Data Mining Practical Machine Learning Tools and Techniques

Information technology’s introduction of online retail has deeply influenced methods of doing business. However, offline retail has not changed as radically in comparison to online retailing. Recently, studies in computer science have suggested new technology that can support offline retailers, including sensors, indoor positioning, augmented reality, vision, and interactive systems. Retailers have recently shown interest in these technologies and rapidly adopted them in order to improve operational efficiency and customer experience in their retail shops. Marketing studies also address immersive marketing that employs these technologies in order to change ways of doing offline retail business. Even though there is much discussion concerning new trends, technologies, and marketing concepts, there is, as of yet, no investigation that comprehensively explains how they can be combined together seamlessly in the real world retail environment. This paper employs the term “smart store” to indicate retail stores equipped with these new technologies and modern marketing concepts. This paper aims to summarize discussions related to smart stores and their possible applications in a real business environment. Furthermore, we present a case study of a business that applies the smart store concept to its fashion retail shops in Korea.

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Use of the Smart Store for Persuasive Marketing and Immersive Customer Experiences: A Case Study of Korean Apparel Enterprise

A person's well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos, and three-dimensional scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data.

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Mirror Mirror on the Wall... An Unobtrusive Intelligent Multisensory Mirror for Well-Being Status Self-Assessment and Visualization

Left-right confusion occurs across the entire population and refers to an impeded ability to distinguish between left and right. In medicine this phenomenon is particularly relevant as left and right are always defined with respect to the patient's point of view, i.e. the doctor's right is the patient's left. Traditional anatomy learning resources such as illustrations in textbooks naturally consider this by consistently depicting the anatomy of a patient as seen by an observer standing in front. Augmented Reality Magic Mirrors (MM) are one example of novel anatomy teaching resources and show a user's digital mirror image augmented with virtual anatomy on a large display. As left and right appear to be reversed in such MM setups, similar to real-world physical mirrors, intriguing perceptual questions arise: is a non-reversing MM (NRMM) the more natural choice for the task of anatomy learning and do users even learn anatomy the wrong way with a traditional, reversing MM (RMM)? In this paper, we explore the perceptual differences between an NRMM and RMM design and present the first empirical study comparing these two concepts for the purpose of anatomy learning. Experimental results demonstrate that medical students perform significantly better at identifying anatomically correct placement of virtual organs in an NRMM. However, interaction was significantly more difficult compared to an RMM. We explore the underlying psychological effects and discuss the implications of using an NRMM on user perception, knowledge transfer, and interaction. This study is relevant for the design of future MM systems in the medical domain and lessons-learned can be transferred to other application domains.

Empirical Study of Non-Reversing Magic Mirrors for Augmented Reality Anatomy Learning

This paper will discuss the concept of a smart mirror for healthier living, the FitMirror. Many people have serious problems to get up after sleeping, to get motivated for the day, or are tired and in a bad mood in the morning. The goal of FitMirror is to positively affect the user's feelings by increasing his/her motivation, mood and feeling of fitness. While concepts for these isolated problems exist, none of these combine them into one system. FitMirror is implemented to combine them and evaluate them in a study. It consists of a monitor with spy-foil, a Microsoft Kinect v2 and a Wii Balance Board and can recognize users and their gestures with these elements. Several hypotheses about the system regarding motivation, fun, difficulty and getting awake were investigated. Participants were grouped by the factors sportspersons and morning persons to investigate the effect based on these aspects. Results show that FitMirror can help users get awake in the morning, raise their motivation to do sports and motivate them for the day.

Fitmirror: a smart mirror for positive affect in everyday user morning routines

We propose a smart mirror system to display an augmented 3D representation of the user with makeup features. In this approach the user is able to view the possible outcomes of different makeup applications in the smart mirror without affecting the real face appearance in the process. The system incorporates 3D face construction, IR based face tracking and OpenGL material extensive rendering approach to deliver the augmented made-up face. We argue that by viewing the augmented grooming features the users will be able to flexibly decide the makeup products of their choice.

Augmented Rendering of Makeup Features in a Smart Interactive Mirror System for Decision Support in Cosmetic Products Selection

Internet of Things (IoT) has been the driving force for many smart city applications. The huge volume of IoT data generated from these applications require efficient processing to get the insight, which poses significant difficulty. Data mining and machine learning (DM) algorithms are used to minimize such difficulty. However, it is still very challenging to select a particular DM algorithm that can process a dynamic IoT dataset based on some application-specific goals to achieve better accuracy. This paper proposes a knowledge-driven framework that considers the knowledge of datasets, available DM algorithms, and application goals to select the suitable DM algorithm for performing a target data processing task. This work considers data from cultural domain, health domain, and transportation domain in the experiment. The results show that the proposed approach dynamically selects the best-suited DM algorithms for the available datasets and target goals that exhibits satisfactory performance in obtaining accurate results compared to the existing work. The proposed approach not only provides flexibility in conducting dynamic IoT data mining tasks, but also reduces the complexity that would otherwise be necessary while adopting the traditional data mining approaches.

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Sk Alamgir Hossain

Papers

Augmented Rendering of Makeup Features in a Smart Interactive Mirror System for Decision Support in Cosmetic Products Selection

A Novel Framework for Recommending Data Mining Algorithm in Dynamic IoT Environment