A multilabel classification approach for complex human activities using a combination of emerging patterns and fuzzy sets

In our daily lives, humans perform different Activities of Daily Living (ADL), such as cooking, and studying. According to the nature of humans, they perform these activities in a sequential/simple or an overlapping/complex scenario. Many research attempts addressed simple activity recognition, but complex activity recognition is still a challenging issue. Recognition of complex activities is a multilabel classification problem, such that a test instance is assigned to a multiple overlapping activities. Existing data-driven techniques for complex activity recognition can recognize a maximum number of two overlapping activities and require a training dataset of complex (i.e. multilabel) activities. In this paper, we propose a multilabel classification approach for complex activity recognition using a combination of Emerging Patterns and Fuzzy Sets. In our approach, we require a training dataset of only simple (i.e. single-label) activities. First, we use a pattern mining technique to extract discriminative features called Strong Jumping Emerging Patterns (SJEPs) that exclusively represent each activity. Then, our scoring function takes SJEPs and fuzzy membership values of incoming sensor data and outputs the activity label(s). We validate our approach using two different dataset. Experimental results demonstrate the efficiency and superiority of our approach against other approaches

Machine and deep learning techniques for detecting internet protocol version six attacks: a review

The rapid development of information and communication technologies has increased the demand for internet-facing devices that require publicly accessible internet protocol (IP) addresses, resulting in the depletion of internet protocol version 4 (IPv4) address space. As a result, internet protocol version 6 (IPv6) was designed to address this issue. However, IPv6 is still not widely used because of security concerns. An intrusion detection system (IDS) is one example of a security mechanism used to secure networks. Lately, the use of machine learning (ML) or deep learning (DL) detection models in IDSs is gaining popularity due to their ability to detect threats on IPv6 networks accurately. However, there is an apparent lack of studies that review ML and DL in IDS. Even the existing reviews of ML and DL fail to compare those techniques. Thus, this paper comprehensively elucidates ML and DL techniques and IPv6-based distributed denial of service (DDoS) attacks. Additionally, this paper includes a qualitative comparison with other related works. Moreover, this work also thoroughly reviews the existing ML and DL-based IDSs for detecting IPv6 and IPv4 attacks. Lastly, researchers could use this review as a guide in the future to improve their work on DL and ML-based IDS

Unobtrusive hand gesture recognition using ultra-wide band radar and deep learning

Hand function after stroke injuries is not regained rapidly and requires physical rehabilitation for at least 6 months. Due to the heavy burden on the healthcare system, assisted rehabilitation is prescribed for a limited time, whereas so-called home rehabilitation is offered. It is therefore essential to develop robust solutions that facilitate monitoring while preserving the privacy of patients in a home-based setting. To meet these expectations, an unobtrusive solution based on radar sensing and deep learning is proposed. The multi-input multi-output convolutional eXtra trees (MIMO-CxT) is a new deep hybrid model used for hand gesture recognition (HGR) with impulse-radio ultra-wide band (IR-UWB) radars. It consists of a lightweight architecture based on a multi-input convolutional neural network (CNN) used in a hybrid configuration with extremely randomized trees (ETs). The model takes data from multiple sensors as input and processes them separately. The outputs of the CNN branches are concatenated before the prediction is made by the ETs. Moreover, the model uses depthwise separable convolution layers, which reduce computational cost and learning time while maintaining high performance. The model is evaluated on a publicly available dataset of gestures collected by three IR-UWB radars and achieved an average accuracy of 98.86%

Mining and Analyzing the Academic Network

Social Network research has attracted the interests of many researchers, not only in analyzing the online social networking applications, such as Facebook and Twitter, but also in providing comprehensive services in scientific research domain. We define an Academic Network as a social network which integrates scientific factors, such as authors, papers, affiliations, publishing venues, and their relationships, such as co-authorship among authors and citations among papers. By mining and analyzing the academic network, we can provide users comprehensive services as searching for research experts, published papers, conferences, as well as detecting research communities or the evolutions hot research topics. We can also provide recommendations to users on with whom to collaborate, whom to cite and where to submit.In this dissertation, we investigate two main tasks that have fundamental applications in the academic network research. In the first, we address the problem of expertise retrieval, also known as expert finding or ranking, in which we identify and return a ranked list of researchers, based upon their estimated expertise or reputation, to user-specified queries. In the second, we address the problem of research action recommendation (prediction), specifically, the tasks of publishing venue recommendation, citation recommendation and coauthor recommendation. For both tasks, to effectively mine and integrate heterogeneous information and therefore develop well-functioning ranking or recommender systems is our principal goal. For the task of expertise retrieval, we first proposed or applied three modified versions of PageRank-like algorithms into citation network analysis; we then proposed an enhanced author-topic model by simultaneously modeling citation and publishing venue information; we finally incorporated the pair-wise learning-to-rank algorithm into traditional topic modeling process, and further improved the model by integrating groups of author-specific features. For the task of research action recommendation, we first proposed an improved neighborhood-based collaborative filtering approach for publishing venue recommendation; we then applied our proposed enhanced author-topic model and demonstrated its effectiveness in both cited author prediction and publishing venue prediction; finally we proposed an extended latent factor model that can jointly model several relations in an academic environment in a unified way and verified its performance in four recommendation tasks: the recommendation on author-co-authorship, author-paper citation, paper-paper citation and paper-venue submission. Extensive experiments conducted on large-scale real-world data sets demonstrated the superiority of our proposed models over other existing state-of-the-art methods

Exploring Patterns of Epigenetic Information With Data Mining Techniques

[Abstract] Data mining, a part of the Knowledge Discovery in Databases process (KDD), is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Analyses of epigenetic data have evolved towards genome-wide and high-throughput approaches, thus generating great amounts of data for which data mining is essential. Part of these data may contain patterns of epigenetic information which are mitotically and/or meiotically heritable determining gene expression and cellular differentiation, as well as cellular fate. Epigenetic lesions and genetic mutations are acquired by individuals during their life and accumulate with ageing. Both defects, either together or individually, can result in losing control over cell growth and, thus, causing cancer development. Data mining techniques could be then used to extract the previous patterns. This work reviews some of the most important applications of data mining to epigenetics.Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT-0366Galicia. Consellería de Economía e Industria; 10SIN105004PRInstituto de Salud Carlos III; RD07/0067/000

Enhanced sequence labeling based on latent variable conditional random fields

Natural language processing is a useful processing technique of language data, such as text and speech. Sequence labeling represents the upstream task of many natural language processing tasks, such as machine translation, text classification, and sentiment classification. In this paper, the focus is on the sequence labeling task, in which semantic labels are assigned to each unit of a given input sequence. Two frameworks of latent variable conditional random fields (CRF) models (called LVCRF-I and LVCRF-II) are proposed, which use the encoding schema as a latent variable to capture the latent structure of the hidden variables and the observed data. Among the two designed models, the LVCRF-I model focuses on the sentence level, while the LVCRF-II works in the word level, to choose the best encoding schema for a given input sequence automatically without handcraft features. In the experiments, the two proposed models are verified by four sequence prediction tasks, including named entity recognition (NER), chunking, reference parsing and POS tagging. The proposed frameworks achieve better performance without using other handcraft features than the conventional CRF model. Moreover, these designed frameworks can be viewed as a substitution of the conventional CRF models. In the commonly used LSTM-CRF models, the CRF layer can be replaced with our proposed framework as they use the same training and inference procedure. The experimental results show that the proposed models exhibit latent variable and provide competitive and robust performance on all three sequence prediction tasks

ChimeRScope: a novel alignment-free algorithm for fusion gene prediction using paired-end short reads

Fusion genes are those that result from the fusion of two or more genes, and they are typically generated due to the perturbations in the genome structure in cancer cells. In turn, fusion genes can contribute to tumor formation and progression by promoting the expression of an oncogene, deregulation of a tumor-suppressor, or producing much more active abnormal proteins. More importantly, oncogenic fusion genes are specifically expressed in the tumor cells, which provide enormous diagnostic and therapeutic advantages for cancer treatment. With the development of next-generation sequencing (NGS) technology, RNA-Seq becomes increasingly popular for transcriptomic study because of its high sensitivity and the capability of detecting novel transcripts including fusion genes. To date, many fusion gene detection tools have been developed, most of which attempt to find reliable alignment evidence for chimeric transcripts from RNA-Seq data. It is well accepted that the alignment quality of sequencing reads against the reference genome is often limited when significant differences in the genomes exist, which is the case with cancer genomes that contain many genomic perturbations and structural variations. Hence, regions where fusion genes occur in the cancer genome tend to be largely different from those in the reference genome, which prevents the alignment-based fusion gene detection methods from achieving good accuracies. We developed a tool called ChimeRScope. ChimeRScope, being an alignment-free method, bypasses the sequence alignment step by assessing the gene fingerprint profiles (in the form of k-mers) from RNA-Seq paired-end reads for fusion gene prediction (Chapter Two). We also optimized the data structure and ChimeRScope algorithms, in order to overcome the common limitations (memory-utilization, low accuracies) that are commonly seen in alignment-free methods (Chapter Two). Results on simulated datasets, previously studied cancer RNA-Seq datasets, and experimental validations on in-house datasets have shown that ChimeRScope consistently performed better than other popular alignment-based methods irrespective of the read length and depth of sequencing coverage (Chapter Three). ChimeRScope also generates graphical outputs for illustrations of the fusion patterns. Lastly, we also developed downloadable software for ChimeRScope and implemented an online data analysis server using the Galaxy platform (Chapter Four). ChimeRScope is available at https://github.com/ChimeRScope/ChimeRScope/

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study