Pub Date : 2018-12-01DOI: 10.1109/ICACAT.2018.8933674
K. Pandey, D. Shukla
The current time technology is growing very faster and data is generating very fast so data characteristics have changed in form of data to big data. If anybody wants to mine some related data in big data environment then present data mining algorithm fails to mine relationship in big data and it takes a lot of time for processing. MapReduce approach is a most efficient algorithm in big data framework which handles a huge amount of data and gives fast result. The Apriori algorithm is more powerful algorithm for mining on interesting relationships between dataset in any type of databases or same databases. In present time a lot of MapReduce base Apriori algorithms are available but its Map and Reduce function run to multiple times and works only for the transaction database. This paper describes what is big data with its characteristics, concept of Association rules with the Apriori algorithm in big data, problems in the existing MapReduce base Apriori algorithm. We propose new improve MapReduce approach base Apriori algorithm for mining on a relationship with the help of given one suitable example where Reduce function runs only one time after running on Map function and this proposed algorithm run on any type of database.
{"title":"Mining on Relationships in Big Data era using Improve Apriori Algorithm with MapReduce Approach","authors":"K. Pandey, D. Shukla","doi":"10.1109/ICACAT.2018.8933674","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933674","url":null,"abstract":"The current time technology is growing very faster and data is generating very fast so data characteristics have changed in form of data to big data. If anybody wants to mine some related data in big data environment then present data mining algorithm fails to mine relationship in big data and it takes a lot of time for processing. MapReduce approach is a most efficient algorithm in big data framework which handles a huge amount of data and gives fast result. The Apriori algorithm is more powerful algorithm for mining on interesting relationships between dataset in any type of databases or same databases. In present time a lot of MapReduce base Apriori algorithms are available but its Map and Reduce function run to multiple times and works only for the transaction database. This paper describes what is big data with its characteristics, concept of Association rules with the Apriori algorithm in big data, problems in the existing MapReduce base Apriori algorithm. We propose new improve MapReduce approach base Apriori algorithm for mining on a relationship with the help of given one suitable example where Reduce function runs only one time after running on Map function and this proposed algorithm run on any type of database.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"59 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84484259","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933784
Neeraj Chandnani, C. N. Khairnar
Internet of Things (IoT) is a network paradigm in which data aggregation and data security plays a vital role. Data aggregation in IoT describes collection of information from different users and data security means encryption of collected data using cryptography method. The proposed work comprises of devices and gateway to perform data aggregation and data encryption. Data aggregation is performed using clustering in which data are clustered and secured by Particle Swarm Optimization (PSO) algorithm which finds the cluster head. After finding cluster head, nodes requests to join as cluster member. PSO computes fitness function using metrics i.e. energy, end-to-end delay, scoring factor, packet drops and successful packet transformation. After completion of clustering process, data encryption process is held in which, cluster head collects data from the cluster members and encrypts it using Elliptic Curve Cryptography (ECC) method. Finally, encrypted data are dispatched to gateway device. Experimental result shows, the proposed work on Secure Particle Swarm Optimization (SPSO)prompts better performance in following metrics i.e. delay, throughput and energy consumption.
{"title":"A Novel Secure Data Aggregation in IoT using Particle Swarm Optimization Algorithm","authors":"Neeraj Chandnani, C. N. Khairnar","doi":"10.1109/ICACAT.2018.8933784","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933784","url":null,"abstract":"Internet of Things (IoT) is a network paradigm in which data aggregation and data security plays a vital role. Data aggregation in IoT describes collection of information from different users and data security means encryption of collected data using cryptography method. The proposed work comprises of devices and gateway to perform data aggregation and data encryption. Data aggregation is performed using clustering in which data are clustered and secured by Particle Swarm Optimization (PSO) algorithm which finds the cluster head. After finding cluster head, nodes requests to join as cluster member. PSO computes fitness function using metrics i.e. energy, end-to-end delay, scoring factor, packet drops and successful packet transformation. After completion of clustering process, data encryption process is held in which, cluster head collects data from the cluster members and encrypts it using Elliptic Curve Cryptography (ECC) method. Finally, encrypted data are dispatched to gateway device. Experimental result shows, the proposed work on Secure Particle Swarm Optimization (SPSO)prompts better performance in following metrics i.e. delay, throughput and energy consumption.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"112 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86758220","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933588
Shweta Jain, Saurabh Jain
Data centers spread around the world at different geographical location with the variation of time and location. This paper offers distributed weighted round robin (DWRR) scheduling algorithm for large-scale distributed data centers using Markov Chain Model. DWRR provides a platform to achieve fairness for all data centers. This proposed algorithm evaluates and optimizes the performance that reduces the operation costs, balances the load effectively, improves fairness and produces maximum throughput of data centers to study the transition behaviour of threads among different data centers.
{"title":"A Markov-Chain Model Based Study of Distributed Weighted Round-Robin Scheduling for Data Centers","authors":"Shweta Jain, Saurabh Jain","doi":"10.1109/ICACAT.2018.8933588","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933588","url":null,"abstract":"Data centers spread around the world at different geographical location with the variation of time and location. This paper offers distributed weighted round robin (DWRR) scheduling algorithm for large-scale distributed data centers using Markov Chain Model. DWRR provides a platform to achieve fairness for all data centers. This proposed algorithm evaluates and optimizes the performance that reduces the operation costs, balances the load effectively, improves fairness and produces maximum throughput of data centers to study the transition behaviour of threads among different data centers.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"416 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84902729","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933769
Saurabh
As we are becoming more and more dependent on computers the attack vectors on them are increasing day by day. The cyberspace is becoming the battlefield of the 21st century as we are witnessing the increasing potential of a cyber-attack on the critical infrastructure. Malware are the most sophisticated evil code It is designed to damage computer systems without the knowledge of the owner these days malware are made up with special arbitrary to evade detection from the antivirus [1] with a huge potential to damage computer systems. Malware analysis is a process for studying the components and the behavior of malware. For analyzing malware we will use two types of methods static analysis and the dynamic analysis. In the static analysis the malware are examined without running it, whereas in dynamic analysis the malware is analyzed while running it in a virtual and controlled environment. In this research we are going to focus on malware analysis using the static and the dynamic method which will help us to access damage, to know the indicators of compromise and to determine the sophistication level of an intruder and to catch the creator of the malware.
{"title":"Advance Malware Analysis Using Static and Dynamic Methodology","authors":"Saurabh","doi":"10.1109/ICACAT.2018.8933769","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933769","url":null,"abstract":"As we are becoming more and more dependent on computers the attack vectors on them are increasing day by day. The cyberspace is becoming the battlefield of the 21st century as we are witnessing the increasing potential of a cyber-attack on the critical infrastructure. Malware are the most sophisticated evil code It is designed to damage computer systems without the knowledge of the owner these days malware are made up with special arbitrary to evade detection from the antivirus [1] with a huge potential to damage computer systems. Malware analysis is a process for studying the components and the behavior of malware. For analyzing malware we will use two types of methods static analysis and the dynamic analysis. In the static analysis the malware are examined without running it, whereas in dynamic analysis the malware is analyzed while running it in a virtual and controlled environment. In this research we are going to focus on malware analysis using the static and the dynamic method which will help us to access damage, to know the indicators of compromise and to determine the sophistication level of an intruder and to catch the creator of the malware.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"44 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74076110","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933676
Raghav Prasad Parouha
This article is to presents an enhanced DE (differential evolution) through memory based mechanism of PSO (particle swarm optimization). Because of uses the memory concept of PSO, the proposed DE is termed as ‘MBDE (memory based differential evolution)’ where new mutation and crossover operators are introduced. This proposed technique is implemented on four typical benchmark functions available in literature. Experimental results prove that the proposed technique produce faster and more accurate solutions than classical DE, traditional PSO and PSODE (an effective hybrid variant of PSO and DE).
{"title":"An Enhanced Differential Evolution through Memory Based Mechanism","authors":"Raghav Prasad Parouha","doi":"10.1109/ICACAT.2018.8933676","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933676","url":null,"abstract":"This article is to presents an enhanced DE (differential evolution) through memory based mechanism of PSO (particle swarm optimization). Because of uses the memory concept of PSO, the proposed DE is termed as ‘MBDE (memory based differential evolution)’ where new mutation and crossover operators are introduced. This proposed technique is implemented on four typical benchmark functions available in literature. Experimental results prove that the proposed technique produce faster and more accurate solutions than classical DE, traditional PSO and PSODE (an effective hybrid variant of PSO and DE).","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"1 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89727820","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933583
E. Choudhary, S. Sharma, Pranay Yadav
In this research work design a modified wideband bow-tie antenna for Wi-Fi ranges. Now a day’s demand of wireless device increases in rapidly due to require a wide range application antenna. Wide band antennas are those antennas full fill the demand of current generation communication devices. In this presented research work design a bow-tie antenna for 1 to 6 GHz frequency range. In this current generation most of the wireless devices exist in this range. This research work shows Bow-Tie Shape antenna with bow shape slot. In slotted Bow-Tie antenna on patch side, and apply defected ground structure on the ground side. On the ground side apply two right angle cuts with parabolic cut on the ground side. For the enhancement of bandwidth apply CPW feed in the structure and obtain enhanced bandwidth as well as return loss. The proposed bow-tie antenna shows result in the range of 3.0 GHz to 4.87 GHz range and the total bandwidth obtain 1.86 GHz. In this region got two impedance frequency on 3.47 GHz and 4.41 GHz with return loss is -32.45 dB and -34.45 dB. Designed antenna is shows wide band (WB) nature also shows dual band nature. The result of proposed design show good return loss as well as VSWR.
{"title":"A Modified Wideband Bow-Tie Antenna with DGS for Wireless Fidelity Range","authors":"E. Choudhary, S. Sharma, Pranay Yadav","doi":"10.1109/ICACAT.2018.8933583","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933583","url":null,"abstract":"In this research work design a modified wideband bow-tie antenna for Wi-Fi ranges. Now a day’s demand of wireless device increases in rapidly due to require a wide range application antenna. Wide band antennas are those antennas full fill the demand of current generation communication devices. In this presented research work design a bow-tie antenna for 1 to 6 GHz frequency range. In this current generation most of the wireless devices exist in this range. This research work shows Bow-Tie Shape antenna with bow shape slot. In slotted Bow-Tie antenna on patch side, and apply defected ground structure on the ground side. On the ground side apply two right angle cuts with parabolic cut on the ground side. For the enhancement of bandwidth apply CPW feed in the structure and obtain enhanced bandwidth as well as return loss. The proposed bow-tie antenna shows result in the range of 3.0 GHz to 4.87 GHz range and the total bandwidth obtain 1.86 GHz. In this region got two impedance frequency on 3.47 GHz and 4.41 GHz with return loss is -32.45 dB and -34.45 dB. Designed antenna is shows wide band (WB) nature also shows dual band nature. The result of proposed design show good return loss as well as VSWR.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"53 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75955105","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933729
V. Trivedi, P. Shukla, H. Gupta
Since the foggy images undergo from low contrast and resolution due to diffusion of light and poor visibility conditions. So, Fog elimination is extremely preferred in both estimation picture making and computer vision applications. Proposed technique uses a Dark Channel Prior with contrast stretching to remove fog and improve the contrast of fog free image, respectively. Using Dark Channel Prior method one can directly take away the thickness of the haze and recover a high quality haze free image. Contrast Stretching is applied in the resulted image of dark channel prior method to improve the contrast of image. The noise that affect foggy image can also be ease by using the median low pass filter. By using this technique the visual quality and color of the foggy image can be correct effectively. Experiments are conducted on PSNR and RMSE parameters. Experimental Result shows that proposed method contains least average RMSE values and Higher PSNR values among other methods.
{"title":"Dark Channel Prior and Global Contrast Stretching based Hybrid Defogging Image Technique","authors":"V. Trivedi, P. Shukla, H. Gupta","doi":"10.1109/ICACAT.2018.8933729","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933729","url":null,"abstract":"Since the foggy images undergo from low contrast and resolution due to diffusion of light and poor visibility conditions. So, Fog elimination is extremely preferred in both estimation picture making and computer vision applications. Proposed technique uses a Dark Channel Prior with contrast stretching to remove fog and improve the contrast of fog free image, respectively. Using Dark Channel Prior method one can directly take away the thickness of the haze and recover a high quality haze free image. Contrast Stretching is applied in the resulted image of dark channel prior method to improve the contrast of image. The noise that affect foggy image can also be ease by using the median low pass filter. By using this technique the visual quality and color of the foggy image can be correct effectively. Experiments are conducted on PSNR and RMSE parameters. Experimental Result shows that proposed method contains least average RMSE values and Higher PSNR values among other methods.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"20 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91530278","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933684
V. Shrivastava, Vivek Richhariya, Vineet Richhariya
Emotions steer both active and passive semantics of human interactions. Precise analysis of these emotions is indispensable to ensure a meaningful communication. Humans, in general, express their emotions in various forms. In order to encompass multiple dimensions of these expressions, this paper proposes a triple-layer (facial, verbal, and vocal) sentiment analysis system based on an application of deep-learning concepts. As such, in our experiment, first we separately examined facial expressions, verbal sentiments and vocal characteristics of a speaker and then mapped the individual results to perform a complete multimodal sentiment analysis. As a part of our two-stage facial expression analysis algorithm, we trained three multi-layer perceptrons using backpropagation technique to recognize a number of action units in human faces and seven single layer perceptrons each to identify one of seven basic human emotions (happiness, sadness, surprise, anger, fear, contempt or disgust, and neutral) expressed by the action units. In our vocal analysis module, we extracted important features (such as, jittering, shimmering, etc.) from sampled audio signals using standard formulae and used those features in a Bayesian Classifier to determine the type of sentiment (positive, negative, or neutral) in the voice. In the final segment of our experiment, we trained seven one dimensional convolutional neural networks to analyze verbal sentiments using the results of vocal analysis module as a bias. We were able to obtain results with as high as 91.80% (training) and 88% (testing) accuracies in our vocal and verbal analysis module; whereas, our facial expression analysis module provided results with 93.71% (training) and 92% (testing) accuracies.
{"title":"Puzzling Out Emotions: A Deep-Learning Approach to Multimodal Sentiment Analysis","authors":"V. Shrivastava, Vivek Richhariya, Vineet Richhariya","doi":"10.1109/ICACAT.2018.8933684","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933684","url":null,"abstract":"Emotions steer both active and passive semantics of human interactions. Precise analysis of these emotions is indispensable to ensure a meaningful communication. Humans, in general, express their emotions in various forms. In order to encompass multiple dimensions of these expressions, this paper proposes a triple-layer (facial, verbal, and vocal) sentiment analysis system based on an application of deep-learning concepts. As such, in our experiment, first we separately examined facial expressions, verbal sentiments and vocal characteristics of a speaker and then mapped the individual results to perform a complete multimodal sentiment analysis. As a part of our two-stage facial expression analysis algorithm, we trained three multi-layer perceptrons using backpropagation technique to recognize a number of action units in human faces and seven single layer perceptrons each to identify one of seven basic human emotions (happiness, sadness, surprise, anger, fear, contempt or disgust, and neutral) expressed by the action units. In our vocal analysis module, we extracted important features (such as, jittering, shimmering, etc.) from sampled audio signals using standard formulae and used those features in a Bayesian Classifier to determine the type of sentiment (positive, negative, or neutral) in the voice. In the final segment of our experiment, we trained seven one dimensional convolutional neural networks to analyze verbal sentiments using the results of vocal analysis module as a bias. We were able to obtain results with as high as 91.80% (training) and 88% (testing) accuracies in our vocal and verbal analysis module; whereas, our facial expression analysis module provided results with 93.71% (training) and 92% (testing) accuracies.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"137 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86771978","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933652
Nikky Ahuja, P. Kanungo, S. Katiyal
In the present era of cut-throat competition, IT tools and services provided by cloud computing environment plays an important role in the success of any business organization. These services are offered by the SaaS providers, who act as a bridge between organizations and the available resources. These providers focus on the optimum utilization of available resources without violating the quality constraints and maximization of profit for gaining competitive advantage. The present research is an effort to maximize the profit of SaaS providers by devising an algorithm to overcome the limitation of present system. Renting of resources in heterogeneous environment has been suggested as a way to improve earnings. Also, dynamic scheduling and re-scheduling of available resources has been used to improve utilization of available resources. Aim is to control cost and gain customer loyalty. Thus, the objectives of the research are: 1) to design a knowledge-based algorithm for optimal allocation of resources; 2) to design an algorithm for scheduling of resources to maximize profit and control cost and 3) to propose a system model to earn maximum customer satisfaction.
{"title":"Profit Maximization for SaaS Provider using Improved Strategy for Resource Allocation in Cloud Computing Environment","authors":"Nikky Ahuja, P. Kanungo, S. Katiyal","doi":"10.1109/ICACAT.2018.8933652","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933652","url":null,"abstract":"In the present era of cut-throat competition, IT tools and services provided by cloud computing environment plays an important role in the success of any business organization. These services are offered by the SaaS providers, who act as a bridge between organizations and the available resources. These providers focus on the optimum utilization of available resources without violating the quality constraints and maximization of profit for gaining competitive advantage. The present research is an effort to maximize the profit of SaaS providers by devising an algorithm to overcome the limitation of present system. Renting of resources in heterogeneous environment has been suggested as a way to improve earnings. Also, dynamic scheduling and re-scheduling of available resources has been used to improve utilization of available resources. Aim is to control cost and gain customer loyalty. Thus, the objectives of the research are: 1) to design a knowledge-based algorithm for optimal allocation of resources; 2) to design an algorithm for scheduling of resources to maximize profit and control cost and 3) to propose a system model to earn maximum customer satisfaction.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"6 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78523626","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933696
Mranali Yadav, Manish Rai, Mohit Gangwar
With the invent of low cost cameras the uses of imaging data has exponentially increased in last two decades. Due to availability of huge data on web, demand of efficient image retrieval techniques have also increased. Many feature based local and global methods have been designed in past but they were either too complex or only case specific. In this paper a simple and efficient statistical soft computing and texture based content based retrieval system is proposed and designed. The method is designed to match the quarry and template images based on histogram and their statistical properties as statistical absolute mean difference and 2D normalized correlation of texture images. Method first resizes the quarry and template image to same size and then calculates the statistical parameters in RGB domain and compares the same. In addition Local binary pattern (LBP) is calculated for comparing the local texture feature of the quarry and template images. The performance of our proposed method is tested and evaluated using the standard large image-vary dataset of color images.
{"title":"An Efficient Content Based Image Retrieval using Statistical Soft Computing and Texture Features","authors":"Mranali Yadav, Manish Rai, Mohit Gangwar","doi":"10.1109/ICACAT.2018.8933696","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933696","url":null,"abstract":"With the invent of low cost cameras the uses of imaging data has exponentially increased in last two decades. Due to availability of huge data on web, demand of efficient image retrieval techniques have also increased. Many feature based local and global methods have been designed in past but they were either too complex or only case specific. In this paper a simple and efficient statistical soft computing and texture based content based retrieval system is proposed and designed. The method is designed to match the quarry and template images based on histogram and their statistical properties as statistical absolute mean difference and 2D normalized correlation of texture images. Method first resizes the quarry and template image to same size and then calculates the statistical parameters in RGB domain and compares the same. In addition Local binary pattern (LBP) is calculated for comparing the local texture feature of the quarry and template images. The performance of our proposed method is tested and evaluated using the standard large image-vary dataset of color images.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"75 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79495514","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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