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COMPUTATION OF b-CHROMATIC TOPOLOGICAL INDICES OF SOME GRAPHS AND ITS DERIVED GRAPHS
The two fastest-growing subfields of graph theory are graph coloring and topological indices. Graph coloring is assigning the colors/values to the edges/vertices or both. A proper coloring of the graph G is assigning colors/values to the vertices/edges or both so that no two adjacent vertices/edges share the same color/value. Recently, studies involving Chromatic Topological indices that dealt with different graph coloring were studied. In such studies, the vertex degrees get replaced with the colors, and the computation is carried out based on the topological index of our choice. We focus on b-Chromatic Zagreb indices and b-Chromatic irregularity indices in this work. This paper discusses the b-Chromatic Zagreb indices and b-Chromatic irregularity indices of the gear graph, star graph, and its derived graphs such as the line and middle graph. 2023, RAMANUJAN SOCIETY OF MATHEMATICS AND MATHEMATICAL SCIENCES. All rights reserved. -
RAINBOW CHROMATIC TOPOLOGICAL INDICES OF CENTRAL GRAPHS OF SOME GRAPHS
The chromatic topological indices concept was introduced recently. Many other variations concerning the chromatic topological indices have been studied lately. In this paper, we have calculated the first and second rainbow chromatic Zagreb indices and rainbow chromatic irregularity indices for central graph of some standard graph classes. Palestine Polytechnic University-PPU 2024. -
LRD: Loop Free Routing Using Distributed Intermediate Variable in Mobile Adhoc Network
One of the critical challenges in the design of the mobile adhoc networks is to design an efficient routing protocol. Mobility is an unique characteristics of wireless network, which leads to unreliable communication links and loss of data packets. We present a new algorithm, Loop Free Routing with DIV (LRD) is introduced which prevents loops and count to infinity problem using intermediate variables. In addition it finds the shortest path between source and destination. The analysis shows that DIV is compatible with all the routing protocol as it is independent of the underlying environment. The proposed algorithm LRD is compared with the existing algorithm of DIV to prove its applicability in the any routing environment. The simulation results show that LRD excels AODV routing protocol while considering throughput and packet delivery ratio. The new algorithm assures that the routing protocol is shortest loop-free path and outperforms all other loop-free routing algorithms previously proposed from the stand point of complexities and computations. Springer Nature Switzerland AG 2020. -
Decision Tree Based Routing Protocol (DTRP) for Reliable Path in MANET
In mobile ad hoc network due to node movements, there exists route failure in active data transmission which results in data loss and communication overheads. Hence, in such a dynamic network, routing through reliable path is one of the tedious tasks. In this paper, we propose a novel Decision Tree based Routing Protocol (DTRP) a data mining technique in route selection process from source to destination. The proposed DTRP protocol selects the one hop neighbors based on the parameters such as speed, Link Expiration Time, trip_time and node life time. Thus the performance of a route discovery mechanism is enhanced by selecting the stable one-hop neighbors along the path to reach the destination. The simulated results show that the lifetime of the route is increased and hence the data loss and end to end delay are minimized thereby increasing the throughput of the network using the proposed DTRP routing protocol compared to existing routing protocols. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
IoT enabled lung cancer detection and routing algorithm using CBSOA-based ShCNN
The Internet of Things (IoT) has tremendously spread worldwide, and it influenced the world through easy connectivity, interoperability, and interconnectivity using IoT devices. Numerous techniques have been developed using IoT-enabled health care systems for cancer detection, but some limitations exist in transmitting the health data to the cloud. The limitations can be accomplished using the proposed chronological-based social optimization algorithm (CBSOA) that effectively transmits the patient's health data using IoT network, thereby detecting lung cancer in an effective way. Initially, nodes in the IoT network are simulated such that patient's health data are collected, and for transmission of such data, routing is performed in order to transmit the health data from source to destination through a gateway based on cloud service using CBSOA. The fitness is newly modeled by assuming the factors like energy, distance, trust, delay, and link quality. Finally, lung cancer detection is carried out at the destination point. At the destination point, the acquired input data is fed to preprocessing phase to make the data acceptable for further mechanism using data normalization. Once the feature selection is done using Canberra distance, then the lung cancer detection is performed using shepard convolutional neural network (ShCNN). The process of routing as well as training of ShCNN is performed using the CBSOA algorithm, which is devised by the inclusion of the chronological concept into the social optimization algorithm. The proposed approach has achieved a maximum accuracy of 0.940, maximum sensitivity of 0.941, maximum specificity of 0.928, and minimum energy of 0.452. 2022 John Wiley & Sons Ltd. -
Forecasting NIFTY 50 in Volatile Markets Using RNNLSTM: A Study on the Performance of Neural Network Models During the COVID-19 Pandemic
The COVID-19 pandemic has shown us how the market can be highly uncertain and volatile at certain times. This brings a new level of challenges to all the investors and active traders in the market, as they have not seen such a movement in the past. However, as technology is evolving, highly sophisticated tools and techniques are being used by hedge funds and other investment banks to track down these movements and turn this into an opportunity. In this paper, we try to analyse how recurrent neural network (RNN) with long- and short-term memory architecture performs under volatile market conditions. For this study, we tried to perform a comparative analysis between two models within two successive time periods, where one is trained in a volatile market condition and the other in a relatively low volatile market condition. The results showed that the RNN model is less accurate in predicting the prices in a volatile market compared to a relatively low volatile market. We also compared these two models to a separate model where we trained using the combined data from the two successive time periods. Even though the addition in data points for the neural network produced a better result compared to the model trained under volatile conditions, it did not significantly perform better than the model, which was trained in the low volatile period. 2022 Management Development Institute. -
Smartphone based indoor localization and tracking model using bat algorithm and Kalman filter
In recent days, accurate localization becomes essential for enabling smartphone-based navigation to attain maximum accuracy in the construction of the real world.Fingerprint-based localization is the widespread solution to achieve and assure effective performance. In this study, a new fingerprint-based localization model using a bat algorithm (BA) is presented stimulated by the echolocation nature of microbats. The presented model adapts BA for estimating the location information. Initially, the presented model applies a Bayesian-rule based objective function. Then, the BA is used for improving the accuracy and analyzing the effects of the initial position of the bats on the localization outcome. For mitigating the estimation error, the Kalman filter is employed for updating the initially determined position using the BA for tracking purposes. The experimental analysis indicated an improvement in real-time performance and decrease in computation complexity. The presented model also obtained maximum localization accuracy with minimum localization error over the compared methods. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature. -
An Approach to Introduce Mobile Application Development for Teaching and Learning by Adapting Allans Dual Coding Theory
This paper reveals around methodology that could be powerful to teach mobile applications in a class by including a decent utilization of innovative technology alongside a system. Appraisal instruments within the cloud were utilized to encourage this sort of methodology toward teaching application development. The new methodology is executed by teaching in the lab with desktops or in the classroom with students laptops. It adapted Allans dual code Theory. The adequacy of this methodology is obvious through an examination of outcomes. 2020, Springer Nature Switzerland AG. -
Sustainable Supply Chain Analytics for Anomalously Potential Fraudulent Logistics
The primary focus of this research is to detect potential anomalous and fraudulent cotton ginning transactions. The analysis of monitoring systems utilizing substantial analytics is often time-consuming and requires painstaking analysis afterward. In addition, the paper discusses how Third-Party Logistics affects the warehousing process and its antidromic role in distribution channels. Data for this study came from an established cotton gin operation in Tanganyika/Tanzania-East Africa. Ultimately, the results should allow cotton ginning to be improved by understanding anomalous activities. Cotton ginning fraud will be explained for the first time in scholarly journals using supply chain analytics. 2022 IEEE. -
Understanding the antecedents of service decisions: An integration of service promiscuity and customer citizenship behaviour
Promiscuity being casual and unrestrained towards any kind of service, the purpose of this article is to contribute to service literature by investigating the influence of customer citizenship behaviour and service promiscuity in the decision-making process in the context of public house services. This paper empirically draws a historic sum-up on the roots of service promiscuity towards the decision-making process. A questionnaire was administered to 1,509 pub customers using retrospective experience sampling technique. The proposed hypotheses were tested using structural equation modelling. Results from this research yielded novel insights into the dual antecedents extending to customer decision making process through customer citizenship behaviour and service promiscuity. The findings have implications for the ongoing argumentation on the practicality of customer promiscuity, thereby broadening the theoretical understanding of 'why customers' decision-making process establishes such an efficacious effect in the service environment? Further, these new and interesting results enlighten the insights of consumer behaviour and more importantly contribute substantially to the existing knowledge of service marketing literature. The results provide managers with specific decision-making process variables and substantial service strategies. 2019 Inderscience Enterprises Ltd. -
An analysis of policy prospective of taxi aggregators and consumers in digital eco-system
The term digital trade is becoming more prevalent in the modern era. Newer company structures have evolved to replace traditional methods with online companies as digitalisation has become the standard. Taxi aggregators are one of the most prevalent digital business concepts. With this particular model, which is now known as taxi aggregators, you may quickly book a cab using your smartphone for transportation inside and outside the city limits. They are also inexpensive to use. Nevertheless, as lawmakers created new and revised rules to control these business models, the last two years have been very difficult for application-based taxi providers like Ola and Uber. The regulations are being developed by legislators in several nations, but the pace and the scope are much slower than necessary. This essay will examine past and present taxi market scenarios before suggesting ways to enhance them in the future. Copyright 2024 Inderscience Enterprises Ltd. -
Growth of mobile applications and the rise of privacy issues
Mobile apps are used by more and more internet users for daily chores, but processing personal data with them poses a major security risk. The wide range of data and sensors in mobile devices, the use of different types of identifiers and the increased ability to monitor consumers, the complex mobile application ecosystem and application developer limitations, and the extensive use of third-party technologies and services, are the main risks. Privacy concerns extend beyond mobile users. Corporate executives need fast, global access to their database. White goods/smart building equipment suppliers offer mobile apps for remote use. This research study will integrate these concerns. Due to these factors, smartphone applications have struggled to enforce the General Data Protection Regulations (GDPR) data protection rules. Mobile app designers and service providers may struggle to meet GDPR rules for data disclosure and permission, data protection by design and default, and operational secrecy. Copyright 2024 Inderscience Enterprises Ltd. -
Implementing privacy and data confidentiality within the framework of the Internet of Things
Throughout the current and future worldwide Web network infrastructure, the notion of the Internet of Things (IoT) foresees the pervasive interconnection and cooperation of intelligent things. As such, the IoT is simply the next logical step in the expansion of the Web into the real world, ushering in a plethora of unique services that will enhance peoples lives, give rise to entirely new economic sectors and smarten up the physical infrastructure upon which we rely, including buildings, cities and transportation networks. As smart devices permit widespread information collection or tracking, the IoT will not be able to reach its full potential if the vision for the IoT is not implemented appropriately. These helpful characteristics are countered by concerns over confidentiality, which have, to date, hindered the viability of IoT aspirations. In the face of widespread surveillance, the management of private information and the development of tools to limit or evade pervasive monitoring and analysis are two examples of the new difficulties brought about by such dangers. This paper considers the privacy concerns raised by the Internet of Things in depth. Henry Stewart Publications 2398-1679 (2023). -
Growth of online social networking and artificial intelligence in digital domain
In this millennium years of technology, machinery is evolving daily. There is plethora of things being affected by this evolution. One of which is our practices of social networking which is largely veering to the internet. Internet social networking has become one of the biggest buzzwords. From a child to an old person, everyone is on these social networking sites and applications. Within the span of 5 to 10 years these so-called Internet social networking sites and applications have taken over the real social gathering or meetings. Now with single click you can buy or sell goods and services at any place and any time. People can connect to one another even when far from home. The pandemic times and demonetization are the two instances that made everyone switch to and accustomed to the aspects of social networking. In the particular research paper, the researcher will put forth how data privacy and security is one of the biggest concerns in this social networking. Secondly, the researcher will understand the role of Artificial Intelligence in Online Social networking and whether it is helpful or not. 2023 Author(s). -
Government is trying but consumers are not buying: A barrier analysis for electric vehicle sales in India
It is a harsh fact that the introduction of various government schemes to push electric vehicle (EV) utilisation does not seem to appeal to the consumers. There are a few barriers that prevent consumers from purchasing EVs. Thus, in the present study, we have tried to identify and analyse the prominent barriers to the adoption of EVs by scrutinising the existing literature and defining new barriers. From the literature review, 35 barriers have been initially identified in the context of the Indian market. The study uses the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach for its analysis. The prominence and causal relationship analyses indicate that familial factors that hinder the decision to buy an EV and unclear government policies regarding EVs were the primary concerns with regard to these vehicles getting traction in the market. The results of this study illustrate the causal relationships amongst the identified barriers. On the other hand, the study reveals that the pricing of the EVs is not a major issue and that the consumers are more concerned about the availability of maintenance support post purchasing of the vehicles. 2021 Institution of Chemical Engineers -
Consumption Behaviour of Poor Consumers: A Bibliometric and Content Analysis
The poor people segment has gained considerable scholarly attention, particularly after eight scholars established it as an untapped market. This study attempts to understand the nine consumption behaviours of a poor consumer. Therefore, we collate 384 scholarly papers indexed in the Scopus database, published during the period 19752020. Both evaluative and bibliometric relationship techniques, namely, Biblioshiny and VOSviewer, are utilized to understand the domains progress in the past 45 years. Besides this, content analysis is conducted via hand search to identify the most utilized research methods, popularly applied theories and most explored contexts. Results show that research in this field has grown exponentially, particularly after 2007, with research focusing on low-income consumers, bottom-of-pyramid consumers and mobile money users. The findings indicate this domain demonstrates skewed development, and there is a huge room for future studies. 2022 Fortune Institute of International Business. -
APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN BUSINESS AND FINANCE 5.0
AI has evolved as a burgeoning technology in many industries, including the financial and banking sectors, which are facing greater challenges in data management, identity theft, and fraud as transactions and other company processes shift online and gain popularity. As systems using deep learning technology are able to detect data patterns and spot suspicious activity and probable fraud, AI can advance many financial and business activities. This book, Applications of Artificial Intelligence in Business and Finance 5.0, provides a valuable overview of how artificial intelligence (AI) applications are transforming global businesses and financial organizations, looking at the newest artificial intelligence-based solutions for e-commerce, corporate management, finance, banking and trading, and more. Chapters look at using artificial intelligence and machine learning techniques to forecast and assess different financial risks such as liquidity risk, volatility risk, and credit risk. The book also describes the use of natural language processing and text mining paired with machine learning models to assist in guiding sophisticated investors and corporate managers in financial decision making. Other topics include cryptocurrency in emerging markets; the role of artificial intelligence in making a positive impact on sustainable development; the use of fintech for micro, small and medium enterprises; the role of AI in financial education; the application of artificial intelligence in cyber security; and more. With a cross-disciplinary theme, this volume will be helpful to those in the corporate world, including professionals in business, finance, the e-commerce, economic sociology, political science, public administration, mass media and communications, information systems, development studies, among others. 2025 by Apple Academic Press, Inc. -
Use Cases of Intelligent Manufacturing
In the sphere of manufacturing, the adoption of Intelligent Manufacturing (IM) has become crucial for maintaining competitiveness and efficiency. This abstract chapter explores the paradigms and progression of IM, tracing the evolution from Industry 1.0 to Industry 5.0. It highlights the significance of digital manufacturing and the Internet of Things in enhancing connectivity. The chapter demonstrates the importance of IM through its diverse applications, including supply chain management, logistics, and warehouse automation. The authors examine the transformative influence of artificial intelligence on order management, predictive maintenance, and the creation of virtual twins. Real-world examples, such as GEs utilization of AI to accelerate product design and Toyotas partnership with Invisible AI for optimizing production quality control, illustrate the practical advantages of IM in driving innovation and operational efficiency. This chapter provides a comprehensive overview of the various aspects of IM, highlighting its current and future impact on the manufacturing sector. 2025 selection and editorial matter, Alka Chaudhary, Vandana Sharma, and Ahmed Alkhayyat individual chapters, the contributors. -
Positive ageing: self-compassion as a mediator between forgiveness and psychological well-being in older adults
Purpose: Positive aging aims to promote the physical health and psychological well-being of older adults for them to age successfully. Under the domain of positive aging, this study aims to explore the mediating role of self-compassion between forgiveness and psychological well-being in older adults. Design/methodology/approach: It was based on a quantitative research design, with a sample of 250 individuals within the age group of 6075 years. Data was collected using Self-compassion Scale (2003), Heartland Forgiveness Scale (2005) and Psychological Well-being Scale. Analysis was performed using Pearsons correlation, linear regression, followed by the generalised linear model of mediation. Findings: The results revealed a significant (p ? 0.001), high and positive correlation between self-compassion and forgiveness (r = 0.821), forgiveness and psychological well-being (r = 0.852) and self-compassion and psychological well-being (r = 0.802). Linear regression suggested that self-compassion and forgiveness are significant (p ? 0.001) predictors of psychological well-being, causing a variance of 75.6%. Mediation revealed significant (p ? 0.001) direct, indirect and total effect between the variables, showing that self-compassion partially mediates the relationship between forgiveness and psychological well-being. Research limitations/implications: The findings provide valuable insights on how fostering self-compassion along with forgiveness can improve psychological well-being among the elderly, however, research on additional variables, drawing comparisons between gender, economic status and clinical populations can be further explored. Nevertheless, this study can be used to develop interventions and therapeutic techniques to enhance self-compassion and forgiveness to improve psychological well-being among older adults. Originality/value: As per the best knowledge of the researcher, this work is original as it is a primary research and no data has been collected of a similar nature from the participants. 2024, Emerald Publishing Limited. -
An Innovative Method for Housing Price Prediction using Least Square - SVM
The House Price Prediction is often employed to forecast housing market shifts. Individual house prices cannot be predicted using HPI alone due to the substantial correlation between housing price and other characteristics like location, area, and population. While several articles have used conventional machine learning methods to predict housing prices, these methods tend to focus on the market as a whole rather than on the performance of individual models. In addition, good data pretreatment methods are intended to be established to boost the precision of machine learning algorithms. The data is normalized and put to use. Features are selected using the correlation coefficient, and LSSVM is employed for model training. The proposed approach outperforms other models such as CNN and SVM. 2023 IEEE.