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Land Cover Clustering for Change Detection using Deep Belief Network

Land Cover Clustering for Change Detection using Deep Belief...

引用

2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Sivasankari, A. Jayalakshmi, S. Vistas Dept. of Computer Science Chennai600117 India Vistas Dept. of Computer Applications Chennai600117 India

ISBN: (纸本)9781665484251

Change detection in recent era plays a major role in the remote sensing areas that includes land cover and water bodies. The application of change detection in remote sensing is considered complicated due to continuous interaction of humans with nature. Further, the change detection in land cover and water bodies plays a major role in global change. Manual change detection process via experts consumes more time to survey the land, whereas detection of changes in land cover and water bodies via an artificial intelligence (AI) model enables faster change detection. However, multi-sensor and multi-temporal satellite data calibration poses a serious problem in remote-sensing application. It makes the entire clustering process a difficult one and hence the accuracy of change detection degrades. In this paper, we apply the task of unsupervised learning to a deep learning model namely deep belief network. Here, the land cover images are clustered using the unlabeled data, where the algorithms are trained in unsupervised manner. The intelligent decisions are made available via deep belief network that clusters the uncategorized the land covers and water bodies images from the satellite image database. The dataset includes Landsat sensors' datasets in dry semi-arid river basin in Western India. The performance of the unsupervised deep belief network is evaluated in terms of various performance metrics that includes accuracy, precision, recall, f-measure, geometric mean, peak signal noise ratio and mean average percentage error. © 2022 IEEE.

关键词： Change detection

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Succeeding with Agile software development

Succeeding with Agile software development

引用

1st International Conference on Advances in Engineering, science and Management, ICAESM-2012

作者： Murugaiyan, M. Sundararajan Balaji, S. Chennai India Computer Science Dept. Indira Institute of Computer Applications Chennai India

ISBN: (纸本)9781467302135

The focus of Agile software development has been on methods and practices. How to take the methods effectively in use has received less attention. Especially in large organization it is not trivial to take agile methods in use. Based on the experiences, a life cycle model that describes phases in a software process improvement project deploying an agile method to an individual project was developed. The model defines the phases and the key decision points in the improvement project. It also defines some alternative processes for projects in different situations. The main benefit of the model is that it gives the big picture of the agile method deployment project. The model helps to understand which steps are needed for initially deploying an agile method and continuously improving the process. © 2012 Pillay Engineering College.

关键词： Software design

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MPCS: A secure Mobile Payment Consortia System for higher educational institutions 09

MPCS: A secure Mobile Payment Consortia System for higher ed...

引用

2nd International Conference on Interaction sciences: Information Technology, Culture and Human, ICIS 2009

作者： Kumar, S.Britto R. Rabara, S.Albert Martin, J.Ronal Dept. of Computer Applications Tiruchirappalli - 620 017 India Dept. of Computer Science Tiruchirappalli - 620 002 India

ISBN: (纸本)9781605587103

The emergence of wireless and mobile networks has made possible the introduction of a new research area M-Commerce or Mobile Commerce. Mobile payment is a natural successor to web centric payments which has emerged as one of the sub domains of mobile commerce applications. A study reveals that there are wide ranges of mobile payment solutions and models which are available with the aid of various services such as Short Message Service (SMS), but there is no specific mobile payment system for educational institutions to collect the fees as well as for student community to pay the fees without huge investment. This paper proposes a secured framework for Mobile Payment Consortia System (MPCS) to carry out the transactions from the bank to the academic institutions for the payment of fees by students through mobile phone. Mobile Payment Consortia System provides an end-to-end security using Public Key Infrastructure (PKI) through a Mobile Information Device Profile (MIDP)-enabled mobile device. This framework provides an efficient, reliable and secured system to perform mobile payment transactions and reduces transactional cost for both students and educational institutions. Mobile Payment Consortia System is designed with strong authentication and non-repudiation by employing digital signatures. Confidentiality and message integrity are also provided by encrypting the messages at application level and by using public key certificates and digital signature envelops. Copyright © 2009 ACM.

关键词： Mobile commerce

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Improved Ensemble Classification Method for Thyroid Disease Using data Mining Technologies 5

Improved Ensemble Classification Method for Thyroid Disease ...

引用

5th International Conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Parimala, S. Vadivu, P.Senthil Srm Institute of Science and Technology Dept. of Computer Science Chennai India Hindustan College of Arts and Science Dept. of Computer Applications Coimbatore India

ISBN: (纸本)9781665415712

data mining plays an important role in various healthcare services. The most complicated task is to identify the disease and diagnosing it at a prompt phase. One of the most important functions of the health-care providers is to diagnose illness and save lives. The main aim of any healthcare domain is to discover and diagnose the disease at premature stages with higher accuracy. For providing the treatment in low cost, data mining is considered as the best technique. In healthcare industry, the usage of automated diagnostic systems has become more common in recent years. These systems offer several advantages in the diagnosis process. This paper will explain the aspects of Feature Engineering to solve the problem of dealing with large data sets. Also, the proposed research work has used the concept of feature engineering to solve the issue of large data sets with the algorithm of Instance Selection that combines the two algorithms of Instance Selection using drop1 algorithm and Instance Selection algorithm using Boosting. © 2021 IEEE.

关键词： data mining

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MotionNet architecture with randomized emission training strategy for adaptive bit rate video compression 6

MotionNet architecture with randomized emission training str...

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6th International Conference on Parallel, Distributed and Grid Computing, PDGC 2020

作者： Sangeeta Gulia, Preeti Maharshi Dayanand University Dept. of Computer Science and Applications Rohtak India

ISBN: (纸本)9781728171326

The traditional methods of video compression are hand designed and fundamentally based on frame prediction strategies. Even though they are working efficiently but significant improvements have been observed by some deep learning based enhancements. In the way, to explore pure deep learning based approaches, the commendable performance motivated further research in the same domain. Adaptive bit rate representation of the videos is preferred over fixed bit representation providing flexibility for adjusting video quality with varying bandwidth in addition to efficient bit utilization. The proposed pure deep learning video compression architecture comprises of frame autoencoder and motion prediction network to propagate motion information for reconstruction of predicted frames. The network was trained with both fixed and random emission steps independently. The experimental results reveal that randomized emission training strategy gives better results for adaptive bit-rate video compression when compared in SSIM, PSNR, EPE and TPF performance parameters. © 2020 IEEE.

关键词： Image compression

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An Advanced Ensemble Directionality Pattern (EDP) based Block Ensemble Neural Network (BENN) Classification Model for Face Recognition System

An Advanced Ensemble Directionality Pattern (EDP) based Bloc...

引用

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Harshitha, C.J. Bharathi, R.K. Jss Science and Technology University Dept. of Computer Applications Mysuru India

ISBN: (纸本)9781665483162

Developing the face detection and recognition system is one of the most demanding and challenging tasks in the recent days. For this purpose, there are different types of image processing techniques have been developed in the conventional works, which are mainly focusing on detecting the facial images under occlusions. Still, it limits with the major problems of high complexity in algorithm design, does not have the ability to handle large dimensional data, requires more time consumption for training the model, and increased misclassification results. Thus, this paper intends to develop an efficient face recognition system for detecting the occluded faces by implementing an advanced image processing techniques using COFW-100 dataset which includes 507 occluded images. Here, the Gaussian filter technique is utilized to improve the overall quality of original image by suppressing the noise/artifacts. After that, an Ensemble Directionality Pattern (EDP) extraction technique is applied to extract the texture features from the normalized image by estimating the similarity between the pixel intensity in different angles of projection plane. It is mainly used to obtain the clear features of the person in the cell of each image frame and, this type of feature extraction helps to increase the accuracy of overall classification system. Then, the Block Ensemble Neural Network (BENN) classification model is deployed to accurately detect the occluded faces by processing the image with separate patterns of features. During experimentation, the performance of both existing and proposed techniques is validated and compared by using various evaluation metrics. Then, the results show that the proposed technique outperforms the other approaches by accurately detecting the face based on the image patterns with separate blocks. © 2022 IEEE.

关键词： Face recognition

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A MMAMF Mitigation Strategy for Overcoming Cyber Vulnerabilties in a Cyber-Physical System 3

A MMAMF Mitigation Strategy for Overcoming Cyber Vulnerabilt...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ahuja, Sidak Rana, Lakshya Wason, Ritika Arora, Parul Hoda, M.N. Arora, Devansh Dept. of Computer Science and Applications Delhi India Sprinklr Gurugram India

ISBN: (纸本)9798331518592

The rise in the frequency and complexity of cyber-attacks against all enterprises alike, has fueled the research on mitigation techniques for cyber-physical vulnerabilities. The 2014 Sony Pictures cyber-attack was a significant event that was unique in its nature, orchestration as well as effects. This manuscript provides a detailed assessment of the cyber physical vulnerabilities exposed by the attackers while assessing the implications for future mitigation strategies. The manuscript also lays an effective mitigation framework to avoid such attacks in the future on any significant, cyberphysical systems. © 2024 IEEE.

关键词： Cyber attacks

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From data to Harvest through Machine Learning based Crop Yield Forecasting 2

From Data to Harvest through Machine Learning based Crop Yie...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Budarapu, Aadithya Vikram Gaddam, John Hyde Boyedi, Ramu Muthyala, Thoshan Kumer Kathirisetty, Nikhila Dept. of Information Technology Hyderabad India Dept. of Computer Science and Data Science Hyderabad India

ISBN: (纸本)9798350370188

The maintenance and enhancement of dynamic soil characteristics are the primary focus of soil management in agriculture to increase crop productivity. Higher productivity may result from efficient soil control of resources and corrective micronutrient treatments. Using CNN and 'KNN' algorithms, the 'soil land classification and crop prediction system' application was created. In this study, two datasets are used: one to obtain crop prediction and the other for soil land categorization. The kind of soils are trained using CNN ('VGG- 19') algorithm, and the accuracy of the model is calculated. The trained model is then utilized in the Flask web app to forecast the type of soil. Another data set with nitrogen, phosphorus, potassium, pH, and temperature as features and the class type of crop as a label is used to forecast crop production. These two algorithms are used to create the flask website, which accepts inputs such as soil picture, soil type prediction, and land parameter inputs for crop prediction. © 2024 IEEE.

关键词： Soils

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An Intelligent LLM-Powered Personalized Assistant for Digital Banking Using LangGraph and Chain of Thoughts 22

An Intelligent LLM-Powered Personalized Assistant for Digita...

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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Easin, Arafat Md Sourav, Saha Tamas, Orosz Data Science and Engineering Dept. Budapest Hungary Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385601

Text-based applications and chatbots are increasingly popular for delivering banking services and educational tools, offering convenient and efficient solutions for users. Whereas, personalized assistants have transformed user engagement in the digital banking space by utilizing Large Language Models (LLMs) in conjunction with autonomous agents. This study proposes the development of an intelligent personalized assistant for digital banking, utilizing a multi-agent framework based on the LangGraph and Chain of Thoughts (COT) prompting. While COT guarantees context-aware replies, the LangGraph design maps characteristics to nodes to improve user interactions. The objectives of this system are to enhance task efficiency and elevate the capabilities of digital banking assistants. We present a customizable digital banking system powered by LLM-based models, designed to deliver an interactive and personalized banking experience. The system supports a range of services, including adding money, transferring funds, paying bills, accessing telco services like mobile recharge, managing savings interest rates, DPS schemes, fixed deposits, and answering FAQs related to banking information. Therefore, integrating COT for logical reasoning enhances the effectiveness of multi-agent systems, as each single agent benefits from the structured reasoning process. In addition, LangGraph is employed for structured data management, enabling the assistant to support and accelerate various digital banking processes efficiently. The code implementation of this work is available for public access at: https://***/srv-sh/digital-agent. © 2024 IEEE.

关键词： Information management

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Locality premised reducer scheduling in Hadoop 5

Locality premised reducer scheduling in Hadoop

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5th International Conference on System Modeling and Advancement in Research Trends, SMART 2016

作者： Fatma, Nusrat Singh, Hemant Kr. Ahmad, Shafeeq Srivastava, Prachi Dept. of Computer Science and Engg Azad IET Lucknow India Dept. of Computer Applications Azad IET Lucknow India

ISBN: (纸本)9781509035434

data is generated in high speed and volume called Big data. Hadoop has ability to handle Big data. Programs get executed in parallel manner in Hadoop. In Hadoop the problem gets divide in two parts namely, map task and reduce task. In the existing Hadoop version map task scheduling premise is the locality of input data lowers the network traffic and hence improve performance of the mappers. But reduce task get scheduled without any consideration of data locality, resulting to poor performance at requesting node. This paper propose a modified reduce task scheduling algorithm on the basis of data locality that will minimize data-local traffic. In the evaluation of algorithm it is observed that up to 80 % of bytes shuffling has reduced in Hadoop clusters. © 2016 IEEE.

关键词： Big data

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