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.

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

IEEE International Conference on Image Processing

Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

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A survey of visual analytics techniques for machine learning

Multi-focus image fusion: A Survey of the state of the art

The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email's structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers' identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject - appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection.

/pdf/a-comprehensive-survey-for-intelligent-spam-email-detection-21ufiarb2l.pdf

A Comprehensive Survey for Intelligent Spam Email Detection

Naive Bayes classifiers are widely used to filter spam emails, however, the strong independence assumptions between features limit their performance in accurately identifying spams. To address this issue, we proposed a support machine vector based naive Bayes — SVM-NB — filtering system. The SVM-NB first constructs an optimal separating hyperplane that divides samples in the training set into two categories. For samples located nearby the hyperplane, if they are in different categories, one of them will be eliminated from the training set. In this way, the dependence between samples is reduced and the entire training sample space is simplified. With the trimmed training set, the naive Bayes algorithm is applied to classify emails in the test set. The SVM-NB system is evaluated with the dataset obtained from DATAMALL. Experiment results demonstrate that SVM-NB can achieve a higher spam-detection accuracy and a faster classification speed.

A support vector machine based naive Bayes algorithm for spam filtering

Unlike weld seam detection in a welding process, weld line localization for inspection is usually performed outdoors and challenged by noise and variation of illumination intensity. In this paper, we propose a weld line localization approach for mobile platform via a cross structured light (CSL) device and spatial-temporal cascaded hidden Markov models (HMMs). A CSL device is designed to project cross red laser stripes on weldment surfaces and capture the weld convexity in video sequences. Stripe edge images are extracted and then a spatial HMM is designed to detect the regions of interest (ROIs) in the video frames. Detected ROIs in successive video frames are fed to the proposed temporal HMM as observations to track the weld lines. In this way, we incorporate both the spatial characteristics of laser stripes and the continuity of the weld lines in an optimal framework. Experiments show that the proposed approach can effectively reduce the influence of illumination and noise, contributing a robust weld line detection and tracking system.

Weld Line Detection and Tracking via Spatial-Temporal Cascaded Hidden Markov Models and Cross Structured Light

Medical image fusion technique can help the physician to execute combined diagnosis, preoperative planning, intraoperative guidance, and interventional treatment in many clinical applications by deriving the complementary information from medical images with different modalities. In this paper, we propose an end-to-end deep learning network, multi-layer concatenation fusion network (MCFNet), for feature extraction, feature fusion, and image reconstruction without manually designing complex feature extraction and fusion rules. MCFNet connects the feature map of each layer in the image reconstruction network with the source map, which has the same resolution due to down-sampling. And the spatial information lost in the fused medical image during two down-sampling processes is supplemented. The up-sampling operation at the image reconstruction stage uses fast up-convolution to reduce the checkerboard effect of traditional deconvolution. The proposed fusion method is tested on many clinical medical images and compared with several commonly used image fusion methods. Experimental results show that the proposed fusion method can provide better fusion results in terms of visual quality and objective evaluation.

MCFNet: Multi-Layer Concatenation Fusion Network for Medical Images Fusion

In this paper, we propose an efficient and energy-saving distributed network architecture based on clustering stratification to solve the information security problem of unmanned aerial vehicle ad hoc network communication. And a double-authentication watermarking strategy is designed. In order to ensure that the data collected by nodes can be sent securely to the cluster head node, we use the self-characteristic of the collected data to generate the authentication watermark and insert it into the collected data at random. The cluster head node first verifies the integrity of collected data and deletes the suspicious data. Then, the authentication information is generated by combining the chaotic mapping method, and the watermark is hidden by changing the parity of the least significant bits of the data. Experimental results show that the proposed security strategy can resist most attacks, such as selective forwarding, data replay and tampering. Meanwhile, it has low energy consumption and low latency.

A data authentication scheme for UAV ad hoc network communication

Analyzing students’ emotions from classroom videos can help both teachers and parents quickly know the engagement of students in class. The availability of high-definition cameras creates opportunities to record class scenes. However, watching videos is time-consuming, and it is challenging to gain a quick overview of the emotion distribution and find abnormal emotions. In this article, we propose EmotionCues , a visual analytics system to easily analyze classroom videos from the perspective of emotion summary and detailed analysis, which integrates emotion recognition algorithms with visualizations. It consists of three coordinated views: a summary view depicting the overall emotions and their dynamic evolution, a character view presenting the detailed emotion status of an individual, and a video view enhancing the video analysis with further details. Considering the possible inaccuracy of emotion recognition, we also explore several factors affecting the emotion analysis, such as face size and occlusion. They provide hints for inferring the possible inaccuracy and the corresponding reasons. Two use cases and interviews with end users and domain experts are conducted to show that the proposed system could be useful and effective for analyzing emotions in the classroom videos.

Liguo Zhang

Papers

A support vector machine based naive Bayes algorithm for spam filtering

Weld Line Detection and Tracking via Spatial-Temporal Cascaded Hidden Markov Models and Cross Structured Light

MCFNet: Multi-Layer Concatenation Fusion Network for Medical Images Fusion

A data authentication scheme for UAV ad hoc network communication

EmotionCues : Emotion-Oriented Visual Summarization of Classroom Videos