Papers by Samit Shivadekar
Lecture notes in computer science, 2024
Multimedia tools and applications, Jan 30, 2024
Recent advancements in technology have enabled the storage of voluminous data. As this data is ab... more Recent advancements in technology have enabled the storage of voluminous data. As this data is abundant, there is a need to create summaries that would capture the relevant details of the original source. Since manual summarization is a very taxing process, researchers have been actively trying to automate this process using modern computers that could try to comprehend and generate natural human language. Automated text summarization has been one of the most researched areas in the realm of Natural Language Processing (NLP). Extractive and abstractive summarization are two of the most commonly used techniques for generating summaries. In this study, we present a new methodology that takes the aforementioned summarization techniques into consideration and based on the input, generates a summary that is seemingly better than that generated using a single approach. Further, we have made an attempt to provide this methodology as a service that is deployed on the internet and is remotely accessible from anywhere. This service provided is scalable, fully responsive, and configurable. Next, we also discuss the evaluation process through which we came up with the best model out of many candidate models. Lastly, we conclude by discussing the inferences that we gained out of this study and provide a brief insight into future directions that we could explore.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Jan 2, 2024
International journal of scientific research in science, engineering and technology, Apr 13, 2024
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
Providing climate data records to infer seasonal and interannual variations from multiple heterog... more Providing climate data records to infer seasonal and interannual variations from multiple heterogeneous sources is a challenging data fusion. We combine the Compressive Sensing (CS) and Deep Learning (DL) into a single framework for fusing data from multiple sensors to improve spatial and temporal resolution for long term analysis. CS is used as an initial guess to combine data from multiple sources. DL models, using Long Short-Term Memory Neural Network (LSTM/RNN), Convolutional Neural Network (CNN), refine and further improve the data fusion from CS algorithm's output. The proposed framework has been tested the using daily global observations from two satellites the NASA Orbiting Carbon Observatory-2 (OCO-2) and the JAXA Greenhouse gases from Orbiting Satellites (GOSAT). Our framework achieves lower errors and high correlation compared with the original data. The quality of fused data is evaluated by comparing again AmeriFlux ground station's datasets. Long term trends using fused data over the United States indicate an increase of 8 parts per million (ppm) annually in XCO2 over four years with Root Mean Square Errors of 0.39 ppm and correlation of 0.98 compared with original data. Interannual variability of the seasonal cycle shows an increase in years 2015 - 2017, but a sharp decrease in 2018.
AGU Fall Meeting Abstracts, Dec 1, 2019
International journal of computer applications, Mar 15, 2017
Due to emerging interest in videos, there are various sites which provides with different kinds o... more Due to emerging interest in videos, there are various sites which provides with different kinds of videos but it is not necessary that every video hold original content. Video Copy Detection process comes into picture to differentiate between original and duplicate videos. Video Copy Detection basically deals with finding out similarities between the content of two given videos. Hadoop is a distributed platform which makes use of MapReduce programming model. It has two phases i.e. Mapping and Reducing phase. Brightness Sequence algorithm along with TIRI-DCT algorithm is implemented to overcome the problems in the existing system. OCR is used in order to detect the copied videos based on subtitles or any other form of text present in the video. The framegrabber(), which is a JAVA method, is used to convert the videos into multiple frames at different time instincts.
Aerosols are collections of suspended solid or liquid particles in the gaseous atmosphere, such a... more Aerosols are collections of suspended solid or liquid particles in the gaseous atmosphere, such as dust, sulfates and nitrate molecules, black and organic carbon, sea salt ocean droplets that can absorb and scatter solar radiation, act as nuclei in forming liquid rain and ice droplets in clouds, influence local convective storms, tropical cyclones and can destabilize the planetary boundary layer height (PBLH). Aerosol observations are required on an hourly basis to follow the changes in the PBLH. Multiple satellite-based instruments are becoming available to observe aerosol distributions. However, they still mostly measure total-column quantities or vertical profiles with low-resolution near the ground, limited frequent coverage leading to a difficult
Soft Computing, Jun 15, 2023
International journal of computer applications, May 20, 2015
Enhancement is among the challenging factors in image processing. The goal of enhancement is to e... more Enhancement is among the challenging factors in image processing. The goal of enhancement is to enhance the structural appearance of a picture without the degradation in the input image. The enhancement techniques make the identification of key features easier by eliminating noise and other artifacts within an image. In this paper, we present an overview of image enhancement processing techniques applied on visibility restoration. More specifically, we categorize processing methods based on representative techniques of Image enhancement.
When it entered into the era of big data, Earth observing systems developed into a new stage, nam... more When it entered into the era of big data, Earth observing systems developed into a new stage, namely characterized by low cost, multinational , multi-sensor and multi-modal with varying spatial and spectral resolutions confronting new challenges and opportunities. Climate data records from multiple data sources are used to infer seasonal and interannual variations which will advance and promote the development of data fusion methods. Compressed sensing is a new framework in which data acquisition and data processing are merged. It provides a new fantastic way to handle multiple observations of the same field view from complementary remote sensing instruments, allowing us to recover information at very low signal-to-noise ratio. We will particularly point out that a Compressive Sensing based framework is flexible enough for combining the two measurement systems by fusing the data from the two satellites, NASA Orbiting Carbon Observatory-2 (OCO-2) and the JAXA Greenhouse gases from Orbiting Satellites (GOSAT) to calculate the interannual Net XCO2 variability over land for three latitudinal regions, Alaska/Canada, United States and the Amazon/Brazil. The OCO-2 design is optimized for sensitivity to XCO2 variations, with an unprecedented combination of spatial resolution (about 3km) with narrow nadir coverage, while GOSAT provides broader spatial coverage (10km) with wider scanning coverage. There are different temporal degradations of both instruments over time because GOSAT was launched in 2009 and OCO-2 was launched in 2014. Both instruments infer CO2 concentration from high-resolution measurements of reflected sunlight and use similar inversion algorithms to retrieve CO2 concentrations. Both are passive satellites providing on-orbit global measurements of the greenhouse gas, XCO2, for the years 2015-2018. The results of the CS data fusion framework show that the fused data have Root Mean Square Error (RMSE) varying from 1.31 ppm to 4.12 ppm compared with original data, depending on the region of study and gridding resolution. Validation of fused data compared with AmeriFlux station towers observations shows RMSE of 2.68 ppm.
International Education and Research Journal, Apr 18, 2017
Counterfeit money is imitation currency produced without the legal sanction of the state or gover... more Counterfeit money is imitation currency produced without the legal sanction of the state or government. Producing or using this fake money is a form of fraud or forgery. Counterfeiting is as old as money itself, and is sufficiently prevalent throughout history that it has been called "the world's second oldest profession.. This has led to the increaseof corruption in our country hindering country’s growth. Common man became a scapegoat for the fake currency circulation, let us suppose that a common man went to a bank to deposit money in bank but only to see that some of the notes are fake, in this case he has to take the blame. Counterfeiting, of whatever kind, may be that has been occurring ever since humans grasped the concept of valuable items, and there has been an ongoing race between certifier like (banks, for example) and counterfeiter ever since. Some of the effects that counterfeit money has on society include a reduction in the value of real money; and inflation due to more money getting circulated in the society or economy which in turn dampen our economy and growth - an unauthorized artificial increase in the money supply; a decrease in the acceptability of paper money; and losses. And this Some of the methods to detect fake currency are water marking, optically variable ink, security thread, latent image, techniques like counterfeit detection pen and using MATLAB.
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
Providing climate data records to infer seasonal and interannual variations from multiple heterog... more Providing climate data records to infer seasonal and interannual variations from multiple heterogeneous sources is a challenging data fusion. We combine the Compressive Sensing (CS) and Deep Learning (DL) into a single framework for fusing data from multiple sensors to improve spatial and temporal resolution for long term analysis. CS is used as an initial guess to combine data from multiple sources. DL models, using Long Short-Term Memory Neural Network (LSTM/RNN), Convolutional Neural Network (CNN), refine and further improve the data fusion from CS algorithm's output. The proposed framework has been tested the using daily global observations from two satellites the NASA Orbiting Carbon Observatory-2 (OCO-2) and the JAXA Greenhouse gases from Orbiting Satellites (GOSAT). Our framework achieves lower errors and high correlation compared with the original data. The quality of fused data is evaluated by comparing again AmeriFlux ground station's datasets. Long term trends using fused data over the United States indicate an increase of 8 parts per million (ppm) annually in XCO2 over four years with Root Mean Square Errors of 0.39 ppm and correlation of 0.98 compared with original data. Interannual variability of the seasonal cycle shows an increase in years 2015 - 2017, but a sharp decrease in 2018.
Aerosols are collections of suspended solid or liquid particles in the gaseous atmosphere, such a... more Aerosols are collections of suspended solid or liquid particles in the gaseous atmosphere, such as dust, sulfates and nitrate molecules, black and organic carbon, sea salt ocean droplets that can absorb and scatter solar radiation, act as nuclei in forming liquid rain and ice droplets in clouds, influence local convective storms, tropical cyclones and can destabilize the planetary boundary layer height (PBLH). Aerosol observations are required on an hourly basis to follow the changes in the PBLH. Multiple satellite-based instruments are becoming available to observe aerosol distributions. However, they still mostly measure total-column quantities or vertical profiles with low-resolution near the ground, limited frequent coverage leading to a difficult
International Education and Research Journal, 2017
Counterfeit money is imitation currency produced without the legal sanction of the state or gover... more Counterfeit money is imitation currency produced without the legal sanction of the state or government. Producing or using this fake money is a form of fraud or forgery. Counterfeiting is as old as money itself, and is sufficiently prevalent throughout history that it has been called "the world's second oldest profession.. This has led to the increaseof corruption in our country hindering country’s growth. Common man became a scapegoat for the fake currency circulation, let us suppose that a common man went to a bank to deposit money in bank but only to see that some of the notes are fake, in this case he has to take the blame. Counterfeiting, of whatever kind, may be that has been occurring ever since humans grasped the concept of valuable items, and there has been an ongoing race between certifier like (banks, for example) and counterfeiter ever since. Some of the effects that counterfeit money has on society include a reduction in the value of real money; and inflation ...
50th International Conference on Parallel Processing Workshop, 2021
Our goals are to address challenges such as latency, scalability, throughput and heterogeneous da... more Our goals are to address challenges such as latency, scalability, throughput and heterogeneous data sources of streaming analytics and deep learning pipelines in science sensors and medical imaging applications. We present a prototype Intelligent Parallel Distributed Streaming Framework (IPDSF) that is capable of distributed streaming processing as well as performing distributed deep training in batch mode. IPDSF is designed to run streaming Artificial Intelligent (AI) analytic tasks using data parallelism including partitions of multiple streams of short time sensing data and high-resolution 3D medical images, and fine grain tasks distribution. We will show the implementation of IPDSF for two real world applications, (i) an Air Quality Index based on near real time streaming of aerosol Lidar backscatter and (ii) data generation of Covid-19 Computing Tomography (CT) scans using deep learning. We evaluate the latency, throughput, scalability, and quantitative evaluation of training and prediction compared against a baseline single instance. As the results, IPDSF scales to process thousands of streaming science sensors in parallel for Air Quality Index application. IPDSF uses novel 3D conditional Generative Adversarial Network (cGAN) training using parallel distributed Graphic Processing Units (GPU) nodes to generate realistic 3D high resolution Computed Tomography scans of Covid-19 patient lungs. We will show that IPDSF can reduce cGAN training time linearly with the number of GPUs.
International Journal of Computer Applications, 2017
Due to emerging interest in videos, there are various sites which provides with different kinds o... more Due to emerging interest in videos, there are various sites which provides with different kinds of videos but it is not necessary that every video hold original content. Video Copy Detection process comes into picture to differentiate between original and duplicate videos. Video Copy Detection basically deals with finding out similarities between the content of two given videos. Hadoop is a distributed platform which makes use of MapReduce programming model. It has two phases i.e. Mapping and Reducing phase. Brightness Sequence algorithm along with TIRI-DCT algorithm is implemented to overcome the problems in the existing system. OCR is used in order to detect the copied videos based on subtitles or any other form of text present in the video. The framegrabber(), which is a JAVA method, is used to convert the videos into multiple frames at different time instincts.
International Journal of Computer Applications, 2015
Ubiquitous Search engine is a non-conventional search engine. It is built with an intended functi... more Ubiquitous Search engine is a non-conventional search engine. It is built with an intended function of finding the most influential node in a network or given data set. The objective is to find centers of influence in social networks. It can be used as a tool for data mining and analyzing it further for optimum use of the user's benefit. The system is implemented using Hadoop and Big Data. It aims at increasing the performance of the system and rendering results in fastest possible way by implementing suitable algorithm for the same. Hadoop is used to support parallel computing whi0ch provides a base for simultaneous search on multiple machines. Big data is a large amount of data which can be analyzed and converted into useful information. The data set taken for this project is of 'Twitter', a micro blogging website as it uses a follower relationship rather than friend concept.
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Papers by Samit Shivadekar