@upatras.gr
Post Doctoral Researcher, Department of Fisheries and Aquaculture, School of Agricultural Sciences
Professor John A. Theodorou
Doctor of Philosophy - PhD in Artificial Intelligence and Marketing, Department of Business Administration of Food and Agricultural Enterprises, School of Economics and Business, Department of Business Administration of Food and Agricultural Enterprises, University of Patras. Thesis: Data mining for enhanced decision making. Applications in consumers’ behavior data in online and offline environment using a machine learning model.
Master of Science - MSc in e-Business and Digital Marketing, Department of Science and Technology, School of Science and Technology, International Hellenic University.
Master of Science - MSc in Computer Science (Artificial Intelligence and Agents), School of Computer Science and Electronic Engineering, University of Essex.
Bachelor of Science - BSc in Informatics Engineering, Department of Informatics and Computer Engineering, School of Engineering, Technology Institute of Athens (University of West Attica).
Artificial intelligence, Machine learning, Digital Strategy, e-Business, Bioinformatics, e-Governance
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Andreas Karydis-Messinis, Christina Kyriakaki, Eleni Triantafyllou, Kyriaki Tsirka, Christina Gioti, Dimitris Gkikas, Konstantinos Nesseris, Dimitrios A. Exarchos, Spyridoula Farmaki, Aris E. Giannakas,et al.
MDPI AG
The increasing global concern over plastic waste and its environmental impact has led to a growing interest in the development of sustainable packaging alternatives. This study focuses on the innovative use of expired dairy products as a potential resource for producing edible packaging materials. Expired milk and yogurt were selected as the primary raw materials due to their protein and carbohydrate content. The extracted casein was combined with various concentrations of chitosan, glycerol, and squid ink, leading to the studied samples. Chitosan was chosen due to its appealing characteristics, including biodegradability, and film-forming properties, and casein was utilized for its superior barrier and film-forming properties, as well as its biodegradability and non-toxic nature. Glycerol was used to further improve the flexibility of the materials. The prepared hydrogels were characterized using various instrumental methods, and the findings reveal that the expired dairy-based edible packaging materials exhibited promising mechanical properties comparable to conventional plastic packaging and improved barrier properties with zero-oxygen permeability of the hydrogel membranes, indicating that these materials have the potential to effectively protect food products from external factors that could compromise quality and shelf life.
Dimitris C. Gkikas, Marios C. Gkikas, and John A. Theodorou
MDPI AG
A proposal has been put forward advocating a data-driven strategy that employs classifiers from data mining to foresee and categorize instances of fish mortality. This addresses the increasing concerns regarding the death rates in caged fish environments because of the unsustainable fish farming techniques employed and environmental variables involved. The aim of this research is to enhance the competitiveness of Greek fish farming through the development of an intelligent system that is able to diagnose fish diseases in farms. This system concurrently addresses medication and dosage issues. To achieve this, a comprehensive dataset derived from various aquaculture sources was used, including various factors such as the geographic locations, farming techniques, and indicative parameters such as the water quality, climatic conditions, and fish biological characteristics. The main objective of the research was to categorize fish mortality cases through predictive models. Advanced data mining classification methods, specifically decision trees (DTs), were used for the comparison, aiming to recognize the most appropriate method with high precision and recall rates in predicting fish death rates. To ensure the reliability of the results, a methodical evaluation process was adopted, including cross-validation and a classification performance assessment. In addition, a statistical analysis was performed to gain insights into the factors that identify the correlations between the various factors affecting fish mortality. This analysis contributes to the development of targeted conservation and restoration action strategies. The research results have important implications for sustainable management actions, enabling stakeholders to proactively address issues and monitor aquaculture practices. This proactive approach ensures the protection of farmed fish quantities while meeting global seafood requirements. The data mining using a classification approach coincides with the general context of the UN sustainability goals, reducing the losses in seafood management and production when dealing with the consequences of climate change.
Vasileios P. Georgopoulos, Dimitris C. Gkikas, and John A. Theodorou
MDPI AG
Food production faces significant challenges, mainly due to the increase in the Earth’s population, combined with climate change. This will create extreme pressure on food industries, which will have to respond to the demand while protecting the environment and ensuring high food quality. It is, therefore, imperative to adopt innovative technologies, such as Artificial Intelligence, in order to aid in this cause. To do this, we first need to understand the adoption process that enables the deployment of those technologies. Therefore, this research attempts to identify the factors that encourage and discourage the adoption of Artificial Intelligence technologies by professionals working in the fields of agriculture, livestock farming and aquaculture, by examining the available literature on the subject. This is a systematic literature review that follows the PRISMA 2020 guidelines. The research was conducted on 38 articles selected from a pool of 225 relevant articles, and led to the identification of 20 factors that encourage and 21 factors that discourage the adoption of Artificial Intelligence. The factors that appeared most were of economic nature regarding discouragement (31.5%) and product-related regarding encouragement (28.1%). This research does not aim to quantify the importance of each factor—since more original research becoming available is needed for that—but mainly to construct a list of factors, using spreadsheets, which could then be used to guide further future research towards understanding the adoption mechanism.
Dimitris C. Gkikas, Prokopis K. Theodoridis, Theodoros Theodoridis, and Marios C. Gkikas
MDPI AG
This study aims to provide a method that will assist decision makers in managing large datasets, eliminating the decision risk and highlighting significant subsets of data with certain weight. Thus, binary decision tree (BDT) and genetic algorithm (GA) methods are combined using a wrapping technique. The BDT algorithm is used to classify data in a tree structure, while the GA is used to identify the best attribute combinations from a set of possible combinations, referred to as generations. The study seeks to address the problem of overfitting that may occur when classifying large datasets by reducing the number of attributes used in classification. Using the GA, the number of selected attributes is minimized, reducing the risk of overfitting. The algorithm produces many attribute sets that are classified using the BDT algorithm and are assigned a fitness number based on their accuracy. The fittest set of attributes, or chromosomes, as well as the BDTs, are then selected for further analysis. The training process uses the data of a chemical analysis of wines grown in the same region but derived from three different cultivars. The results demonstrate the effectiveness of this innovative approach in defining certain ingredients and weights of wine’s origin.
Dimitris C. Gkikas, Prokopis K. Theodoridis, and Grigorios N. Beligiannis
MDPI AG
An excessive amount of data is generated daily. A consumer’s journey has become extremely complicated due to the number of electronic platforms, the number of devices, the information provided, and the number of providers. The need for artificial intelligence (AI) models that combine marketing data and computer science methods is imperative to classify users’ needs. This work bridges the gap between computer and marketing science by introducing the current trends of AI models on marketing data. It examines consumers’ behaviour by using a decision-making model, which analyses the consumer’s choices and helps the decision-makers to understand their potential clients’ needs. This model is able to predict consumer behaviour both in the digital and physical shopping environments. It combines decision trees (DTs) and genetic algorithms (GAs) through one wrapping technique, known as the GA wrapper method. Consumer data from surveys are collected and categorised based on the research objectives. The GA wrapper was found to perform exceptionally well, reaching classification accuracies above 90%. With regard to the Gender, the Household Size, and Household Monthly Income classes, it manages to indicate the best subsets of specific genes that affect decision making. These classes were found to be associated with a specific set of variables, providing a clear roadmap for marketing decision-making.
Dimitris C Gkikas, Katerina Tzafilkou, Prokopis K Theodoridis, Aristogiannis Garmpis, and Marios C Gkikas
Elsevier BV
Dimitris C. Gkikas and Prokopis K. Theodoridis
Springer International Publishing
Dimitris C. Gkikas, Georgia Tzavella, Melpomeni Tzioli, Georgia Vlachopoulou, Isidora Kondili, and Ioannis Magnisalis
IGI Global
This research revealed the importance of public service web portals for an e-government information system. An e-government portal is interacting with its administrators, citizens, businesses and other governments helping them increase their operations performance. The authors have developed, modeled, formulated and compared an efficient assessment framework for e-government portals. In order to accomplish such task many quantitative factors and indicators were taken under consideration; also, other frameworks have been studied and compared. The authors focused on the web portals services quantity that the interested parties should use, in order to create an well designed public services’ web portal. This research provides a framework model to evaluate the basic common digital public services that a government offers to its interactive stakeholders, so that all other countries across the world can predefine weaknesses and strengths, improve existing or formulating new e-services. The importance of the assessment framework model is thoroughly explained through the results.
Prokopis K. Theodoridis and Dimitris C. Gkikas
Springer International Publishing
Prokopis K. Theodoridis and Dimitris C. Gkikas
Springer International Publishing
Dimitris C. Gkikas and Prokopis K. Theodoridis
Springer International Publishing