Ana Shohibul Manshur Al Ahmad, Loso Judijanto, D. Tooy, Purnama Putra, Muhammad Hermansyah, Maria Kumalasanti, Alamsyah Agit
INTRODUCTION: This study introduces a groundbreaking approach that integrates Artificial Intelligence (AI) with macro-economic analysis to address a critical gap in existing economic forecasting methodologies. By leveraging diverse economic data sources, the study aims to transcend traditional analytical boundaries and provide a more comprehensive understanding of macroeconomic trends. OBJECTIVE: The primary objective is to pioneer a scalable framework for economic data analysis by combining AI with macroeconomic analysis. The study aims to utilize advanced machine learning algorithms to analyze and synthesize macroeconomic indicators, offering enhanced accuracy and predictive power. A key focus is on dynamically incorporating real-time data to adapt to evolving economic landscapes. METHODS: The research employs advanced machine learning algorithms to analyze and synthesize macroeconomic indicators. The integration of AI allows for a more nuanced understanding of complex economic dynamics. The methodology uniquely adapts to real-time data, providing a scalable framework for economic data analysis. RESULTS: The findings demonstrate the model's efficacy in predicting economic trends, surpassing conventional models in both precision and reliability. The study showcases the potential of AI-driven economic analysis to offer insights into economic dynamics with unprecedented accuracy. CONCLUSION: This study significantly contributes to the fields of AI and economics by proposing a transformative approach to macroeconomic analysis. The integration of technology and economics sets a new precedent, paving the way for future innovations in economic forecasting. The research also explores the implications of AI-driven economic analysis for policy-making, emphasizing its potential to inform more effective economic strategies.
{"title":"Integration of Artificial Intelligence and Macro-Economic Analysis: A Novel Approach with Distributed Information Systems","authors":"Ana Shohibul Manshur Al Ahmad, Loso Judijanto, D. Tooy, Purnama Putra, Muhammad Hermansyah, Maria Kumalasanti, Alamsyah Agit","doi":"10.4108/eetsis.4452","DOIUrl":"https://doi.org/10.4108/eetsis.4452","url":null,"abstract":"INTRODUCTION: This study introduces a groundbreaking approach that integrates Artificial Intelligence (AI) with macro-economic analysis to address a critical gap in existing economic forecasting methodologies. By leveraging diverse economic data sources, the study aims to transcend traditional analytical boundaries and provide a more comprehensive understanding of macroeconomic trends. OBJECTIVE: The primary objective is to pioneer a scalable framework for economic data analysis by combining AI with macroeconomic analysis. The study aims to utilize advanced machine learning algorithms to analyze and synthesize macroeconomic indicators, offering enhanced accuracy and predictive power. A key focus is on dynamically incorporating real-time data to adapt to evolving economic landscapes. METHODS: The research employs advanced machine learning algorithms to analyze and synthesize macroeconomic indicators. The integration of AI allows for a more nuanced understanding of complex economic dynamics. The methodology uniquely adapts to real-time data, providing a scalable framework for economic data analysis. RESULTS: The findings demonstrate the model's efficacy in predicting economic trends, surpassing conventional models in both precision and reliability. The study showcases the potential of AI-driven economic analysis to offer insights into economic dynamics with unprecedented accuracy. CONCLUSION: This study significantly contributes to the fields of AI and economics by proposing a transformative approach to macroeconomic analysis. The integration of technology and economics sets a new precedent, paving the way for future innovations in economic forecasting. The research also explores the implications of AI-driven economic analysis for policy-making, emphasizing its potential to inform more effective economic strategies.","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139249261","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 fundamental method for pre-processing speech or text data that enables computers to comprehend human language is known as natural language processing. Numerous models have been developed to date to pre-process data in the English language; however, the Hindi language does not support these models. India's national tongue is Hindi. In order to help the locals, the authors of this study used supervised learning methods like Linear Regression, SVM, and Naive Bayes algorithm to investigate a dataset of complicated terms in the Hindi language. Additionally, a sophisticated Hindi word classification model is suggested employing several methods based on the forecasts as well as collective learning strategies like Random Forest, Adaboost, and Decision Tree. Depending on how well the user's language is understood, the suggested model will assist in simplifying Hindi text. Authors attempt to classify the uncharted dataset using deep learning algorithms like Bi-LSTM and GRU approaches in further processing.
{"title":"A Novel Ensemble Model for Complex Entities Identification in Low Resource Language","authors":"Preeti Vats, Nonita Sharma, Deepak Kumar Sharma","doi":"10.4108/eetsis.4434","DOIUrl":"https://doi.org/10.4108/eetsis.4434","url":null,"abstract":"The fundamental method for pre-processing speech or text data that enables computers to comprehend human language is known as natural language processing. Numerous models have been developed to date to pre-process data in the English language; however, the Hindi language does not support these models. India's national tongue is Hindi. In order to help the locals, the authors of this study used supervised learning methods like Linear Regression, SVM, and Naive Bayes algorithm to investigate a dataset of complicated terms in the Hindi language. Additionally, a sophisticated Hindi word classification model is suggested employing several methods based on the forecasts as well as collective learning strategies like Random Forest, Adaboost, and Decision Tree. Depending on how well the user's language is understood, the suggested model will assist in simplifying Hindi text. Authors attempt to classify the uncharted dataset using deep learning algorithms like Bi-LSTM and GRU approaches in further processing.","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"51 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139251117","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}
These days movies are one of the most important part of entertainment industry and back in the days you could see everyday people standing outside theatres, or watching movies in OTT platforms. But due to busy schedules not many people are watching every movie. They go over the internet and search for top rated movies and go to theatres. And creating a successful movie is no easy job. Thus, this study helps movie producers to consider what are the important factors that influence a movie to be successful. this study applied neural network model to the IMDb dataset and then due to its complex nature in order to achieve the local explainability and global explainability for the enhanced analysis, study have used SHAP (Shapley additive explanations) to analysis.
如今，电影是娱乐业最重要的组成部分之一，在过去，你可以看到每天都有人站在影院门口，或在 OTT 平台上观看电影。但由于工作繁忙，看电影的人并不多。他们会上网搜索评分最高的电影，然后去影院观看。创作一部成功的电影并非易事。本研究将神经网络模型应用于 IMDb 数据集，然后由于其复杂性，为了实现增强分析的局部可解释性和全局可解释性，研究使用了 SHAP（夏普利加法解释）进行分析。
{"title":"Explainable Neural Network analysis on Movie Success Prediction","authors":"S. Bhavesh Kumar, Sagar Dhanaraj Pande","doi":"10.4108/eetsis.4435","DOIUrl":"https://doi.org/10.4108/eetsis.4435","url":null,"abstract":"These days movies are one of the most important part of entertainment industry and back in the days you could see everyday people standing outside theatres, or watching movies in OTT platforms. But due to busy schedules not many people are watching every movie. They go over the internet and search for top rated movies and go to theatres. And creating a successful movie is no easy job. Thus, this study helps movie producers to consider what are the important factors that influence a movie to be successful. this study applied neural network model to the IMDb dataset and then due to its complex nature in order to achieve the local explainability and global explainability for the enhanced analysis, study have used SHAP (Shapley additive explanations) to analysis.","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"420 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139250826","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：