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Further results on induced graphoidal decomposition
Discrete Mathematics, Algorithms and applications Vol.5, No.1, ISSN No.1793-8317 -
Further results on induced graphoidal decomposition
Let G be a nontrivial, simple, finite, connected and undirected graph. A graphoidal decomposition (GD) of G is a collection ? of paths and cycles in G that are internally disjoint such that every edge of G lies in exactly one member of ?. As a variation of GD the notion of induced graphoidal decomposition (IGD) was introduced in [S. Arumugam, Path covers in graphs (2006)] which is a GD all of whose members are either induced paths or induced cycles. The minimum number of elements in such a decomposition of a graph G is called the IGD number, denoted by ?i(G). In this paper, we extend the study of the parameter ?i by establishing bounds for ?i(G) in terms of the diameter, girth and the maximum degree along with characterization of graphs achieving the bounds. 2013 World Scientific Publishing Company. -
Further Results on the 3-Consecutive Vertex Coloring Number of Certain Graphs
A 3-consecutive vertex coloring is an assignment of colors on vertices of a graph G such that for any 3-consecutive vertices a, b and c, the color of b is the same as the color of a or c. ? 3c(G) denotes the maximum number of colors that can be used to 3-consecutive vertex color a graph G. The main aim of this article is to give the value of ? 3c(G) for some particular types of graphs, which includes: necklace graphs; the Cartesian product of two paths, a cycle and a path, and two cycles; the corona product of a path and a clique; Mobius Ladder graphs; the 3rd edge line graph; triangular snake graphs, double triangular snake graphs, triple triangular snake graphs, quadrilateral snake graphs and the alternative versions of them; Hanoi graphs; Sun graphs; Barbel graphs; the n-pan graph. The objective of this article is to explore some important results on ? 3c(G). 2024 The Authors. -
Further studies on chromatic completion of graphs
The chromatic completion graph of G with respect to a proper vertex coloring c of G, denoted by Gc?, is the graph obtained by adding all possible edges to G without violating the proper coloring protocol. The maximum number of edges added to G to obtain the chromatic completion graph is the chromatic completion number ??(G). Equitable chromatic completion graph Ge? of a graph G and equitable chromatic completion number ??e(G) are the equitable analogues of Gc? and ??(G), respectively. In this paper, we present various structural aspects of chromatic completion graphs and equitable chromatic completion graphs. Also, the chromatic completion and the related parameter are described in terms of adjacency matrix and color matrix of graphs. The equitable chromatic completion graph is shown to be a Tur graph. More relevantly, we obtained the equitable chromatic completion number of an arbitrary graph G. World Scientific Publishing Company. -
Further studies on circulant completion of graphs
A circulant graph C(n, S) is a graph having its adjacency matrix as a circulant matrix. It can also be interpreted as a graph with vertices v0, v1,,vn?1 that are in one-to-one correspondence with the members of Zn and with edge set {vivj: i ? j ? S}, where S known as the connection set or symbol, is a subset of non-identity members of Zn that is closed under inverses. This work extends the study of circulant completion and general formulae for calculating circulant completion numbers in two different perspectives, one in terms of circulant span and the other in terms of the adjacency matrix. (2024), (SciELO-Scientific Electronic Library Online). All Rights Reserved. -
Further Study on the s-Shunt Intersection Graph of a Graph
For an integer s ? 1, an s-arc in a graph G is a sequence of (s + 1) vertices (v1, v2, , vs+1) of G such that any two consecutive vertices are adjacent in G and vi ? vi+2; 1 ? i ? s ? 1. Certain structural properties of an intersection graph defined on the set of all s-arcs on distinct vertices of a graph G, that can be shunted onto another s-arc on distinct vertices of G, known as the s-shunt intersection graph of G is studied. 2026, SINUS Association. All rights reserved. -
Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction
Stock market prediction is one of the most important financial subjects that have drawn researchers attention for many years. Several factors affecting the stock market make stock market forecasting highly complicated and a difficult task. The successful prediction of a stock market may promise attractive benefits. Various data mining methods such as artificial neural network (ANN), fuzzy system (FS), and adaptive neuro-fuzzy inference system (ANFIS) etc are being widely used for predicting stock prices. The goal of this paper is to find out an efficient soft computing technique for stock prediction. In this paper, time series prediction model of closing price via fusion of wavelet-adaptive network-based fuzzy inference system (WANFIS) is formulated, which is capable of predicting stock market. The data used in this study were collected from the internet sources. The fusion forecasting model uses the discrete wavelet transform (DWT) to decompose the financial time series data. The obtained approximation and detailed coefficients after decomposition of the original time series data are used as input variables of ANFIS to forecast the closing stock prices. The proposed model is applied on four different companies previous data such as opening price, lowest price, highest price and total volume share traded. The day end closing price of stock is the outcome of WANFIS model. Numerical illustration is provided to demonstrate the efficiency of the proposed model and is compared with the existing techniques namely ANN and hybrid of ANN and wavelet to prove its effectiveness. The experimental results reveal that the proposed fusion model achieves better forecasting accuracy than either of the models used separately. From the results, it is suggested that the fusion model WANFIS provides a promising alternative for stock market prediction and can be a useful tool for practitioners and economists dealing with the prediction of stock market. 2019, Springer-Verlag GmbH Germany, part of Springer Nature. -
Fusion of medical image using STSVD
The process of uniting medical images which are taken from different types of images to make them as one image is a Medical Image Fusion. This is performed to increase the image information content and also to reduce the randomness and redundancy which is used for clinical applicability. In this paper a new method called Shearlet Transform (ST) is applied on image by using the Singular Value Decomposition (SVD) to improve the information content of the images. Here two different images Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) are taken for fusing. Initially the ST is applied on the two input images, then for low frequency coefficients the SVD method is applied for fusing purpose and for high frequency coefficients different method is applied. Then fuse the low and high frequency coefficients. Then the Inverse Shearlet Transform (IST) is applied to rebuild the fused image. To carry out the experiments three benchmark images are used and are compared with the progressive techniques. The results show that the proposed method exceeds many progressive techniques. Springer Nature Singapore Pte Ltd. 2017. -
Fusion Techniques for medical imaging and clinical data towards precision diagnostics and personalized care
The combination of clinical data with medical imaging has created a revolution in modern healthcare, which provides a clear insight into patient's health sometimes in an earlier stage itself. Magnetic resonance imaging, computed tomography scans, ultrasounds, and X-rays are a few of the medical imaging techniques that offer high-resolution representations of the body's structure and physiological processes. Clinical data, such as physical examinations, test findings, and medical histories, help to contextualize these photographs, which enhances treatment planning and broadens diagnostic discoveries. Fusion approaches combine data from multiple sources to create a unified dataset, making diagnoses more accurate and treatments more personalized. This chapter emphasizes the importance of the fusion of heterogeneous medical data along with various fusion techniques, including deep learning and attention mechanisms, to align medical images with clinical data for meaningful insights. By using fusion techniques, healthcare professionals can make real-time decisions, identify diseases with better accuracy, and derive insights that can lead to actionable treatment or management strategies. Advanced fusion methods enable healthcare providers to obtain a holistic view of a patient's health, allowing advancements in precision medicine and customized treatment plans. 2026 Elsevier Inc. All rights reserved. -
FusionBotSentinel: A Framework to Mitigate Probable Social Bots Spreading False Information in Cyber Physical Systems
The escalating dissemination of fake news across social media networks has emerged as a concerning societal issue and a threat to cyber physical systems. Bots, often employed to propagate such misinformation, present a formidable challenge in their detection and elimination. Bot prediction have been pivotal in identifying and curbing these deceptive bot activities within social media networks. Twitchs live streaming content is readily scrapable and totally accessible. But quite understudied. Recent studies scrutinized these frameworks, revealing significant strides in their development while acknowledging the need for further enhancements in both predictions for proactive measures. FusionBotSentinel proposes a novel architecture that underscores the imperative for future research to concentrate on fortifying these frameworks, ensuring they are more resilient and adaptable in mitigating and predicting the spread of fake news by social bots. Another focus is on enhancing the effectiveness of deep learning models through a refined understanding of data quality with a largest dataset available and employing better hybrid techniques that bolster the generalizability and robustness helping in forecasting bot activities in combatting this escalating problem within cyber physical systems. Since bots are seen to be the source of the present problems with cyber physical systems, including privacy, security, safety, and ethical difficulties, it is necessary to recognize these gaps. Our suggested FusionBotSentinelprovides a revolutionary significance by contributing to in combatting fake news in the society by achieving up to 99% in accuracy, 98% in precision, 100% in recall, 99% in sensitivity with F1 score as 99% in social bot prediction offering 20% more efficiency when compared to the most advanced existing models proving its superiority. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Future Battlefield System Using Graph Database and Internet of Things (IoT)
The Internet of Things (IoT) concept is rapidly evolving and is expected to influence each field of the computational realm. These advances have an impact on any nations defence force. The defense industrys solution mostly depends on detectors and their installations. The major goal of sensory statistics is to provide information that may be used for strategic choices and evaluation in future battling fields. Each piece of statistics, from documenting a soldiers essential health metrics to its ammunition, weapons, and position circumstance, has a function and is especially important to the strategic commander stationed in the control unit. This research proposes an innovative approach that combines the IoTs with the growing graph database to produce a contextual consciousness regarding each characteristic of the personnel on the battlefield. We show a projected future battlefield application condition in which we explore the graph database for contextual consciousness patterns to gain a strategic benefit over our competitors. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Future Crop Designing: Antistress Capacities Gained by CRISPRmediated Releasing the Potential of Functional Genomes
Abiotic stresses, including temperature fluctuations, salinity, and drought, as well as biotic stresses such as viral, bacterial, and fungal infections, exert detrimental effects on plant growth and development, thereby significantly impeding overall plant productivity and crop yield. Traditionally, the sustainable mitigation of abiotic stress has been achieved through the breeding of tolerant cultivars; however, this process is characterized by its timeconsuming and labor-intensive nature, as well as its inherent lack of precision. Thus, there is a pressing need to adopt advanced genome technology to address these limitations and enhance the efficacy of stress-tolerance breeding efforts. This can be addressed by facilitating site-specific modifications of selected functional genomic elements, thus providing a potential avenue for introducing desired traits to combat adverse stress conditions. Among various genome engineering methodologies, CRISPR-Cas9 has emerged as the most promising genomeediting tool, attributed to its notable efficiency, precision, and rapidity. This study offers insights into the prospective trajectory of crop improvement through the advancement of crop enhancement strategies, employing CRISPR technology to enhance crop resilience against stress conditions by selectively modifying or activating specific functional genomes. CAB International 2025. All rights reserved. -
Future Inclusive Education
The United Nations (UN) Sustainable Development Goals (SDGs) ensure inclusive and equitable quality education for promoting lifelong learning. Inclusive education fosters an environment for access to quality education by addressing diversity and barriers that can cause exclusion. COVID-19 has reimagined Higher Education with new challenges and opportunities for the present and future. Digital divide, gender inequality, addressing specially-abled students, and a non-inclusive learning environment are the major barriers to inclusive education. Inclusive education ensures that no one leaves behind, and higher education institutes can enhance their capacity building to promote inclusivity for the common good. Employability is one of the key concepts in higher education that builds the workforce and contributes to nation-building. With COVID-19, nature of work has seen radical changes; hence, graduate attributes have evolved with the 21st-century skills. The chapter emphasizes the role of inclusive education and reimagining higher education with suggestions to using existing strategies in life-long and futuristic inclusive learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2024. -
Future Innovation in Healthcare by Spatial Computing using ProjectDR
Spatial Computation is the next step in the continuing convergence between the digital and physical realms. It is a set of inventions and developments that can better our lives through learning the real world, acknowledging and connecting our connection to, and traveling through various locations in the world. The lack of modern, precise, and effective diagnosis limits the rehabilitation of patients, despite technical advancements in medicines. The capabilities of spatial computing are expanded in a healthcare framework during the care and treatment of the patient. In this article, our purpose is to clarify the function of ProjectDR in the field of healthcare, which enables the display of medical images, such as CT scans and MRI results, directly on the patient's body in a manner that moves as patients do. 2021 IEEE. -
Future of customer engagement through marketing intelligence
In the competitive world of contemporary business, the challenge of developing marketing strategies that bridge the gap between traditional and innovative techniques has become more critical than ever. As marketing shifts between physical and digital realms, companies grapple with the central question of how to navigate this evolution successfully. The key lies in data - the linchpin that can unravel vital problems in modern marketing. The need for sustainable and effective marketing strategies permeates all sectors, emphasizing the urgency for businesses to combine traditional methods with innovative approaches, such as harnessing alternative data and leveraging AI-based solutions. Future of Customer Engagement Through Marketing Intelligence emerges as a compelling solution to the pressing challenges faced by businesses in this transformative landscape. It offers a step-by-step roadmap, guiding readers on how market intelligence can utilize data and transform it into actionable insights. By emphasizing the crucial role of data in crafting great marketing strategies, the book advocates for a deep understanding of market-supported content and factual data. It asserts that marketing intelligence, encompassing data collection, analysis, and strategic utilization, is the key to becoming customer-centric, understanding market demands, and gaining a competitive advantage. Designed with a comprehensive and practical approach, the book's objectives align with addressing the emerging trends and challenges in customer engagement driven by marketing intelligence. It caters to a diverse audience, including marketing professionals, data analysts, business leaders, academics, researchers, consultants, technology developers, and policymakers. By delving into various topics, from AI-driven customer experiences to the application of advanced technologies like text mining and blockchain, the book serves as a valuable resource for navigating the evolving landscape of customer engagement and marketing intelligence. Ultimately, it stands as a beacon, illuminating the path toward sustainable and responsible customer engagement strategies in the ever-evolving world of marketing. 2024 by IGI Global. All rights reserved. -
Future of knowledge management in investment banking: Role of personal intelligent assistants
Purpose: The studys objective focuses on investigating the involvement of Personal Intelligent Assistants (PIAs) in the Knowledge Management Process (KMP) in Investment Banking Companies leading to Industrial Revolution 5.0 leading to effective Organizational Knowledge Management. Design/Methodology: A Self-administered Survey Questionnaire was circulated to 695 employees of Investment Banking Companies operating in Bangalore, Mumbai, Delhi, Hyderabad, Chennai, and Pune using the Cluster Sampling method. The Covariance-based Structural Equation Modelling (CB-SEM) and Gradient Boosting Regression technique of Machine Learning were used to validate the hypothesis through JASP V.18 Software. Knowledge Creation, Knowledge Sharing, Knowledge Retrieval, Knowledge Application, and Organizational Knowledge Management are the crucial constructs considered in the study. Findings: The results revealed that Knowledge Application is the most influencing factor in effective organizational Knowledge management among the Investment Banks followed by Knowledge Sharing. It also emphasizes that they have a weak Knowledge retrieval process and minimal efforts taken to create knowledge within these banks. Implications: The PIAs can facilitate effective Data Analysis and research in managing vast data eliminating the repeated tasks in portfolio reconciliation and offering personalized recommendations to manage portfolios. It enables in compliance, risk management, client relationship management, real-time monitoring and leveraged decision-making through predictive analysis. The Author(s) 2024. -
Future of Work in Creative Industries
Digital technologies, platform- based labour and AI are causing a significant transformation in the future of work in creative industries. This chapter looks at how the changes are transforming creative professions, work models and career expectations. In the digital transition, numerous cultural barriers to entry have been eliminated and individuals can tap into international markets without institutional intermediaries. With creative employees becoming more freelance and web based entrepreneurs, artists have to manage an increased pressure to balance between creativity and commercial and technological concerns. Lastly, it also gives future research and policy implications such as the need to provide structural systems that safeguard mental health, equitable pay and inclusiveness to opportunities. This chapter contends that creators, platforms, educators and policymakers must work together in order to achieve a healthy and equitable creative economy and sustainable creative futures. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Future of Work in the Age of Automation in the Global Scenario: Navigating Imposter Syndrome and Identity Crisis - Routing AI, Job Displacement, and Workforce Renovation
Automation and artificial intelligence continue to disrupt the workforce economy, transforming industries and institutions, as well as traditional employment models. Although automation promises to improve productivity and efficiency, it poses its own problems in job displacement and skill gaps like imposter syndrome and identity crises as the replacement of human workforce to AI and rebotics. In order to cope with this transition, companies, government bodies and educational institutions need to take initiative, in the form of retraining initiatives, AI-human collaboration, labour policies etc. This discussion must also include ethical considerations, regulatory frameworks, and equitable opportunities for employment as AI continues to develop. As automation progresses the societies should shape it by augmentation of human potential instead of replacement, which could help facilitate sustainable economic growth and resilient labour workforce. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Future Perspectives of Microplastic towards Environmental Assessment
Microplastic (MP) pollution is an outcome of the widespread use of non-biodegradable plastic and improper disposal. This leads to contamination of environmental resources, such as landfills, and all kinds of water reservoirs including but not limited to sea, fresh water, drinking water, and even wastewater. Recent reports have highlighted the presence of MPs in the human body, including blood, lungs, placentas, and breast milk, indicating the severity of the issue. It is thus crucial to eliminate these hazardous contaminants from the environment. One of the effective methods to address the concern while reducing the adverse effects is to remove the MPs at their discharge points. Nanomaterials with exceptional properties like high surface area, ease of functionalization, and high affinity toward various pollutants act as excellent adsorbents. In this chapter, we present an overview of emerging nanomaterial-based adsorbents, such as photocatalysts, metal-organic frameworks, carbon-based nanomaterials, and nanocomposites, for effective removal of MPs from aqueous media via adsorption, photo-catalysis, and membrane filtration. However, considering that the research in the area of MP pollution is still in its infant stage, we aim to provide a brief account of the strengths, weaknesses, and future research dimensions of nanomaterial-based adsorbents for removing MPs from aqueous media. 2025 selection and editorial matter, Nirmala Kumari Jangid and Rekha Sharma; individual chapters, the contributors. -
Future Perspectives of Microplastic towards Environmental Assessment
Microplastic (MP) pollution is an outcome of the widespread use of non-biodegradable plastic and improper disposal. This leads to contamination of environmental resources, such as landfills, and all kinds of water reservoirs including but not limited to sea, fresh water, drinking water, and even wastewater. Recent reports have highlighted the presence of MPs in the human body, including blood, lungs, placentas, and breast milk, indicating the severity of the issue. It is thus crucial to eliminate these hazardous contaminants from the environment. One of the effective methods to address the concern while reducing the adverse effects is to remove the MPs at their discharge points. Nanomaterials with exceptional properties like high surface area, ease of functionalization, and high affinity toward various pollutants act as excellent adsorbents. In this chapter, we present an overview of emerging nanomaterial-based adsorbents, such as photocatalysts, metal-organic frameworks, carbon-based nanomaterials, and nanocomposites, for effective removal of MPs from aqueous media via adsorption, photo-catalysis, and membrane filtration. However, considering that the research in the area of MP pollution is still in its infant stage, we aim to provide a brief account of the strengths, weaknesses, and future research dimensions of nanomaterial-based adsorbents for removing MPs from aqueous media. 2025 selection and editorial matter, Nirmala Kumari Jangid and Rekha Sharma; individual chapters, the contributors.