Pub Date : 2023-08-28DOI: 10.3390/informatics10030070
Ashlin Lee
The digital humanities and social sciences are critical for addressing societal challenges such as climate change and disaster risk reduction. One way in which the digital humanities and social sciences add value, particularly in an increasingly digitised society, is by engaging different communities through digital services and products. Alongside this observation, the field of user experience (UX) has also become popular in industrial settings. UX specifically concerns designing and developing digital products and solutions, and, while it is popular in business and other academic domains, there is disquiet in the digital humanities/social sciences towards UX and a general lack of engagement. This paper shares the reflections and insights of a digital humanities/social science practitioner working on a UX project to build a prototype demonstrator for disaster risk reduction. Insights come from formal developmental and participatory evaluation activities, as well as qualitative self-reflection. The paper identifies lessons learnt, noting challenges experienced—including feelings of uncertainty and platform dependency—and reflects on the hesitancy practitioners may have and potential barriers in participation between UX and the digital humanities/social science. It concludes that digital humanities/social science practitioners have few skill barriers and offer a valued perspective, but unclear opportunities for critical engagement may present a barrier.
{"title":"What Is It Like to Make a Prototype? Practitioner Reflections on the Intersection of User Experience and Digital Humanities/Social Sciences during the Design and Delivery of the “Getting to Mount Resilience” Prototype","authors":"Ashlin Lee","doi":"10.3390/informatics10030070","DOIUrl":"https://doi.org/10.3390/informatics10030070","url":null,"abstract":"The digital humanities and social sciences are critical for addressing societal challenges such as climate change and disaster risk reduction. One way in which the digital humanities and social sciences add value, particularly in an increasingly digitised society, is by engaging different communities through digital services and products. Alongside this observation, the field of user experience (UX) has also become popular in industrial settings. UX specifically concerns designing and developing digital products and solutions, and, while it is popular in business and other academic domains, there is disquiet in the digital humanities/social sciences towards UX and a general lack of engagement. This paper shares the reflections and insights of a digital humanities/social science practitioner working on a UX project to build a prototype demonstrator for disaster risk reduction. Insights come from formal developmental and participatory evaluation activities, as well as qualitative self-reflection. The paper identifies lessons learnt, noting challenges experienced—including feelings of uncertainty and platform dependency—and reflects on the hesitancy practitioners may have and potential barriers in participation between UX and the digital humanities/social science. It concludes that digital humanities/social science practitioners have few skill barriers and offer a valued perspective, but unclear opportunities for critical engagement may present a barrier.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42635990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-26DOI: 10.3390/informatics10030069
B. Chaudhry, Shekufeh Shafeie, M.S.A. Mohamed
Theoretical models play a vital role in understanding the barriers and facilitators for the acceptance or rejection of emerging technologies. We conducted a narrative review of theoretical models predicting acceptance and adoption of human enhancement embeddable technologies to assess how well those models have studied unique attributes and qualities of embeddables and to identify gaps in the literature. Our broad search across multiple databases and Google Scholar identified 15 relevant articles published since 2016. We discovered that three main theoretical models: the technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), and cognitive–affective–normative (CAN) model have been consistently used and refined to explain the acceptance of human enhancement embeddable technology. Psychological constructs such as self-efficacy, motivation, self-determination, and demographic factors were also explored as mediating and moderating variables. Based on our analysis, we collated the verified determinants into a comprehensive model, modifying the CAN model. We also identified gaps in the literature and recommended a further exploration of design elements and psychological constructs. Additionally, we suggest investigating other models such as the matching person and technology model (MPTM), the hedonic-motivation system adoption model (HMSAM), and the value-based adoption model (VAM) to provide a more nuanced understanding of embeddable technologies’ adoption. Our study not only synthesizes the current state of research but also provides a robust framework for future investigations. By offering insights into the complex interplay of factors influencing the adoption of embeddable technologies, we contribute to the development of more effective strategies for design, implementation, and acceptance, thereby paving the way for the successful integration of these technologies into everyday life.
{"title":"Theoretical Models for Acceptance of Human Implantable Technologies: A Narrative Review","authors":"B. Chaudhry, Shekufeh Shafeie, M.S.A. Mohamed","doi":"10.3390/informatics10030069","DOIUrl":"https://doi.org/10.3390/informatics10030069","url":null,"abstract":"Theoretical models play a vital role in understanding the barriers and facilitators for the acceptance or rejection of emerging technologies. We conducted a narrative review of theoretical models predicting acceptance and adoption of human enhancement embeddable technologies to assess how well those models have studied unique attributes and qualities of embeddables and to identify gaps in the literature. Our broad search across multiple databases and Google Scholar identified 15 relevant articles published since 2016. We discovered that three main theoretical models: the technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), and cognitive–affective–normative (CAN) model have been consistently used and refined to explain the acceptance of human enhancement embeddable technology. Psychological constructs such as self-efficacy, motivation, self-determination, and demographic factors were also explored as mediating and moderating variables. Based on our analysis, we collated the verified determinants into a comprehensive model, modifying the CAN model. We also identified gaps in the literature and recommended a further exploration of design elements and psychological constructs. Additionally, we suggest investigating other models such as the matching person and technology model (MPTM), the hedonic-motivation system adoption model (HMSAM), and the value-based adoption model (VAM) to provide a more nuanced understanding of embeddable technologies’ adoption. Our study not only synthesizes the current state of research but also provides a robust framework for future investigations. By offering insights into the complex interplay of factors influencing the adoption of embeddable technologies, we contribute to the development of more effective strategies for design, implementation, and acceptance, thereby paving the way for the successful integration of these technologies into everyday life.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44682566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-25DOI: 10.3390/informatics10030068
Konstantinos I. Roumeliotis, N. Tselikas
In the present-day digital landscape, websites have increasingly relied on digital marketing practices, notably search engine optimization (SEO), as a vital component in promoting sustainable growth. The traffic a website receives directly determines its development and success. As such, website owners frequently engage the services of SEO experts to enhance their website’s visibility and increase traffic. These specialists employ premium SEO audit tools that crawl the website’s source code to identify structural changes necessary to comply with specific ranking criteria, commonly called SEO factors. Working collaboratively with developers, SEO specialists implement technical changes to the source code and await the results. The cost of purchasing premium SEO audit tools or hiring an SEO specialist typically ranges in the thousands of dollars per year. Against this backdrop, this research endeavors to provide an open-source Python-based Machine Learning SEO software tool to the general public, catering to the needs of both website owners and SEO specialists. The tool analyzes the top-ranking websites for a given search term, assessing their on-page and off-page SEO strategies, and provides recommendations to enhance a website’s performance to surpass its competition. The tool yields remarkable results, boosting average daily organic traffic from 10 to 143 visitors.
{"title":"A Machine Learning Python-Based Search Engine Optimization Audit Software","authors":"Konstantinos I. Roumeliotis, N. Tselikas","doi":"10.3390/informatics10030068","DOIUrl":"https://doi.org/10.3390/informatics10030068","url":null,"abstract":"In the present-day digital landscape, websites have increasingly relied on digital marketing practices, notably search engine optimization (SEO), as a vital component in promoting sustainable growth. The traffic a website receives directly determines its development and success. As such, website owners frequently engage the services of SEO experts to enhance their website’s visibility and increase traffic. These specialists employ premium SEO audit tools that crawl the website’s source code to identify structural changes necessary to comply with specific ranking criteria, commonly called SEO factors. Working collaboratively with developers, SEO specialists implement technical changes to the source code and await the results. The cost of purchasing premium SEO audit tools or hiring an SEO specialist typically ranges in the thousands of dollars per year. Against this backdrop, this research endeavors to provide an open-source Python-based Machine Learning SEO software tool to the general public, catering to the needs of both website owners and SEO specialists. The tool analyzes the top-ranking websites for a given search term, assessing their on-page and off-page SEO strategies, and provides recommendations to enhance a website’s performance to surpass its competition. The tool yields remarkable results, boosting average daily organic traffic from 10 to 143 visitors.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43648525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-18DOI: 10.3390/informatics10030066
Thanapong Champahom, Panuwat Wisutwattanasak, Chamroeun Se, Chinnakrit Banyong, Sajjakaj Jomnonkwao, V. Ratanavaraha
Among several approaches to analyzing crash research, the use of machine learning and econometric analysis has found potential in the analysis. This study aims to empirically examine factors influencing the single-vehicle crash for personal cars and trucks using decision trees (DT) and mixed binary logit with heterogeneity in means and variances (RPBLHMV) and compare model accuracy. The data in this study were obtained from the Department of Highway during 2011–2017, and the results indicated that the RPBLHMV was superior due to its higher overall prediction accuracy, sensitivity, and specificity values when compared to the DT model. According to the RPBLHMV results, car models showed that injury severity was associated with driver gender, seat belt, mount the island, defect equipment, and safety equipment. For the truck model, it was found that crashes located at intersections or medians, mounts on the island, and safety equipment have a significant influence on injury severity. DT results also showed that running off-road and hitting safety equipment can reduce the risk of death for car and truck drivers. This finding can illustrate the difference causing the dependent variable in each model. The RPBLHMV showed the ability to capture random parameters and unobserved heterogeneity. But DT can be easily used to provide variable importance and show which factor has the most significance by sequencing. Each model has advantages and disadvantages. The study findings can give relevant authorities choices for measures and policy improvement based on two analysis methods in accordance with their policy design. Therefore, whether advocating road safety or improving policy measures, the use of appropriate methods can increase operational efficiency.
{"title":"Analysis of Factors Associated with Highway Personal Car and Truck Run-Off-Road Crashes: Decision Tree and Mixed Logit Model with Heterogeneity in Means and Variances Approaches","authors":"Thanapong Champahom, Panuwat Wisutwattanasak, Chamroeun Se, Chinnakrit Banyong, Sajjakaj Jomnonkwao, V. Ratanavaraha","doi":"10.3390/informatics10030066","DOIUrl":"https://doi.org/10.3390/informatics10030066","url":null,"abstract":"Among several approaches to analyzing crash research, the use of machine learning and econometric analysis has found potential in the analysis. This study aims to empirically examine factors influencing the single-vehicle crash for personal cars and trucks using decision trees (DT) and mixed binary logit with heterogeneity in means and variances (RPBLHMV) and compare model accuracy. The data in this study were obtained from the Department of Highway during 2011–2017, and the results indicated that the RPBLHMV was superior due to its higher overall prediction accuracy, sensitivity, and specificity values when compared to the DT model. According to the RPBLHMV results, car models showed that injury severity was associated with driver gender, seat belt, mount the island, defect equipment, and safety equipment. For the truck model, it was found that crashes located at intersections or medians, mounts on the island, and safety equipment have a significant influence on injury severity. DT results also showed that running off-road and hitting safety equipment can reduce the risk of death for car and truck drivers. This finding can illustrate the difference causing the dependent variable in each model. The RPBLHMV showed the ability to capture random parameters and unobserved heterogeneity. But DT can be easily used to provide variable importance and show which factor has the most significance by sequencing. Each model has advantages and disadvantages. The study findings can give relevant authorities choices for measures and policy improvement based on two analysis methods in accordance with their policy design. Therefore, whether advocating road safety or improving policy measures, the use of appropriate methods can increase operational efficiency.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44963196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-18DOI: 10.3390/informatics10030067
Fatma Taher, Omar Al Fandi, Mousa Al Kfairy, Hussam Al Hamadi, S. Alrabaee
There are a variety of reasons why smartphones have grown so pervasive in our daily lives. While their benefits are undeniable, Android users must be vigilant against malicious apps. The goal of this study was to develop a broad framework for detecting Android malware using multiple deep learning classifiers; this framework was given the name DroidMDetection. To provide precise, dynamic, Android malware detection and clustering of different families of malware, the framework makes use of unique methodologies built based on deep learning and natural language processing (NLP) techniques. When compared to other similar works, DroidMDetection (1) uses API calls and intents in addition to the common permissions to accomplish broad malware analysis, (2) uses digests of features in which a deep auto-encoder generates to cluster the detected malware samples into malware family groups, and (3) benefits from both methods of feature extraction and selection. Numerous reference datasets were used to conduct in-depth analyses of the framework. DroidMDetection’s detection rate was high, and the created clusters were relatively consistent, no matter the evaluation parameters. DroidMDetection surpasses state-of-the-art solutions MaMaDroid, DroidMalwareDetector, MalDozer, and DroidAPIMiner across all metrics we used to measure their effectiveness.
{"title":"A Proposed Artificial Intelligence Model for Android-Malware Detection","authors":"Fatma Taher, Omar Al Fandi, Mousa Al Kfairy, Hussam Al Hamadi, S. Alrabaee","doi":"10.3390/informatics10030067","DOIUrl":"https://doi.org/10.3390/informatics10030067","url":null,"abstract":"There are a variety of reasons why smartphones have grown so pervasive in our daily lives. While their benefits are undeniable, Android users must be vigilant against malicious apps. The goal of this study was to develop a broad framework for detecting Android malware using multiple deep learning classifiers; this framework was given the name DroidMDetection. To provide precise, dynamic, Android malware detection and clustering of different families of malware, the framework makes use of unique methodologies built based on deep learning and natural language processing (NLP) techniques. When compared to other similar works, DroidMDetection (1) uses API calls and intents in addition to the common permissions to accomplish broad malware analysis, (2) uses digests of features in which a deep auto-encoder generates to cluster the detected malware samples into malware family groups, and (3) benefits from both methods of feature extraction and selection. Numerous reference datasets were used to conduct in-depth analyses of the framework. DroidMDetection’s detection rate was high, and the created clusters were relatively consistent, no matter the evaluation parameters. DroidMDetection surpasses state-of-the-art solutions MaMaDroid, DroidMalwareDetector, MalDozer, and DroidAPIMiner across all metrics we used to measure their effectiveness.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43687534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-08DOI: 10.3390/informatics10030065
Aditya Singhal, Vijay K. Mago
The use of Twitter by healthcare organizations is an effective means of disseminating medical information to the public. However, the content of tweets can be influenced by various factors, such as health emergencies and medical breakthroughs. In this study, we conducted a discourse analysis to better understand how public and private healthcare organizations use Twitter and the factors that influence the content of their tweets. Data were collected from the Twitter accounts of five private pharmaceutical companies, two US and two Canadian public health agencies, and the World Health Organization from 1 January 2020, to 31 December 2022. The study applied topic modeling and association rule mining to identify text patterns that influence the content of tweets across different Twitter accounts. The findings revealed that building a reputation on Twitter goes beyond just evaluating the popularity of a tweet in the online sphere. Topic modeling, when applied synchronously with hashtag and tagging analysis can provide an increase in tweet popularity. Additionally, the study showed differences in language use and style across the Twitter accounts’ categories and discussed how the impact of popular association rules could translate to significantly more user engagement. Overall, the results of this study provide insights into natural language processing for health literacy and present a way for organizations to structure their future content to ensure maximum public engagement.
{"title":"Exploring How Healthcare Organizations Use Twitter: A Discourse Analysis","authors":"Aditya Singhal, Vijay K. Mago","doi":"10.3390/informatics10030065","DOIUrl":"https://doi.org/10.3390/informatics10030065","url":null,"abstract":"The use of Twitter by healthcare organizations is an effective means of disseminating medical information to the public. However, the content of tweets can be influenced by various factors, such as health emergencies and medical breakthroughs. In this study, we conducted a discourse analysis to better understand how public and private healthcare organizations use Twitter and the factors that influence the content of their tweets. Data were collected from the Twitter accounts of five private pharmaceutical companies, two US and two Canadian public health agencies, and the World Health Organization from 1 January 2020, to 31 December 2022. The study applied topic modeling and association rule mining to identify text patterns that influence the content of tweets across different Twitter accounts. The findings revealed that building a reputation on Twitter goes beyond just evaluating the popularity of a tweet in the online sphere. Topic modeling, when applied synchronously with hashtag and tagging analysis can provide an increase in tweet popularity. Additionally, the study showed differences in language use and style across the Twitter accounts’ categories and discussed how the impact of popular association rules could translate to significantly more user engagement. Overall, the results of this study provide insights into natural language processing for health literacy and present a way for organizations to structure their future content to ensure maximum public engagement.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48427857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-02DOI: 10.3390/informatics10030064
P. Lahande, Parag Ravikant Kaveri, Jatinderkumar R. Saini
Cloud computing delivers robust computational services by processing tasks on its virtual machines (VMs) using resource-scheduling algorithms. The cloud’s existing algorithms provide limited results due to inappropriate resource scheduling. Additionally, these algorithms cannot process tasks generating faults while being computed. The primary reason for this is that these existing algorithms need an intelligence mechanism to enhance their abilities. To provide an intelligence mechanism to improve the resource-scheduling process and provision the fault-tolerance mechanism, an algorithm named reinforcement learning-shortest job first (RL-SJF) has been implemented by integrating the RL technique with the existing SJF algorithm. An experiment was conducted in a simulation platform to compare the working of RL-SJF with SJF, and challenging tasks were computed in multiple scenarios. The experimental results convey that the RL-SJF algorithm enhances the resource-scheduling process by improving the aggregate cost by 14.88% compared to the SJF algorithm. Additionally, the RL-SJF algorithm provided a fault-tolerance mechanism by computing 55.52% of the total tasks compared to 11.11% of the SJF algorithm. Thus, the RL-SJF algorithm improves the overall cloud performance and provides the ideal quality of service (QoS).
{"title":"Reinforcement Learning for Reducing the Interruptions and Increasing Fault Tolerance in the Cloud Environment","authors":"P. Lahande, Parag Ravikant Kaveri, Jatinderkumar R. Saini","doi":"10.3390/informatics10030064","DOIUrl":"https://doi.org/10.3390/informatics10030064","url":null,"abstract":"Cloud computing delivers robust computational services by processing tasks on its virtual machines (VMs) using resource-scheduling algorithms. The cloud’s existing algorithms provide limited results due to inappropriate resource scheduling. Additionally, these algorithms cannot process tasks generating faults while being computed. The primary reason for this is that these existing algorithms need an intelligence mechanism to enhance their abilities. To provide an intelligence mechanism to improve the resource-scheduling process and provision the fault-tolerance mechanism, an algorithm named reinforcement learning-shortest job first (RL-SJF) has been implemented by integrating the RL technique with the existing SJF algorithm. An experiment was conducted in a simulation platform to compare the working of RL-SJF with SJF, and challenging tasks were computed in multiple scenarios. The experimental results convey that the RL-SJF algorithm enhances the resource-scheduling process by improving the aggregate cost by 14.88% compared to the SJF algorithm. Additionally, the RL-SJF algorithm provided a fault-tolerance mechanism by computing 55.52% of the total tasks compared to 11.11% of the SJF algorithm. Thus, the RL-SJF algorithm improves the overall cloud performance and provides the ideal quality of service (QoS).","PeriodicalId":37100,"journal":{"name":"Informatics","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48904289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-21DOI: 10.3390/informatics10030063
Dimitris C. Gkikas, Prokopis K. Theodoridis, Theodoros Theodoridis, Marios C. Gkikas
This study aims to provide a method that will assist decision makers in managing large datasets, eliminating the decision risk and highlighting significant subsets of data with certain weight. Thus, binary decision tree (BDT) and genetic algorithm (GA) methods are combined using a wrapping technique. The BDT algorithm is used to classify data in a tree structure, while the GA is used to identify the best attribute combinations from a set of possible combinations, referred to as generations. The study seeks to address the problem of overfitting that may occur when classifying large datasets by reducing the number of attributes used in classification. Using the GA, the number of selected attributes is minimized, reducing the risk of overfitting. The algorithm produces many attribute sets that are classified using the BDT algorithm and are assigned a fitness number based on their accuracy. The fittest set of attributes, or chromosomes, as well as the BDTs, are then selected for further analysis. The training process uses the data of a chemical analysis of wines grown in the same region but derived from three different cultivars. The results demonstrate the effectiveness of this innovative approach in defining certain ingredients and weights of wine’s origin.
{"title":"Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification","authors":"Dimitris C. Gkikas, Prokopis K. Theodoridis, Theodoros Theodoridis, Marios C. Gkikas","doi":"10.3390/informatics10030063","DOIUrl":"https://doi.org/10.3390/informatics10030063","url":null,"abstract":"This study aims to provide a method that will assist decision makers in managing large datasets, eliminating the decision risk and highlighting significant subsets of data with certain weight. Thus, binary decision tree (BDT) and genetic algorithm (GA) methods are combined using a wrapping technique. The BDT algorithm is used to classify data in a tree structure, while the GA is used to identify the best attribute combinations from a set of possible combinations, referred to as generations. The study seeks to address the problem of overfitting that may occur when classifying large datasets by reducing the number of attributes used in classification. Using the GA, the number of selected attributes is minimized, reducing the risk of overfitting. The algorithm produces many attribute sets that are classified using the BDT algorithm and are assigned a fitness number based on their accuracy. The fittest set of attributes, or chromosomes, as well as the BDTs, are then selected for further analysis. The training process uses the data of a chemical analysis of wines grown in the same region but derived from three different cultivars. The results demonstrate the effectiveness of this innovative approach in defining certain ingredients and weights of wine’s origin.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47556918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-14DOI: 10.3390/informatics10030062
A. Berrones-Santos, F. Bagnoli
The dichotomy in power consumption between digital and biological information processing systems is an intriguing open question related at its core with the necessity for a more thorough understanding of the thermodynamics of the logic of computing. To contribute in this regard, we put forward a model that implements the Boltzmann machine (BM) approach to computation through an electric substrate under thermal fluctuations and dissipation. The resulting network has precisely defined statistical properties, which are consistent with the data that are accessible to the BM. It is shown that by the proposed model, it is possible to design neural-inspired logic gates capable of universal Turing computation under similar thermal conditions to those found in biological neural networks and with information processing and storage electric potentials at comparable scales.
{"title":"Biologically Plausible Boltzmann Machine","authors":"A. Berrones-Santos, F. Bagnoli","doi":"10.3390/informatics10030062","DOIUrl":"https://doi.org/10.3390/informatics10030062","url":null,"abstract":"The dichotomy in power consumption between digital and biological information processing systems is an intriguing open question related at its core with the necessity for a more thorough understanding of the thermodynamics of the logic of computing. To contribute in this regard, we put forward a model that implements the Boltzmann machine (BM) approach to computation through an electric substrate under thermal fluctuations and dissipation. The resulting network has precisely defined statistical properties, which are consistent with the data that are accessible to the BM. It is shown that by the proposed model, it is possible to design neural-inspired logic gates capable of universal Turing computation under similar thermal conditions to those found in biological neural networks and with information processing and storage electric potentials at comparable scales.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43635864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.3390/informatics10030061
Andrew W. Vargo, Benjamin Tag, Christopher Blakely, K. Kise
Online knowledge-based peer-production communities, like question and answer sites (Q&A), often rely on gamification, e.g., through reputation points, to incentivize users to contribute frequently and effectively. These gamification techniques are important for achieving the critical mass that sustains a community and enticing new users to join. However, aging communities tend to build “poverty traps” that act as barriers for new users. In this paper, we present our investigation of 32 domain communities from Stack Exchange and our analysis of how different subjects impact the development of early user advantage. Our results raise important questions about the accessibility of knowledge-based peer-production communities. We consider the analysis results in the context of changing information needs and the relevance of Q&A in the future. Our findings inform policy design for building more equitable knowledge-based peer-production communities and increasing the accessibility to existing ones.
{"title":"Poverty Traps in Online Knowledge-Based Peer-Production Communities","authors":"Andrew W. Vargo, Benjamin Tag, Christopher Blakely, K. Kise","doi":"10.3390/informatics10030061","DOIUrl":"https://doi.org/10.3390/informatics10030061","url":null,"abstract":"Online knowledge-based peer-production communities, like question and answer sites (Q&A), often rely on gamification, e.g., through reputation points, to incentivize users to contribute frequently and effectively. These gamification techniques are important for achieving the critical mass that sustains a community and enticing new users to join. However, aging communities tend to build “poverty traps” that act as barriers for new users. In this paper, we present our investigation of 32 domain communities from Stack Exchange and our analysis of how different subjects impact the development of early user advantage. Our results raise important questions about the accessibility of knowledge-based peer-production communities. We consider the analysis results in the context of changing information needs and the relevance of Q&A in the future. Our findings inform policy design for building more equitable knowledge-based peer-production communities and increasing the accessibility to existing ones.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41378613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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