Browsing by Author "Anand Singh"

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Electrical Resistivity and Induced Polarization signatures to delineate the near-surface aquifers contaminated with seawater invasion in Digha, West-Bengal, India
(Elsevier B.V., 2021) Prashant Kumar; Prarabdh Tiwari; Anand Singh; Arkoprovo Biswas; Tapas Acharya
The Digha-Shankarur-Tajpur-Mandarmani (DSTM) area in West-Bengal is well known for seawater invasion in shallow aquifers. In the present work, geophysical investigations such as Electrical Resistivity Imaging (ERI) and Induced Polarization (IP) data were used to delineate the possible zones of shallow aquifers contaminated with seawater. Smoothness-constrained least square and unstructured grid-based finite element methods were applied to interpret ERI data. The finite element method based inversion technique adopted in this study is better in terms of the final resolution of interpretation. Furthermore, the resistivity imaging survey showed low resistive zones (0–5 Ωm) in shallow aquifers. Subsequently, the IP data with low chargeability indicates probable zones of seawater invasion. These affected zones are associated mostly with clay and sandy layers up to a depth of 30 m. Moreover, the geophysical data suggests that the seawater invasion zones do not show any linear relationship throughout the area. This is mainly because the anthropogenic activities have overshadow geogenic activities. Hence, human-induced activities should be restricted in the present study area on the landward side to reduce the hazards related to seawater invasion in the groundwater aquifers in the region. © 2021 Elsevier B.V.
Global particle swarm optimization technique in the interpretation of residual magnetic anomalies due to simple geo-bodies with idealized structure
(Elsevier, 2020) Anand Singh; Arkoprovo Biswas
The Global Particle Swarm Optimization (GPSO) method using a MATLAB programme was developed for the interpretation of residual magnetic anomaly data produced by hidden subsurface bodies with simple geometrical bodies such as spheres, horizontal cylinders, and thin dyke and thin sheet-like structures. Inversion parameters such as the depth of the body, the location of the anomaly, amplitude coefficient, effective magnetization angle, and shape factor were inverted. The GPSO inversion method was tested on noise-free synthetic data, synthetic data with 10% Random noise and 20% Gaussian noise and four field examples were interpreted. The inversion results showed excellent fit with the earlier results got using different inversion and interpretation techniques. The present study also shows that the optimization technique is able to delineate all the model parameters correctly when shape factor is fixed. The computation period for GPSO optimization process is very short (less than 1seconds) for a constrained number of swarm sizes. The GPSO method is enormously fast and does not require any suppositions about the nature of the source of the residual magnetic anomaly. © 2021 Elsevier Inc. All rights reserved.
Performance benchmarking of different convolutional neural network architectures on COVID-19 dataset
(Bentham Science Publishers, 2024) Harsh Kumar Mishra; Anand Singh; Ayushi Rastogi
The utilization of chest X-rays could offer valuable assistance in the initial screening of patients before undergoing RT-PCR testing. This potential approach holds promise within hospital environments grappling with the challenge of categorizing patients for either general ward placement or isolation within designated COVID-19 zones. This study investigates the use of chest X-rays as a preliminary screening technique for suspected COVID-19 cases in hospital settings, given the limited testing capacity and probable delays for RT-PCR testing. We assess how well several neural network architectures perform in automated COVID-19 identification in X-rays with the goal of locating a model that has the highest levels of sensitivity, low latency, and accuracy. The results reveal that InceptionV3 exhibits better robustness while MobileNet obtains the maximum accuracy. This strategy may help healthcare organisations better manage patients and allocate resources optimally, especially when radiologists are hard to come by. This will help in choosing an architecture that has better accuracy, sensitivity, and lower latency. The chosen models are pre-trained using the technique of transfer learning to save computation power and time. After the training and testing of the model, we observed that while MobileNet gave the best accuracy among all the models (VGG16, VGG19, MobileNet and InceptionV3), IncpetionV3 was still better when it comes to robustness. © 2024, Bentham Books imprint. All rights reserved.
Response of potato (Solanum tuberosum l.) to irrigation methods, moisture regimes and integrated nitrogen management
(Indian Society of Agronomy, 2021) Sanjeev Singh; Bhoop Narayan Singh; Adesh Singh; Rakesh Chandra Tiwari; Mohammad Hasanain; Tejbal Singh; Anand Singh; Yakshi Agrawal
A field experiment was conducted in split-plot design during the winter (rabi) season of 2015–16 at Agronomy Research Farm, Kumarganj, Ayodhya (Uttar Pradesh) to evaluate the response of potato (Solanum tubersum L.) to irrigation methods, moisture regimes and nitrogen management. Treatments effect was non-significant on initial plant stand [15 days after planting (DAP)]. The plant height, number of haulms, number of leaves at 90 DAP and dry weight of haulms were significantly superior under regular furrow irrigation method, 1.0 irrigation water: cumulative pan evaporation (IW: CPE) and 75% dose of urea through N + 25% dose of N through FYM over their counterparts. All the growth characters were found highest under nitrogen-management treatment 75% dose of urea through N + 25% dose of N through FYM, which was statistically superior to rest of the nitrogen-management treatments. Yield attributes, viz. number of tubers (grade-wise)/hill, weight of tubers (grade-wise) (kg/plot), and tuber yield (q/ha) were found highest under regular furrow method of irrigation (M1 ) and moisture regime, 1.0 IW: CPE (I2 ) over 0.8 IW: CPE (I1 ) and 1.2 IW: CPE (I3 ). Moisture regimes significantly influenced number of all grade of tubers. All the yield attributes were significantly higher under nitrogen management treatment N2, and was statistically superior to rest of the nitrogen management treatments. © 2021, Indian Society of Agronomy. All rights reserved.