Daphne Lopez - Academia.edu (original) (raw)

Papers by Daphne Lopez

Advances in data mining and database management book series, 2018

Cloud Computing is a new computing model that distributes the computation on a resource pool. The... more Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.

International Journal on Recent and Innovation Trends in Computing and Communication

Interactive and Backtracking applications often require undoing certain recent operations and upd... more Interactive and Backtracking applications often require undoing certain recent operations and updates made to the underlying data structures. The concept of dancing links has made such reverting operations easier and efficient by repeatedly performing unlinking and re-linking of pointers in complex data structures involving circular multiply linked lists. This paper extends the idea of dancing links to XOR linked lists, the memory efficient counterpart of doubly linked lists to develop XDLX, a more space efficient algorithm than DLX to solve exact cover problems without compromising the timing efficiency. Owing to the NP-Complete nature of the exact cover problem, any NP-complete problem can be reduced to it and solved using the proposed memory-efficient dancing links based algorithm, XDLX. The algorithm can be effectively used to solve any backtracking application and will prove to be a significant contribution towards the programming of memory-constrained environments such as embe...

International Journal on Recent and Innovation Trends in Computing and Communication, Feb 6, 2023

Interactive and Backtracking applications often require undoing certain recent operations and upd... more Interactive and Backtracking applications often require undoing certain recent operations and updates made to the underlying data structures. The concept of dancing links has made such reverting operations easier and efficient by repeatedly performing unlinking and re-linking of pointers in complex data structures involving circular multiply linked lists. This paper extends the idea of dancing links to XOR linked lists, the memory efficient counterpart of doubly linked lists to develop XDLX, a more space efficient algorithm than DLX to solve exact cover problems without compromising the timing efficiency. Owing to the NP-Complete nature of the exact cover problem, any NPcomplete problem can be reduced to it and solved using the proposed memory-efficient dancing links based algorithm, XDLX. The algorithm can be effectively used to solve any backtracking application and will prove to be a significant contribution towards the programming of memory-constrained environments such as embedded systems.

Applied Sciences

Catastrophic forgetting is a significant challenge in deep reinforcement learning (RL). To addres... more Catastrophic forgetting is a significant challenge in deep reinforcement learning (RL). To address this problem, researchers introduce the experience replay (ER) concept to complement the training of a deep RL agent. However, the buffer size, experience selection, and experience retention strategies adopted for the ER can negatively affect the agent’s performance stability, especially for complex continuous state action problems. This paper investigates how to address the stability problem using an enhanced ER method that combines a replay policy network, a dual memory, and an alternating transition selection control (ATSC) mechanism. Two frameworks were designed: an experience replay optimisation via alternating transition selection control (ERO-ATSC) without a transition storage control (TSC) and an ERO-ATSC with a TSC. The first is a hybrid of experience replay optimisation (ERO) and dual-memory experience replay (DER) and the second, which has two versions of its kind, integrate...

IGI Global eBooks, 2019

Ambient intelligence is an emerging platform that provides advances in sensors and sensor network... more Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

Health and technology, Jul 20, 2023

Biomedical Research-tokyo, 2017

arXiv (Cornell University), Jun 23, 2023

This paper focuses on studying the impact of climate data and vector larval indices on dengue out... more This paper focuses on studying the impact of climate data and vector larval indices on dengue outbreak. After a comparative study of the various LSTM models, Bidirectional Stacked LSTM network is selected to analyze the time series climate data and health data collected for the state of Tamil Nadu (India), for the period 2014 to 2020. Prediction accuracy of the model is significantly improved by including the mosquito larval index, an indication of VBD control measure.

IGI Global eBooks, 2022

Big Data has been playing a vital role in almost all environments such as healthcare, education, ... more Big Data has been playing a vital role in almost all environments such as healthcare, education, business organizations and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Big data consists of huge unstructured data that require advance real-time analysis. Thus, nowadays many of the researchers are interested in developing advance technologies and algorithms to solve the issues when dealing with big data. Big Data has gained much attention from many private organizations, public sector and research institutes. This chapter provides an overview of the state-of-the-art algorithms for processing big data, as well as the characteristics, applications, opportunities and challenges of big data systems. This chapter also presents the challenges and issues in human computer interaction with big data analytics.

International Journal of Infectious Diseases, 2020

Concurrency and Computation: Practice and Experience, 2020

SummaryInsights from geohash coding algorithms introduce significant opportunities for various sp... more SummaryInsights from geohash coding algorithms introduce significant opportunities for various spatial applications. However, these algorithms require massive storage, complex bit manipulation, and extensive code modification when scaled to higher dimensions. In this article, we have developed a two‐bit geohash coding algorithm that divides the search space into four equal partitions where each partition is assigned a two‐bit label as 00, 01, 10, and 11, which helps to uniquely identify a chosen data point and the two neighbors on its either side, taken along a particular dimension. This salient feature of the algorithm simplifies the generation of geohash code for the neighboring grid cells. In addition, it achieves efficient memory utilization by storing the geohash values of the training points as integers. Demonstrated by experiments for climate data assimilation, model‐to‐observation space mapping with a geohash code length of 24 bits for Lat‐Lon extent of India has shown favor...

India, being an agricultural country analysis of so il texture and water resource plays a vital r... more India, being an agricultural country analysis of so il texture and water resource plays a vital role in increasing the productivity of crops. The area of study is Vellore district in Tamil Nadu where agric ulture is the main occupation. Though it is a fact, most o f the land remains uncultivated due to the lack of knowledge of soil and water wealth. This paper prop oses a method to analyze the suitability of land ba sed on soil and water resource and predicts the yeild. Initially we apply Analytical Hierarchical Process to evaluate the influence of these factors on producti vity. Weighted Overlay Analysis is then used for de riving at the productivity map by imposing the soil and wa ter resource map over the base map. The resultant m ap is used to mine interesting patterns which gives us potential results in the form of rules that has a high societal impact. The Agricultural administrative au thorities can take a decision and help people in converting a barren land into a fertile la...

Feature selection has become an important task for effective application of data mining technique... more Feature selection has become an important task for effective application of data mining techniquesin real-world high dimensional datasets. It is a process that selects a subset of original features by removing irrelevant and redundant features on the basis of the evaluation criteria without loss of information content. A feature selection method helps to reduce computational complexity of learning algorithm, improve prediction performance, better data understanding and reduce data storage space. Feature selectionhas gained more popularity in data mining and machine learning applications. The general procedure of feature selection process and overview of filter, wrapper and embedded method present in literature form the subject matter of this paper. Keyword: Feature Selection, Filter method, Wrapper method and embedded method

Big Data and Software Defined Networks, 2018

Big Data refers to a collection of massive volume of data that cannot be processed by conventiona... more Big Data refers to a collection of massive volume of data that cannot be processed by conventional data processing tools and technologies. In recent years, the data production sources are enlarged noticeably, such as high-end streaming devices, wireless sensor networks, satellite, wearable Internet of Things devices. These data generation sources generate massive amount of data in continuous manner. Nowadays, Big Data analytics plays a significant role in various environments it includes business monitoring, healthcare applications, production development, research and development, share market prediction, business process, industrial applications, social network analysis, weather analysis and environmental monitoring. A data center is a facility composed of networked computers and storage that businesses or other organizations use to process, analyze, store and distribute huge volume of data. In recent years, cloud data centers have been used to store and process the Big Data. This chapter reviews various architectures to store and process the Big Data in cloud data centers. In addition, this chapter also describesthe challenges and applications of Big Data analytics in cloud data centers.

International Journal of Ambient Computing and Intelligence, 2017

Ambient intelligence is an emerging platform that provides advances in sensors and sensor network... more Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

Advances in data mining and database management book series, 2018

Cloud Computing is a new computing model that distributes the computation on a resource pool. The... more Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.

International Journal on Recent and Innovation Trends in Computing and Communication

Interactive and Backtracking applications often require undoing certain recent operations and upd... more Interactive and Backtracking applications often require undoing certain recent operations and updates made to the underlying data structures. The concept of dancing links has made such reverting operations easier and efficient by repeatedly performing unlinking and re-linking of pointers in complex data structures involving circular multiply linked lists. This paper extends the idea of dancing links to XOR linked lists, the memory efficient counterpart of doubly linked lists to develop XDLX, a more space efficient algorithm than DLX to solve exact cover problems without compromising the timing efficiency. Owing to the NP-Complete nature of the exact cover problem, any NP-complete problem can be reduced to it and solved using the proposed memory-efficient dancing links based algorithm, XDLX. The algorithm can be effectively used to solve any backtracking application and will prove to be a significant contribution towards the programming of memory-constrained environments such as embe...

International Journal on Recent and Innovation Trends in Computing and Communication, Feb 6, 2023

Interactive and Backtracking applications often require undoing certain recent operations and upd... more Interactive and Backtracking applications often require undoing certain recent operations and updates made to the underlying data structures. The concept of dancing links has made such reverting operations easier and efficient by repeatedly performing unlinking and re-linking of pointers in complex data structures involving circular multiply linked lists. This paper extends the idea of dancing links to XOR linked lists, the memory efficient counterpart of doubly linked lists to develop XDLX, a more space efficient algorithm than DLX to solve exact cover problems without compromising the timing efficiency. Owing to the NP-Complete nature of the exact cover problem, any NPcomplete problem can be reduced to it and solved using the proposed memory-efficient dancing links based algorithm, XDLX. The algorithm can be effectively used to solve any backtracking application and will prove to be a significant contribution towards the programming of memory-constrained environments such as embedded systems.

Applied Sciences

Catastrophic forgetting is a significant challenge in deep reinforcement learning (RL). To addres... more Catastrophic forgetting is a significant challenge in deep reinforcement learning (RL). To address this problem, researchers introduce the experience replay (ER) concept to complement the training of a deep RL agent. However, the buffer size, experience selection, and experience retention strategies adopted for the ER can negatively affect the agent’s performance stability, especially for complex continuous state action problems. This paper investigates how to address the stability problem using an enhanced ER method that combines a replay policy network, a dual memory, and an alternating transition selection control (ATSC) mechanism. Two frameworks were designed: an experience replay optimisation via alternating transition selection control (ERO-ATSC) without a transition storage control (TSC) and an ERO-ATSC with a TSC. The first is a hybrid of experience replay optimisation (ERO) and dual-memory experience replay (DER) and the second, which has two versions of its kind, integrate...

IGI Global eBooks, 2019

Ambient intelligence is an emerging platform that provides advances in sensors and sensor network... more Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

Health and technology, Jul 20, 2023

Biomedical Research-tokyo, 2017

arXiv (Cornell University), Jun 23, 2023

This paper focuses on studying the impact of climate data and vector larval indices on dengue out... more This paper focuses on studying the impact of climate data and vector larval indices on dengue outbreak. After a comparative study of the various LSTM models, Bidirectional Stacked LSTM network is selected to analyze the time series climate data and health data collected for the state of Tamil Nadu (India), for the period 2014 to 2020. Prediction accuracy of the model is significantly improved by including the mosquito larval index, an indication of VBD control measure.

IGI Global eBooks, 2022

Big Data has been playing a vital role in almost all environments such as healthcare, education, ... more Big Data has been playing a vital role in almost all environments such as healthcare, education, business organizations and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Big data consists of huge unstructured data that require advance real-time analysis. Thus, nowadays many of the researchers are interested in developing advance technologies and algorithms to solve the issues when dealing with big data. Big Data has gained much attention from many private organizations, public sector and research institutes. This chapter provides an overview of the state-of-the-art algorithms for processing big data, as well as the characteristics, applications, opportunities and challenges of big data systems. This chapter also presents the challenges and issues in human computer interaction with big data analytics.

International Journal of Infectious Diseases, 2020

Concurrency and Computation: Practice and Experience, 2020

SummaryInsights from geohash coding algorithms introduce significant opportunities for various sp... more SummaryInsights from geohash coding algorithms introduce significant opportunities for various spatial applications. However, these algorithms require massive storage, complex bit manipulation, and extensive code modification when scaled to higher dimensions. In this article, we have developed a two‐bit geohash coding algorithm that divides the search space into four equal partitions where each partition is assigned a two‐bit label as 00, 01, 10, and 11, which helps to uniquely identify a chosen data point and the two neighbors on its either side, taken along a particular dimension. This salient feature of the algorithm simplifies the generation of geohash code for the neighboring grid cells. In addition, it achieves efficient memory utilization by storing the geohash values of the training points as integers. Demonstrated by experiments for climate data assimilation, model‐to‐observation space mapping with a geohash code length of 24 bits for Lat‐Lon extent of India has shown favor...

India, being an agricultural country analysis of so il texture and water resource plays a vital r... more India, being an agricultural country analysis of so il texture and water resource plays a vital role in increasing the productivity of crops. The area of study is Vellore district in Tamil Nadu where agric ulture is the main occupation. Though it is a fact, most o f the land remains uncultivated due to the lack of knowledge of soil and water wealth. This paper prop oses a method to analyze the suitability of land ba sed on soil and water resource and predicts the yeild. Initially we apply Analytical Hierarchical Process to evaluate the influence of these factors on producti vity. Weighted Overlay Analysis is then used for de riving at the productivity map by imposing the soil and wa ter resource map over the base map. The resultant m ap is used to mine interesting patterns which gives us potential results in the form of rules that has a high societal impact. The Agricultural administrative au thorities can take a decision and help people in converting a barren land into a fertile la...

Feature selection has become an important task for effective application of data mining technique... more Feature selection has become an important task for effective application of data mining techniquesin real-world high dimensional datasets. It is a process that selects a subset of original features by removing irrelevant and redundant features on the basis of the evaluation criteria without loss of information content. A feature selection method helps to reduce computational complexity of learning algorithm, improve prediction performance, better data understanding and reduce data storage space. Feature selectionhas gained more popularity in data mining and machine learning applications. The general procedure of feature selection process and overview of filter, wrapper and embedded method present in literature form the subject matter of this paper. Keyword: Feature Selection, Filter method, Wrapper method and embedded method

Big Data and Software Defined Networks, 2018

Big Data refers to a collection of massive volume of data that cannot be processed by conventiona... more Big Data refers to a collection of massive volume of data that cannot be processed by conventional data processing tools and technologies. In recent years, the data production sources are enlarged noticeably, such as high-end streaming devices, wireless sensor networks, satellite, wearable Internet of Things devices. These data generation sources generate massive amount of data in continuous manner. Nowadays, Big Data analytics plays a significant role in various environments it includes business monitoring, healthcare applications, production development, research and development, share market prediction, business process, industrial applications, social network analysis, weather analysis and environmental monitoring. A data center is a facility composed of networked computers and storage that businesses or other organizations use to process, analyze, store and distribute huge volume of data. In recent years, cloud data centers have been used to store and process the Big Data. This chapter reviews various architectures to store and process the Big Data in cloud data centers. In addition, this chapter also describesthe challenges and applications of Big Data analytics in cloud data centers.

International Journal of Ambient Computing and Intelligence, 2017

Ambient intelligence is an emerging platform that provides advances in sensors and sensor network... more Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.