. The summation of absolute eigenvalues of SC(G) is called sum-connectivity energy and denoted as E[SC(G)].In the article, we determine sum-connectivity energy for some generalized graph operations other than product graphs.Further, we establish the results in terms of the base graphs.
Evaluating AI for Time- Series Forecasting C. Rohith Bhat, S. Sripriya, R.Sumathi, Abhay Nagale, Rohit Bansal, A. Jeeva Proceeding of 2024 International Conference on Communication Computing and Energy Efficient Technologies I3ceet 2024, 2024 For instance, we may use time series forecasting, one of the most significant issue types in many machine learning scenarios, to estimate not just power consumption but also air quality or traffic patterns. Techniques like autoregressive integrated moving averages, rolling averages, and vector auto-regression are used in traditional forecasting models. On the other hand, more recent studies use deep learning methods and vector factorization to provide better results [24] – [26]. When compared to what the older methodologies give, the intricacy of these more advanced models is undoubtedly a drawback. In order to build a machine learning baseline, this research compares well-known deep learning models to the well-respected Gradient Booster Logistic Tree (GBRT) model. In deep neural network models, we consider time series forecasting as a window-based regression issue. To build the input/output structure for each training window of the GBRT model, we similarly flatten target values and external characteristics across all of the windows. This allows you to make a single input instance out of numerous outputs. Through an extensive comparison on nine datasets and eight recent popular examples of the state-of-the-art architectures, we show that the proposed window-based input perturbation method leads to significant performance improvements for a vanilla GBRT framework, with results even surpassing those obtained by modern deep learning models which were reported at top-level conferences.
Total Neighbourhood Prime Labeling of Certain Graphs S. Sripriya, K. Ramachandran Journal of Physics Conference Series, 2019 In this paper, we investigate the total neighbourhood prime labelling of Lily graphs, Umbrella graph, Friendship graph Fn, Generalised friendship graph and .
