Pub Date : 2020-11-09DOI: 10.1108/ijpcc-10-2020-0175
Ameer Alhasan, L. Audah, Ishaq Ibrahim, Ammar Al-Sharaa, Ali Saadon Al-Ogaili, Jabiry M. Mohammed
PurposeSeveral countries have been using internet of things (IoT) devices in the healthcare sector to combat COVID-19. Therefore, this study aims to examine the doctors’ intentions to use IoT healthcare devices in Iraq during the COVID-19 pandemic.Design/methodology/approachThis study proposed a model based on the integration of the innovation diffusion theory (IDT). This included compatibility, trialability and image and a set of exogenous factors such as computer self-efficacy, privacy and cost into the technology acceptance model comprising perceived ease of use, perceived usefulness, attitude and behavioral intention to use.FindingsThe findings revealed that compatibility and image of the IDT factors, have a significant impact on the perceived ease of use, perceived usefulness and behavioral intention, but trialability has a significant impact on perceived ease of use, perceived usefulness and insignificant impact on behavioral intention. Additionally, external factors such as privacy and cost significantly impacted doctors’ behavioral intention to use. Moreover, doctors’ computer self-efficacy significantly influenced the perceived ease of use, perceived usefulness and behavioral intention to use. Furthermore, perceived ease of use has a significant impact on perceived usefulness and attitude, perceived usefulness has a significant impact on attitude, which, in turn, significantly impacting doctors' behavior toward an intention to use.Research limitations/implicationsThe limitations of the present study are the retractions of the number of participants and the lack of qualitative methods.Originality/valueThe finding of this study could benefit researchers, doctors and policymakers in the adaption of IoT technologies in the health sectors, especially in developing counties.
{"title":"A case-study to examine doctors' intentions to use IoT healthcare devices in Iraq during COVID-19 pandemic","authors":"Ameer Alhasan, L. Audah, Ishaq Ibrahim, Ammar Al-Sharaa, Ali Saadon Al-Ogaili, Jabiry M. Mohammed","doi":"10.1108/ijpcc-10-2020-0175","DOIUrl":"https://doi.org/10.1108/ijpcc-10-2020-0175","url":null,"abstract":"PurposeSeveral countries have been using internet of things (IoT) devices in the healthcare sector to combat COVID-19. Therefore, this study aims to examine the doctors’ intentions to use IoT healthcare devices in Iraq during the COVID-19 pandemic.Design/methodology/approachThis study proposed a model based on the integration of the innovation diffusion theory (IDT). This included compatibility, trialability and image and a set of exogenous factors such as computer self-efficacy, privacy and cost into the technology acceptance model comprising perceived ease of use, perceived usefulness, attitude and behavioral intention to use.FindingsThe findings revealed that compatibility and image of the IDT factors, have a significant impact on the perceived ease of use, perceived usefulness and behavioral intention, but trialability has a significant impact on perceived ease of use, perceived usefulness and insignificant impact on behavioral intention. Additionally, external factors such as privacy and cost significantly impacted doctors’ behavioral intention to use. Moreover, doctors’ computer self-efficacy significantly influenced the perceived ease of use, perceived usefulness and behavioral intention to use. Furthermore, perceived ease of use has a significant impact on perceived usefulness and attitude, perceived usefulness has a significant impact on attitude, which, in turn, significantly impacting doctors' behavior toward an intention to use.Research limitations/implicationsThe limitations of the present study are the retractions of the number of participants and the lack of qualitative methods.Originality/valueThe finding of this study could benefit researchers, doctors and policymakers in the adaption of IoT technologies in the health sectors, especially in developing counties.","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115252844","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 : 2020-11-06DOI: 10.1108/ijpcc-10-2020-0166
S. Praveen, Rajesh Ittamalla, Dhilip Subramanian
Purpose Despite numerous positive aspects of digital contact tracing, the implied nature of contact tracing is still viewed with skepticism. Those in favor of contact tracing often undermine various risks involved with it, while those against it often undermine its positive benefits. However, unless the government and the app makers can convince a significant section of the population to use digital contact apps, desired results cannot be achieved. This study aims to focus on analyzing the perception of citizens belonging to developing countries about digital contact tracing. Design/methodology/approach For this study, data were collected from Twitter. Tweets containing hashtag and the word “contact tracing” were crawled using Python library Tweepy. Tweets across the top five developing countries (India, Brazil, South Africa, Argentina and Columbia) with high COVID-19 cases were collected for this study. After eliminating tweets of other languages, we selected 50,000 unique English tweets for this study. Using the machine learning algorithm, we have detected the sentiment of all the tweets belonging to each country. Structural topic modeling was performed for the tweets to understand the concerns shared by citizens of the developing countries about digital contact tracing. Findings The study was conducted in two parts. Study 1 results show that Indians and Brazilians citizens record more negative sentiments toward “digital contact tracing” than other major developing countries. Surprisingly, the citizens of India and Brazil also records more positive sentiments about contact tracing. This shows the polarized nature of the population of both countries while dealing with digital contact tracing. Overall, only 33.3% of total tweets were positively related to contact tracing, while 53.7% of the total tweets were neutral. Study 2 results show that factors such as the reliability of the contact tracing apps, contact tracing may lead to unnecessary panic, invasion of privacy and data misuse as the prominent reasons why the citizens of the five countries feel pessimistic about contact tracing. Originality/value After the COVID-19 strikes, numerous studies were conducted to analyze and suggest the best possible way of implementing digital contact tracing to curb COVID. However, only a handful of studies were conducted examining how the general public perceives the concept of digital contact tracing, especially pertaining to developing countries. This study fills that gap.
{"title":"How optimistic do citizens feel about digital contact tracing? - Perspectives from developing countries","authors":"S. Praveen, Rajesh Ittamalla, Dhilip Subramanian","doi":"10.1108/ijpcc-10-2020-0166","DOIUrl":"https://doi.org/10.1108/ijpcc-10-2020-0166","url":null,"abstract":"\u0000Purpose\u0000Despite numerous positive aspects of digital contact tracing, the implied nature of contact tracing is still viewed with skepticism. Those in favor of contact tracing often undermine various risks involved with it, while those against it often undermine its positive benefits. However, unless the government and the app makers can convince a significant section of the population to use digital contact apps, desired results cannot be achieved. This study aims to focus on analyzing the perception of citizens belonging to developing countries about digital contact tracing.\u0000\u0000\u0000Design/methodology/approach\u0000For this study, data were collected from Twitter. Tweets containing hashtag and the word “contact tracing” were crawled using Python library Tweepy. Tweets across the top five developing countries (India, Brazil, South Africa, Argentina and Columbia) with high COVID-19 cases were collected for this study. After eliminating tweets of other languages, we selected 50,000 unique English tweets for this study. Using the machine learning algorithm, we have detected the sentiment of all the tweets belonging to each country. Structural topic modeling was performed for the tweets to understand the concerns shared by citizens of the developing countries about digital contact tracing.\u0000\u0000\u0000Findings\u0000The study was conducted in two parts. Study 1 results show that Indians and Brazilians citizens record more negative sentiments toward “digital contact tracing” than other major developing countries. Surprisingly, the citizens of India and Brazil also records more positive sentiments about contact tracing. This shows the polarized nature of the population of both countries while dealing with digital contact tracing. Overall, only 33.3% of total tweets were positively related to contact tracing, while 53.7% of the total tweets were neutral. Study 2 results show that factors such as the reliability of the contact tracing apps, contact tracing may lead to unnecessary panic, invasion of privacy and data misuse as the prominent reasons why the citizens of the five countries feel pessimistic about contact tracing.\u0000\u0000\u0000Originality/value\u0000After the COVID-19 strikes, numerous studies were conducted to analyze and suggest the best possible way of implementing digital contact tracing to curb COVID. However, only a handful of studies were conducted examining how the general public perceives the concept of digital contact tracing, especially pertaining to developing countries. This study fills that gap.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"167 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113990692","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 : 2020-10-28DOI: 10.1108/ijpcc-09-2020-0147
S. Praveen, Rajesh Ittamalla, Dhilip Subramanian
Purpose The word “digital contact tracing” is often met with different reactions: the reaction that passionately supports it, the reaction that neither supports nor oppose and the one that vehemently opposes it. Those who support the notion of digital contact tracing vouch for its effectiveness and how the complicated process can be made simpler by implementing digital contact tracing, and those who oppose it often criticize the imminent threats it possesses. However, without earning the support of a large population, it would be difficult for any government to implement digital contact tracing to perfection. The purpose of this paper is to analyze, using machine learning, how different continents have different sentiments over digital contact tracing being used as a measure to curb COVID-19. Design/methodology/approach For the analysis, data were collected from Twitter. Tweets that contain the hashtag and the word “digital contact tracing” were crawled using Python library Tweepy. Tweets across countries of four continents were collected from March 2020 to August 2020. In total, 70,212 tweets were used for this study. Using the machine learning algorithm, the authors detected the sentiment of all the tweets belonging to each continent. Structural topic modeling was used to understand the overall significant issues people voice out by global citizens while sharing their opinions on digital contact tracing. Findings This study was conducted in two parts. Study one results show that North American and European citizens share more negative sentiments toward “digital contact tracing.” The citizens of the Asian and South American continent mostly share neutral sentiments regarding the digital contact tracing. Overall, only 33% of total tweets were positively related to contact tracing, whereas 52% of the total tweets were neutral. Study two results show that factors such as fear of government using contact tracing to spy on its people, the feeling of being unsafe and contact tracing being used to promote an agenda were the three major issues concerning the overall general public. Originality/value Despite numerous studies being conducted about how to implement the contact tracing efficiently, minimal studies were done to explore the possibility and challenges in implementing it. This study fills the gap.
{"title":"Challenges in successful implementation of Digital contact tracing to curb COVID-19 from global citizen's perspective: a text analysis study","authors":"S. Praveen, Rajesh Ittamalla, Dhilip Subramanian","doi":"10.1108/ijpcc-09-2020-0147","DOIUrl":"https://doi.org/10.1108/ijpcc-09-2020-0147","url":null,"abstract":"\u0000Purpose\u0000The word “digital contact tracing” is often met with different reactions: the reaction that passionately supports it, the reaction that neither supports nor oppose and the one that vehemently opposes it. Those who support the notion of digital contact tracing vouch for its effectiveness and how the complicated process can be made simpler by implementing digital contact tracing, and those who oppose it often criticize the imminent threats it possesses. However, without earning the support of a large population, it would be difficult for any government to implement digital contact tracing to perfection. The purpose of this paper is to analyze, using machine learning, how different continents have different sentiments over digital contact tracing being used as a measure to curb COVID-19.\u0000\u0000\u0000Design/methodology/approach\u0000For the analysis, data were collected from Twitter. Tweets that contain the hashtag and the word “digital contact tracing” were crawled using Python library Tweepy. Tweets across countries of four continents were collected from March 2020 to August 2020. In total, 70,212 tweets were used for this study. Using the machine learning algorithm, the authors detected the sentiment of all the tweets belonging to each continent. Structural topic modeling was used to understand the overall significant issues people voice out by global citizens while sharing their opinions on digital contact tracing.\u0000\u0000\u0000Findings\u0000This study was conducted in two parts. Study one results show that North American and European citizens share more negative sentiments toward “digital contact tracing.” The citizens of the Asian and South American continent mostly share neutral sentiments regarding the digital contact tracing. Overall, only 33% of total tweets were positively related to contact tracing, whereas 52% of the total tweets were neutral. Study two results show that factors such as fear of government using contact tracing to spy on its people, the feeling of being unsafe and contact tracing being used to promote an agenda were the three major issues concerning the overall general public.\u0000\u0000\u0000Originality/value\u0000Despite numerous studies being conducted about how to implement the contact tracing efficiently, minimal studies were done to explore the possibility and challenges in implementing it. This study fills the gap.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123736640","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 : 2020-10-19DOI: 10.1108/ijpcc-09-2020-0150
Udhaya Sankar S.M., Ganesan R., Jeevaa Katiravan, R. M, Ruhin Kouser R.
PurposeIt has been six months from the time the first case was registered, and nations are still working on counter steering regulations. The proposed model in the paper encompasses a novel methodology to equip systems with artificial intelligence and computational audition techniques over voice recognition for detecting the symptoms. Regular and irregular speech/voice patterns are recognized using in-built tools and devices on a hand-held device. Phenomenal patterns can be contextually varied among normal and presence of asymptotic symptoms.Design/methodology/approachThe lives of patients and healthy beings are seriously affected with various precautionary measures and social distancing. The spread of virus infection is mitigated with necessary actions by governments and nations. Resulting in increased death ratio, the novel coronavirus is certainly a serious pandemic which spreads with unhygienic practices and contact with air-borne droplets of infected patients. With minimal measures to detect the symptoms from the early onset and the rise of asymptotic outcomes, coronavirus becomes even difficult for detection and diagnosis.FindingsA number of significant parameters are considered for the analysis, and they are dry cough, wet cough, sneezing, speech under a blocked nose or cold, sleeplessness, pain in chests, eating behaviours and other potential cases of the disease. Risk- and symptom-based measurements are imposed to deliver a symptom subsiding diagnosis plan. Monitoring and tracking down the symptoms inflicted areas, social distancing and its outcomes, treatments, planning and delivery of healthy food intake, immunity improvement measures are other areas of potential guidelines to mitigate the disease.Originality/valueThis paper also lists the challenges in actual scenarios for a solution to work satisfactorily. Emphasizing on the early detection of symptoms, this work highlights the importance of such a mechanism in the absence of medication or vaccine and demand for large-scale screening. A mobile and ubiquitous application is definitely a useful measure of alerting the officials to take necessary actions by eliminating the expensive modes of tests and medical investigations.
{"title":"Mobile application based speech and voice analysis for COVID-19 detection using computational audit techniques","authors":"Udhaya Sankar S.M., Ganesan R., Jeevaa Katiravan, R. M, Ruhin Kouser R.","doi":"10.1108/ijpcc-09-2020-0150","DOIUrl":"https://doi.org/10.1108/ijpcc-09-2020-0150","url":null,"abstract":"PurposeIt has been six months from the time the first case was registered, and nations are still working on counter steering regulations. The proposed model in the paper encompasses a novel methodology to equip systems with artificial intelligence and computational audition techniques over voice recognition for detecting the symptoms. Regular and irregular speech/voice patterns are recognized using in-built tools and devices on a hand-held device. Phenomenal patterns can be contextually varied among normal and presence of asymptotic symptoms.Design/methodology/approachThe lives of patients and healthy beings are seriously affected with various precautionary measures and social distancing. The spread of virus infection is mitigated with necessary actions by governments and nations. Resulting in increased death ratio, the novel coronavirus is certainly a serious pandemic which spreads with unhygienic practices and contact with air-borne droplets of infected patients. With minimal measures to detect the symptoms from the early onset and the rise of asymptotic outcomes, coronavirus becomes even difficult for detection and diagnosis.FindingsA number of significant parameters are considered for the analysis, and they are dry cough, wet cough, sneezing, speech under a blocked nose or cold, sleeplessness, pain in chests, eating behaviours and other potential cases of the disease. Risk- and symptom-based measurements are imposed to deliver a symptom subsiding diagnosis plan. Monitoring and tracking down the symptoms inflicted areas, social distancing and its outcomes, treatments, planning and delivery of healthy food intake, immunity improvement measures are other areas of potential guidelines to mitigate the disease.Originality/valueThis paper also lists the challenges in actual scenarios for a solution to work satisfactorily. Emphasizing on the early detection of symptoms, this work highlights the importance of such a mechanism in the absence of medication or vaccine and demand for large-scale screening. A mobile and ubiquitous application is definitely a useful measure of alerting the officials to take necessary actions by eliminating the expensive modes of tests and medical investigations.","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115667209","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 : 2020-10-06DOI: 10.1108/IJPCC-05-2020-0037
Josemila Baby Jesuretnam, Jeba James Rose
Purpose This paper aims to propose a multi-dimensional hierarchical K-means clustering algorithm for the purpose of intrusion detection. Initially, the clustering set of rules is proposed to shape some of clusters in the network and then the most beneficial clusters are decided on by the use of Cuckoo search optimization set of rules. Finally, an Artificial Bee Colony primarily based selection tree (ABC-DT) classifier is rented to classify the regular and unusual instances present in the network with the aid of the extracted features. Design/methodology/approach Intrusion detection system (IDS) is crucial for the network system; the intruder can take sensitive details about the network. IDS are said to be more effective when it has both high intrusion detection rate and low false alarm rate. Numerous strategies including gadget mastering, records mining and statistical techniques were tested for IDS mission. Recent study reveals that combining multiple classifiers, i.e. classifiers ensemble, can also own better performance than unmarried classifier. In this paper, a comparative study is conducted of the overall performance of four classifiers, i.e. hybrid ABC-DT particle swarm optimization-based K-means clustering (PSO-KM), help vector device (SVM) and K-Nearest neighbour (KNN). All the four classifiers are tested with exceptional packet sizes 1470, 1024, 512 and 256. The experiment is carried out for the speed ranging from turned into done for the velocity ranging from 250Mbps, 500Mbps, 750Mbps, 1.0Gpbs, 1.5Gbps, and 2.0Gbps in terms of accuracy, detection charge, specificity, false alarm charge and computational time. The experimental results reveals that the hybridization of classifiers performs better than the base classifiers in all scenarios. Findings This study analyses the performance of hybrid ABC-DT classifier and compares the performance against three well-known classifiers such as PSO-KM, SVM and K-NN. The performances of all the four classifiers are tested with Discovery in Data Mining (KDD) CUP 99 dataset with different packet sizes 1470, 1024, 512 and 256. The results show the classifier performance variations with different speed ranges. From the experimental results and analysis, the hybridization of classifiers such as ABC-DT outperforms the base classifiers in all scenarios. Originality/value The novel approach in this paper is used to study the hybrid ABC-DT classifier and compare the performance against three well-known classifiers such as PSO-KM, SVM and K-NN. The discussed concept is used within the network to monitor the traffic to and from all the devices connected in that network.
目的提出一种用于入侵检测的多维分层k均值聚类算法。首先,提出聚类规则集来塑造网络中的一些聚类,然后使用布谷鸟搜索优化规则集来确定最有利的聚类。最后,利用基于人工蜂群的选择树(ABC-DT)分类器,利用提取的特征对网络中存在的规则和异常实例进行分类。入侵检测系统(IDS)对网络系统至关重要;入侵者可以获取有关网络的敏感细节。当入侵检测率高、误报率低时,入侵检测的效率更高。IDS任务测试了许多策略,包括工具掌握、记录挖掘和统计技术。最近的研究表明,组合多个分类器,即分类器集成,也比单一分类器具有更好的性能。本文对基于ABC-DT混合粒子群优化的k -均值聚类(PSO-KM)、帮助向量设备(SVM)和k -近邻(KNN)四种分类器的总体性能进行了比较研究。所有四种分类器都用异常数据包大小1470、1024、512和256进行了测试。从准确率、检测费用、专一性、虚警费用和计算时间等方面,在250Mbps、500Mbps、750Mbps、1.0Gpbs、1.5Gbps和2.0Gbps的速度范围内,对从转弯到完成的速度进行了实验。实验结果表明,混合分类器在所有场景下的性能都优于基本分类器。本研究分析了ABC-DT混合分类器的性能,并与PSO-KM、SVM和K-NN三种知名分类器进行了性能比较。在数据挖掘中的发现(KDD) CUP 99数据集上测试了这四种分类器的性能,数据集的数据包大小分别为1470、1024、512和256。结果表明,在不同的转速范围内,分类器的性能发生了变化。从实验结果和分析来看,ABC-DT等混合分类器在所有场景下的性能都优于基本分类器。本文采用新颖的方法对ABC-DT混合分类器进行了研究,并与PSO-KM、SVM和K-NN等三种知名分类器进行了性能比较。所讨论的概念在网络中用于监视网络中连接的所有设备之间的流量。
{"title":"Performance analysis of optimal cluster selection and intrusion detection by hierarchical K-means clustering with hybrid ABC-DT","authors":"Josemila Baby Jesuretnam, Jeba James Rose","doi":"10.1108/IJPCC-05-2020-0037","DOIUrl":"https://doi.org/10.1108/IJPCC-05-2020-0037","url":null,"abstract":"\u0000Purpose\u0000This paper aims to propose a multi-dimensional hierarchical K-means clustering algorithm for the purpose of intrusion detection. Initially, the clustering set of rules is proposed to shape some of clusters in the network and then the most beneficial clusters are decided on by the use of Cuckoo search optimization set of rules. Finally, an Artificial Bee Colony primarily based selection tree (ABC-DT) classifier is rented to classify the regular and unusual instances present in the network with the aid of the extracted features.\u0000\u0000\u0000Design/methodology/approach\u0000Intrusion detection system (IDS) is crucial for the network system; the intruder can take sensitive details about the network. IDS are said to be more effective when it has both high intrusion detection rate and low false alarm rate. Numerous strategies including gadget mastering, records mining and statistical techniques were tested for IDS mission. Recent study reveals that combining multiple classifiers, i.e. classifiers ensemble, can also own better performance than unmarried classifier. In this paper, a comparative study is conducted of the overall performance of four classifiers, i.e. hybrid ABC-DT particle swarm optimization-based K-means clustering (PSO-KM), help vector device (SVM) and K-Nearest neighbour (KNN). All the four classifiers are tested with exceptional packet sizes 1470, 1024, 512 and 256. The experiment is carried out for the speed ranging from turned into done for the velocity ranging from 250Mbps, 500Mbps, 750Mbps, 1.0Gpbs, 1.5Gbps, and 2.0Gbps in terms of accuracy, detection charge, specificity, false alarm charge and computational time. The experimental results reveals that the hybridization of classifiers performs better than the base classifiers in all scenarios.\u0000\u0000\u0000Findings\u0000This study analyses the performance of hybrid ABC-DT classifier and compares the performance against three well-known classifiers such as PSO-KM, SVM and K-NN. The performances of all the four classifiers are tested with Discovery in Data Mining (KDD) CUP 99 dataset with different packet sizes 1470, 1024, 512 and 256. The results show the classifier performance variations with different speed ranges. From the experimental results and analysis, the hybridization of classifiers such as ABC-DT outperforms the base classifiers in all scenarios.\u0000\u0000\u0000Originality/value\u0000The novel approach in this paper is used to study the hybrid ABC-DT classifier and compare the performance against three well-known classifiers such as PSO-KM, SVM and K-NN. The discussed concept is used within the network to monitor the traffic to and from all the devices connected in that network.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114548720","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 : 2020-10-01DOI: 10.1108/IJPCC-05-2020-0036
G. Sreeram, S. Pradeep, K. S. Rao, B. Raju, Nikhat Parveen
Purpose The paper aims to precise and fast categorization on to transaction evolves into indispensible. The effective capacity difficulty of all the IDS simulates today at below discovery amount of fewer regular barrage associations and therefore the next warning rate. Design/methodology/approach The reticulum perception is that the methods which examine and determine the scheme of contact on unearths toward number of dangerous and perchance fateful interchanges occurring toward the system. Within character of guaran-teeing the slumberous, opening and uprightness count of to socialize for professional. The precise and fast categorization on to transaction evolves into indispensible. The effective capacity difficulty of all the intrusion detection simulation (IDS) simulates today at below discovery amount of fewer regular barrage associations and therefore the next warning rate. The container with systems of connections are reproduction everything beacon subject to the series of actions to achieve results accepts exists a contemporary well-known method. At the indicated motivation a hybrid methodology supported pairing distinct ripple transformation and human intelligence artificial neural network (ANN) for IDS is projected. The lack of balance of the situation traversing the space beyond information range was eliminated through synthetic minority oversampling technique-based oversampling have low regular object and irregular below examine of the dominant object. We are binding with three layer ANN is being used for classification, and thus the experimental results on knowledge discovery databases are being used for the facts in occurrence of accuracy rate and disclosure estimation toward identical period. True and false made up accepted. Findings At the indicated motivation a hybrid methodology supported pairing distinct ripple transformation and human intelligence ANN for IDS is projected. The lack of balance of the situation traversing the space beyond information range was eliminated through synthetic minority oversampling technique-based oversampling have low regular object and irregular below examine of the dominant object. Originality/value Chain interruption discovery is the series of actions for the results knowing the familiarity opening and honor number associate order, the scientific categorization undertaking become necessary. The capacity issues of invasion discovery is the order to determine and examine. The arrangement of simulations at the occasion under discovery estimation for low regular aggression associations and above made up feeling sudden panic amount.
{"title":"Moving ridge neuronal espionage network simulation for reticulum invasion sensing","authors":"G. Sreeram, S. Pradeep, K. S. Rao, B. Raju, Nikhat Parveen","doi":"10.1108/IJPCC-05-2020-0036","DOIUrl":"https://doi.org/10.1108/IJPCC-05-2020-0036","url":null,"abstract":"\u0000Purpose\u0000The paper aims to precise and fast categorization on to transaction evolves into indispensible. The effective capacity difficulty of all the IDS simulates today at below discovery amount of fewer regular barrage associations and therefore the next warning rate.\u0000\u0000\u0000Design/methodology/approach\u0000The reticulum perception is that the methods which examine and determine the scheme of contact on unearths toward number of dangerous and perchance fateful interchanges occurring toward the system. Within character of guaran-teeing the slumberous, opening and uprightness count of to socialize for professional. The precise and fast categorization on to transaction evolves into indispensible. The effective capacity difficulty of all the intrusion detection simulation (IDS) simulates today at below discovery amount of fewer regular barrage associations and therefore the next warning rate. The container with systems of connections are reproduction everything beacon subject to the series of actions to achieve results accepts exists a contemporary well-known method. At the indicated motivation a hybrid methodology supported pairing distinct ripple transformation and human intelligence artificial neural network (ANN) for IDS is projected. The lack of balance of the situation traversing the space beyond information range was eliminated through synthetic minority oversampling technique-based oversampling have low regular object and irregular below examine of the dominant object. We are binding with three layer ANN is being used for classification, and thus the experimental results on knowledge discovery databases are being used for the facts in occurrence of accuracy rate and disclosure estimation toward identical period. True and false made up accepted.\u0000\u0000\u0000Findings\u0000At the indicated motivation a hybrid methodology supported pairing distinct ripple transformation and human intelligence ANN for IDS is projected. The lack of balance of the situation traversing the space beyond information range was eliminated through synthetic minority oversampling technique-based oversampling have low regular object and irregular below examine of the dominant object.\u0000\u0000\u0000Originality/value\u0000Chain interruption discovery is the series of actions for the results knowing the familiarity opening and honor number associate order, the scientific categorization undertaking become necessary. The capacity issues of invasion discovery is the order to determine and examine. The arrangement of simulations at the occasion under discovery estimation for low regular aggression associations and above made up feeling sudden panic amount.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129192896","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 : 2020-09-30DOI: 10.1108/IJPCC-09-2020-0121
Praveen S.V., Rajesh Ittamalla
Purpose Governments worldwide are taking various measures to prevent the spreading of COVID virus. One such effort is digital contact tracing. However, the aspect of digital contact tracing was met with criticism, as many critics view this as an attempt of the government to control people and a fundamental breach of privacy. Using machine learning techniques, this study aims to deal with understanding the general public’s emotions toward contact tracing and determining whether there is a change in the attitude of the general public toward digital contact tracing in various months of crises. This study also analyzes the significant concerns voiced out by the general public regarding digital contact tracing. Design/methodology/approach For the analysis, data were collected from Reddit. Reddit posts discussing the digital contact tracing during COVID-19 crises were collected from February 2020 to July 2020. A total of 5,025 original Reddit posts were used for this study. Natural language processing, which is a part of machine learning, was used for this study to understand the sentiments of the general public about contact tracing. Latent Dirichlet allocation was used to understand the significant issues voiced out by the general public while discussing contact tracing. Findings This study was conducted in two parts. Study 1 results show that the percentage of general public viewing the aspect of contact tracing positively had not changed throughout the time period of Data frame (March 2020 to July 2020). However, compared to the initial month of the crises, the later months saw a considerable increase in negative sentiments and a decrease in neutral sentiments regarding the digital contact tracing. Study 2 finds out the significant issues public voices out in their negative sentiments are a violation of privacy, fear of safety and lack of trust in government. Originality/value Although numerous studies were conducted on how to implement contact tracing effectively, to the best of the authors’ knowledge, this is the first study conducted with an objective of understanding the general public’s perception of contact tracing.
{"title":"General public's attitude toward governments implementing digital contact tracing to curb COVID-19 - a study based on natural language processing","authors":"Praveen S.V., Rajesh Ittamalla","doi":"10.1108/IJPCC-09-2020-0121","DOIUrl":"https://doi.org/10.1108/IJPCC-09-2020-0121","url":null,"abstract":"\u0000Purpose\u0000Governments worldwide are taking various measures to prevent the spreading of COVID virus. One such effort is digital contact tracing. However, the aspect of digital contact tracing was met with criticism, as many critics view this as an attempt of the government to control people and a fundamental breach of privacy. Using machine learning techniques, this study aims to deal with understanding the general public’s emotions toward contact tracing and determining whether there is a change in the attitude of the general public toward digital contact tracing in various months of crises. This study also analyzes the significant concerns voiced out by the general public regarding digital contact tracing.\u0000\u0000\u0000Design/methodology/approach\u0000For the analysis, data were collected from Reddit. Reddit posts discussing the digital contact tracing during COVID-19 crises were collected from February 2020 to July 2020. A total of 5,025 original Reddit posts were used for this study. Natural language processing, which is a part of machine learning, was used for this study to understand the sentiments of the general public about contact tracing. Latent Dirichlet allocation was used to understand the significant issues voiced out by the general public while discussing contact tracing.\u0000\u0000\u0000Findings\u0000This study was conducted in two parts. Study 1 results show that the percentage of general public viewing the aspect of contact tracing positively had not changed throughout the time period of Data frame (March 2020 to July 2020). However, compared to the initial month of the crises, the later months saw a considerable increase in negative sentiments and a decrease in neutral sentiments regarding the digital contact tracing. Study 2 finds out the significant issues public voices out in their negative sentiments are a violation of privacy, fear of safety and lack of trust in government.\u0000\u0000\u0000Originality/value\u0000Although numerous studies were conducted on how to implement contact tracing effectively, to the best of the authors’ knowledge, this is the first study conducted with an objective of understanding the general public’s perception of contact tracing.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"41 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114099207","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 : 2020-09-23DOI: 10.1108/IJPCC-08-2020-0107
Rajeesh Kumar N.V., Arun M., Baraneetharan E., Stanly Jaya Prakash J., Kanchana A., Prabu S.
Purpose Many investigations are going on in monitoring, contact tracing, predicting and diagnosing the COVID-19 disease and many virologists are urgently seeking to create a vaccine as early as possible. Even though there is no specific treatment for the pandemic disease, the world is now struggling to control the spread by implementing the lockdown worldwide and giving awareness to the people to wear masks and use sanitizers. The new technologies, including the Internet of things (IoT), are gaining global attention towards the increasing technical support in health-care systems, particularly in predicting, detecting, preventing and monitoring of most of the infectious diseases. Similarly, it also helps in fighting against COVID-19 by monitoring, contract tracing and detecting the COVID-19 pandemic by connection with the IoT-based smart solutions. IoT is the interconnected Web of smart devices, sensors, actuators and data, which are collected in the raw form and transmitted through the internet. The purpose of this paper is to propose the concept to detect and monitor the asymptotic patients using IoT-based sensors. Design/methodology/approach In recent days, the surge of the COVID-19 contagion has infected all over the world and it has ruined our day-to-day life. The extraordinary eruption of this pandemic virus placed the World Health Organization (WHO) in a hazardous position. The impact of this contagious virus and scarcity among the people has forced the world to get into complete lockdown, as the number of laboratory-confirmed cases is increasing in millions all over the world as per the records of the government. Findings COVID-19 patients are either symptomatic or asymptotic. Symptomatic patients have symptoms such as fever, cough and difficulty in breathing. But patients are also asymptotic, which is very difficult to detect and monitor by isolating them. Originality/value Asymptotic patients are very hazardous because without knowing that they are infected, they might spread the infection to others, also asymptotic patients might be having very serious lung damage. So, earlier prediction and monitoring of asymptotic patients are mandatory to save their life and prevent them from spreading.
{"title":"Detection and monitoring of the asymptotic COVID-19 patients using IoT devices and sensors","authors":"Rajeesh Kumar N.V., Arun M., Baraneetharan E., Stanly Jaya Prakash J., Kanchana A., Prabu S.","doi":"10.1108/IJPCC-08-2020-0107","DOIUrl":"https://doi.org/10.1108/IJPCC-08-2020-0107","url":null,"abstract":"\u0000Purpose\u0000Many investigations are going on in monitoring, contact tracing, predicting and diagnosing the COVID-19 disease and many virologists are urgently seeking to create a vaccine as early as possible. Even though there is no specific treatment for the pandemic disease, the world is now struggling to control the spread by implementing the lockdown worldwide and giving awareness to the people to wear masks and use sanitizers. The new technologies, including the Internet of things (IoT), are gaining global attention towards the increasing technical support in health-care systems, particularly in predicting, detecting, preventing and monitoring of most of the infectious diseases. Similarly, it also helps in fighting against COVID-19 by monitoring, contract tracing and detecting the COVID-19 pandemic by connection with the IoT-based smart solutions. IoT is the interconnected Web of smart devices, sensors, actuators and data, which are collected in the raw form and transmitted through the internet. The purpose of this paper is to propose the concept to detect and monitor the asymptotic patients using IoT-based sensors.\u0000\u0000\u0000Design/methodology/approach\u0000In recent days, the surge of the COVID-19 contagion has infected all over the world and it has ruined our day-to-day life. The extraordinary eruption of this pandemic virus placed the World Health Organization (WHO) in a hazardous position. The impact of this contagious virus and scarcity among the people has forced the world to get into complete lockdown, as the number of laboratory-confirmed cases is increasing in millions all over the world as per the records of the government.\u0000\u0000\u0000Findings\u0000COVID-19 patients are either symptomatic or asymptotic. Symptomatic patients have symptoms such as fever, cough and difficulty in breathing. But patients are also asymptotic, which is very difficult to detect and monitor by isolating them.\u0000\u0000\u0000Originality/value\u0000Asymptotic patients are very hazardous because without knowing that they are infected, they might spread the infection to others, also asymptotic patients might be having very serious lung damage. So, earlier prediction and monitoring of asymptotic patients are mandatory to save their life and prevent them from spreading.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133360924","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 : 2020-09-17DOI: 10.1108/IJPCC-04-2020-0029
M.Aftab Alam, Mahak, R. Haidri, D. Yadav
Purpose Cloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization. Design/methodology/approach Cloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing. Findings To evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time. Originality/value The simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.
{"title":"Efficient task scheduling on virtual machine in cloud computing environment","authors":"M.Aftab Alam, Mahak, R. Haidri, D. Yadav","doi":"10.1108/IJPCC-04-2020-0029","DOIUrl":"https://doi.org/10.1108/IJPCC-04-2020-0029","url":null,"abstract":"\u0000Purpose\u0000Cloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization.\u0000\u0000\u0000Design/methodology/approach\u0000Cloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing.\u0000\u0000\u0000Findings\u0000To evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time.\u0000\u0000\u0000Originality/value\u0000The simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129873718","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 : 2020-09-17DOI: 10.1108/IJPCC-06-2020-0059
J. B. Abdo, S. Zeadally
Purpose The purpose of this paper is to design a sustainable development platform for water and energy peer-to-peer trading that is financially and economically feasible. Water and other resources are becoming scarcer every day, and developing countries are the neediest for an immediate intervention. Water, as a national need, is considered to be one of the most precious commodities, but it is also one of the main causes for conflicts in the 21st century. Rainwater harvesting and peer-to-peer trading of the harvested water is one of the most convenient, scalable and sustainable solutions but faces organization challenges such as the absence of suitable business models motivating normal users to sell their generated resources (such as water and energy), currency and financial settlement complexities and single utility markets. Design/methodology/approach This paper proposes a multi-utility trading platform based on the blockchain technology which can address the challenges faced by peer-to-peer trading for resources such as energy and water. Findings This paper presents a peer-to-peer multi-utility trading platform that solves the shortcomings of existing utility frameworks reported in the current literature. Originality/value This proposed platform meets the needs of developing countries as well as rural areas of developed countries. The open nature of the proposed design makes it suitable for adoption and use by various stakeholders.
{"title":"Multi-utility framework: blockchain exchange platform for sustainable development","authors":"J. B. Abdo, S. Zeadally","doi":"10.1108/IJPCC-06-2020-0059","DOIUrl":"https://doi.org/10.1108/IJPCC-06-2020-0059","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to design a sustainable development platform for water and energy peer-to-peer trading that is financially and economically feasible. Water and other resources are becoming scarcer every day, and developing countries are the neediest for an immediate intervention. Water, as a national need, is considered to be one of the most precious commodities, but it is also one of the main causes for conflicts in the 21st century. Rainwater harvesting and peer-to-peer trading of the harvested water is one of the most convenient, scalable and sustainable solutions but faces organization challenges such as the absence of suitable business models motivating normal users to sell their generated resources (such as water and energy), currency and financial settlement complexities and single utility markets.\u0000\u0000\u0000Design/methodology/approach\u0000This paper proposes a multi-utility trading platform based on the blockchain technology which can address the challenges faced by peer-to-peer trading for resources such as energy and water.\u0000\u0000\u0000Findings\u0000This paper presents a peer-to-peer multi-utility trading platform that solves the shortcomings of existing utility frameworks reported in the current literature.\u0000\u0000\u0000Originality/value\u0000This proposed platform meets the needs of developing countries as well as rural areas of developed countries. The open nature of the proposed design makes it suitable for adoption and use by various stakeholders.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122183200","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}