Neuromarketing Trends and Opportunities for Companies

2020 ◽  
pp. 82-103
Author(s):  
Arabinda Bhandari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Sensing ◽  
Decision Making ◽  
Brand Management ◽  
Technological Development ◽  
Research Direction ◽  
Future Research ◽  
Design Decision ◽  
Future Research Direction

The main purpose of this chapter is to concisely describe the origin of neuromarketing, its applications in the organization, and to explore consumer behavior with the help of different neuromarketing technologies like fMRI, EEG, and MEG. This chapter gives a guideline on how neuromarketing would be used in different areas of organization functions, like, brand management, advertisement, communication, product design, decision making, etc. with the help of data mining, artificial intelligence, social media, machine learning, remote sensing, AR, and VR. The chapter identifies the opportunities of neuromarketing with the latest technological development to understand the customer mindset so that it would be easy to formulate neurostrategy for an organization. This chapter gives a future research direction with strategic management, so that it will be helpful for a professional to create a more accurate strategy in a VUCA (volatility, uncertainty, complexity, ambiguity) environment, predict, and fulfill the “institution void” situation with more accuracy in an emerging developing market.

A Context-Based Approach to Engineering Design Negotiation

2005 ◽  
Author(s):  
Mathieu Geslin ◽  
Yan Jin
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Collaborative Design ◽  
Large Scale ◽  
Research Direction ◽  
Distributed Artificial Intelligence ◽  
Future Research ◽  
Design Decision ◽  
Context Model ◽  
Engineering Design Problems

Complex and large-scale engineering design problems require a collaborative approach in order to be completed in a timely manner. Designers involved in such work are making collaborative design decision, and often have to negotiate to address intricate problems and resolve their discrepancies while exploring the design space, generating new ideas and compromising for agreement. Advances in negotiation research have been made in social psychology, distributed artificial intelligence, and decision theory, but few have been applied to design. We advocate that design context information is of paramount importance in the decision-making process. In this paper, an argumentation-based negotiation model is introduced to support collaborative design decision-making. This model relies on clear design context model, argument model, negotiation protocol and strategies. In this paper, we successively detail each of these components and conclude with a discussion on a real-world case example and our future research direction.

Co-Enzyme Q10 to Treat Neurological Disorders: Basic Mechanisms, Clinical Outcomes, and Future Research Direction

2013 ◽  
Vol 12 (5) ◽  
pp. 641-664 ◽  
Author(s):  
Mohamed Salama ◽  
Ti-Fei Yuan ◽  
Sergio Machado ◽  
Eric Murillo-Rodriguez ◽  
Jose Vega ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Neurological Disorders ◽  
Research Direction ◽  
Future Research ◽  
Future Research Direction

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Federated Quantum Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 460
Author(s):  
Samuel Yen-Chi Chen ◽  
Shinjae Yoo
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Research Direction ◽  
Future Research ◽  
Quantum Computers ◽  
Training Time ◽  
Quantum Neural Network ◽  
Distributed Training ◽  
Machine Learning Model ◽  
Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

scholarly journals A New Model Averaging Approach in Predicting Credit Risk Default

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 114
Author(s):  
Paritosh Navinchandra Jha ◽  
Marco Cucculelli
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Model Averaging ◽  
Research Direction ◽  
Future Research ◽  
Classification Problems ◽  
Average Technique ◽  
Model Average ◽  
Novel Approach ◽  
Unbalanced Dataset

The paper introduces a novel approach to ensemble modeling as a weighted model average technique. The proposed idea is prudent, simple to understand, and easy to implement compared to the Bayesian and frequentist approach. The paper provides both theoretical and empirical contributions for assessing credit risk (probability of default) effectively in a new way by creating an ensemble model as a weighted linear combination of machine learning models. The idea can be generalized to any classification problems in other domains where ensemble-type modeling is a subject of interest and is not limited to an unbalanced dataset or credit risk assessment. The results suggest a better forecasting performance compared to the single best well-known machine learning of parametric, non-parametric, and other ensemble models. The scope of our approach can be extended to any further improvement in estimating weights differently that may be beneficial to enhance the performance of the model average as a future research direction.

Central government patronage, donor aid, and budget performance in local government: testing a mediation effect

2016 ◽  
Vol 28 (2) ◽  
pp. 139-170 ◽  
Author(s):  
Paul Onyango-Delewa
Keyword(s):  
Local Government ◽  
Fiscal Federalism ◽  
Central Government ◽  
Research Direction ◽  
Theory And Practice ◽  
Mediation Effect ◽  
Future Research ◽  
Future Research Direction ◽  
Mixed Methods Design ◽  
Donor Agencies

Drawing on network and fiscal federalism theories, we investigated central government patronage and donor aid as antecedents of budget performance in local government (LG). A mixed methods design with data collected from 18 LGs, two ministries, and four donor agencies in Uganda was employed. Results revealed that both central government patronage and donor aid predict budget performance. Moreover, autonomy does not mediate the interactions as initially hypothesized. Implications for theory and practice are discussed and future research direction is provided.

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