Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Bird classification based on their sound patterns(Springer New York LLC barbara.b.bertram@gsk.com, 2016) Raghuram, M.A.; Chavan, N.R.; Belur, R.; Koolagudi, S.G.In this paper we focus on automatic bird classification based on their sound patterns. This is useful in the field of ornithology for studying bird species and their behavior based on their sound. The proposed methodology may be used to conduct survey of birds. The proposed methods may be used to automatically classify birds using different audio processing and machine learning techniques on the basis of their chirping patterns. An effort has been made in this work to map characteristics of birds such as size, habitat, species and types of call, on to their sounds. This study is also part of a broader project that includes development of software and hardware systems to monitor the bird species that appear in different geographical locations which helps ornithologists to monitor environmental conditions with respect to specific bird species. © 2016, Springer Science+Business Media New York.Item Laboratory investigations of wave attenuation by simulated vegetation of varying densities(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2019) John, B.M.; Shirlal, K.G.; Rao, S.Coastal communities across the world are facing the need to adapt to rising sea levels, an increase in the frequency of natural hazards like storm surges, cyclones, tsunamis, and an increase in beach erosion. This present-day scenario calls for a sustainable, environment-friendly, and cost efficient solution for coastal protection. Under these circumstances, the role of vegetation in providing ecosystem services to coastal populations is becoming increasingly prominent. This work presents the results of an experimental study carried out with simulated rigid submerged and emergent vegetation meadows of varying plant densities in a wave flume 50 m long, 0.71 m wide and 1.1 m deep. The material used for modeling the vegetation is nylon. The tests are carried out with regular waves for water depths of 0.40 and 0.45 m, and wave periods 1.4–2 s at an interval of 0.2 s. Five different wave heights ranging from 0.08 to 0.16 m at an interval of 0.02 m are generated. Measurements of wave heights at different locations indicate an exponential decay in wave height along the vegetation meadow which leads to wave attenuation and confirms that vegetation can be a viable option for coastal protection. © 2017, © 2017 Indian Society for Hydraulics.Item International efforts for children online safety: A survey(Inderscience Publishers, 2020) Andrews, D.; Alathur, S.; Chetty, N.Children online safety is a global issue and attaining international attention to address it. Often, children are vulnerable to online threats. Aim of this paper is to review children online safety issues and identify existing international efforts for reducing online risks. In this regard, efforts from available international bodies for providing online safety to children are reviewed and reported. To overcome online risks, understanding the behaviour of online ecosystem and coping after facing risks are most important. The ecosystem involves different stakeholders such as service providers, physical network, online users being connected, social media sites and tools and technology. Elimination of online risks is difficult but the intensity of risks can be reduced. © © 2020 Inderscience Enterprises Ltd.Item Public expenditure effectiveness for biodiversity conservation: Understanding the trends for project tiger in India(Now Publishers Inc, 2020) Nayak, B.P.; Jena, P.R.; Chaudhury, S.Project Tiger, a flagship programme for conservation of the tiger launched in 1973 in India has expanded over the years in terms of its geographical coverage and volume of expenditure. However, the tiger is still an endangered species in India and conservation efforts face multiple challenges like widespread loss of tiger habitat, decline in the density of prey animals, illegal poaching, human-animal conflicts and revenge killing. This study explores the trends and patterns of government expenditure over the years by reviewing the annual plan of operation of different tiger reserves and examines whether the volume or the pattern of expenditure has any relationship with performance, measured by the change in the number of tigers and occupancy in 28 tiger reserves. Analysis of the financial outlay data in the Annual Plan of Operation of the tiger reserves suggest that habitat improvement, which includes relocation, gets the highest share whereas human-animal conflict and eco-development gets the least, though more than 0.5 million households are located in and around the tiger reserves 0.3 million. Allocations are neither proportional to the size of the reserve nor to the tiger population. The relationships between expenditure categories and tiger populations are explored through a negative binomial regression model. Among the expenditure categories, expenditure on habitat improvement, excluding relocation, is found to be negatively related to tiger population whereas all other expenditures like infrastructure, protection, and human-animal conflict are positively related. © 2020 B. P. Nayak and P. R. Jena and S. ChaudhuryItem A Quantitative Method for Measuring Health of Authoritative Name Servers(IGI Global, 2022) Adiwal, S.; Rajendran, B.; Shetty D, P.D.The domain name system (DNS) is regarded as one of the critical infrastructure components of the global internet because a large-scale DNS outage would effectively take a typical user offline. Therefore, the internet community should ensure that critical components of the DNS ecosystem—that is, root name servers, top-level domain registrars and registries, authoritative name servers, and recursive resolvers—function smoothly. To this end, the community should monitor them periodically and provide public alerts about abnormal behavior. The authors propose a novel quantitative approach for evaluating the health of authoritative name servers – a critical, core, and a large component of the DNS ecosystem. The performance is typically measured in terms of response time, reliability, and throughput for most of the internet components. This research work proposes a novel list of parameters specifically for determining the health of authoritative name servers: DNS attack permeability, latency comparison, and DNSSEC validation. The aim is to understand the general behavior of authoritative name servers, detect sluggishness in their performance, and arrive at a score of their health through the aforesaid parameters. The effectiveness of identified parameters is evaluated by devising the corresponding probing algorithms and experimented with them among the authoritative name servers serving the world’s top 500 domains. This approach could be used periodically to assess and take necessary measures to protect authoritative domain name servers from abuse. © © 2022, IGI Global.Item Groundwater level modeling using Augmented Artificial Ecosystem Optimization(Elsevier B.V., 2023) Nguyen, N.; Deb Barma, S.D.; van Lam, T.; Kisi, O.; Mahesha, A.Nature-inspired optimization is an active area of research in the artificial intelligence (AI) field and has recently been adopted in hydrology for the calibration (training) of both process-based and statistical models. This study proposes an improved AI model, Augmented Artificial Ecosystem Optimization-based Multi-Layer Perceptron (AAEO-MLP), to build a monthly groundwater level (GWL) forecasting model. AAEO-MLP model is built on the novel Augmented version of Artificial Ecosystem Optimization and traditional MLP network. In AAEO, Levy-flight trajectory and Gaussian random are utilized in exploration and exploitation to improve the optimizing ability. The AAEO-MLP model is tested on two time-series (1989–2012) datasets collected at two wells in India. Various explanatory variables such as monthly cumulative precipitation, mean temperature, tidal height, and previous measurements of GWL were considered for predicting 1-month ahead of GWL. The performance of AAEO-MLP was benchmarked against 17 different models (original AEO, 3 different variants of AEO, and 13 well-known models) in terms of forecasting accuracy based on six metrics (e.g., mean absolute error, root mean square error, Kling–Gupta efficiency, normalized Nash–Sutcliffe efficiency, Pearson's correlation index, a20 index). Furthermore, convergence analysis and model stability are employed to indicate the effectiveness of AAEO-MLP. The compared results express that the AAEO-MLP is superior to other models in terms of prediction accuracy, convergence, and stability. Overall, the results depict that the AAEO is a promising approach for optimizing machine learning models (e.g., MLP) and should be explored for other hydrological forecasting applications (e.g., streamflow, rainfall) to further assess its strengths over existing methods. © 2022 Elsevier B.V.Item Assessing forest health using remote sensing-based indicators and fuzzy analytic hierarchy process in Valmiki Tiger Reserve, India(Institute for Ionics, 2023) Roshani; Sajjad, H.; Rahaman, M.H.; Rehman, S.; Masroor, M.; Ahmed, R.Anthropogenic activities, climate variability and environmental stresses have greatly affected forest ecosystems globally. Thus, monitoring of forest health is essential for proper planning and effective management. The present study employed an integrated approach of remote sensing and fuzzy analytic hierarchy process to assess the forest health in the Valmiki Tiger Reserve in India. Advanced vegetation index, normalized difference vegetation index, normalized difference moisture index, forest fragmentation, rainfall and soil types were derived from remote sensing data. Multiple buffer zones of villages, roads, railways and canals were also determined for analyzing the forest health status. These layers were prepared in the geographical information system. These layers were given weightage using fuzzy analytic hierarchy process. These layers were integrated to prepare forest health map using weighted overlay method. The results revealed that the largest forest area was found under moderately healthy forest (37%) followed by healthy forest (31%) and unhealthy forest (13%). Of the total area of the Reserve, 19% area was under non-forest category. Human-induced disturbances such as encroachment, illegal sand mining, livestock grazing and forest conversion to agriculture have been attributed to the unhealthy forest in the Reserve. The receiver operating characteristic curve value and area under curve (0.792) show reliability of forest health map. The findings of this study may be helpful for forest managers, conservationists and local communities in devising sustainable strategies for effective management of the forest. The methodological framework adopted in this study may be utilized in other geographical regions interested in assessing forest health. © 2022, The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University.Item Multi-criteria decision-making and machine learning-based CMIP6 general circulation model ensemble for climate projections in a tropical river basin in India(Springer Science and Business Media Deutschland GmbH, 2025) Kumar, G.P.; Vinod, D.; Dwarakish, G.S.; Mahesha, A.General circulation models (GCMs) are vital for accurate climate prediction and informing strategic water resource planning. The investigation explores the performance of five machine learning (ML) algorithms for ensembling the GCMs for top-5 and least-5 ranked models in multi-criteria decision-making (MCDM) in addition to 28 GCMs applicable to a tropical river basin in India and the performance of their ensemble using statistical metrics. The gridded datasets from the India Meteorological Department (IMD) are used as observed data. From the statistical metrics, an entire 28 GCMs ensemble showed superiority over top-5 and least-5 ranked ensembles for three meteorological variables. The random forest (RF) algorithm consistently demonstrated high accuracy and reliability in ensembling the GCMs for the three meteorological variables, followed by support vector machine (SVM) and multiple linear regression (MLR). By implementing the proposed approach, researchers can minimize biases, enable resource-efficient modeling, and deliver practical insights through robust and reliable climate projections. These results highlight the importance of thoughtful ensemble design, advocating using multi-model ensembles (MMEs) in comprehensive climate studies to ensure accurate predictions across diverse climate indices. The findings provide valuable insights into local climate conditions, supporting ecosystem management and informing policy decisions. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.