Dimitris C. Gkikas
@upatras.gr
Post Doctoral Researcher, Department of Fisheries and Aquaculture, School of Agricultural Sciences
Professor John A. Theodorou
EDUCATION
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).
RESEARCH INTERESTS
Artificial intelligence, Machine learning, Digital Strategy, e-Business, Bioinformatics, e-Governance
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
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