This paper undertakes a hauntological reading of time and existence, in Amitav Ghosh’s Gun Island (2019). Hauntology, Jacques Derrida’s portmanteau coinage (haunt-plus-ontology, or haunted-ontology, Spectres of Marx, 1994), is a concept that avers that “this instant…is not docile to time, at least to what we call time.” Therefore positing existence as something of “doubtful contemporaneity,” simultaneously contemporaneous and intergenerational, making the present non-contemporaneous, detecting in the present both past and future. Unlike Heidegger’s “care” where the individual is finitely contained within death’s horizon, hauntology ideates a collective global existence beyond the “living present,” enfolding “the ghosts of those who are not yet born or who are already dead.” Not to be confused with cliched ghosts, hauntology, holds time and existence as “spectral” because “effective presence” (norm, law, hegemonic discourse) and its “other” (disarticulated, invisibilised, spectralised presence- as-non-present) both constitute our existence. Gun Island, often called the “Bengali Da Vinci Code,” foregrounds 21st century’s human and eco-displacements inside a detective story. Its central query, “Desh Koi” (where’s home?), problematises our current existence into one of identitylessness, most evident in current refugee/migrant crises as-well-as ecological disasters. Ghosh avers that these are fuelled by disavowed criminal nexus between corporates, smuggling syndicates, and governments/regimes, whose closest analogues are archaic systemic oppressions, thus gridlocking us into a perennially violently unequal world. This paper asks why Ghosh theorises our contemporary constitutedness thus. It argues that Ghosh, in seeing our post-fordist contemporaneity as continuums of archaic habits of systemic injustices, considers our 21st century temporality as a cancellation of future, thus liquifying the concept of a homogeneous, empty, calendar, or teleologically linear time. Detective fiction, with its meta-reading detective figure and analeptic closure of the narratorial logico-temporal gap provides a structural shorthand for Gun Island’s braiding of past, present, and future, where medieval Bengal, and Venice, contemporary India, America, and Italy, coalesce, to detect our existences as globally interconnected continuums of transhistoric, transcultural processes, which manifest as temporal bleeds, indexing hegemonic injustices, despite their systemic disarticulations. Like Giorgio Agamben, Ghosh too believes that the hidden foundation of all regimes lies in the prestidigitated inner solidarity between imperii arcana (archaic sovereignty), democracy and totalitarianism (Agamben, Homo Sacer). Gun Island, limns these disavowed processes and avers that our conventional empirical sense-making systems remain short-sighted in noticing these connections. Ghosh asserts that until the spectral, that-is, spectres and spectre-making (spectragenic) processes, are visibilised, our identities and politics will remain congealed in unequal, violent relatedness, and time will bring forth nothing new. Gun Island imagines a just world fuelled by a collective awareness of equality without leaving anyone out. This entails transitioning from the tyranny of presences to an awareness of the spectral third, beyond the present/absent, seen/unseen totalising binaries. This third, a clarifying agent for the ineffable failure of our future, requires a spectral historiography wherein time and being is placed outside commonplace tempero-spatial fixities. This paper therefore studies Gun Island’s treatment of existence and temporality as attempts towards historicising those no-longer- here, and those yet-to-be.Keywords- Spectres, Hauntology, Detective Fiction, Temporality, Absent presences.

English

(SoHSS) School of Humanities and Social Sciences

Re-Temporalising the Cultural in India

Haunted Time in Amitav Ghosh's Gun Island

Selecting a business site is a significant decision in managing a commercial organization. It becomes more complex in countries like India, where population density is high, and it is tough to distribute services fairly. With this difficulty, investing in a location without a thorough survey is challenging. This study aims to provide an information map that can help choose the optimal neighborhood for a particular business. The research aims to use data science powers to provide an unsupervised machine-learning approach to offer a recommendation-based solution that suggests the best location for a given use case. The objective of this paper is the in-depth analysis for site-finding that includes clustering regions based on various factors such as geographical locations (like schools, bus and railway stations, and hospitals), sanitation, distance, footfall, and other entities. The idea is to obtain the user's use case query and then create hotspots on the map that show the most suitable locations where the specific use case will benefit the stakeholders the most. We need to identify neighborhoods where there may be an unsatisfied need for Indian restaurants. Additionally, we require places that are uncrowded and have little competition. If the first two requirements are satisfied, we would also like a location close to a prominent city neighborhood. The advantages of each area will then be clearly expressed so that stakeholders can choose the best possible final location.

Computer Science and Engineering

(SoE) School of Engineering

2024 IEEE International Conference on Contemporary Computing and Communications

Location-Based Ideal Site Selection using Clustering

This research explores the intricate relationship between weather-related variables and the transmission dynamics of two pivotal diseases: COVID-19 and dengue. Our primary goal is to advance our understanding of how climate factors influence the occurrence and spread of these diseases through a comparative analysis. In the context of the pressing need for accurate prediction systems, our research aims to contribute to the refinement of public health planning and decision-making strategies. In this work, our findings underscore the superiority of certain models in predicting epidemic outbreaks. Notably, Random Forest consistently demonstrated exceptional performance across all metrics for both COVID-19 and Dengue predictions. The model exhibited superior accuracy, precision, and correlation with actual values.

Asian Conference on Intelligent Information and Database Systems

Predicting Epidemic Outbreak Using Climatic Factors

Economics

Physics

Shiv Nadar University

(SoNS) School of Natural Sciences

Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.