An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Machine learning(ML)techniques are used to assist healthcare providers in better diagnosing heart disease.This study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart disease.The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository.Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics.Specifically,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method.Results were recorded before and after the data preprocessing techniques were applied.Out of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed model.The proposed model yielded better results with gradient boosting in terms of precision,recall,and f1-score.It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.

基于双正交小波信道估计改进的TR-UWB系统

针对超宽带(UWB)信号因脉冲持续时间短、时域分辨率高、在传播中出现密集多径现象的特点,利用小波函数和超宽带脉冲之间的相似特性,提出了一种基于双正交小波基的高效估计多径衰落幅度和时延等信道参数的信道估计算法,以便接收机能捕获足够的信号能量。通过滑动相关实现了同尺度上信号与小波的内积运算,避免了峰值搜索过程,简化了估计器的结构。同时,基于由信道冲激响应估计值合成的本地相关模板信号,提出了改进的发射-参考超宽带(TR-UWB)系统。Monte-Carlo 仿真结果表明,该算法的归一化均方误差较小,改进的 TR-UWB 系统能有效抑制参考符号中噪声的影响,系统性能优于传统 TR 接收机。

Learning to Optimize for Resource Allocation in LTE-U Networks

This paper proposes a deep learning(DL)resource allocation framework to achieve the harmonious coexistence between the transceiver pairs(TPs)and the Wi-Fi users in LTE-U networks.The nonconvex resource allocation is considered as a constrained learning problem and the deep neural network(DNN)is employed to approximate the optimal resource allocation decisions through unsupervised manner.A parallel DNN framework is proposed to deal with the two optimization variables in this problem,where one is the licensed power allocation unit and the other is the unlicensed time fraction occupied unit.Besides,to guarantee the feasibility of the proposed algorithm,the Lagrange dual method is used to relax the constraints into the DNN training process.Then,the dual variable and the DNN parameter are alternating update via the batch-based gradient decent method until the training process converges.Numerical results show that the proposed algorithm is feasible and has better performance than other general algorithms.

Towards Aspect Based Components Integration Framework for Cyber-Physical System

Cyber-Physical Systems(CPS)comprise interactive computation,networking,and physical processes.The integrative environment of CPS enables the smart systems to be aware of the surrounding physical world.Smart systems,such as smart health care systems,smart homes,smart transportation,and smart cities,are made up of complex and dynamic CPS.The components integration development approach should be based on the divide and conquer theory.This way multiple interactive components can reduce the development complexity inCPS.As reusability enhances efficiency and consistency in CPS,encapsulation of component functionalities and a well-designed user interface is vital for the better end-user’s Quality of Experience(QoE).Thus,incorrect interaction of interfaces in the cyber-physical system causes system failures.Usually,interface failures occur due to false,and ambiguous requirements analysis and specification.Therefore,to resolve this issue semantic analysis is required for different stakeholders’viewpoint analysis during requirement specification and components analysis.This work proposes a framework to improve the CPS component integration process,starting from requirement specification to prioritization of components for configurable.For semantic analysis and assessing the reusability of specifications,the framework uses text mining and case-based reasoning techniques.The framework has been tested experimentally,and the results show a significant reduction in ambiguity,redundancy,and irrelevancy,as well as increasing accuracy of interface interactions,component selection,and higher user satisfaction.

Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network

Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people,and its high number of deaths also by 7%.For that purpose,a proposed model of several stages was developed.The first stage is optimizing the images using dynamic adaptive histogram equalization,performing a semantic segmentation using DeepLabv3Plus,then augmenting the data by flipping it horizontally,rotating it,then flipping it vertically.The second stage builds a custom convolutional neural network model using several pre-trained ImageNet.Finally,the model compares the pre-trained data to the new output,while repeatedly trimming the best-performing models to reduce complexity and improve memory efficiency.Several experiments were done using different techniques and parameters.Accordingly,the proposed model achieved an average accuracy of 99.6%and an area under the curve of 0.996 in the Covid-19 detection.This paper will discuss how to train a customized intelligent convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.

Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

In mobile edge computing(MEC),one of the important challenges is how much resources of which mobile edge server(MES)should be allocated to which user equipment(UE).The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only.This paper presents a novel comprehensive utility function for resource allocation in MEC.The utility function considers the heterogeneous nature of applications that a UE offloads to MES.The proposed utility function considers all important parameters,including CPU,RAM,hard disk space,required time,and distance,to calculate a more realistic utility value for MESs.Moreover,we improve upon some general algorithms,used for resource allocation in MEC and cloud computing,by considering our proposed utility function.We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes.The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources.The utility function depends upon the UE requests and the distance between UEs and MES,and serves as a realistic means of comparison between different types of UE requests.Choosing(or selecting)an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task.We show that MES resource allocation is sub-optimal if CPU is the only resource considered.By taking into account the other resources,i.e.,RAM,disk space,request time,and distance in the utility function,we demonstrate improvement in the resource allocation algorithms in terms of service rate,utility,and MES energy consumption.

A Review of Wireless Body Area Networks for Medical Applications

Recent advances in Micro-Electro-Mechanical Systems (MEMS) technology, integrated circuits, and wireless communication have allowed the realization of Wireless Body Area Networks (WBANs). WBANs promise unobtrusive ambulatory health monitoring for a long period of time, and provide real-time updates of the patient’s status to the physician. They are widely used for ubiquitous healthcare, entertainment, and military applications. This paper reviews the key aspects of WBANs for numerous applications. We present a WBAN infrastructure that provides solutions to on-demand, emergency, and normal traffic. We further discuss in-body antenna design and low-power MAC protocol for a WBAN. In addition, we briefly outline some of the WBAN applications with examples. Our discussion realizes a need for new power-efficient solu-tions towards in-body and on-body sensor networks.

中继通信优化传输基础理论与关键技术研究

无线中继通信是信息与通信工程领域的前沿课题,涉及通信信号处理、协作通信、多址接入和信息论等范畴。随着新一代无线通信系统对全覆盖、全频谱、全应用和强安全的迫切需求,无线中继通信成为国内外的研究热点,是下一代新基建信息基础设施的核心技术之一。1项日主要技术创新点在国家自然科学基金和山东省自然科学基金等项目的支持下。