Browsing by Author "Sumith, N."

Filter results by typing the first few letters
Now showing 1 - 7 of 7
  • No Thumbnail Available
    Item
    A holistic approach to influence maximization
    (Springer International Publishing, 2017) Sumith, N.; Annappa, B.; Bhattacharya, S.
    A social network is an Internet-based collaboration platform that plays a vital role in information spread, opinion-forming, trend-setting, and keeps everyone connected. Moreover, the popularity of web and social networks has interesting applications including viral marketing, recommendation systems, poll analysis, etc. In these applications, user influence plays an important role. This chapter discusses how effectively social networks can be used for information propagation in the context of viral marketing. Picking the right group of users, hoping they will cause a chain effect of marketing, is the core of viral marketing applications. The strategy used to select the correct group of users is the influence maximization problem. This chapter proposes one of the viable solutions to influence maximization. The focus is to find those users in the social networks who would adopt and propagate information, thus resulting in an effective marketing strategy. The three main components that would help in the effective spread of information in the social networks are: the network structure, the user's influence on others, and the seeding algorithm. Amalgamation of these three aspects provides a holistic solution to influence maximization. © Springer International Publishing AG 2017. All rights reserved.
  • No Thumbnail Available
    Item
    A holistic approach to influence maximization in social networks: STORIE
    (Elsevier Ltd, 2018) Sumith, N.; Annappa, B.; Bhattacharya, S.
    Crowd sourcing techniques are used in social networks to propagate information at a faster pace through campaigns. One of the challenges of crowd sourcing system is to recruit right users to be a part of successful campaigns. Fetching this right group of people, who influence a vast population to adopt information, is termed as influence maximization. Concerns of scalability and effectiveness need an effective and a viable solution. This paper proposes the solution in three stages. At the first stage, the large social network is pruned based on the nodal properties to make the solution scalable. At the second stage, Outdegree Rank (OR), is proposed and at the third stage, Influence Estimation (IE) approach estimates user influence. This work amalgamates aspects of structure, heuristic and user influence, to form STORIE. The proposed approach is compared to standard heuristics, on various experimental setups such as RNNDp, RNUDp and TVM. The spread of information is observed for HEP, PHY, Twitter, Infectious and YouTube data, under Independent Cascade model and STORIE gives optimal results, with an increase up to 50%. Although the paper discusses influence maximization, the proposed approach is also applicable to understand the spread of epidemics, computer virus, and rumor spreading in the real world and can also be extended to detect anomalies in web and social networks. © 2017 Elsevier B.V.
  • No Thumbnail Available
    Item
    Influence maximization in large social networks: Heuristics, models and parameters
    (Elsevier B.V., 2018) Sumith, N.; Annappa, B.; Bhattacharya, S.
    Online social networks play a major role not only in socio psychological front, but also in the economic aspect. The way social network serves as a platform of information spread, has attracted a wide range of applications at its doorstep. In recent years, lot of efforts are directed to use the phenomenon of vast spread of information, via social networks, in various applications, ranging from poll analysis, product marketing, identifying influential users and so on. One such application that has gained research attention is the influence maximization problem. The influence maximization problem aims to fetch the top influential users in the social networks. The aim of the paper is to provide a comprehensive analysis on the state of art approaches towards identifying influential users. In this review, we discuss various challenges and approaches to identify influential users in online social networks. This review concludes with future research direction, helping researchers to bring possible improvements to the existing body of work. © 2018 Elsevier B.V.
  • No Thumbnail Available
    Item
    Optimizing Solid Waste Management: A Holistic Approach by Informed Carbon Emission Reduction
    (Institute of Electrical and Electronics Engineers Inc., 2024) Hegde, S.; Sumith, N.; Pinto, T.; Shukla, S.; Patidar, V.
    Reducing carbon monoxide (CO) emissions is imperative for safeguarding human health and environment. CO adversely affects respiratory health, contributing to respiratory problems and, in severe cases, fatalities. Its reduction aligns with the broader efforts to combat climate change, as CO is often emitted alongside other greenhouse gases. Environmental consequences include air pollution and its detrimental impact on ecosystems. Compliance with emission standards is essential, and reducing Carbon emissions can lead to social and economic benefits, such as increased productivity and reduced healthcare costs. Moreover, the focus on emission reduction drives technological innovation, fostering the development of cleaner and sustainable technologies. In essence, addressing CO emissions is vital for creating a healthier, more sustainable future. However, in most of the cases, there has been no much importance given in scientific management of solid wastes. This has therefore resulted in large magnitude of carbon emission causing serious implications. This paper presents a novel approach to solid waste management, combining carbon emission assessment with advanced object detection technology. We develop an integrated waste management model that employs machine learning techniques for the identification and categorization of metals, non-metals, and plastics within the solid waste stream. To optimize waste sorting and recycling processes, we implement an efficient object detection system that leverages computer vision algorithms. This system enhances the precision of material identification within solid waste, thereby improving sorting accuracy. Additionally, we establish a database to quantify carbon emissions associated with distinct waste management methods, encompassing incineration, composting, recycling, bioremediation, and landfills is used for this work. The novelty of the work lies in the integration of CO2 emissions data and object detection resulting into a decision-making model, providing a holistic evaluation of the environmental impact of varied waste management scenarios. The formulation of recommendations for sustainable waste management practices based on the integrated assessment of carbon footprints and material identification is easy to implement in real world.The technical framework proposed here, aims to inform decision-makers on adopting environmentally conscious strategies for waste management. © 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
  • No Thumbnail Available
    Item
    RnSIR: A new model of information spread in online social networks
    (2017) Sumith, N.; Annappa, B.; Bhattacharya, S.
    There is a close resemblance between the dynamism of epidemic spread and information spread. For this reason, the Susceptible-Infected-Recovered(SIR) model, rooted in epidemiology, is been used to understand the information spread in online social networks. This model is based on homogeneous mixing of population, where an individual is equally likely to be infected by others. However, the degree of sparsity in interactions among the users will invalidate the homogeneous mixing concept. For this reason, SIR model fails to map the complete scenario of information spread among the users. In this paper, to fill in the gap seen in SIR, a new model RnSIR is developed. The proposed model is able to make a clear distinction between the restrained and susceptible. To this end, the new model is applied to viral marketing to understand its authenticity. The contribution is shown by the increase in spread of information reaching as far as 50% of the susceptible population in the RnSIR model, when compared to the SIR model. Although the paper discusses the dynamism of information spread in online social networks, the proposed model can be used to understand the spread of epidemics, spread of computer virus, rumors and also analyze the role of users. � 2016 IEEE.
  • No Thumbnail Available
    Item
    RnSIR: A new model of information spread in online social networks
    (Institute of Electrical and Electronics Engineers Inc., 2017) Sumith, N.; Annappa, B.; Bhattacharya, S.
    There is a close resemblance between the dynamism of epidemic spread and information spread. For this reason, the Susceptible-Infected-Recovered(SIR) model, rooted in epidemiology, is been used to understand the information spread in online social networks. This model is based on homogeneous mixing of population, where an individual is equally likely to be infected by others. However, the degree of sparsity in interactions among the users will invalidate the homogeneous mixing concept. For this reason, SIR model fails to map the complete scenario of information spread among the users. In this paper, to fill in the gap seen in SIR, a new model RnSIR is developed. The proposed model is able to make a clear distinction between the restrained and susceptible. To this end, the new model is applied to viral marketing to understand its authenticity. The contribution is shown by the increase in spread of information reaching as far as 50% of the susceptible population in the RnSIR model, when compared to the SIR model. Although the paper discusses the dynamism of information spread in online social networks, the proposed model can be used to understand the spread of epidemics, spread of computer virus, rumors and also analyze the role of users. © 2016 IEEE.
  • No Thumbnail Available
    Item
    Social network pruning for building optimal social network: A user perspective
    (Elsevier B.V., 2017) Sumith, N.; Annappa, B.; Bhattacharya, S.
    Social networks with millions of nodes and edges are difficult to visualize and understand. Therefore, approaches to simplify social networks are needed. This paper addresses the problem of pruning social network while not only retaining but also improving its information propagation properties. The paper presents an approach which examines the nodal attribute of a node and develops a criterion to retain a subset of nodes to form a pruned graph of the original social network. To authenticate feasibility of the proposed approach to information propagation process, it is evaluated on small world properties such as average clustering coefficient, diameter, path length, connected components and modularity. The pruned graph, when compared to original social network, shows improvement in small world properties which are essential for information propagation. Results also give a significantly more refined picture of social network, than has been previously highlighted. The efficacy of the pruned graph is demonstrated in the information diffusion process under Independent Cascade (IC) and Linear Threshold (LT) models on various seeding strategies. In all size ranges and across various seeding strategies, the proposed approach performs consistently well in IC model and outperforms other approaches in LT model. Although, the paper discusses the problem with the context of information propagation for viral marketing, the pruned graph generated from the proposed approach is also suitable for any application, where information propagation has to take place reasonably fast and effectively. © 2016 Elsevier B.V.