Artificial Intelligence Trends最新文献

A Context-Aware Vocabulary Management and Reading Assistance System using Machine Learning and Natural Language Processing 使用机器学习和自然语言处理的上下文感知词汇管理和阅读辅助系统

Artificial Intelligence Trends Pub Date : 2022-06-18 DOI: 10.5121/csit.2022.121007

Zhanhao Cao

{"title":"A Context-Aware Vocabulary Management and Reading Assistance System using Machine Learning and Natural Language Processing","authors":"Zhanhao Cao","doi":"10.5121/csit.2022.121007","DOIUrl":"https://doi.org/10.5121/csit.2022.121007","url":null,"abstract":"Through the increase in the popularity of online reading, many people rely on online dictionaries to further understand the text [1]. However, looking up a word manually is a great inconvenience as well as a form of distraction [2]. This paper develops a chrome extension to automatically detect the difficult words for each user, and provide the words’ associated definition with a mouse hover. The chrome extension can be customized by adding and removing personal difficult words and personal easy words [3]. Also, the chrome extension offers a deeper level of analytic, including the system analyzing part of speech of the world, to further understand the definition of a selected word or sentence. The chrome extension is applied to a school/work setting in order to improve the working efficiency by providing a simple model to analyze the word definition; it is also useful for casual reading, especially to those that aren’t fluent in English. Following the strict SDLC model, the end of the testing stage reflects that most of the users gave positive feedback to the chrome extension with most of the comments centered around convenience and accuracy [4]. Through alpha testing and a small sample of beta testing, the Chrome extension presents productivity improvement on difficult texts.","PeriodicalId":402252,"journal":{"name":"Artificial Intelligence Trends","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123217033","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

AI_Birder: An Intelligent Mobile Application to Automate Bird Classification using Artificial Intelligence and Deep Learning AI_Birder:一个使用人工智能和深度学习实现鸟类自动分类的智能移动应用程序

Artificial Intelligence Trends Pub Date : 2022-06-18 DOI: 10.5121/csit.2022.121005

Charles Tian, Yu Sun

{"title":"AI_Birder: An Intelligent Mobile Application to Automate Bird Classification using Artificial Intelligence and Deep Learning","authors":"Charles Tian, Yu Sun","doi":"10.5121/csit.2022.121005","DOIUrl":"https://doi.org/10.5121/csit.2022.121005","url":null,"abstract":"Birds are everywhere around us and are easy to spot. However, for many beginner birders, identifying the birds is a hard task [8]. There are many apps that help the birder to identify the birds, but they are often too complicated and require good internet to give a result. A better app is needed so that birders can identify birds while not depending on internet connection. My app, AI_Bider, is mainly built in android studio using flutter and firebase, and the AI engine is coded with TensorFlow and trained with images from the internet [9]. To test my AI engine, I made six different prototypes, each having a different number of times that the code will train from the dataset of pictures. I then selected 5 birds that are in my dataset and found 5 pictures on the internet for each of them, which I then uploaded to the app. My app will then give me 3 bird species that most closely resemble the image, as well as the app’s confidence in its choices, which are listed as percentages. I recorded down the percentages of accuracy for each picture. After taking the average percentage of all the models, I selected the most successful model, which had an average percent of accuracy of 79%.","PeriodicalId":402252,"journal":{"name":"Artificial Intelligence Trends","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115980116","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

Media Legitimacy Detection: A Data Science Approach to Locate Falsehoods and Bias using Supervised Machine Learning and Natural-Language Processing 媒体合法性检测:使用监督机器学习和自然语言处理定位谎言和偏见的数据科学方法

Artificial Intelligence Trends Pub Date : 2022-06-18 DOI: 10.5121/csit.2022.121003

N. Ji, Yu Sun

引用次数: 0

An Intelligent Sensor Mobile Phone Assisting System using AI and Machine Learning 基于人工智能和机器学习的智能传感器手机辅助系统

Artificial Intelligence Trends Pub Date : 2022-06-18 DOI: 10.5121/csit.2022.121004

Rui-qiong Liang, Yu Sun

{"title":"An Intelligent Sensor Mobile Phone Assisting System using AI and Machine Learning","authors":"Rui-qiong Liang, Yu Sun","doi":"10.5121/csit.2022.121004","DOIUrl":"https://doi.org/10.5121/csit.2022.121004","url":null,"abstract":"Technology is taking over the world [7]. Thus, how elderly people can request for help when they use a mobile device if there is anybody around them? In this paper, we address this issue by providing a system that can share and remote control a mobile device in real time [8]. An Android mobile app has been developed as an assistant tool. Thus, when a user needs help, she/he uses an unique ID, sends a request and shares the mobile screen, so the helper sees the sharing screen in his/her device and assists the person who needs help. We applied our application to data analysis and accurate measurements. For the accurate measurement, we conducted diverse experiments to observe the stability of use in different devices, and the influence of geographic, environmental, and network factors. The result shows there are no interrupts during the 30 experiments, which means that the system is stable for use and the network speed is the main factor which affects the average connection delay. For the data analysis, we advertised the Mobile App in communities and schools and received a total of 20 feedback questionnaires. We observe that users from 66 - 70 yield the highest positive score.","PeriodicalId":402252,"journal":{"name":"Artificial Intelligence Trends","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115638932","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

An Intelligent Mobile Application for Depression Relief using Artificial Intelligence and Natural Language Processing 基于人工智能和自然语言处理的抑郁症智能手机应用

Artificial Intelligence Trends Pub Date : 2022-06-18 DOI: 10.5121/csit.2022.121006

Zhishuo Zhang, Yu Sun, Ryan Yan

{"title":"An Intelligent Mobile Application for Depression Relief using Artificial Intelligence and Natural Language Processing","authors":"Zhishuo Zhang, Yu Sun, Ryan Yan","doi":"10.5121/csit.2022.121006","DOIUrl":"https://doi.org/10.5121/csit.2022.121006","url":null,"abstract":"“What is an simple yet effective method to improve the mental health of individuals?” is the question that we chose to tackle [7]. The solution that we came up with was having a deep conversation with another person. From personal experiences, having deep conversations with another person seemed to be one of the most effective ways to keep someone's mental health issues under control and maintain a more positive outlook on life. Sharing similar experiences with another person can demonstrate to people that they are not alone and there is always someone who can relate to them and lead them down the right path. In order to provide people with an easier method to have deep conversations with one another, we decided to create an application called Affinity, which was developed using Flutter [8]. In this application, users with various mental health issues will be able to talk with other users who have shared similar experiences. Users can connect to each other based on similar mental health issues, and they can engage in deep conversations with one another through a chat messaging system. We tested the results by providing twelve participants with two surveys. One survey measured a self-given score regarding the participant’s levels of stress and anxiety before using Affinity as well as after one week of using Affinity, and the other survey asks participants to tally the number of conversation partners that shared at least one mental health issue or experience with them compared to the total number of conversation partners. The results we have found are that daily usage of this application will generally reduce levels of stress and anxiety, and the majority of the individuals that the application will offer as conversation partners will be able to connect to a user through at least one additional similar shared experience or mental health issue.","PeriodicalId":402252,"journal":{"name":"Artificial Intelligence Trends","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126810359","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

CalixBoost: A Stock Market Index Predictor using Gradient Boosting Machines Ensemble CalixBoost:一个使用梯度增强机器集合的股票市场指数预测器

Artificial Intelligence Trends Pub Date : 2022-06-18 DOI: 10.5121/csit.2022.121009

Jarrett Yeo Shan Wei, Yeo Chai Kiat

{"title":"CalixBoost: A Stock Market Index Predictor using Gradient Boosting Machines Ensemble","authors":"Jarrett Yeo Shan Wei, Yeo Chai Kiat","doi":"10.5121/csit.2022.121009","DOIUrl":"https://doi.org/10.5121/csit.2022.121009","url":null,"abstract":"The potential of machine learning has sustained the interest of both academia and industry in stock market prediction over the past decade. This paper aims to integrate modern techniques such as Gradient Boosting Machines (GBMs) into a novel ensemble called CalixBoost which is a resource-efficient and accurate stock index predictor. Data comprising macro-economic metrics and technical financial indicators, as well as sentiment analysis of social media using a simple and fast but effective rule-based model are used in this paper. Other techniques include model tuning with Bayesian Optimization, temporal consistency analysis for invariant feature selection over random trial-and-error, feature importance and inter-feature relationships analysis using a unified game theory approach using Shapley values. Lastly, the model will be evaluated using a novel holdout method, viz. on two separate test datasets whose datapoints are collected under (i) normal economic activity and (ii) during a black swan (financial downturn). The experimental results show that our model outperforms previous methods and can achieve a good prediction performance with 84.88% accuracy, 0.0956 RMSE, 0.0573 MAE and 4.19% MAPE.","PeriodicalId":402252,"journal":{"name":"Artificial Intelligence Trends","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124393707","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

An Intelligent News-based Stock Pricing Prediction using AI and Natural Language Processing 利用人工智能和自然语言处理的基于新闻的智能股票定价预测

Artificial Intelligence Trends Pub Date : 2022-06-18 DOI: 10.5121/csit.2022.121011

Sirui Liu, Yu Sun

引用次数: 0

Identifying a Default of Credit Card Clients by using a LSTM Method: A Case Study 使用LSTM方法识别信用卡客户端违约:案例研究

Artificial Intelligence Trends Pub Date : 2022-06-18 DOI: 10.5121/csit.2022.121012

Jui-Yu Wu, Peiyan Liu

{"title":"Identifying a Default of Credit Card Clients by using a LSTM Method: A Case Study","authors":"Jui-Yu Wu, Peiyan Liu","doi":"10.5121/csit.2022.121012","DOIUrl":"https://doi.org/10.5121/csit.2022.121012","url":null,"abstract":"Detecting fraudulent transactions is critical and challenging for financial banks and institutes. This study used a deep learning technique, which is a long short-term memory (LSTM) method, for identifying a default of credit card clients (an imbalanced dataset). To evaluate the performance of optimizers for the LSTM approach, this study employed three optimizers based on gradient methods, such as adaptive moment estimation (Adam), stochastic gradient descent with momentum (Sgdm) and root mean square propagation (Rmsprop). This study used 10-fold cross-validation. Moreover, this study compared the best numerical results of the LSTM method with those of supervised machine learning classifiers, which are back-propagation neural network (BPNN) with a gradient descent algorithm (GDA) and a scaled conjugate gradient algorithm (SCGA). Numerical results indicate that the LSTM-Adam and the BPNN-SCGA classifiers have identical performance, and that selecting an appropriate classification threshold value is important for an imbalanced dataset. Based on the numerical results, the LSTM-Adam classifier can be considered for dealing with credit scoring problems, which are binary classification problems.","PeriodicalId":402252,"journal":{"name":"Artificial Intelligence Trends","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115301979","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

Video Content Development Guides based on Teaching Experiences 基于教学经验的视频内容开发指南

Artificial Intelligence Trends Pub Date : 2022-06-18 DOI: 10.5121/csit.2022.121001

Zolzaya Badamjav, U. Tudevdagva

{"title":"Video Content Development Guides based on Teaching Experiences","authors":"Zolzaya Badamjav, U. Tudevdagva","doi":"10.5121/csit.2022.121001","DOIUrl":"https://doi.org/10.5121/csit.2022.121001","url":null,"abstract":"This paper describes a research study on video content development. Due to the COVID-19 pandemic, all kinds of education and training switched from traditional classroom teaching to online and distance learning. The effect of e-learning will be the integral part of the higher education’s primary structure. The challenge of online teaching in higher education is to prepare learning materials for students with corresponding quality in various types. The video contents are one of important type of teaching and learning materials. This is one of most welcomed learning materials by students during online and distance teaching. Advantages of video contents are easy to follow focus of lesson, can hear and watch simultaneously, or just can hear if want, or just can watch if not possible to hear, more realistic, gives feeling like takes lesson in classroom. But, to prepare video contents requests a lot of time and preparation. It needs corresponding skills from teacher and it is costly. To support video content with high quality can be offer well defined guidance which helps to prepare good video contents. In this study authors are explained experience-oriented guidance for video content development.","PeriodicalId":402252,"journal":{"name":"Artificial Intelligence Trends","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130545062","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: 1

Enhancing Networking Cipher Algorithms with Natural Language 用自然语言增强网络密码算法

Artificial Intelligence Trends Pub Date : 2022-06-18 DOI: 10.5121/csit.2022.121013

John E. Ortega

引用次数: 0