In vehicular ad hoc networks(VANETs),the topology information(TI)is updated frequently due to vehicle mobility.These frequent changes in topology increase the topology maintenance overhead.To reduce the control messag...In vehicular ad hoc networks(VANETs),the topology information(TI)is updated frequently due to vehicle mobility.These frequent changes in topology increase the topology maintenance overhead.To reduce the control message overhead,cluster-based routing schemes are proposed.In clusterbased routing schemes,the nodes are divided into different virtual groups,and each group(logical node)is considered a cluster.The topology changes are accommodated within each cluster,and broadcasting TI to the whole VANET is not required.The cluster head(CH)is responsible for managing the communication of a node with other nodes outside the cluster.However,transmitting real-time data via a CH may cause delays in VANETs.Such real-time data require quick service and should be routed through the shortest path when the quality of service(QoS)is required.This paper proposes a hybrid scheme which transmits time-critical data through the QoS shortest path and normal data through CHs.In this way,the real-time data are delivered efciently to the destination on time.Similarly,the routine data are transmitted through CHs to reduce the topology maintenance overhead.The work is validated through a series of simulations,and results show that the proposed scheme outperforms existing algorithms in terms of topology maintenance overhead,QoS and real-time and routine packet transmission.展开更多
Clustering algorithms optimization can minimize topology maintenance overhead in large scale vehicular Ad hoc networks(VANETs)for smart transportation that results from dynamic topology,limited resources and noncentra...Clustering algorithms optimization can minimize topology maintenance overhead in large scale vehicular Ad hoc networks(VANETs)for smart transportation that results from dynamic topology,limited resources and noncentralized architecture.The performance of a clustering algorithm varies with the underlying mobility model to address the topology maintenance overhead issue in VANETs for smart transportation.To design a robust clustering algorithm,careful attention must be paid to components like mobility models and performance objectives.A clustering algorithm may not perform well with every mobility pattern.Therefore,we propose a supervisory protocol(SP)that observes the mobility pattern of vehicles and identies the realistic Mobility model through microscopic features.An analytical model can be used to determine an efcient clustering algorithm for a specic mobility model(MM).SP selects the best clustering scheme according to the mobility model and guarantees a consistent performance throughout VANET operations.The simulation has performed in three parts that is the central part simulation for setting up the clustering environment,In the second part the clustering algorithms are tested for efciency in a constrained atmosphere for some time and the third part represents the proposed scheme.The simulation results show that the proposed scheme outperforms clustering algorithms such as honey bee algorithm-based clustering and memetic clustering in terms of cluster count,re-afliation rate,control overhead and cluster lifetime.展开更多
Software testing is an important and cost intensive activity in software development.The major contribution in cost is due to test case generations.Requirement-based testing is an approach in which test cases are deri...Software testing is an important and cost intensive activity in software development.The major contribution in cost is due to test case generations.Requirement-based testing is an approach in which test cases are derivative from requirements without considering the implementation’s internal structure.Requirement-based testing includes functional and nonfunctional requirements.The objective of this study is to explore the approaches that generate test cases from requirements.A systematic literature review based on two research questions and extensive quality assessment criteria includes studies.The study identies 30 primary studies from 410 studies spanned from 2000 to 2018.The review’s nding shows that 53%of journal papers,42%of conference papers,and 5%of book chapters’address requirementsbased testing.Most of the studies use UML,activity,and use case diagrams for test case generation from requirements.One of the signicant lessons learned is that most software testing errors are traced back to errors in natural language requirements.A substantial amount of work focuses on UML diagrams for test case generations,which cannot capture all the system’s developed attributes.Furthermore,there is a lack of UML-based models that can generate test cases from natural language requirements by rening them in context.Coverage criteria indicate how efciently the testing has been performed 12.37%of studies use requirements coverage,20%of studies cover path coverage,and 17%study basic coverage.展开更多
Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018.There are several imaging tests like Comput...Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018.There are several imaging tests like Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver.These tests are costly and time-consuming.This paper proposed that image processing through deep learning Convolutional Neural Network(CNNs)ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods.The existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest(ROI).This study uses ResUNet,an updated version of U-Net and ResNet Models that utilize the service of Residential blocks.We apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected patients.The results showed the True Value Accuracy around 99%and F1 score performance around 95%.This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.展开更多
Text Summarization is an essential area in text mining,which has procedures for text extraction.In natural language processing,text summarization maps the documents to a representative set of descriptive words.Therefo...Text Summarization is an essential area in text mining,which has procedures for text extraction.In natural language processing,text summarization maps the documents to a representative set of descriptive words.Therefore,the objective of text extraction is to attain reduced expressive contents from the text documents.Text summarization has two main areas such as abstractive,and extractive summarization.Extractive text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding principles.All such text extractions have limitations in providing the basic theme of the underlying documents.In this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word embedding.The study compared these forms of the text summarizations with the actual text,by calculating cosine similarities.Furthermore,TF-IDF based PageRank keywords are extracted from the other two extractive summarizations.An intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is performed.This technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each document.This form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized text.It also solves the issue of deciding the number of representative keywords for a specific text document.To evaluate the technique,the study used a sample of more than eighteen hundred text documents.The abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text summarization.The proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.展开更多
Internet of Everything(IoE)indicates a fantastic vision of the future,where everything is connected to the internet,providing intelligent services and facilitating decision making.IoE is the collection of static and m...Internet of Everything(IoE)indicates a fantastic vision of the future,where everything is connected to the internet,providing intelligent services and facilitating decision making.IoE is the collection of static and moving objects able to coordinate and communicate with each other.The moving objects may consist of ground segments and ying segments.The speed of ying segment e.g.,Unmanned Ariel Vehicles(UAVs)may high as compared to ground segment objects.The topology changes occur very frequently due to high speed nature of objects in UAV-enabled IoE(Ue-IoE).The routing maintenance overhead may increase when scaling the Ue-IoE(number of objects increases).A single change in topology can force all the objects of the Ue-IoE to update their routing tables.Similarly,the frequent updating in routing table entries will result more energy dissipation and the lifetime of the Ue-IoE may decrease.The objects consume more energy on routing computations.To prevent the frequent updation of routing tables associated with each object,the computation of routes from source to destination may be limited to optimum number of objects in the Ue-IoE.In this article,we propose a routing scheme in which the responsibility of route computation(from neighbor objects to destination)is assigned to some IoE-objects in the Ue-IoE.The route computation objects(RCO)are selected on the basis of certain parameters like remaining energy and mobility.The RCO send the routing information of destination objects to their neighbors once they want to communicate with other objects.The proposed protocol is simulated and the results show that it outperform state-of-the-art protocols in terms of average energy consumption,messages overhead,throughput,delay etc.展开更多
基金supported by Taif University Researchers Supporting Project Number(TURSP-2020/231),Taif University,Taif,Saudi Arabia.
文摘In vehicular ad hoc networks(VANETs),the topology information(TI)is updated frequently due to vehicle mobility.These frequent changes in topology increase the topology maintenance overhead.To reduce the control message overhead,cluster-based routing schemes are proposed.In clusterbased routing schemes,the nodes are divided into different virtual groups,and each group(logical node)is considered a cluster.The topology changes are accommodated within each cluster,and broadcasting TI to the whole VANET is not required.The cluster head(CH)is responsible for managing the communication of a node with other nodes outside the cluster.However,transmitting real-time data via a CH may cause delays in VANETs.Such real-time data require quick service and should be routed through the shortest path when the quality of service(QoS)is required.This paper proposes a hybrid scheme which transmits time-critical data through the QoS shortest path and normal data through CHs.In this way,the real-time data are delivered efciently to the destination on time.Similarly,the routine data are transmitted through CHs to reduce the topology maintenance overhead.The work is validated through a series of simulations,and results show that the proposed scheme outperforms existing algorithms in terms of topology maintenance overhead,QoS and real-time and routine packet transmission.
基金The authors extend their appreciation to King Saud University for funding this work through Researchers supporting project number(RSP-2020/133),King Saud University,Riyadh,Saudi Arabia.
文摘Clustering algorithms optimization can minimize topology maintenance overhead in large scale vehicular Ad hoc networks(VANETs)for smart transportation that results from dynamic topology,limited resources and noncentralized architecture.The performance of a clustering algorithm varies with the underlying mobility model to address the topology maintenance overhead issue in VANETs for smart transportation.To design a robust clustering algorithm,careful attention must be paid to components like mobility models and performance objectives.A clustering algorithm may not perform well with every mobility pattern.Therefore,we propose a supervisory protocol(SP)that observes the mobility pattern of vehicles and identies the realistic Mobility model through microscopic features.An analytical model can be used to determine an efcient clustering algorithm for a specic mobility model(MM).SP selects the best clustering scheme according to the mobility model and guarantees a consistent performance throughout VANET operations.The simulation has performed in three parts that is the central part simulation for setting up the clustering environment,In the second part the clustering algorithms are tested for efciency in a constrained atmosphere for some time and the third part represents the proposed scheme.The simulation results show that the proposed scheme outperforms clustering algorithms such as honey bee algorithm-based clustering and memetic clustering in terms of cluster count,re-afliation rate,control overhead and cluster lifetime.
文摘Software testing is an important and cost intensive activity in software development.The major contribution in cost is due to test case generations.Requirement-based testing is an approach in which test cases are derivative from requirements without considering the implementation’s internal structure.Requirement-based testing includes functional and nonfunctional requirements.The objective of this study is to explore the approaches that generate test cases from requirements.A systematic literature review based on two research questions and extensive quality assessment criteria includes studies.The study identies 30 primary studies from 410 studies spanned from 2000 to 2018.The review’s nding shows that 53%of journal papers,42%of conference papers,and 5%of book chapters’address requirementsbased testing.Most of the studies use UML,activity,and use case diagrams for test case generation from requirements.One of the signicant lessons learned is that most software testing errors are traced back to errors in natural language requirements.A substantial amount of work focuses on UML diagrams for test case generations,which cannot capture all the system’s developed attributes.Furthermore,there is a lack of UML-based models that can generate test cases from natural language requirements by rening them in context.Coverage criteria indicate how efciently the testing has been performed 12.37%of studies use requirements coverage,20%of studies cover path coverage,and 17%study basic coverage.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group No RG-1438-089.
文摘Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018.There are several imaging tests like Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver.These tests are costly and time-consuming.This paper proposed that image processing through deep learning Convolutional Neural Network(CNNs)ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods.The existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest(ROI).This study uses ResUNet,an updated version of U-Net and ResNet Models that utilize the service of Residential blocks.We apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected patients.The results showed the True Value Accuracy around 99%and F1 score performance around 95%.This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.
文摘Text Summarization is an essential area in text mining,which has procedures for text extraction.In natural language processing,text summarization maps the documents to a representative set of descriptive words.Therefore,the objective of text extraction is to attain reduced expressive contents from the text documents.Text summarization has two main areas such as abstractive,and extractive summarization.Extractive text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding principles.All such text extractions have limitations in providing the basic theme of the underlying documents.In this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word embedding.The study compared these forms of the text summarizations with the actual text,by calculating cosine similarities.Furthermore,TF-IDF based PageRank keywords are extracted from the other two extractive summarizations.An intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is performed.This technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each document.This form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized text.It also solves the issue of deciding the number of representative keywords for a specific text document.To evaluate the technique,the study used a sample of more than eighteen hundred text documents.The abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text summarization.The proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.
基金supported by Taif University Researchers Supporting Project number(TURSP-2020/231),Taif University,Taif,Saudi Arabia.
文摘Internet of Everything(IoE)indicates a fantastic vision of the future,where everything is connected to the internet,providing intelligent services and facilitating decision making.IoE is the collection of static and moving objects able to coordinate and communicate with each other.The moving objects may consist of ground segments and ying segments.The speed of ying segment e.g.,Unmanned Ariel Vehicles(UAVs)may high as compared to ground segment objects.The topology changes occur very frequently due to high speed nature of objects in UAV-enabled IoE(Ue-IoE).The routing maintenance overhead may increase when scaling the Ue-IoE(number of objects increases).A single change in topology can force all the objects of the Ue-IoE to update their routing tables.Similarly,the frequent updating in routing table entries will result more energy dissipation and the lifetime of the Ue-IoE may decrease.The objects consume more energy on routing computations.To prevent the frequent updation of routing tables associated with each object,the computation of routes from source to destination may be limited to optimum number of objects in the Ue-IoE.In this article,we propose a routing scheme in which the responsibility of route computation(from neighbor objects to destination)is assigned to some IoE-objects in the Ue-IoE.The route computation objects(RCO)are selected on the basis of certain parameters like remaining energy and mobility.The RCO send the routing information of destination objects to their neighbors once they want to communicate with other objects.The proposed protocol is simulated and the results show that it outperform state-of-the-art protocols in terms of average energy consumption,messages overhead,throughput,delay etc.
