Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis

NetFPGA: status, uses, developments, challenges, and evaluation

The constant growth of the Internet, driven by the demand for timely access to data center networks; has meant that the technological platforms necessary to achieve this purpose are outside the current budgets. In this order to make and validate relevant, timely and relevant contributions; it is necessary that a wider community, access to evaluation, experimentation and demonstration environments with specifications that can be compared with existing networking solutions. This article introduces the NetFPGA, which is a platform to develop network hardware for reconfigurable and rapid prototyping. It’s introduces the application areas in high-performance networks, advantages for traffic analysis, packet flow, hardware acceleration, power consumption and parallel processing in real time. Likewise, it presents the advantages of the platform for research, education, innovation, and future trends of this platform. Finally, we present a performance evaluation of the tool called OSNT (Open-Source Network Tester) and shows that OSNT has 95% accuracy of timestamp with resolution of 10ns for the generation of TCP traffic, and 90% efficiency capturing packets at 10Gbps of full line-rate

A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis Distance

A cyber-attack detection is currently essential for computer network protection. The fundamentals of protection are to detect cyber-attack effectively with the ability to combat it in various ways and with constant data learning such as internet traffic. With these functions, each cyber-attack can be memorized and protected effectively any time. This research will present procedures for a cyber-attack detection system Incremental Decision Tree Learning (IDTL) that use the principle through Incremental Linear Discriminant Analysis (ILDA) together with Mahalanobis distance for classification of the hierarchical tree by reducing data features that enhance classification of a variety of malicious data. The proposed model can learn a new incoming datum without involving the previous learned data and discard this datum after being learned. The results of the experiments revealed that the proposed method can improve classification accuracy as compare with other methods. They showed the highest accuracy when compared to other methods. If comparing with the effectiveness of each class, it was found that the proposed method can classify both intrusion datasets and other datasets efficiently

Urban traffic density estimation based on ultrahigh-resolution UAV video and deep neural network

This paper presents an advanced urban traffic density estimation solution using the latest deep learning techniques to intelligently process ultrahigh-resolution traffic videos taken from an unmanned aerial vehicle (UAV). We first capture nearly an hour-long ultrahigh-resolution traffic video at five busy road intersections of a modern megacity by flying a UAV during the rush hours. We then randomly sampled over 17 K 512×512 pixel image patches from the video frames and manually annotated over 64 K vehicles to form a dataset for this paper, which will also be made available to the research community for research purposes. Our innovative urban traffics analysis solution consists of an advanced deep neural network (DNN) based vehicle detection and localization, type (car, bus, and truck) recognition, tracking, and vehicle counting over time. We will present extensive experimental results to demonstrate the effectiveness of our solution. We will show that our enhanced single shot multibox detector (Enhanced-SSD) outperforms other DNN-based techniques and that deep learning techniques are more effective than traditional computer vision techniques in traffic video analysis. We will also show that ultrahigh-resolution video provides more information that enables more accurate vehicle detection and recognition than lower resolution contents. This paper not only demonstrates the advantages of using the latest technological advancements (ultrahigh-resolution video and UAV), but also provides an advanced DNN-based solution for exploiting these technological advancements for urban traffic density estimation

Incremental learning algorithms and applications

International audienceIncremental learning refers to learning from streaming data, which arrive over time, with limited memory resources and, ideally, without sacrificing model accuracy. This setting fits different application scenarios where lifelong learning is relevant, e.g. due to changing environments , and it offers an elegant scheme for big data processing by means of its sequential treatment. In this contribution, we formalise the concept of incremental learning, we discuss particular challenges which arise in this setting, and we give an overview about popular approaches, its theoretical foundations, and applications which emerged in the last years

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction

Analysis of the impact in the maritime traffics of the Nicaragua canal

One of the primary causes for the increased traffic through the Panama Canal and the expansion of the US East coast ports has been the importing of Chinese manufactured products. The new canal will offer a shorter route between China and the US East coast and will compete with the current expanded Panama Canal. To analyze how the shipping routes would change, three scenarios have been studied, each one focused on a different aspect: economics, environment, and politics. For each one, similar conclusions have been extracted: the canal’s feasibility is in doubt, since it does not provide enough guarantees for its construction. Comparisons with different routes were made, including both Panama and Suez canals and the Magellan strait, in order to evaluate the different distances and transit times so to measure the impact of the Nicaragua Canal. The main objective of this project is to assess if the construction of the new canal is going to influence future shipping routes, and how

Analysis of the actual shipping situation and the Port of bBarcelona : Strategy for the near future and application of Horizon 2020 policies.

The shipping industry is making the ports to change continuously in order to adapt their strategies to new challenges to attract more traffic to their ports and to take into account seriously the impact of the industry to the environment. The Port of Barcelona (PoB) is not an exception for this and needs to detect its main weaknesses and strengths in order to improve its position. Nowadays, PoB is very far from some of its competitors despite being located near Africa and being one of the European closest port to Asia. Accordingly, it is needed to strengthen its positive factors and to improve the ones that are worst. For this reason, PoB needs a change of strategy focusing on its main market (China) and trying to catch traffic mostly from ports located in Africa (because its proximity) or United Arab Emirates (constantly growing during last years). In addition, for becoming a model to follow about green transport, it is urgent to have an international gauge to connect the port to Europe, build the Mediterranean Corridor and apply some policies explained in the program of the European Commission Horizon 2020 about inland connections (awards to the cleanest carriers, for example). Applying those measures, PoB could become a more competitive port and respectful with the environment. The methodology used in the study consists on a deep explanation about the PoB, a comparison between the port and the Port of Rotterdam (because its leadership in Europe), Valencia Port (because it is the closest and the second most important port of Spain) and Port of Marseille (because its proximity going to the North and its competitiveness). After the comparison, Horizon 2020 is applied to Port of Barcelona for finalizing making proposals that connect both parts of the work (traffics and environment)

Performance Modeling and Analysis of Wireless Local Area Networks with Bursty Traffic

The explosive increase in the use of mobile digital devices has posed great challenges in the design and implementation of Wireless Local Area Networks (WLANs). Ever-increasing demands for high-speed and ubiquitous digital communication have made WLANs an essential feature of everyday life. With audio and video forming the highest percentage of traffic generated by multimedia applications, a huge demand is placed for high speed WLANs that provide high Quality-of-Service (QoS) and can satisfy end user’s needs at a relatively low cost. Providing video and audio contents to end users at a satisfactory level with various channel quality and current battery capacities requires thorough studies on the properties of such traffic. In this regard, Medium Access Control (MAC) protocol of the 802.11 standard plays a vital role in the management and coordination of shared channel access and data transmission. Therefore, this research focuses on developing new efficient analytical models that evaluate the performance of WLANs and the MAC protocol in the presence of bursty, correlated and heterogeneous multimedia traffic using Batch Markovian Arrival Process (BMAP). BMAP can model the correlation between different packet size distributions and traffic rates while accurately modelling aggregated traffic which often possesses negative statistical properties. The research starts with developing an accurate traffic generator using BMAP to capture the existing correlations in multimedia traffics. For validation, the developed traffic generator is used as an arrival process to a queueing model and is analyzed based on average queue length and mean waiting time. The performance of BMAP/M/1 queue is studied under various number of states and maximum batch sizes of BMAP. The results clearly indicate that any increase in the number of states of the underlying Markov Chain of BMAP or maximum batch size, lead to higher burstiness and correlation of the arrival process, prompting the speed of the queue towards saturation. The developed traffic generator is then used to model traffic sources in IEEE 802.11 WLANs, measuring important QoS metrics of throughput, end-to-end delay, frame loss probability and energy consumption. Performance comparisons are conducted on WLANs under the influence of multimedia traffics modelled as BMAP, Markov Modulated Poisson Process and Poisson Process. The results clearly indicate that bursty traffics generated by BMAP demote network performance faster than other traffic sources under moderate to high loads. The model is also used to study WLANs with unsaturated, heterogeneous and bursty traffic sources. The effects of traffic load and network size on the performance of WLANs are investigated to demonstrate the importance of burstiness and heterogeneity of traffic on accurate evaluation of MAC protocol in wireless multimedia networks. The results of the thesis highlight the importance of taking into account the true characteristics of multimedia traffics for accurate evaluation of the MAC protocol in the design and analysis of wireless multimedia networks and technologies