Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM...Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.展开更多
Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood cells.In severe forms,it can lead to death.This genetic disorder h...Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood cells.In severe forms,it can lead to death.This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival.Therefore,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia carriers.Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health affairs.Exploring individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the public.An effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main tools.VADER represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social media.The contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform Twitter.In this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,respectively.The tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.展开更多
In every network,delay and energy are crucial for communication and network life.In wireless sensor networks,many tiny nodes create networks with high energy consumption and compute routes for better communication.Wir...In every network,delay and energy are crucial for communication and network life.In wireless sensor networks,many tiny nodes create networks with high energy consumption and compute routes for better communication.Wireless Sensor Networks(WSN)is a very complex scenario to compute minimal delay with data aggregation and energy efficiency.In this research,we compute minimal delay and energy efficiency for improving the quality of service of any WSN.The proposed work is based on energy and distance parameters as taken dependent variables with data aggregation.Data aggregation performs on different models,namely Hybrid-Low Energy Adaptive Clustering Hierarchy(H-LEACH),Low Energy Adaptive Clustering Hierarchy(LEACH),and Multi-Aggregator-based Multi-Cast(MAMC).The main contribution of this research is to a reduction in delay and optimized energy solution,a novel hybrid model design in this research that ensures the quality of service in WSN.This model includes a whale optimization technique that involves heterogeneous functions and performs optimization to reach optimized results.For cluster head selection,Stable Election Protocol(SEP)protocol is used and Power-Efficient Gathering in Sensor Information Systems(PEGASIS)is used for driven-path in routing.Simulation results evaluate that H-LEACH provides minimal delay and energy consumption by sensor nodes.In the comparison of existing theories and our proposed method,HLEACH is providing energy and delay reduction and improvement in quality of service.MATLAB 2019 is used for simulation work.展开更多
In the network field,Wireless Sensor Networks(WSN)contain prolonged attention due to afresh augmentations.Industries like health care,traffic,defense,and many more systems espoused the WSN.These networks contain tiny ...In the network field,Wireless Sensor Networks(WSN)contain prolonged attention due to afresh augmentations.Industries like health care,traffic,defense,and many more systems espoused the WSN.These networks contain tiny sensor nodes containing embedded processors,TinyOS,memory,and power source.Sensor nodes are responsible for forwarding the data packets.To manage all these components,there is a need to select appropriate parameters which control the quality of service of WSN.Multiple sensor nodes are involved in transmitting vital information,and there is a need for secure and efficient routing to reach the quality of service.But due to the high cost of the network,WSN components have limited resources to manage the network.There is a need to design a lightweight solution that ensures the quality of service in WSN.In this given manner,this study provides the quality of services in a wireless sensor network with a security mechanism.An incorporated hybrid lightweight security model is designed in which random waypoint mobility(RWM)model and grey wolf optimization(GWO)is used to enhance service quality and maintain security with efficient routing.MATLAB version 16 andNetwork Stimulator 2.35(NS2.35)are used in this research to evaluate the results.The overall cost factor is reduced at 60%without the optimization technique and 90.90%reduced by using the optimization technique,which is assessed by calculating the signal-to-noise ratio,overall energy nodes,and communication overhead.展开更多
Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is...Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN.Many routing protocols are available,but the issue is still alive.Clustering is one of the most important techniques in the existing routing protocols.In the clustering-based model,the important thing is the selection of the cluster heads.In this paper,we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster.Initially,the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance.The proposed scheme performs hierarchal routing and direct routing with some energy thresholds.The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance.Moreover,the simulations will be performed in two scenarios,gate-way-based and without gateway to achieve more energy-efficient results.展开更多
IPv6 over Low PowerWireless Personal Area Network(6LoWPAN)provides IP connectivity to the highly constrained nodes in the Internet of Things(IoTs).6LoWPANallows nodeswith limited battery power and storage capacity to ...IPv6 over Low PowerWireless Personal Area Network(6LoWPAN)provides IP connectivity to the highly constrained nodes in the Internet of Things(IoTs).6LoWPANallows nodeswith limited battery power and storage capacity to carry IPv6 datagrams over the lossy and error-prone radio links offered by the IEEE 802.15.4 standard,thus acting as an adoption layer between the IPv6 protocol and IEEE 802.15.4 network.The data link layer of IEEE 802.15.4 in 6LoWPAN is based on AES(Advanced Encryption Standard),but the 6LoWPANstandard lacks and has omitted the security and privacy requirements at higher layers.The sensor nodes in 6LoWPANcan join the network without requiring the authentication procedure.Therefore,from security perspectives,6LoWPAN is vulnerable to many attacks such as replay attack,Man-in-the-Middle attack,Impersonation attack,and Modification attack.This paper proposes a secure and efficient cluster-based authentication scheme(CBAS)for highly constrained sensor nodes in 6LoWPAN.In this approach,sensor nodes are organized into a cluster and communicate with the central network through a dedicated sensor node.The main objective of CBAS is to provide efficient and authentic communication among the 6LoWPAN nodes.To ensure the low signaling overhead during the registration,authentication,and handover procedures,we also introduce lightweight and efficient registration,de-registration,initial authentication,and handover procedures,when a sensor node or group of sensor nodes join or leave a cluster.Our security analysis shows that the proposed CBAS approach protects against various security attacks,including Identity Confidentiality attack,Modification attack,Replay attack,Man-in-the-middle attack,and Impersonation attack.Our simulation experiments show that CBAS has reduced the registration delay by 11%,handoff authentication delay by 32%,and signaling cost by 37%compared to the SGMS(Secure GroupMobility Scheme)and LAMS(Light-Wight Authentication&Mobility Scheme).展开更多
Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging environments.In aut...Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging environments.In autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs.In this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results.Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs.However,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle prediction.Thus,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction problem.To validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear unit.As per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower ones.Results show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning approach.Infield testing was also performed using the model trained with the optimal architecture,which we developed using our approach.展开更多
In Wuhan,China,a novel Corona Virus(COVID-19)was detected in December 2019;it has changed the entire world and to date,the number of diagnosed cases is 38,756,2891 and 1,095,2161 people have died.This happened because...In Wuhan,China,a novel Corona Virus(COVID-19)was detected in December 2019;it has changed the entire world and to date,the number of diagnosed cases is 38,756,2891 and 1,095,2161 people have died.This happened because a large number of people got affected and there is a lack of hospitals for COVID-19 patients.One of the precautionary measures for COVID-19 patients is isolation.To support this,there is an urgent need for a platform that makes treatment possible from a distance.Telemedicine systems have been drastically increasing in number and size over recent years.This increasing number intensies the extensive need for telemedicine for the national healthcare system.In this paper,we present Tele-COVID which is a telemedicine application to treat COVID-19 patients from a distance.Tele-COVID is uniquely designed and implemented in Service-Oriented Architecture(SOA)to avoid the problem of interoperability,vendor lock-in,and data interchange.With the help of Tele-COVID,the treatment of patients at a distance is possible without the need for them to visit hospitals;in case of emergency,necessary services can also be provided.展开更多
Sentiment analysis task has widely been studied for various languages such as English and French.However,Roman Urdu sentiment analysis yet requires more attention from peer-researchers due to the lack of Off-the-Shelf...Sentiment analysis task has widely been studied for various languages such as English and French.However,Roman Urdu sentiment analysis yet requires more attention from peer-researchers due to the lack of Off-the-Shelf Natural Language Processing(NLP)solutions.The primary objective of this study is to investigate the diverse machine learning methods for the sentiment analysis of Roman Urdu data which is very informal in nature and needs to be lexically normalized.To mitigate this challenge,we propose a fine-tuned Support Vector Machine(SVM)powered by Roman Urdu Stemmer.In our proposed scheme,the corpus data is initially cleaned to remove the anomalies from the text.After initial pre-processing,each user review is being stemmed.The input text is transformed into a feature vector using the bag-of-word model.Subsequently,the SVM is used to classify and detect user sentiment.Our proposed scheme is based on a dictionary based Roman Urdu stemmer.The creation of the Roman Urdu stemmer is aimed at standardizing the text so as to minimize the level of complexity.The efficacy of our proposed model is also empirically evaluated with diverse experimental configurations,so as to fine-tune the hyper-parameters and achieve superior performance.Moreover,a series of experiments are conducted on diverse machine learning and deep learning models to compare the performance with our proposed model.We also introduced the largest dataset on Roman Urdu,i.e.,Roman Urdu e-commerce dataset(RUECD),which contains 26K+user reviews annotated by the group of experts.The RUECD is challenging and the largest dataset available of Roman Urdu.The experiments show that the newly generated dataset is quite challenging and requires more attention from the peer researchers for Roman Urdu sentiment analysis.展开更多
In today’s competitive business environment,the cost of a product is one of the most important considerations for its sale.Businesses are heavily involved in research strategies to minimize the cost of elements that ...In today’s competitive business environment,the cost of a product is one of the most important considerations for its sale.Businesses are heavily involved in research strategies to minimize the cost of elements that can impact on the final price of the product.Logistics is one such factor.Numerous products arrive from diverse locations to consumers in today’s digital era of online businesses.Clearly,the logistics sector faces several dilemmas from order attributes to environmental changes in this regard.This has specially been noted during the ongoing Covid-19 pandemic where the demands on online businesses have increased several fold.Consequently,the methodology to optimise delivery cost and its impact on environmental focus by reducing CO_(2) emissions has gained relevance.The resultant strategy of Shipment Consolidation that has evolved is an approach that combines one or more transport orders in the same vehicle for delivery.Shipment Consolidation has been categorized in three order scheduling approaches:Time based consolidation,Quantity based consolidation,and a Hybrid(Time-Quantity)based consolidation.In this paper,a new Hybrid Consolidation approach is presented.Using the Hybrid approach,it has been shown that order delivery can be facilitated by taking into account not only the order pick up time,but also the total order quantity.These results have shown that if a time window is available in respect of the order delivery time,then the order can be delayed from pickup to consolidate it with other orders for cost optimization.This hybrid approach is based on four consolidation principles,two of which work on fixed departure and two,on demand departure.Three of these rules have been implemented and tested here with an application case study.Statistical analysis of the results is illustrated with different planning evaluation indicators.The Result analyses indicate that consolidation of orders is increased with each implemented rule hence motivating us towards the implementation of the fourth rule.Testing with bigger data sets is required.展开更多
Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy o...Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy of heart disease is low.The coronary heart disorder determines the state that influences the heart valves,causing heart disease.Two indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep throat.This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness.At first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)algorithm.With this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was decreased.The CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods face.Our proposed method offers an illustrative framework that helps predict heart attacks with high accuracy.The proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.展开更多
Warning alerts are specially designed to protect user rights and safety to avoid serious damage caused by overlooking the essence of warning alerts. Today’s world of Information Communication Technology (ICT) needs i...Warning alerts are specially designed to protect user rights and safety to avoid serious damage caused by overlooking the essence of warning alerts. Today’s world of Information Communication Technology (ICT) needs improvement and to review the decisions of security experts in terms of improving warning designs and dialogues, and timely inform the authorities to take quick action at the right time and choice. Human behaviour is deeply involved in most of the security failures and its poor response. If we are able to check and monitor human behaviour in any organisation, we can achieve quality assurance and provide best services to our customers. We have arranged a study in the Center of Post Graduate Studies, International Islamic University, Malaysia (CPS-IIUM), department of Hajj Services-Makkah, and Hospital Management System-Makkah comprised of Observation, Interviews, Questionnaire and discussion based on organizational structure and job activities of people involved in different scenarios and positions under one umbrella of organizational objectives in order to trap the human error in order to take rapid action and response from the management team. Human behaviour is deeply observed and checked while performing different job activities in order to identify the serious errors at the right time during job performance at various levels. We have applied the concept of Brahm’s Language for the simulation of human behaviour which proves an opportunity to simulate human behaviour while performing job activities. Customer service can be improved easily if necessary measures and decisions are taken at the right time and place in any organisation.展开更多
基金authors are thankful to the Deanship of Scientific Research at Najran University for funding this work,under the Research Groups Funding Program Grant Code(NU/RG/SERC/12/27).
文摘Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.
基金The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Collaboration Funding program grant coder NU/RC/SERC/11/5.
文摘Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood cells.In severe forms,it can lead to death.This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival.Therefore,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia carriers.Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health affairs.Exploring individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the public.An effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main tools.VADER represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social media.The contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform Twitter.In this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,respectively.The tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.
基金The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Collaboration Funding program Grant Code NU/RC/SERC/11/7.
文摘In every network,delay and energy are crucial for communication and network life.In wireless sensor networks,many tiny nodes create networks with high energy consumption and compute routes for better communication.Wireless Sensor Networks(WSN)is a very complex scenario to compute minimal delay with data aggregation and energy efficiency.In this research,we compute minimal delay and energy efficiency for improving the quality of service of any WSN.The proposed work is based on energy and distance parameters as taken dependent variables with data aggregation.Data aggregation performs on different models,namely Hybrid-Low Energy Adaptive Clustering Hierarchy(H-LEACH),Low Energy Adaptive Clustering Hierarchy(LEACH),and Multi-Aggregator-based Multi-Cast(MAMC).The main contribution of this research is to a reduction in delay and optimized energy solution,a novel hybrid model design in this research that ensures the quality of service in WSN.This model includes a whale optimization technique that involves heterogeneous functions and performs optimization to reach optimized results.For cluster head selection,Stable Election Protocol(SEP)protocol is used and Power-Efficient Gathering in Sensor Information Systems(PEGASIS)is used for driven-path in routing.Simulation results evaluate that H-LEACH provides minimal delay and energy consumption by sensor nodes.In the comparison of existing theories and our proposed method,HLEACH is providing energy and delay reduction and improvement in quality of service.MATLAB 2019 is used for simulation work.
基金The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Collaboration Funding program grant code NU/RC/SERC/11/7。
文摘In the network field,Wireless Sensor Networks(WSN)contain prolonged attention due to afresh augmentations.Industries like health care,traffic,defense,and many more systems espoused the WSN.These networks contain tiny sensor nodes containing embedded processors,TinyOS,memory,and power source.Sensor nodes are responsible for forwarding the data packets.To manage all these components,there is a need to select appropriate parameters which control the quality of service of WSN.Multiple sensor nodes are involved in transmitting vital information,and there is a need for secure and efficient routing to reach the quality of service.But due to the high cost of the network,WSN components have limited resources to manage the network.There is a need to design a lightweight solution that ensures the quality of service in WSN.In this given manner,this study provides the quality of services in a wireless sensor network with a security mechanism.An incorporated hybrid lightweight security model is designed in which random waypoint mobility(RWM)model and grey wolf optimization(GWO)is used to enhance service quality and maintain security with efficient routing.MATLAB version 16 andNetwork Stimulator 2.35(NS2.35)are used in this research to evaluate the results.The overall cost factor is reduced at 60%without the optimization technique and 90.90%reduced by using the optimization technique,which is assessed by calculating the signal-to-noise ratio,overall energy nodes,and communication overhead.
文摘Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN.Many routing protocols are available,but the issue is still alive.Clustering is one of the most important techniques in the existing routing protocols.In the clustering-based model,the important thing is the selection of the cluster heads.In this paper,we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster.Initially,the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance.The proposed scheme performs hierarchal routing and direct routing with some energy thresholds.The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance.Moreover,the simulations will be performed in two scenarios,gate-way-based and without gateway to achieve more energy-efficient results.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation,Ministry of Education,Kingdom of Saudi Arabia for this research through a Grant(NU/IFC/INT/01/008)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘IPv6 over Low PowerWireless Personal Area Network(6LoWPAN)provides IP connectivity to the highly constrained nodes in the Internet of Things(IoTs).6LoWPANallows nodeswith limited battery power and storage capacity to carry IPv6 datagrams over the lossy and error-prone radio links offered by the IEEE 802.15.4 standard,thus acting as an adoption layer between the IPv6 protocol and IEEE 802.15.4 network.The data link layer of IEEE 802.15.4 in 6LoWPAN is based on AES(Advanced Encryption Standard),but the 6LoWPANstandard lacks and has omitted the security and privacy requirements at higher layers.The sensor nodes in 6LoWPANcan join the network without requiring the authentication procedure.Therefore,from security perspectives,6LoWPAN is vulnerable to many attacks such as replay attack,Man-in-the-Middle attack,Impersonation attack,and Modification attack.This paper proposes a secure and efficient cluster-based authentication scheme(CBAS)for highly constrained sensor nodes in 6LoWPAN.In this approach,sensor nodes are organized into a cluster and communicate with the central network through a dedicated sensor node.The main objective of CBAS is to provide efficient and authentic communication among the 6LoWPAN nodes.To ensure the low signaling overhead during the registration,authentication,and handover procedures,we also introduce lightweight and efficient registration,de-registration,initial authentication,and handover procedures,when a sensor node or group of sensor nodes join or leave a cluster.Our security analysis shows that the proposed CBAS approach protects against various security attacks,including Identity Confidentiality attack,Modification attack,Replay attack,Man-in-the-middle attack,and Impersonation attack.Our simulation experiments show that CBAS has reduced the registration delay by 11%,handoff authentication delay by 32%,and signaling cost by 37%compared to the SGMS(Secure GroupMobility Scheme)and LAMS(Light-Wight Authentication&Mobility Scheme).
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation,Ministry of Education,Kingdom of Saudi Arabia for this research through a grant(NU/IFC/INT/01/008)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging environments.In autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs.In this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results.Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs.However,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle prediction.Thus,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction problem.To validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear unit.As per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower ones.Results show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning approach.Infield testing was also performed using the model trained with the optimal architecture,which we developed using our approach.
文摘In Wuhan,China,a novel Corona Virus(COVID-19)was detected in December 2019;it has changed the entire world and to date,the number of diagnosed cases is 38,756,2891 and 1,095,2161 people have died.This happened because a large number of people got affected and there is a lack of hospitals for COVID-19 patients.One of the precautionary measures for COVID-19 patients is isolation.To support this,there is an urgent need for a platform that makes treatment possible from a distance.Telemedicine systems have been drastically increasing in number and size over recent years.This increasing number intensies the extensive need for telemedicine for the national healthcare system.In this paper,we present Tele-COVID which is a telemedicine application to treat COVID-19 patients from a distance.Tele-COVID is uniquely designed and implemented in Service-Oriented Architecture(SOA)to avoid the problem of interoperability,vendor lock-in,and data interchange.With the help of Tele-COVID,the treatment of patients at a distance is possible without the need for them to visit hospitals;in case of emergency,necessary services can also be provided.
基金the Deputy for Study and Innovation,Ministry of Education,Kingdom of Saudi Arabia,for funding this research through a Grant(NU/IFC/INT/01/008)from the Najran University Institutional Funding Committee.
文摘Sentiment analysis task has widely been studied for various languages such as English and French.However,Roman Urdu sentiment analysis yet requires more attention from peer-researchers due to the lack of Off-the-Shelf Natural Language Processing(NLP)solutions.The primary objective of this study is to investigate the diverse machine learning methods for the sentiment analysis of Roman Urdu data which is very informal in nature and needs to be lexically normalized.To mitigate this challenge,we propose a fine-tuned Support Vector Machine(SVM)powered by Roman Urdu Stemmer.In our proposed scheme,the corpus data is initially cleaned to remove the anomalies from the text.After initial pre-processing,each user review is being stemmed.The input text is transformed into a feature vector using the bag-of-word model.Subsequently,the SVM is used to classify and detect user sentiment.Our proposed scheme is based on a dictionary based Roman Urdu stemmer.The creation of the Roman Urdu stemmer is aimed at standardizing the text so as to minimize the level of complexity.The efficacy of our proposed model is also empirically evaluated with diverse experimental configurations,so as to fine-tune the hyper-parameters and achieve superior performance.Moreover,a series of experiments are conducted on diverse machine learning and deep learning models to compare the performance with our proposed model.We also introduced the largest dataset on Roman Urdu,i.e.,Roman Urdu e-commerce dataset(RUECD),which contains 26K+user reviews annotated by the group of experts.The RUECD is challenging and the largest dataset available of Roman Urdu.The experiments show that the newly generated dataset is quite challenging and requires more attention from the peer researchers for Roman Urdu sentiment analysis.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation,Ministry of Education,Kingdom of Saudi Arabia for this research through a grant(NU/IFC/INT/01/008)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘In today’s competitive business environment,the cost of a product is one of the most important considerations for its sale.Businesses are heavily involved in research strategies to minimize the cost of elements that can impact on the final price of the product.Logistics is one such factor.Numerous products arrive from diverse locations to consumers in today’s digital era of online businesses.Clearly,the logistics sector faces several dilemmas from order attributes to environmental changes in this regard.This has specially been noted during the ongoing Covid-19 pandemic where the demands on online businesses have increased several fold.Consequently,the methodology to optimise delivery cost and its impact on environmental focus by reducing CO_(2) emissions has gained relevance.The resultant strategy of Shipment Consolidation that has evolved is an approach that combines one or more transport orders in the same vehicle for delivery.Shipment Consolidation has been categorized in three order scheduling approaches:Time based consolidation,Quantity based consolidation,and a Hybrid(Time-Quantity)based consolidation.In this paper,a new Hybrid Consolidation approach is presented.Using the Hybrid approach,it has been shown that order delivery can be facilitated by taking into account not only the order pick up time,but also the total order quantity.These results have shown that if a time window is available in respect of the order delivery time,then the order can be delayed from pickup to consolidate it with other orders for cost optimization.This hybrid approach is based on four consolidation principles,two of which work on fixed departure and two,on demand departure.Three of these rules have been implemented and tested here with an application case study.Statistical analysis of the results is illustrated with different planning evaluation indicators.The Result analyses indicate that consolidation of orders is increased with each implemented rule hence motivating us towards the implementation of the fourth rule.Testing with bigger data sets is required.
文摘Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy of heart disease is low.The coronary heart disorder determines the state that influences the heart valves,causing heart disease.Two indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep throat.This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness.At first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)algorithm.With this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was decreased.The CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods face.Our proposed method offers an illustrative framework that helps predict heart attacks with high accuracy.The proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.
文摘Warning alerts are specially designed to protect user rights and safety to avoid serious damage caused by overlooking the essence of warning alerts. Today’s world of Information Communication Technology (ICT) needs improvement and to review the decisions of security experts in terms of improving warning designs and dialogues, and timely inform the authorities to take quick action at the right time and choice. Human behaviour is deeply involved in most of the security failures and its poor response. If we are able to check and monitor human behaviour in any organisation, we can achieve quality assurance and provide best services to our customers. We have arranged a study in the Center of Post Graduate Studies, International Islamic University, Malaysia (CPS-IIUM), department of Hajj Services-Makkah, and Hospital Management System-Makkah comprised of Observation, Interviews, Questionnaire and discussion based on organizational structure and job activities of people involved in different scenarios and positions under one umbrella of organizational objectives in order to trap the human error in order to take rapid action and response from the management team. Human behaviour is deeply observed and checked while performing different job activities in order to identify the serious errors at the right time during job performance at various levels. We have applied the concept of Brahm’s Language for the simulation of human behaviour which proves an opportunity to simulate human behaviour while performing job activities. Customer service can be improved easily if necessary measures and decisions are taken at the right time and place in any organisation.
