scholarly journals Classification of Upper Bound Sequences of Local Fractional Metric Dimension of Rotationally Symmetric Hexagonal Planar Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Shahbaz Ali ◽  
Muhammad Khalid Mahmood ◽  
Fairouz Tchier ◽  
F. M. O. Tawfiq
Keyword(s):  
Image Processing ◽  
Chemical Engineering ◽  
Chemical Compounds ◽  
Vital Role ◽  
Metric Dimension ◽  
Integer Programming Problem ◽  
Rotationally Symmetric ◽  
Metric Dimensions ◽  
Planar Networks

The term metric or distance of a graph plays a vital role in the study to check the structural properties of the networks such as complexity, modularity, centrality, accessibility, connectivity, robustness, clustering, and vulnerability. In particular, various metrics or distance-based dimensions of different kinds of networks are used to resolve the problems in different strata such as in security to find a suitable place for fixing sensors for security purposes. In the field of computer science, metric dimensions are most useful in aspects such as image processing, navigation, pattern recognition, and integer programming problem. Also, metric dimensions play a vital role in the field of chemical engineering, for example, the problem of drug discovery and the formation of different chemical compounds are resolved by means of some suitable metric dimension algorithm. In this paper, we take rotationally symmetric and hexagonal planar networks with all possible faces. We find the sequences of the local fractional metric dimensions of proposed rotationally symmetric and planar networks. Also, we discuss the boundedness of sequences of local fractional metric dimensions. Moreover, we summarize the sequences of local fractional metric dimension by means of their graphs.

scholarly journals On the Upper Bounds of Fractional Metric Dimension of Symmetric Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Muhammad Javaid ◽  
Muhammad Kamran Aslam ◽  
Jia-Bao Liu
Keyword(s):  
Image Processing ◽  
Drug Discovery ◽  
Computer Science ◽  
Chemical Compounds ◽  
Upper Bounds ◽  
Metric Dimension ◽  
Rotationally Symmetric ◽  
Metric Dimensions ◽  
Structural Aspects ◽  
Symmetric Networks

Distance-based numeric parameters play a pivotal role in studying the structural aspects of networks which include connectivity, accessibility, centrality, clustering modularity, complexity, vulnerability, and robustness. Several tools like these also help to resolve the issues faced by the different branches of computer science and chemistry, namely, navigation, image processing, biometry, drug discovery, and similarities in chemical compounds. For this purpose, in this article, we are considering a family of networks that exhibits rotationally symmetric behaviour known as circular ladders consisting of triangular, quadrangular, and pentagonal faced ladders. We evaluate their upper bounds of fractional metric dimensions of the aforementioned networks.

scholarly journals Lower and Upper Bounds of Fractional Metric Dimension of Connected Networks

2021 ◽  
Vol 5 (4) ◽  
pp. 276
Author(s):  
Muhammad Javaid ◽  
Muhammad Kamran Aslam ◽  
Muhammad Imran Asjad ◽  
Bander N. Almutairi ◽  
Mustafa Inc ◽  
...  
Keyword(s):  
Large Scale ◽  
Chemical Compounds ◽  
Vital Role ◽  
Molecular Networks ◽  
Metric Dimension ◽  
Weighted Version ◽  
Shortest Distance ◽  
Metric Dimensions ◽  
Large Scale Networks ◽  
2D And 3D

The distance centric parameter in the theory of networks called by metric dimension plays a vital role in encountering the distance-related problems for the monitoring of the large-scale networks in the various fields of chemistry and computer science such as navigation, image processing, pattern recognition, integer programming, optimal transportation models and drugs discovery. In particular, it is used to find the locations of robots with respect to shortest distance among the destinations, minimum consumption of time, lesser number of the utilized nodes, and to characterize the chemical compounds, having unique presentations in molecular networks. After the arrival of its weighted version, known as fractional metric dimension, the rectification of distance-related problems in the aforementioned fields has revived to a great extent. In this article, we compute fractional as well as local fractional metric dimensions of web-related networks called by subdivided QCL, 2-faced web, 3-faced web, and antiprism web networks. Moreover, we analyse their final results using 2D and 3D plots.

scholarly journals Computing Analysis of Zagreb Indices for Generalized Sum Graphs under Strong Product

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Muhammad Javaid ◽  
Saira Javed ◽  
Abdulaziz Mohammed Alanazi ◽  
Majdah R. Alotaibi
Keyword(s):  
Chemical Engineering ◽  
Chemical Properties ◽  
Chemical Compounds ◽  
Vital Role ◽  
Molecular Structures ◽  
Zagreb Indices ◽  
Graph Theoretic ◽  
Strong Product ◽  
Physicochemical And Structural Properties ◽  
Product Of Graphs

Numerous studies based on mathematical models and tools indicate that there is a strong inherent relationship between the chemical properties of the chemical compounds and drugs with their molecular structures. In the last two decades, the graph-theoretic techniques are frequently used to analyse the various physicochemical and structural properties of the molecular graphs which play a vital role in chemical engineering and pharmaceutical industry. In this paper, we compute Zagreb indices of the generalized sum graphs in the form of the different indices of their factor graphs, where generalized sum graphs are obtained under the operations of subdivision and strong product of graphs. Moreover, the obtained results are illustrated with the help of particular classes of graphs and analysed to find the efficient subclass with dominant indices.

scholarly journals Local Fractional Metric Dimensions of Rotationally Symmetric and Planar Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 82404-82420 ◽  
Author(s):  
Jia-Bao Liu ◽  
Muhammad Kamran Aslam ◽  
Muhammad Javaid
Keyword(s):  
Rotationally Symmetric ◽  
Metric Dimensions ◽  
Planar Networks

scholarly journals Boundedness of Convex Polytopes Networks via Local Fractional Metric Dimension

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Muhammad Javaid ◽  
Hassan Zafar ◽  
Amer Aljaedi ◽  
Abdulaziz Mohammad Alanazi
Keyword(s):  
Convex Polytopes ◽  
Chemical Properties ◽  
Chemical Compounds ◽  
Molecular Networks ◽  
Metric Dimension ◽  
Optimal Solutions ◽  
Weighted Version ◽  
Shortest Distance ◽  
Metric Dimensions ◽  
And Chemical Properties

Metric dimension is one of the distance-based parameter which is frequently used to study the structural and chemical properties of the different networks in the various fields of computer science and chemistry such as image processing, pattern recognition, navigation, integer programming, optimal transportation models, and drugs discovery. In particular, it is used to find the locations of robots with respect to shortest distance among the destinations, minimum consumption of time, and lesser number of the utilized nodes and to characterize the chemical compounds having unique presentation in molecular networks. The fractional metric dimension being a latest developed weighted version of the metric dimension is used in the distance-related problems of the aforementioned fields to find their nonintegral optimal solutions. In this paper, we have formulated the local resolving neighborhoods with their cardinalities for all the edges of the convex polytopes networks to compute their local fractional metric dimensions in the form of exact values and sharp bounds. Moreover, the boundedness of all the obtained results is also proved.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

High-precision Classification of Small Insects with Similar Color and Shape Using Image Processing

2016 ◽  
Vol 136 (8) ◽  
pp. 1120-1127 ◽  
Author(s):  
Naoya Ikemoto ◽  
Kenji Terada ◽  
Yuta Takashina ◽  
Akio Nakano
Keyword(s):  
Image Processing ◽  
High Precision

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification
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