IAES International Journal of Artificial Intelligence (IJ-AI)最新文献_第4页

Stock market prediction employing ensemble methods: the Nifty50 index 采用集合方法预测股市：Nifty50 指数

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2049-2059

Chinthakunta Manjunath, Balamurugan Marimuthu, Bikramaditya Ghosh

{"title":"Stock market prediction employing ensemble methods: the Nifty50 index","authors":"Chinthakunta Manjunath, Balamurugan Marimuthu, Bikramaditya Ghosh","doi":"10.11591/ijai.v13.i2.pp2049-2059","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2049-2059","url":null,"abstract":"Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141233969","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

Low-resolution facial emotion recognition on low-cost devices 低成本设备上的低分辨率面部情绪识别

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2201-2211

M. D. Putro, Jane Litouw, V. Poekoel

引用次数: 0

Factor analysis influencing Mobile JKN user experience using sentiment analysis 利用情感分析影响移动 JKN 用户体验的因素分析

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1782-1793

Muhammad Yazid Al Qahar, Y. Ruldeviyani, Ulfah Nur Mukharomah, Miftahul Agtamas Fidyawan, Ramadhoni Putra

{"title":"Factor analysis influencing Mobile JKN user experience using sentiment analysis","authors":"Muhammad Yazid Al Qahar, Y. Ruldeviyani, Ulfah Nur Mukharomah, Miftahul Agtamas Fidyawan, Ramadhoni Putra","doi":"10.11591/ijai.v13.i2.pp1782-1793","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1782-1793","url":null,"abstract":"Social security administration for health or Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS Kesehatan), as a public legal entity, has a critical role in the health of the Indonesian population. BPJS Kesehatan introduced the Mobile national health insurance or jaminan kesehatan nasional (JKN) application to enhance its services, enabling Indonesians to access it directly. Nevertheless, the rating of the Mobile JKN application on the Google Play Store has shown a gradual decline over time. Therefore, this study was conducted to analyze the factors influencing the user experience of the Mobile JKN application, utilizing the review data obtained from the Google Play Store. Sentiment analysis using the Naïve Bayes (NB) classification model and support vector machine (SVM) combined with synthetic minority oversampling technique (SMOTE) and slang word replacement. The results obtained an accuracy value of 93.33%, precision of 93.76%, recall of 93.33%, and F1-score of 93.43%. A further analysis was conducted using online service quality factors to obtain the main factors influencing the experience of Mobile JKN application users. The evaluation findings revealed that factors of security, ease of use, and timeliness are three fundamental aspects that should be given immediate attention by BPJS Kesehatan while improving the Mobile JKN application in the future.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231144","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 multilingual semantic search chatbot framework 多语言语义搜索聊天机器人框架

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2333-2341

Vinay R, Thejas B U, H. A. V. Sharma, Shobha G, Poonam Ghuli

引用次数: 0

An auto-encoder bio medical signal transmission through custom convolutional neural network 通过定制卷积神经网络传输生物医疗信号的自动编码器

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1312-1325

Usha Muniraju, Thangamuthu Senthil Kumaran

{"title":"An auto-encoder bio medical signal transmission through custom convolutional neural network","authors":"Usha Muniraju, Thangamuthu Senthil Kumaran","doi":"10.11591/ijai.v13.i2.pp1312-1325","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1312-1325","url":null,"abstract":"The transmission of biomedical signals in real-time is extremely difficult and necessitates the use of cloud and internet of things (IoT) infrastructure. Security is also an important consideration, however, to achieve this, a reconstruction method is developed where the entire signal is fed as an input, just the primary portion, the entire signal is taken then encoded, and then deliver to the destination. It is unlocked using a reconstruction technique without any signal attenuation. The key difficulty is how to manage the sensor network once the input is prepared for transmission. This has problems with extremely high network energy consumption and accurate data collection. The accuracy of data reconstruction through is improved by compressive sensing. The lifespan of the network as a whole could be extended, in this study; the proposed proposed system convolutional neural network (PS-CNN) is an integrated model that combines feature selection and auto-encoder. In order to produce the most useful features for particular tasks, our algorithm can eventually separate the appropriate task units from the irrelevant tasks.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230085","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

User interface design of context-input-process-product evaluation application based on weighted product 基于加权产品的情境-输入-流程-产品评估应用程序的用户界面设计

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1388-1397

Dewa Gede, Hendra Divayana, A. Adiarta, N. Santiyadnya, P. Wayan, Arta Suyasa, M. Lissia, Andayani, I. Nyoman, Indhi Wiradika, I. Kadek, Arta Wiguna

{"title":"User interface design of context-input-process-product evaluation application based on weighted product","authors":"Dewa Gede, Hendra Divayana, A. Adiarta, N. Santiyadnya, P. Wayan, Arta Suyasa, M. Lissia, Andayani, I. Nyoman, Indhi Wiradika, I. Kadek, Arta Wiguna","doi":"10.11591/ijai.v13.i2.pp1388-1397","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1388-1397","url":null,"abstract":"This study aimed to show the user interface design form of the context-input-process-product (CIPP) evaluation application based on weighted product as a measuring tool for the effectiveness level of blended learning in health colleges. This research approach was development research. The development model used was Borg and Gall. It focused on the design stage, initial trials, and revisions. The initial test of the user interface design involved 32 respondents. The tool for conducting it was in the form of a questionnaire, which contains 16 questions. The research was at the health colleges in Buleleng Regency. The data analysis technique of the initial test results was quantitative descriptive. It compared the percentage level of user interface design quality from the weighted product-based CIPP evaluation application with a quality standard which referred to a five scale. The results of this study indicated that the quality of the user interface design was relatively good. The research result’s impact on educational evaluation was new knowledge for pedagogic evaluators in maximizing the development of digital-based evaluation tools by integrating the decision support system method (weighted product) with the educational evaluation model (CIPP model).","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141233412","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 novel fusion-based approach for the classification of packets in wireless body area networks 基于融合的无线体域网络数据包分类新方法

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1450-1458

Hanaa M. Mushgil, Khairiyah Saeed Abduljabbar, Baydaa Mohammad Mushgil

{"title":"A novel fusion-based approach for the classification of packets in wireless body area networks","authors":"Hanaa M. Mushgil, Khairiyah Saeed Abduljabbar, Baydaa Mohammad Mushgil","doi":"10.11591/ijai.v13.i2.pp1450-1458","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1450-1458","url":null,"abstract":"This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivotal role in expediting diagnosis and treatment processes during medical emergencies. This study introduces an innovative protocol termed collaborative binary Naive Bayes decision tree (CBNBDT) designed to enhance packet classification and transmission prioritization. Through the utilization of this protocol, incoming packets are categorized based on their respective classes, enabling subsequent prioritization. Thorough simulations have demonstrated the superior performance of the proposed CBNBDT protocol compared to baseline approaches.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141235307","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

Model for motivating learners with personalized learning objects in a hypermedia adaptive learning system 在超媒体自适应学习系统中利用个性化学习对象激励学习者的模型

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1282-1293

Chelliq Ikram, Anoir Lamya, Erradi Mohamed, Khaldi Mohamed

{"title":"Model for motivating learners with personalized learning objects in a hypermedia adaptive learning system","authors":"Chelliq Ikram, Anoir Lamya, Erradi Mohamed, Khaldi Mohamed","doi":"10.11591/ijai.v13.i2.pp1282-1293","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1282-1293","url":null,"abstract":"A number of weaknesses were demonstrated in the E-learning platforms during the Covid-19 pandemic despite the efforts invested. This has negatively influenced learners' motivation and consequently their performance. With the proliferation of technology and the revolution of information and communication technologies (ICT), learning objects have become new epitomes widely used, accessible, and implemented with educational resources and technological support. The integration of learning objects into E-learning has enhanced educational progress, but during critical periods, it is crucial to ensure pedagogical continuity and learner motivation. Based on this observation, we will propose architecture of a personalized learning object model in the context of an adaptive hypermedia learning system (AHS). The objective of our model is to increase the motivation factor which is a determining element in the success of E-learning, our model aims to improve the performance of the learners in order to avoid the abounding of learning and to promote the attendance of the learners. This will be useful later for any design or development of learning objects in hypermedia learning systems that are adaptive to the needs of the learners and in line with their preferences and profiles throughout the learning process offered by the system. ","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230837","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

Methodology for eliminating plain regions from captured images 从拍摄的图像中消除平原区域的方法

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1358-1370

Shiva Shankar Reddy, Vuddagiri MNSSVKR. Gupta, Lokavarapu V. Srinivas, Chigurupati Ravi Swaroop

{"title":"Methodology for eliminating plain regions from captured images","authors":"Shiva Shankar Reddy, Vuddagiri MNSSVKR. Gupta, Lokavarapu V. Srinivas, Chigurupati Ravi Swaroop","doi":"10.11591/ijai.v13.i2.pp1358-1370","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1358-1370","url":null,"abstract":"Finding relevant content and extracting information from images is highly significant. Still, it may be challenging to do so because of changes within the textual contents, such as typefaces, size, line orientation, sophisticated backgrounds in images, and non-uniform illuminations. Despite these challenges, extracting content from captured images is still very important. Proficient textual content image recognition abilities extract text from the images to get over these issues. Despite the availability of several optical character recognition (OCR) techniques, this issue has yet to be resolved. Captured images with text are a rich source of information that should be presented so that viewers may make informed decisions. Because of this, it has become a complicated process to extract the text from an image because the text might be of poor quality, has a variety of fonts and styles, and occasionally have a complicated backdrop, among other things. Several approaches have been tried. However, finding a solution remains challenging. The maximally stable external regions (MSER) approach is developed to identify the text region in a picture. MSER is utilized to elevate the plain regions outside the text and non-text areas using geometric features and stroke width variation qualities.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229737","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

Evaluation of Indonesia’s police public service platforms through sentiment and thematic analysis 通过情感和主题分析评估印度尼西亚警察公共服务平台

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1596-1607

Hasna Melani Puspasari, Ilham Zharif Mustaqim, Avita Tri Utami, Rahmad Syalevi, Y. Ruldeviyani

{"title":"Evaluation of Indonesia’s police public service platforms through sentiment and thematic analysis","authors":"Hasna Melani Puspasari, Ilham Zharif Mustaqim, Avita Tri Utami, Rahmad Syalevi, Y. Ruldeviyani","doi":"10.11591/ijai.v13.i2.pp1596-1607","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1596-1607","url":null,"abstract":"The Indonesian national police (Polri) offer public services through mobile apps: Digital korlantas polri (DigiKorlantas) and samsat digital nasional (SIGNAL). Sentiment analysis gauges public perceptions, serving as a basis for e-government evaluation using user ratings and comments from app stores. Keyword relevance is assessed via feature extraction and Naïve Bayes classification. Thematic analysis is implemented using N-grams methods to identify the factors affecting the effectiveness based on user experiences. The accuracy of the model reaches 81.09% where it indicates a high performance. DigiKorlantas acquires slightly more negative reviews in comparation with positive reviews which are 51% and 49% respectively. In contrast, positive sentiment is dominant on SIGNAL which reach 58%, compared with negative sentiment that in 42%. N-grams reveal similar review patterns for both apps. Some of the solutions are Korlantas Polri should enhance the verification functionality with several techniques such as retinex algorithms or optical character recognition pipeline and increase the capacity of supporting server then releasing an updated version of application to address errors or bugs. This analysis can be alternative evaluation by the Polri to measure the success of the application and find out the continuous improvement of the process and the system.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229482","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