Pub Date : 2023-01-01DOI: 10.1504/ijcat.2023.10058751
A. Azar
{"title":"Bio-inspired method for segmenting the optic disc and macula in retinal images","authors":"A. Azar","doi":"10.1504/ijcat.2023.10058751","DOIUrl":"https://doi.org/10.1504/ijcat.2023.10058751","url":null,"abstract":"","PeriodicalId":46624,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75109557","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 : 2023-01-01DOI: 10.1504/ijcat.2023.133036
Tanvi Dalal, Jyotsna Yadav
Support vector machines are widely utilised in the field of Face Recognition (FR) but it suffers from the drawback of high-computational time. In proposed work, new active set strategy is utilised for support vector machines on Integer Wavelet Transform (IWT) based large scale facial features with low-computational time. Lifting scheme-based significant localised wavelet features are extracted using IWT based on orthogonal wavelets. Large Scale Orthogonal-IWT (LSOI) features with maximum covariance are then projected into eigen space from where robust training and testing features are selected. For classification of data, Active Support Vector Machine (ASVM) based machine learning technique is utilised which generates a less complex procedure compared to traditional support vector machine. ASVM aims to solve a fixed number of linear equations for One-vs-One (OVO) and One-vs-All (OVA) multiclass FR. Extensive experiments on Yale, ORL, AR, JAFFE and Georgia-Tech databases have revealed high performance compared to existing FR techniques.
{"title":"Large-scale orthogonal integer wavelet transform features-based active support vector machine for multi-class face recognition","authors":"Tanvi Dalal, Jyotsna Yadav","doi":"10.1504/ijcat.2023.133036","DOIUrl":"https://doi.org/10.1504/ijcat.2023.133036","url":null,"abstract":"Support vector machines are widely utilised in the field of Face Recognition (FR) but it suffers from the drawback of high-computational time. In proposed work, new active set strategy is utilised for support vector machines on Integer Wavelet Transform (IWT) based large scale facial features with low-computational time. Lifting scheme-based significant localised wavelet features are extracted using IWT based on orthogonal wavelets. Large Scale Orthogonal-IWT (LSOI) features with maximum covariance are then projected into eigen space from where robust training and testing features are selected. For classification of data, Active Support Vector Machine (ASVM) based machine learning technique is utilised which generates a less complex procedure compared to traditional support vector machine. ASVM aims to solve a fixed number of linear equations for One-vs-One (OVO) and One-vs-All (OVA) multiclass FR. Extensive experiments on Yale, ORL, AR, JAFFE and Georgia-Tech databases have revealed high performance compared to existing FR techniques.","PeriodicalId":46624,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135002321","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 : 2023-01-01DOI: 10.1504/ijcat.2023.133297
Shubham P. Kulkarni, Sanjeev Patel
{"title":"Ensemble-based software fault prediction with two staged data pre-processing","authors":"Shubham P. Kulkarni, Sanjeev Patel","doi":"10.1504/ijcat.2023.133297","DOIUrl":"https://doi.org/10.1504/ijcat.2023.133297","url":null,"abstract":"","PeriodicalId":46624,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135400456","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 : 2023-01-01DOI: 10.1504/ijcat.2023.133040
Arshad Ahmad Khan Mohammad, Arif Mohammad Abdul
Wireless infrastructure-less networks comprise mobile devices disseminated in radio communication areas without any central coordinator. The nodes communicate directly if they are within the radio range; otherwise, they rely on other nodes. Thus, nodes should act as a router to forward the information to other nodes. The network permits the nodes to be mobile freely and organises arbitrarily; therefore, any node can participate or leave the network independently. Further, nodes in the network consist of constrained heterogeneous resources. Proper utilisation and management of network resources and characteristics are needed to achieve performance efficiency in communication. The paper designs an Optimised Reactive Resource Aware Routing Mechanism to utilise resources effectively and adequately control network characteristics. The proposed mechanism is validated with NS2, and performance is compared with current routing protocols. Results indicate that the proposed mechanism outperforms energy efficiency, packet delivery and resource utilisation.
{"title":"Optimised reactive resource aware routing for wireless infrastructure-less networks","authors":"Arshad Ahmad Khan Mohammad, Arif Mohammad Abdul","doi":"10.1504/ijcat.2023.133040","DOIUrl":"https://doi.org/10.1504/ijcat.2023.133040","url":null,"abstract":"Wireless infrastructure-less networks comprise mobile devices disseminated in radio communication areas without any central coordinator. The nodes communicate directly if they are within the radio range; otherwise, they rely on other nodes. Thus, nodes should act as a router to forward the information to other nodes. The network permits the nodes to be mobile freely and organises arbitrarily; therefore, any node can participate or leave the network independently. Further, nodes in the network consist of constrained heterogeneous resources. Proper utilisation and management of network resources and characteristics are needed to achieve performance efficiency in communication. The paper designs an Optimised Reactive Resource Aware Routing Mechanism to utilise resources effectively and adequately control network characteristics. The proposed mechanism is validated with NS2, and performance is compared with current routing protocols. Results indicate that the proposed mechanism outperforms energy efficiency, packet delivery and resource utilisation.","PeriodicalId":46624,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135002274","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 : 2023-01-01DOI: 10.1504/ijcat.2023.133836
U.K. Jena, M.R. Kabat, P.K. Das
Load balancing is one of the primary aspects of cloud computing to avoid situations of being overloaded or underloaded in the node. The paper aims to carry out the dynamic load balancing of non-determent independent tasks in the cloud network and resolved through the hybridisation of an improved version of the Harris Hawks Optimisation Algorithm (HHO) improved by differential perturbed velocity and Thermal Exchange Optimisation (TEO). The main motivations of hybridising are to intensify the diversification ability of the device through the load balance with the VMs, optimise different matrices and enhance the convergence speed. The strength of the algorithm has been authenticated by relating the outcome gained from simulation and real platform processes with the surviving load balancing. The conclusions drawn from the simulation and comparison results illustrate that the projected procedure is outstripping its opponent in the manner of different matrices.
{"title":"Efficient load balancing in cloud computing using HHO improved by differential perturbed velocity and TEO","authors":"U.K. Jena, M.R. Kabat, P.K. Das","doi":"10.1504/ijcat.2023.133836","DOIUrl":"https://doi.org/10.1504/ijcat.2023.133836","url":null,"abstract":"Load balancing is one of the primary aspects of cloud computing to avoid situations of being overloaded or underloaded in the node. The paper aims to carry out the dynamic load balancing of non-determent independent tasks in the cloud network and resolved through the hybridisation of an improved version of the Harris Hawks Optimisation Algorithm (HHO) improved by differential perturbed velocity and Thermal Exchange Optimisation (TEO). The main motivations of hybridising are to intensify the diversification ability of the device through the load balance with the VMs, optimise different matrices and enhance the convergence speed. The strength of the algorithm has been authenticated by relating the outcome gained from simulation and real platform processes with the surviving load balancing. The conclusions drawn from the simulation and comparison results illustrate that the projected procedure is outstripping its opponent in the manner of different matrices.","PeriodicalId":46624,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136003522","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 : 2023-01-01DOI: 10.1504/ijcat.2023.133296
Megha Agarwal
{"title":"An automatic image retrieval system using multi-scale local ternary pattern","authors":"Megha Agarwal","doi":"10.1504/ijcat.2023.133296","DOIUrl":"https://doi.org/10.1504/ijcat.2023.133296","url":null,"abstract":"","PeriodicalId":46624,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135400460","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 : 2023-01-01DOI: 10.1504/ijcat.2023.133307
Ahmad Taher Azar
{"title":"Hybrid machine learning approach for human activity recognition","authors":"Ahmad Taher Azar","doi":"10.1504/ijcat.2023.133307","DOIUrl":"https://doi.org/10.1504/ijcat.2023.133307","url":null,"abstract":"","PeriodicalId":46624,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135400577","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 : 2023-01-01DOI: 10.1504/ijcat.2023.133037
Mohammad Ahmad Ansari, Krishnan Rajkumar, Poonam Agarwal
In this paper, supervised machine learning regression techniques: Support Vector Machine (SVM), Random Forest and Deep Neural Network (DNN) models, are demonstrated to predict the characteristic impedance of the microstrip transmission line. Here, microstrip transmission line width, substrate height and substrate dielectric constant are taken as the input and characteristics impedance as the output parameter. To train the models, the data set is created using microstrip transmission line analytical models. DNN models are developed using Feed-forward Back-propagation learning algorithm, where 'adam' is used as optimiser and 'relu' as the activation function. The regression predictive model of SVM and Random Forest model of ensemble learning using bagging technique are developed. It is found that minimum MSE of DNN model is 0.04191 with high execution time 1114.179655 sec, whereas SVM model shows low execution time of 0.8327 sec with MSE of 0.49. Random Forest model showed the MSE of 0.14 with execution time 1.4296 sec.
{"title":"To predict the characteristic impedance of the microstrip transmission line using supervised machine learning regression techniques","authors":"Mohammad Ahmad Ansari, Krishnan Rajkumar, Poonam Agarwal","doi":"10.1504/ijcat.2023.133037","DOIUrl":"https://doi.org/10.1504/ijcat.2023.133037","url":null,"abstract":"In this paper, supervised machine learning regression techniques: Support Vector Machine (SVM), Random Forest and Deep Neural Network (DNN) models, are demonstrated to predict the characteristic impedance of the microstrip transmission line. Here, microstrip transmission line width, substrate height and substrate dielectric constant are taken as the input and characteristics impedance as the output parameter. To train the models, the data set is created using microstrip transmission line analytical models. DNN models are developed using Feed-forward Back-propagation learning algorithm, where 'adam' is used as optimiser and 'relu' as the activation function. The regression predictive model of SVM and Random Forest model of ensemble learning using bagging technique are developed. It is found that minimum MSE of DNN model is 0.04191 with high execution time 1114.179655 sec, whereas SVM model shows low execution time of 0.8327 sec with MSE of 0.49. Random Forest model showed the MSE of 0.14 with execution time 1.4296 sec.","PeriodicalId":46624,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135002004","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 : 2023-01-01DOI: 10.1504/ijcat.2023.133038
Juan C. Tejada, Alejandro Toro Ossaba, Juan Berruecos, Santiago Rúa, Daniel Sanin Villa, Alexandro López González
Collaborative robots (cobot) are a great solution for companies that must automate processes without modifying the production line, however, these robots lose flexibility in the application when they need to be located in another point of the production line performing a different task than usual because this action involves programming changes. This paper presents the creation of a programming platform that facilitates the configuration of cobots, for the operator to reprogram the robot simply by writing the task to be performed in a web application, without any programming structure, compatible with the flexibility and adaptability of the collaborative robot. To develop this platform, a technology called Robotic Process Automation (RPA) will be used, with which a bot will be created with the ability to interpret user instructions, structure the corresponding code and program the cobot. Thus, a platform is obtained capable of implementing without the need for additional hardware.
{"title":"A systems engineering approach for Baxter Assistant: programming platform to facilitate the configuration of CoBots through natural language","authors":"Juan C. Tejada, Alejandro Toro Ossaba, Juan Berruecos, Santiago Rúa, Daniel Sanin Villa, Alexandro López González","doi":"10.1504/ijcat.2023.133038","DOIUrl":"https://doi.org/10.1504/ijcat.2023.133038","url":null,"abstract":"Collaborative robots (cobot) are a great solution for companies that must automate processes without modifying the production line, however, these robots lose flexibility in the application when they need to be located in another point of the production line performing a different task than usual because this action involves programming changes. This paper presents the creation of a programming platform that facilitates the configuration of cobots, for the operator to reprogram the robot simply by writing the task to be performed in a web application, without any programming structure, compatible with the flexibility and adaptability of the collaborative robot. To develop this platform, a technology called Robotic Process Automation (RPA) will be used, with which a bot will be created with the ability to interpret user instructions, structure the corresponding code and program the cobot. Thus, a platform is obtained capable of implementing without the need for additional hardware.","PeriodicalId":46624,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135002294","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}