A predictive Decision Support System (DSS) for a microalgae production plant based on Internet of Things paradigm

Abstract

Summary The production of microalgae represents a large and rapidly expanding market with several applications in the fields of food, pharmaceutics, cosmetics, and energy. Microalgae are photosynthetic aquatic microorganisms whose growth is mainly controlled by a few environmental parameters: temperature, light, pH, and nutrient availability. For this reason, monitoring and controlling such parameters is crucial for their production. At the same time, the development of mathematical models to simulate the behavior of biological systems has become a major predictive and control tool of production processes. In this paper, we present an Internet of Things (IoT) system that can couple sensor data collected directly by biotechnological cultivations with a predictive simulation model. The IoT system constitutes the core of a Decision Support System developed to help the end‐user in the management of industrial production processes.

Sustainable Change Management through Employee Readiness: Decision Support System Adoption in Technology-Intensive British E-Businesses

“…Although in recent years there has been a plethora of studies on technology readiness and technology acceptance from the consumer's perspective, industrial context, or in the organizational adoption as a whole [5,10,14,[27][28][29][30][31][32][33][34][35][36][37], there is scarcity of research from an employee's perspective as the unit of analysis for readiness and technology acceptance [11,36,[38][39][40][41][42] in e-business environments. Moreover, no studies of such nature are found for technology intensive environments.…”

Prediction of environmental indicators in land leveling using artificial intelligence techniques

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“…Using computers and the Internet has shown a great potential to solve these types of problems by reducing the aforementioned undesirable effects. There are myriad of computer-based techniques and recently IoT that are used widely to solve engineering problems [9]. ANNs are one of these methods.…”

APEX2S: A two‐layer machine learning model for discovery of host‐pathogen protein‐protein interactions on cloud‐based multiomics data

“…11 The major impediments toward the success of scientific workflows may be due to, first, the specific domain knowledge, and second, the distributed data over the internet, although the internet of data paradigm has stimulated numerous data analysis and mining applications. [12][13][14] In our research, we focus on host-pathogen protein-protein interactions, which we anticipate in delivering a workflow to bridge the aforementioned research gaps. This work is a substantial extension of the conference version, 3 which has initially investigated the performance of the two-layer model by one feature representation algorithm.…”