N. Nandhagopal, S. Navaneethan, V. Nivedita, A. Parimala, Dinesh Valluru
The pupil detection system plays a vital role in ophthalmology diagnosis equipments because pupil has a center place of human eye to locate the exact position. To identify the exact human eye pupil region in near infrared (NIR) images, this work proposes the Center of gravity method and its real time FPGA hardware implementation. The proposed work involves global threshold method to segment the pupil region from human eye and the bright spot suppression process removes the light reflections on the pupil due to the IR (Infra red) rays then the morphology dilation process removes unnecessary black pixels other than pupil region on the image. Finally, center of gravity (COG) method provides the exact pupil center coordinate and radius of the human eye. CASIA IRIS V4 and UBIRIS iris database images used in this work and achieved 90-95% of recognition rate.
{"title":"Human Eye Pupil Detection System for Different IRIS Database Images","authors":"N. Nandhagopal, S. Navaneethan, V. Nivedita, A. Parimala, Dinesh Valluru","doi":"10.1166/JCTN.2021.9390","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9390","url":null,"abstract":"The pupil detection system plays a vital role in ophthalmology diagnosis equipments because pupil has a center place of human eye to locate the exact position. To identify the exact human eye pupil region in near infrared (NIR) images, this work proposes the Center of gravity method\u0000 and its real time FPGA hardware implementation. The proposed work involves global threshold method to segment the pupil region from human eye and the bright spot suppression process removes the light reflections on the pupil due to the IR (Infra red) rays then the morphology dilation process\u0000 removes unnecessary black pixels other than pupil region on the image. Finally, center of gravity (COG) method provides the exact pupil center coordinate and radius of the human eye. CASIA IRIS V4 and UBIRIS iris database images used in this work and achieved 90-95% of recognition rate.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49436366","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 efficient message routing is highly challenging in terms of low power and lossy networks (loT) for transmission of data with overhead and delay. The protocols used for routing need to be designed such that they should be working efficiently. Efficiency in calculated in terms of energy and delivery of packets. RPL protocol is also designed with the aim of making these two parameters efficient. Even then it contains drawbacks. Trickle algorithm is designed with a goal to reduce the drawbacks in RPL. Trickle algorithm is used in RPL protocols for creation of routes between nodes in the network with different intervals. Unfortunately, there exists some more downsides for the trickle algorithm, which made design of several algorithms inorder to analyse different drawbacks. In this paper, on analysing different types of trickle algorithms and locating the drawback in every algorithm, a novel algorithm is designed which helps in reduction of the drawbacks that are found. The description of this algorithm along with the simulation results done using Cooja 3.0 simulator is also discussed in this paper. The Simulation of the algorithm that is newly designed is done by assuming a network with different count of nodes and comparing the results with the previously introduced Trickle algorithms.
{"title":"Design and Implementation of an Adaptable Trickle Algorithm for Amelioration of RPL Usage in Internet of Things Networks","authors":"J. Kumar, D. Suresh","doi":"10.1166/JCTN.2021.9382","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9382","url":null,"abstract":"The efficient message routing is highly challenging in terms of low power and lossy networks (loT) for transmission of data with overhead and delay. The protocols used for routing need to be designed such that they should be working efficiently. Efficiency in calculated in terms of\u0000 energy and delivery of packets. RPL protocol is also designed with the aim of making these two parameters efficient. Even then it contains drawbacks. Trickle algorithm is designed with a goal to reduce the drawbacks in RPL. Trickle algorithm is used in RPL protocols for creation of routes\u0000 between nodes in the network with different intervals. Unfortunately, there exists some more downsides for the trickle algorithm, which made design of several algorithms inorder to analyse different drawbacks. In this paper, on analysing different types of trickle algorithms and locating the\u0000 drawback in every algorithm, a novel algorithm is designed which helps in reduction of the drawbacks that are found. The description of this algorithm along with the simulation results done using Cooja 3.0 simulator is also discussed in this paper. The Simulation of the algorithm that is newly\u0000 designed is done by assuming a network with different count of nodes and comparing the results with the previously introduced Trickle algorithms.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49110891","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}
{"title":"A Special Section on Electronical and Computer Communication Systems","authors":"V. Gopal","doi":"10.1166/JCTN.2021.9405","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9405","url":null,"abstract":"","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46932185","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}
By the CAD diagnosis using various clinical parameters such as plate-ratio (commander) optical cup are determined to diagnose glaucoma. Hough converter and circular return view disk fundus image is taken. Level compilation methods and integrated space-based blur package are proposed to analyze the optical cup area from the viewing disk. The experiments were performed using the MATLAB software HRF database using the proposed approach to fundus imaging from images in the hospital. The Linear regression fit is intent to discover the Gold standard assessment for the evaluating attained CDR based on the fundus image acquired from the hospital database. Them CDR values is obtained through Bayesian classifies to train the dataset. Consequences formed from the sorting achieve data results, the sensitivity of 96.47%, specificity of 92.85% and an accuracy of 94.83%. Receiver operating characteristic curve is plotted for the observed and gold standard values of CDR. With this approach, the boundaries of the region can be accurately identified and the target mass of screening retinal images for early detection of glaucoma can be used and the resulting segmentation in consistent areas can be made firmer.
{"title":"Computer Aided Design Diagnosis for Glaucoma Detection in Retinal Images by Spatial Fuzzy C Means with Level Set Segmentation","authors":"S. Shoba","doi":"10.1166/JCTN.2020.9457","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9457","url":null,"abstract":"By the CAD diagnosis using various clinical parameters such as plate-ratio (commander) optical cup are determined to diagnose glaucoma. Hough converter and circular return view disk fundus image is taken. Level compilation methods and integrated space-based blur package are proposed\u0000 to analyze the optical cup area from the viewing disk. The experiments were performed using the MATLAB software HRF database using the proposed approach to fundus imaging from images in the hospital. The Linear regression fit is intent to discover the Gold standard assessment for the evaluating\u0000 attained CDR based on the fundus image acquired from the hospital database. Them CDR values is obtained through Bayesian classifies to train the dataset. Consequences formed from the sorting achieve data results, the sensitivity of 96.47%, specificity of 92.85% and an accuracy of 94.83%. Receiver\u0000 operating characteristic curve is plotted for the observed and gold standard values of CDR. With this approach, the boundaries of the region can be accurately identified and the target mass of screening retinal images for early detection of glaucoma can be used and the resulting segmentation\u0000 in consistent areas can be made firmer.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45162035","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 Internet has enormous information and it is growing rapidly. The vast amount of data creates challenges in relation to effective Information Retrieval (IR). The scope of the Information Retrieval System (IRS) is to provide the most relevant data for user query from large datasets. However the current IR system fails to provide the hidden and up to date data. This paper focused on soft computing techniques to overcome the above mentioned issues. Particle Swarm Optimization (PSO) is used to compute the fitness function to optimize the retrieval result. PSO has an efficient capability in global search and the implementation is easy to develop. The implementation result of the present study is feasible, that improves the retrieval effect and the accuracy of hidden data retrieval.
{"title":"An Innovative Information Retrieval Model Implementing Particle Swarm Optimization Technique","authors":"S. Surya, P. Sumitra","doi":"10.1166/JCTN.2020.9460","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9460","url":null,"abstract":"The Internet has enormous information and it is growing rapidly. The vast amount of data creates challenges in relation to effective Information Retrieval (IR). The scope of the Information Retrieval System (IRS) is to provide the most relevant data for user query from large datasets.\u0000 However the current IR system fails to provide the hidden and up to date data. This paper focused on soft computing techniques to overcome the above mentioned issues. Particle Swarm Optimization (PSO) is used to compute the fitness function to optimize the retrieval result. PSO has an efficient\u0000 capability in global search and the implementation is easy to develop. The implementation result of the present study is feasible, that improves the retrieval effect and the accuracy of hidden data retrieval.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49146817","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}
This research article has explored the electronic behaviour of CdTe nanowire. The present study has evolved the structural dependence of electronic properties of CdTe nanowire. The shapes for which this dependence has been studied are 2 atoms linear, 2 atoms zigzag, 4 atoms square and 6 atoms hexagonal for CdTe nanowire. We have used ABINIT code for this study. We have explored the geometrical optimization, band structure and stability of proposed structures. The structure which has come out to be the most stable amongst the all is 4 atom square nanowire where as the findings of the study for band structure reveal that CdTe nanowires may have insulating as well semiconducting nature depending on the shape of the nanowire.
{"title":"First Principles Study of Structural Stability and Electronic Properties of CdTe Nanowires","authors":"S. Kaushik, S. Singh, R. Thakur","doi":"10.1166/JCTN.2020.9631","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9631","url":null,"abstract":"This research article has explored the electronic behaviour of CdTe nanowire. The present study has evolved the structural dependence of electronic properties of CdTe nanowire. The shapes for which this dependence has been studied are 2 atoms linear, 2 atoms zigzag, 4 atoms square and\u0000 6 atoms hexagonal for CdTe nanowire. We have used ABINIT code for this study. We have explored the geometrical optimization, band structure and stability of proposed structures. The structure which has come out to be the most stable amongst the all is 4 atom square nanowire where as the findings\u0000 of the study for band structure reveal that CdTe nanowires may have insulating as well semiconducting nature depending on the shape of the nanowire.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43374890","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}
Cervical cancer is a commonly occurring deadliest disease among women, which needs earlier diagnosis to reduce the prevalence. Pap-smear is considered as a widely employed technique to screen and diagnose cervical cancer. Since classical manual screening techniques are inefficient in the identification of cervical cancer, several research works have been started to develop automated machine learning (ML) and deep learning (DL) tools for cervical cancer diagnosis. This paper surveys the recent works made on cervical cancer diagnosis and classification. The recently presently ML and DL models for cervical cancer diagnosis and classification has been reviewed in detail. Besides, segmentation techniques developed for cervical cancer diagnosis also surveyed. At the end of the survey, a brief comparative study has been carried out to identify the significance of the reviewed methods.
{"title":"An Extensive Review on Machine Learning and Deep Learning Based Cervical Cancer Diagnosis and Classification Models","authors":"C. Suguna, S. Balamurugan","doi":"10.1166/JCTN.2020.9437","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9437","url":null,"abstract":"Cervical cancer is a commonly occurring deadliest disease among women, which needs earlier diagnosis to reduce the prevalence. Pap-smear is considered as a widely employed technique to screen and diagnose cervical cancer. Since classical manual screening techniques are inefficient in\u0000 the identification of cervical cancer, several research works have been started to develop automated machine learning (ML) and deep learning (DL) tools for cervical cancer diagnosis. This paper surveys the recent works made on cervical cancer diagnosis and classification. The recently presently\u0000 ML and DL models for cervical cancer diagnosis and classification has been reviewed in detail. Besides, segmentation techniques developed for cervical cancer diagnosis also surveyed. At the end of the survey, a brief comparative study has been carried out to identify the significance of the\u0000 reviewed methods.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42244009","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}
In the era of technology advancement and COVID-19 outbreak period, all physical classes have been converted to online classes through social network platforms. Having online classes through social networks are actually very comfortable and flexible for students as they can have their classes at various places. This paper is focuses on the relationship between usages of social network and the quality of education during COVID-19 outbreak.
{"title":"The Usage of Social Network to Students: Does It Improve Student’s Education Quality During COVID-19 Outbreak","authors":"Indraah Kolandaisamy, Raenu Kolandaisamy","doi":"10.1166/JCTN.2020.9412","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9412","url":null,"abstract":"In the era of technology advancement and COVID-19 outbreak period, all physical classes have been converted to online classes through social network platforms. Having online classes through social networks are actually very comfortable and flexible for students as they can have their\u0000 classes at various places. This paper is focuses on the relationship between usages of social network and the quality of education during COVID-19 outbreak.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48072941","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}
P. Meenal, P. Gowr, A. Ram, A. Rajini, B. Abishek, D. Ravikumar
Excess amount of insulin in human blood might affect the retina in eyes and cause abnormalities in human vision, which is generally termed as Diabetic Retinopathy (DR). Many diabetic patients are often saved by the earlier diagnosis of Diabetic Retinopathy. The surface of retinal layer that has the earlier signs of Diabetic Retinopathy. This type of abnormalities are detected using traditional image processing methods which includes stages such as capturing fundus images, preprocessing, feature extraction and finally classification is performed to classify it as retinal and healthy images. (The proposed system, this detection is completed by Fuzzy-C Means (FCM) clustering). The proposed automated system consists of four phases which includes, preprocessing of the captured fundus images in which the image is resized and the second stage involves CLAHE. Images has to enhanced in order to boost up the features for which Contrast adjustment is performed in the third phase and before classification the grey and green channels of the images are extracted from the processed images. This detection process provides better results than the prevailing method. SVM classifier has been used in the proposed framework which classified the malady level of diabetic retinopathy in eye. The proposed system manages to provide better classification rates compared to the previous methodologies. The accuracy, sensitivity and specificity of the developed automated system was found to be 94.4%, 100% and 85.7%, which was promising than the compared methods.
{"title":"Automatic Detection of Diabetic Retinopathy Using Support Vector Machine","authors":"P. Meenal, P. Gowr, A. Ram, A. Rajini, B. Abishek, D. Ravikumar","doi":"10.1166/JCTN.2020.9456","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9456","url":null,"abstract":"Excess amount of insulin in human blood might affect the retina in eyes and cause abnormalities in human vision, which is generally termed as Diabetic Retinopathy (DR). Many diabetic patients are often saved by the earlier diagnosis of Diabetic Retinopathy. The surface of retinal layer\u0000 that has the earlier signs of Diabetic Retinopathy. This type of abnormalities are detected using traditional image processing methods which includes stages such as capturing fundus images, preprocessing, feature extraction and finally classification is performed to classify it as retinal\u0000 and healthy images. (The proposed system, this detection is completed by Fuzzy-C Means (FCM) clustering). The proposed automated system consists of four phases which includes, preprocessing of the captured fundus images in which the image is resized and the second stage involves CLAHE. Images\u0000 has to enhanced in order to boost up the features for which Contrast adjustment is performed in the third phase and before classification the grey and green channels of the images are extracted from the processed images. This detection process provides better results than the prevailing method.\u0000 SVM classifier has been used in the proposed framework which classified the malady level of diabetic retinopathy in eye. The proposed system manages to provide better classification rates compared to the previous methodologies. The accuracy, sensitivity and specificity of the developed automated\u0000 system was found to be 94.4%, 100% and 85.7%, which was promising than the compared methods.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44628913","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}
Early detection of heart disease may prevent myocardial infarction. Electrocardiogram (ECG) is the most widely used signal in clinical practice for the diagnosis of cardiovascular diseases such as arrhythmias and myocardial infarction. Human interpretation is time-consuming, and long-term ECG records are difficult to detect in small differences.Therefore, automated recognition of myocardial infarction using a Computer-Aided Diagnosis (CAD) system is the research interest, which can be used effectively to reduce mortality among cardiovascular disease patients. The most important step in the analysis of complex R-peak/QRS signals using an automated process of ECG signal. To automate the cardiovascular disease detection process, an adequate mechanism is required to characterize ECG signals, which are unknown features according to the similarities between ECG signals. If the classification can find similarities accurately and the probability of arrhythmia detection increases, the algorithm can become an effective method in the laboratory. In this research work, a new classification strategy is proposed to all the more precisely order ECG signals dependent on a powerful model of ECG signals. In this proposed method, a Nonlinear Vector Decomposed Neural Network (NVDN) is developed, and its simulation results show that this classifier can isolate the ECGs with high productivity. This proposed technique expands the exactness of the ECG classification concerning increasingly exact arrhythmia discovery.
{"title":"ECG Classification Framework for Cardiac Disease Prediction Using Nonlinear Vector Decomposed Neural Network","authors":"M. Suhail, T. .. Razak","doi":"10.1166/JCTN.2020.9453","DOIUrl":"https://doi.org/10.1166/JCTN.2020.9453","url":null,"abstract":"Early detection of heart disease may prevent myocardial infarction. Electrocardiogram (ECG) is the most widely used signal in clinical practice for the diagnosis of cardiovascular diseases such as arrhythmias and myocardial infarction. Human interpretation is time-consuming, and long-term\u0000 ECG records are difficult to detect in small differences.Therefore, automated recognition of myocardial infarction using a Computer-Aided Diagnosis (CAD) system is the research interest, which can be used effectively to reduce mortality among cardiovascular disease patients. The most important\u0000 step in the analysis of complex R-peak/QRS signals using an automated process of ECG signal. To automate the cardiovascular disease detection process, an adequate mechanism is required to characterize ECG signals, which are unknown features according to the similarities between ECG signals.\u0000 If the classification can find similarities accurately and the probability of arrhythmia detection increases, the algorithm can become an effective method in the laboratory. In this research work, a new classification strategy is proposed to all the more precisely order ECG signals dependent\u0000 on a powerful model of ECG signals. In this proposed method, a Nonlinear Vector Decomposed Neural Network (NVDN) is developed, and its simulation results show that this classifier can isolate the ECGs with high productivity. This proposed technique expands the exactness of the ECG classification\u0000 concerning increasingly exact arrhythmia discovery.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43871500","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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