The demand for many agricultural tracking applications is timely and reliable information on crop... more The demand for many agricultural tracking applications is timely and reliable information on crop quality and development. The most widely used source that yields forecasts and appraisal is coarse spatial resolution satellite image as it provides coverage and temporal resolution at local and national scales. For measuring the quantity of green biomass in agricultural areas, NDVI is used to monitor crop stress, and NDWI to monitor drought stress. Precision agriculture, forest health monitoring, and other applications where the state of plants is sensitive to changes in water content are all examples of where NDWI is widely used. In this paper, the main focus is to extract complete information on the cultivation and harvesting of wheat crops for proper field monitoring. In total, five Sentinel-2A images from January 2020 to May 2020 have been downloaded and processed to monitor the status of wheat crop cultivation, harvesting, and the post-harvesting cycle. Therefore, by extracting meaningful information on the cultivation and harvesting process of the wheat crop, a sustainable approach is identified for developing a Machine Learning (ML)-based Smart Agriculture System. The proposed framework will help to monitor crop health and crop growth and will provide predictions on crop stress and drought stress. ML-based solutions are currently addressing crop-specific issues; however, farming methods will move to knowledge-based agriculture as automated data recording, data analysis, and decision-making are combined into an integrated framework, which will enhance productivity levels and product quality.
International Journal of Innovative Technology and Exploring Engineering, 2020
Mango is a very important fruit which is liked by majority of the population due to its nutrition... more Mango is a very important fruit which is liked by majority of the population due to its nutritional value and excellent taste. India is the largest producer of mango in the world. Accurate information is required for policy decision making in terms of providing subsidy, area expansion, and crop insurance planning. Hence, this type of information may be retrieve through satellite images by using the image classification techniques, which are playing a crucial role in crop cover classification, yield prediction and crop monitoring etc. Classification of optical satellite images is still a challenging task due to effect of changing atmospheric conditions such as cloud, snow, haze, dust, fog, and rain etc. In this paper, knowledge based decision tree classification (DTC) has been proposed to classify the mango orchards of Lucknow district using multi-temporal Landsat 8 operational land imager (OLI) images from year 2015 to 2017 and further mango orchard area were also estimated. In orde...
Floods in India is among the perilous natural disasters with a high impact on its economic sector... more Floods in India is among the perilous natural disasters with a high impact on its economic sectors. One of the critical factors to handle such hazardous events is monitoring the affected areas and changes in flood patterns. Flood management is a very complex issue, largely owing to the growing population and investments in flood-affected regions. Satellite images especially Synthetic Aperture Radar (SAR) images are very useful and effective because SAR images are acquired day and night in all types of weather conditions. This research analyzes a combination of machine learning algorithms implemented on Sentinel-1A (SAR) data using supervised classification techniques to monitor the flooded areas in the North Indian region. Random Forest (RF) and the K-nearest neighbour (KNN) classification is applied to classify the different land covers such as water bodies, land, vegetation, and bare soil land covers. The outcomes of the presented work depict that the SAR data provides efficient i...
Internet of Medical Things is the interconnectivity of highly sophisticated medical grade devices... more Internet of Medical Things is the interconnectivity of highly sophisticated medical grade devices and software applications to work together and provide a new improved version of health care environment in this era. Wearable and standalone devices like Fitbit, remote patient monitoring system use sensor technology to exchange medical information of patients from remote areas to health workforces’ over wireless network. Apart from being advantageous IoMT faces challenges like safety, cyber security and scalability, interoperability, verification and validation. Interoperability is a facility through which m-health devices, databases share and maintains health records seamlessly. Interoperability basically deals with the exchange, interpretation and usage of data delivered through different computerized gadgets without restrictions. Currently there are numerous government-certified EHR products each of which has different technical, clinical and serviceable capabilities. Being heterogeneous in nature it becomes hard to achieve a single standard interoperability format to exchange data.
International Journal of Innovative Technology and Exploring Engineering, 2020
Coal fires, also known as subsurface fires or hot spots are all-inclusive issues in coal mines ev... more Coal fires, also known as subsurface fires or hot spots are all-inclusive issues in coal mines everywhere throughout the globe. Aimless mining over a period of past 100 years has prompted large scale damages to the ecosystem of the earth. For example, debasement in nature of water, soil, air, vegetation dissemination and variations in land topography have caused degradation. Research is needed to be more attentive on developing the prospective use of the satellite image analysis for hot spot detection because ground-based hot spots monitoring is time-taking, complex, cumbrous and very expensive. In this paper, a two-stage model has been developed to extract the hot spot delineated boundaries in Jharia coal field (JCF) region. In the first stage, contextual thresholding (CT) technique has been used to classify the hot spot and non-hot spot regions. After thorough processing, hot spots regions have been retrieved and for performance evaluation sensitivity and specificity are calculate...
Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images i... more Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.
The demand for many agricultural tracking applications is timely and reliable information on crop... more The demand for many agricultural tracking applications is timely and reliable information on crop quality and development. The most widely used source that yields forecasts and appraisal is coarse spatial resolution satellite image as it provides coverage and temporal resolution at local and national scales. For measuring the quantity of green biomass in agricultural areas, NDVI is used to monitor crop stress, and NDWI to monitor drought stress. Precision agriculture, forest health monitoring, and other applications where the state of plants is sensitive to changes in water content are all examples of where NDWI is widely used. In this paper, the main focus is to extract complete information on the cultivation and harvesting of wheat crops for proper field monitoring. In total, five Sentinel-2A images from January 2020 to May 2020 have been downloaded and processed to monitor the status of wheat crop cultivation, harvesting, and the post-harvesting cycle. Therefore, by extracting meaningful information on the cultivation and harvesting process of the wheat crop, a sustainable approach is identified for developing a Machine Learning (ML)-based Smart Agriculture System. The proposed framework will help to monitor crop health and crop growth and will provide predictions on crop stress and drought stress. ML-based solutions are currently addressing crop-specific issues; however, farming methods will move to knowledge-based agriculture as automated data recording, data analysis, and decision-making are combined into an integrated framework, which will enhance productivity levels and product quality.
International Journal of Innovative Technology and Exploring Engineering, 2020
Mango is a very important fruit which is liked by majority of the population due to its nutrition... more Mango is a very important fruit which is liked by majority of the population due to its nutritional value and excellent taste. India is the largest producer of mango in the world. Accurate information is required for policy decision making in terms of providing subsidy, area expansion, and crop insurance planning. Hence, this type of information may be retrieve through satellite images by using the image classification techniques, which are playing a crucial role in crop cover classification, yield prediction and crop monitoring etc. Classification of optical satellite images is still a challenging task due to effect of changing atmospheric conditions such as cloud, snow, haze, dust, fog, and rain etc. In this paper, knowledge based decision tree classification (DTC) has been proposed to classify the mango orchards of Lucknow district using multi-temporal Landsat 8 operational land imager (OLI) images from year 2015 to 2017 and further mango orchard area were also estimated. In orde...
Floods in India is among the perilous natural disasters with a high impact on its economic sector... more Floods in India is among the perilous natural disasters with a high impact on its economic sectors. One of the critical factors to handle such hazardous events is monitoring the affected areas and changes in flood patterns. Flood management is a very complex issue, largely owing to the growing population and investments in flood-affected regions. Satellite images especially Synthetic Aperture Radar (SAR) images are very useful and effective because SAR images are acquired day and night in all types of weather conditions. This research analyzes a combination of machine learning algorithms implemented on Sentinel-1A (SAR) data using supervised classification techniques to monitor the flooded areas in the North Indian region. Random Forest (RF) and the K-nearest neighbour (KNN) classification is applied to classify the different land covers such as water bodies, land, vegetation, and bare soil land covers. The outcomes of the presented work depict that the SAR data provides efficient i...
Internet of Medical Things is the interconnectivity of highly sophisticated medical grade devices... more Internet of Medical Things is the interconnectivity of highly sophisticated medical grade devices and software applications to work together and provide a new improved version of health care environment in this era. Wearable and standalone devices like Fitbit, remote patient monitoring system use sensor technology to exchange medical information of patients from remote areas to health workforces’ over wireless network. Apart from being advantageous IoMT faces challenges like safety, cyber security and scalability, interoperability, verification and validation. Interoperability is a facility through which m-health devices, databases share and maintains health records seamlessly. Interoperability basically deals with the exchange, interpretation and usage of data delivered through different computerized gadgets without restrictions. Currently there are numerous government-certified EHR products each of which has different technical, clinical and serviceable capabilities. Being heterogeneous in nature it becomes hard to achieve a single standard interoperability format to exchange data.
International Journal of Innovative Technology and Exploring Engineering, 2020
Coal fires, also known as subsurface fires or hot spots are all-inclusive issues in coal mines ev... more Coal fires, also known as subsurface fires or hot spots are all-inclusive issues in coal mines everywhere throughout the globe. Aimless mining over a period of past 100 years has prompted large scale damages to the ecosystem of the earth. For example, debasement in nature of water, soil, air, vegetation dissemination and variations in land topography have caused degradation. Research is needed to be more attentive on developing the prospective use of the satellite image analysis for hot spot detection because ground-based hot spots monitoring is time-taking, complex, cumbrous and very expensive. In this paper, a two-stage model has been developed to extract the hot spot delineated boundaries in Jharia coal field (JCF) region. In the first stage, contextual thresholding (CT) technique has been used to classify the hot spot and non-hot spot regions. After thorough processing, hot spots regions have been retrieved and for performance evaluation sensitivity and specificity are calculate...
Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images i... more Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.
We are delighted to intimate you that after the resounding success of past fifteen editions of IN... more We are delighted to intimate you that after the resounding success of past fifteen editions of INDIACom, since its inception in 2007; INDIACom – 2023; 17th INDIACom – 2023 10th International Conference on Computing for Sustainable Global Development, Technically approved by IEEE Delhi Section and supported and promoted by GGSIP University, New Delhi, IETE Delhi Centre, ISTE Delhi Section, CSI Div – II, IV & CSI Delhi Chapter and IET Delhi Local Networks to be held during 15th – 17th March, 2023 at Bharati Vidyapeeth, New Delhi (INDIA). On the eve of the occasion of INDIACom-2023 a special session on “Green ICT: Sustainable Development Goals (SDGs) Roadmap” will be conducted by Dr. Mohammad Faisal as session chairperson and Dr Tasneem Ahmed as session Co-Chair. Please visit http://www.bvicam.ac.in/indiacom/specialSessions.asp for more details.
This book aims to create awareness of the information threats that these technologies play on per... more This book aims to create awareness of the information threats that these technologies play on personal, societal, business and governmental levels. It will provide a platform to discuss the development of Information and Communication Technologies (ICT), its connection with the cyber revolution and the impact that it has on every facets of humans’ lives. This awareness, knowledge gained through discussions, and understanding the security concerns will help create a more secure cyberspace. This will be an essential reference source which will offer further research opportunities by going through the existing literature and finding the research gaps to help formulate policies and concepts related to Cyberology/cyberspace.
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