Expert System Application – Computer-aided design is receiving enormous attention in chemical engineering for high-performance pharmaceutical materials in the manufacture of dosage forms. In order to accelerate the development of new quality design methods, the preformulation algorithm of the SeDeM Expert system was developed as a tool for designing solid drug delivery systems and predicting the direct production of complex formulations. This research aims to integrate the SeDeM expert system in the design of composite engineering components against hardness in order to develop a new, self-softening and automatic composite, using corn starch and microcrystalline cellulose powder as basic models.
The data and information generated by the expert system clearly defined the mass characteristics of the main beneficiaries, could calculate the optimal mixing ratio of the ingredients, and verified the synergistic effect of the product’s functionality. The experimental activity (7.78 ± 0.17), the pressure functions (5.16 ± 0.14), the dimensional profile (0.92) and the parametric profile index (6.72 ± 0.27) of the technical component were at acceptable levels. With a constant reliability of 0.961, the net direct correlation coefficient expressed as a positive compression index (6.46 ± 0.26) is higher than that of the main beneficiaries, but comparable to the processed reference materials, StarLac® (6, 44 ± 0.14) and MicroceLac ® 100 (6.58 ± 0.03 ).
Expert System Application
The implementation of the SeDeM expert system in collaborative materials engineering has provided an improved dynamic process for the design of automatically engineered compressed materials in a design-oriented manner. It assumes a systematic four-step approach to stakeholder engagement. Phase I contains characterization of CMAs for both damaged and repair derivatives and verification of their physico-mechanical limitations using SeDeM diagrams. Phase II includes calculating the yield stress carrying capacity using the solvent potential equation. Phase III consists of selecting a collaborative technique based on the main desired material properties as revealed by the data obtained in Phase I. Phase IV evaluates the effectiveness of the collaboration by monitoring the critical behavior of the engineering mix while deciding whether to accept or reject a product.
Solution: 2022 Lec 6 Expert System
Materials: A mainly reactive plastic material – microcrystalline cellulose powder (Avicel PH101, FMC Corporation, UK) and a rather coarse material – corn starch powder (CDH Chemicals, India), served as a template for the main extractants. Polyvinylpyrrolidone (CDH Chemicals, India) was used as a binder during dispersion. Magnesium stearate, colloidal silicon dioxide, talc (Merck, Sigma-Aldrich) were used as lubricants and flow agents only for the cornstarch mixture. Two commercially available compressible preparations, MicroceLac®100 (75% lactose monohydrate + 25% microcrystalline cellulose) and StarLac® (85% lactose monohydrate + 15% white corn starch) were kindly provided by the donation of Meggle Wasserburg GmbH & Co. KG, Germany, were used as reference standards for comparison.
Article information: Salim, I., Olowosulu, A.K., Abdulsamad, A. et al. Application of a SeDeM expert system to develop a novel novel that can be compressed directly into custom beneficiaries with the help of processing. Futur J Pharm Sci 7, 135 (2021). https://doi.org/10.1186/s43094-021-00253-zDefinition: It is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial intelligence (AI) is a branch of computer science that deals with artificial intelligence where an intelligent agent is a system that performs actions that increase the chances of success. It is the exploration of ideas that allows computers to do the things that make humans intelligent.
4 principles of artificial intelligence The main principles of artificial intelligence include intelligence, knowledge, planning, learning, communication, perception and the ability to move and manage objects.
Make sense of ambiguous or contradictory messages Respond quickly and successfully to new situations Use logic to solve problems
Expert Systems: Leveraging Ai For Decision Making
A computer can only be considered intelligent when the human interviewer can “converse” with both an invisible person and an invisible computer.
8 artificial intelligence methods The characterization of artificial intelligence, which focuses on the development of knowledge-based systems (KBS), including methods such as neural networks (NN), fuzzy systems (FS) and evolutionary computing
Patterns in the way we think patterns that apply our intelligence can make computers easier to use can make more knowledge available to create parts of the human brain
KBS can be defined as a computerized system capable of providing advice in a specific field, using knowledge provided by a human expert. The distinguishing characteristic of KBS lies in the separation behind the knowledge, which can be represented in various ways such as rules, cases, and an inference engine or algorithm that uses the knowledge base to reach a conclusion.
Application Of Expert Systems
Decision makers in management are knowledge workers They use knowledge to make decisions Knowledge problems Knowledge-based expert systems: applied artificial intelligence
Expert systems do not replace experts, but make their knowledge and experience more available by enabling non-experts to work better
15 Expert Broad, task-specific knowledge acquired through training, reading, and experience Attitudes about the problem area Hard and fast rules Rules (heuristics) Global strategies Epistemology (knowledge of knowledge) Facts enable experts to improve faster than non-experts
It is often related to the acquisition of knowledge, but not always the most intelligent person, it is often related to a large amount of knowledge. Nice memories
Pdf) Expert System And Its Applications For A Sustainable Environment Management
Identify and define the problem Solve problems quickly and efficiently Explain the solution Learn from experience Restructure knowledge Break the rules
The purpose of an expert system is to transfer knowledge from the expert to the computer system and then to other people (non-experts).
Knowledge acquisition is the collection, transfer, and conversion of expert problem-solving skills and/or documented knowledge resources into computer software to build or expand a knowledge base. Requires knowledge engineering
27 Knowledge base The knowledge base consists of the knowledge needed to understand, formulate and solve problems The two knowledge bases Knowledge bases Heuristic facts or specific rules that guide the use of knowledge Knowledge is the basic tool for knowledge representation in ES Incorporated
Expert Systems: Principles And… By Giarratano, Joseph C
30 Justifier looks for responsibility and explains the SE’s behavior by interactively answering the questions: Why? – as? -What is? (Where? When? Who?) Knowledge Refinement System Learn to improve performance
32 An expert has special knowledge, judgment, experience and methods for giving advice and solving problems.
Helps experts model problem areas by interpreting and synthesizing human responses to questions, drawing examples, presenting counterexamples, and highlighting conceptual issues. Usually also a systems architect
A non-expert client who wants direct advice (ES acts as a consultant or advisor) A student who wants to learn (instruct) An ES builder who improves or expands the knowledge base An engineer must anticipate user needs and limitations during ES design
Architecture Of Expert System
The knowledge is not always available to an expert and can be difficult to extract from humans. Each expert’s method may be different, but it is correct. It is difficult, even for a highly qualified specialist, to work under pressure. limited knowledge
The vocabulary of the experts is often limited and high. Knowledge engineers are rare and expensive Lack of trust from end users Knowledge transfer depends on hosting theory and judgment ES may not be able to reach correct conclusions. ES sometimes makes wrong recommendations.
39 Success 1. Business applications that demonstrate strategic impact (competitive advantage) 2. Applications that are well defined and understood (knowledge-based systems are more general than expert systems)
The role of the DSS systems specialist is the injection of the knowledge of the information systems specialist. Automated Decision Making Decision environments have alternative structures and often predetermined goals. An expert system can eventually replace the human decision maker. Decision support system Extract or augment knowledge from computer systems Facilitate decision making in an unstructured environment Alternatives may not yet be fully realized Use system goals and data to determine alternatives and outcomes, if they are not good enough.
Artificial Intelligence Icons Including Black Box Learning, Reinforcement, Supervised, Unsupervised, Transfer, Cognition, Sensorimotor Skill, Ai Knowledge, Expert System, Representation, Automated Planning, Computational, Multi Agent, Application
3 learning objectives Learn the advantages, limitations and main success factors of rule-based expert systems for decision support Learn the correct ES method Learn the interaction between networks and rule-based expert systems in the context of DSS Learn the tools and DSS-based rule generation technique Deepen your knowledge in the development environment of the expert system through practical exercises
A subset of computer science that deals with symbolic logic and problem solving, artificial intelligence has many definitions… machine behavior that, if performed by a human, would be considered intelligent “…for a moment, humans are good at understanding how humans work” the brain works
Understand what intelligence is. Making machines smarter and more useful. Intelligent signals
Decision Support System (dss): What It Is And How Businesses Use Them
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