User Answers Research Articles

Penerapan Metode Certainty Factor Untuk Mediagnosa Penyakit Depresi Pada Manusia Menggunakan PHP Dan Mysql

The purpose were design and develop software engineering web based expert system to diagnose depression on human to get solution and information easily and rapidly. This research used Certainty Factor of analyzed method base of user answer for question in this expert system. Output were showed as user condition related depression and were completed with disease explanation and medical solution which showed in website with PHP programming of MySQL database. The conclution in this research were PHP programming and MySQL database evidently could be implemented in expert system engineering to diagnose depression. Certainty Factor method evidently could commit searching depression symtomp easily and rapidly. Online system could support user to get information about kind of disease, symptom, and medical solution for depression.

DQueST: dynamic questionnaire for search of clinical trials.

ObjectiveInformation overload remains a challenge for patients seeking clinical trials. We present a novel system (DQueST) that reduces information overload for trial seekers using dynamic questionnaires.Materials and MethodsDQueST first performs information extraction and criteria library curation. DQueST transforms criteria narratives in the ClinicalTrials.gov repository into a structured format, normalizes clinical entities using standard concepts, clusters related criteria, and stores the resulting curated library. DQueST then implements a real-time dynamic question generation algorithm. During user interaction, the initial search is similar to a standard search engine, and then DQueST performs real-time dynamic question generation to select criteria from the library 1 at a time by maximizing its relevance score that reflects its ability to rule out ineligible trials. DQueST dynamically updates the remaining trial set by removing ineligible trials based on user responses to corresponding questions. The process iterates until users decide to stop and begin manually reviewing the remaining trials.ResultsIn simulation experiments initiated by 10 diseases, DQueST reduced information overload by filtering out 60%–80% of initial trials after 50 questions. Reviewing the generated questions against previous answers, on average, 79.7% of the questions were relevant to the queried conditions. By examining the eligibility of random samples of trials ruled out by DQueST, we estimate the accuracy of the filtering procedure is 63.7%. In a study using 5 mock patient profiles, DQueST on average retrieved trials with a 1.465 times higher density of eligible trials than an existing search engine. In a patient-centered usability evaluation, patients found DQueST useful, easy to use, and returning relevant results.ConclusionDQueST contributes a novel framework for transforming free-text eligibility criteria to questions and filtering out clinical trials based on user answers to questions dynamically. It promises to augment keyword-based methods to improve clinical trial search.

Integrating Multi-level Tag Recommendation with External Knowledge Bases for Automatic Question Answering

We focus on using natural language unstructured textual Knowledge Bases (KBs) to answer questions from community-based Question-and-Answer (Q8A) websites. We propose a novel framework that integrates multi-level tag recommendation with external KBs to retrieve the most relevant KB articles to answer user posted questions. Different from many existing efforts that primarily rely on the Q8A sites’ own historical data (e.g., user answers), retrieving answers from authoritative external KBs (e.g., online programming documentation repositories) has the potential to provide rich information to help users better understand the problem, acquire the knowledge, and hence avoid asking similar questions in future. The proposed multi-level tag recommendation best leverages the rich tag information by first categorizing them into different semantic levels based on their usage frequencies. A post-tag co-clustering model, augmented by a two-step tag recommender, is used to predict tags at different levels for a given user posted question. A KB article retrieval component leverages the recommended multi-level tags to select the appropriate KBs and search/rank the matching articles thereof. We conduct extensive experiments using real-world data from a Q8A site and multiple external KBs to demonstrate the effectiveness of the proposed question-answering framework.

HILC

Creating automation scripts for tasks involving Graphical User Interface (GUI) interactions is hard. It is challenging because not all software applications allow access to a program’s internal state, nor do they all have accessibility APIs. Although much of the internal state is exposed to the user through the GUI, it is hard to programmatically operate the GUI’s widgets. To that end, we developed a system prototype that learns by demonstration, called HILC (Help, It Looks Confusing). Users, both programmers and non-programmers, train HILC to synthesize a task script by demonstrating the task. A demonstration produces the needed screenshots and their corresponding mouse-keyboard signals. After the demonstration, the user answers follow-up questions. We propose a user-in-the-loop framework that learns to generate scripts of actions performed on visible elements of graphical applications. Although pure programming by demonstration is still unrealistic due to a computer’s limited understanding of user intentions, we use quantitative and qualitative experiments to show that non-programming users are willing and effective at answering follow-up queries posed by our system, to help with confusing parts of the demonstrations. Our models of events and appearances are surprisingly simple but are combined effectively to cope with varying amounts of supervision. The best available baseline, Sikuli Slides, struggled to assist users in the majority of the tests in our user study experiments. The prototype with our proposed approach successfully helped users accomplish simple linear tasks, complicated tasks (monitoring, looping, and mixed), and tasks that span across multiple applications. Even when both systems could ultimately perform a task, ours was trained and refined by the user in less time.

Diabetes Mellitus Diagnosis Expert System With Web-Based Forward Chaining

Diabetes Mellitus Diagnosis Expert System with Web-Based Forward Chaining Method. An expert system is one branch of artificial intelligence technology that combines the knowledge of an expert with tracing data to solve problems that are normally carried out by an expert. The development of information technology now makes it possible to access information from anywhere and anytime. So the role of information technology is increasingly useful to be able to develop in various fields including in the health sector. One of them is the system used to diagnose diabetes mellitus. Diabetes Mellitus is one of the most common diseases in the world. The inability of the pancreas to produce insulin normally becomes one of the reasons a person has this disease. In this research, the method used is Forward Chaining which sorts all data obtained before getting the final conclusion. The final results obtained from this study is an expert system application for diagnosing diabetes with a forward chaining method, where the user answers questions based on perceived symptoms, then the results obtained in the form of disease and explanation of the disease.

Developing an Expert System for Diagnosing Liver Diseases

The liver plays an important role in regulating the body's internal interactions as the largest and the most complex organs in the body. Such interactions are essential in preserving life and proper body function. The eaten foods are not consumed in the same way in the body. In fact, it should pass through the liver and after absorption and entering the blood, and the liver plays an important role in the storage of eaten substances. Accordingly, the speed of diagnosing and treating liver diseases are highly effective in improving patients' health; however, there is always a lack of immediate access to specialized physicians. The present study aims to provide an expert system using the VP-Expert shell for diagnosing liver disease. Also, block and Mockler charts have been used to provide the necessary knowledge in the expert system. In this regard, the factors affecting the diagnosis of the disease were firstly categorized into three groups of blood tests, clinical symptoms of illness and disease time on the block diagram for disease diagnose, and in the next step, the Mockler chart was designed according to the three groups of factors on the block chart. The three categories of disease diagnostic factors, questions corresponding sub-sections, and the response range are determined on Mockler chart, and finally, the tables are designed to express the system inference according to the user's answers to the questions.

Developing an Expert System for Diagnosing Liver Diseases

The liver plays an important role in regulating the body's internal interactions as the largest and the most complex organs in the body. Such interactions are essential in preserving life and proper body function. The eaten foods are not consumed in the same way in the body. In fact, it should pass through the liver and after absorption and entering the blood, and the liver plays an important role in the storage of eaten substances. Accordingly, the speed of diagnosing and treating liver diseases are highly effective in improving patients' health; however, there is always a lack of immediate access to specialized physicians. The present study aims to provide an expert system using the VP-Expert shell for diagnosing liver disease. Also, block and Mockler charts have been used to provide the necessary knowledge in the expert system. In this regard, the factors affecting the diagnosis of the disease were firstly categorized into three groups of blood tests, clinical symptoms of illness and disease time on the block diagram for disease diagnose, and in the next step, the Mockler chart was designed according to the three groups of factors on the block chart. The three categories of disease diagnostic factors, questions corresponding sub-sections, and the response range are determined on Mockler chart, and finally, the tables are designed to express the system inference according to the user's answers to the questions.

SmartQ: A Question and Answer System for Supplying High-Quality and Trustworthy Answers

Question and Answer (QA SmartQ incorporates a lightweight spammer detection method to identify potential spammers. In order to reduce the loads of experts, we propose a strategy to recommend suggested answers from similar questions to each new question. Our trace-driven simulation on PeerSim demonstrates the effectiveness of SmartQ in providing good user experience. We then develop a real application of SmartQ and deploy it for use in a student group in Clemson University. The user feedback shows that SmartQ can provide high-quality answers for users in a community.

Interactive Text2Pickup Networks for Natural Language-Based Human–Robot Collaboration

In this letter, we propose the Interactive Text2Pickup (IT2P) network for human-robot collaboration that enables an effective interaction with a human user despite the ambiguity in user's commands. We focus on the task where a robot is expected to pick up an object instructed by a human, and to interact with the human when the given instruction is vague. The proposed network understands the command from the human user and estimates the position of the desired object first. To handle the inherent ambiguity in human language commands, a suitable question which can resolve the ambiguity is generated. The user's answer to the question is combined with the initial command and given back to the network, resulting in more accurate estimation. The experiment results show that given unambiguous commands, the proposed method can estimate the position of the requested object with an accuracy of 98.49% based on the test dataset. Given ambiguous language commands, we show that the accuracy of the pick up task increases by 1.94 times after incorporating the information obtained from the interaction.

Interactive Game Development to Assist Cultural Heritage

Serious educational games provide a novel way to transfer knowledge on cultural heritage, which can especially attract young people by using interactive multimedia technology. The paper presents a new flexible tool for developing, managing and presenting games. This server-based solution can facilitate the game development by applying game templates, layout styles and question pools. It also supports the development of game packages for multiple languages and multiple platforms (Web and mobile). The games can easily be customised for various learning domains. Complex games can be composed from several socalled minigames (e.g. puzzle, multiple choice, memory game, crossword etc.) and the tool also supports the evaluation of the user answers and organising competitions. The games were applied to deepen knowledge on various topics related to our cultural heritage. The latest game development is represented by the serious educational game “The Thracians” running in a virtual 360º panorama environment, which presents the life, beliefs and traditions of the ancient tribes.

Declarative debugging of concurrent Erlang programs

Despite the growing number of concurrent applications in nowadays systems, debugging concurrent programs remains an open issue due to its intrinsic complexity. In this work, we present a declarative debugger for the concurrent language Erlang. Following the ideas of algorithmic debugging, our tool asks questions to the user about the validity of transitions between those program points that involve message passing, and also about the expected results of the function calls occurred during the computation. The differences between the user answers, which represent the intended behavior of the program, and the actual program execution, allow the debugger to detect the function responsible for the error, and to point out the pieces of source code responsible for the bugs. The proposal is based on a concurrent calculus for Erlang programs, and we show a benchmark that assesses its scalability both in terms of resources usage and considering the number of questions asked to the user and their complexity. Furthermore, we check the usability of our proposal, applying the debugger to the Erlang library poolboy.

Expert ranking techniques for online rated forums

Web 2.0 or social web applications such as online discussion forums, blogs and Wikipedia have improved knowledge sharing by providing an environment in which users can generate and find their favorite content in, a flexible way. With the passage of time, online discussion forums accumulate a huge amount of content and this can introduce issues of content quality and user credibility. A poor-quality answer in a discussion forum indicates the presence of unprofessional or unqualified users; therefore, a priority is to find experts or reputable users. Most of the existing expert-ranking approaches consider basic features, such as the total number of answers provided by a user, but ignore the quality and consistency of the user's answer. In this paper, expert-ranking techniques using g-index are proposed, and are applied to a StackOverflow forum dataset. Three techniques are proposed including Exp-PC, Rep-FS and Weighted Exp-PC. Exp-PC is an adaptation of g-index for ranking experts in StackOverflow forum. In Rep-FS, several features like voters reputation, vote ratio are proposed to measure users' expertise while Weighted Exp-PC computes user expertise by combining their Exp-PC and Rep-FS scores. We measure users' reputation and expertise according to both the quality of their answer and their consistency in providing quality answers. The experimental results of the proposed expert-ranking techniques, Exp-PC and Weighted Exp-PC in particular, validate that these methods identify genuine experts in a more effective way.

Evaluation of Standard Concepts Design of Library Interior Physical Environment

Currently the function of a room is not only used as a shelter, the function of the room itself to be increased as a refreshing or relaxation area for users to follow the development of creativity and technology in the field of design. The comfortable factor becomes the main factor that indicates a successful process of creating a space. No exception library. The nature of library seemed stiff because of its function as a place to read, now can be developed and made into more dynamic with the special design concepts or color patterns used. Libraries can be created a special concept that suits the characteristics of the users themselves. Most users of the library, especially in college libraries are teenagers. Naturally, teenagers like to gather with their friends and we have to facilitate this activity in our library design concept. In addition we can also determine the needs of users through research by questionnaire method. The answers of users can be mapped and drawn conclusions. To explore the research, the author reviewed some literature about library interior design and observed the library of Ma Chung University as a case study. The combined results of the method can be concluded and the discovery of ideal standards of physical environment. So, the library can be made as a comfortable reading environment so as to increased interest in reading behavior and the frequent visits of students in the library

Analyzing the Impact of Different Feature Queries in Active Learning for Social Robots

In recent years, the role of social robots is gaining popularity in our society but still learning from humans is a challenging problem that needs to be addressed. This paper presents an experiment where, after teaching poses to a robot, a group of users are asked several questions whose answers are used to create feature filters in the robot’s learning space. We study how the answers to different types of questions affect the learning accuracy of a social robot when it is trained to recognize human poses. We considered three types of questions: “Free Speech Queries”, “Yes/No Queries”, and “Rank Queries”, building a feature filter for each type of question. Besides, we provide another filter to help the robot to reduce the effects of inaccurate answers: the Extended Filter. We compare the performance of a robot that learned the same poses with Active Learning (using the four feature filters) versus Passive Learning (without filters). Our results show that, despite the fact that Active Learning can improve the robot’s learning accuracy, there are some cases where this approach, using the feature filters, achieves significant worse results than Passive Learning if the user provides inaccurate feedback when asked. However, the Extended Filter has proven to maintain the benefits of Active Learning even when the user answers are not accurate.

Feedback Big Data-Based Lie Robot

Existing robots only try to give reasonable answers to users, not considering if the answer can make user happy or not. This paper proposed a new kind of robot, lie robot. Lie robot does not like existing robots which always select answers for users according to the truth, lie robot select answers for users mainly according to the user preferences in order to make user happier. The main idea of lie robot is to predict the user feedbacks for answers based on the big data of the user past feedbacks detected by the lie robot.

DIETOS: A dietary recommender system for chronic diseases monitoring and management

Background and objectiveUse of mobile and web-based applications for diet and weight management is currently increasing. However, the impact of known apps on clinical outcomes is not well-characterized so far. Moreover, availability of food recommender systems providing high quality nutritional advices to both healthy and diet-related chronic diseases users is very limited. In addition, the potentiality of nutraceutical properties of typical regional foods for improving app utility has not been exerted to this end. We present DIETOS, a recommender system for the adaptive delivery of nutrition contents to improve the quality of life of both healthy subjects and patients with diet-related chronic diseases. DIETOS provides highly specialized nutritional advices in different health conditions. MethodsDIETOS was projected to provide users with health profile and individual nutritional recommendation. Health profiling was based on user answers to dynamic real-time medical questionnaires. Furthermore, DIETOS contains catalogs of typical foods from Calabria, a southern Italian region. Several Calabrian foods have been inserted because of their nutraceutical properties widely reported in several quality studies. DIETOS includes some well known methods for user profiling (overlay profiling) and content adaptation (content selection) coming from general purpose adaptive web systems. ResultsDIETOS has been validated for usability for both patients and specialists and for assessing the correctness of the profiling and recommendation, by enrolling 20 chronic kidney disease (CKD) patients at the Department of Nephrology and Dialysis, University Hospital, Catanzaro (Italy) and 20 age-matched healthy controls. Recruited subjects were invited to register to DIETOS and answer to medical questions to determine their health status. Based on our results, DIETOS has high specificity and sensitivity, allowing to determine a medical-controlled user’s health profile and to perform a fine-grained recommendation that is better adapted to each user health status. The current version of DIETOS, available online at http://www.easyanalysis.it/dietos is not intended to be used by general users, but only for review purpose. ConclusionsDIETOS is a novel food recommender system for healthy people and individuals affected by diet-related chronic diseases. The proposed system builds a users health profile and, accordingly, provides individualized nutritional recommendations, also with attention to food geographical origin.

Evaluation of Standard Concepts Design of Library Interior Physical Environment (Case Study at University of Ma Chung)

Currently the function of a room is not only used as a shelter, the function of the room itself to be increased as a refreshing or relaxation area for users to follow the development of creativity and technology in the field of design. The comfortable factor becomes the main factor that indicates a successful process of creating a space. No exception library. The nature of library seemed stiff because of its function as a place to read, now can be developed and made into more dynamic with the special design concepts or color patterns used. Libraries can be created a special concept that suits the characteristics of the users themselves. Most users of the library, especially in college libraries are teenagers. Naturally, teenagers like to gather with their friends and we have to facilitate this activity in our library design concept. In addition we can also determine the needs of users through research by questionnaire method. The answers of users can be mapped and drawn conclusions. To explore the research, the author reviewed some literature about library interior design and observed the library of Ma Chung University as a case study. The combined results of the method can be concluded and the discovery of ideal standards of physical environment. So, the library can be made as a comfortable reading environment so as to increased interest in reading behavior and the frequent visits of students in the library.

VICO: Ontology-based representation and integrative analysis of Vaccination Informed Consent forms.

BackgroundAlthough signing a vaccination (or immunization) informed consent form is not a federal requirement in the US and Canada, such a practice is required by many states and pharmacies. The content and structures of these informed consent forms vary, which makes it hard to compare and analyze without standardization. To facilitate vaccination informed consent data standardization and integration, it is important to examine various vaccination informed consent forms, patient answers, and consent results. In this study, we report a Vaccination Informed Consent Ontology (VICO) that extends the Informed Consent Ontology and integrates related OBO foundry ontologies, such as the Vaccine Ontology, with a focus on vaccination screening questionnaire in the vaccination informed consent domain.ResultsCurrent VICO contains 993 terms, including 248 VICO specific terms and 709 terms imported from 17 OBO Foundry ontologies. VICO ontologically represents and integrates 12 vaccination informed consent forms from the Walgreens, Costco pharmacies, Rite AID, University of Maryland College Park, and the government of Manitoba, Canada. VICO extends Informed Consent Ontology (ICO) with vaccination screening questionnaires and questions. Our use cases and examples demonstrate five usages of VICO. First, VICO provides standard, robust and consistent representation and organization of the knowledge in different vaccination informed consent forms, questionnaires, and questions. Second, VICO integrates prior knowledge, e.g., the knowledge of vaccine contraindications imported from the Vaccine Ontology (VO). Third, VICO helps manage the complexity of the domain knowledge using logically defined ontological hierarchies and axioms. VICO glues multiple schemas that represent complex vaccination informed consent contents defined in different organizations. Fourth, VICO supports efficient query and comparison, e.g., through the Description Language (DL)-Query and SPARQL. Fifth, VICO helps discover new knowledge. For instance, by integrating the prior knowledge imported from the VO with a user’s answer to informed consent questions (e.g., allergic reaction question) for a specific vaccination, we can infer whether or not the patient can be vaccinated with the vaccine.ConclusionsThe Vaccination Informed Consent Ontology (VICO) represents entities related to vaccination informed consents with a special focus on vaccination informed consent forms, and questionnaires and questions in the forms. Our use cases and examples demonstrated how VICO could support a platform for vaccination informed consent data standardization, data integration, and data queries.

Rancang Bangun Aplikasi Pembelajaran Baca Tulis Huruf Jawa Dengan Metode Rule Based

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Design of an expert system for diagnosis of thyroid cancer

Abstract. Expert systems has designed for the non-specialist people with the aim of make available of skills of specialist people.these plans simulation of throught pattern and the method of human performance, and make a relation ship between an expert system's function and human performance or a specialist person. Up to now different expert systems has provided in medical science ground and of this regard,it\s one of the pioneer sciences.Promptitude has effect sn diagnose and cure of thyroid cancer and recovery of patient condition,but sometimes access to specialiste is not available for patients.so design of a system with the knowledge of specialist provides an appropriate diagnosis to sicks. Method:this research provides an expert system for diagnosis of thyroid cancer, using C# and necessary knowledge for diagnose for diagnosis reserves in the form of rules in knowledge unit of system. system with due attention to user's answers shows the probability of cancer. Finding:Using intelligent software, if there are no experties, we can diagnose and show the way of treatment, to the reliable extent. Resulting: The use of intelligent and half-intelligent of systems such as expert systems, can provides for users in the form of companion's decision.However at present it can replace of experties, by no means .