{"title":"June 2021","authors":"","doi":"10.36548/jtcsst.2021.2","DOIUrl":"https://doi.org/10.36548/jtcsst.2021.2","url":null,"abstract":"","PeriodicalId":107574,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121566081","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 : 2020-07-23DOI: 10.36548/jscp.2020.3.005
Dr. P. Karuppusamy
In medical image processing, segmentation and extraction of tumor portion from brain MRI is a complex task. It consumes more time and human effort to differentiate the normal and abnormal tissue. Clinical experts need more time to provide accurate results, recent technology developments in image processing reduces the human effort and provides more accurate results which reduces time and death rates by identifying the issues in early stage itself. Machine learning based algorithms occupies a major role in bio medical image processing applications. The performance of machine learning models is in satisfactory levels, but it could be improved by introducing optimization in feature selection stage itself. The research work provides a hybrid manta ray foraging optimization for feature selection from brain tumor MRI images. Convolution neural network is used to test the optimized features and detects the early stage brain tumors. The experimental model is compared with existing artificial neural network, particle swarm optimization algorithm and acquires a better detection and classification accuracy.
{"title":"Hybrid Manta Ray Foraging Optimization for Novel Brain Tumor Detection","authors":"Dr. P. Karuppusamy","doi":"10.36548/jscp.2020.3.005","DOIUrl":"https://doi.org/10.36548/jscp.2020.3.005","url":null,"abstract":"In medical image processing, segmentation and extraction of tumor portion from brain MRI is a complex task. It consumes more time and human effort to differentiate the normal and abnormal tissue. Clinical experts need more time to provide accurate results, recent technology developments in image processing reduces the human effort and provides more accurate results which reduces time and death rates by identifying the issues in early stage itself. Machine learning based algorithms occupies a major role in bio medical image processing applications. The performance of machine learning models is in satisfactory levels, but it could be improved by introducing optimization in feature selection stage itself. The research work provides a hybrid manta ray foraging optimization for feature selection from brain tumor MRI images. Convolution neural network is used to test the optimized features and detects the early stage brain tumors. The experimental model is compared with existing artificial neural network, particle swarm optimization algorithm and acquires a better detection and classification accuracy.","PeriodicalId":107574,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124068525","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 : 2020-05-10DOI: 10.36548/jtcsst.2020.2.001
Sathish
Patient preference management is an essential work for any healthcare scheme to give priority to the needy patient. The work is generally carryout by a caretaker in the healthcare block to enroll their details of the patient on computer to find out and suggest an available consultant and time slot for the patient. These kind of usual works can be helpful up to certain normal conditions only. During uncertain times like viral explosion or war or nature disaster, the usual system will make the patient to wait in a queue for enrollment process. Most of the time it is intolerable to make a severe injured person to wait in the queue for the treatment. At the same time, during viral explosion the people were asked to stay at their home and for treatment they have to make a phone call to the care taking team for expressing their situation and health status. Attending a huge number of phone calls manually and providing a good suggestion to the caller is a challenging work for any healthcare team. The proposed IoT based computer vision system suggests the patient to send their status through a mobile phone message or email to the healthcare server to segregate the status of patient as emergency, severe and follow-up categories. This makes the healthcare team to identify the needy patient at right time to serve them. The proposed system is simulated with different computer vision algorithm and analyses its accuracy, time delay and drop rate to make a reliable patient preference management system.
{"title":"Computer Vision on IOT Based Patient Preference Management System","authors":"Sathish","doi":"10.36548/jtcsst.2020.2.001","DOIUrl":"https://doi.org/10.36548/jtcsst.2020.2.001","url":null,"abstract":"Patient preference management is an essential work for any healthcare scheme to give priority to the needy patient. The work is generally carryout by a caretaker in the healthcare block to enroll their details of the patient on computer to find out and suggest an available consultant and time slot for the patient. These kind of usual works can be helpful up to certain normal conditions only. During uncertain times like viral explosion or war or nature disaster, the usual system will make the patient to wait in a queue for enrollment process. Most of the time it is intolerable to make a severe injured person to wait in the queue for the treatment. At the same time, during viral explosion the people were asked to stay at their home and for treatment they have to make a phone call to the care taking team for expressing their situation and health status. Attending a huge number of phone calls manually and providing a good suggestion to the caller is a challenging work for any healthcare team. The proposed IoT based computer vision system suggests the patient to send their status through a mobile phone message or email to the healthcare server to segregate the status of patient as emergency, severe and follow-up categories. This makes the healthcare team to identify the needy patient at right time to serve them. The proposed system is simulated with different computer vision algorithm and analyses its accuracy, time delay and drop rate to make a reliable patient preference management system.","PeriodicalId":107574,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127580920","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 : 2020-03-25DOI: 10.36548/jtcsst.2020.1.005
Dr. Subarna Shakya, Dr. Smys S.
A novel platform of dispersed streaming is developed by the fog paradigm for the applications associated with the internet of things. The sensed information’s of the IOT plat form is collected from the edge device closer to the user from the lower plane and moved to the fog in the middle of the cloud and edge and then further pushed to the cloud at the top most plane. The information’s gathered at the lower plane often holds unanticipated values that are of no use in the application. These unanticipated or the unexpected data’s are termed as anomalies. These unexpected data’s could emerge either due to the improper edge device functioning which is usually the mobile devices, sensors or the actuators or the coincidences or purposeful attacks or due to environmental changes. The anomalies are supposed to be removed to retain the efficiency of the network and the application. The deep learning frame work developed in the paper involves the hardware techniques to detect the anomalies in the fog paradigm. The experimental analysis showed that the deep learning models are highly grander compared to the rest of the basic detection structures on the terms of the accuracy in detecting, false-alarm and elasticity.
{"title":"Anomalies Detection in Fog Computing Architectures Using Deep Learning","authors":"Dr. Subarna Shakya, Dr. Smys S.","doi":"10.36548/jtcsst.2020.1.005","DOIUrl":"https://doi.org/10.36548/jtcsst.2020.1.005","url":null,"abstract":"A novel platform of dispersed streaming is developed by the fog paradigm for the applications associated with the internet of things. The sensed information’s of the IOT plat form is collected from the edge device closer to the user from the lower plane and moved to the fog in the middle of the cloud and edge and then further pushed to the cloud at the top most plane. The information’s gathered at the lower plane often holds unanticipated values that are of no use in the application. These unanticipated or the unexpected data’s are termed as anomalies. These unexpected data’s could emerge either due to the improper edge device functioning which is usually the mobile devices, sensors or the actuators or the coincidences or purposeful attacks or due to environmental changes. The anomalies are supposed to be removed to retain the efficiency of the network and the application. The deep learning frame work developed in the paper involves the hardware techniques to detect the anomalies in the fog paradigm. The experimental analysis showed that the deep learning models are highly grander compared to the rest of the basic detection structures on the terms of the accuracy in detecting, false-alarm and elasticity.","PeriodicalId":107574,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":" 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141220932","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 : 2020-03-10DOI: 10.36548/jtcsst.2020.1.002
S. Mugunthan
The cyber-attacks nowadays are becoming more and more erudite causing challenges in distinguishing them and confining. These attacks affect the sensitized information’s of the network by penetrating into the network and behaving normally. The paper devises a system for such interference recognition in the internet of things architecture that is aided by the FOG. The proposed system is a combination of variety of classifiers that are founded on the decision tree as well as the rule centered conceptions. The system put forth involves the JRip and the REP tree algorithm to utilize the features of the data set as input and distinguishes between the benign and the malicious traffic in the network and includes an decision forest that is improved with the penalizing attributes of the previous trees in the final stage to classify the traffic in the network utilizing the initial data set as well as the outputs of the classifiers that were engaged in the former stages. The proffered system was examined using the dataset such BOT-Internet of things and the CICIDS2017 to evince its competence in terms of rate of false alarm, detection, and accuracy. The attained results proved that the performance of the proposed system was better compared to the exiting methodologies to recognize the interference.
当今的网络攻击越来越广泛，给网络攻击的识别和控制带来了挑战。这类攻击通过渗透到网络中并正常活动来影响网络的敏感信息。本文设计了一种基于光纤陀螺的物联网干扰识别系统。所提出的系统是基于决策树和以规则为中心概念的各种分类器的组合。系统提出涉及JRip和代表树算法利用数据集的特征作为输入,并区分良性和恶意的交通网络中,包括一个提高决策森林与以前的惩罚属性树最后阶段分类的交通网络利用初始数据集以及分类器的输出,从事前阶段。使用BOT-Internet of things和CICIDS2017等数据集对所提供的系统进行了检查，以证明其在误报率、检测率和准确性方面的能力。实验结果表明，与现有的干扰识别方法相比，该方法具有更好的识别性能。
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Pub Date : 2019-12-29DOI: 10.36548/jtcsst.2019.2.006
Dr. Suma V.
The paper is a review on the computer vision that is helpful in the interaction between the human and the machines. The computer vision that is termed as the subfield of the artificial intelligence and the machine learning is capable of training the computer to visualize, interpret and respond back to the visual world in a similar way as the human vision does. Nowadays the computer vision has found its application in broader areas such as the heath care, safety security, surveillance etc. due to the progress, developments and latest innovations in the artificial intelligence, deep learning and neural networks. The paper presents the enhanced capabilities of the computer vision experienced in various applications related to the interactions between the human and machines involving the artificial intelligence, deep learning and the neural networks.
{"title":"COMPUTER VISION FOR HUMAN-MACHINE INTERACTION-REVIEW","authors":"Dr. Suma V.","doi":"10.36548/jtcsst.2019.2.006","DOIUrl":"https://doi.org/10.36548/jtcsst.2019.2.006","url":null,"abstract":"The paper is a review on the computer vision that is helpful in the interaction between the human and the machines. The computer vision that is termed as the subfield of the artificial intelligence and the machine learning is capable of training the computer to visualize, interpret and respond back to the visual world in a similar way as the human vision does. Nowadays the computer vision has found its application in broader areas such as the heath care, safety security, surveillance etc. due to the progress, developments and latest innovations in the artificial intelligence, deep learning and neural networks. The paper presents the enhanced capabilities of the computer vision experienced in various applications related to the interactions between the human and machines involving the artificial intelligence, deep learning and the neural networks.","PeriodicalId":107574,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132136866","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 : 2019-12-19DOI: 10.36548/jtcsst.2019.2.005
Wang Haoxiang
The Internet of things is the basic paradigm with the cluster of techniques that ensure innovations in the service rendered in various applications. It aims to develop a seamless connection between the tangible objects around and the information network in turn to provide a well-structured servicing to its users. Though the IOT service seems to be promising, the risks still prevail in the form of privacy and the security in user acceptance in utilizing the internet of things services, and its application. This makes the trust management very important for the internet of things. So the paper puts forth the distributed block chain involved trust system to manage the conveyance infrastructures of the internet of things paradigm. The evaluation of the proposed model evinces the enhanced security provided for the nodes of the IOT as well as its information exchange.
{"title":"TRUST MANAGEMENT OF COMMUNICATION ARCHITECTURES OF INTERNET OF THINGS","authors":"Wang Haoxiang","doi":"10.36548/jtcsst.2019.2.005","DOIUrl":"https://doi.org/10.36548/jtcsst.2019.2.005","url":null,"abstract":"The Internet of things is the basic paradigm with the cluster of techniques that ensure innovations in the service rendered in various applications. It aims to develop a seamless connection between the tangible objects around and the information network in turn to provide a well-structured servicing to its users. Though the IOT service seems to be promising, the risks still prevail in the form of privacy and the security in user acceptance in utilizing the internet of things services, and its application. This makes the trust management very important for the internet of things. So the paper puts forth the distributed block chain involved trust system to manage the conveyance infrastructures of the internet of things paradigm. The evaluation of the proposed model evinces the enhanced security provided for the nodes of the IOT as well as its information exchange.","PeriodicalId":107574,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132296406","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 : 2019-12-08DOI: 10.36548/jtcsst.2019.2.002
D. Sivaganesan
The advancements in the technologies and the increase in the digital miniaturization day by day are causing devices to become smarter and smarter and the emergence of the internet of things and the cloud has made things even better with insightful suggestions for organization as well as the way the people work and lead their life. The limitations in the cloud paradigm in terms of processing complexity, the latency in the service provisioning and improper resource scheduling, remains as a reason leading to shifting of applications from cloud to edge. More over the emergence of the artificial intelligence in the edge computing has turned out to be center of attention as it improves the speed and the range of the IOT applications. The paper also puts forth the design of the AI-enabled Edge computing for developing a Smart Farming.
{"title":"DESIGN AND DEVELOPMENT AI-ENABLED EDGE COMPUTING FOR INTELLIGENT-IOT APPLICATIONS","authors":"D. Sivaganesan","doi":"10.36548/jtcsst.2019.2.002","DOIUrl":"https://doi.org/10.36548/jtcsst.2019.2.002","url":null,"abstract":"The advancements in the technologies and the increase in the digital miniaturization day by day are causing devices to become smarter and smarter and the emergence of the internet of things and the cloud has made things even better with insightful suggestions for organization as well as the way the people work and lead their life. The limitations in the cloud paradigm in terms of processing complexity, the latency in the service provisioning and improper resource scheduling, remains as a reason leading to shifting of applications from cloud to edge. More over the emergence of the artificial intelligence in the edge computing has turned out to be center of attention as it improves the speed and the range of the IOT applications. The paper also puts forth the design of the AI-enabled Edge computing for developing a Smart Farming.","PeriodicalId":107574,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133502128","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 : 2019-12-03DOI: 10.36548/jtcsst.2019.2.001
R. Bestak, S. Smys
The internet connectivity extended by the internet of things to all the tangible things lying around and used by us in our day today life has convert the devices into smart objects and led to huge set of data generation that holds both the valuable and invaluable information. In order to perfectly handle the information’s generated and mine the valuables from them, the analytics are engaged by the cloud. To have a timely access, most probably the fog services are preferred than the cloud as they bring down the service of the cloud to the user edge and reduces the time complexity in accessing of the information. So the paper proposes the big data analytics for the fog assisted health care application to effectively handle the health information’s diagnosed for the aged persons. The proposed model is simulated using the IFogSim toolkit to examine the performance fogassisted smart healthcare application.
{"title":"BIG DATA ANALYTICS FOR SMART CLOUD-FOG BASED APPLICATIONS","authors":"R. Bestak, S. Smys","doi":"10.36548/jtcsst.2019.2.001","DOIUrl":"https://doi.org/10.36548/jtcsst.2019.2.001","url":null,"abstract":"The internet connectivity extended by the internet of things to all the tangible things lying around and used by us in our day today life has convert the devices into smart objects and led to huge set of data generation that holds both the valuable and invaluable information. In order to perfectly handle the information’s generated and mine the valuables from them, the analytics are engaged by the cloud. To have a timely access, most probably the fog services are preferred than the cloud as they bring down the service of the cloud to the user edge and reduces the time complexity in accessing of the information. So the paper proposes the big data analytics for the fog assisted health care application to effectively handle the health information’s diagnosed for the aged persons. The proposed model is simulated using the IFogSim toolkit to examine the performance fogassisted smart healthcare application.","PeriodicalId":107574,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126955466","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 : 2019-09-22DOI: 10.36548/jtcsst.2019.1.005
Bhalaji N Dr
Cloud computing being a promising paradigm has become very prominent among a wide range of applications due to their timely service rendering capability. Attracted to the type of servicing and the way of servicing lots and lots of users, adapt to the cloud computing. This makes the time servicing of the cloud computing a tedious job. So in order to effectively handle the tasks the scheduling approach is entailed in the cloud computing. The paper proposes an efficient task scheduling for the heterogeneous cloud to render service at a minimized delay utilizing the genetic algorithm. The proposed method is validated through the, cloud simulator to understand the efficiency of the same in terms of delay and the quality of service.
{"title":"DELAY DIMINISHED EFFICIENT TASK SCHEDULING AND ALLOCATION FOR\u0000HETEROGENEOUS CLOUD ENVIRONMENT","authors":"Bhalaji N Dr","doi":"10.36548/jtcsst.2019.1.005","DOIUrl":"https://doi.org/10.36548/jtcsst.2019.1.005","url":null,"abstract":"Cloud computing being a promising paradigm has become very prominent among a wide range of applications due to their timely service rendering capability. Attracted to the type of servicing and the way of servicing lots and lots of users, adapt to the cloud computing. This makes the time servicing of the cloud computing a tedious job. So in order to effectively handle the tasks the scheduling approach is entailed in the cloud computing. The paper proposes an efficient task scheduling for the heterogeneous cloud to render service at a minimized delay utilizing the genetic algorithm. The proposed method is validated through the, cloud simulator to understand the efficiency of the same in terms of delay and the quality of service.","PeriodicalId":107574,"journal":{"name":"Journal of Trends in Computer Science and Smart Technology","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127033561","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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