Development of a Kansei Engineering Artificial Intelligence Sightseeing Application

Purpouse: Based on Kansei Engineering, the existing problems of smart seals are analyzed, and the perceptual needs of users of smart seals are extracted and transformed into the modeling features of smart seals. Methods: Based on the related theories of Kansei Engineering, use questionnaires and other methods to select perceptual vocabulary. Through data analysis and processing, a mapping relationship between perceptual vocabulary and modeling features is established, and modeling features that meet the perceptual needs of users are determined and their feasibility is proved by practice. Conclusion: Kansei engineering has positive significance for the design of smart seals, and it has a certain reference for the design of such products.

With the overwhelming increase in the amount of texts on the web, it is almost impossible for people to keep abreast of up-to-date information. Text mining is a process by which interesting information is derived from text through the discovery of patterns and trends. Text mining algorithms are used to guarantee the quality of extracted knowledge. However, the extracted patterns using text or data mining algorithms or methods leads to noisy patterns and inconsistency. Thus, different challenges arise, such as the question of how to understand these patterns, whether the model that has been used is suitable, and if all the patterns that have been extracted are relevant. Furthermore, the research raises the question of how to give a correct weight to the extracted knowledge. To address these issues, this paper presents a text post-processing method, which uses a pattern co-occurrence matrix to find the relation between extracted patterns in order to reduce noisy patterns. The main objective of this paper is not only reducing the number of closed sequential patterns, but also improving the performance of pattern mining as well. The experimental results on Reuters Corpus Volume 1 data collection and TREC filtering topics show that the proposed method is promising.

Background: While it is commonly recognized that almost every work and research discipline utilize their own taxonomy, the language used within a specific discipline may also vary depending on numerous factors, including the desired effect of the information being communicated and the intended audience. Different audiences are reached through publication of information, including research results, in different types of publication outlets such as newspapers, newsletters, magazines, websites, and journals. Prior research has shown that students, both undergraduate and graduate, as well as faculty may have a difficult time locating information in different publication outlet types (e.g., magazines, newspapers, journals). The type of publication may affect the ease of understanding and also the confidence placed in the acquired information. A text analytics tool for classifying the source of research as a newsletter (used as a substitute for newspaper articles), a magazine, or an academic journal article has been developed to assist students, faculty, and researchers in identifying the likely source type of information and classifying their own writings with respect to these possible publication outlet types. Literature Review: Literature on information literacy is discussed as this forms the motivation for the reported research. Additionally, prior research on using text mining and text analytics is examined to better understand the methodology employed, including a review of the original Scale of Theoretical and Applied Research system, adapted for the current research. Research Questions: The primary research question is: Can a text mining and text analytics approach accurately determine the most probable publication source type with respect to being from a newsletter, magazine, or journal? Methodology: A text mining and text analytics algorithm, STAR’ (System for Text Analytics-based Ranking), was developed from a previously researched text mining tool, STAR (Scale of Theoretical and Applied Research), that was used to classify the research type of articles between theoretical and applied research. The new text mining method, STAR’, analyzes the language used in manuscripts to determine the type of publication. This method first mines all words from corresponding publication source types to determine a keyword corpus. The corpus is then used in a text analytics process to classify full newsletters, magazine articles, and journal articles with respect to their publication source. All newsletters, magazine articles, and journal articles are from the library and information sciences (LIS) domain. Results: The STAR’ text analytics method was evaluated as a proof of concept on a specific LIS organizational newsletter, as well as articles from a single LIS magazine and a single LIS journal. STAR’ was able to classify the newsletters, magazine articles, and journal articles with 100% accuracy. Random samples from another similar LIS newsletter and a different LIS journal were also evaluated to examine the robustness of the STAR’ method in the initial proof of concept. Following the positive results of the proof of concept, additional journal, magazine, and newsletter articles were used to evaluate the generalizability of STAR’. The second-round results were very positive for differentiating journals and newsletters from other publication types, but revealed potential issues for distinguishing magazine articles from other types of publications. Discussion: STAR’ demonstrates that the language used for transferring information within a specific discipline does differ significantly depending on the intended recipients of the research knowledge. Further work is needed to examine language usage specific to magazine articles. Conclusions: The STAR’ method may be used by students and faculty to identify the likely source of research or discipline-specific information. This may improve trust in the reliability of information due to different levels of rigor applied to different types of publications. Additionally, the STAR’ classifications may be used by students, faculty, or researchers to determine the most appropriate type of outlet and correspondingly the most appropriate type of audience for the reported information in their own manuscripts, thereby improving the chance for successful sharing of information to appropriate audiences who will deem the information to be reliable, through publication in the most relevant outlet type.

This paper illustrates the design methods of machine tools based on the theory of Kansei Engineering. General attitudes towards the machine tools are measured by the Semantic Differential Method, the mapping between the design elements and the cognitive image is established through the Orthogonal Experiments, and the specific element style that significantly results in a certain image as well as the order of contribution degree from design elements towards images are obtained. Finally, the effectiveness of this method is verified by designing practice of a pipe processing machine.

We recently released a plugin to Pathvisio to assist the curator with automatic integration of heterogeneous pathway knowledge. Pathway curation involves gathering this knowledge not only through discussions with peers, but also from biological databases and the scientific literature. Integrating this all into one pathway is challenging and to date still mainly a laborious manual process.More and more biological databases provide webservices (ie. REST or SOAP). The main objective of webservices is to provide an interface for other computer applications. Our plug-in uses these interfaces to gather binary relationships of a given pathway entity and present the relation in star shaped figures to the curator. The plug-in also uses functionality of Pathvisio to project and visualize research data on pathways. If research data exists, this can then be projected on to the suggested potential novel entities. The curator can then make an educated decision on extending pathways with database knowledge.Different text mining methods exist to process large quantities of available literature. Text mining results can be very valuable in pathway curation. Integrating them in a similar way as with biological databases is difficult. It requires a text-mining output standard. In our current plug-in we included text-mining support in our current version of the plug-in.We like to propose here a standard for text mining output. The output standard is a data object that captures the relationships found with text mining, the document identifiers, the sentences from where the results originate, the method used, and its metrics. Such an output standard would enable a user-oriented comparison of text mining results. Currently, text-mining results are mainly evaluated on their performance in finding relevant results. By enabling a text mining out standard, the results become available to the larger user community. The data is immediately accessible to a wide range of applications. If a text mining method supports our output standard, the pathway curation community can use it in Pathvisio through our plug-in.

We recently released a plugin to Pathvisio to assist the curator with automatic integration of heterogeneous pathway knowledge. Pathway curation involves gathering this knowledge not only through discussions with peers, but also from biological databases and the scientific literature. Integrating this all into one pathway is challenging and to date still mainly a laborious manual process.More and more biological databases provide webservices (ie. REST or SOAP). The main objective of webservices is to provide an interface for other computer applications. Our plug-in uses these interfaces to gather binary relationships of a given pathway entity and present the relation in star shaped figures to the curator. The plug-in also uses functionality of Pathvisio to project and visualize research data on pathways. If research data exists, this can then be projected on to the suggested potential novel entities. The curator can then make an educated decision on extending pathways with database knowledge.Different text mining methods exist to process large quantities of available literature. Text mining results can be very valuable in pathway curation. Integrating them in a similar way as with biological databases is difficult. It requires a text-mining output standard. In our current plug-in we included text-mining support in our current version of the plug-in.We like to propose here a standard for text mining output. The output standard is a data object that captures the relationships found with text mining, the document identifiers, the sentences from where the results originate, the method used, and its metrics. Such an output standard would enable a user-oriented comparison of text mining results. Currently, text-mining results are mainly evaluated on their performance in finding relevant results. By enabling a text mining out standard, the results become available to the larger user community. The data is immediately accessible to a wide range of applications. If a text mining method supports our output standard, the pathway curation community can use it in Pathvisio through our plug-in.

Information could be power if most technological approach engaged in the era of IT. Web based text mining is one of the approach that could be analyzed in many ways through soft-computing methods and techniques. The analytical result has shown that different methods of text mining have several advantages with the gap of knowledge that is required to improve. This paper explores the performance of various methods and its impact on specific text mining field such as web based financial text analysis for stock prediction. Key research area of financial text mining is becoming one of the potential research field based on the source of online news, forums, blogs or social media.

<p><strong>A common problem often encountered in sanitary napkin packaging is inadequate product protection. Failure to open or reseal the packaging and the use of materials that cannot be reused are complaints from 97% of consumers regarding sanitary napkin products currently on the market. This research aims to analyze packaging design elements according to consumer preferences. In this research, the Kansei Engineering and Rough Set methods were used. Kansei Engineering has the advantage of being able to translate consumer emotions into design elements. The design element analysis process is carried out using the Rough Set method. The research results obtained consumer emotions in the form of 19 Kansei words and 29 packaging samples as design references. Analysis of sanitary napkin packaging design elements obtained recommendations in the form of cardboard material, folding cardboard packaging type, no additional features, box body, opening type with flap, medium size, feminine design, script typography, and image elements in the form of pictures and illustrations. This research succeeded in proving Kansei Engineering's ability to produce packaging designs according to consumer preferences through quantitative data analysis<em>.</em></strong></p><p><strong><em>Keywords</em></strong> - <em>Model Kano, Rough Set, Design Element, Packaging</em></p>

This paper aims to identify the trends in machine learning research using text mining. The researcharticles contain significant knowledge and research results. However, they are long and have many noisy results such that it takes a lot of human efforts to analyze them. Text mining can be used to analyze and extract useful information from a large number of research articles quickly and automatically. Text mining is the method of defining innovative, and unseen knowledge from unstructured, semi-structured and structured textual data. This knowledge contributed to very important information that can derive from textual data. In this paper, text mining methods are applied to detect trends of terms that occur in the research articles and how they varies over time. We collected 21,906 scientific papers from six top journals in the field of machine learning published in period 1988-2017 and analyzed them using text mining. Our result analysis shows a changing trend of various terms in Machine learning research in three decades. The analysis of our study helps the upcoming researchers to explore the significant research area of machine learning.

This study proposes a new model for service quality measurement using sentiment analysis and text mining techniques. This model aims to overcome traditional methods' time, cost and implementation difficulties and provide a more dynamic and efficient approach to service quality measurement. In addition, in this model, instead of the dimensions used in service quality measurements, such as SERVQUAL or SERVPERF, it is shown how to determine new categories and keywords specific to the service sector in which the model is used by text mining. Thus, it is aimed at something other than reaching more accurate results in service quality measurement. To achieve the model’s purpose, it aims to develop a service quality measurement model using social media data processed by text mining and sentiment analysis. To find an answer to this question, the keywords "flood", "meter", "rain", "irrigation", "infrastructure", "sewerage", "sewage", "maintenance hole ", "aski", "waterless", "water" were extracted from 109.844 tweets sent to the Twitter account of a municipality between 2016 and 2022 by text mining method. Service quality was measured by subjecting 5766 tweets containing the keywords extracted to sentiment analysis. As a result of the service quality measurement, 1922 negative, 973 positive and 2871 neutral tweets were identified. The average negative score was 0.51, the average positive score was 0.11, and the average neutral score was 0.38.

To introduce the research methods of computerized text mining and its possible applications in suicide research and to demonstrate the procedures of applying a specific text mining area, document classification, to a suicide-related study. A systematic search of academic papers that applied text mining methods to suicide research was conducted. Relevant papers were reviewed focusing on their research objectives and sources of data. Furthermore, a case of using natural language processing and document classification methods to analyze a large amount of suicide news was elaborated to showcase the methods. Eighty-six papers using text mining methods for suicide research have been published since 2001. The most common research objective (72.1%) was to classify which documents exhibit suicide risk or were written by suicidal people. The most frequently used data source was online social media posts (45.3%), followed by e-healthcare records (25.6%). For the news classification case, the top three classifiers trained for classification tasks achieved 84% or higher accuracy. Computerized text mining methods can help to scale up content analysis capacity and efficiency and uncover new insights and perspectives for suicide research.

The aim of the study was to examine the possibility of converting subjective textual data written in the free column space of the Mother and Child Handbook (MCH) into objective information using text mining and to compare any monthly changes in the words written by the mothers. Pregnant women without complications (n = 60) were divided into two groups according to State-Trait Anxiety Inventory grade: low trait anxiety (group I, n = 39) and high trait anxiety (group II, n = 21). Exploratory analysis of the textual data from the MCH was conducted by text mining using the Word Miner software program. Using 1203 structural elements extracted after processing, a comparison of monthly changes in the words used in the mothers' comments was made between the two groups. The data was mainly analyzed by a correspondence analysis. The structural elements in groups I and II were divided into seven and six clusters, respectively, by cluster analysis. Correspondence analysis revealed clear monthly changes in the words used in the mothers' comments as the pregnancy progressed in group I, whereas the association was not clear in group II. The text mining method was useful for exploratory analysis of the textual data obtained from pregnant women, and the monthly change in the words used in the mothers' comments as pregnancy progressed differed according to their degree of unease.

To provide scientific institutions with comprehensive and well-maintained documentation of their research information in a current research information system (CRIS), they have the best prerequisites for the implementation of text and data mining (TDM) methods. Using TDM helps to better identify and eliminate errors, improve the process, develop the business, and make informed decisions. In addition, TDM increases understanding of the data and its context. This not only improves the quality of the data itself, but also the institution’s handling of the data and consequently the analyses. This present paper deploys TDM in CRIS to analyze, quantify, and correct the unstructured data and its quality issues. Bad data leads to increased costs or wrong decisions. Ensuring high data quality is an essential requirement when creating a CRIS project. User acceptance in a CRIS depends, among other things, on data quality. Not only is the objective data quality the decisive criterion, but also the subjective quality that the individual user assigns to the data.

BackgroundInternet is a particularly dynamic way to quickly capture the perceptions of a population in real time. Complementary to traditional face-to-face communication, online social networks help patients to improve self-esteem and self-help.ObjectiveThe aim of this study was to use text mining on material from an online forum exploring patients’ concerns about treatment (antidepressants and anxiolytics).MethodsConcerns about treatment were collected from discussion titles in patients’ online community related to antidepressants and anxiolytics. To examine the content of these titles automatically, we used text mining methods, such as word frequency in a document-term matrix and co-occurrence of words using a network analysis. It was thus possible to identify topics discussed on the forum.ResultsThe forum included 2415 discussions on antidepressants and anxiolytics over a period of 3 years. After a preprocessing step, the text mining algorithm identified the 99 most frequently occurring words in titles, among which were escitalopram, withdrawal, antidepressant, venlafaxine, paroxetine, and effect. Patients’ concerns were related to antidepressant withdrawal, the need to share experience about symptoms, effects, and questions on weight gain with some drugs.ConclusionsPatients’ expression on the Internet is a potential additional resource in addressing patients’ concerns about treatment. Patient profiles are close to that of patients treated in psychiatry.

Emotional design is one of the most important methods in modern product design. It was highlighted after Donald A. Norman published his book Emotional design: why we love (or hate) everyday things. Emotional design addresses people’s needs and hopes which is in the center of the development of product design and product technology. Due to the lack of strong theoretical basis and quantitative analysis, many products can’t reach the goal of their designers with Emotional feelings, nor can consumer’s emotional understandings be similar to the designers’ emotional expectations. This paper analyzes the concept of emotional design and some difficulties which designers must to be faced in their emotional designing works, discusses on product shape design according to emotional design. On this basis, the Kansei engineering and emotional design methods are described in details.