Indonesian Journal of Artificial Intelligence and Data Mining最新文献_第2页

Optimizing Malware Detection Using Back Propagation Neural Network and Hyperparameter Tuning 利用反向传播神经网络和超参数调整优化恶意软件检测

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-08-09 DOI: 10.24014/ijaidm.v6i2.24731

Annisa Arrumaisha Siregar, Sopian Soim, Mohammad Fadhli

引用次数: 0

Virtual Assistant for Thesis Technical Guide Using Artificial Neural Network 使用人工神经网络的论文技术指南虚拟助理

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-08-01 DOI: 10.24014/ijaidm.v6i2.23473

Mohammad Ovi Sanjaya, S. Bukhori, Muhammad `Ariful Furqon

引用次数: 0

Hybrid Machine Learning Techniques for Comparative Opinion Mining 用于比较意见挖掘的混合机器学习技术

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-08-01 DOI: 10.24014/ijaidm.v6i2.22644

B. Ondara, Stephen Waithaka, John Kandiri, Lawrence Muchemi

{"title":"Hybrid Machine Learning Techniques for Comparative Opinion Mining","authors":"B. Ondara, Stephen Waithaka, John Kandiri, Lawrence Muchemi","doi":"10.24014/ijaidm.v6i2.22644","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i2.22644","url":null,"abstract":"Comparative opinion mining has lately gained traction among individuals and businesses due to its growing range of applications in brand reputation monitoring and consumer decision making among others. Past research in sub-field of opinion mining have mostly explored single-entity opinion mining models and the mining of comparative sentences suing single classifiers. Most of these studies relied on a limited number of comparative opinion labels and datasets while applying the techniques in limited domains. Consequently, the reported performances of the techniques might not be optimal in some cases like working with big data. In this study, however, we developed four hybrid machine learning techniques, with which we performed multi-class based comparative opinion mining using three datasets from different domains. From our results, the best-performing hybrid machine learning technique for comparative opinion mining using a multi-layer perceptron as the base estimator was the Multilayer Perceptron + Random Forest (MLP + RF). This technique had an average accuracy of 93.0% and an F1-score of 93.0%. These results show that our hybrid machine learning techniques could reliably be used for comparative opinion mining to support business needs like brand reputation monitoring.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"86 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139352294","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

Sentiment Analysis on IMDB Movie Reviews using BERT 使用 BERT 对 IMDB 电影评论进行情感分析

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-08-01 DOI: 10.24014/ijaidm.v6i2.24239

Rani Puspita, Cindy Rahayu

引用次数: 0

Trends and Advances on The K-Hyperparameter Tuning Techniques in High-Dimensional Space Clustering 高维空间聚类中 K 参数调整技术的趋势与进展

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-08-01 DOI: 10.24014/ijaidm.v6i2.22718

Rufus Gikera, Jonathan Mwaura, Elizaphan Maina, S. Mambo

{"title":"Trends and Advances on The K-Hyperparameter Tuning Techniques in High-Dimensional Space Clustering","authors":"Rufus Gikera, Jonathan Mwaura, Elizaphan Maina, S. Mambo","doi":"10.24014/ijaidm.v6i2.22718","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i2.22718","url":null,"abstract":"Clustering is one of the tasks performed during exploratory data analysis with an extensive and wealthy history in a variety of disciplines. Application of clustering in computational medicine is one such application of clustering that has proliferated in the recent past. K-means algorithms are the most popular because of their ability to adapt to new examples besides scaling up to large datasets. They are also easy to understand and implement. However, with k-means algorithms, k-hyperparameter tuning is a long standing challenge. The sparse and redundant nature of the high-dimensional datasets makes the k-hyperparameter tuning in high-dimensional space clustering a more challenging task. A proper k-hyperparameter tuning has a significant effect on the clustering results. A number of state-of-the art k-hyperparameter tuning techniques in high-dimensional space have been proposed. However, these techniques perform differently in a variety of high-dimensional datasets and data-dimensionality reduction methods. This article uses a five-step methodology to investigate the trends and advances on the state of the art k-hyperparameter tuning techniques in high-dimensional space clustering, data dimensionality reduction methods used with these techniques, their tuning strategies, nature of the datasets applied with them as well as the challenges associated with the cluster analysis in high-dimensional spaces. The metrics used in evaluating these techniques are also reviewed. The results of this review, elaborated in the discussion section, makes it efficient for data science researchers to undertake an empirical study among these techniques; a study that subsequently forms the basis for creating improved solutions to this k-hyperparameter tuning problem.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139352958","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

Fuzzy Tsukamoto-Based Detection of Ping of Death Attacks: Advancing Network Security with Precise Classification 基于模糊冢本的死亡 Ping 攻击检测：通过精确分类推进网络安全

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-08-01 DOI: 10.24014/ijaidm.v6i2.23858

Muhammad Adam Hawari, Wahyu Adi Prabowo, Rifki Adhitama

引用次数: 0

The Use of Large Databases for Diagnosing Human Diseases at Early Stage 利用大型数据库早期诊断人类疾病

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-08-01 DOI: 10.24014/ijaidm.v6i2.24525

A. M. Al-Ansi, V. Ryabtsev, Tatyana Utkina

{"title":"The Use of Large Databases for Diagnosing Human Diseases at Early Stage","authors":"A. M. Al-Ansi, V. Ryabtsev, Tatyana Utkina","doi":"10.24014/ijaidm.v6i2.24525","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i2.24525","url":null,"abstract":"The purpose of this article is to demonstrate the ability of the Eidos intellectual system to recognize human diseases at an early stage by processing large databases containing signs of diseases. To study the signs of diseases, it is proposed to use an automated system-cognitive analysis implemented in the Eidos intellectual system. Automated system-cognitive analysis extracts information from large databases and forms knowledge from them that makes it possible to recognize human diseases. In the process of forming models, the amount of information is calculated in the value of the factor by which the modeling object will pass under its influence to a certain state corresponding to the class. This allows for comparable and correct processing of heterogeneous information about observations of the object of modeling, presented in different types of measuring scales and different units of measurement. The results of recognition of the following diseases were obtained with high reliability: chronic kidney disease, lung cancer, breast cancer, liver disease, risks of developing diabetes and stroke. The results of the study can be applied in medical institutions in many countries, since the Eidos system is freely available on the Internet.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139352550","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

Harnessing the Power of Stacked GRU for Accurate Weather Predictions 利用叠加 GRU 的力量进行准确的天气预测

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-08-01 DOI: 10.24014/ijaidm.v6i2.24769

Mohammad Diqi, Ahmad Wakhid, Wayan Ordiyasa, Nurhadi Wijaya, Marselina Endah Hiswati, Article Info

{"title":"Harnessing the Power of Stacked GRU for Accurate Weather Predictions","authors":"Mohammad Diqi, Ahmad Wakhid, Wayan Ordiyasa, Nurhadi Wijaya, Marselina Endah Hiswati, Article Info","doi":"10.24014/ijaidm.v6i2.24769","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i2.24769","url":null,"abstract":"This research proposed a novel approach using Stacked GRU (Gated Recurrent Unit) models to address the problem of weather prediction and aimed to improve forecasting accuracy in sectors like agriculture, transportation, and disaster management. The key idea involved leveraging the temporal dependencies and memory management capabilities of Stacked GRU to model complex weather patterns effectively. Comprehensive data preprocessing ensured data quality and fine-tuning of the model architecture and hyperparameters optimized performance. The research demonstrated the Stacked GRU model's effectiveness in accurately forecasting temperature, pressure, humidity, and wind speed, validated by low RMSE and MAE scores and high R2 coefficients. However, challenges in forecasting humidity and a percentage discrepancy in wind speed predictions were observed. Overfitting and computational complexity were identified as potential limitations. Despite these constraints, the study concluded that the Stacked GRU model showed promise in weather forecasting and warranted further refinement for broader applications in time-series prediction tasks.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139352629","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

Artificial General Intelligence (AGI) and Its Implications For Contract Law 人工智能（AGI）及其对合同法的影响

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-07-31 DOI: 10.24014/ijaidm.v6i1.24704

Wahyudi Umar, Sudirman Sudirman, Rasmuddin Rasmuddin

{"title":"Artificial General Intelligence (AGI) and Its Implications For Contract Law","authors":"Wahyudi Umar, Sudirman Sudirman, Rasmuddin Rasmuddin","doi":"10.24014/ijaidm.v6i1.24704","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i1.24704","url":null,"abstract":"The development of artificial intelligence technology has presented AGI as an exciting future potential. In contract law, AGI can change the landscape of agreements and contract execution. The existence of AGI will raise various legal challenges and questions, such as whether AGI can be a legal party to a contract, whether AGI can execute contracts effectively, and how legal responsibility AGI is in contract execution. This study aims to analyze and identify the legal implications that may arise with the existence of AGI in the context of contract law. In this regard, the research will try to understand how AGI can influence existing principles of contract law. This study uses normative research methods by collecting and analyzing relevant legal sources, including legal literature, regulations, and court rulings related to contract law. This research also involves a comparative study of existing contract law with possible future situations with the existence of AGI. The results of this study show that the presence of AGI has the potential to change important aspects of contract law. Some of the implications identified include questions about AGI's legal status as a legal subject, AGI's legal liability in the performance of contracts, aspects of the validity and interpretation of contracts involving AGI, and legal protection for parties entering transactions with AGI. This research provides a crucial initial understanding in dealing with legal challenges that may arise due to the existence of AGI in the context of contract law","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139353533","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

Data Sharing Technique for Electronic Health Record (EHR) Classification using Support Vector Machine Algorithm 使用支持向量机算法进行电子健康记录（EHR）分类的数据共享技术

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-07-31 DOI: 10.24014/ijaidm.v6i1.24794

Moh. Erkamim, Said Thaufik Rizaldi, Sepriano Sepriano, Khoirun Nisa, Sulhatun Sulhatun, Zilrahmi Zilrahmi, Winalia Agwil

引用次数: 0