Knowledge-based (expert) systems in engineering applications: A survey

Valavanis, Kimon P.; Kokkinaki, Angelika; Tzafestas, Spyros G.

doi:10.1007/bf01258225

Cited by 12 publications

(5 citation statements)

References 72 publications

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“…It contains also the identification of the metallic plant with a summary description of the structure, while the first set of guidelines is then activated as a result of this given information. In first, module (1) concerns the accumulation of relative data to damage type developed by the metallic structure [6] , at this stage, the user introduces the visual observations and other data from damage across the entire interface. Module (2) is related to the assessment of damage using the second set of guidelines saddled to the damage on the structure.…”

Section: Methodsmentioning

confidence: 99%

Knowledge base system (KBS) applied on corrosion damage assessment on metallic structure pipes

Cheriet

Bacha

Skender

2018

Heliyon

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This paper aims to outline a proposed knowledge base system (KBS) for the assessment of corrosive damage on metallic pipe conduits. KBS is developed by using two information sources available for assessing the corrosive damage of pipes which are mainly: expert knowledge and field data. In our knowledge, it's a new method that assists the engineer in his task for assessing the degree of damage on metallic pipes, yielding therefore to a rational evaluation of the corrosive damage (hence the ECOR system) on metallic structures of pipeline, and permits to assign the damage degree on the structure qualitatively while using a ladder of indications representing the severity of each damage type (pitting, crater, weight loss and crack), regarding the priority of either repairing or replacing the metal piece. Furthermore, the analytic hierarchy process is implemented overall the system to determine whether the damage severity requires the engineer intervention in a systematic and rational way.

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Section: Methodsmentioning

confidence: 99%

Knowledge base system (KBS) applied on corrosion damage assessment on metallic structure pipes

Cheriet

Bacha

Skender

2018

Heliyon

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“…Despite the uncertainty, ML technology can detect patterns or predict solutions (Valavanis et al. , 1994).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Despite the uncertainty, ML technology can detect patterns or predict solutions (Valavanis et al, 1994). To detect patterns or predict solutions, ML uses algorithms.…”

Section: Machine Learning Technology To Automate Tasksmentioning

confidence: 99%

Identifying the limitations associated with machine learning techniques in performing accounting tasks

Smith,

Lamprecht

2024

JFRA

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Purpose In a virtual interconnected digital space, the metaverse encompasses various virtual environments where people can interact, including engaging in business activities. Machine learning (ML) is a strategic technology that enables digital transformation to the metaverse, and it is becoming a more prevalent driver of business performance and reporting on performance. However, ML has limitations, and using the technology in business processes, such as accounting, poses a technology governance failure risk. To address this risk, decision makers and those tasked to govern these technologies must understand where the technology fits into the business process and consider its limitations to enable a governed transition to the metaverse. Using selected accounting processes, this study aims to describe the limitations that ML techniques pose to ensure the quality of financial information. Design/methodology/approach A grounded theory literature review method, consisting of five iterative stages, was used to identify the accounting tasks that ML could perform in the respective accounting processes, describe the ML techniques that could be applied to each accounting task and identify the limitations associated with the individual techniques. Findings This study finds that limitations such as data availability and training time may impact the quality of the financial information and that ML techniques and their limitations must be clearly understood when developing and implementing technology governance measures. Originality/value The study contributes to the growing literature on enterprise information and technology management and governance. In this study, the authors integrated current ML knowledge into an accounting context. As accounting is a pervasive aspect of business, the insights from this study will benefit decision makers and those tasked to govern these technologies to understand how some processes are more likely to be affected by certain limitations and how this may impact the accounting objectives. It will also benefit those users hoping to exploit the advantages of ML in their accounting processes while understanding the specific technology limitations on an accounting task level.

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“…In the food industry, expert systems, as a knowledge-based artificial intelligence technology [87,88], can simulate the thinking style and knowledge structure of experts to achieve automated decision-making and problem-solving [89]. The system can use the knowledge and experience of domain experts, combined with techniques such as machine learning and natural language processing, to build a system with certain reasoning capabilities to automate the solution of complex problems.…”

Section: Knowledge-based Expert Systems In the Food Industrymentioning

confidence: 99%

The Application of Artificial Intelligence and Big Data in the Food Industry

Ding,

Tian,

et al. 2023

Foods

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Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.

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Knowledge-based (expert) systems in engineering applications: A survey

Cited by 12 publications

References 72 publications

Knowledge base system (KBS) applied on corrosion damage assessment on metallic structure pipes

Knowledge base system (KBS) applied on corrosion damage assessment on metallic structure pipes

Identifying the limitations associated with machine learning techniques in performing accounting tasks

The Application of Artificial Intelligence and Big Data in the Food Industry

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