The 5G mobile wireless network systems faces a lot of security issues due to the opening of network and its insecurity. The insecure network prone to various attacks and it disrupts secure data communications between legitimate users. Many works have addressed the security problems in 3G and 4G networks in efficient way through authentication and cryptographic techniques. But, the security in 5G networks during data communication was not improved. Subtractive Gradient Boost Clustered Node Authentication (SGBCNA) Method is introduced to perform secure data communication. The subtractive gradient boost clustering technique is applied to authenticate the mobile node as normal nodes and malicious nodes based on the selected features. The designed ensemble clustering model combines the weak learners to make final strong clustering results with minimum loss. Finally, the malicious nodes are eliminated and normal mobile nodes are taken for performing the secured communication in 5G networks. Simulation is carried out on factors such as authentication accuracy, computation overhead and security level with respect to a number of mobile nodes and data packets. The observed outcomes clearly illustrate that the SGBCNA Method efficiently improves node authentication accuracy, security level with minimum overhead than the state-of-the-art-methods.
{"title":"Subtractive Gradient Boost Clustering for Mobile Node Authentication in Internet of Things Aware 5G Networks","authors":"M. Haripriya, P. Venkadesh","doi":"10.1166/JCTN.2021.9394","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9394","url":null,"abstract":"The 5G mobile wireless network systems faces a lot of security issues due to the opening of network and its insecurity. The insecure network prone to various attacks and it disrupts secure data communications between legitimate users. Many works have addressed the security problems\u0000 in 3G and 4G networks in efficient way through authentication and cryptographic techniques. But, the security in 5G networks during data communication was not improved. Subtractive Gradient Boost Clustered Node Authentication (SGBCNA) Method is introduced to perform secure data communication.\u0000 The subtractive gradient boost clustering technique is applied to authenticate the mobile node as normal nodes and malicious nodes based on the selected features. The designed ensemble clustering model combines the weak learners to make final strong clustering results with minimum loss. Finally,\u0000 the malicious nodes are eliminated and normal mobile nodes are taken for performing the secured communication in 5G networks. Simulation is carried out on factors such as authentication accuracy, computation overhead and security level with respect to a number of mobile nodes and data packets.\u0000 The observed outcomes clearly illustrate that the SGBCNA Method efficiently improves node authentication accuracy, security level with minimum overhead than the state-of-the-art-methods.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1287-1293"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46052184","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}
The task of recognizing human activities directs extensive divergence of various functions and applications. Despite analysing the intricate activity it endures demanding requirements in contemporary field of research. A subject performs a definite task at a particular time by determining the activity by using sensor data. In this research task we appraise a unique way by using data with supervised learning techniques by placing sensors on the human body by contingent upon classification process at different stages. The State-of-art machine learning approach random forests are widely discussed in terms of covering practical and theoretical aspects of body sensing. The eventual target is the superior rate of accurate predictions effecting Human Activity Recognition further effective for behavioural monitoring, medical and healthcare sectors. Classification processes are deployed for pairs of activities that are distracted often and this work attempts to analyse the essential sensors for the improved prediction. The results shows the best accuracy scores and the remaining of our findings we expose the outline, exhibiting the degree of distraction between features of ranking and human activities which renders back to sensor ranking.
{"title":"Contemporary Human Activity Recognition Based Predictions by Sensors Using Random Forest Classifier","authors":"S. Anand, S. Magesh, I. Arockiamary","doi":"10.1166/JCTN.2021.9404","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9404","url":null,"abstract":"The task of recognizing human activities directs extensive divergence of various functions and applications. Despite analysing the intricate activity it endures demanding requirements in contemporary field of research. A subject performs a definite task at a particular time by determining\u0000 the activity by using sensor data. In this research task we appraise a unique way by using data with supervised learning techniques by placing sensors on the human body by contingent upon classification process at different stages. The State-of-art machine learning approach random forests\u0000 are widely discussed in terms of covering practical and theoretical aspects of body sensing. The eventual target is the superior rate of accurate predictions effecting Human Activity Recognition further effective for behavioural monitoring, medical and healthcare sectors. Classification processes\u0000 are deployed for pairs of activities that are distracted often and this work attempts to analyse the essential sensors for the improved prediction. The results shows the best accuracy scores and the remaining of our findings we expose the outline, exhibiting the degree of distraction between\u0000 features of ranking and human activities which renders back to sensor ranking.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1243-1250"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41956662","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}
S. Manjula, R. Karthikeyan, S. Karthick, N. Logesh, M. Logeshkumar
An optimized high gain low power low noise amplifier (LNA) is presented using 90 nm CMOS process at 2.4 GHz frequency for Zigbee applications. For achieving desired design specifications, the LNA is optimized by particle swarm optimization (PSO). The PSO is successfully implemented for optimizing noise figure (NF) when satisfying all the design specifications such as gain, power dissipation, linearity and stability. PSO algorithm is developed in MATLAB to optimize the LNA parameters. The LNA with optimized parameters is simulated using Advanced Design System (ADS) Simulator. The LNA with optimized parameters produces 21.470 dB of voltage gain, 1.031 dB of noise figure at 1.02 mW power consumption with 1.2 V supply voltage. The comparison of designed LNA with and without PSO proves that the optimization improves the LNA results while satisfying all the design constraints.
{"title":"Optimized Design of Low Power Complementary Metal Oxide Semiconductor Low Noise Amplifier for Zigbee Application","authors":"S. Manjula, R. Karthikeyan, S. Karthick, N. Logesh, M. Logeshkumar","doi":"10.1166/JCTN.2021.9387","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9387","url":null,"abstract":"An optimized high gain low power low noise amplifier (LNA) is presented using 90 nm CMOS process at 2.4 GHz frequency for Zigbee applications. For achieving desired design specifications, the LNA is optimized by particle swarm optimization (PSO). The PSO is successfully implemented\u0000 for optimizing noise figure (NF) when satisfying all the design specifications such as gain, power dissipation, linearity and stability. PSO algorithm is developed in MATLAB to optimize the LNA parameters. The LNA with optimized parameters is simulated using Advanced Design System (ADS) Simulator.\u0000 The LNA with optimized parameters produces 21.470 dB of voltage gain, 1.031 dB of noise figure at 1.02 mW power consumption with 1.2 V supply voltage. The comparison of designed LNA with and without PSO proves that the optimization improves the LNA results while satisfying all the design constraints.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1327-1330"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47726113","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}
Stream is endlessly inbound sequence of information, streamed information is unbounded and every information are often examined one time. Streamed information are often noisy and therefore the variety of clusters within the information and their applied mathematics properties will change over time, wherever random access to the information isn’t possible and storing all the arriving information is impractical. When applying data set processing techniques and specifically stream clustering Algorithms to real time information streams, limitation in execution time and memory have to be oblige to be thought-about carefully. The projected hymenopteran colony stream clustering Algorithmic is a clustering Algorithm which forms cluster according to density variation, in which clusters are separated by high density features from low density feature region with mounted movement of hymenopteran. Result shows that it created denser cluster than antecedently projected Algorithmic program. And with mounted movement of ants conjointly it decreases the loss of data points. And conjointly the changed radius formula of cluster is projected so as to increase performance of model to create it a lot of dynamic with continuous flow of information. And also we changed probability formula for pick up and drop to reduce oulier. Results from hymenopteran experiments conjointly showed that sorting is disbursed in 2 phases, a primary clustering episode followed by a spacing part. In this paper, we have also compared proposed Algorithm with particle swarm optimization and genetic optimization using DBSCAN and k -means clustering.
{"title":"Hymenopteran Colony Stream Clustering Algorithm and Comparison with Particle Swarm Optimization and Genetic Optimization Clustering","authors":"Nikhil Parafe, M. Venkatesan, Prabhavathy Panner","doi":"10.1166/JCTN.2021.9402","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9402","url":null,"abstract":"Stream is endlessly inbound sequence of information, streamed information is unbounded and every information are often examined one time. Streamed information are often noisy and therefore the variety of clusters within the information and their applied mathematics properties will change\u0000 over time, wherever random access to the information isn’t possible and storing all the arriving information is impractical. When applying data set processing techniques and specifically stream clustering Algorithms to real time information streams, limitation in execution time and memory\u0000 have to be oblige to be thought-about carefully. The projected hymenopteran colony stream clustering Algorithmic is a clustering Algorithm which forms cluster according to density variation, in which clusters are separated by high density features from low density feature region with mounted\u0000 movement of hymenopteran. Result shows that it created denser cluster than antecedently projected Algorithmic program. And with mounted movement of ants conjointly it decreases the loss of data points. And conjointly the changed radius formula of cluster is projected so as to increase performance\u0000 of model to create it a lot of dynamic with continuous flow of information. And also we changed probability formula for pick up and drop to reduce oulier. Results from hymenopteran experiments conjointly showed that sorting is disbursed in 2 phases, a primary clustering episode followed by\u0000 a spacing part. In this paper, we have also compared proposed Algorithm with particle swarm optimization and genetic optimization using DBSCAN and k -means clustering.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1336-1341"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49421103","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}
Text clustering has now a days become a very major technique in many fields including data mining, Natural Language Processing etc. It’s also broadly used for information retrieval and assimilation of textual data. Majority of the works which were carried out previously focuses on the clustering algorithms where feature extraction is done without considering the semantic meaning of word based on its context. In the given work, we introduce a double clustering algorithm using K -Means, by using in conjuction, a Bi-directional Long Short-Term Memory and a Convolutional Neural Network for the purpose of feature extraction, so that the semantic meaning is also considered. Recurrent neural network (RNN) has the ability to study long-term dependencies prevailing in input whereas CNN models are for long known to be effective in feature extraction of local features of given input data. Unlike all the works previously carried out, this proposed work considers and carries out extraction of features and clustering of documents as one combined mechanism. Here result of clustering is send back to the model as feedback information thereby optimizing the parameters of the network model dynamically. Clustering in a double-clustering manner is implemented, which increases the time efficiency.
{"title":"Double Clustering Based Neural Feedback Method for Unstructured Text Data","authors":"S. Sarannya, M. Venkatesan, Prabhavathy Panner","doi":"10.1166/JCTN.2021.9385","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9385","url":null,"abstract":"Text clustering has now a days become a very major technique in many fields including data mining, Natural Language Processing etc. It’s also broadly used for information retrieval and assimilation of textual data. Majority of the works which were carried out previously focuses\u0000 on the clustering algorithms where feature extraction is done without considering the semantic meaning of word based on its context. In the given work, we introduce a double clustering algorithm using K -Means, by using in conjuction, a Bi-directional Long Short-Term Memory and a Convolutional\u0000 Neural Network for the purpose of feature extraction, so that the semantic meaning is also considered. Recurrent neural network (RNN) has the ability to study long-term dependencies prevailing in input whereas CNN models are for long known to be effective in feature extraction of local features\u0000 of given input data. Unlike all the works previously carried out, this proposed work considers and carries out extraction of features and clustering of documents as one combined mechanism. Here result of clustering is send back to the model as feedback information thereby optimizing the parameters\u0000 of the network model dynamically. Clustering in a double-clustering manner is implemented, which increases the time efficiency.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1306-1311"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41550733","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}
R. Ferraz, Raiff Sales da Fonseca, Cláudio Bastos da Silva, H. T. Filho
In this paper, it is described a new design of a digital Lock-In amplifier applied to 4-Kelvin- probe electrodes for the measurement of complex electrical variables. The proposed design is based on the operation of a Phase Sensitive Detection (PSD) circuit and on signal acquisition by the Nyquist principle. The hardware basically consists of a programmable embedded system and an analog interfacing circuit. The microcontroller within the circuit was programmed using standard C language for portability and performs the acquisition of the resulting signal along with mathematical operations. Experimental tests on the prototype have shown that it performs as theoretically predicted.
{"title":"Development of an Impedance Meter Based on a Digital Lock-In Amplifier with 4-Kelvin-Probe Electrodes","authors":"R. Ferraz, Raiff Sales da Fonseca, Cláudio Bastos da Silva, H. T. Filho","doi":"10.1166/JCTN.2021.9715","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9715","url":null,"abstract":"In this paper, it is described a new design of a digital Lock-In amplifier applied to 4-Kelvin- probe electrodes for the measurement of complex electrical variables. The proposed design is based on the operation of a Phase Sensitive Detection (PSD) circuit and on signal acquisition\u0000 by the Nyquist principle. The hardware basically consists of a programmable embedded system and an analog interfacing circuit. The microcontroller within the circuit was programmed using standard C language for portability and performs the acquisition of the resulting signal along with mathematical\u0000 operations. Experimental tests on the prototype have shown that it performs as theoretically predicted.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1171-1176"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41840050","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}
Big data defines the state where the size, speed and kind of data go beyond a memory or executing capabilities for precise and timely decision-making. Big data analytics is integrated with ML and statistical methods for processing big data and recognizes the important data. At present times, the generation of online product reviews has exponentially increased at each and every second. These applications have resulted in developing the volumes of data which can be used for prediction and classification for decision making process. Compared with other models, various techniques are applied in solving the big data problem, feature selection (FS) is known to be an efficient method. FS operations could be exploring with the application of a subset of features which is related to the topic of précised definition of the existing datasets. Deplorably, search using this type of sub-sets results in the problems of combinatorial as well as maximum time consuming. The meta-heuristic approaches are typically employed to facilitate the choice of features. This paper presents an optimal extreme learning machine (ELM) based binary particle swarm optimization to precede the FS process. The proposed method develops a Fitness Function (FF) by applying ELM. And the best solution of the FF has been explored under the application of BPSO technique. For instance, the dataset of product review which are derived from Amazon including synthetic data, which is comprised with total of 235,000 positive and 147,000 negative review records is used. The experimental result implied that the ELM-BPSO technique is comparably best
{"title":"An Optimal Binary Particle Swarm Optimization Based Feature Selection Model for Big Data Analysis of Product Assessment","authors":"R. Sathya, L. Babu","doi":"10.1166/JCTN.2021.9384","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9384","url":null,"abstract":"Big data defines the state where the size, speed and kind of data go beyond a memory or executing capabilities for precise and timely decision-making. Big data analytics is integrated with ML and statistical methods for processing big data and recognizes the important data. At present\u0000 times, the generation of online product reviews has exponentially increased at each and every second. These applications have resulted in developing the volumes of data which can be used for prediction and classification for decision making process. Compared with other models, various techniques\u0000 are applied in solving the big data problem, feature selection (FS) is known to be an efficient method. FS operations could be exploring with the application of a subset of features which is related to the topic of précised definition of the existing datasets. Deplorably, search using\u0000 this type of sub-sets results in the problems of combinatorial as well as maximum time consuming. The meta-heuristic approaches are typically employed to facilitate the choice of features. This paper presents an optimal extreme learning machine (ELM) based binary particle swarm optimization\u0000 to precede the FS process. The proposed method develops a Fitness Function (FF) by applying ELM. And the best solution of the FF has been explored under the application of BPSO technique. For instance, the dataset of product review which are derived from Amazon including synthetic data, which\u0000 is comprised with total of 235,000 positive and 147,000 negative review records is used. The experimental result implied that the ELM-BPSO technique is comparably best","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1233-1238"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49660863","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}
An image is a two-dimensional function that is expressed through spatial coordinates X, Y. At any pair of coordinates (x, y), the amplitude of a point is called the intensity of that pixel. Digital Image comprises a predictable number of components, each of which has a precise value at a given region. Those components are called pixels. Image Fusion is a phenomenon of transforming data from two or more images of a scenario into a single, more descriptive image taken than both of the input images, and is more appropriate for information processing. Image Fusion (IF) has been utilized in numerous application regions/areas. Remote Sensing Satellites (RSS) produce different images based on their sensory characteristics. Among those images, Panchromatic (PAN) and Multi-Spectral (MS) images are widely used in Satellite Image Fusion (SIF). The Image Fusion (IF) techniques are broadly classified as methods for the Spatial and Frequency domains. Wavelet Fusion Techniques (WFT) based on the Frequency-Domain (FD) are having applications in medical, space, and military applications. This literature delivers a study of some of the Image Fusion (IF) techniques. Remote Sensing Image (RSI) and Data Fusion (DF) seeks to merge the data acquired from sensors installed on satellites, airborne platforms, and ground-based sensors with specific spatial, spectral and temporal resolutions to produce merged data containing more accurate information than is found in each of the individual data sources.
{"title":"Image Fusion-The Pioneering Technique for Real-Time Image Processing Applications","authors":"P. Sreedhar, N. Nandhagopal","doi":"10.1166/JCTN.2021.9403","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9403","url":null,"abstract":"An image is a two-dimensional function that is expressed through spatial coordinates X, Y. At any pair of coordinates (x, y), the amplitude of a point is called the intensity of that pixel. Digital Image comprises a predictable number of components, each of which has a precise\u0000 value at a given region. Those components are called pixels. Image Fusion is a phenomenon of transforming data from two or more images of a scenario into a single, more descriptive image taken than both of the input images, and is more appropriate for information processing. Image Fusion (IF)\u0000 has been utilized in numerous application regions/areas. Remote Sensing Satellites (RSS) produce different images based on their sensory characteristics. Among those images, Panchromatic (PAN) and Multi-Spectral (MS) images are widely used in Satellite Image Fusion (SIF). The Image Fusion\u0000 (IF) techniques are broadly classified as methods for the Spatial and Frequency domains. Wavelet Fusion Techniques (WFT) based on the Frequency-Domain (FD) are having applications in medical, space, and military applications. This literature delivers a study of some of the Image Fusion (IF)\u0000 techniques. Remote Sensing Image (RSI) and Data Fusion (DF) seeks to merge the data acquired from sensors installed on satellites, airborne platforms, and ground-based sensors with specific spatial, spectral and temporal resolutions to produce merged data containing more accurate information\u0000 than is found in each of the individual data sources.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1208-1212"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64584778","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}
We propose an alternative algorithm referred to RVM (relevance vector machine) to circumvent the support vector machine’s (SVM) unnecessary use of basic functions, a large number of support vectors, lack of probabilistic prediction, and longer time computation complexity (TCC). Global annual land-ocean average temperature (GASAT) data and WTI oil market price data extracted from the National Aeronautic and Space Administration US and the US Energy Administration, respectively. The data were preprocessed and used to build RVM models. To evaluate the proposed RVM, its performance was compared to that of a SVM. The results were validated using ANOVA. A significant correlation between the two datasets was found. The relevance vectors for the RVM were significantly less than the support vectors for the SVM, and the TCC for the RVM was significantly better than the TCC for the SVM. The prediction accuracy of both the RVM and the SVM were found to be statistically equal. The RVM model was able to project the impact of GASAT on WTI crude oil prices from 2014 to 2023. The projection can be used by intergovernmental organizations to formulate a global response to combat WTI crude oil price negative impact, which is expected to worsen in the next decade.
{"title":"Projection of the Impact of Climate Change on Crude Oil Prices Based on Relevance Vector Machine","authors":"L. A. Gabralla","doi":"10.1166/JCTN.2021.9714","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9714","url":null,"abstract":"We propose an alternative algorithm referred to RVM (relevance vector machine) to circumvent the support vector machine’s (SVM) unnecessary use of basic functions, a large number of support vectors, lack of probabilistic prediction, and longer time computation complexity (TCC).\u0000 Global annual land-ocean average temperature (GASAT) data and WTI oil market price data extracted from the National Aeronautic and Space Administration US and the US Energy Administration, respectively. The data were preprocessed and used to build RVM models. To evaluate the proposed RVM,\u0000 its performance was compared to that of a SVM. The results were validated using ANOVA. A significant correlation between the two datasets was found. The relevance vectors for the RVM were significantly less than the support vectors for the SVM, and the TCC for the RVM was significantly better\u0000 than the TCC for the SVM. The prediction accuracy of both the RVM and the SVM were found to be statistically equal. The RVM model was able to project the impact of GASAT on WTI crude oil prices from 2014 to 2023. The projection can be used by intergovernmental organizations to formulate a\u0000 global response to combat WTI crude oil price negative impact, which is expected to worsen in the next decade.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1162-1170"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46109028","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}
R. Rahim, S. Murugan, R. Manikandan, Ambeshwar Kumar
In Polarimetric SAR (PolSAR) and synthetic aperture radar (SAR) requires efficient filtering approach for effective processing of captured quad-polarized images. Due to polarization speckles arises in the captured PolSAR images which causes complexity in collection of data from PolSAR images. This limitation has been overcome through application of effective despeckling technique for efficient processing of PolSAR images. Several techniques have been evolved over past three decades to reduce those speckle in image captured by PolSAR, and recent research studies illustrated a efficient trend for filtering local single-point to non-local patch-based or global collaborative filtering. In this paper, applied contourlet technique for effective despeckling of PolSAR images. This contourlet transformation technique perform thresholding and transformation for efficient despeckling of captured images. Contourlet transformation technique is applied in PolSAR images with speckle and performance metrices are observed. Contourlet technique is comparatively examined with conventional transformation technique which involved in despeckling of images. Analysis of results exhibited that DWT (Discrete Wavelet Transform) approach exhibits similar performance related to contourlet transformation technique. Through analysis of results it is observed that computation time also significantly reduced in contourlet transformation rather than conventional technique. Further mean and window sizing also significantly maintained in contourlet technique for various noise variance.
{"title":"Efficient Contourlet Transformation Technique for Despeckling of Polarimetric Synthetic Aperture Radar Image","authors":"R. Rahim, S. Murugan, R. Manikandan, Ambeshwar Kumar","doi":"10.1166/JCTN.2021.9396","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9396","url":null,"abstract":"In Polarimetric SAR (PolSAR) and synthetic aperture radar (SAR) requires efficient filtering approach for effective processing of captured quad-polarized images. Due to polarization speckles arises in the captured PolSAR images which causes complexity in collection of data from PolSAR\u0000 images. This limitation has been overcome through application of effective despeckling technique for efficient processing of PolSAR images. Several techniques have been evolved over past three decades to reduce those speckle in image captured by PolSAR, and recent research studies illustrated\u0000 a efficient trend for filtering local single-point to non-local patch-based or global collaborative filtering. In this paper, applied contourlet technique for effective despeckling of PolSAR images. This contourlet transformation technique perform thresholding and transformation for efficient\u0000 despeckling of captured images. Contourlet transformation technique is applied in PolSAR images with speckle and performance metrices are observed. Contourlet technique is comparatively examined with conventional transformation technique which involved in despeckling of images. Analysis of\u0000 results exhibited that DWT (Discrete Wavelet Transform) approach exhibits similar performance related to contourlet transformation technique. Through analysis of results it is observed that computation time also significantly reduced in contourlet transformation rather than conventional technique.\u0000 Further mean and window sizing also significantly maintained in contourlet technique for various noise variance.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1312-1320"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48653475","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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