Data Mining Practical Machine Learning Tools and Techniques

Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions.

A survey towards an integration of big data analytics to big insights for value-creation

Functional polysaccharides can be derived from plants (including herbs), animals and microorganisms. They have been widely used in a broad of biomedical applications, such as immunoregulatory agents or drug delivery vehicles. In the past few years, increasing studies have started to develop natural polysaccharides-based biomaterials for various applications in tissue engineering and regenerative medicine. We discuss in this article the emerging applications of natural polysaccharides—particularly those derived from Chinese medicine—for wound healing. First, we introduce natural polysaccharides of three natural sources and their biological activities. Then, we focus on certain natural polysaccharides with growth factor-binding affinities and their inspired polymeric tools, with an emphasis on how these polysaccharides could possibly benefit wound healing. Finally, we report the latest progress in the discovery of polysaccharides from Chinese medicinal herbs with identified activities favouring tissue repair. Natural polysaccharides with clearly elucidated compositions/structures, identified cellular activities, as well as desirable physical properties have shown the potential to serve as therapeutic tools for tissue regeneration.

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Bioactive polysaccharides from natural resources including Chinese medicinal herbs on tissue repair.

In this paper, a hybrid model using modular neural networks and fuzzy logic was designed to provide the hypertension risk diagnosis of a person. This model considers age, risk factors and behavior of the blood pressure in a period of 24 h, using as a basis the Framingham Heart Study. Records of blood pressure are collected with the ambulatory blood pressure monitoring (ABPM), a device which takes readings for a period of time of 24 h. A modular neural network was designed, with three modules, of which the first and second modules correspond to the systolic and diastolic pressures and the last one to the heart rate. Each module is trained with the data obtained by the ABPM of different patients, this in order that the neural network learns the different behaviors that the blood pressure may have. Also, different architectures and learning methods are considered to obtain the best possible architecture. In addition, two fuzzy inference systems (FISs) for classification purpose are proposed, the first one for the heart rate level and the second one for the night profile of the patient. These were tested with different types of membership functions and then selecting the FIS that obtained the best results. Furthermore, a third FIS as a blood pressure classifier is also used. The different proposed methodologies were tested, in the case of the modular neural network to find the architecture that produces better results and in the fuzzy inference systems to find which membership functions were the ideal ones for the case study, in this way obtaining overall good results. For the case of the modular neural network, the learning accuracy in the first module is 98%, in the second module is 97.62% and the third module is 97.83% respectively. For the night profile, the fuzzy system is compared to a traditional system of production rules, and it is noted that the first one gives all correct outputs and the second one just gives 53% of the outputs, this is due to the uncertainty handling that fuzzy systems can provide, which the traditional system cannot because its rules are very strict. Hybrid intelligent systems for the solution of this kind of complex problems have excellent performance, due to the good learning in each module of the neural network and the classification uncertainty that is well managed by the fuzzy systems, obtaining with this a hybrid combination for achieving good results.

A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis

The field of adaptive e-learning is continuously developing. More research is being conducted in this area as adaptive e-learning aims to provide learners with adaptive learning paths and content, according to their individual characteristics and needs, which makes e-learning more efficient and effective. The learner model, which is a representation of different learner’s characteristics, plays a key role in this adaptation. This paper presents a systematic literature review about learner modelling during the last 5 years, describing the different modelled characteristics and the adopted modelling techniques and modeling types: automatic modeling and collaborative modeling. 107 publications were selected and analyzed, and six categories of the modelled characteristics were identified. This literature review contributes to the identification of the learners’ individual traits and presents the most used modelling techniques for each of them. It also identifies the latest research trends of Learner Modeling and generates future research directions in this field.

Learner modelling: systematic review of the literature from the last 5 years

Transcutaneous electrical stimulation can depolarize nerve or muscle cells applying impulses through electrodes attached on the skin. For these applications, the electrode-skin impedance is an important factor which influences effectiveness. Various models describe the interface using constant or current-depending resistive-capacitive equivalent circuit. Here, we develop a dynamic impedance model valid for a wide range stimulation intensities. The model considers electroporation and charge-dependent effects to describe the impedance variation, which allows to describe high-charge pulses. The parameters were adjusted based on rectangular, biphasic stimulation pulses generated by a stimulator, providing optionally current or voltage-controlled impulses, and applied through electrodes of different sizes. Both control methods deliver a different electrical field to the tissue, which is constant throughout the impulse duration for current-controlled mode or have a very current peak for voltage-controlled. The results show a predominant dependence in the current intensity in the case of both stimulation techniques that allows to keep a simple model. A verification simulation using the proposed dynamic model shows coefficient of determination of around 0.99 in both stimulation types. The presented method for fitting electrode-skin impedance can be simple extended to other stimulation waveforms and electrode configuration. Therefore, it can be embedded in optimization algorithms for designing electrical stimulation applications even for pulses with high charges and high current spikes.

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Dynamic impedance model of the skin-electrode interface for transcutaneous electrical stimulation.

We developed a virtual assistant that enables students to access interactive content adapted for an introductory undergraduate course on artificial intelligence. This chatbot is able to show answers to frequently asked questions in a hierarchical structured manner, leading students by either voice, text or tactile input to the content that better solves their questions and doubts. It was developed using Google Dialogflow as a simple way to generate and train a natural language model. Another convenience of this platform is its ability to collect usage data that is potentially useful for lecturers as learning indicators. The main purpose of this paper is to outline the methodology that guided our implementation so that it can be reproduced in different educational contexts and study chatbots as tools for learning. At the moment, several articles, news and blogs are writing about the potential, implementation and impact chatbots have in general contexts, however there is little to no literature proposing a methodology to reproduce them for educational purposes. In that respect, we developed four main categories as a generic structure of course content and focused on quick implementation, easy updating and generalization. The final product received a general approbation of the students due to its accessibility and well structured data.

Methodology for the Implementation of Virtual Assistants for Education Using Google Dialogflow

A funder is missing from the manuscript’s Funding section. The correct funding statement is as follows: JLVL was supported by the Mexican Council of Research and Technology (CONACYT) grant: 236850 (www.conacyt.mx). This work was also supported by the Vienna Science and Technology Fund (WWTF), Proj.Nr. LS11-057 (www.wwtf.at), and the Wings for Life Spinal Cord Research Foundation (WfL), Proj.Nr. WFL-AT-007/11 (http://www.wingsforlife.com). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Correction: Dynamic Impedance Model of the Skin-Electrode Interface for Transcutaneous Electrical Stimulation.

The invention relates to a device for taking a liquid bone marrow sample (aspiration) and a solid bone sample (biopsy) in the same procedure, which can replace the special needles used to obtain bone marrow and those used exclusively for bone biopsy. The aforementioned device includes a simple mechanism employing two catches formed by grooves. In addition, the device can be easily dismantled for sterilisation and the handle thereof is designed to facilitate puncture. The needle of the device is hollow and contains a guide having two sections of different diameters. The guide enables the channel of the cavity of the needle to be opened so that the bone marrow liquid can flow therethrough and be extracted, as well as enabling said channel to be closed so that the tissue can be penetrated. The aim of the invention is to provide bone marrow biopsy and aspiration methods that reduce both patient discomfort and procedure times.

Bone biopsy and bone marrow aspiration device

Neuromuscular electrical stimulation (NMES) is a widely used technique for clinical diagnostic, treatment, and research Normally, it applies charge-balanced biphasic pulses, which several publications have reported to be less efficient than monophasic pulses A good alternative is the use of interphase intervals (IPI) on biphasic pulses that allows to achieve similar responses than those evoked by monophasic stimulation This study analyzes the enhancing mechanism of the IPI and provides guidelines on how to optimize the IPI in order to reduce secondary effects such as the electrode corrosion The tibial nerve was excited by NMES biphasic pulses with different IPI durations and polarities Then, the elicited responses were recorded on the soleus muscle via electromyography When cathodic-first pulses were applied, the responses increased proportionally to the IPI until the duration of 250 µs, where the increase saturated at 30% of the original amplitude The responses evoked during anodic-first were 6% to 30% smaller than those evoked during cathodic-first pulses and continuously increased until the IPI duration of 2500 µs, where the responses reached an increase of around 30% The results suggest that when a cathodic-first pulse is used, the IPI could be optimized (based on the setup geometry) to allow the action potentials to travel out of the hyperpolarization zone induced by the anodic phase When anodic-first stimuli are applied, the IPI duration allows the fiber to recover from an apparent insensitive state induced by the anodic phase The use of IPI is a viable option to improve the efficiency of actual stimulation systems, since only small modifications are required to significantly reduce the electrical charge required and boost the stimulation efficiency

Jorge Armando Cortes Ramirez

Papers

Dynamic impedance model of the skin-electrode interface for transcutaneous electrical stimulation.

Methodology for the Implementation of Virtual Assistants for Education Using Google Dialogflow

Correction: Dynamic Impedance Model of the Skin-Electrode Interface for Transcutaneous Electrical Stimulation.

Bone biopsy and bone marrow aspiration device

Optimization of Interphase Intervals to Enhance the Evoked Muscular Responses of Transcutaneous Neuromuscular Electrical Stimulation