AI Pub Date : 2024-04-05 DOI: 10.3390/ai5020023

Gülsüm Alicioğlu, Bo Sun

引用次数: 0

Single Image Super Resolution Using Deep Residual Learning 利用深度残差学习实现单张图像超分辨率

AI Pub Date : 2024-03-21 DOI: 10.3390/ai5010021

Moiz Hassan, K. Illanko, Xavier N Fernando

引用次数: 0

Trust-Aware Reflective Control for Fault-Resilient Dynamic Task Response in Human–Swarm Cooperation 面向人群合作中故障弹性动态任务响应的信任意识反射控制

AI Pub Date : 2024-03-21 DOI: 10.3390/ai5010022

Yibei Guo, Yijiang Pang, Joseph Lyons, Michael Lewis, K. Sycara, Rui Liu

{"title":"Trust-Aware Reflective Control for Fault-Resilient Dynamic Task Response in Human–Swarm Cooperation","authors":"Yibei Guo, Yijiang Pang, Joseph Lyons, Michael Lewis, K. Sycara, Rui Liu","doi":"10.3390/ai5010022","DOIUrl":"https://doi.org/10.3390/ai5010022","url":null,"abstract":"Due to the complexity of real-world deployments, a robot swarm is required to dynamically respond to tasks such as tracking multiple vehicles and continuously searching for victims. Frequent task assignments eliminate the need for system calibration time, but they also introduce uncertainty from previous tasks, which can undermine swarm performance. Therefore, responding to dynamic tasks presents a significant challenge for a robot swarm compared to handling tasks one at a time. In human–human cooperation, trust plays a crucial role in understanding each other’s performance expectations and adjusting one’s behavior for better cooperation. Taking inspiration from human trust, this paper introduces a trust-aware reflective control method called “Trust-R”. Trust-R, based on a weighted mean subsequence reduced algorithm (WMSR) and human trust modeling, enables a swarm to self-reflect on its performance from a human perspective. It proactively corrects faulty behaviors at an early stage before human intervention, mitigating the negative influence of uncertainty accumulated from dynamic tasks. Three typical task scenarios {Scenario 1: flocking to the assigned destination; Scenario 2: a transition between destinations; and Scenario 3: emergent response} were designed in the real-gravity simulation environment, and a human user study with 145 volunteers was conducted. Trust-R significantly improves both swarm performance and trust in dynamic task scenarios, marking a pivotal step forward in integrating trust dynamics into swarm robotics.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140224103","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}

引用次数: 0

Few-Shot Fine-Grained Image Classification: A Comprehensive Review 少镜头精细图像分类：全面回顾

AI Pub Date : 2024-03-06 DOI: 10.3390/ai5010020

Jie Ren, Changmiao Li, Yaohui An, Weichuan Zhang, Changming Sun

引用次数: 0

A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring 全面评述人工智能技术，解决求职招聘中的算法偏见问题

AI Pub Date : 2024-02-07 DOI: 10.3390/ai5010019

Elham Albaroudi, Taha Mansouri, Ali Alameer

{"title":"A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring","authors":"Elham Albaroudi, Taha Mansouri, Ali Alameer","doi":"10.3390/ai5010019","DOIUrl":"https://doi.org/10.3390/ai5010019","url":null,"abstract":"The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms’ fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139858052","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}

引用次数: 0

A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring 全面评述人工智能技术，解决求职招聘中的算法偏见问题

AI Pub Date : 2024-02-07 DOI: 10.3390/ai5010019

Elham Albaroudi, Taha Mansouri, Ali Alameer

{"title":"A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring","authors":"Elham Albaroudi, Taha Mansouri, Ali Alameer","doi":"10.3390/ai5010019","DOIUrl":"https://doi.org/10.3390/ai5010019","url":null,"abstract":"The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms’ fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139798182","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}

引用次数: 0

Automated Classification of User Needs for Beginner User Experience Designers: A Kano Model and Text Analysis Approach Using Deep Learning 面向用户体验设计初学者的用户需求自动分类：使用深度学习的卡诺模型和文本分析方法

AI Pub Date : 2024-02-02 DOI: 10.3390/ai5010018

Zhejun Zhang, Huiying Chen, Ruonan Huang, Lihong Zhu, Shengling Ma, Larry Leifer, Wei Liu

{"title":"Automated Classification of User Needs for Beginner User Experience Designers: A Kano Model and Text Analysis Approach Using Deep Learning","authors":"Zhejun Zhang, Huiying Chen, Ruonan Huang, Lihong Zhu, Shengling Ma, Larry Leifer, Wei Liu","doi":"10.3390/ai5010018","DOIUrl":"https://doi.org/10.3390/ai5010018","url":null,"abstract":"This study introduces a novel tool for classifying user needs in user experience (UX) design, specifically tailored for beginners, with potential applications in education. The tool employs the Kano model, text analysis, and deep learning to classify user needs efficiently into four categories. The data for the study were collected through interviews and web crawling, yielding 19 user needs from Generation Z users (born between 1995 and 2009) of LEGO toys (Billund, Denmark). These needs were then categorized into must-be, one-dimensional, attractive, and indifferent needs through a Kano-based questionnaire survey. A dataset of over 3000 online comments was created through preprocessing and annotating, which was used to train and evaluate seven deep learning models. The most effective model, the Recurrent Convolutional Neural Network (RCNN), was employed to develop a graphical text classification tool that accurately outputs the corresponding category and probability of user input text according to the Kano model. A usability test compared the tool’s performance to the traditional affinity diagram method. The tool outperformed the affinity diagram method in six dimensions and outperformed three qualities of the User Experience Questionnaire (UEQ), indicating a superior UX. The tool also demonstrated a lower perceived workload, as measured using the NASA Task Load Index (NASA-TLX), and received a positive Net Promoter Score (NPS) of 23 from the participants. These findings underscore the potential of this tool as a valuable educational resource in UX design courses. It offers students a more efficient and engaging and less burdensome learning experience while seamlessly integrating artificial intelligence into UX design education. This study provides UX design beginners with a practical and intuitive tool, facilitating a deeper understanding of user needs and innovative design strategies.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139869814","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}

引用次数: 0

Automated Classification of User Needs for Beginner User Experience Designers: A Kano Model and Text Analysis Approach Using Deep Learning 面向用户体验设计初学者的用户需求自动分类：使用深度学习的卡诺模型和文本分析方法

AI Pub Date : 2024-02-02 DOI: 10.3390/ai5010018

Zhejun Zhang, Huiying Chen, Ruonan Huang, Lihong Zhu, Shengling Ma, Larry Leifer, Wei Liu

{"title":"Automated Classification of User Needs for Beginner User Experience Designers: A Kano Model and Text Analysis Approach Using Deep Learning","authors":"Zhejun Zhang, Huiying Chen, Ruonan Huang, Lihong Zhu, Shengling Ma, Larry Leifer, Wei Liu","doi":"10.3390/ai5010018","DOIUrl":"https://doi.org/10.3390/ai5010018","url":null,"abstract":"This study introduces a novel tool for classifying user needs in user experience (UX) design, specifically tailored for beginners, with potential applications in education. The tool employs the Kano model, text analysis, and deep learning to classify user needs efficiently into four categories. The data for the study were collected through interviews and web crawling, yielding 19 user needs from Generation Z users (born between 1995 and 2009) of LEGO toys (Billund, Denmark). These needs were then categorized into must-be, one-dimensional, attractive, and indifferent needs through a Kano-based questionnaire survey. A dataset of over 3000 online comments was created through preprocessing and annotating, which was used to train and evaluate seven deep learning models. The most effective model, the Recurrent Convolutional Neural Network (RCNN), was employed to develop a graphical text classification tool that accurately outputs the corresponding category and probability of user input text according to the Kano model. A usability test compared the tool’s performance to the traditional affinity diagram method. The tool outperformed the affinity diagram method in six dimensions and outperformed three qualities of the User Experience Questionnaire (UEQ), indicating a superior UX. The tool also demonstrated a lower perceived workload, as measured using the NASA Task Load Index (NASA-TLX), and received a positive Net Promoter Score (NPS) of 23 from the participants. These findings underscore the potential of this tool as a valuable educational resource in UX design courses. It offers students a more efficient and engaging and less burdensome learning experience while seamlessly integrating artificial intelligence into UX design education. This study provides UX design beginners with a practical and intuitive tool, facilitating a deeper understanding of user needs and innovative design strategies.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139809990","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}

引用次数: 0

New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification 基于卷积神经网络和图卷积网络的新架构在阿尔茨海默病和痴呆症阶段分类中的人工智能应用

AI Pub Date : 2024-02-01 DOI: 10.3390/ai5010017

Md Easin Hasan, A. Wagler

{"title":"New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification","authors":"Md Easin Hasan, A. Wagler","doi":"10.3390/ai5010017","DOIUrl":"https://doi.org/10.3390/ai5010017","url":null,"abstract":"Neuroimaging experts in biotech industries can benefit from using cutting-edge artificial intelligence techniques for Alzheimer’s disease (AD)- and dementia-stage prediction, even though it is difficult to anticipate the precise stage of dementia and AD. Therefore, we propose a cutting-edge, computer-assisted method based on an advanced deep learning algorithm to differentiate between people with varying degrees of dementia, including healthy, very mild dementia, mild dementia, and moderate dementia classes. In this paper, four separate models were developed for classifying different dementia stages: convolutional neural networks (CNNs) built from scratch, pre-trained VGG16 with additional convolutional layers, graph convolutional networks (GCNs), and CNN-GCN models. The CNNs were implemented, and then the flattened layer output was fed to the GCN classifier, resulting in the proposed CNN-GCN architecture. A total of 6400 whole-brain medical reasoning imaging scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative database to train and evaluate the proposed methods. We applied the 5-fold cross-validation (CV) technique for all the models. We presented the results from the best fold out of the five folds in assessing the performance of the models developed in this study. Hence, for the best fold of the 5-fold CV, the above-mentioned models achieved an overall accuracy of 45.47%, 71.17%, 99.06%, and 100%, respectively. The CNN-GCN model, in particular, demonstrates excellent performance in classifying different stages of dementia. Understanding the stages of dementia can assist biotech industry researchers in uncovering molecular markers and pathways connected with each stage.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139876100","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}

引用次数: 0

New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification 基于卷积神经网络和图卷积网络的新架构在阿尔茨海默病和痴呆症阶段分类中的人工智能应用

AI Pub Date : 2024-02-01 DOI: 10.3390/ai5010017

Md Easin Hasan, A. Wagler

{"title":"New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification","authors":"Md Easin Hasan, A. Wagler","doi":"10.3390/ai5010017","DOIUrl":"https://doi.org/10.3390/ai5010017","url":null,"abstract":"Neuroimaging experts in biotech industries can benefit from using cutting-edge artificial intelligence techniques for Alzheimer’s disease (AD)- and dementia-stage prediction, even though it is difficult to anticipate the precise stage of dementia and AD. Therefore, we propose a cutting-edge, computer-assisted method based on an advanced deep learning algorithm to differentiate between people with varying degrees of dementia, including healthy, very mild dementia, mild dementia, and moderate dementia classes. In this paper, four separate models were developed for classifying different dementia stages: convolutional neural networks (CNNs) built from scratch, pre-trained VGG16 with additional convolutional layers, graph convolutional networks (GCNs), and CNN-GCN models. The CNNs were implemented, and then the flattened layer output was fed to the GCN classifier, resulting in the proposed CNN-GCN architecture. A total of 6400 whole-brain medical reasoning imaging scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative database to train and evaluate the proposed methods. We applied the 5-fold cross-validation (CV) technique for all the models. We presented the results from the best fold out of the five folds in assessing the performance of the models developed in this study. Hence, for the best fold of the 5-fold CV, the above-mentioned models achieved an overall accuracy of 45.47%, 71.17%, 99.06%, and 100%, respectively. The CNN-GCN model, in particular, demonstrates excellent performance in classifying different stages of dementia. Understanding the stages of dementia can assist biotech industry researchers in uncovering molecular markers and pathways connected with each stage.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139816376","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}

引用次数: 0

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