Blockchain Technology (BT) is rapidly becoming one of the most promising emerging economy innovations. Financial Technology (FinTech) has been disrupted by blockchain, and its market size is growing by the day. Payments are closely related to banking, and blockchain has become very famous in the banking industry. This study aims to analyse the factors influencing behavioural intention to adopt blockchain in FinTech. Total 13 factors were extracted from the literature, and later relations among these variables were analysed using Interpretive Structural Modelling (ISM). The study's conceptual model was built and validated by academic experts working in blockchain. Later, MICMAC analysis was performed to study these variables' driving and dependence power. Blockchain has various challenges as well as opportunities but due to its advantages its implementation is recommended for FinTech. As per our results, the implementation of blockchain in FinTech is required as it promotes data privacy and traceability and involves more trust than traditional means.
{"title":"Factors of Blockchain Adoption for FinTech Sector: An Interpretive Structural Modelling Approach","authors":"Somya Gupta, Ganesh Prasad Sahu","doi":"10.14201/adcaij.28395","DOIUrl":"https://doi.org/10.14201/adcaij.28395","url":null,"abstract":"Blockchain Technology (BT) is rapidly becoming one of the most promising emerging economy innovations. Financial Technology (FinTech) has been disrupted by blockchain, and its market size is growing by the day. Payments are closely related to banking, and blockchain has become very famous in the banking industry. This study aims to analyse the factors influencing behavioural intention to adopt blockchain in FinTech. Total 13 factors were extracted from the literature, and later relations among these variables were analysed using Interpretive Structural Modelling (ISM). The study's conceptual model was built and validated by academic experts working in blockchain. Later, MICMAC analysis was performed to study these variables' driving and dependence power. Blockchain has various challenges as well as opportunities but due to its advantages its implementation is recommended for FinTech. As per our results, the implementation of blockchain in FinTech is required as it promotes data privacy and traceability and involves more trust than traditional means.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"26 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84161650","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}
This study examines the problem of Twitter sentimental analysis, which categorizes Tweets as positive or negative. Many applications require analyzing public mood, including organizations attempting to determine the market response to their products, political election forecasting, and macroeconomic phenomena such as stock exchange forecasting. Twitter is a social networking microblogging and digital platform that allows users to update their status in a maximum of 140 characters. It is a rapidly expanding platform with over 200 million registered users, 100 million active users, and half of the people log on every day, tweeting out over 250 million tweets. Public opinion analysis is critical for applications, including firms looking to understand market responses to their products, predict political choices, and forecast socio-economic phenomena like bonds. Through the deep learning methodologies, a recurrent neural network with convolutional neural network models was constructed to do Twitter sentiment analysis to predict if a tweet is positive or negative using a dataset of tweets. The applied methods were trained using a publicly available dataset of 1,600,000 tweets. Several model architectures were trained, with the best one achieving a (93.91%) success rate in recognizing the tweets' matching sentiment. The model's high success rate makes it a valuable advisor and a technique that might be improved to enable an integrated sentiment analyzer system that can work in real-world situations for political marketing.
{"title":"Analyzing Social Media Sentiment: Twitter as a Case Study","authors":"Y. Jasim, M. G. Saeed, M. Raewf","doi":"10.14201/adcaij.28394","DOIUrl":"https://doi.org/10.14201/adcaij.28394","url":null,"abstract":"This study examines the problem of Twitter sentimental analysis, which categorizes Tweets as positive or negative. Many applications require analyzing public mood, including organizations attempting to determine the market response to their products, political election forecasting, and macroeconomic phenomena such as stock exchange forecasting. Twitter is a social networking microblogging and digital platform that allows users to update their status in a maximum of 140 characters. It is a rapidly expanding platform with over 200 million registered users, 100 million active users, and half of the people log on every day, tweeting out over 250 million tweets. Public opinion analysis is critical for applications, including firms looking to understand market responses to their products, predict political choices, and forecast socio-economic phenomena like bonds. Through the deep learning methodologies, a recurrent neural network with convolutional neural network models was constructed to do Twitter sentiment analysis to predict if a tweet is positive or negative using a dataset of tweets. The applied methods were trained using a publicly available dataset of 1,600,000 tweets. Several model architectures were trained, with the best one achieving a (93.91%) success rate in recognizing the tweets' matching sentiment. The model's high success rate makes it a valuable advisor and a technique that might be improved to enable an integrated sentiment analyzer system that can work in real-world situations for political marketing.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"9 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77415638","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}
Vishakha Arya, A. Mishra, Alfonso González-Briones
The novel Coronavirus disease of 2019 (COVID-19) has subsequently named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have tormented the lives of millions of people worldwide. Effective and safe vaccination might curtail the pandemic. This study aims to apply the VADER lexicon, TextBlob and machine learning approach: to analyze and detect the ongoing sentiments during the affliction of the Covid-19 pandemic on Twitter, to understand public reaction worldwide towards vaccine and concerns about the effectiveness of the vaccine. Over 200000 tweets vaccine-related using hashtags #CovidVaccine #Vaccines #CornavirusVaccine were retrieved from 18 August 2020 to 20 July 2021. Data analysis conducted by VADER lexicon method to predict sentiments polarity, counts and sentiment distribution, TextBlob to determine the subjectivity and polarity, and also compared with two other models such as Random Forest (RF) and Logistic Regression (LR). The results determine sentiments that public have a positive stance towards a vaccine follows by neutral and negative. Machine learning classification models performed better than the VADER lexicon method on vaccine Tweets. It is anticipated this study aims to help the government in long run, to make policies and a better environment for people suffering from negative thoughts during the ongoing pandemic.
{"title":"Sentiments Analysis of Covid-19 Vaccine Tweets Using Machine Learning and Vader Lexicon Method","authors":"Vishakha Arya, A. Mishra, Alfonso González-Briones","doi":"10.14201/adcaij.27349","DOIUrl":"https://doi.org/10.14201/adcaij.27349","url":null,"abstract":"The novel Coronavirus disease of 2019 (COVID-19) has subsequently named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have tormented the lives of millions of people worldwide. Effective and safe vaccination might curtail the pandemic. This study aims to apply the VADER lexicon, TextBlob and machine learning approach: to analyze and detect the ongoing sentiments during the affliction of the Covid-19 pandemic on Twitter, to understand public reaction worldwide towards vaccine and concerns about the effectiveness of the vaccine. Over 200000 tweets vaccine-related using hashtags #CovidVaccine #Vaccines #CornavirusVaccine were retrieved from 18 August 2020 to 20 July 2021. Data analysis conducted by VADER lexicon method to predict sentiments polarity, counts and sentiment distribution, TextBlob to determine the subjectivity and polarity, and also compared with two other models such as Random Forest (RF) and Logistic Regression (LR). The results determine sentiments that public have a positive stance towards a vaccine follows by neutral and negative. Machine learning classification models performed better than the VADER lexicon method on vaccine Tweets. It is anticipated this study aims to help the government in long run, to make policies and a better environment for people suffering from negative thoughts during the ongoing pandemic.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"28 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74056826","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}
Muhammad Hamayon Khan Vardag, Ali Saeed, Umer Hayat, Muhammad Farhat Ullah, Naveed Hussain
Textual emotion detection aims to discover human emotions from written text. Textual emotion detection is a significant challenge due to the unavailability of facial and voice expressions. Considerable research has been done to identify textual emotions in high-resource languages such as English, French, Chinese, and others. Despite having over 300 million speakers and large volumes of literature available online, Urdu has not been properly investigated for the textual emotion detection task. To address this gap, this study makes two contributions: (1) the creation of a novel dialog-based corpus for Urdu (Contextual Urdu Text Emotion Detection Corpus). CUTEC contains 30,160 training and 5,509 testing labelled dialogues, where each dialogue consists of three Urdu contextual sentences. In addition, all dialogues are labelled using four emotion classes, i.e., Happy, Sad, Angry, and Other. As a second contribution (2) five deep learning models, i.e., RNN, LSTM, Bi- LSTM, GRU, and Bi-GRU have been trained and tested using CUTEC with different parametric settings. The highest results (Accuracy = 87.28 and F1 = 0.87) are attained using a GRU-based architecture.
{"title":"Contextual Urdu Text Emotion Detection Corpus and Experiments using Deep Learning Approaches","authors":"Muhammad Hamayon Khan Vardag, Ali Saeed, Umer Hayat, Muhammad Farhat Ullah, Naveed Hussain","doi":"10.14201/adcaij.30128","DOIUrl":"https://doi.org/10.14201/adcaij.30128","url":null,"abstract":"Textual emotion detection aims to discover human emotions from written text. Textual emotion detection is a significant challenge due to the unavailability of facial and voice expressions. Considerable research has been done to identify textual emotions in high-resource languages such as English, French, Chinese, and others. Despite having over 300 million speakers and large volumes of literature available online, Urdu has not been properly investigated for the textual emotion detection task. To address this gap, this study makes two contributions: (1) the creation of a novel dialog-based corpus for Urdu (Contextual Urdu Text Emotion Detection Corpus). CUTEC contains 30,160 training and 5,509 testing labelled dialogues, where each dialogue consists of three Urdu contextual sentences. In addition, all dialogues are labelled using four emotion classes, i.e., Happy, Sad, Angry, and Other. As a second contribution (2) five deep learning models, i.e., RNN, LSTM, Bi- LSTM, GRU, and Bi-GRU have been trained and tested using CUTEC with different parametric settings. The highest results (Accuracy = 87.28 and F1 = 0.87) are attained using a GRU-based architecture.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"82 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76107951","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}
Containerization is a technique for lightweight virtualization of programs in cloud computing, which leads to the widespread use of cloud computing. It has a positive impact on both the development and deployment of software. Containers can be divided into two groups based on their setup. The Application Container and the System Container are two types of containers. A container is a user-space that is contained within another container, while a system container is a user-space that is contained within another container. This study compares and contrasts several container architectures and their organization in micro-hosting environments for containers.
{"title":"Containerization and its Architectures: A Study","authors":"S. Verma, Brijesh Pandey, Bineet Kumar Gupta","doi":"10.14201/adcaij.28351","DOIUrl":"https://doi.org/10.14201/adcaij.28351","url":null,"abstract":"Containerization is a technique for lightweight virtualization of programs in cloud computing, which leads to the widespread use of cloud computing. It has a positive impact on both the development and deployment of software. Containers can be divided into two groups based on their setup. The Application Container and the System Container are two types of containers. A container is a user-space that is contained within another container, while a system container is a user-space that is contained within another container. This study compares and contrasts several container architectures and their organization in micro-hosting environments for containers.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"11 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85426960","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}
The need for a low power system on a chip for embedded systems has increased enormously for human to machine interaction. The primary constraint of such embedded system is to consume less power and improve the battery performance of the device. We propose energy efficient, low power hybrid 1-bit full adder circuit in this paper, which may be integrated on chip to improve the overall performance of embedded systems. The proposed 1-bit hybrid full adder circuit designed at 130 nm technology was simulated using Mentor Graphics EDA tool. Further, a comparison is made with the previously proposed full adders, using metrics such as power dissipation, propagation delay and power delay product. Comparative performance shows that the proposed 1-bit full adder shows average improvement in terms of power dissipation (31.62 nW and 20.84 nW) and average delay (5.07ns and 11.41ns) over the existing 1-bit hybrid and cell 3 full adder circuit.
{"title":"Performance Evaluation of Efficient Low Power 1-bit Hybrid Full Adder","authors":"R. Upadhyay, R. Chauhan, Manish Kumar","doi":"10.14201/adcaij.28558","DOIUrl":"https://doi.org/10.14201/adcaij.28558","url":null,"abstract":"The need for a low power system on a chip for embedded systems has increased enormously for human to machine interaction. The primary constraint of such embedded system is to consume less power and improve the battery performance of the device. We propose energy efficient, low power hybrid 1-bit full adder circuit in this paper, which may be integrated on chip to improve the overall performance of embedded systems. The proposed 1-bit hybrid full adder circuit designed at 130 nm technology was simulated using Mentor Graphics EDA tool. Further, a comparison is made with the previously proposed full adders, using metrics such as power dissipation, propagation delay and power delay product. Comparative performance shows that the proposed 1-bit full adder shows average improvement in terms of power dissipation (31.62 nW and 20.84 nW) and average delay (5.07ns and 11.41ns) over the existing 1-bit hybrid and cell 3 full adder circuit.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"70 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89867170","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 : 2023-04-29DOI: 10.5121/csit.2023.130712
Dinesh Kalla, Arvind Chandrasekaran
Heart disease is most common disease reported currently in the United States among both the genders and according to official statistics about fifty percent of the American population is suffering from some form of cardiovascular disease. This paper performs chi square tests and linear regression analysis to predict heart disease based on the symptoms like chest pain and dizziness. This paper will help healthcare sectors to provide better assistance for patients suffering from heart disease by predicting it in beginning stage of disease. Chi square test is conducted to identify whether there is a relation between chest pain and heart disease cases in the United States by analyzing heart disease dataset from IEEE Data Port. The test results and analysis show that males in the United States are most likely to develop heart disease with the symptoms like chest pain, dizziness, shortness of breath, fatigue, and nausea. This test also shows that there is a week corelation of 0.5 is identified which shows people with all ages including teens can face heart diseases and its prevalence increase with age. Also, the tests indicate that 90 percent of the participant who are facing severe chest pain is suffering from heart disease where majority of the successful heart disease identified is in males and only 10 percent participants are identified as healthy. The evaluated p-values are much greater than the statistical threshold of 0.05 which concludes factors like sex, Exercise angina, Cholesterol, old peak, ST_Slope, obesity, and blood sugar play significant role in onset of cardiovascular disease. We have tested the dataset with prediction model built on logistic regression and observed an accuracy of 85.12 percent.
{"title":"Heart Disease Prediction using Chi-Square Test and Linear Regression","authors":"Dinesh Kalla, Arvind Chandrasekaran","doi":"10.5121/csit.2023.130712","DOIUrl":"https://doi.org/10.5121/csit.2023.130712","url":null,"abstract":"Heart disease is most common disease reported currently in the United States among both the genders and according to official statistics about fifty percent of the American population is suffering from some form of cardiovascular disease. This paper performs chi square tests and linear regression analysis to predict heart disease based on the symptoms like chest pain and dizziness. This paper will help healthcare sectors to provide better assistance for patients suffering from heart disease by predicting it in beginning stage of disease. Chi square test is conducted to identify whether there is a relation between chest pain and heart disease cases in the United States by analyzing heart disease dataset from IEEE Data Port. The test results and analysis show that males in the United States are most likely to develop heart disease with the symptoms like chest pain, dizziness, shortness of breath, fatigue, and nausea. This test also shows that there is a week corelation of 0.5 is identified which shows people with all ages including teens can face heart diseases and its prevalence increase with age. Also, the tests indicate that 90 percent of the participant who are facing severe chest pain is suffering from heart disease where majority of the successful heart disease identified is in males and only 10 percent participants are identified as healthy. The evaluated p-values are much greater than the statistical threshold of 0.05 which concludes factors like sex, Exercise angina, Cholesterol, old peak, ST_Slope, obesity, and blood sugar play significant role in onset of cardiovascular disease. We have tested the dataset with prediction model built on logistic regression and observed an accuracy of 85.12 percent.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"95 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91353305","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 : 2023-04-29DOI: 10.5121/csit.2023.130701
Samira Sorayaasa, M. Ahmadi
Hyperbolic tangent (Tanh) activation function is used in multilayered artificial neural networks (ANN). This activation function contains exponential and division terms in its expressions which makes its accurate digital implementation difficult. In this paper we present two different approximation techniques for digital implementation of Tanh function using power of two and coordinate rotation digital computer (CORDIC) methods. A comparative study of both techniques in terms of accuracy of their approximations in hardware costs as well as their speed when implemented on FPGA is also explained
{"title":"Efficient Implementation of Tanh: A Comparative Study of New Results","authors":"Samira Sorayaasa, M. Ahmadi","doi":"10.5121/csit.2023.130701","DOIUrl":"https://doi.org/10.5121/csit.2023.130701","url":null,"abstract":"Hyperbolic tangent (Tanh) activation function is used in multilayered artificial neural networks (ANN). This activation function contains exponential and division terms in its expressions which makes its accurate digital implementation difficult. In this paper we present two different approximation techniques for digital implementation of Tanh function using power of two and coordinate rotation digital computer (CORDIC) methods. A comparative study of both techniques in terms of accuracy of their approximations in hardware costs as well as their speed when implemented on FPGA is also explained","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"68 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83866488","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 : 2023-04-29DOI: 10.5121/csit.2023.130704
Yu-Hua Mo, Chao Deng, Feijie Huang, Qian Tan, Yuan-Kun Li
In order to accurately predict the ef ect of new product cigarette marketing strategy.We take 18 months of cigarette sales data in city B of province A as the research sample, take new cigarette C as the researchobject, and use the random forest method to fix the errors and missing data. Then, we first use the mature cigarette brand's short-term historical sales and multiple labeling systems including the mature cigarette brand's historical sales data, retailer sales data, merchant circle crowd portrait data. Based on various machine learning method, we calculate the fitting weights of mature cigarettes to new cigarettes and thensimulate and predict the sales trend of new cigarettes. The application ef ect test found the accuracy of new cigarette sales prediction based on the traditional LSTM model was only 33.31%. In comparison, the prediction accuracy of the new model we constructed can reach 94.17%. We address the limitations encountered in new cigarette sales prediction, and fill the research gap in new cigarette launch models.
{"title":"Predicting New Cigarette Launch Strategy based on Synthetic Control Method","authors":"Yu-Hua Mo, Chao Deng, Feijie Huang, Qian Tan, Yuan-Kun Li","doi":"10.5121/csit.2023.130704","DOIUrl":"https://doi.org/10.5121/csit.2023.130704","url":null,"abstract":"In order to accurately predict the ef ect of new product cigarette marketing strategy.We take 18 months of cigarette sales data in city B of province A as the research sample, take new cigarette C as the researchobject, and use the random forest method to fix the errors and missing data. Then, we first use the mature cigarette brand's short-term historical sales and multiple labeling systems including the mature cigarette brand's historical sales data, retailer sales data, merchant circle crowd portrait data. Based on various machine learning method, we calculate the fitting weights of mature cigarettes to new cigarettes and thensimulate and predict the sales trend of new cigarettes. The application ef ect test found the accuracy of new cigarette sales prediction based on the traditional LSTM model was only 33.31%. In comparison, the prediction accuracy of the new model we constructed can reach 94.17%. We address the limitations encountered in new cigarette sales prediction, and fill the research gap in new cigarette launch models.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"25 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84868970","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 : 2023-04-29DOI: 10.5121/csit.2023.130706
Zixuan Cheng, Zihang Cheng, Ang Li
The paper discusses the issue of stress among students and proposes using lighting to alleviate stress levels [3]. The authors discuss various techniques for managing stress, including exercise, sleep, and socialization, and suggest that lighting can be used to address seasonal affective disorder (SAD) [4][5]. The paper outlines the challenges faced during the experiment and design, including creating a reliable survey, user interface design, and data privacy. The authors propose using a weighted score for survey responses and adopting simple designs for the app interface. The paper concludes by discussing the potential benefits of using lighting to alleviate stress levels and identifying areas for future research.
{"title":"Psychological Lights: An Intelligent LED System to Relief Youth Stress Level using AI and Internet of the things","authors":"Zixuan Cheng, Zihang Cheng, Ang Li","doi":"10.5121/csit.2023.130706","DOIUrl":"https://doi.org/10.5121/csit.2023.130706","url":null,"abstract":"The paper discusses the issue of stress among students and proposes using lighting to alleviate stress levels [3]. The authors discuss various techniques for managing stress, including exercise, sleep, and socialization, and suggest that lighting can be used to address seasonal affective disorder (SAD) [4][5]. The paper outlines the challenges faced during the experiment and design, including creating a reliable survey, user interface design, and data privacy. The authors propose using a weighted score for survey responses and adopting simple designs for the app interface. The paper concludes by discussing the potential benefits of using lighting to alleviate stress levels and identifying areas for future research.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"32 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87293970","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：