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On L? (2, 1)-Edge Coloring Number of Regular Grids
In this paper, we study multi-level distance edge labeling for infinite rectangular, hexagonal and triangular grids. We label the edges with non-negative integers. If the edges are adjacent, then their color difference is at least 2 and if they are separated by exactly a single edge, then their colors must be distinct. We find the edge coloring number of these grids to be 9, 7 and 16, respectively so that we could color the edges of a rectangular, hexagonal and triangular grid with at most 10, 8 and 17 colors, respectively using this coloring technique. Repeating the sequence pattern for different grids, we can color the edges of a grid of larger size. 2019 D. Deepthy et al. -
INDUCED nK2 DECOMPOSITION OF INFINITE SQUARE GRIDS AND INFINITE HEXAGONAL GRIDS
The induced nK2 decomposition of infinite square grids and hexagonal grids are described here. We use the multi-level distance edge labeling as an effective technique in the decomposition of square grids. If the edges are adjacent, then their color difference is at least 2 and if they are separated by exactly a single edge, then their colors must be distinct. Only non-negative integers are used for labeling. The proposed partitioning technique per the edge labels to get the induced nK2 decomposition of the ladder graph is the square grid and the hexagonal grid. 2022, Krasovskii Institute of Mathematics and Mechanics. All rights reserved. -
Deposition and characterization of ZnO/CdSe/SnSe ternary thin film based photocatalyst for an enhanced visible light-driven photodegradation of model pollutants
A heterogeneous photocatalytic pathway is a possible approach to global energy and environmental issues. Sol-gel spin coating and physical vapour deposition were used to create a new ternary ZnO/CdSe/SnSe nanocomposite thin film photocatalyst. X-ray diffractometry, energy-dispersive X-ray spectroscopy (EDS), field emission-scanning electron microscopy, UV-Vis, and photoluminescence (PL) spectrophotometers were used to characterize the deposited films. When exposed to solar light, the ternary photocatalyst exhibits high photocatalytic activity in photocatalytic dye degradation processes. it demonstrates excellent visible light absorption, enhanced charge carrier separation, and solar light simulation. It was proposed that the charge in the ternary ZnO/CdSe/SnSe photocatalyst moves in a double type-II and cascade manner between the various components. In this study, ternary thin film heterostructures are synthesized, exhibiting outstanding stability and solar light-induced photocatalytic activity.The thin film composed of ZnO/CdSe/SnSe exhibits a degradation efficiency of 96% when exposed to visible light, and a degradation efficiency of 90% for methylene blue under sunlight within a time period of 150 min. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
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MRSP-Multi Routing Systems and Parameter Explanations to Build the Path in Underwater Sensor Network
The underwater network is currently widely used to locate moving objects beneath the sea, monitor marine security, and detect changes in the sea water. A large number of sensors, as well as a precise methodology, are necessary to detect changes in sea depth. The protocol should be revised in response to environmental and chronological changes. The sensor should have been designed with multiple knowledge to route packets in order to optimise transmissions. Because the node will choose the best route based on the circumstances, especially in an underwater network, the paper MRSP - multi routing systems and parameter validations to create the path in an underwater sensor network is discussed in the multi routing knowledge sensor operations, energy saving systems, redundancy reduction, and so on. All of these measures, combined with secure transmission with trusted neighbour selection, result in safer transmissions and more accurate path selection. 2022 IEEE. -
Dynamics of chaotic waterwheel model with the asymmetric flow within the frame of Caputo fractional operator
The chaotic waterwheel model is a mechanical model that exhibits chaos and is also a practical system that justifies the Lorenz system. The chaotic waterwheel model (or Malkus waterwheel model) is modified with the addition of asymmetric water inflow to the system. The hereditary property of the modified chaotic waterwheel model is analyzed to determine the system's stability and identify the parameter that contributes to the stability We also examine the factor that leads to the bifurcation. We determine the well-posed nature of the modified system. The modified chaotic waterwheel model is defined with the Caputo fractional operator. The existence and uniqueness, boundedness, stability, Lyapunov stability, and numerical simulation are studied for the modified fractional waterwheel model. The bifurcation parameter and Lyapunov exponent are examined to study the chaotic nature of the system with respect to the fractional order. The nature of the system is captured with the help of the efficient numerical approach AdamsBashforthMoulton Method. The numerical approach demonstrates that the chaotic nature of the modified chaotic waterwheel is changed into unstable nature, which could further reduce to the stable case with suitable values of the parameter. This analysis is justified with the help of Lyapunov exponent. We consider irrational order (?,e) in the present work to illustrate the reliability of fractional order. 2023 Elsevier Ltd -
Modified Genesio-Tesi systems with trigonometric functions and the Caputo fractional derivative
The new fractional-order Genesio-Tesi system is introduced, and its boundedness, stability of the equilibrium points, Lyapunov stability, uniqueness of the solution, and bifurcation are all discussed in this paper. Using the efficient predictor-corrector approach, we statistically analyze the Genesio-Tesi system in fractional order. The results effectively conceptualize and visualize the novel fractional order Genesio-Tesi systems that are suggested. When the systems order shifts from integer to fractional, the revolution around the fixed point increases. The chaotic character of the modified Genesio-Tesi system is comparable to that of the original Genesio system. The major changes were made to the Geensio-Tesi system by including the trigonometric functions, keeping the initial conditions and parameter values intact. The system is fractionalised with the help of Caputo fractional operator. In particular, the modified systems nature is more complex, which may aid in signal processing and secure communication. Future research on the modified Genesio-Tesi system can now proceed in light of this finding. This article offers a fresh approach to utilizing and thoroughly researching the Genesio-Tesi systems that have been provided. CSP - Cambridge, UK; I&S - Florida, USA, 2024 -
A computational approach for the generalised GenesioTesi systems using a novel fractional operator
This article presents the novel fractional-order GenesioTesi system, along with discussions of its boundedness, stability of the equilibrium points, Lyapunov stability, uniqueness of the solution and bifurcation. The efficient predictorcorrector approach is employed to quantitatively analyse the GenesioTesi system in fractional order. The findings enable conceptualisation and visualisation of the presented novel fractional-order GenesioTesi systems. The modified systems are proposed for future study on chaos control and applying the same for secure communication. Bifurcation analysis is carried out to see the variation in the systems behaviour from stability to chaos. The results of the bifurcation analysis support the results obtained for the stability of the equilibrium points. The system behaves chaotically since all the equilibrium points are unstable. The findings demonstrate a torus attractor for some of the suggested systems and a chaotic attractor for some of the novel fractional-order GenesioTesi systems. The systems torus attractor changes into a steady state when the order is reduced from integer to fractional. Changing the parameter values for one of the modified systems also shifts the systems behaviour, with the point attractor replacing the torus attractor. The point attractor of one of the systems changes into a steady character when the systems order is reduced from integer to fractional. The behaviour for one modified system is the same for fractional and integer orders. This discovery paves the way for the future study of the modified GenesioTesi system. This article gives a new direction to utilise these proposed GenesioTesi systems and study them extensively. The chaotic behaviour of the modified system can be used for secure communication. The synchronisation and chaos control of the modified system is recommended. 2024, Indian Academy of Sciences. -
Financial inclusion and poverty alleviation: The alternative state-led microfinance model of Kudumbashree in Kerala, India
The study examines the microfinance and microenterprise model of Kudumbashree, the state poverty eradication mission of Kerala, and its impact on poverty alleviation in the state of Kerala in India. Kudumbashree's method of identification of the poor is seen to be superior to the conventional head count ratio as it captures the multidimensional characteristics of poverty leading to lesser chances of exclusion of vulnerable families. The microenterprise-linked microfinance model of Kudumbashree has established itself as an effective model linking the state, community, and financial organizations, differentiating itself from other NABARD-led self-help group (SHG) programmes or the Grameena model of microfinance institutions in the country. The fundamental idea of local economic development on which the microenterprise business is built is, however, not free from limitations. Heavy reliance on local markets for procuring inputs and selling outputs makes the products less competitive, questioning the sustainability of a business-led model in the absence of state subsidy in the longer run. Copyright 2014 Practical Action Publishing. -
Parametrical variation and its effects on characteristics of microstrip rectangular patch antenna
This paper represents a brief description about design of rectangular microstrip patch antenna and its parameter effects in size, efficiency and compactness and parametric analysis in terms of return loss, bandwidth, directivity and gain by using same and different dielectric substrate materials with same and different thickness of rectangular microstrip patch antenna. The important parameters of patch such as L, W, r and h has its own impact in antenna characteristics. This parametrical impact is studied and verified. As thickness of dielectric substrate increases, the gain & directivity of rectangular microstrip patch antenna decreases and bandwidth increases. As r increases, the size of the antenna decreases but when height of dielectric substrate increase antenna size also increases. There will be always a compromise between miniaturization and other antenna characteristics. This antenna is designed for microstrip feed line technique and with center frequency (f0) at 4GHz. The parametric analysis is obtained by comparing the simulated results of rectangular microstrip patch antenna for different cases. The proposed antenna is simulated using HFSS tool at resonance frequency of 4 GHz. 2017 IEEE. -
Comic Memes and Sexist Humor in India: Tools for Reinforcement of Female Body-Image Stereotypes
Memes have been described as communicative and aesthetic practices that serve cultural, social, political purpose on a digital platform. Several studies, in the last decade, have attempted to study this digital aesthetic knowledge production as a powerful tool for political, racial, and gender-related discourses. Most often this knowledge is produced through comic multi-media texts. Many theorists believe that, digital media reinforces inequality, marginalization and such other social issues through the audio-visual-textual medium as much as it establishes the counter-discourses for equality, body activism, racial activism and the like. Speed and lack of censorship can be the cardinal reasons for the popularity of these memes. Among the mass-influencing gender-related memes are those encouraging fat-talk and body-image stereotypes. In the Indian context, 'Tag a Friend' memes is one such widely circulated meme which communicates body-shaming messages through sexist humor. It mainly targets the fat/colored/transgender women. The current study examines these memes using multimodal discourse analysis methodology. The paper attempts to investigate the revival/reproduction potential of color-shaming and body-shaming stereotypes via comic memes through Shiffman's memetic dimensions. The analysis establishes that memes can be a prominent site for the re-production of the problematic ideology of body/color shaming even in the 21st century. AesthetixMS 2021 -
Longitudinal study on noncommunicable diseases using machine learning
This longitudinal case study thoroughly explores the intricate connection between body mass index (BMI) and four key factors: physical health, psychological well-being, lifestyle choices, and the impact of diet on health. Through the analysis of longitudinal data, notable trends emerge, revealing an increase in risk factors for noncommunicable diseases (NCDs) and unhealthy behaviors over time. This highlights the combined impact of these interconnected factors on health outcomes and the risk of developing NCDs like heart disease, diabetes, and cancer. Leveraging machine learning, the study effectively identifies individuals at elevated risk for NCDs and dispels common health misconceptions, underscoring the significance of holistic wellness approaches. Serving as a beacon for the next generation, this study provides insights that contribute to shaping a healthier future. 2025 selection and editorial matter, Arun Kumar Rana, Vishnu Sharma, Sanjeev Kumar Rana, and Vijay Shanker Chaudhary; individual chapters, the contributors. All rights reserved. -
A hybridized semantic trust-based framework for personalized web page recommendation
The World Wide Web is constantly evolving and is the most dynamic information repository in the world that has ever existed. Since the information on the web is changing continuously and owing to the presence of a large number of similar web pages, it is very challenging to retrieve the most relevant information. With a large number of malicious and fake web pages, it is required to retrieve Web Pages that are trustworthy. Personalization of the recommendation of web pages is certainly necessary to estimate the user interests for suggesting web pages as per their choices. Moreover, the Web is tending towards a more organized Semantic Web which primarily requires semantic techniques for recommending the Web Pages. In this paper, a framework for personalized web page recommendation based on a hybridized strategy is proposed. Web Pages are recommended based on the user query by analyzing the Web Usage Data of the users. An array of strategies is intelligently integrated together to achieve an efficient Web Page Recommendation system. Latent Semantic Analysis is applied to the User-Term Matrix and the Term-Frequency Matrix that are built from the Web Usage Information to form a Term Prioritization Vector. Further, techniques like Latent Dirichlet Allocation for Topic-based Segregation of the URLs and Normalized Pointwise Mutual Information strategies are used for recommending web pages based on users queries. The Personalization is achieved by prioritizing the Web pages based on the Prioritization Vector. Also, a unique methodology is incorporated into the system to retrieve trustworthy websites. An overall Accuracy of 0.84 is achieved which is better than the existing strategies. 2018 Informa UK Limited, trading as Taylor & Francis Group. -
A hybrid semantic algorithm for web image retrieval incorporating ontology classification and user-driven query expansion
There is always a need to increase the overall relevance of results in Web search systems. Most existing web search systems are query-driven and give the least preferences to the users needs. Specifically, mining images from the Web are a highly cumbersome task as there are so many homonyms and canonically synonymous terms. An ideal Web image recommendation system must understand the needs of the user. A system that facilitates modeling of homonymous and synonymous ontologies that understands the users need for images is proposed. A Hybrid Semantic Algorithm that computes the semantic similarity using APMI is proposed. The system also classifies the ontologies using SVM and facilitates a homonym lookup directory for classifying the semantically related homonymous ontologies. The users intentions are dynamically captured by presenting images based on the initial OntoPath and recording the user click. Strategic expansion of OntoPath based on the users choice increases the recommendation relevance. An overall accuracy of 95.09% is achieved by the proposed system. 2018, Springer Nature Singapore Pte Ltd. -
JRHDLSI: An Approach Towards Job Recommendation Hybridizing Deep Learning and Semantic Intelligence
The requirement of the job for people and employees for employers are al-ways in demand. This is due to the lack of proper infrastructure to reduce the unmatching job application for employers and inappropriate job recommendations for people. This chapter proposes a strategic framework with machine learning and knowledge integration to increase accuracy in the provided recommendations and increase the chance of getting a job offer. The usage of'user's search data intends job recommended more in liking of the users, and the machine learning helps in finding the accurate job recommendation. The machine learning technique used here is Radial Basis Function Neural Net-work for the classification and Knowledge Integrated using Analysis of Variance - Web Point Wise Mutual Information and Kullback Leibler (KL) divergence. All the job providers ads are retrieved from the top websites using beautiful soup. The proposed JRHDLSI architecture achieved an accuracy of 94.99% which outperformed the baseline models and was much superior. 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) -
ATRSI: Automatic Tag Recommendation for Videos Encompassing Semantic Intelligence
There is a requirement for an automatic semantic-oriented framework for Web video tagging in the epoch of Web 3.0, as Web 3.0 is much denser, intelligent, but more cohesive compared to Web 2.0. This paper proposes the ATRSI framework which is the Automatic Tag Recommender framework which encompasses the semantic-oriented Artificial Intelligence that outgrows the dataset by making the use of informative terms using TF-IDF and bag of words model to build the intermediate semantic network which is further organized using an Lin similarity measure and is optimized using red deer optimization by encompassing the entities from the World Wide Web to focused crawling. RNN is a classifier that is used for the classification of the dataset, it is a strong deep-learning classifier. Semantic-oriented Intelligence is achieved using the CoSim rank and Morisita's overlap index. The bag of lightweight graphs is obtained from the semantic network which is an intermediate knowledge representation mechanism that is further embedded in the intrinsic model. A semantically consistent system for video recommendation, ATRSI outperforms the other baseline models in terms of average accuracy, average precision and F-measure for a variety of recommendations. 2024 IEEE. -
Descriptive Answer Evaluation using NLP Processes Integrated with Strategically Constructed Semantic Skill Ontologies
The world is moving towards an online methodology of education. One of the key challenges is the assessment of questions which do not have a definite answer and have several correct answers. To solve this problem, and for quality evaluation of descriptive answers online, an automatic evaluation methodology is proposed in this work. A language model is modelled from the expected answer key, and entity graphs are generated from the ontology modelled using the input answer to be evaluated. Natural Language Processing (NLP) techniques like Stemming, Summarization, and Polarity Analysis are integrated in this work with Ontologies for the efficient evaluation of descriptive answers. Several challenges which come across evaluating descriptive answers are discussed in this chapter, and they have been solved in order to obtain a dynamic and robust evaluating system. Finally, the system is evaluated using a user-feedback methodology comprising a panel of 100 students and 100 professors. 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) -
An intelligent inventive system for personalised webpage recommendation based on ontology semantics
Owing to the information diversity in the web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organised semantic web, the incorporation of semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant webpages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant webpages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalisation is achieved by prioritisation of webpages by content-based analysis of the users web usage data. An overall accuracy of 87.73% is achieved by the proposed approach. Copyright 2019 Inderscience Enterprises Ltd. -
Hereditary factor-based multi-featured algorithm for early diabetes detection using machine learning
Today's advent in the medical industry have given numerous chances to improve the quality of detection and reporting the diseases at the early stages for a better diagnosis. Modern day datasets generate fruitful information for timely and periodic monitoring of patients' health conditions. Such information is hidden to a naked eye or hidden in multiple track records of highly affected population. Diabetes mellitus is one such disease which is predominant among a global population which ultimately leads to blindness and death in some cases. The model proposed in this system attempts to design and deliver an intelligent solution for predicting diabetes in the early stages and address the problem of late detection and diagnosis. Intensive research is carried out in many tropical countries for automating this process through a machine learning model. The accuracy of machine learning algorithms is more than satisfactory in the detection of Type 2 diabetes from the dataset of PIMA Indians Diabetes Dataset. An additional feature of hereditary factor is implemented to the existing multiple objective fuzzy classifiers. The proposed model has improved the accuracy to 83% in the training and tested datasets when compared to NGSA model of prediction. 2022 Scrivener Publishing LLC. -
Performance Analysis of Deep Learning Pretrained Image Classifiation Models
Convolutional Neural Networks (CNNs) is revolutionized in the field of computer vision, with the high accuracy and capability to learn features from raw data. In this research work focused on a comparative analysis of two popular CNN architectures, VGG16 and VGG19. The CIFAR dataset consists of 60,000 images, each with a resolution of 32x32 and it's belong to one of the 10 classes. Experimental results are compared with VGG16 and VGG19 in terms of their accuracy and training time, and to identify any differences in their ability to learn features from the CIFAR-10 dataset. The results of this research can aid in directing the choice of appropriate architectures for image classification tasks as well as the advantages of optimisation strategies for enhancing the efficiency of deep learning models. In order to enhance the performance of these structures, more optimisation methods and datasets may be investigated in subsequent research. 2023 IEEE.