Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234509
S. Bhattacharjee, Yumnam Jayanta Singh, D. Ray
Machine learning classifiers help physicians to make near-perfect diagnoses, minimizing costs and time. Since medical data usually contains a high degree of uncertainty and ambiguity, proper ordering and classification require a proper comparative performance analysis of machine learning classifiers. Machine learning classifiers are applied on the Ovarian Cancer Dataset. Ovarian cancer is the fifth leading cause of cancer-related death among women, and is the deadliest of gynecological cancers. The mortality rate of ovarian cancer ranks first. Thus, early diagnosis and treatment are critical for improving the patients' cure rate and prolonging their survival. Here we have investigated Mass spectrometry (MS) field data to develop a computer-aided system for the purpose. Using machine learning techniques, data is classified in different categories to identify benign and malignant cancerous cells and a comparative study has been done to identify the most suitable technique under different operational conditions and datasets. Our Comparative studies show that the Multilayer Perceptron (MLP) is the best options for such detection considering its performance metrics such as Accuracy, Sensitivity, Specificity and Errors
{"title":"Comparative performance analysis of machine learning classifiers on ovarian cancer dataset","authors":"S. Bhattacharjee, Yumnam Jayanta Singh, D. Ray","doi":"10.1109/ICRCICN.2017.8234509","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234509","url":null,"abstract":"Machine learning classifiers help physicians to make near-perfect diagnoses, minimizing costs and time. Since medical data usually contains a high degree of uncertainty and ambiguity, proper ordering and classification require a proper comparative performance analysis of machine learning classifiers. Machine learning classifiers are applied on the Ovarian Cancer Dataset. Ovarian cancer is the fifth leading cause of cancer-related death among women, and is the deadliest of gynecological cancers. The mortality rate of ovarian cancer ranks first. Thus, early diagnosis and treatment are critical for improving the patients' cure rate and prolonging their survival. Here we have investigated Mass spectrometry (MS) field data to develop a computer-aided system for the purpose. Using machine learning techniques, data is classified in different categories to identify benign and malignant cancerous cells and a comparative study has been done to identify the most suitable technique under different operational conditions and datasets. Our Comparative studies show that the Multilayer Perceptron (MLP) is the best options for such detection considering its performance metrics such as Accuracy, Sensitivity, Specificity and Errors","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130647516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234510
S. S. Sandhu, Ashwin R. Jadhav, B. Tripathy
The practice of using divide and conquer techniques to solve complex, time-consuming problems has been in use for a very long time. Here we evaluate the performance of centroid-based clustering techniques, specifically k-means and its two approximation algorithms, the k-means++ and k-means|| (also known as Scalable k-means++), as divide and conquer paradigms applied for the creation of minimum spanning trees. The algorithms will be run on different datasets to get a good evaluation of their respective performances. This is a continuation of our previous work carried out in developing the KMST+ algorithm in the context of fast minimum spanning tree (FMST) frameworks.
{"title":"Comparison of centroid-based clustering algorithms in the context of divide and conquer paradigm based FMST framework","authors":"S. S. Sandhu, Ashwin R. Jadhav, B. Tripathy","doi":"10.1109/ICRCICN.2017.8234510","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234510","url":null,"abstract":"The practice of using divide and conquer techniques to solve complex, time-consuming problems has been in use for a very long time. Here we evaluate the performance of centroid-based clustering techniques, specifically k-means and its two approximation algorithms, the k-means++ and k-means|| (also known as Scalable k-means++), as divide and conquer paradigms applied for the creation of minimum spanning trees. The algorithms will be run on different datasets to get a good evaluation of their respective performances. This is a continuation of our previous work carried out in developing the KMST+ algorithm in the context of fast minimum spanning tree (FMST) frameworks.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116297738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234494
Mohammad Rezaul Islam, F. H. Chowdhury, Sifat Rezwan, Mohammed Jawad Ishaque, Jamir Uddin Akanda, Abu Shaid Tuhel, Benazir Bashar Riddhe
This paper introduces a mars exploration robot based system that useful to explore the planet mars. Mars Rover NSU is a semi-autonomous robot that is developed based on sensors and interactive applications. The robot is capable of completing human assistant tasks, Astronaut assistance task, collecting resource from planet mars, giving a feedback of soils condition such as temperature, moisture, pH. This robotics system includes a web based mother station from where the rover is controlled and given instructions to complete tasks. The mars rover will be used in planetary exploration research to explore life on mars. This rover has rocker-bogie suspension system, robotics arm, Drilling mechanism, live feed camera, aluminum wheels suitable to explore planet mars. This research is mainly focused on robotic system and computational efficiency. The system is consist of a GPS system for mapping purpose and smooth controlling features. In his paper we will discuss about different functionalities of mars rover. The end result will show the efficiency, impact and performance analysis of the system.
{"title":"Novel design and performance analysis of a Mars exploration robot: Mars rover mongol pothik","authors":"Mohammad Rezaul Islam, F. H. Chowdhury, Sifat Rezwan, Mohammed Jawad Ishaque, Jamir Uddin Akanda, Abu Shaid Tuhel, Benazir Bashar Riddhe","doi":"10.1109/ICRCICN.2017.8234494","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234494","url":null,"abstract":"This paper introduces a mars exploration robot based system that useful to explore the planet mars. Mars Rover NSU is a semi-autonomous robot that is developed based on sensors and interactive applications. The robot is capable of completing human assistant tasks, Astronaut assistance task, collecting resource from planet mars, giving a feedback of soils condition such as temperature, moisture, pH. This robotics system includes a web based mother station from where the rover is controlled and given instructions to complete tasks. The mars rover will be used in planetary exploration research to explore life on mars. This rover has rocker-bogie suspension system, robotics arm, Drilling mechanism, live feed camera, aluminum wheels suitable to explore planet mars. This research is mainly focused on robotic system and computational efficiency. The system is consist of a GPS system for mapping purpose and smooth controlling features. In his paper we will discuss about different functionalities of mars rover. The end result will show the efficiency, impact and performance analysis of the system.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116456105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234486
S. Deb, K. Kalita, Xiao-zhi Gao, K. Tammi, P. Mahanta
The growing apprehension regarding greenhouse gas emission accompanied by fossil fuel depletion has instigated the electrification of transportation sector. As a consequence of this Electric Vehicle (EV) has emerged as an environment friendly solution for the automobile industry. For large scale deployment of EVs development of proper charging infrastructure is indispensable. Charging stations (CS) must be placed in the transport network in such a way that the distribution network parameters are least affected. This work proposes a novel approach for co-ordinated planning of EV charging infrastructures considering superimposition of both transport and distribution network. This approach is validated on IEEE 33 bus distribution network superimposed with 25 node road network. The capability of a new hybrid algorithm which is an amalgamation of Chicken Swarm Algorithm (CSO) and Teaching Learning Based Optimization Algorithm (TLBO) is utilized in this work for attaining optimal solution.
{"title":"Optimal placement of charging stations using CSO-TLBO algorithm","authors":"S. Deb, K. Kalita, Xiao-zhi Gao, K. Tammi, P. Mahanta","doi":"10.1109/ICRCICN.2017.8234486","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234486","url":null,"abstract":"The growing apprehension regarding greenhouse gas emission accompanied by fossil fuel depletion has instigated the electrification of transportation sector. As a consequence of this Electric Vehicle (EV) has emerged as an environment friendly solution for the automobile industry. For large scale deployment of EVs development of proper charging infrastructure is indispensable. Charging stations (CS) must be placed in the transport network in such a way that the distribution network parameters are least affected. This work proposes a novel approach for co-ordinated planning of EV charging infrastructures considering superimposition of both transport and distribution network. This approach is validated on IEEE 33 bus distribution network superimposed with 25 node road network. The capability of a new hybrid algorithm which is an amalgamation of Chicken Swarm Algorithm (CSO) and Teaching Learning Based Optimization Algorithm (TLBO) is utilized in this work for attaining optimal solution.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122137717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234503
T. Chakraborty, Tanima Chakraborty
Intelligent Transportation System (ITS) is integral towards envisioning a truly mobile and digital India. While existing solutions like smart vehicles and autonomous driving are inapplicable for Indian cities, transport surveys in India have provided offline solutions through GPS data. Without analytical formulations, they are prone to failure in ever-changing Indian urban landscape. This paper addresses this contemporary problem and provides an analytical model that serves as the backbone for developing ITS in India. Contribution of this paper is the formulation of objective function using road networks that reduces congestion by minimizing vehicular density under constraints of feasible paths and traffic flows. Novelty of this work is further established by exhaustive studies that determine important traffic metrics and further establish relationships among critical congestion-queue parameters. Additionally, indepth studies of Indian cities under this model justify our motivation for ITS-enabled India. Finally, validation with theoretical model establishes the credibility of this work.
{"title":"Design and analysis of a novel mathematical model towards realizing the need for intelligent transportation system in Indian cities","authors":"T. Chakraborty, Tanima Chakraborty","doi":"10.1109/ICRCICN.2017.8234503","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234503","url":null,"abstract":"Intelligent Transportation System (ITS) is integral towards envisioning a truly mobile and digital India. While existing solutions like smart vehicles and autonomous driving are inapplicable for Indian cities, transport surveys in India have provided offline solutions through GPS data. Without analytical formulations, they are prone to failure in ever-changing Indian urban landscape. This paper addresses this contemporary problem and provides an analytical model that serves as the backbone for developing ITS in India. Contribution of this paper is the formulation of objective function using road networks that reduces congestion by minimizing vehicular density under constraints of feasible paths and traffic flows. Novelty of this work is further established by exhaustive studies that determine important traffic metrics and further establish relationships among critical congestion-queue parameters. Additionally, indepth studies of Indian cities under this model justify our motivation for ITS-enabled India. Finally, validation with theoretical model establishes the credibility of this work.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129600818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234517
Khaled Ahmed, A. Hassanien, S. Bhattacharyya
Feature selection is an important task in data mining, which aims to reduce the dimensionality of the data sets while at least maintaining the classification performance. Chicken swarm optimization algorithm (CSO) has been widely applied to feature selection because of its efficiency and effectiveness. However, since feature selection is a challenging task with a complex search space, CSO quickly gets stuck the local minimum problem. This paper aims to improve the CSO searching ability by applying logistic and tend chaotic maps to assist the CSO swarm in exploring the search space better. The proposed chaotic chicken swarm algorithm (CCSO)-based feature selection algorithm is compared with four feature selection algorithms on five benchmark data sets. A comparison among several types of popular classifiers is done to figure out the sensitivity of each classifier corresponding to the selected features and the dimension reduction. During iterations, the best fitness value shows remarkable improvement of the classification accuracy.
{"title":"A novel chaotic chicken swarm optimization algorithm for feature selection","authors":"Khaled Ahmed, A. Hassanien, S. Bhattacharyya","doi":"10.1109/ICRCICN.2017.8234517","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234517","url":null,"abstract":"Feature selection is an important task in data mining, which aims to reduce the dimensionality of the data sets while at least maintaining the classification performance. Chicken swarm optimization algorithm (CSO) has been widely applied to feature selection because of its efficiency and effectiveness. However, since feature selection is a challenging task with a complex search space, CSO quickly gets stuck the local minimum problem. This paper aims to improve the CSO searching ability by applying logistic and tend chaotic maps to assist the CSO swarm in exploring the search space better. The proposed chaotic chicken swarm algorithm (CCSO)-based feature selection algorithm is compared with four feature selection algorithms on five benchmark data sets. A comparison among several types of popular classifiers is done to figure out the sensitivity of each classifier corresponding to the selected features and the dimension reduction. During iterations, the best fitness value shows remarkable improvement of the classification accuracy.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"10 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117287328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234492
Kunal Hossain, Susovan Jana, S. Mukherjee, R. Parekh
Digital representation of biomedical images and videos has grown enormously in scope and gained importance in recent years. Preserving security and integrity in wireless transmission of biomedical images and videos is an utmost challenge. Manipulation of sensitive biomedical data in the path of transmission can mislead critical diagnosis and treatment. To overcome this, multi-level security is provided by applying encryption and stegonagraphy techniques to protect biomedical images and videos. In the proposed approach, biomedical images are considered to be RGB images and each segmented video frames are treated as RGB images. Discrete Cosine Transform (DCT), Pixel flipping and Least Significant Bit (LSB) substitution mechanisms are applied to form the stego-image followed by image encryption that increases robustness and hiding technique. Original images and videos can be retrieved by using key-based decryption algorithm, LSB, and Pixel flipping mechanism.
{"title":"A novel approach to secure biomedical images and videos for transmission","authors":"Kunal Hossain, Susovan Jana, S. Mukherjee, R. Parekh","doi":"10.1109/ICRCICN.2017.8234492","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234492","url":null,"abstract":"Digital representation of biomedical images and videos has grown enormously in scope and gained importance in recent years. Preserving security and integrity in wireless transmission of biomedical images and videos is an utmost challenge. Manipulation of sensitive biomedical data in the path of transmission can mislead critical diagnosis and treatment. To overcome this, multi-level security is provided by applying encryption and stegonagraphy techniques to protect biomedical images and videos. In the proposed approach, biomedical images are considered to be RGB images and each segmented video frames are treated as RGB images. Discrete Cosine Transform (DCT), Pixel flipping and Least Significant Bit (LSB) substitution mechanisms are applied to form the stego-image followed by image encryption that increases robustness and hiding technique. Original images and videos can be retrieved by using key-based decryption algorithm, LSB, and Pixel flipping mechanism.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115483746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234482
Apurv Saha, Akash Kumar, Aishwarya Sahu
Paper presents a brief idea about drones, their technology, the process to make that technology energy-efficient and can be used for different purposes. Major aim of our prototype is for surveillance of terrifying notions and hidden activities which can be captured in the camera which gives us an aerial view of objects. Our major aim is to increase the flight time of the drone as much as possible so to get more time for surveillance and to reduce the noise produced by cloaking it. An additional feature which is added to the prototype is transmission of video in FPV goggles. Our aim is making it available at an affordable rate for the private agencies, institutions and governmental bodies for efficient surveillance. There is lot of hidden observations and development for this prototype in future. Quad copters are types of drone which have four wings attached to them. Depending upon the criteria different feature can be added.
{"title":"FPV drone with GPS used for surveillance in remote areas","authors":"Apurv Saha, Akash Kumar, Aishwarya Sahu","doi":"10.1109/ICRCICN.2017.8234482","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234482","url":null,"abstract":"Paper presents a brief idea about drones, their technology, the process to make that technology energy-efficient and can be used for different purposes. Major aim of our prototype is for surveillance of terrifying notions and hidden activities which can be captured in the camera which gives us an aerial view of objects. Our major aim is to increase the flight time of the drone as much as possible so to get more time for surveillance and to reduce the noise produced by cloaking it. An additional feature which is added to the prototype is transmission of video in FPV goggles. Our aim is making it available at an affordable rate for the private agencies, institutions and governmental bodies for efficient surveillance. There is lot of hidden observations and development for this prototype in future. Quad copters are types of drone which have four wings attached to them. Depending upon the criteria different feature can be added.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132676235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234513
Sourav Das, A. Kolya
In this modern era of communication, people are always connected to the internet. Hence, everyone tends to express their opinions on social media or e-commerce websites about commercial products, movies, sports, social and geopolitical matters and even on government policies. These opinions reflect the corresponding person's view or sentiment about that particular matter, which ultimately leads to the formation of opinion polarity regarding a specific issue. In today's context, Twitter, Facebook or other social platforms often witness a lot of opinion waves regarding some of the today's most hot topics, and one of them surely is the introduction of Goods and Services Tax or GST in India. The rising sentiment analysis and opinion mining regarding this issue is helping researchers to understand the insight of public emotion. It can be also implemented to gain an idea of allover opinion polarity of people in this matter. GST was one of the most rending topic on social network platforms during Jun-July 2017, so in this paper, we present a simple and robust work to gather, analyze and graphically represent people's opinion about India's new taxation system using Naive Bayes algorithm.
{"title":"Sense GST: Text mining & sentiment analysis of GST tweets by Naive Bayes algorithm","authors":"Sourav Das, A. Kolya","doi":"10.1109/ICRCICN.2017.8234513","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234513","url":null,"abstract":"In this modern era of communication, people are always connected to the internet. Hence, everyone tends to express their opinions on social media or e-commerce websites about commercial products, movies, sports, social and geopolitical matters and even on government policies. These opinions reflect the corresponding person's view or sentiment about that particular matter, which ultimately leads to the formation of opinion polarity regarding a specific issue. In today's context, Twitter, Facebook or other social platforms often witness a lot of opinion waves regarding some of the today's most hot topics, and one of them surely is the introduction of Goods and Services Tax or GST in India. The rising sentiment analysis and opinion mining regarding this issue is helping researchers to understand the insight of public emotion. It can be also implemented to gain an idea of allover opinion polarity of people in this matter. GST was one of the most rending topic on social network platforms during Jun-July 2017, so in this paper, we present a simple and robust work to gather, analyze and graphically represent people's opinion about India's new taxation system using Naive Bayes algorithm.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116584404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234528
Mayank Kumar
Quite often we come across data which is large in size such as the identification numbers of employees working in a multinational firm or climate records of a place over a period of time or the memory addresses of the storage device. These types of relatively large data are often hard to handle and organize. The paper puts forward a new data model aiming to provide efficient organization in storing large-sized data with fast search and traversal mechanisms. To do so it incorporates some of the powerful principles of a ‘Binary Tree’. The data structure intends at creating an efficient storage system for large data allowing data members to be searched and (or) appended further by faster and simpler ways.
{"title":"Y-Trees: An extending non-linear data structure for better organization of large-sized data","authors":"Mayank Kumar","doi":"10.1109/ICRCICN.2017.8234528","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234528","url":null,"abstract":"Quite often we come across data which is large in size such as the identification numbers of employees working in a multinational firm or climate records of a place over a period of time or the memory addresses of the storage device. These types of relatively large data are often hard to handle and organize. The paper puts forward a new data model aiming to provide efficient organization in storing large-sized data with fast search and traversal mechanisms. To do so it incorporates some of the powerful principles of a ‘Binary Tree’. The data structure intends at creating an efficient storage system for large data allowing data members to be searched and (or) appended further by faster and simpler ways.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122762385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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