Mobile networks possess significant information and thus are considered a gold mine for the researcher’s community.The call detail records(CDR)of a mobile network are used to identify the network’s efficacy and the ...Mobile networks possess significant information and thus are considered a gold mine for the researcher’s community.The call detail records(CDR)of a mobile network are used to identify the network’s efficacy and the mobile user’s behavior.It is evident from the recent literature that cyber-physical systems(CPS)were used in the analytics and modeling of telecom data.In addition,CPS is used to provide valuable services in smart cities.In general,a typical telecom company hasmillions of subscribers and thus generatesmassive amounts of data.From this aspect,data storage,analysis,and processing are the key concerns.To solve these issues,herein we propose a multilevel cyber-physical social system(CPSS)for the analysis and modeling of large internet data.Our proposed multilevel system has three levels and each level has a specific functionality.Initially,raw Call Detail Data(CDR)was collected at the first level.Herein,the data preprocessing,cleaning,and error removal operations were performed.In the second level,data processing,cleaning,reduction,integration,processing,and storage were performed.Herein,suggested internet activity record measures were applied.Our proposed system initially constructs a graph and then performs network analysis.Thus proposed CPSS system accurately identifies different areas of internet peak usage in a city(Milan city).Our research is helpful for the network operators to plan effective network configuration,management,and optimization of resources.展开更多
Community detection in social networks is a hard problem because of the size,and the need of a deep understanding of network structure and functions.While several methods with significant effort in this direction have...Community detection in social networks is a hard problem because of the size,and the need of a deep understanding of network structure and functions.While several methods with significant effort in this direction have been devised,an outstanding open problem is the unknown number of communities,it is generally believed that the role of influential nodes that are surrounded by neighbors is very important.In addition,the similarity among nodes inside the same cluster is greater than among nodes from other clusters.Lately,the global and local methods of community detection have been getting more attention.Therefore,in this study,we propose an advanced communitydetection model for social networks in order to identify network communities based on global and local information.Our proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label propagation.This process is conducted with the same color till nodes reach maximum similarity.Finally,the communities are formed,and a clear community graph is displayed to the user.Our proposed model is completely parameter-free,and therefore,no prior information is required,such as the number of communities,etc.We perform simulations and experiments using well-known synthetic and real network benchmarks,and compare them with well-known state-of-the-art models.The results prove that our model is efficient in all aspects,because it quickly identifies communities in the network.Moreover,it can easily be used for friendship recommendations or in business recommendation systems.展开更多
The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses.Social network analysis(SNA)is a good example...The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses.Social network analysis(SNA)is a good example that has recently gained a lot of scientific attention.It has its roots in social and economic research,as well as the evaluation of network science,such as graph theory.Scientists in this area have subverted predefined theories,offering revolutionary ones regarding interconnected networks,and they have highlighted the mystery of six degrees of separation with confirmation of the small-world phenomenon.The motivation of this study is to understand and capture the clustering properties of large networks and social networks.We present a network growth model in this paper and build a scale-free artificial social network with controllable clustering coefficients.The random walk technique is paired with a triangle generating scheme in our proposed model.As a result,the clustering controlmechanism and preferential attachment(PA)have been realized.This research builds on the present random walk model.We took numerous measurements for validation,including degree behavior and the measure of clustering decay in terms of node degree,among other things.Finally,we conclude that our suggested random walk model is more efficient and accurate than previous state-of-the-art methods,and hence it could be a viable alternative for societal evolution.展开更多
The networks are fundamental to our modern world and they appear throughout science and society.Access to a massive amount of data presents a unique opportunity to the researcher’s community.As networks grow in size ...The networks are fundamental to our modern world and they appear throughout science and society.Access to a massive amount of data presents a unique opportunity to the researcher’s community.As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace.Therefore,this paper initiates a discussion on graph signal processing for large-scale data analysis.We first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,etc.Graph filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of noise.We then consider a graph filter as a model that helps to extend the application of GSP methods to large datasets.To show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the filter.After several rounds of simulation results.We see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based signal.By using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.展开更多
In recent years,the Internet of Things(IoT)has played a vital role in providing various services to users in a smart city.However,searching for services,objects,data,and frameworks remains a concern.The technological ...In recent years,the Internet of Things(IoT)has played a vital role in providing various services to users in a smart city.However,searching for services,objects,data,and frameworks remains a concern.The technological advancements in Cyber-Physical Systems(CPSs)and the Social Internet of Things(SIoT)open a new era of research.Thus,we propose a Cyber-Physical-Social Systems(CPSs)for service search.Herein,service search and object discovery operation carries with the suitable selection of friends in the network.Our proposed model constructs a graph and performs social network analysis(SNA).We suggest degree centrality,clustering,and scalefree emergence and show that a rational selection of friends per service exploration increases the overall network navigability.The efficiency of our proposed system is verified using real-world datasets based on service processing time,path length,giant component,and network diameter.The simulation results proved that our proposed system is efficient,robust,and scalable.展开更多
The social internet of things(SIoT)is one of the emerging paradigms that was proposed to solve the problems of network service discovery,navigability,and service composition.The SIoT aims to socialize the IoT devices ...The social internet of things(SIoT)is one of the emerging paradigms that was proposed to solve the problems of network service discovery,navigability,and service composition.The SIoT aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings.In IoT,an object can offer multiple services and different objects can offer the same services with different parameters and interest factors.The proliferation of offered services led to difficulties during service customization and service filtering.This problem is known as service explosion.The selection of suitable service that fits the requirements of applications and objects is a challenging task.To address these issues,we propose an efficient automated query-based service search model based on the local network navigability concept for the SIoT.In the proposed model,objects can use information from their friends or friends of their friends while searching for the desired services,rather than exploring a global network.We employ a centrality metric that computes the degree of importance for each object in the social IoT that helps in selecting neighboring objects with high centrality scores.The distributed nature of our navigation model results in high scalability and short navigation times.We verified the efficacy of our model on a real-world SIoT-related dataset.The experimental results confirm the validity of our model in terms of scalability,navigability,and the desired objects that provide services are determined quickly via the shortest path,which in return improves the service search process in the SIoT.展开更多
Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intell...Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intelligence(AI)and deep learning,there has been potential to improve diagnostic accuracy,especially with Magnetic Resonance Imaging(MRI).However,traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation.Thus,our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model.The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification.The proposed model is first trained and later evaluated using the BraTS 2020 dataset.In our proposed model preprocessing consists of normalization,noise reduction,and data augmentation to improve model robustness.The attention mechanism and dilated convolutions were introduced to increase the model’s focus on critical regions and capture finer spatial details without compromising image resolution.We have performed experimentation to measure efficiency.For this,we have used various metrics including accuracy,sensitivity,and curve(AUC-ROC).The proposed model achieved a high accuracy of 94%,a sensitivity of 93%,a specificity of 92%,and an AUC-ROC of 0.98,outperforming traditional diagnostic models in brain tumor detection.The proposed model accurately identifies tumor regions,while dilated convolutions enhanced the segmentation accuracy,especially for complex tumor structures.The proposed model demonstrates significant potential for clinical application,providing reliable and precise brain tumor detection in MRI.展开更多
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493).
文摘Mobile networks possess significant information and thus are considered a gold mine for the researcher’s community.The call detail records(CDR)of a mobile network are used to identify the network’s efficacy and the mobile user’s behavior.It is evident from the recent literature that cyber-physical systems(CPS)were used in the analytics and modeling of telecom data.In addition,CPS is used to provide valuable services in smart cities.In general,a typical telecom company hasmillions of subscribers and thus generatesmassive amounts of data.From this aspect,data storage,analysis,and processing are the key concerns.To solve these issues,herein we propose a multilevel cyber-physical social system(CPSS)for the analysis and modeling of large internet data.Our proposed multilevel system has three levels and each level has a specific functionality.Initially,raw Call Detail Data(CDR)was collected at the first level.Herein,the data preprocessing,cleaning,and error removal operations were performed.In the second level,data processing,cleaning,reduction,integration,processing,and storage were performed.Herein,suggested internet activity record measures were applied.Our proposed system initially constructs a graph and then performs network analysis.Thus proposed CPSS system accurately identifies different areas of internet peak usage in a city(Milan city).Our research is helpful for the network operators to plan effective network configuration,management,and optimization of resources.
基金This research was supported by the Ministry of Trade,Industry&Energy(MOTIE,Korea)under the Industrial Technology Innovation Program,No.10063130by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2019R1A2C1006159)by the Ministry of Science and ICT(MSIT),Korea,under the Information Technology Research Center(ITRC)support program(IITP-2019-2016-0-00313)supervised by the Institute for Information&communications Technology Promotion(IITP).
文摘Community detection in social networks is a hard problem because of the size,and the need of a deep understanding of network structure and functions.While several methods with significant effort in this direction have been devised,an outstanding open problem is the unknown number of communities,it is generally believed that the role of influential nodes that are surrounded by neighbors is very important.In addition,the similarity among nodes inside the same cluster is greater than among nodes from other clusters.Lately,the global and local methods of community detection have been getting more attention.Therefore,in this study,we propose an advanced communitydetection model for social networks in order to identify network communities based on global and local information.Our proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label propagation.This process is conducted with the same color till nodes reach maximum similarity.Finally,the communities are formed,and a clear community graph is displayed to the user.Our proposed model is completely parameter-free,and therefore,no prior information is required,such as the number of communities,etc.We perform simulations and experiments using well-known synthetic and real network benchmarks,and compare them with well-known state-of-the-art models.The results prove that our model is efficient in all aspects,because it quickly identifies communities in the network.Moreover,it can easily be used for friendship recommendations or in business recommendation systems.
基金This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant NRF-2019R1A2C1006159 and Grant NRF-2021R1A6A1A03039493in part by the 2021 Yeungnam University Research Grant。
文摘The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses.Social network analysis(SNA)is a good example that has recently gained a lot of scientific attention.It has its roots in social and economic research,as well as the evaluation of network science,such as graph theory.Scientists in this area have subverted predefined theories,offering revolutionary ones regarding interconnected networks,and they have highlighted the mystery of six degrees of separation with confirmation of the small-world phenomenon.The motivation of this study is to understand and capture the clustering properties of large networks and social networks.We present a network growth model in this paper and build a scale-free artificial social network with controllable clustering coefficients.The random walk technique is paired with a triangle generating scheme in our proposed model.As a result,the clustering controlmechanism and preferential attachment(PA)have been realized.This research builds on the present random walk model.We took numerous measurements for validation,including degree behavior and the measure of clustering decay in terms of node degree,among other things.Finally,we conclude that our suggested random walk model is more efficient and accurate than previous state-of-the-art methods,and hence it could be a viable alternative for societal evolution.
基金supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2019R1A2C1006159)and(NRF-2021R1A6A1A03039493)by the 2021 Yeungnam University Research Grant.
文摘The networks are fundamental to our modern world and they appear throughout science and society.Access to a massive amount of data presents a unique opportunity to the researcher’s community.As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace.Therefore,this paper initiates a discussion on graph signal processing for large-scale data analysis.We first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,etc.Graph filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of noise.We then consider a graph filter as a model that helps to extend the application of GSP methods to large datasets.To show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the filter.After several rounds of simulation results.We see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based signal.By using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.
基金supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF2019R1A2C1006159)and(NRF-2021R1A6A1A03039493).
文摘In recent years,the Internet of Things(IoT)has played a vital role in providing various services to users in a smart city.However,searching for services,objects,data,and frameworks remains a concern.The technological advancements in Cyber-Physical Systems(CPSs)and the Social Internet of Things(SIoT)open a new era of research.Thus,we propose a Cyber-Physical-Social Systems(CPSs)for service search.Herein,service search and object discovery operation carries with the suitable selection of friends in the network.Our proposed model constructs a graph and performs social network analysis(SNA).We suggest degree centrality,clustering,and scalefree emergence and show that a rational selection of friends per service exploration increases the overall network navigability.The efficiency of our proposed system is verified using real-world datasets based on service processing time,path length,giant component,and network diameter.The simulation results proved that our proposed system is efficient,robust,and scalable.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(2020R1A2B5B01002145).
文摘The social internet of things(SIoT)is one of the emerging paradigms that was proposed to solve the problems of network service discovery,navigability,and service composition.The SIoT aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings.In IoT,an object can offer multiple services and different objects can offer the same services with different parameters and interest factors.The proliferation of offered services led to difficulties during service customization and service filtering.This problem is known as service explosion.The selection of suitable service that fits the requirements of applications and objects is a challenging task.To address these issues,we propose an efficient automated query-based service search model based on the local network navigability concept for the SIoT.In the proposed model,objects can use information from their friends or friends of their friends while searching for the desired services,rather than exploring a global network.We employ a centrality metric that computes the degree of importance for each object in the social IoT that helps in selecting neighboring objects with high centrality scores.The distributed nature of our navigation model results in high scalability and short navigation times.We verified the efficacy of our model on a real-world SIoT-related dataset.The experimental results confirm the validity of our model in terms of scalability,navigability,and the desired objects that provide services are determined quickly via the shortest path,which in return improves the service search process in the SIoT.
基金supported by the European University of Atlantic.
文摘Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intelligence(AI)and deep learning,there has been potential to improve diagnostic accuracy,especially with Magnetic Resonance Imaging(MRI).However,traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation.Thus,our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model.The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification.The proposed model is first trained and later evaluated using the BraTS 2020 dataset.In our proposed model preprocessing consists of normalization,noise reduction,and data augmentation to improve model robustness.The attention mechanism and dilated convolutions were introduced to increase the model’s focus on critical regions and capture finer spatial details without compromising image resolution.We have performed experimentation to measure efficiency.For this,we have used various metrics including accuracy,sensitivity,and curve(AUC-ROC).The proposed model achieved a high accuracy of 94%,a sensitivity of 93%,a specificity of 92%,and an AUC-ROC of 0.98,outperforming traditional diagnostic models in brain tumor detection.The proposed model accurately identifies tumor regions,while dilated convolutions enhanced the segmentation accuracy,especially for complex tumor structures.The proposed model demonstrates significant potential for clinical application,providing reliable and precise brain tumor detection in MRI.
