Opportunistic Mobile Data Offloading Using Machine Learning Approach

Wireless Personal Communications - Currently, cellular networks both 3G and 4G are heavily overloaded due to increasing usage of mobile applications. Offloading mobile data traffic through...     

Green Computing Process and its Optimization Using Machine Learning Algorithm in Healthcare Sector

Mobile Networks and Applications - Handling the information is crucial task in healthcare sector; the data mining techniques will be right choice to address the complex problems. The hybridized...     

Healthcare Services Monitoring in Cloud Using Secure and Robust Healthcare-Based BLOCKCHAIN(SRHB)Approach

Mobile Networks and Applications - Health Electronic Records (HER) share the data to improve the quality and decrease the cost of Healthcare. It is challenging because of techniques complexities...     

Adaptive Context-Aware Energy Optimization for Services on Mobile Devices with Use of Machine Learning

Wireless Personal Communications - In this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms...     

Modelling and Analysis of Cuk Converter Using Current Injected Equivalent Circuit Approach

The current-injected equivalent-circuit approach has been developed for modelling and analysis of switching dc-dc converters and is very versatile. This approach can also be applied for modelling and analysis of complex converters or cascaded converters. To demonstrate the ability of the current-injected equivalent-circuit approach, the modelling and analysis of a Cuk converter is carried out. A small signal equivalent-circuit model is obtained which represents both input and output properties of the nonlinear converter. The results are presented in the form of linear equivalent-circuit models, as well as transfer functions.     

Predictive Analysis and Prognostic Approach of Diabetes Prediction with Machine Learning Techniques

Wireless Personal Communications - Medical experts indulge in numerous strategies for efficient and predictive measures to model the health status of patients and formulate the patterns that are...     

Characterisation of Signal Modality: Exploiting Signal Nonlinearity in Machine Learning and Signal Processing

A novel method for online tracking of the changes in the nonlinearity within both real-domain and complex–valued signals is introduced. This is achieved by a collaborative adaptive signal processing approach based on a hybrid filter. By tracking the dynamics of the adaptive mixing parameter within the employed hybrid filtering architecture, we show that it is possible to quantify the degree of nonlinearity within both real- and complex-valued data. Implementations for tracking nonlinearity in general and then more specifically sparsity are illustrated on both benchmark and real world data. It is also shown that by combining the information obtained from hybrid filters of different natures it is possible to use this method to gain a more complete understanding of the nature of the nonlinearity within a signal. This also paves the way for building multidimensional feature spaces and their application in data/information fusion.     

A Machine Learning Approach for Automated Evaluation of Short Answers Using Text Similarity Based on WordNet Graphs

Wireless Personal Communications - Answer sheet evaluation is a time-consuming task that requires lot of efforts by the teachers and hence there is a strong need of automation for the same. This...     

Superquantile-Based Learning: A Direct Approach Using Gradient-Based Optimization

Journal of Signal Processing Systems - We consider a formulation of supervised learning that endows models with robustness to distributional shifts from training to testing. The formulation hinges...     

A Supervised Machine Learning Approach for the Credibility Assessment of User-Generated Content

Wireless Personal Communications - Consumers increasingly rely on online reviews to assist them in their buying decisions. The rising popularity of e-commerce websites, hotel reviews, and social...     

Machine Learning Approach Electric Appliance Consumption and Peak Demand Forecasting of Residential Customers Using Smart Meter Data

Wireless Personal Communications - Electric consumption forecasting using smart meter dataset is one of the aspects in which machine learning approach is highly applied. Forecasting peak demand and...     

SVM算法在硬件木马旁路分析检测中的应用

集成电路(ICs)面临着硬件木马(HTs)造成的严峻威胁。传统的旁路检测手段中黄金模型不易获得,且隐秘的木马可以利用固硬件联合操作将恶意行为隐藏在常规的芯片运行中,更难以检测。针对这种情况,该文提出利用机器学习支持向量机(SVM)算法从系统操作层次对旁路分析检测方法进行改进。使用现场可编程门阵列(FPGA)验证的实验结果表明,存在黄金模型时,有监督SVM可得到86.8%的训练及测试综合的平均检测准确率,进一步采用分组和归一化去离群点方法可将检测率提升4%。若黄金模型无法获得,则可使用半监督SVM方法进行检测,平均检测率为52.9%～79.5%。与现有同类方法相比,验证了SVM算法在指令级木马检测中的有效性,明确了分类学习条件与检测性能的关系。

     

Decision Based Model for Real-Time IoT Analysis Using Big Data and Machine Learning

Use of internet of things (IoT) in different fields including smart cities, health care, manufacturing, and surveillance is growing rapidly, which results in massive amount of data generated by IoT devices. Real-time processing of large-scale data streams is one of the main challenges of IoT systems. Analyzing IoT data can help in providing better services, predicting trends and timely decision making for industries. The systematic structure of IoT data follows the pattern of big data. In this paper, a novel approach is proposed in which big data tools are used to perform real-time stream processing and analysis on IoT data. We have also applied Spark’s built-in support of the machine learning library in order to make real-time predictions. The efficiency of the proposed system is evaluated by conducting experiments and reporting results on the case scenario of IoT based weather station.

     

基于机器学习的蛋白质亚细胞定位预测

基因的表达使得蛋白质在核糖体内的合成成为可能,也使得遗传信息进行传递。蛋白质合成后必须被转运到特定的亚细胞器中,才能正常发挥其生物学功能,使整个生命机体正常运转。如果合成的蛋白质定位与其天然定位发生偏差,细胞的各种功能及其构成的生命机体将遭受重大影响。     

Semiconductor Yield Analysis and Multi-Chip Package(MCP) Dic Pairing Optimization using Machine Learning

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一种自动选择参数的雷达辐射源SVC分选方法

雷达信号分选的容差问题一直是雷达对抗情报处理中的难题,而支持向量聚类法（SVC）是一种能够有效避免容差问题的聚类方法,但现有参数搜索方法不能快速准确地确定SVC最优聚类结构,从而限制了支持向量聚类法的广泛应用。针对这一问题,提出了一种可以自动选择参数的SVC聚类方法。它通过采用一种综合的参数搜索方法,自动选择惩罚因子和核函数宽度两个参数,从而确定最优的聚类结构。仿真实验表明,此方法可在较少的迭代次数下获得最优的聚类结构,提高了雷达信号的分选正确率。     

预失真OFDM的一种新方法

OFDM信号大的峰均功率比以及过零点,通过一些模拟器件后,由于它们的非线性,会使信号严重失真,降低了整个系统的性能,文中提出了一种全新的OFDM信号预失真方法,将幅度和相位预失真结合起来,用以减小峰均功率比和避免信号过零点,在一定的条件下,该方法达到了设计目的,同时对抗干扰能力影响不大。     

基于深度学习网络的神经元自适应投影分类方法

传统的基于几何形态的神经元分类方法依赖于神经元空间结构特征的提取与选择,会损失大量有用的神经元分类信息.应用自适应投影算法将三维神经元进行转换,不需要提取神经元的几何特征,提出了一种基于深度学习网络的神经元几何形态分类方法.该方法将原始神经元数据进行三维体素重建,经过自适应投影过程构成二维神经元图像数据,并构建了基于双卷积门限循环神经网络的深度学习模型对神经元进行分类.将该方法应用于三种神经元分类数据集,通过与基于特征提取的神经元分类方法相比,实验结果表明该方法具有更高的分类准确率和良好的适应能力.     

Editorial: Machine Learning and Intelligent Communications (MLICOM 2018)

Mobile Networks and Applications -