School of Basic Sciences

Permanent URI for this collection

http://10.10.25.38:4000/handle/123456789/643

Browse

Browsing School of Basic Sciences by Title

Now showing 1 - 20 of 221
  • No Thumbnail Available
    Item
    A next generation probabilistic approach to analyze cancer patients data with inference and applications
    (2025) Kumar, Anoop
    This study addresses the critical challenges faced in cancer care, particularly in predicting survival times for patients with lung cancer and acute myeloid leukemia. Despite recent advancements in medical science, existing models often fall short in accurately capturing disease progression, leading to less effective clinical decision making, and compromised patient outcomes. The need for advanced predictive models is urgent to improve survival time forecasts and enhance treatment strategies. In response to this, we introduce a novel probabilistic approach, the New Weibull (NEWE) model, which is part of a newly generated class designed to model cancer patient data more effectively. Our methodology includes using seven well-known estimation methods, each rigorously evaluated for consistency through Monte Carlo simulation studies focused on key metrics such as absolute bias, mean square error, and mean relative error. The datasets analyzed include survival times for twenty acute myeloid leukemia patients, 121 breast cancer patients from 1929 to 1938, 33 patients with acute myelogenous leukemia, data from eighteen individuals who died from causes unrelated to cancer, and survival times of advanced lung cancer patients undergoing standard chemotherapy. The NEWE model outperformed competing models, particularly in Anderson-Darling, Cramer-von Mises, and Kolmogorov-Smirnov tests, with significantly higher p-values. These findings highlight the NEWE model’s potential to transform predictive oncology by offering more precise survival time predictions, improving the quality of care and decision-making in cancer treatment.
  • Thumbnail Image
    Item
    Advances in Optical Visual Information Security: A Comprehensive Review
    (2024-01) Sachin; Kumar, R; Sakshi; Yadav, R; Reddy, S; Yadav, A; Singh, P
    In the modern era, the secure transmission and storage of information are among the utmost priorities. Optical security protocols have demonstrated significant advantages over digital counterparts, i.e., a high speed, a complex degree of freedom, physical parameters as keys (i.e., phase, wavelength, polarization, quantum properties of photons, multiplexing, etc.) and multi-dimension processing capabilities. This paper provides a comprehensive overview of optical cryptosystems developed over the years. We have also analyzed the trend in the growth of optical image encryption methods since their inception in 1995 based on the data collected from various literature libraries such as Google Scholar, IEEE Library and Science Direct Database. The security algorithms developed in the literature are focused on two major aspects, i.e., symmetric and asymmetric cryptosystems. A summary of state-of-the-art works is described based on these two aspects. Current challenges and future perspectives of the field are also discussed.
  • Thumbnail Image
    Item
    Advances in Optical Visual Information Security: A Comprehensive Review
    (2024-01) Sachin; Kumar, R; Sakshi; Yadav, R; Yadav, AK
    In the modern era, the secure transmission and storage of information are among the utmost priorities. Optical security protocols have demonstrated significant advantages over digital counterparts, i.e., a high speed, a complex degree of freedom, physical parameters as keys (i.e., phase, wavelength, polarization, quantum properties of photons, multiplexing, etc.) and multi-dimension processing capabilities. This paper provides a comprehensive overview of optical cryptosystems developed over the years. We have also analyzed the trend in the growth of optical image encryption methods since their inception in 1995 based on the data collected from various literature libraries such as Google Scholar, IEEE Library and Science Direct Database. The security algorithms developed in the literature are focused on two major aspects, i.e., symmetric and asymmetric cryptosystems. A summary of state-of-the-art works is described based on these two aspects. Current challenges and future perspectives of the field are also discussed.
  • No Thumbnail Available
    Item
  • Thumbnail Image
    Item
    Alpha logarithm transformed fréchetdistribution: Properties and estimation
    (Austrian Journal of Statistics, 2019) Dey, Sanku; Nassar, Mazen; Kumar, Devendra; Alaboud, Fahad
    In this paper, a new three-parameter distribution called the Alpha Logarithm Transformed Fr echet (ALTF) distribution is introduced which o ers a more exible distribution for modeling lifetime data. Various properties of the proposed distribu- tion, including explicit expressions for the quantiles, moments, incomplete moments, conditional moments, moment generating function R enyi and -entropies, stochastic ordering, stress-strength reliability and order statistics are derived. The new dis- tribution can have decreasing, reversed J-shaped and upside-down bathtub failure rate functions depending on its parameter values. The maximum likelihood method is used to estimate the distribution parameters. A simulation study is conducted to evaluate the performance of the maximum likelihood estimates. Finally, the pro- posed extended model is applied on real data sets and the results are given which illustrate the superior performance of the ALTF distribution compared to some other well-known distributions.
  • Thumbnail Image
    Item
    Alpha power transformed inverse Lindley distribution: A distribution with an upside-down bathtub-shaped hazard function
    (2017-08) De, S; Nassar, M; Kumar, D
    The inverse Lindley distribution has been generalized by many authors in recent years. Here, we introduce a new generalization called alpha power transformed inverse Lindley (APTIL) distribution that provides better fits than the inverse Lindley distribution and some of its known generalizations. The new model includes the inverse Lindley distribution as a special case. Various properties of the proposed distribution, including explicit expres sions for the mode, moments, conditional moments, mean residual lifetime, Bonferroni and Lorenz curves, entropies, stochastic ordering, stress–strength reliability and order statistics are derived. The new distribution can have an upside-down bathtub failure rate function depending on its parameters. The model parameters are obtained by the method of maximum likelihood estimation. The approximate confidence intervals of the model parameters are also obtained. A simulation study is carried out to examine the performance of the maximum likelihood estimators of the parameters. Finally, two data sets have been analyzed to show how the proposed model works in practice.
  • Thumbnail Image
    Item
  • No Thumbnail Available
    Item
    Analyzing landscape changes and their relationship with land surface temperature and vegetation indices using remote sensing and AI techniques
    (2025) Kumar, Pankaj
    Land use patterns and consumption occur widely due to fast industrialization and development in the previous several decades, which might lead to problems such as over-exploitation of land resources, food shortages, and pol lution. Monitoring and subsequent modeling of land use land cover (LULC) changes has become critical. A study of the variations in the LULC pattern of the Baghpat District of Uttar Pradesh, India, was attempted. This study assessed spatial patterns and fluctuations in growth in the Baghpat District of Uttar Pradesh (India) from 1991 to 2021. The study also analyzed the land cover changes and their effects on land surface temperature (LST), normalized difference vegetation index (NDVI), and soil indices in the Baghpat district. Decadal land use and land cover (LULC) changes were analyzed using Multitemporal Landsat Imagery and applying the maximum likelihood classifier in ENVI (Image Processing Software). Post-classification spatial measures were used to examine changes in LULC and the spatial distribution of urban growth, as well as to identify changes using ArcMap (GIS Software) across the period. Various AI techniques were used to show the trend variation in NDVI, LST, and SAVI indices using IBM-SPSS, Microsoft Office, OriginLab, and MATLAB for the study area to comprehend the variation in the index within the given period. The f indings indicated significant improvements in agriculture between 1991 and 2021 (from 58.94 to 84.79%), but sig nificant declines in vegetation cover (from 29.53 to 1.14%). The yearly percentage growth of the parallel built-up area was 3.77%, 5.59%, 6.71%, and 6.90%, respectively. Approximately 43.85% of the increase in agricultural land between 1991 and 2021 came from the conversion of vegetation covers, which fell by 96.13%. The analysis of LST, NDVI, and SAVI data revealed a substantial negative association for all years, except a slight positive correlation. NDVI and SAVI values were highest in agricultural fields with the lowest LST values, whereas fallow land regions exhibited the reverse pattern. With the help of these findings, urban planners and designers may reduce various socio-eco nomic and environmental consequences. Keywords Spatio-temporal changes, Geospatial techniques, LST, NDVI, SAVI
  • Thumbnail Image
    Item
    Analyzing the emerging patterns of SARS‐CoV‐2 Omicron subvariants for the development of next‐gen vaccine: An observational study
    (2023-09) Mohapatra, RK; Mishra, S; Kandi, V; Branda, F; Ansari, A
    Background and Aim: Understanding the prevalence and impact of SARS‐CoV‐2 variants has assumed paramount importance. This study statistically analyzed to effectively track the emergence and spread of the variants and highlights the importance of such investigations in developing potential next‐gen vaccine to combat the continuously emerging Omicron subvariants. Methods: Transmission fitness advantage and effective reproductive number (Re)of epidemiologically relevant SARS‐CoV‐2 sublineages through time during the study period based on the GISAID data were estimated. Results: The analyses covered the period from January to June 2023 around an array of sequenced samples. The dominance of the XBB variant strain, accounting for approximately 57.63% of the cases, was identified during the timeframe. XBB.1.5 exhibited 37.95% prevalence rate from March to June 2023. Multiple variants showed considerable global influence throughout the study, as sporadically documented. Notably, the XBB variant demonstrated an estimated relative 28% weekly growth advantage compared with others. Numerous variants were resistant to the over‐the‐counter vaccines and breakthrough infections were reported. Similarly, the efficacy of mAB‐based therapy appeared limited. However, it's important to underscore the perceived benefits of these preventive and therapeutic measures were restricted to specific variants. Conclusion: Given the observed trends, a comprehensive next‐gen vaccine coupled with an advanced vaccination strategy could be a potential panacea in the fight against the pandemic. The findings suggest that targeted vaccine development could be an effective strategy to prevent infections. The study also highlights the need of global collaborations to rapidly develop and distribute the vaccines to ensure global human health.
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring Une localisation basée sur un ancrage dans les réseaux de capteurs sans fil sous-marins pour la surveillance des oléoducs industriels
    (2022-08) Goyal, N; Nain, M
    Industries need solutions that can automatically monitor oil leakage from deployed underwater pipelines and to rapidly report any damage. The location prediction of mineral reservoirs like oil, gas, or metals in deep water is a challenge during the extraction of these resources. Moreover, the problem of ores and mineral deposits on the seafloor comes into play. The abovementioned challenges necessitate for the deployment of underwater wireless sensor networks (UWSNs). Anchor-based localization techniques are segregated into range-free and range-based processes. Range-based schemes depend on various techniques like angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), and received signal strength indicator (RSSI). In this article, the localization of these leakages is performed by using range-based metrics for calculating the distance among anchor nodes (ANs) and target nodes (TNs). This estimated distance is further optimized to minimize the estimation error. A multilateralism procedure is used to estimate the optimal position of each TN. The results exhibit that the proposed algorithm shows a high performance when compared to previous works, in terms of minimum energy consumption, lower packet loss, rapid location estimation, and lowest localization error. The benefit of using the proposed methodology greatly impacts on identifying the leakage area in mobility-assisted UWSN, where rapid reporting helps to lower the loss of resources.
  • No Thumbnail Available
    Item
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    Application of geospatial tools in the assessment of Flood hazard impact on social vulnerability of Malda district, West Bengal, India
    (2023-11) Mandal, K; Dharanirajan, K; Meena, M; Jaman, T; Rana, S
    Social vulnerability assessment is a dynamic process, which varies from place to place. In the present study, the social vulnerability index (SVI) of Malda district has been prepared because of several impacts of flood inunda tion. The flood inundation layer has been generated using multi-temporal remote sensing data. The flood inun dation layer is prepared from real-time Synthetic Aperture Radar (SAR) data. For social vulnerability assessment, the most efficient indicators are household composition, age & sex composition, and underprivileged population (SC& ST). Economic and educational data has been collected from the Census of India Handbook 2011. All these data are combined with the district's village database on the GIS platform. The weightage overlay analysis method is applied to generate the social vulnerability index of the study area, where the multi-influencing factor (MIF) technique has been used for determining the influencing factors. The social vulnerability index has categories into Very High (4%), High (37%), Moderate (32%) and Low (27%). The social vulnerability index is being further intersected with the flood inundation layer to build a database for the most vulnerable area of this district. It has been observed that 70 villages are in Very High zones, 662 villages are in High, 578 villages are in Moderate and 479 villages are in Low zones. This study will help the disaster manager and stakeholders about the vulnerable situation of the study area and also depict the importance of geospatial techniques in disaster management.
  • Thumbnail Image
    Item
    Approaches towards the synthesis of 5-aminopyrazoles
    (Beilstein Journal of Organic Chemistry, 2011) Kumar, Vinod
  • No Thumbnail Available
    Item
    Approximation by B´ezier-Baskakov-Jain type operators
    (2025) Yadav,Jyoti
    t. In this paper, we introduce the B´ezier variant of the α-Baskakov-Jain type operators. We explore the elements of the Lipschitz type space, propose a direct approximation theorem using the modulus of continuity, and assess the approximation rate for functions possessing derivatives of bounded variation. The use of computer graphics lends support to the validity of the theoretical components in this study.
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    Approximation by α-Baskakov−Jain Type Operators
    (Filomat, 2022) Kajla, Arun
  • Thumbnail Image
    Item
    Assessment and Mapping of Riverine Flood Susceptibility (RFS) in India through Coupled Multicriteria Decision Making Models and Geospatial Techniques
    (2023-11) Kumar, R; Kumar, M; Tiwari, A
    Abstract: Progressive environmental and climatic changes have significantly increased hydrometeo rological threats all over the globe. Floods have gained global significance owing to their devastating impact and their capacity to cause economic and human loss. Accurate flood forecasting and the identification of high-risk areas are essential for preventing flood impacts and implementing strategic measures to mitigate flood-related damages. In this study, an assessment of the susceptibility to riverine flooding in India was conducted utilizing Multicriteria Decision making (MCDM) and an extensive geospatial database was created through the integration of fourteen geomorphological, meteorological, hydroclimatic, and anthropogenic factors. The coupled methodology incorporates a Fuzzy Analytical Hierarchy Process (FAHP) model, which utilizes Triangular Fuzzy Numbers (TFN) to determine the Importance Weights (IWs) of various parameters and their subclasses based on the Saaty scale. Based on the determined IWs, this study identifies proximity to rivers, drainage density, and mean annual rainfall as the key factors that contribute significantly to the occurrence of riverine floods. Furthermore, as the Geographic Information System (GIS) was employed to create the Riverine Flood Susceptibility (RFS) map of India by overlaying the weighted factors, it was found that high, moderate, and low susceptibility zones across the country span of 15.33%, 26.30%, and 31.35% of the total area of the country, respectively. The regions with the highest susceptibility to flooding are primarily concentrated in the Brahmaputra, Ganga, and Indus River basins, which happen to encompass a significant portion of the country’s agricultural land (334,492 km2 ) potentially posing a risk to India’s food security. Approximately 28.13% of built-up area in India falls in the highly susceptible zones, including cities such as Bardhaman, Silchar, Kharagpur, Howrah, Kolkata, Patna, Munger, Bareilly, Allahabad, Varanasi, Lucknow, and Muzaffarpur, which are particularly susceptible to flooding. RFS is moderate in the Kutch-Saurashtra-Luni, Western Ghats, and Krishna basins. On the other hand, areas on the outskirts of the Ganga, Indus, and Brahmaputra basins, as well as the middle and outer portions of the peninsular basins, show a relatively low likelihood of riverine flooding. The RFS map created in this research, with an 80.2% validation accuracy assessed through AUROC analysis, will function as a valuable resource for Indian policymakers, urban planners, and emergency management agencies. It will aid them in prioritizing and executing efficient strategies to reduce flood risks effectively.
  • Thumbnail Image
    Item
    Asymmetric double image encryption, compression and watermarking scheme based on orthogonal-triangular decomposition with column pivoting
    (2021-03) ANJANA S; RAKHEJA, P; YADAV, A; SINGH, P
    A novel asymmetric scheme for double image encryption, compression and watermarking based on QR decomposition in the Fresnel domain has been presented. The QR decomposition provides a permutation matrix as a ciphertext, and the product of orthogonal and triangular matrix as a key. The ciphertext obtained through this process is a sparse matrix that is compressed by the CSR meth od to give compressed encrypted data, which when combined with a host image, gives a watermarked image. Thus, a cryptosystem that involves compression and watermarking is proposed. The proposed scheme is validated for grayscale images. To check the efficacy of the proposed scheme, histo grams, statistical parameters, and key sensitivity are analyzed. The scheme is also tested against various attacks. Numerical simulations are performed to validate the security of the scheme.