BITS Faculty Publications

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    Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques
    (Springer Nature, 2025-07) Srinivas, Rallapalli; Chalapathi, G.S.S.; Singh, Amit Rajnarayan
    Modeling the spatial variability and uncertainty of soil fertility parameters is crucial for sustainable agriculture but remains a challenge due to complex interactions between soil properties. Traditional models often assess individual parameters, such as pH or nitrogen (N), without considering their combined influence and uncertainty. This study develops a fuzzy logic and geoinformatics-based approach to simultaneously assess multiple soil fertility parameters. The model integrates 80 fuzzy rules to evaluate macro- and micronutrients, incorporating 250 soil samples analyzed using the PUSA Soil Test and Fertilizer Recommendation Meter (STFR). Experimental results showed soil fertility parameter ranges: pH (7.46–8.26), ECe (0.267–0.807 dS m−1), organic carbon (0.24–0.56%), N (85.56–146.32 kg ha−1), P (21.99–34.28 kg ha−1), K (116.41–156.16 kg ha−1), S (5.60–20.86 mg kg−1), Fe (1.065–5.095 mg kg−1), Mn (2.058–2.637 mg kg−1), Zn (0.748–1.105 mg kg−1), B (0.372–0.530 mg kg−1), and Cu (0.230–0.788 mg kg−1). The fuzzy model-derived fertility scores ranged from 41.55 to 52.60, with pH, organic carbon, nitrogen, phosphorus, potassium, and iron as critical parameters influencing fertility. Geostatistical kriging interpolation estimated fertility values at unsampled locations, generating a continuous, high-resolution soil fertility map for precision agriculture. Validation with crop yield data ranked suitability as: Pearl millet (0.919) > Mustard (0.890) > Wheat (0.863) > Barley (0.861). Multi-criteria decision analysis confirmed pearl millet as the most suitable crop based on fertility and yield potential. The study categorizes soil into low and moderate fertility zones across Jhunjhunu, Rajasthan, ensuring a systematic assessment for optimal nutrient management. By integrating fuzzy logic with GIS-based spatial modeling, this study enhances soil fertility classification, site-specific nutrient recommendations, and sustainable crop planning, reinforcing the role of fuzzy-GIS frameworks in precision agriculture.
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    Modelling the risk of COVID-19 based on major clinical factors: A fuzzy rule approach
    (IEEE, 2021) Das, Dhiraj Kumar
    In this article, a Mamdani type fuzzy inference system is formulated in order to identify possible COVID-19 infected individuals based on three major clinical factors namely body-temperature, body-immunity level and vaccination efficacy. Measurements of the system's input and output parameters are considered as linguistic variable and assumed to follow trapezoidal type membership functions. The system based on total 27 fuzzy If-Then rules and called as Fuzzy Inference System (FIS) of Mamdani type. The system is analyzed using the Fuzzy Logic Toolbox of MATLAB. It has been found that with highly efficient vaccine a person with low body-immunity can escape the disease. On contrary, high body-temperature with high body-immunity power is not sufficient to exclude a person from the risk of having COVID-19.
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    Optimizing UAV Swarm Communication in Urban Scenarios: An Enhanced Fuzzy LEACH Algorithm Based Approach
    (IEEE, 2024-03) Joshi, Sandeep
    With the rapid improvement in drone technology, autonomous unmanned aerial vehicle (UAV) swarms are increasingly finding applications in traffic management, pollution monitoring, package delivery, security surveillance, and other essential services. In this paper, we propose an enhanced low-energy adaptive clustering hierarchy (LEACH) algorithm for UAV swarm communication in urban scenarios. We consider that the cluster head (CH) is selected based on attributes like cluster size, distance from the base station (BS), battery percentage, and line-of-sight (LOS), which enhances the effective packet delivery and helps the system sustain a longer duration. We propose a two-level hierarchy where the CHs are selected based on the enhanced LEACH algorithm from a dedicated pool of parent drones (PDs). We ensure that the PDs are always in the LOS of the BS to prevent the link outage. We perform LOS and Non-LOS link loss analysis based on the COST 231 Walfisch-Ikegami model. We show through simulation results that the proposed algorithm optimizes the existing LEACH algorithm, increasing system lifetime by around 66%, improving packet throughput, and the overall packet delivery by almost double.
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    A novel Venus’ visible image processing neoteric workflow for improved planetary surface feature analysis
    (Springer, 2024-03) Rohil, Mukesh Kumar
    The article presents a novel methodology that comprises of end-to-end Venus’ visible image processing neoteric workflow. The visible raw image is denoised using Tri-State median filter with background dark subtraction, and then enhanced using Contrast Limited Adaptive Histogram Equalization. The multi-modal image registration technique is developed using Segmented Affine Scale Invariant Feature Transform and Motion Smoothness Constraint outlier removal for co-registration of Venus’ visible and radar image. A novel image fusion algorithm using guided filter is developed to merge multi-modal Visible-Radar Venus’ image pair for generating the fused image. The Venus’ visible image quality assessment is performed at each processing step, and results are quantified and visualized. In addition, fuzzy color-coded segmentation map is generated for crucial information retrieval about Venus’ surface feature characteristics. It is found that Venus’ fused image clearly demarked planetary morphological features and validated with publicly available Venus’ radar nomenclature map.
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    An innovative approach for predicting pandemic hotspots in complex wastewater networks using graph theory coupled with fuzzy logic
    (Springer, 2023-05) Srinivas, Rallapalli
    Early prediction of COVID-19 infected communities (potential hotspots) is essential to limit the spread of virus. Diagnostic testing has limitations in big populations because it cannot deliver information at a fast enough rate to stop the spread in its early phases. Wastewater based epidemiology (WBE) experiments showed promising results for brisk detection of ‘SARS CoV-2’ RNA in urban wastewater. However, a systematic and targeted approach to track COVID-19 virus in the complex wastewater networks at a community level is lacking. This research combines graph network (GN) theory with fuzzy logic to determine the chances of a specific community being a COVID-19 hotspot in a wastewater network. To detect 'SARS-CoV-2' RNA, GN divides wastewater network into communities and fuzzy logic-based inference system is used to identify targeted communities. For the propose of tracking, 4000 sample cases from Minnesota (USA) were tested based on various contributing factors. With a probability score of greater than 0.8, 42% of cases were likely to be designated as COVID-19 hotspots based on multiple demographic characteristics. The research enhances the conventional WBE approach through two novel aspects, viz. (1) by integrating graph theory with fuzzy logic for quick prediction of potential hotspot along with its likelihood percentage in a wastewater network, and (2) incorporating the uncertainty associated with COVID-19 contributing factors using fuzzy membership functions. The targeted approach allows for rapid testing and implementation of vaccination campaigns in potential hotspots. Consequently, governmental bodies can be well prepared to check future pandemics and variant spreading in a more planned manner.
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    Experimental Analysis of Energy Efficient Building Air Conditioning System Using Fuzzy Logic Controller
    (AIT, 2009-06) Parameshwaran, R.
    The present work is focused on investigating the thermal comfort and indoor air quality (IAQ) in buildings through the use of energy efficient air conditioning (A/C) system. In this context, a combined variable air volume (VAV) and variable refrigerant volume (VRV) system is developed and tested with different ventilation strategies for summer and winter design conditions. The proposed system is controlled by the intelligent fuzzy logic controller that enhanced the overall system performance. The proposed system is tested under fixed ventilation, demand controlled ventilation (DCV) and combined DCV and economizer cycle (EC) ventilation that ensured better indoor thermal comfort and IAQ without compromising on the energy efficiency. The test results infer that the proposed air conditioning system controlled by fuzzy logic methodology yield a maximum of 34% and 52% of per day energy savings in summer and winter design conditions respectively. The test results for each technique in terms of thermal comfort, IAQ and energy savings potential are presented.
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    Optimization of energy conservation potential for VAV air conditioning system using fuzzy based genetic algorithm
    (World Academy of Science, Engineering and Technology, 2008) Parameshwaran, R.
    The objective of this study is to present the test results of variable air volume (VAV) air conditioning system optimized by two objective genetic algorithm (GA). The objective functions are energy savings and thermal comfort. The optimal set points for fuzzy logic controller (FLC) are the supply air temperature (T s ), the supply duct static pressure (P s ), the chilled water temperature (T w ), and zone temperature (T z ) that is taken as the problem variables. Supply airflow rate and chilled water flow rate are considered to be the constraints. The optimal set point values are obtained from GA process and assigned into fuzzy logic controller (FLC) in order to conserve energy and maintain thermal comfort in real time VAV air conditioning system. A VAV air conditioning system with FLC installed in a software laboratory has been taken for the purpose of energy analysis. The total energy saving obtained in VAV GA optimization system with FLC compared with constant air volume (CAV) system is expected to achieve 31.5%. The optimal duct static pressure obtained through Genetic fuzzy methodology attributes to better air distribution by delivering the optimal quantity of supply air to the conditioned space. This combination enhanced the advantages of uniform air distribution, thermal comfort and improved energy savings potential
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    FUGEN: a tool for the design of layouts for cellular manufacturing systems
    (Inder Science, 2009-05) Sangwan, Kuldip Singh
    Design of layouts for Cellular Manufacturing Systems (CMSs) has not received attention by researchers as much as cell formation in cellular manufacturing. In this paper a mathematical model, formulated as a multicriteria Quadratic Assignment Problem (QAP) is proposed for the design of layouts for CMSs with the objective of minimising the material handling cost (quantitative) and maximising the closeness rating (qualitative). A Genetic Algorithm (GA)-based tool called FUGEN, which makes use of crossover, mutation (inversion) and direct entry (elitism) with embedded fuzzy logic and Analytic Hierarchy Process (AHP) models, is developed for the design of layouts for CMSs. An attempt is made to make the model practical by considering the production volume, transfer batch size and operational sequence of parts. The proposed tool is validated using an example from the literature.
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    A fuzzy-based decision support framework for product recovery process selection in reverse logistics
    (Inder Science, 2016-11) Sangwan, Kuldip Singh
    Owing to the increasing attention to sustainability and green manufacturing as well as the legislations and competition, product recovery management has become an important issue to extend the product's life. It is an environmentally, economically and socially sound way to achieve many of the goals of sustainable development. In this paper, a fuzzy-based multi-criteria decision making framework has been proposed for the evaluation of alternate product recovery processes. The evaluation has been done based on the criteria of operating cost, value added recovery, environmental impact, market demand, technical/operational feasibility, and corporate social responsibility. The five alternative product recovery processes identified in the study are repair, refurbishing, remanufacturing, cannibalising, and recycling. The novelty of the framework is that it takes care of the inherent uncertainties in reverse logistics environment; and managers can provide different weights to different criteria depending upon the company strengths, weaknesses, opportunities and threats in the business environment.
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    Analysis of supply chain vulnerability factors in manufacturing enterprises: a fuzzy DEMATEL approach
    (Taylor & Francis, 2022-06) Sharma, Satyendra Kumar; Routroy, Srikanta
    With an increase in supply chain disruptions, managing supply chain vulnerability has become a key factor to build resilient supply chains. Although supply chain vulnerability and associated factors are mostly studied individually or with selected groups, the supply chain literature lacks comprehensive and systematic studies. The current study aims to identify and segregate the supply chain vulnerability factors for manufacturing enterprises based on causal–effect relationships that exist between them. The supply chain vulnerability factors are identified from the extant literature review. Its applicability in Indian manufacturing enterprises is discussed with experts drawn from industry and academia. The cause–effect relationships of selected supply chain vulnerability factors are analysed using the fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) approach. The obtained results indicate that supply design and supply chain efficiency-related factors are effect factors, whereas supply chain collaboration and information technology-related factors are causal factors. Complexity, centralisation, supplier concentration and low-cost sourcing are found to be the most prominent factors. This research contributes to the literature on supply chain vulnerability by describing the causal relationships among key factors impacting it. It would help managers to develop appropriate disruptions mitigation strategy(s) to make a resilient and robust supply chain.