2,074 research outputs found

    Adaptive Feature Engineering Modeling for Ultrasound Image Classification for Decision Support

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    Ultrasonography is considered a relatively safe option for the diagnosis of benign and malignant cancer lesions due to the low-energy sound waves used. However, the visual interpretation of the ultrasound images is time-consuming and usually has high false alerts due to speckle noise. Improved methods of collection image-based data have been proposed to reduce noise in the images; however, this has proved not to solve the problem due to the complex nature of images and the exponential growth of biomedical datasets. Secondly, the target class in real-world biomedical datasets, that is the focus of interest of a biopsy, is usually significantly underrepresented compared to the non-target class. This makes it difficult to train standard classification models like Support Vector Machine (SVM), Decision Trees, and Nearest Neighbor techniques on biomedical datasets because they assume an equal class distribution or an equal misclassification cost. Resampling techniques by either oversampling the minority class or under-sampling the majority class have been proposed to mitigate the class imbalance problem but with minimal success. We propose a method of resolving the class imbalance problem with the design of a novel data-adaptive feature engineering model for extracting, selecting, and transforming textural features into a feature space that is inherently relevant to the application domain. We hypothesize that by maximizing the variance and preserving as much variability in well-engineered features prior to applying a classifier model will boost the differentiation of the thyroid nodules (benign or malignant) through effective model building. Our proposed a hybrid approach of applying Regression and Rule-Based techniques to build our Feature Engineering and a Bayesian Classifier respectively. In the Feature Engineering model, we transformed images pixel intensity values into a high dimensional structured dataset and fitting a regression analysis model to estimate relevant kernel parameters to be applied to the proposed filter method. We adopted an Elastic Net Regularization path to control the maximum log-likelihood estimation of the Regression model. Finally, we applied a Bayesian network inference to estimate a subset for the textural features with a significant conditional dependency in the classification of the thyroid lesion. This is performed to establish the conditional influence on the textural feature to the random factors generated through our feature engineering model and to evaluate the success criterion of our approach. The proposed approach was tested and evaluated on a public dataset obtained from thyroid cancer ultrasound diagnostic data. The analyses of the results showed that the classification performance had a significant improvement overall for accuracy and area under the curve when then proposed feature engineering model was applied to the data. We show that a high performance of 96.00% accuracy with a sensitivity and specificity of 99.64%) and 90.23% respectively was achieved for a filter size of 13 × 13

    Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

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    Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

    A Machine Learning Enhanced Scheme for Intelligent Network Management

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    The versatile networking services bring about huge influence on daily living styles while the amount and diversity of services cause high complexity of network systems. The network scale and complexity grow with the increasing infrastructure apparatuses, networking function, networking slices, and underlying architecture evolution. The conventional way is manual administration to maintain the large and complex platform, which makes effective and insightful management troublesome. A feasible and promising scheme is to extract insightful information from largely produced network data. The goal of this thesis is to use learning-based algorithms inspired by machine learning communities to discover valuable knowledge from substantial network data, which directly promotes intelligent management and maintenance. In the thesis, the management and maintenance focus on two schemes: network anomalies detection and root causes localization; critical traffic resource control and optimization. Firstly, the abundant network data wrap up informative messages but its heterogeneity and perplexity make diagnosis challenging. For unstructured logs, abstract and formatted log templates are extracted to regulate log records. An in-depth analysis framework based on heterogeneous data is proposed in order to detect the occurrence of faults and anomalies. It employs representation learning methods to map unstructured data into numerical features, and fuses the extracted feature for network anomaly and fault detection. The representation learning makes use of word2vec-based embedding technologies for semantic expression. Next, the fault and anomaly detection solely unveils the occurrence of events while failing to figure out the root causes for useful administration so that the fault localization opens a gate to narrow down the source of systematic anomalies. The extracted features are formed as the anomaly degree coupled with an importance ranking method to highlight the locations of anomalies in network systems. Two types of ranking modes are instantiated by PageRank and operation errors for jointly highlighting latent issue of locations. Besides the fault and anomaly detection, network traffic engineering deals with network communication and computation resource to optimize data traffic transferring efficiency. Especially when network traffic are constrained with communication conditions, a pro-active path planning scheme is helpful for efficient traffic controlling actions. Then a learning-based traffic planning algorithm is proposed based on sequence-to-sequence model to discover hidden reasonable paths from abundant traffic history data over the Software Defined Network architecture. Finally, traffic engineering merely based on empirical data is likely to result in stale and sub-optimal solutions, even ending up with worse situations. A resilient mechanism is required to adapt network flows based on context into a dynamic environment. Thus, a reinforcement learning-based scheme is put forward for dynamic data forwarding considering network resource status, which explicitly presents a promising performance improvement. In the end, the proposed anomaly processing framework strengthens the analysis and diagnosis for network system administrators through synthesized fault detection and root cause localization. The learning-based traffic engineering stimulates networking flow management via experienced data and further shows a promising direction of flexible traffic adjustment for ever-changing environments

    An Overview on Application of Machine Learning Techniques in Optical Networks

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    Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

    A Game-Theoretic Based QoS-Aware Capacity Management for Real-Time EdgeIoT Applications

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    More and more real-time IoT applications such as smart cities or autonomous vehicles require big data analytics with reduced latencies. However, data streams produced from distributed sensing devices may not suffice to be processed traditionally in the remote cloud due to: (i) longer Wide Area Network (WAN) latencies and (ii) limited resources held by a single Cloud. To solve this problem, a novel Software-Defined Network (SDN) based InterCloud architecture is presented for mobile edge computing environments, known as EdgeIoT. An adaptive resource capacity management approach is proposed to employ a policy-based QoS control framework using principles in coalition games with externalities. To optimise resource capacity policy, the proposed QoS management technique solves, adaptively, a lexicographic ordering bi-criteria Coalition Structure Generation (CSG) problem. It is an onerous task to guarantee in a deterministic way that a real-time EdgeIoT application satisfies low latency requirement specified in Service Level Agreements (SLA). CloudSim 4.0 toolkit is used to simulate an SDN-based InterCloud scenario, and the empirical results suggest that the proposed approach can adapt, from an operational perspective, to ensure low latency QoS for real-time EdgeIoT application instances

    V-Cache: Towards Flexible Resource Provisioning for Multi-tier Applications in IaaS Clouds

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    Abstract—Although the resource elasticity offered by Infrastructure-as-a-Service (IaaS) clouds opens up opportunities for elastic application performance, it also poses challenges to application management. Cluster applications, such as multi-tier websites, further complicates the management requiring not only accurate capacity planning but also proper partitioning of the resources into a number of virtual machines. Instead of burdening cloud users with complex management, we move the task of determining the optimal resource configuration for cluster applications to cloud providers. We find that a structural reorganization of multi-tier websites, by adding a caching tier which runs on resources debited from the original resource budget, significantly boosts application performance and reduces resource usage. We propose V-Cache, a machine learning based approach to flexible provisioning of resources for multi-tier applications in clouds. V-Cache transparently places a caching proxy in front of the application. It uses a genetic algorithm to identify the incoming requests that benefit most from caching and dynamically resizes the cache space to accommodate these requests. We develop a reinforcement learning algorithm to optimally allocate the remaining capacity to other tiers. We have implemented V-Cache on a VMware-based cloud testbed. Exper-iment results with the RUBiS and WikiBench benchmarks show that V-Cache outperforms a representative capacity management scheme and a cloud-cache based resource provisioning approach by at least 15 % in performance, and achieves at least 11 % and 21 % savings on CPU and memory resources, respectively. I
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