News

Cityscape-with-network.png

AI-Based Solution

AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems

Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus, AI-based modeling is the key to build automated, intelligent, and smart systems according to today’s needs. To solve real-world issues, various types of AI such as analytical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of an application. However, developing an effective AI model is a challenging task due to the dynamic nature and variation in real-world problems and data. In this paper, we present a comprehensive view on “AI-based Modeling” with the principles and capabilities of potential AI techniques that can play an important role in developing intelligent and smart systems in various real-world application areas including business, finance, healthcare, agriculture, smart cities, cybersecurity and many more. We also emphasize and highlight the research issues within the scope of our study. Overall, the goal of this paper is to provide a broad overview of AI-based modeling that can be used as a reference guide by academics and industry people as well as decision-makers in various real-world scenarios and application domains.

This revolution is impacting almost every industry in every country and causing a tremendous change in a non-linear manner at an unprecedented rate, with implications for all disciplines, industries, and economies. Three key terms Automation, i.e., reducing human interaction in operations, Intelligent, i.e., ability to extract insights or usable knowledge from data, and Smart computing, i.e., self-monitoring, analyzing, and reporting, known as self-awareness, have become fundamental criteria in designing today’s applications and systems in every sector of our lives since the current world is more reliant on technology than ever before. The use of modern smart technologies enables making smarter, faster decisions regarding the business process, ultimately increasing the productivity and profitability of the overall operation, where Artificial Intelligence (AI) is known as a leading technology in the area. The AI revolution, like earlier industrial revolutions that launched massive economic activity in manufacturing, commerce, transportation, and other areas, has the potential to lead the way of progress. As a result, the impact of AI on the fourth industrial revolution motivates us to focus briefly on “AI-based modeling”.


What is AI

Artificial intelligence (AI) is a broad field of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. In other words, we can say that it aims is to make computers smart and intelligent by giving them the ability to think and learn using computer programs or machines, i.e., can think and function in the same way that people do. From a philosophical perspective, AI has the potential to help people live more meaningful lives without having to work as hard, as well as manage the massive network of interconnected individuals, businesses, states, and nations in a way that benefits everyone. Thus, the primary goal of AI is to enable computers and machines to perform cognitive functions such as problem-solving, decision making, perception, and comprehension of human communication. Therefore, AI-based modeling is the key to building automated, intelligent and smart systems according to today’s needs, which has emerged as the next major technological milestone, influencing the future of practically every business by making every process better, faster, and more precise.

AI technology has become one of the core technologies to achieve the goal. However, developing an effective AI model is a challenging task due to the dynamic nature and variation in real-world problems and data. Thus, we take into account several AI categories: The first one is “Analytical AI” with the capability of extracting insights from data to ultimately produce recommendations and thus contributing to data-driven decision-making; the Second one is “Functional AI” which is similar to analytical AI; however, instead of giving recommendations, it takes actions; the Third one is “Interactive AI” that typically allows businesses to automate communication without compromising on interactivity like smart personal assistants or chatbots; the Fourth one is “Textual AI” that covers textual analytics or natural language processing through which business can enjoy text recognition, speech-to-text conversion, machine translation, and content generation capabilities; and finally the Fifth one is “Visual AI” that covers computer vision or augmented reality fields, discussed briefly in “Why artificial intelligence in today’s research and applications?”.

To build AI-based models;

We classify various AI techniques into ten categories: (1) machine learning; (2) neural networks and deep learning; (3) data mining, knowledge discovery and advanced analytics; (4) rule-based modeling and decision-making; (5) fuzzy logic-based approach; (6) knowledge representation, uncertainty reasoning, and expert system modeling; (7) case-based reasoning; (8) text mining and natural language processing; (9) visual analytics, computer vision and pattern recognition; (10) hybridization, searching and Optimization

These techniques can play an important role in developing intelligent and smart systems in various real-world applicationareas that include business, finance, healthcare, agriculture, smart cities, cybersecurity, and many more, depending on the nature of the problem and target solution.

 

Understanding Various Types of Artificial Intelligence

 

rtificial intelligence (AI) is primarily concerned with comprehending and carrying out intelligent tasks such as thinking, acquiring new abilities, and adapting to new contexts and challenges. AI is thus considered a branch of science and engineering that focuses on simulating a wide range of issues and functions in the field of human intellect. However, due to the dynamic nature and diversity of real-world situations and data, building an effective AI model is a challenging task. Thus, to solve various issues in today’s Fourth Industrial Revolution, we explore various types of AI that include analytical, functional, interactive, textual, and visual, to understand the theme of the power of AI,

 

  • Analytical AI: Analytics typically refers to the process of identifying, interpreting, and communicating meaningful patterns of data. Thus, Analytical AI aims to discover new insights, patterns, and relationships or dependencies in data and to assist in data-driven decision-making. Therefore, in the domain of today’s business intelligence, it becomes a core part of AI that can provide insights to an enterprise and generate suggestions or recommendations through its analytical processing capability. Various machine learning and deep learning techniques can be used to build an analytical AI model to solve a particular real-world problem. For instance, to assess business risk, a data-driven analytical model can be used.

  • Functional AI: Functional AI works similarly to analytical AI because it also explores massive quantities of data for patterns and dependencies. Functional AI, on the other hand, executes actions rather than making recommendations. For instance, a functional AI model could be useful in robotics and IoT applications to take immediate actions.

  • Interactive AI: Interactive AI typically enables efficient and interactive communication automation, which is well established in many aspects of our daily lives, particularly in the commercial sphere. For instance, to build chatbots and smart personal assistants an interactive AI model could be useful. While building an interactive AI model, a variety of techniques such as machine learning, frequent pattern mining, reasoning, AI heuristic search can be employed.

  • Textual AI: Textual AI typically covers textual analytics or natural language processing through which businesses can enjoy text recognition, speech-to-text conversion, machine translation as well as content generation capabilities. For instance, an enterprise may use textual AI to support an internal corporate knowledge repository to provide relevant services, e.g., answering consumers’ queries.

  • Visual AI: Visual AI is typically capable to recognize, classify, and sorting items, as well as converting images and videos into insights. Thus, visual AI can be considered as a branch of computer science that trains machines to learn images and visual data in the same manner that humans do. This sort of AI is often used in fields such as computer vision and augmented reality.

 

The Relation of AI with ML and DL

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are three prominent terminologies used interchangeably nowadays to represent intelligent systems or software. The position of machine learning and deep learning within the artificial intelligence field is depicted in Fig. 2. According to Fig. 2, DL is a subset of ML which is also a subset of AI. In general, AI [77] combines human behavior and intelligence into machines or systems, whereas ML is a way of learning from data or experience, which automates analytical model building. Deep learning also refers to data-driven learning approaches that use multi-layer neural networks and processing to compute.

Thus, both ML and DL can be considered as essential AI technologies, as well as a frontier for AI that can be used to develop intelligent systems and automate processes. It also takes AI to a new level, termed “Smarter AI” with data-driven learning. There is a significant relationship with “Data Science” as well because both ML and DL can learn from data. These learning methods can also play a crucial role in advanced analytics and intelligent decision-making in data science, which typically refers to the complete process of extracting insights in data in a certain problem domain. Overall, we can conclude that both ML and DL technologies have the potential to transform the current world, particularly in terms of a powerful computational engine, and to contribute to technology-driven automation, smart and intelligent systems. In addition to these learning techniques, several others can play the role in the development of AI-based models in various real-world application areas, depending on the nature of the problem and the target solution

 

„AI-based modeling which is considered a key component of the fourth industrial revolution.“

Emil Mitry