In Wireless Sensor Networks (WSNs), sensor nodes are developed densely. They have limit processing ca-pability and low power resources. Thus, energy is one of most important constraints in these networks. In some appl...In Wireless Sensor Networks (WSNs), sensor nodes are developed densely. They have limit processing ca-pability and low power resources. Thus, energy is one of most important constraints in these networks. In some applications of sensor networks, sensor nodes sense data from the environment periodically and trans-mit these data to sink node. In order to decrease energy consumption and so, increase network’s lifetime, volume of transmitted data should be decreased. A solution, which is suggested, is aggregation. In aggrega-tion mechanisms, the nodes aggregate received data and send aggregated result instead of raw data to sink, so, the volume of the transmitted data is decreased. Aggregation algorithms should construct aggregation tree and transmit data to sink based on this tree. In this paper, we propose an automaton based algorithm to con-struct aggregation tree by using energy and distance parameters. Automaton is a decision-making machine that is able-to-learn. Since network’s topology is dynamic, algorithm should construct aggregation tree peri-odically. In order to aware nodes of topology and so, select optimal path, routing packets must be flooded in entire network that led to high energy consumption. By using automaton machine which is in interaction with environment, we solve this problem based on automat learning. By using this strategy, aggregation tree is reconstructed locally, that result in decreasing energy consumption. Simulation results show that the pro-posed algorithm has better performance in terms of energy efficiency which increase the network lifetime and support better coverage.展开更多
In a sensor network with a large number of densely populated sensor nodes, a single target of interest may be detected by multiple sensor nodes simultaneously. Data collected from the sensor nodes are usually highly c...In a sensor network with a large number of densely populated sensor nodes, a single target of interest may be detected by multiple sensor nodes simultaneously. Data collected from the sensor nodes are usually highly correlated, and hence energy saving using in-network data fusion becomes possible. A traditional data fusion scheme starts with dividing the network into clusters, followed by electing a sensor node as cluster head in each cluster. A cluster head is responsible for collecting data from all its cluster members, performing data fusion on these data and transmitting the fused data to the base station. Assuming that a sensor node is only capable of handling a single node-to-node transmission at a time and each transmission takes T time-slots, a cluster head with n cluster members will take at least nT time-slots to collect data from all its cluster members. In this paper, a tree-based network structure and its formation algorithms are proposed. Simulation results show that the proposed network structure can greatly reduce the delay in data collection.展开更多
Two important performance indicators for data mining algorithms are accuracy of classification/ prediction and time taken for training. These indicators are useful for selecting best algorithms for classification/pred...Two important performance indicators for data mining algorithms are accuracy of classification/ prediction and time taken for training. These indicators are useful for selecting best algorithms for classification/prediction tasks in data mining. Empirical studies on these performance indicators in data mining are few. Therefore, this study was designed to determine how data mining classification algorithm perform with increase in input data sizes. Three data mining classification algorithms—Decision Tree, Multi-Layer Perceptron (MLP) Neural Network and Naïve Bayes— were subjected to varying simulated data sizes. The time taken by the algorithms for trainings and accuracies of their classifications were analyzed for the different data sizes. Results show that Naïve Bayes takes least time to train data but with least accuracy as compared to MLP and Decision Tree algorithms.展开更多
This paper presents a new method for obtaining network properties from incomplete data sets. Problems associated with missing data represent well-known stumbling blocks in Social Network Analysis. The method of “esti...This paper presents a new method for obtaining network properties from incomplete data sets. Problems associated with missing data represent well-known stumbling blocks in Social Network Analysis. The method of “estimating connectivity from spanning tree completions” (ECSTC) is specifically designed to address situations where only spanning tree(s) of a network are known, such as those obtained through respondent driven sampling (RDS). Using repeated random completions derived from degree information, this method forgoes the usual step of trying to obtain final edge or vertex rosters, and instead aims to estimate network-centric properties of vertices probabilistically from the spanning trees themselves. In this paper, we discuss the problem of missing data and describe the protocols of our completion method, and finally the results of an experiment where ECSTC was used to estimate graph dependent vertex properties from spanning trees sampled from a graph whose characteristics were known ahead of time. The results show that ECSTC methods hold more promise for obtaining network-centric properties of individuals from a limited set of data than researchers may have previously assumed. Such an approach represents a break with past strategies of working with missing data which have mainly sought means to complete the graph, rather than ECSTC’s approach, which is to estimate network properties themselves without deciding on the final edge set.展开更多
Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learni...Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.展开更多
分析了移动网络优化的工作内容和工作流程,对网络中产生的海量数据进行了科学地分析与处理,提出了应用Web数据仓库和OLAP(联机分析处理)技术为手段构建GSM网络优化系统。详细说明了数据仓库的主题设计、维度和层次、度量指标和多维数据...分析了移动网络优化的工作内容和工作流程,对网络中产生的海量数据进行了科学地分析与处理,提出了应用Web数据仓库和OLAP(联机分析处理)技术为手段构建GSM网络优化系统。详细说明了数据仓库的主题设计、维度和层次、度量指标和多维数据模型设计,利用SQL Server 2000实现了数据仓库的建设,并使用前端分析工具实现对数据的多维分析,从而实现为网优人员提供数据分析。展开更多
文摘In Wireless Sensor Networks (WSNs), sensor nodes are developed densely. They have limit processing ca-pability and low power resources. Thus, energy is one of most important constraints in these networks. In some applications of sensor networks, sensor nodes sense data from the environment periodically and trans-mit these data to sink node. In order to decrease energy consumption and so, increase network’s lifetime, volume of transmitted data should be decreased. A solution, which is suggested, is aggregation. In aggrega-tion mechanisms, the nodes aggregate received data and send aggregated result instead of raw data to sink, so, the volume of the transmitted data is decreased. Aggregation algorithms should construct aggregation tree and transmit data to sink based on this tree. In this paper, we propose an automaton based algorithm to con-struct aggregation tree by using energy and distance parameters. Automaton is a decision-making machine that is able-to-learn. Since network’s topology is dynamic, algorithm should construct aggregation tree peri-odically. In order to aware nodes of topology and so, select optimal path, routing packets must be flooded in entire network that led to high energy consumption. By using automaton machine which is in interaction with environment, we solve this problem based on automat learning. By using this strategy, aggregation tree is reconstructed locally, that result in decreasing energy consumption. Simulation results show that the pro-posed algorithm has better performance in terms of energy efficiency which increase the network lifetime and support better coverage.
基金The Hong Kong Polytechnic University under internal Grant No. G-YF51.
文摘In a sensor network with a large number of densely populated sensor nodes, a single target of interest may be detected by multiple sensor nodes simultaneously. Data collected from the sensor nodes are usually highly correlated, and hence energy saving using in-network data fusion becomes possible. A traditional data fusion scheme starts with dividing the network into clusters, followed by electing a sensor node as cluster head in each cluster. A cluster head is responsible for collecting data from all its cluster members, performing data fusion on these data and transmitting the fused data to the base station. Assuming that a sensor node is only capable of handling a single node-to-node transmission at a time and each transmission takes T time-slots, a cluster head with n cluster members will take at least nT time-slots to collect data from all its cluster members. In this paper, a tree-based network structure and its formation algorithms are proposed. Simulation results show that the proposed network structure can greatly reduce the delay in data collection.
文摘Two important performance indicators for data mining algorithms are accuracy of classification/ prediction and time taken for training. These indicators are useful for selecting best algorithms for classification/prediction tasks in data mining. Empirical studies on these performance indicators in data mining are few. Therefore, this study was designed to determine how data mining classification algorithm perform with increase in input data sizes. Three data mining classification algorithms—Decision Tree, Multi-Layer Perceptron (MLP) Neural Network and Naïve Bayes— were subjected to varying simulated data sizes. The time taken by the algorithms for trainings and accuracies of their classifications were analyzed for the different data sizes. Results show that Naïve Bayes takes least time to train data but with least accuracy as compared to MLP and Decision Tree algorithms.
文摘This paper presents a new method for obtaining network properties from incomplete data sets. Problems associated with missing data represent well-known stumbling blocks in Social Network Analysis. The method of “estimating connectivity from spanning tree completions” (ECSTC) is specifically designed to address situations where only spanning tree(s) of a network are known, such as those obtained through respondent driven sampling (RDS). Using repeated random completions derived from degree information, this method forgoes the usual step of trying to obtain final edge or vertex rosters, and instead aims to estimate network-centric properties of vertices probabilistically from the spanning trees themselves. In this paper, we discuss the problem of missing data and describe the protocols of our completion method, and finally the results of an experiment where ECSTC was used to estimate graph dependent vertex properties from spanning trees sampled from a graph whose characteristics were known ahead of time. The results show that ECSTC methods hold more promise for obtaining network-centric properties of individuals from a limited set of data than researchers may have previously assumed. Such an approach represents a break with past strategies of working with missing data which have mainly sought means to complete the graph, rather than ECSTC’s approach, which is to estimate network properties themselves without deciding on the final edge set.
基金Taif University Researchers Supporting Project Number(TURSP-2020/73)Taif University,Taif,Saudi Arabia.
文摘Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.
文摘分析了移动网络优化的工作内容和工作流程,对网络中产生的海量数据进行了科学地分析与处理,提出了应用Web数据仓库和OLAP(联机分析处理)技术为手段构建GSM网络优化系统。详细说明了数据仓库的主题设计、维度和层次、度量指标和多维数据模型设计,利用SQL Server 2000实现了数据仓库的建设,并使用前端分析工具实现对数据的多维分析,从而实现为网优人员提供数据分析。
