Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

Artificial Intelligence Review

2018 ◽  
pp. 106-119 ◽  
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this paper, we present a profound literature review of the Artificial Intelligence (AI). After defining it, we briefly cover its history and enumerate its principal fields of application. We name for example information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called The Turing test, is also defined and detailed. Afterwards, we describe some AI tools such as Fuzzy logic, genetic algorithms and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. We also present the future research directions and ethics.

MetaLearning with Graph Neural Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 13-22
Author(s):  
Debmalya Mandal ◽  
Sourav Medya ◽  
Brian Uzzi ◽  
Charu Aggarwal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Graph Problems ◽  
Meta Learning ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

Applying Fuzzy Logic and Fuzzy Methods to Marketing

2012 ◽  
pp. 1056-1068
Author(s):  
Laurent Donzé ◽  
Andreas Meier
Keyword(s):  
Fuzzy Logic ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Future Research ◽  
Computing With Words ◽  
Linguistic Variables ◽  
Research Directions ◽  
Fuzzy Methods ◽  
Future Research Directions

Marketing deals with identifying and meeting the needs of customers. It is therefore both an art and a science. To bridge the gap between art and science, soft computing, or computing with words, could be an option. This chapter introduces fundamental concepts such as fuzzy sets, fuzzy logic, and computing with linguistic variables and terms. This set of fuzzy methods can be applied in marketing and customer relationship management. In the conclusion, future research directions are given for applying fuzzy logic to marketing and customer relationship management.

Artificial Intelligence

2019 ◽  
pp. 13-22
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Future Research ◽  
Human Intelligence ◽  
Research Directions ◽  
Intelligent Machines ◽  
History Of ◽  
Future Research Directions ◽  
New Research

Artificial intelligence is the science of creating intelligent machines. Human intelligence is comprised of numerous pieces of knowledge as well as processes for utilizing this knowledge to solve problems. Artificial intelligence seeks to emulate and surpass human intelligence in problem solving. Current research tends to be focused within narrow, well-defined domains, but new research is looking to expand this to create global intelligence. This chapter seeks to define the various fields that comprise artificial intelligence and look at the history of AI and suggest future research directions.

scholarly journals Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

2020 ◽  
Vol 9 (2) ◽  
pp. 21 ◽  
Author(s):  
Martins O. Osifeko ◽  
Gerhard P. Hancke ◽  
Adnan M. Abu-Mahfouz
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Energy Efficient ◽  
State Of The Art ◽  
Future Internet ◽  
Future Research ◽  
Research Directions ◽  
Efficient Data ◽  
Future Research Directions ◽  
Efficient Data Collection

Smart, secure and energy-efficient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage. One such solution, termed cognitive sensing (CS) describes the use of smart sensors to intelligently perceive inputs from the environment. Further, CS has been proposed for use in FIoT in order to facilitate smart, secure and energy-efficient data collection processes. In this article, we provide a survey of different Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for different FIoT applications. We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions. This survey contributes to a better understanding of AI techniques deployed for cognitive sensing in FIoT as well as future research directions in this regard.

Machine Learning and Cognitive Algorithms for Engineering Applications

2013 ◽  
Vol 7 (4) ◽  
pp. 64-82 ◽  
Author(s):  
Leonid Perlovsky ◽  
Gary Kuvich
Keyword(s):  
Fuzzy Logic ◽  
Cognitive Processes ◽  
Future Research ◽  
Abstract Concepts ◽  
Cognitive Mechanisms ◽  
Research Directions ◽  
Semiotic Approach ◽  
Future Research Directions ◽  
The Brain ◽  
Universal Representation

Mind is based on intelligent cognitive processes, which are not limited by language and logic only. The thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures, and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized. Semiotic approach allows for the universal representation of such diagrams. In that approach, logic is a way of synthesis of such structures, which is a small but clearly visible top of the iceberg. The most efforts were traditionally put into logics without paying much attention to the rest of the mechanisms that make the entire thought system working autonomously. Dynamic fuzzy logic is reviewed and its connections with semiotics are established. Dynamic fuzzy logic extends fuzzy logic in the direction of logic-processes, which include processes of fuzzification and defuzzification as parts of logic. The paper reviews basic cognitive mechanisms, including instinctual drives, emotional and conceptual mechanisms, perception, cognition, language, a model of interaction between language and cognition upon the new semiotic models. The model of interacting cognition and language is organized in an approximate hierarchy of mental representations from sensory percepts at the “bottom” to objects, contexts, situations, abstract concepts-representations, and to the most general representations at the “top” of mental hierarchy. Knowledge Instinct and emotions are driving feedbacks for these representations. Interactions of bottom-up and top-down processes in such hierarchical semiotic representation are essential for modeling cognition. Dynamic fuzzy logic is analyzed as a fundamental mechanism of these processes. Future research directions are discussed.

scholarly journals Graph Neural Networks: Taxonomy, Advances, and Trends

2022 ◽  
Vol 13 (1) ◽  
pp. 1-54
Author(s):  
Yu Zhou ◽  
Haixia Zheng ◽  
Xin Huang ◽  
Shufeng Hao ◽  
Dengao Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Research Community ◽  
Future Research ◽  
Research Directions ◽  
Comprehensive Review ◽  
Future Research Directions ◽  
Graph Neural Networks ◽  
Low Dimensional

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers cannot see a panorama of the graph neural networks. This survey aims to overcome this limitation and provide a systematic and comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 327 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the challenges faced. It is expected that more and more scholars can understand and exploit the graph neural networks and use them in their research community.

scholarly journals Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

2021 ◽  
Vol 2 ◽  
pp. 1-21
Author(s):  
Gengchen Mai ◽  
Krzysztof Janowicz ◽  
Rui Zhu ◽  
Ling Cai ◽  
Ni Lao
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Question Answering ◽  
Future Research ◽  
Open Domain ◽  
Future Directions ◽  
Research Directions ◽  
Classification Framework ◽  
Substantial Progress ◽  
Future Research Directions

Abstract. As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language. While there has been substantial progress in open-domain question answering, QA systems are still struggling to answer questions which involve geographic entities or concepts and that require spatial operations. In this paper, we discuss the problem of geographic question answering (GeoQA). We first investigate the reasons why geographic questions are difficult to answer by analyzing challenges of geographic questions. We discuss the uniqueness of geographic questions compared to general QA. Then we review existing work on GeoQA and classify them by the types of questions they can address. Based on this survey, we provide a generic classification framework for geographic questions. Finally, we conclude our work by pointing out unique future research directions for GeoQA.

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