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Das S, Obaidullah SM, Mahmud M, Kaiser MS, Roy K, Saha CK, Goswami K. A machine learning pipeline to classify foetal heart rate deceleration with optimal feature set. Sci Rep 2023; 13:2495. [PMID: 36781920 PMCID: PMC9925757 DOI: 10.1038/s41598-023-27707-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 01/06/2023] [Indexed: 02/15/2023] Open
Abstract
Deceleration is considered a commonly practised means to assess Foetal Heart Rate (FHR) through visual inspection and interpretation of patterns in Cardiotocography (CTG). The precision of deceleration classification relies on the accurate estimation of corresponding event points (EP) from the FHR and the Uterine Contraction Pressure (UCP). This work proposes a deceleration classification pipeline by comparing four machine learning (ML) models, namely, Multilayer Perceptron (MLP), Random Forest (RF), Naïve Bayes (NB), and Simple Logistics Regression. Towards an automated classification of deceleration from EP using the pipeline, it systematically compares three approaches to create feature sets from the detected EP: (1) a novel fuzzy logic (FL)-based approach, (2) expert annotation by clinicians, and (3) calculated using National Institute of Child Health and Human Development guidelines. The classification results were validated using different popular statistical metrics, including receiver operating characteristic curve, intra-class correlation coefficient, Deming regression, and Bland-Altman Plot. The highest classification accuracy (97.94%) was obtained with MLP when the EP was annotated with the proposed FL approach compared to RF, which obtained 63.92% with the clinician-annotated EP. The results indicate that the FL annotated feature set is the optimal one for classifying deceleration from FHR.
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Affiliation(s)
- Sahana Das
- West Bengal State University, Kolkata, 700126, India
| | | | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Nottingham, NG11 8NS, UK.
| | | | - Kaushik Roy
- West Bengal State University, Kolkata, 700126, India
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Lasker A, Ghosh M, Obaidullah SM, Chakraborty C, Roy K. LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery. Multimed Tools Appl 2022; 82:21801-21823. [PMID: 36532598 PMCID: PMC9734972 DOI: 10.1007/s11042-022-14247-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/18/2022] [Accepted: 11/04/2022] [Indexed: 05/27/2023]
Abstract
Automatic detection of lung diseases using AI-based tools became very much necessary to handle the huge number of cases occurring across the globe and support the doctors. This paper proposed a novel deep learning architecture named LWSNet (Light Weight Stacking Network) to separate Covid-19, cold pneumonia, and normal chest x-ray images. This framework is based on single, double, triple, and quadruple stack mechanisms to address the above-mentioned tri-class problem. In this framework, a truncated version of standard deep learning models and a lightweight CNN model was considered to conviniently deploy in resource-constraint devices. An evaluation was conducted on three publicly available datasets alongwith their combination. We received 97.28%, 96.50%, 97.41%, and 98.54% highest classification accuracies using quadruple stack. On further investigation, we found, using LWSNet, the average accuracy got improved from individual model to quadruple model by 2.31%, 2.55%, 2.88%, and 2.26% on four respective datasets.
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Affiliation(s)
- Asifuzzaman Lasker
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Mridul Ghosh
- Department of Computer Science, Shyampur Siddheswari Mahavidyalaya, Howrah, India
| | - Sk Md Obaidullah
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | | | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Barasat, India
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Lasker A, Ghosh M, Obaidullah SM, Chakraborty C, Roy K. LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery. Multimed Tools Appl 2022; 82:1-23. [PMID: 36532598 DOI: 10.1007/s11042-022-13740-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/18/2022] [Accepted: 11/04/2022] [Indexed: 05/23/2023]
Abstract
Automatic detection of lung diseases using AI-based tools became very much necessary to handle the huge number of cases occurring across the globe and support the doctors. This paper proposed a novel deep learning architecture named LWSNet (Light Weight Stacking Network) to separate Covid-19, cold pneumonia, and normal chest x-ray images. This framework is based on single, double, triple, and quadruple stack mechanisms to address the above-mentioned tri-class problem. In this framework, a truncated version of standard deep learning models and a lightweight CNN model was considered to conviniently deploy in resource-constraint devices. An evaluation was conducted on three publicly available datasets alongwith their combination. We received 97.28%, 96.50%, 97.41%, and 98.54% highest classification accuracies using quadruple stack. On further investigation, we found, using LWSNet, the average accuracy got improved from individual model to quadruple model by 2.31%, 2.55%, 2.88%, and 2.26% on four respective datasets.
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Affiliation(s)
- Asifuzzaman Lasker
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Mridul Ghosh
- Department of Computer Science, Shyampur Siddheswari Mahavidyalaya, Howrah, India
| | - Sk Md Obaidullah
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | | | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Barasat, India
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Lasker A, Obaidullah SM, Chakraborty C, Roy K. Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review. SN Comput Sci 2022; 4:65. [PMID: 36467853 PMCID: PMC9702883 DOI: 10.1007/s42979-022-01464-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 10/18/2022] [Indexed: 11/26/2022]
Abstract
Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.
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Affiliation(s)
- Asifuzzaman Lasker
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Sk Md Obaidullah
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Chandan Chakraborty
- Department of Computer Science & Engineering, National Institute of Technical Teachers’ Training & Research Kolkata, Kolkata, India
| | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Barasat, India
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Ghosh M, Obaidullah SM, Gherardini F, Zdimalova M. Classification of Geometric Forms in Mosaics Using Deep Neural Network. J Imaging 2021; 7:149. [PMID: 34460785 PMCID: PMC8404919 DOI: 10.3390/jimaging7080149] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/15/2021] [Accepted: 08/15/2021] [Indexed: 11/23/2022] Open
Abstract
The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.
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Affiliation(s)
- Mridul Ghosh
- Department of Computer Science, Shyampur Siddheswari Mahavidyalaya, Howrah 711312, India;
- Department of Computer Science & Engineering, Aliah University, Kolkata 700160, India;
| | - Sk Md Obaidullah
- Department of Computer Science & Engineering, Aliah University, Kolkata 700160, India;
| | - Francesco Gherardini
- Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, 41121 Modena, Italy
| | - Maria Zdimalova
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, 810 05 Bratislava, Slovakia;
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Mukherjee H, Ghosh S, Dhar A, Obaidullah SM, Santosh KC, Roy K. Shallow Convolutional Neural Network for COVID-19 Outbreak Screening Using Chest X-rays. Cognit Comput 2021:1-14. [PMID: 33564340 PMCID: PMC7863062 DOI: 10.1007/s12559-020-09775-9] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/29/2020] [Indexed: 12/18/2022]
Abstract
Among radiological imaging data, Chest X-rays (CXRs) are of great use in observing COVID-19 manifestations. For mass screening, using CXRs, a computationally efficient AI-driven tool is the must to detect COVID-19-positive cases from non-COVID ones. For this purpose, we proposed a light-weight Convolutional Neural Network (CNN)-tailored shallow architecture that can automatically detect COVID-19-positive cases using CXRs, with no false negatives. The shallow CNN-tailored architecture was designed with fewer parameters as compared to other deep learning models. The shallow CNN-tailored architecture was validated using 321 COVID-19-positive CXRs. In addition to COVID-19-positive cases, another set of non-COVID-19 5856 cases (publicly available, source: Kaggle) was taken into account, consisting of normal, viral, and bacterial pneumonia cases. In our experimental tests, to avoid possible bias, 5-fold cross-validation was followed, and both balanced and imbalanced datasets were used. The proposed model achieved the highest possible accuracy of 99.69%, sensitivity of 1.0, where AUC was 0.9995. Furthermore, the reported false positive rate was only 0.0015 for 5856 COVID-19-negative cases. Our results stated that the proposed CNN could possibly be used for mass screening. Using the exact same set of CXR collection, the current results were better than other deep learning models and major state-of-the-art works.
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Affiliation(s)
- Himadri Mukherjee
- Department of Computer Science, West Bengal State University, Kolkata, India
| | | | - Ankita Dhar
- Department of Computer Science, West Bengal State University, Kolkata, India
| | - Sk Md Obaidullah
- Department of Computer Science and Engineering, Aliah University, Kolkata, India
| | - K. C. Santosh
- Department of Computer Science, The University of South Dakota, Vermillion, SD 57069 USA
| | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Kolkata, India
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Mukherjee H, Ghosh S, Dhar A, Obaidullah SM, Santosh KC, Roy K. Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays. APPL INTELL 2020; 51:2777-2789. [PMID: 34764562 PMCID: PMC7646727 DOI: 10.1007/s10489-020-01943-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2020] [Indexed: 12/24/2022]
Abstract
Since December 2019, the novel COVID-19's spread rate is exponential, and AI-driven tools are used to prevent further spreading [1]. They can help predict, screen, and diagnose COVID-19 positive cases. Within this scope, imaging with Computed Tomography (CT) scans and Chest X-rays (CXRs) are widely used in mass triage situations. In the literature, AI-driven tools are limited to one data type either CT scan or CXR to detect COVID-19 positive cases. Integrating multiple data types could possibly provide more information in detecting anomaly patterns due to COVID-19. Therefore, in this paper, we engineered a Convolutional Neural Network (CNN) -tailored Deep Neural Network (DNN) that can collectively train/test both CT scans and CXRs. In our experiments, we achieved an overall accuracy of 96.28% (AUC = 0.9808 and false negative rate = 0.0208). Further, major existing DNNs provided coherent results while integrating CT scans and CXRs to detect COVID-19 positive cases.
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Affiliation(s)
- Himadri Mukherjee
- Department of Computer Science, West Bengal State University, West Bengal, India
| | | | - Ankita Dhar
- Department of Computer Science, West Bengal State University, West Bengal, India
| | - Sk Md Obaidullah
- Department of Computer Science, Engineering, Aliah University, Kolkata, India
| | - K C Santosh
- Department of Computer Science, The University of South Dakota, Vermillion, SD USA
| | - Kaushik Roy
- Department of Computer Science, West Bengal State University, West Bengal, India
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Das S, Obaidullah SM, Santosh KC, Roy K, Saha CK. Cardiotocograph-based labor stage classification from uterine contraction pressure during ante-partum and intra-partum period: a fuzzy theoretic approach. Health Inf Sci Syst 2020; 8:16. [PMID: 32257127 DOI: 10.1007/s13755-020-00107-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 03/13/2020] [Indexed: 12/01/2022] Open
Abstract
Computerized techniques for Cardiotocograph (CTG) based labor stage classification would support obstetrician for advance CTG analysis and would improve their predictive power for fetal heart rate (FHR) monitoring. Intrapartum fetal monitoring is necessary as it can detect the event, which ultimately leads to hypoxic ischemic encephalopathy, cerebral palsy or even fetal demise. To bridge this gap, in this paper, we propose an automated decision support system that will help the obstetrician identify the status of the fetus during ante-partum and intra-partum period. The proposed algorithm takes 30 min of 275 Cardiotocograph data and applies a fuzzy-rule based approach for identification and classification of labor from 'toco' signal. Since there is no gold standard to validate the outcome of the proposed algorithm, the authors used various statistical means to establish the cogency of the proposed algorithm and the degree of agreement with visual estimation were using Bland-Altman plot, Fleiss kappa (0.918 ± 0.0164 at 95% CI) and Kendall's coefficient of concordance (W = 0.845). Proposed method was also compared against some standard machine learning classifiers like SVM, Random Forest and Naïve Bayes using weighted kappa (0.909), Bland-Altman plot (Limits of Agreement 0.094 to 0.0155 at 95% CI) and AUC-ROC (0.938). The proposed algorithm was found to be as efficient as visual estimation compared to the standard machine learning algorithms and thus can be incorporated into the automated decision support system.
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Affiliation(s)
- Sahana Das
- 1Department of Computer Science, West Bengal State University, Kolkata, 700124 West Bengal India
| | - Sk Md Obaidullah
- 2Department of Computer Science & Engineering, Aliah University, Kolkata, 700156 India
| | - K C Santosh
- 3Department of Computer Science, The University of South Dakota, Vermillion, SD USA
| | - Kaushik Roy
- 1Department of Computer Science, West Bengal State University, Kolkata, 700124 West Bengal India
| | - Chanchal Kumar Saha
- Department of Obstetrics & Gynecology, Biraj Mohini Matri-Sadan & Hospital, Kolkata, West Bengal 700122 India
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Ukil S, Ghosh S, Obaidullah SM, Santosh KC, Roy K, Das N. Improved word-level handwritten Indic script identification by integrating small convolutional neural networks. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04111-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Mukherjee H, Obaidullah SM, Santosh KC, Phadikar S, Roy K. A lazy learning-based language identification from speech using MFCC-2 features. INT J MACH LEARN CYB 2019. [DOI: 10.1007/s13042-019-00928-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Obaidullah SM, Goswami C, Santosh KC, Das N, Halder C, Roy K. Separating Indic Scripts with matra for Effective Handwritten Script Identification in Multi-Script Documents. INT J PATTERN RECOGN 2017. [DOI: 10.1142/s0218001417530032] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We present a novel approach for separating Indic scripts with ‘matra’, which is used as a precursor to advance and/or ease subsequent handwritten script identification in multi-script documents. In our study, among state-of-the-art features and classifiers, an optimized fractal geometry analysis and random forest are found to be the best performer to distinguish scripts with ‘matra’ from their counterparts. For validation, a total of 1204 document images are used, where two different scripts with ‘matra’: Bangla and Devanagari are considered as positive samples and the other two different scripts: Roman and Urdu are considered as negative samples. With this precursor, an overall script identification performance can be advanced by more than 5.13% in accuracy and 1.17 times faster in processing time as compared to conventional system.
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Affiliation(s)
- Sk Md Obaidullah
- Departmemt of Computer Science & Engineering, Aliah University Kolkata, West Bengal, India
| | - Chitrita Goswami
- Departmemt of Computer Science & Engineering, Aliah University Kolkata, West Bengal, India
| | - K. C. Santosh
- Department of Computer Science, The University of South Dakota, SD, USA
| | - Nibaran Das
- Department of Computer Science & Engineering, Jadavpur University, Kolkata, India
| | - Chayan Halder
- Department of Computer Science, West Bengal State University, Kolkata, India
| | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Kolkata, India
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Obaidullah SM, Halder C, Das N, Roy K. A new dataset of word-level offline handwritten numeral images from four official Indic scripts and its benchmarking using image transform fusion. IJIEI 2016. [DOI: 10.1504/ijiei.2016.074497] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Chaturvedi RK, Prasad S, Rana S, Obaidullah SM, Pandey V, Singh H. Effect of dust load on the leaf attributes of the tree species growing along the roadside. Environ Monit Assess 2013; 185:383-91. [PMID: 22367367 DOI: 10.1007/s10661-012-2560-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2011] [Accepted: 01/31/2012] [Indexed: 05/22/2023]
Abstract
Dust is considered as one of the most widespread air pollutants. The objective of the study was to analyse the effect of dust load (DL) on the leaf attributes of the four tree species planted along the roadside at a low pollution Banaras Hindu University (BHU) campus and a highly polluted industrial area (Chunar, Mirzapur) of India. The studied leaf attributes were: leaf area, specific leaf area (SLA), relative water content (RWC), leaf nitrogen content (LNC), leaf phosphorus content (LPC), chlorophyll content (Chl), maximum stomatal conductance (Gs(max)), maximum photosynthetic rate (A (max)) and intrinsic water-use efficiency (WUEi). Results showed significant effect of sites and species for DL and the leaf attributes. Average DL across the four tree species was greater at Chunar, whereas, the average values of leaf attributes were greater at the BHU campus. Maximum DL was observed for Tectona grandis at Chunar site and minimum for Syzygium cumini at BHU campus. Across the two sites, maximum value of SLA, Chl and Gs(max) were exhibited by S. cumini, whereas, the greatest value of RWC, LNC, LPC, A (max) and WUEi were observed in Anthocephalus cadamba. A. cadamba and S. cumini exhibited 28 and 27 times more dust accumulation, respectively, at the most polluted Chunar site as compared to the BHU campus. They also exhibited less reduction in A (max) due to dust deposition as compared to the other two species. Therefore, both these species may be promoted for plantation along the roadside of the sites having greater dust deposition.
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Affiliation(s)
- R K Chaturvedi
- Ecosystems Analysis Laboratory, Department of Botany, Banaras Hindu University, Varanasi 221005, India
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