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108 results on '"Singla, Anshu"'

4. Load Balancing in Multiprocessor Systems Using Modified Real-Coded Genetic Algorithm

Author

Panwar, Poonam, Kaushal, Chetna, Singla, Anshu, Rattan, Vikas, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Roy, Satyabrata, editor, Sinwar, Deepak, editor, Perumal, Thinagaran, editor, Slowik, Adam, editor, and Tavares, João Manuel R. S., editor

Published
2022
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5. ANN-Based Wheat Crop Yield Prediction Technique for Punjab Region

Author

Bali, Nishu, Singla, Anshu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Shetty, N. R., editor, Patnaik, L. M., editor, Nagaraj, H. C., editor, Hamsavath, Prasad N., editor, and Nalini, N., editor

Published
2022
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7. Analysis of Breast Cancer for Histological Dataset Based on Different Feature Extraction and Classification Algorithms

Author

Kaushal, Chetna, Singla, Anshu, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Gupta, Deepak, editor, Khanna, Ashish, editor, Bhattacharyya, Siddhartha, editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2021
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10. A Study of Factors to Predict At-Risk Students Based on Machine Learning Techniques

Author

Marwaha, Anu, Singla, Anshu, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Choudhury, Sushabhan, editor, Mishra, Ranjan, editor, Mishra, Raj Gaurav, editor, and Kumar, Adesh, editor

Published
2020
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13. Context-Sensitive Thresholding Technique Using ABC for Aerial Images

Author

Kirti, Singla, Anshu, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Wang, Jiacun, editor, Reddy, G. Ram Mohana, editor, Prasad, V. Kamakshi, editor, and Reddy, V. Sivakumar, editor

Published
2019
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18. Soil quality assessment and prediction using various AI techniques: A review.

Author

Bali, Nishu, Singla, Anshu, Chaudhary, Deepika, and Nagpal, Pallavi

Subjects
*SOIL quality, *PLANT water requirements, *ARTIFICIAL intelligence, *DEEP learning, *MACHINE learning
Abstract

Agriculture is one of the most important fields of study as it helps in ensuring food for masses. The study involves various important parameters related to climate and soil. Among these, soil is one of the most important parameter as it acts as the base for fulfilling water and nutrient requirements of the plant. The study of soil characteristics has always been a challenge for the researchers as it exhibits large nonlinear variations both spatially and temporally. The advancements in the field of machine learning and deep learning has provided solutions in the form of diverse algorithms for studying the chemical and physical properties of the soil. The present study provides an extensive review of various machine learning and deep learning models used for studying the physical and chemical characteristics of soils of various regions. [ABSTRACT FROM AUTHOR]

Published
2023
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19. PSO Based Context Sensitive Thresholding Technique for Automatic Image Segmentation

Author

Singla, Anshu, Patra, Swarnajyoti, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Deep, Kusum, editor, Bansal, Jagdish Chand, editor, Das, Kedar Nath, editor, Lal, Arvind Kumar, editor, Garg, Harish, editor, Nagar, Atulya K., editor, and Pant, Millie, editor

Published
2017
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23. Identification of influential environmental factors in wheat crop yield based on feature selection techniques

Author

Bali, Nishu and Singla, Anshu

Abstract

Preharvest crop yield prediction is an important field of agriculture. Timely and precise prediction of crop yield can help the farmers in making right assessment of gains they can expect from their crop and can also help them in prior planning to avoid any crisis. Crop yield is dependent on many controlled and uncontrolled factors. An accurate assessment of these factors in terms of their impact on crop yield can help the farmers in taking steps to increase the produce of their crop. The present study is focused on identifying the influential environmental factors in the study of wheat crop yield prediction. Various feature selection techniques categorized under different feature selection methods: filter methods, wrapper methods and embedded methods were employed to select the most influential factors. The results of feature selection were further validated by using the feature subset generated in random forest prediction model. Among all the feature selection techniques, recursive feature elimination technique under embedded methods was found to be the most efficient feature selection technique. Among the environmental factors, maximum and minimum temperature, relative humidity, evaporation, wind direction and solar radiation were found to be the most prominent and influential factors in relation to wheat crop yield.

Published
2024
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24. Estimating the Concentration of Soil Heavy Metals in Agricultural Areas from AVIRIS Hyperspectral Imagery

Author

Annam, Sangeetha and Singla, Anshu

Subjects
AVIRIS satellite images, Soil HM concentration, Correlated bands, Transfer Learning Models
Abstract

Heavy metal contamination in agricultural soil is currently a global issue. The traditional approaches for soil heavy metal (HM) estimation are insufficient for large-scale and in-time monitoring and assessment. AVIRIS hyperspectral imaging can be utilized in better way to estimate HM concentrations in soil. The authors employed transfer learning model to classify the images, further HM concentration estimation was compared with the actual values. Experimental findings show VGG19 outperformed other deep learning and machine learning models and yielded a consistent accuracy of 81.25% starting from epoch 134 to 200 epochs. The root means square error (RMSE) values of different heavy metals, arsenic (As), cadmium (Cd) and lead (Pb) were found to be 2.89, 0.12, and 0.22 and the mean square value (MSE) value was evaluated to be 0.96, 0.01, and 0.04, respectively. The results of HM estimation proves that the proposed technique is efficient and effective.

Published
2023

27. A Brief Survey of Assessment Models to Predict Stress Level of Heavy Metals in Soil

Author

Annam Sangeetha and Singla Anshu

Subjects
Environmental science, Heavy metals, Soil science, Electrical and Electronic Engineering, Electronic, Optical and Magnetic Materials, Stress level
Abstract

Soil is a major and important natural resource, which not only support human life but also furnish commodities for ecological and economic growth. Ecological risk, such as degradation of soil pose a serious threat to the ecosystem. The high-stress level of heavy metals like chromium, copper, cadmium, etc., produce ecological risks, which include: decrease in the fertility of the soil; reduction in crop yield and degradation of metabolism of living beings, and hence ecological health. The ecological risk associated demands the assessment of heavy metal stress levels in soils. As the rate of stress level of heavy metals is exponentially increasing in recent times, it is apparent to assess or predict heavy metal contamination in soil. The assessment will help the concerned authorities to take corrective as well as preventive measures to enhance the ecological, and hence economic growth. This study reviews the efficient assessment models to predict contamination of soil due to heavy metal.

Published
2021

49. A novel image quality assessment method and coefficient of quality for digital solutions of colour blindness.

Author

S, Meenakshi and Singla, Anshu

Subjects
*IMAGE quality analysis, *IMAGE quality in imaging systems, *DIGITAL image processing, *COLOR blindness, *ALGORITHMS
Abstract

Eyesight is one of the primary senses that human beings have. Reports show that colour blindness, a form of colour vision deficiency (CVD), affects about 8% of the male population and 0.5% of female population. The Assistive Technology Act of 2004 lays focus on technologies that help individuals with disabilities and deficiencies. With the rapid advancement in technologies, several assistive solutions are available for visually impaired or CVD patients. Such solutions involve simulation and compensation of conflicting colours to help the colour blind in the visual perception of colours. Given the increased usage of the web, post the pandemic, these solutions improve the quality of life for the colour blind. Defining the image quality assessment criteria for such digital solutions becomes imperative. The study proposes a novel method for image quality assessment of digital solutions aimed at assisting the colour blind users. The proposed coefficient of quality (CQ) would be useful to rank colour compensation and recolouring algorithms. Experiments were conducted with a novel questionnaire set designed for this quality measurement. The results affirm the efficiency of the assessment method proposed. This will also provide objective feedback to the researchers and experts in this area to improve their solutions for CVD patients. [ABSTRACT FROM AUTHOR]

Published
2022
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