The document describes a clinical decision support system project completed by Jayita Roy for her MBA degree, including a declaration that the work is her own, certification by her advisor, and acknowledgements of those who provided guidance and support.
Continued Use Of IDAs And Knowledge AcquisitionMicheal Axelsen
The effects of continued use of intelligent decision aids upon auditor procedural knowledge
Student: Micheal Axelsen
Supervisor: Professor Peter Green, Dr Fiona Rohde
ABSTRACT
This research proposal builds upon the theory of technology dominance (Sutton & Arnold 1998), which has as one of its propositions that the continued use of intelligent decision aids may have the effect of deskilling auditors over time. A theoretical contribution is made through a consideration of this effect through the operation of the anchoring and adjustment heuristic (Epley & Gilovich, 2006; Kowalczyk & Wolfe, 1998; Tversky & Kahnemann, 1974) and cognitive load theory (Mascha & Smedley, 2007; Sweller, 1988). The anchoring and adjustment heuristic is a technique used by people in judgment tasks to remove cognitive burden. In making a judgment, the assessor ‘anchors’ upon the first value provided in making an estimate, and then ‘adjusts’ this estimate until a ‘reasonable’ estimate is reached. This heuristic has the effect of a systematic adjustment bias in the final estimate made. Cognitive load theory finds that an expert uses different and more efficient problem-solving strategies as a result of their past experiences in comparison to the novice. The expert draws upon their experience with past problems to develop their problem-solving strategies. Theoretically the argument is developed that the professional auditor’s ability to develop efficient problem-solving strategies is reduced as a result of their use of the anchoring and adjustment heuristics encouraged by the continued use of intelligent decision aids.
It is proposed that this integrated theory be empirically tested through a series of semi-structured interviews with audit professionals and a survey of public sector auditors designed to test the developed theoretical model. This investigation will consider the role of the continued use of intelligent decision aids and any deskilling effect such use may have upon auditor ‘know-how’, or procedural knowledge.
The contributions of this proposed research are several. Firstly, a theoretical contribution is made through extension and reconciliation of the theory of technology dominance with the anchoring and adjustment heuristic and cognitive load theory. Secondly, a practical contribution is made by extension of the testing of the theory to the field rather than experimentally. A third practical contribution is made through an empirical test of the theory of technology dominance in the context of procedural knowledge (auditor ‘know-how’), which has not previously been tested.
Machine learning models involve a bias-variance tradeoff, where increased model complexity can lead to overfitting training data (high variance) or underfitting (high bias). Bias measures how far model predictions are from the correct values on average, while variance captures differences between predictions on different training data. The ideal model has low bias and low variance, accurately fitting training data while generalizing to new examples.
This document contains a list of 3 employees - Jayita Roy as Director, Neerajita Sarkar as Finance Officer, and Kallol Mukherji as Marketing Officer. It also contains a table with 6 columns labeled with dates from 2017 to 2022 and no data in the rows.
Team A from the University of Kalyani Department of MBA presented on the topic of sexual harassment. They discussed child abuse, sexual harassment of the third gender, sexual harassment of men, and sexual harassment of women. They covered the definition of child abuse and signs of abuse. They also discussed existing Indian laws on sexual harassment including the Sexual Harassment of Women at Workplace Act, the Indecent Representation of Women Act, and the Protection of Children from Sexual Offenses Act. They called for a need of a gender-neutral sexual harassment law.
This document describes the design and implementation of a clinical decision support system for rural women. The system was created to improve healthcare access for rural women by providing diagnoses for five common diseases: dysmenorrhea, ovarian cysts, endometriosis, polycystic ovarian disease, and urinary tract infection. It uses an artificial intelligence-based knowledge base and rule-based inference engine to provide diagnoses and treatment recommendations to users based on their symptoms. The system was tested extensively before deployment to ensure accurate and efficient operation.
The document provides an overview of selling and marketing skills, including different concepts in marketing, the differences between selling and marketing, buying motives, and techniques for questioning customers. It discusses the production concept, product concept, selling concept, and marketing concept. It also summarizes the buying process and need for change and improvement in marketing approaches.
Marketing implementation involves carrying out a marketing plan through execution. There is a close relationship between strategy, which defines what and why, and implementation, which addresses who, where, when and how a plan will be implemented. Effective implementation requires diagnostic, identification, and implementation skills as well as the use of information technologies and software platforms to enable improved execution on a global scale. Proper implementation is critical to realizing the benefits of a well-designed marketing strategy.
Continued Use Of IDAs And Knowledge AcquisitionMicheal Axelsen
The effects of continued use of intelligent decision aids upon auditor procedural knowledge
Student: Micheal Axelsen
Supervisor: Professor Peter Green, Dr Fiona Rohde
ABSTRACT
This research proposal builds upon the theory of technology dominance (Sutton & Arnold 1998), which has as one of its propositions that the continued use of intelligent decision aids may have the effect of deskilling auditors over time. A theoretical contribution is made through a consideration of this effect through the operation of the anchoring and adjustment heuristic (Epley & Gilovich, 2006; Kowalczyk & Wolfe, 1998; Tversky & Kahnemann, 1974) and cognitive load theory (Mascha & Smedley, 2007; Sweller, 1988). The anchoring and adjustment heuristic is a technique used by people in judgment tasks to remove cognitive burden. In making a judgment, the assessor ‘anchors’ upon the first value provided in making an estimate, and then ‘adjusts’ this estimate until a ‘reasonable’ estimate is reached. This heuristic has the effect of a systematic adjustment bias in the final estimate made. Cognitive load theory finds that an expert uses different and more efficient problem-solving strategies as a result of their past experiences in comparison to the novice. The expert draws upon their experience with past problems to develop their problem-solving strategies. Theoretically the argument is developed that the professional auditor’s ability to develop efficient problem-solving strategies is reduced as a result of their use of the anchoring and adjustment heuristics encouraged by the continued use of intelligent decision aids.
It is proposed that this integrated theory be empirically tested through a series of semi-structured interviews with audit professionals and a survey of public sector auditors designed to test the developed theoretical model. This investigation will consider the role of the continued use of intelligent decision aids and any deskilling effect such use may have upon auditor ‘know-how’, or procedural knowledge.
The contributions of this proposed research are several. Firstly, a theoretical contribution is made through extension and reconciliation of the theory of technology dominance with the anchoring and adjustment heuristic and cognitive load theory. Secondly, a practical contribution is made by extension of the testing of the theory to the field rather than experimentally. A third practical contribution is made through an empirical test of the theory of technology dominance in the context of procedural knowledge (auditor ‘know-how’), which has not previously been tested.
Machine learning models involve a bias-variance tradeoff, where increased model complexity can lead to overfitting training data (high variance) or underfitting (high bias). Bias measures how far model predictions are from the correct values on average, while variance captures differences between predictions on different training data. The ideal model has low bias and low variance, accurately fitting training data while generalizing to new examples.
This document contains a list of 3 employees - Jayita Roy as Director, Neerajita Sarkar as Finance Officer, and Kallol Mukherji as Marketing Officer. It also contains a table with 6 columns labeled with dates from 2017 to 2022 and no data in the rows.
Team A from the University of Kalyani Department of MBA presented on the topic of sexual harassment. They discussed child abuse, sexual harassment of the third gender, sexual harassment of men, and sexual harassment of women. They covered the definition of child abuse and signs of abuse. They also discussed existing Indian laws on sexual harassment including the Sexual Harassment of Women at Workplace Act, the Indecent Representation of Women Act, and the Protection of Children from Sexual Offenses Act. They called for a need of a gender-neutral sexual harassment law.
This document describes the design and implementation of a clinical decision support system for rural women. The system was created to improve healthcare access for rural women by providing diagnoses for five common diseases: dysmenorrhea, ovarian cysts, endometriosis, polycystic ovarian disease, and urinary tract infection. It uses an artificial intelligence-based knowledge base and rule-based inference engine to provide diagnoses and treatment recommendations to users based on their symptoms. The system was tested extensively before deployment to ensure accurate and efficient operation.
The document provides an overview of selling and marketing skills, including different concepts in marketing, the differences between selling and marketing, buying motives, and techniques for questioning customers. It discusses the production concept, product concept, selling concept, and marketing concept. It also summarizes the buying process and need for change and improvement in marketing approaches.
Marketing implementation involves carrying out a marketing plan through execution. There is a close relationship between strategy, which defines what and why, and implementation, which addresses who, where, when and how a plan will be implemented. Effective implementation requires diagnostic, identification, and implementation skills as well as the use of information technologies and software platforms to enable improved execution on a global scale. Proper implementation is critical to realizing the benefits of a well-designed marketing strategy.
IRJET- Learning Assistance System for Autistic ChildIRJET Journal
1) The document describes a learning assistance system for autistic children that aims to provide specialized education for autism by detecting affected areas and tailoring learning accordingly.
2) It uses techniques like data mining, neuroimaging, and deep learning to classify autism-related conditions like nasal, tongue, auditory, or brain defects and provide individualized learning based on the child's needs.
3) The system analyzes recorded sounds of the child learning to not only assess their progress but also predict their condition, aiming to improve their reading, understanding, and quality of life through a flexible educational experience.
The document discusses Pekka Silvén's background and work in evaluation, quality and feedback processes. It notes some common problems with traditional evaluation methods and introduces the "Zef-method" as an alternative that provides clearer results and facilitates improvements. The Zef-method is used in various feedback questionnaires and evaluation engines to analyze responses and compare views of different stakeholders.
An expert system is an interactive computer-based decision tool that uses both facts and heuristics to solve difficult decision problems based on knowledge acquired from an expert.
This document summarizes an industrial training report on developing a Smart Reminder App. The report acknowledges those who provided guidance and support for the project. It then provides background on the company where the training took place, Agile Softech Pvt. Ltd., which develops customized software solutions. The report abstract introduces the Smart Reminder App, which allows patients to set medication alarms and search for doctors. Finally, the document discusses system analysis conducted for the app, including identifying user needs, feasibility analysis, and technical requirements.
Amber Logue is seeking a position that utilizes her skills in organization, attention to detail, communication, and experience in healthcare IT and medical records. She has over 15 years of experience as a privacy officer, medical records supervisor, and electronic health records coordinator at an ophthalmology practice. She is pursuing a Bachelor's degree in Information Technology and holds certifications as an ophthalmic scribe and assistant.
This document provides information about a proposed Health Information Card system. Some key points:
- The card would store a person's medical information and history in a centralized digital format similar to a debit/credit card for easy access by doctors.
- It aims to address issues with the current paper-based system like difficulty finding records, long wait times, and needing repeat tests.
- The card would allow fast, paperless retrieval and sharing of health data between patients, doctors, and hospitals using technologies like fingerprint scanning.
- The proposed system is intended to make healthcare more efficient, flexible and user-friendly for all parties by streamlining information management and medical processes.
The document discusses expert systems, which are computer applications that solve complex problems at a human expert level. It describes the characteristics and capabilities of expert systems, why they are useful, and their key components - knowledge base, inference engine, and user interface. The document also outlines common applications of expert systems and the general development process.
An expert system is a computer program that contains knowledge from human experts to solve complex problems in a specific domain. It has four main components: a knowledge base of facts and rules, an inference engine that applies rules to facts to derive new facts, a user interface, and an explanation facility. Expert systems were developed in the 1970s to apply human expertise to problems. They have limitations but can also explain their reasoning, draw complex conclusions, and provide portable knowledge to help humans. Common applications of expert systems include medical diagnosis, materials identification, and credit approval.
The document discusses expert systems, which are designed to solve real problems in a particular domain that normally require human expertise. Developing an expert system involves extracting knowledge from domain experts. The key components of an expert system are the knowledge base, inference engine, explanation facility, knowledge acquisition facility, and user interface. Expert systems use knowledge rather than data to solve problems and can explain their reasoning. They have limitations such as being difficult to maintain and only applicable to narrow problems.
This workshop is a comprehensive introduction to the application of Generative AI in healthcare. It provides healthcare professionals, educators, and researchers with practical experience in using Generative AI for data analysis, predictive modeling, and personalized treatment planning. The workshop also explores the use of Generative AI in medical education and research. No prior AI experience is required, making this a unique opportunity to learn about the latest advancements in Generative AI and its healthcare applications.
The document discusses best practices for visualizing analytics results. It emphasizes that visualization is critical for effectively communicating insights from data analysis. Good visualizations exploit the human visual system by presenting information simply and clearly. Practitioners should understand their data and audience to develop visualizations that tell the right story. Iterative experimentation is important to arrive at visualizations that provide global understanding from the data. Overall the document stresses that visualization is a key part of deriving meaningful insights from analytics work.
The document discusses using artificial intelligence technologies in healthcare, noting opportunities for AI to enhance diagnosis, treatment planning, and research, but also challenges regarding governance, privacy, bias, and other issues. It provides an overview of different applications of AI in healthcare management, clinical decision-making, and patient data analysis, and emphasizes that AI should augment rather than replace human experts in medical fields. The workshop aims to educate participants on utilizing AI, specifically generative AI, in healthcare and medical education.
I probably should have titled this section Who Ensures that Digita.docxsalmonpybus
I probably should have titled this section Who Ensures that Digital Health Tools are Safe?. The Food and Drug Administration (FDA) is largely tasked with ensuring the safety and oversight of "traditional" medical devices. Consumer oriented technologies and many of the digital health tools available today present a more unique challenge for federal regulators. For example, software which reads digital mammograms is clearly a medical device intended to diagnose cancer. However, a mobile application which a user/consumer/patient leverages to track daily blood glucose levels is a very different approach. The mobile application is not making a diagnosis. There is limited risk of harm or change in medical management resulting from use. To address many of these nuances the FDA has created a
digital health resource page. The FDA often issues regulatory guidance documents in the event there is a question surrounding the legal authority provide oversight on a specific tool, device, or software component of a device.
FDA Approach to Risk
The FDA continues to evaluate technologies in the context of risk and classifies technologies in one of three tiers. It is important to note that any mobile app; regardless of if FDA approval is needed or not; may be submitted to the FDA for review. At times a vendor will submit to the FDA for review to increase the marketability of service or solution. The device itself is not required to be evaluated but the vendor chooses to do so anyway. The classes fall into one of the three following categories:
Class I - Low-risk where general reporting of adverse events occurs
Class II - Medium-risk where the FDA must provide review and clearance before marketing of the solution occurs. Review period takes between 60 to 90 days.
Class III - High risk where clearance before marketing must be obtained and the clearance must involve clinical studies showing that the product is safe and effective.
Mobile Medical Applications
The FDA has done a good job of clarifying the types of mobile apps that will not, will, and may be regulated by providing guidance documents. The FDA calls this document
Mobile Medical Applications Guidance. The aim of the guidance document is to clarify where the agency intends to enforce oversight and where the agency will continue to monitor developments. While the guidance document and website is extensive, there are a few key items worth mentioning. 1. The FDA defines what mobile medical applications
ARE. They can be Apps or accessory devices which meet the definition of a medical device (somewhat abridged and abbreviated for simplicity)- recognized by the National Formulary (ie Pharmaceuticals), intended for the use in the diagnosis of disease, cure, mitigation, treatment, or prevention of disease; or affect the structure or function of the body of man or other animals. 2. Consumers can use both medical apps and mobile apps to manage their o.
This document discusses an assignment submitted on expert system design. It begins with defining an expert system as a computer application that performs tasks done by human experts, such as medical diagnosis. It then outlines the advantages and disadvantages of using expert systems. Key advantages include providing consistent solutions, reasonable explanations, and overcoming human limitations. Disadvantages include lacking common sense, high costs, and difficulties creating inference rules. The document also differentiates expert systems from conventional computer systems and describes knowledge acquisition in expert systems as the process of extracting and organizing knowledge from human experts. It discusses forward and backward chaining and declarative versus procedural knowledge. Finally, it presents problems on analyzing a circuit output and applying laws of equivalence to a logical expression, and defining a
An outline of knowledge mining multi tier architecture for decision makingIAEME Publication
1. The document proposes a multi-tier architecture for a knowledge mining tool to support decision making.
2. It describes key aspects of decision making processes, knowledge management systems, and existing approaches to knowledge discovery in databases.
3. The proposed architecture is a three-tier system with graphical interface, client-side services, and centralized middle-tier services that control mining tasks and access data sources to produce results for users.
CompTIA exam study guide presentations by instructor Brian Ferrill, PACE-IT (Progressive, Accelerated Certifications for Employment in Information Technology)
"Funded by the Department of Labor, Employment and Training Administration, Grant #TC-23745-12-60-A-53"
Learn more about the PACE-IT Online program: www.edcc.edu/pace-it
An expert system is a computer application that uses specialized knowledge to guide complex tasks usually requiring human expertise. It uses an inference engine to apply logic rules to a knowledge base of facts to provide advice and explanations. Expert systems are used in fields like accounting, medicine, manufacturing, and human resources to consistently provide answers to repetitive decisions and processes while maintaining large stores of information. They have advantages like constant availability and ability to serve multiple users, but lack common sense reasoning and cannot adapt without changing the knowledge base.
Expert system prepared by fikirte and hayat im assignmentfikir getachew
The document discusses expert systems, their components, types, and uses. An expert system is an intelligent system that can perform complex tasks like a human expert. It consists of a knowledge base, inference engine, user interface, interpreter, and blackboard. Expert systems are classified based on their function, such as for interpretation, prediction, diagnosis, design, or planning. They can benefit industries and countries by advancing fields like agriculture, education, medicine, and more.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
IRJET- Learning Assistance System for Autistic ChildIRJET Journal
1) The document describes a learning assistance system for autistic children that aims to provide specialized education for autism by detecting affected areas and tailoring learning accordingly.
2) It uses techniques like data mining, neuroimaging, and deep learning to classify autism-related conditions like nasal, tongue, auditory, or brain defects and provide individualized learning based on the child's needs.
3) The system analyzes recorded sounds of the child learning to not only assess their progress but also predict their condition, aiming to improve their reading, understanding, and quality of life through a flexible educational experience.
The document discusses Pekka Silvén's background and work in evaluation, quality and feedback processes. It notes some common problems with traditional evaluation methods and introduces the "Zef-method" as an alternative that provides clearer results and facilitates improvements. The Zef-method is used in various feedback questionnaires and evaluation engines to analyze responses and compare views of different stakeholders.
An expert system is an interactive computer-based decision tool that uses both facts and heuristics to solve difficult decision problems based on knowledge acquired from an expert.
This document summarizes an industrial training report on developing a Smart Reminder App. The report acknowledges those who provided guidance and support for the project. It then provides background on the company where the training took place, Agile Softech Pvt. Ltd., which develops customized software solutions. The report abstract introduces the Smart Reminder App, which allows patients to set medication alarms and search for doctors. Finally, the document discusses system analysis conducted for the app, including identifying user needs, feasibility analysis, and technical requirements.
Amber Logue is seeking a position that utilizes her skills in organization, attention to detail, communication, and experience in healthcare IT and medical records. She has over 15 years of experience as a privacy officer, medical records supervisor, and electronic health records coordinator at an ophthalmology practice. She is pursuing a Bachelor's degree in Information Technology and holds certifications as an ophthalmic scribe and assistant.
This document provides information about a proposed Health Information Card system. Some key points:
- The card would store a person's medical information and history in a centralized digital format similar to a debit/credit card for easy access by doctors.
- It aims to address issues with the current paper-based system like difficulty finding records, long wait times, and needing repeat tests.
- The card would allow fast, paperless retrieval and sharing of health data between patients, doctors, and hospitals using technologies like fingerprint scanning.
- The proposed system is intended to make healthcare more efficient, flexible and user-friendly for all parties by streamlining information management and medical processes.
The document discusses expert systems, which are computer applications that solve complex problems at a human expert level. It describes the characteristics and capabilities of expert systems, why they are useful, and their key components - knowledge base, inference engine, and user interface. The document also outlines common applications of expert systems and the general development process.
An expert system is a computer program that contains knowledge from human experts to solve complex problems in a specific domain. It has four main components: a knowledge base of facts and rules, an inference engine that applies rules to facts to derive new facts, a user interface, and an explanation facility. Expert systems were developed in the 1970s to apply human expertise to problems. They have limitations but can also explain their reasoning, draw complex conclusions, and provide portable knowledge to help humans. Common applications of expert systems include medical diagnosis, materials identification, and credit approval.
The document discusses expert systems, which are designed to solve real problems in a particular domain that normally require human expertise. Developing an expert system involves extracting knowledge from domain experts. The key components of an expert system are the knowledge base, inference engine, explanation facility, knowledge acquisition facility, and user interface. Expert systems use knowledge rather than data to solve problems and can explain their reasoning. They have limitations such as being difficult to maintain and only applicable to narrow problems.
This workshop is a comprehensive introduction to the application of Generative AI in healthcare. It provides healthcare professionals, educators, and researchers with practical experience in using Generative AI for data analysis, predictive modeling, and personalized treatment planning. The workshop also explores the use of Generative AI in medical education and research. No prior AI experience is required, making this a unique opportunity to learn about the latest advancements in Generative AI and its healthcare applications.
The document discusses best practices for visualizing analytics results. It emphasizes that visualization is critical for effectively communicating insights from data analysis. Good visualizations exploit the human visual system by presenting information simply and clearly. Practitioners should understand their data and audience to develop visualizations that tell the right story. Iterative experimentation is important to arrive at visualizations that provide global understanding from the data. Overall the document stresses that visualization is a key part of deriving meaningful insights from analytics work.
The document discusses using artificial intelligence technologies in healthcare, noting opportunities for AI to enhance diagnosis, treatment planning, and research, but also challenges regarding governance, privacy, bias, and other issues. It provides an overview of different applications of AI in healthcare management, clinical decision-making, and patient data analysis, and emphasizes that AI should augment rather than replace human experts in medical fields. The workshop aims to educate participants on utilizing AI, specifically generative AI, in healthcare and medical education.
I probably should have titled this section Who Ensures that Digita.docxsalmonpybus
I probably should have titled this section Who Ensures that Digital Health Tools are Safe?. The Food and Drug Administration (FDA) is largely tasked with ensuring the safety and oversight of "traditional" medical devices. Consumer oriented technologies and many of the digital health tools available today present a more unique challenge for federal regulators. For example, software which reads digital mammograms is clearly a medical device intended to diagnose cancer. However, a mobile application which a user/consumer/patient leverages to track daily blood glucose levels is a very different approach. The mobile application is not making a diagnosis. There is limited risk of harm or change in medical management resulting from use. To address many of these nuances the FDA has created a
digital health resource page. The FDA often issues regulatory guidance documents in the event there is a question surrounding the legal authority provide oversight on a specific tool, device, or software component of a device.
FDA Approach to Risk
The FDA continues to evaluate technologies in the context of risk and classifies technologies in one of three tiers. It is important to note that any mobile app; regardless of if FDA approval is needed or not; may be submitted to the FDA for review. At times a vendor will submit to the FDA for review to increase the marketability of service or solution. The device itself is not required to be evaluated but the vendor chooses to do so anyway. The classes fall into one of the three following categories:
Class I - Low-risk where general reporting of adverse events occurs
Class II - Medium-risk where the FDA must provide review and clearance before marketing of the solution occurs. Review period takes between 60 to 90 days.
Class III - High risk where clearance before marketing must be obtained and the clearance must involve clinical studies showing that the product is safe and effective.
Mobile Medical Applications
The FDA has done a good job of clarifying the types of mobile apps that will not, will, and may be regulated by providing guidance documents. The FDA calls this document
Mobile Medical Applications Guidance. The aim of the guidance document is to clarify where the agency intends to enforce oversight and where the agency will continue to monitor developments. While the guidance document and website is extensive, there are a few key items worth mentioning. 1. The FDA defines what mobile medical applications
ARE. They can be Apps or accessory devices which meet the definition of a medical device (somewhat abridged and abbreviated for simplicity)- recognized by the National Formulary (ie Pharmaceuticals), intended for the use in the diagnosis of disease, cure, mitigation, treatment, or prevention of disease; or affect the structure or function of the body of man or other animals. 2. Consumers can use both medical apps and mobile apps to manage their o.
This document discusses an assignment submitted on expert system design. It begins with defining an expert system as a computer application that performs tasks done by human experts, such as medical diagnosis. It then outlines the advantages and disadvantages of using expert systems. Key advantages include providing consistent solutions, reasonable explanations, and overcoming human limitations. Disadvantages include lacking common sense, high costs, and difficulties creating inference rules. The document also differentiates expert systems from conventional computer systems and describes knowledge acquisition in expert systems as the process of extracting and organizing knowledge from human experts. It discusses forward and backward chaining and declarative versus procedural knowledge. Finally, it presents problems on analyzing a circuit output and applying laws of equivalence to a logical expression, and defining a
An outline of knowledge mining multi tier architecture for decision makingIAEME Publication
1. The document proposes a multi-tier architecture for a knowledge mining tool to support decision making.
2. It describes key aspects of decision making processes, knowledge management systems, and existing approaches to knowledge discovery in databases.
3. The proposed architecture is a three-tier system with graphical interface, client-side services, and centralized middle-tier services that control mining tasks and access data sources to produce results for users.
CompTIA exam study guide presentations by instructor Brian Ferrill, PACE-IT (Progressive, Accelerated Certifications for Employment in Information Technology)
"Funded by the Department of Labor, Employment and Training Administration, Grant #TC-23745-12-60-A-53"
Learn more about the PACE-IT Online program: www.edcc.edu/pace-it
An expert system is a computer application that uses specialized knowledge to guide complex tasks usually requiring human expertise. It uses an inference engine to apply logic rules to a knowledge base of facts to provide advice and explanations. Expert systems are used in fields like accounting, medicine, manufacturing, and human resources to consistently provide answers to repetitive decisions and processes while maintaining large stores of information. They have advantages like constant availability and ability to serve multiple users, but lack common sense reasoning and cannot adapt without changing the knowledge base.
Expert system prepared by fikirte and hayat im assignmentfikir getachew
The document discusses expert systems, their components, types, and uses. An expert system is an intelligent system that can perform complex tasks like a human expert. It consists of a knowledge base, inference engine, user interface, interpreter, and blackboard. Expert systems are classified based on their function, such as for interpretation, prediction, diagnosis, design, or planning. They can benefit industries and countries by advancing fields like agriculture, education, medicine, and more.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
1. 1 | P a g e
DECLARATION
I, Jayita Roy, student of MBA program, University of Kalyani of 2014-2016,
hereby declare that the dissertation entitle “Design and Implementation of a
Knowledge Based Clinical Decision Support System for Rural Women”
submitted for the degree of Master of Business Administration (IT) is my own
work.
I, also certify this report has not been submitted by me to any other University or
Institution for the award of any Degree or Diploma.
Jayita Roy
(University of Kalyani)
2. 2 | P a g e
CERTIFICATION
This is to certify that Jayita Roy student of Department of Business
Administration, University of Kalyani, Nadia, West Bengal, Registration No -
KU/PG/00528 of 2014-15 has successfully completed the project entitled –
“Design and Implementation of a Knowledge Based Clinical Decision Support
System for Rural Women” under the guidance of Prof. Manas Kumar Sanyal.
Prof. (Dr.) Manas Kumar Sanyal
(Head of the Department of Business Administration)
Department of Business Administration
University of Kalyani
Kalyani, Nadia-741235
3. 3 | P a g e
ACKNOWLEDGEMENT
At this juncture I feel deeply honored in expressing my sincere thanks to
Professor. (Dr.) Manas Kumar Sanyal (Head of Business Administration) of
University of Kalyani. The door to Prof. Sanyal’s office was always open
whenever I ran into a trouble spot or had a question about my dissertation. He
steered me in the right the direction whenever he thought I needed it.
I would also like to thank Professor. Satyajit Dhar, Professor. Isita Lahiri,
Dr. Tuhin Mukherjee and Thirupathi Chellapalli sir for their critical advice and
guidance, without which this project would not have been possible.
Last but not the least I place a deep sense of gratitude to my Maa, Papa and
Dr. Diganta Chatterjee who have been constant source of inspiration during the
preparation of this project work.
Jayita Roy
Department of Business Administration
University of Kalyani
Kalyani, Nadia-741235
(jayitamoscow@gmail.com)
4. 4 | P a g e
DEDICATION
To my father, who always wanted me to be a doctor! Papa, this is all what I
could do with technology yet.
5. 5 | P a g e
TABLE OF CONTENTS
Chapter No. Content Page No.
1 Introduction
Abstract and Objective
Decision Support System a General Approach
Knowledge Base
System Requirements
7
8-12
13-14
15
2 Analysis, Design and Development
System Development Life Cycle
Context level DFD
Level 1 DFD
E-R Diagram
Screen prints
17-19
20
21
22
23-32
3 Implementation
Coding 34-41
4 Testing and Validation 43-50
5 Conclusion 51
6 References 54
7. 7 | P a g e
INTRODUCTION
Abstract
Women health issues around the world were not considered as significant as
men's health simply because the paying clients were men and hence the doctors
tended to male healthcare. For example breast cancer is secondary in diagnostics
to prostate cancer even though prostate cancer is very late in a human male life
where as the breast cancer can be even in juvenile females. Particularly women of
rural and tribal areas experience extreme levels of health deprivation, due to non
accessibility to public health care and low quality of health care services. We
think that clinical decision support systems or medical expert systems which are
intelligent decision making systems using AI tools, Fuzzy Reasoning and
machine Language features, we can help.
Due to shortage of time this particular Clinical decision support system can
diagnose only FIVE diseases - Dysmenorrhea, Ovarian cysts, Endometriosis,
Polycystic ovarian disease (PCOD) and Urinary tract infection (UTI).
Objectives
To provide free health services to the rural women.
To have an easy and interactive user interface.
To develop a strong knowledge base.
To develop an efficient DSS (Decision Support System).
Accurate diagnosis of diseases.
To provide accurate information about medicine
If critical referring the patient to doctors or specialists.
8. 8 | P a g e
DECISION SUPPORT SYSTEM -
A GENERAL APPROACH
What are Decision Support Systems or Expert Systems?
The expert systems are the computer applications developed to solve complex
problems in a particular domain, at the level of extra-ordinary human
intelligence and expertise.
Characteristics of Expert Systems
High performance
Understandable
Reliable
Highly responsive
Capabilities of Expert Systems
The expert systems are capable of −
Advising
Instructing and assisting human in decision making
Demonstrating
Deriving a solution
Diagnosing
Explaining
Interpreting input
Predicting results
Justifying the conclusion
Suggesting alternative options to a problem
9. 9 | P a g e
They are incapable of −
Substituting human decision makers
Possessing human capabilities
Producing accurate output for inadequate knowledge base
Refining their own knowledge
Components of Expert Systems
The components of ES include −
Knowledge Base
Interface Engine
User Interface
Let us see them one by one briefly −
USER INTERFACEUSER
INTERFACE
ENGINE
KNOWLEDGE
BASE
10. 10 | P a g e
Knowledge Base: It contains domain-specific and high-quality knowledge.
Knowledge is required to exhibit intelligence. The success of any ES majorly
depends upon the collection of highly accurate and precise knowledge. Data,
information, and past experience combined together are termed as knowledge.
Components of Knowledge Base
The knowledge base of an ES is a store of both, factual and heuristic knowledge.
o Factual Knowledge − It is the information widely accepted by the
Knowledge Engineers and scholars in the task domain.
o Heuristic Knowledge − It is about practice, accurate judgment,
one’s ability of evaluation, and guessing.
Knowledge representation: It is the method used to organize and formalize
the knowledge in the knowledge base. It is in the form of IT-THEN-ELSE rules.
Knowledge Acquisition: The success of any expert system majorly depends
on the quality, completeness, and accuracy of the information stored in the
knowledge base.
The knowledge base is formed by readings from various experts, scholars, and
the Knowledge Engineers. The knowledge engineer is a person with the qualities
of empathy, quick learning, and case analyzing skills.
He or She acquires information from subject expert by recording, interviewing,
and observing him at work, etc. He then categorizes and organizes the
information in a meaningful way, in the form of IF-THEN-ELSE rules, to be used
by interference machine. The knowledge engineer also monitors the
development of the ES.
Interface Engine: Use of efficient procedures and rules by the Interface
Engine is essential in deducting a correct, flawless solution.
In case of knowledge-based ES, the Interface Engine acquires and manipulates
the knowledge from the knowledge base to arrive at a particular solution.
11. 11 | P a g e
In case of rule based ES, it −
o Applies rules repeatedly to the facts, which are obtained from earlier
rule application.
o Adds new knowledge into the knowledge base if required.
o Resolves rules conflict when multiple rules are applicable to a
particular case.
To recommend a solution, the interface engine uses the following strategies −
o Forward Chaining
o Backward Chaining
Forward Chaining: It is a strategy of an expert system to answer the question,
“What can happen next?”Here, the interface engine follows the chain of
conditions and derivations and finally deduces the outcome. It considers all the
facts and rules, and sorts them before concluding to a solution. This strategy is
followed for working on conclusion, result, or effect. For example, prediction of
share market status as an effect of changes in interest rates.
FACT 1
FACT 2
FACT 3
FACT 4
AND DECISION 1
DECISION 2OR
AND
FINAL
DECISION
Backward Chaining: With this strategy, an expert system finds out the answer
to the question, “Why this happened?” On the basis of what has already
happened, the interface engine tries to find out which conditions could have
happened in the past for this result. This strategy is followed for finding out
cause or reason. For example, diagnosis of Endometriosis in a woman.
12. 12 | P a g e
FACT 1
FACT 2
FACT 3
FACT 4
AND DECISION 1
DECISION 2OR
AND
FINAL
DECISION
User Interface: User interface provides interaction between user of the ES and
the ES itself. It is generally Natural Language Processing so as to be used by the
user who is well-versed in the task domain. The user of the ES need not be
necessarily an expert in Artificial Intelligence. It explains how the ES has arrived
at a particular recommendation. The explanation may appear in the following
forms −
o Natural language displayed on screen.
o Verbal narrations in natural language.
o Listing of rule numbers displayed on the screen.
The user interface makes it easy to trace the credibility of the deductions.
Requirements of Efficient ES User Interface
o It should help users to accomplish their goals in shortest possible
way.
o It should be designed to work for user’s existing or desired work
practices.
o Its technology should be adaptable to user’s requirements; not the
other way round.
o It should make efficient use of user input.
13. 13 | P a g e
KNOWLEDGE BASE
Symptoms Disease Medicine
1. Abdominal pain
2. Feeling pressure in
abdomen
3. Pain in lower back
4. Loose stools
5. Vomit (Sometimes)
Dysmenorrhea
'IBUPROFEN',
'PARACETAMOL' or
'DICYCLOMINE' for
pain, 'RANTAC D' for
Vomiting and/or Nausea
1. Abdominal bloating or
swelling
2. Pelvic pain before or
during menstrual cycle
3. Painful intercourse
4. Breast tenderness
5. Nausea
6. Vomiting
Ovarian cysts
'OVRAL L', 'MALA N' or
'MALA D' as Oral
contraceptive,
'IBUPROFEN',
'PARACETAMOL' or
'DICYCLOMINE' for
pain, 'RANTAC D' as
antacid
1. Painful Menstrual
Cramps
2. Blood or Pus in the
urine
Endometriosis
'IBUPROFEN',
'PARACETAMOL' or
'DICYCLOMINE' for
pain, 'RANTAC D' for
Vomiting and/or Nausea
1. Irregular menstrual
periods
2. Breathing problem
while sleeping
3. Acne and oily skin
4. Depression and mood
swings
Polycystic ovarian
disease (PCOD)
'ALDACTONE' for Acne
and Oily skin, 'CLOMID'
and 'METFORMIN
ORAL'
14. 14 | P a g e
Symptoms Disease Medicine
1. Frequent urination
2. Blood of pus in urine
3. Fever
4. Strong smelling urine
Urinary tract infection
(UTI)
'AZITHROMYCIN' or
'DOXYCYCLINE' as
antibiotics, 'RANTAC D'
as antacid
This above Knowledge Base is used in our Clinical Decision Support System for
implementation of the expert system.
15. 15 | P a g e
SYSTEM REQUERMENTS
System Requirements
Hardware requirements
Compute Processor : Intel Core i3
Mother Board : Dell A10
Memory : 1GB and above
CPU Speed : 2.66 GHz
RAM : 2GB
HDD : 100GB and above
Monitor : 15” colours (SVGA)
Keyboard
Mouse
Software requirements
Operating System : Windows7 or above
Application Software : Microsoft Visual Studio 2012
Database Requirement : SQL SERVER EXPRESS 2012
17. 17 | P a g e
SOFTWARE DEVELOPMENT LIFE CYCLE
The basic idea of System Development Life Cycle (SDLC) is a well defined
process by which a system is conceived, develop and implemented.
SDLC has two steps:
Systems analysis and
Systems design
Systems analysis involves
Problem identification
Feasibility study and cost benefit analysis
System requirement analysis
System design involves
System design
Coding
Testing
Maintenance
System Analysis
Problem Identification:
One of the most difficult tasks of system analysis is identifying the real
problem of the existing system. Without clear understanding of the problem
in the system, any further work done will lead to wastage of time and energy
at a later stage. Here several questions must be prepared before identifying
the correct problem at this stage. The question may include:
a) What is the actual problem?
b) What are the causes for this problem?
c) Is it important to solve this problem?
18. 18 | P a g e
d) How complex it is?
e) What are the possible solutions for this problem?
f) What type of benefits can be expected once the problem is
solved? and so on
Problem identification also includes identifying the possible opportunities
like real market potential, new technology etc. Before any further steps can
be taken up, the problem must be stated in clear and unambiguous words.
Feasibility Study and Cost benefit Analysis: Feasibly study is carried
out to determine whether it would be financially and technically feasible. It
helps to obtain an overview of the problem and to get rough assessment of
feasible solutions. There are three types of feasibility
a) Technical Feasibility
b) Economic feasibility and
c) Operations feasibility of the project.
Technical Feasibility: Can the work for the project be done with the present
equipment, current procedures, existing software technology and available
manpower? If new technology is needed what alternatives will be needed in the
present structure? This will require a close examination of the present system.
The technical feasibility will ask questions related to:
1. Adequate availability of technology
2. Availability of hardware, computer etc.
Economic feasibility: Economic feasibility analysis requires in making
approximate estimates of the resources required, cost of development,
development time for each of the options. These estimates are used as the basis
for comparing the different solutions.
Calculate the ROI [Return on Investment = Net Earnings / Total Investment.]
on present value method and arrival at the best possible iterative solution.
19. 19 | P a g e
Operational feasibility: Will the system be used if it is implemented? Will there
be any resistance from users? The existing employee normally worried about job
security, loss of jobs etc. whenever new systems are proposed.
Requirement Analysis and Specification: The aim of the requirement
analysis and specification phase is to understand the exact requirements of the
customer and to document it properly. This phase consist of two distinct
activities: Requirements gathering and Analysis and Requirement Specification.
Requirement Gathering and Analysis: The goal of the requirement gathering
and analysis is to collect all relevant information from the customer regarding
the product to be developed with a view to clearly understanding the customer
requirements and examine the incompleteness and inconsistencies in these
requirements. An inconsistent requirement is one where some parts of the
requirements may be omitted.
The requirement analysis activity is begun by collecting all relevant data
regarding the product to be developed from the users through Interview and
Discussions. During this activity the user requirements are systematically
organized into a software requirement specifications (SRS) document.
Software Requirement Specifications (SRS) : The important components of SRS
document are the final requirements, the non-formal requirements and the goals
of implements. Documenting the final requirements marks the Identification of
the functions to be supported by the system. Each function can be characterized
by the input data, the processing required on the input data and output to be
produced. The non-formal requirements identify the performance requirements,
the required standard to be followed etc. The SRS document is written using the
endeavor terminology so that the customer can understand the document. This
makes the document review & approval by the customer.
20. 20 | P a g e
SYSTEM DESIGN
Data Flow Diagram (DFD)
Context Level DFD
provides information provides fact
provides decision makes decision
USER
KNOWLEDGE
BASE
CLINICAL
DECISION
SUPPORT
SYSTEM
21. 21 | P a g e
Level 1 DFD
user name age & symptoms name age & symptoms
user id
symptoms
Disease detection
Disease detection
Medicines name Medicines name
Decision
user information
User
1.0
Collect
information
User_info
Knowledge
2.0
Problem
identification
3.0
Decision
4.0
Prints
coupon
22. 22 | P a g e
Entity Relationship Diagram
User
Knowledge Base
interacts
with
Makes
decision
for
provides
facts
Name
Age
Id
Symptoms
User interface
Disease_name Medicine_info
Stores_info Provides_info
23. 23 | P a g e
SCREEN PRINTS
Accepting name and age from user
40. 40 | P a g e
Part - II
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;
namespace WomenHealth
{
public partial class Form2 : Form
{
public Form2()
{
//this.printDocument1.PrintPage += new
System.Drawing.Printing.PrintPageEventHandler(this.OnPrintPage);
InitializeComponent();
}
private void label4_Click(object sender, EventArgs e)
{
}
private void Form2_Load(object sender, EventArgs e)
{
}
private void printDocument1_PrintPage(object sender,
System.Drawing.Printing.PrintPageEventArgs e)
{
e.Graphics.DrawImage(memoryImage, 0, 0);
}
[System.Runtime.InteropServices.DllImport("gdi32.dll")]
public static extern long BitBlt(IntPtr hdcDest, int nXDest, int
nYDest, int nWidth, int nHeight, IntPtr hdcSrc, int nXSrc, int nYSrc, int
dwRop);
private Bitmap memoryImage;
private void PrintScreen()
{
Graphics mygraphics = this.CreateGraphics();
41. 41 | P a g e
Size s = this.Size;
memoryImage = new Bitmap(s.Width, s.Height, mygraphics);
Graphics memoryGraphics = Graphics.FromImage(memoryImage);
IntPtr dc1 = mygraphics.GetHdc();
IntPtr dc2 = memoryGraphics.GetHdc();
BitBlt(dc2, 0, 0, this.ClientRectangle.Width,
this.ClientRectangle.Height, dc1, 0, 0, 13369376);
mygraphics.ReleaseHdc(dc1);
memoryGraphics.ReleaseHdc(dc2);
}
private void btnPrint_Click(object sender, EventArgs e)
{
PrintScreen();
printPreviewDialog1.ShowDialog();
this.Hide();
}
}
}
43. 43 | P a g e
TESTING
System testing is the stage of implementation, which is aimed at before live
operation commences. Testing is vital to the success of the system. Testing is the
process of executing a program with the explicit intention of findings errors that
is making the program fail. The tester may be analysts, programmer or a
specialist trained for software testing, is actually trying to make the program fail.
Analysts know that an effective testing program doesn’t guarantee system
reliability. Therefore reliability must be designed into the system.
Levels of Testing
Unit Testing: Unit testing is undertaken after a module has been coded
and successfully reviewed. Unit testing (or module testing) is the testing of
different units (or modules) of a system in isolation.
In order to test a single module, a complete environment is needed to
provide all that is necessary for execution of the module. That is, besides
the module under test itself, the following steps are needed in order to be
able to test the module:
o The procedures belonging to the other modules that the module
under test calls.
o Nonlocal data structure that the module accesses.
o A procedure to call the functions of the module under test with
appropriate parameters.
Modules required to provide the necessary environment (which either call
or are called by the module under test) is usually not available until they
too have been until they too have been unit tested, stubs and drivers are
designed to provide the complete environment for a module.
44. 44 | P a g e
Black-box Testing: Black Box Testing, also known as Behavioral Testing,
is a software testing method in which the internal structure/ design/
implementation of the item being tested is not known to the tester. These
tests can be functional or non-functional, though usually functional.
This method is named so because the software program, in the eyes of the
tester, is like a black box; inside which one cannot see. Two main
approaches of to designing black box test cases are:
o Equivalence class partitioning
o Boundary value analysis
Equivalence class partitioning: In this method the input domain data is
divided into different equivalence data classes. This method is typically
used to reduce the total number of test cases to a finite set of testable test
cases, still covering maximum requirements. In short it is the process of
taking all possible test cases and placing them into classes. One test value
is picked from each class while testing.
E.g.: If you are testing for an input box accepting numbers from 1 to 1000
then there is no use in writing thousand test cases for all 1000 valid input
numbers plus other test cases for invalid data. Using equivalence
partitioning method above test cases can be divided into three sets of input
data called as classes. Each test case is a representative of respective class.
45. 45 | P a g e
So in above example we can divide our test cases into three equivalence
classes of some valid and invalid inputs. Test cases for input box accepting
numbers between 1 and 1000 using Equivalence Partitioning:
1) One input data class with all valid inputs. Pick a single value from
range 1 to 1000 as a valid test case. If you select other values between
1 and 1000 then result is going to be same. So one test case for valid
input data should be sufficient.
2) Input data class with all values below lower limit i.e. any value
below 1, as a invalid input data test case.
3) Input data with any value greater than 1000 to represent third
invalid input class.
So using equivalence partitioning you have categorized all possible test
cases into three classes. Test cases with other values from any class should
give you the same result. We have selected one representative from every
input class to design our test cases. Test case values are selected in such a
way that largest number of attributes of equivalence class can be exercised.
Equivalence partitioning uses fewest test cases to cover maximum
requirements.
Boundary value analysis: It’s widely recognized that input values at the
extreme ends of input domain cause more errors in system. More
application errors occur at the boundaries of input domain. ‘Boundary
value analysis’ testing technique is used to identify errors at boundaries
rather than finding those exist in center of input domain.
Boundary value analysis is a next part of Equivalence partitioning for
designing test cases where test cases are selected at the edges of the
equivalence classes.
46. 46 | P a g e
Test cases for input box accepting numbers between 1 and 1000 using
Boundary value analysis:
1) Test cases with test data exactly as the input boundaries of input
domain i.e. values 1 and 1000 in our case.
2) Test data with values just below the extreme edges of input
domains i.e. values 0 and 999.
White-box Testing: White Box Testing (WBT) is also known as Code-Based
Testing or Structural Testing. White box testing is the software testing
method in which internal structure is being known to tester who is going
to test the software. In this method of testing the test cases are calculated
based on analysis internal structure of the system based on Code coverage,
branches coverage, paths coverage, condition Coverage etc.
White box testing involves the testing by looking at the internal structure
of the code & when you completely aware of the internal structure of the
code then you can run your test cases & check whether the system meet
requirements mentioned in the specification document. Based on derived
test cases the user exercised the test cases by giving the input to the system
& checking for expected outputs with actual output. In this is testing
method user has to go beyond the user interface to find the correctness of
the system.
Typically such method are used at Unit Testing of the code but this
different as Unit testing done by the developer & White Box Testing done
by the testers, this is learning the part of the code & finding out the
weakness in the software program under test.
For tester to test the software application under test is like a
white/transparent box where the inside of the box is clearly seen to the
tester (as tester is aware/access of the internal structure of the code), so
this method is called as White Box Testing.
47. 47 | P a g e
The White-box testing is one of the best method to find out the errors in
the software application in early stage of software development life cycle.
In this process the deriving the test cases is most important part. The test
case design strategy include such that all lines of the source code will be
executed at least once or all available functions are executed to complete
100% code coverage of testing.
Integration Testing: Once all the individual units are created and tested,
we start combining those “Unit Tested” modules and start doing the
integrated testing. So the meaning of Integration testing is quite straight
forward- Integrate/combine the unit tested module one by one and test
the behavior as a combined unit.
The main function or goal of Integration testing is to test the interfaces
between the units/modules.
The individual modules are first tested in isolation. Once the modules are
unit tested, they are integrated one by one, till all the modules are
integrated, to check the combinational behavior, and validate whether the
requirements are implemented correctly or not.
There are fundamentally 2 approaches for doing Integration testing:
o Bottom up approach
o Top down approach.
Bottom up approach : Bottom up testing, as the name suggests starts
from the lowest or the innermost unit of the application, and gradually
moves up. The Integration testing starts from the lowest module and
gradually progresses towards the upper modules of the application. This
integration continues till all the modules are integrated and the entire
application is tested as a single unit.
48. 48 | P a g e
In this case, modules B1C1, B1C2 & B2C1, B2C2 are the lowest module
which is unit tested. Module B1 & B2 are not yet developed. The
functionality of Module B1 and B2 is that, it calls the modules B1C1, B1C2
& B2C1, B2C2. Since B1 and B2 are not yet developed, we would need
some program or a “stimulator” which will call the B1C1, B1C2 & B2C1,
B2C2 modules. These stimulator programs are called DRIVERS.
In simple words, DRIVERS are the dummy programs which are used to
call the functions of the lowest module in case when the calling function
does not exists. Bottom up technique requires module driver to feed test
case input to the interface of the module being tested.
Advantage for this approach is that, if a major fault exists at the lowest
unit of the program, it is easier to detect it, and corrective measures can be
taken.
Disadvantage is that the main program actually does not exist until the last
module is integrated and tested. As a result, the higher level design flaws
will be detected only at the end.
Top down approach : This technique starts from the top most module
and gradually progress towards the lower modules. Only the top module
is unit tested in isolation. After this, the lower modules are integrated one
by one. The process is repeated until all the modules are integrated and
tested.
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In the context of our figure, testing starts from Module A, and lower
modules B1 and B2 are integrated one by one. Now here the lower
modules B1 and B2 are not actually available for integration. So in order to
test the top most modules A, we develop “STUBS”.
“Stubs” can be referred to as code a snippet which accepts the inputs /
requests from the top module and returns the results/ response. This way,
in spite of the lower modules do not exist, we are able to test the top
module.
Interface Testing: Interface Testing is performed to evaluate whether
systems or components pass data and control correctly to one another. It is
to verify if all the interactions between these modules are working
properly and errors are handled properly.
Installation Testing: It is performed to verify if the software has been
installed with all the necessary components and the application is working
as expected. This is very important as installation would be the first user
interaction with the end users.
Uninstallation Testing: Uninstallation testing is performed to verify if
all the components of the application is removed during the process or
NOT. All the files related to the application along with its folder structure
have to be removed upon successful uninstallation. Post Uninstallation
System should be able to go back to the stable state.
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Validation Testing: The process of evaluating software during the
development process or at the end of the development process to
determine whether it satisfies specified business requirements. Validation
Testing ensures that the product actually meets the client's needs. It can
also be defined as to demonstrate that the product fulfills its intended use
when deployed on appropriate environment. It answers to the question,
Are we building the right product?
System Testing: System Testing (ST) is a black box testing technique
performed to evaluate the complete system the system's compliance
against specified requirements. In System testing, the functionalities of the
system are tested from an end-to-end perspective.. Some approaches of
this testing are:
1) α testing: It is the system testing performed by the development team.
2) β testing: It is the system testing performed by a friendly set of
customer.
3) acceptance testing: It is the system testing performed by the customer
himself after the product delivery to determine whether to accept or reject
the delivered product.
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CONCLUSION
This Expert System is providing decisions only for FIVE diseases. Due to
shortage of time I could not work on the knowledge base. I would like to enlist
the features I would like to add in future to this system.
More symptoms.
Diagnosis of more diseases.
Providing more detailed diagnosis report.
Providing features for calling the ambulance directly through
system.
Providing list of doctors or specialist to consult.
Providing name of the hospitals.
Inclusion of above features will make the Clinical Decision Support System more
effective for rural women.
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REFERENCES
1. Mathew A. Stoecker, Steve Stein Microsoft.NET Framework 3.5-Windows Forms
Application Development : Microsoft Press
2. Herbert Schildt C# 3.0 Beginner’s guide : Mc Graw Hill
3. Christian Nagel, Bill Evjen, Jay Glynn, Morgan Skinner, Karli Watson
Professional C# 2008 : Wiley Publishing, Inc
4. Sir Stanley Davidson Davidson’s Principles and Practice of Medicine
22nd Edition : Elsevier Limited
5. Atef Darwish Contemporary Gynecologic Practice : InTech
6. Sudha Salhan Text Book of Gynecology : Jaypee
7. Oscar Schaeffer Atlas and Essentials of Gynecology : William Wood and Company
8. www.webmd.com
9. www.mayoclinic.org
10. www.stackoverflow.com
11. www.c-sharpcorner.com
12. www.tutorialspoint.com/sql
13. www.tutorialspoint.com/artificial_intelligence