Intelligent energy management using data mining techniques at Bosch Car Multimedia Portugal facilities

The fusion of emerged technologies such as Artificial Intelligence, cloud computing, big data, and the Internet of Things in manufacturing has pioneered this industry to meet the fourth stage of the industrial revolution (industry 4.0). One major approach to keeping this sector sustainable and productive is intelligent energy demand planning. Monitoring and controlling the consumption of energy under industry 4.0, directly results in minimizing the cost of operation and maximizing efficiency. To advance the research on the adoption of industry 4.0, this study examines CRISP-DM methodology to project data mining approach over data from 2020 to 2021 which was collected from industrial sensors to predict/forecast future electrical consumption at Bosch car multimedia facilities located at Braga, Portugal. Moreover, the influence of indicators such as humidity and temperature on electrical energy consumption was investigated. This study employed five promising regression algorithms and FaceBook prophet (FB prophet) to apply over data belonging to two HVAC (heating, ventilation, and air conditioning) sensors (E333, 3260). Results indicate Random Forest (RF) algorithms as a potential regression approach for prediction and the outcome of FB prophet to forecast the demand of future usage of electrical energy associated with HVAC presented. Based on that, it was concluded that predicting the usage of electrical energy for both data points requires time series techniques. Where "timestamp" was identified as the most effective feature to predict consume of electrical energy by regression technique (RF). The result of this study was integrated with Intelligent Industrial Management System (IIMS) at Bosch Portugal.- (undefined

Retail cost optimisation and the role of information technology

Today retailers need more integrated and reliable strategies and solutions in order to remain competitive at a Global level and one of the important sources of enjoying a competitive advantage can be optimising the retail cost of operation. Information technology as a domain, with its tools for modern retailing can provide the dual benefits of improving retailer, supplier, and customer activities and experiences and at the same time providing an opportunity for retailers to control their operations resulting in cost optimisation. This paper brings out the strategic benefits of using IT solutions to optimise retail cost and addresses the challenges that could be faced by retailers in implementing them and suggests methods to overcome the same

Data mining techniques in psychotherapy: applications for studying therapeutic alliance

Abstract Therapeutic Alliance (TA) has been consistently reported as a robust predictor of therapy outcomes and is one of the most investigated therapy relational factors. Research on therapists' and clients’ contributions to the alliance development and the alliance-outcome relationship had shown mixed results. The relation of the therapist’s and client’s biological markers with the alliance is an important and under-investigated topic. Taking advantage of data mining techniques, this exploratory study aimed to investigate the role of different therapist and client factors, including heart rate (HR) and electrodermal activity (EDA), in relation to TA. Twenty-two dyads with 6 therapists and 22 clients participated in the study. The Working Alliance Inventory (WAI) was used to evaluate the client’s and therapist's perception of the alliance at the end of each session and through the therapy processes. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to explore patterns that may contribute to TA. Machine Learning (ML) models have been employed to provide insights into the predictors and correlates of TA. Our results showed that Linear Regression (LR) was the best technique for predicting the therapist’s TA, with client “Diagnostic” and therapy “Termination” being identified as significant predictors of the therapist’s TA. In addition, for clients’ TA, the Random Forest (RF) was shown to have the best performance. The therapist’s TA and therapy “Outcome” were observed as the most influential predictors for the client’s TA. In addition, while the Heart Rate (therapist) was negatively associated with the therapist’s TA, EDA in the client was a physiological indicator related to the client’s TA. Overall, these findings can assist in identifying key factors that therapists should focus on to enhance the quality of therapeutic alliance. Results are discussed in terms of their consistency with empirical literature, innovative and interdisciplinary research on the therapeutic alliance field, and, in particular, the use of the Data Mining approach in a psychotherapy context