IAES International Journal of Artificial Intelligence (IJ-AI)最新文献_第6页

Enhancing the smart parking assignment system through constraints optimization 通过约束条件优化改进智能停车分配系统

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2374-2385

Nihal Elkhalidi, F. Benabbou, N. Sael

{"title":"Enhancing the smart parking assignment system through constraints optimization","authors":"Nihal Elkhalidi, F. Benabbou, N. Sael","doi":"10.11591/ijai.v13.i2.pp2374-2385","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2374-2385","url":null,"abstract":"Traffic in big cities has become a black spot for drivers. One of the major concerns is the parking problem that hindering urban mobility particularly in the big city and other congested areas; Drivers lose a significant amount of time looking for looking for a parking spot. This leads to an increase in accidents, a big consumption of fuel and a spectacular augmentation of pollution. We present a parking assignment system based on constraint programming in this paper, to meet the need for effective parking management in smart cities, for a group of drivers booking in the same time and area. In this work, we suggest two formulations of the Parking Assignment Problem, The first was established by using Constraint Satisfaction Problems (CSP) and the second is based on Mixed Integer Linear Programing (MILP). An implementation of the model taking advantage of Choco solver dedicate to the constraint programming and the evaluation of its scalability compared to the Mixed Integer Linear Programing solvers. The experiments conducted with Choco and MILP solvers on a real case study in the city of Casablanca showed that the two methods generates promising solutions in terms of scalability and response time.","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":"141235431","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

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia 处理印度尼西亚经济衰退情绪分析中的不平衡数据集问题

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2060-2072

Dinar Ajeng Kristiyanti, Samuel Ady Sanjaya, Vinsencius Christio Tjokro, Jason Suhali

{"title":"Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia","authors":"Dinar Ajeng Kristiyanti, Samuel Ady Sanjaya, Vinsencius Christio Tjokro, Jason Suhali","doi":"10.11591/ijai.v13.i2.pp2060-2072","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2060-2072","url":null,"abstract":"Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.","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":"141228794","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

Coronavirus risk factor by Sugeno fuzzy logic 利用菅野模糊逻辑分析冠状病毒风险因素

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1420-1429

Saba Qasim Hasan, Raid Rafi Omar Al-Nima, Sahar Esmail Mahmmod

引用次数: 0

Balanced clustering for student admission school zoning by parameter tuning of constrained k-means 通过调整受约束 k-means 的参数实现招生学校分区的均衡聚类

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2301-2313

Zahir Zainuddin, Andi Alviadi Nur Risal

{"title":"Balanced clustering for student admission school zoning by parameter tuning of constrained k-means","authors":"Zahir Zainuddin, Andi Alviadi Nur Risal","doi":"10.11591/ijai.v13.i2.pp2301-2313","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2301-2313","url":null,"abstract":"The Indonesian government issued a regulation through the Ministry of Education and Culture, number 51 of 2018, which contains zoning rules to improve the quality of education in school educational institutions. This research aims to compare the performance of the k-means algorithm with the constrained k-means algorithm to model the zoning of each school area based on the shortest distance parameter between the school location and the domicile of prospective students. The study used data from 2248 prospective students and 22 public school locations. The results of testing the k-means algorithm in grouping showed the formation of non-circular patterns in the cluster membership with different numbers of centroid cluster members. In contrast, testing the constrained k-means algorithm showed balanced outcomes in cluster membership with a membership value of 103 for each school as the cluster center. The research findings state that the developed constrained k-means algorithm solves the problem of unbalanced data clustering and overlapping issues in the process of new student admissions. In other words, the constrained k-means algorithm can be a reference for the government in making decisions on new student admissions","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":"141230620","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 efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based 基于机器学习的商业市场客户流失预测效率元启发式模型

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1547-1556

Rahmad B. Y. Syah, Rizki Muliono, Muhammad Akbar Siregar, M. Elveny

{"title":"An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based","authors":"Rahmad B. Y. Syah, Rizki Muliono, Muhammad Akbar Siregar, M. Elveny","doi":"10.11591/ijai.v13.i2.pp1547-1556","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1547-1556","url":null,"abstract":"Metaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer churn in the market business. Particle swam optimization (PSO) was used in this study as a metaheuristic method to provide a strategy to guide the search process for new customers and obtain parameters for processing by support vector regression (SVR). SVR predicts the value of a continuous variable by determining the best decision line to find the best value. The number of transactions, the number of periods, and the conversion value are the parameters that are visible. Efficiency models are added to improve prediction results through two optimizations: prediction flexibility and risk minimization. The findings demonstrate the effectiveness of prediction in reducing customer churn.","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":"141230628","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

Detection of chronic kidney disease using binary whale optimization algorithm 利用二元鲸鱼优化算法检测慢性肾病

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1511-1518

S. Sutikno, Retno Kusumaningrum, Aris Sugiharto, Helmie Arif Wibawa

{"title":"Detection of chronic kidney disease using binary whale optimization algorithm","authors":"S. Sutikno, Retno Kusumaningrum, Aris Sugiharto, Helmie Arif Wibawa","doi":"10.11591/ijai.v13.i2.pp1511-1518","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1511-1518","url":null,"abstract":"Chronic kidney disease (CKD), a medical illness, is characterized by a steady deterioration in kidney function. A disease's ability to be prevented and effectively significantly treated depends on early diagnosis. The addition of filter feature selection to the machine learning algorithm has been done to detect CKD. However, the quality of its feature subset is not optimal. Wrapper feature selection can improve the quality of these feature subsets. Therefore, we proposed wrapper feature selection and binary whale optimization algorithm (BWOA) to enhance the accuracy of early CKD detection. We also make data improvements to improve accuracy, namely the preprocessing process with the median and modus techniques. We used a public dataset of 250 medical records of kidney sufferers and 150 completely healthy people. There are 24 features in this dataset. The test results showed that adding BWOA feature selection can increase accuracy. The proposed method produced an accuracy of 100%. Further research on these methods can be used to develop expert systems for early detection of CKD.","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":"141231033","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 new optimal strategy for energy minimization in wireless sensor networks 无线传感器网络能量最小化的新优化策略

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2265-2274

Hicham Ouchitachen, A. Darif, Mohamed Er-rouidi, Mustapha Johri

引用次数: 0

The rise of AI: a comprehensive research review 人工智能的崛起：全面研究综述

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2226-2235

Chinimilli Venkata Rama Padmaja, Sadasivuni Lakshminarayana

{"title":"The rise of AI: a comprehensive research review","authors":"Chinimilli Venkata Rama Padmaja, Sadasivuni Lakshminarayana","doi":"10.11591/ijai.v13.i2.pp2226-2235","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2226-2235","url":null,"abstract":"Artificial Intelligence (AI) has emerged as a transformative force with far-reaching implications across various domains. This research review provides a comprehensive analysis of the rise of AI, examining its evolution, applications, ethical considerations, and future prospects. The study traces the historical development of AI, highlighting key milestones and technological advancements that have propelled its growth. It explores the wide-ranging applications of AI in sectors such as healthcare, finance, transportation, manufacturing, and entertainment, showcasing its impact on efficiency, decision-making, and user experiences. Ethical considerations surrounding AI, including bias, privacy, and societal implications, are thoroughly discussed. The transformative potential of AI in shaping society is examined, with insights into its effects on employment, education, governance, and societal challenges. Looking ahead, the review identifies emerging technologies and discusses challenges related to data privacy, security, and transparency. The research review concludes by emphasizing the importance of responsible and ethical development of AI, while underscoring the need for continued research and collaboration to fully harness its potential. This comprehensive review serves as a valuable resource for researchers, and practitioners seeking a holistic understanding of the rise of AI and its implications.","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":"141234750","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

Ubiquitous-cloud-inspired deterministic and stochastic service provider models with mixed-integer-programming 采用混合整数编程的泛在云启发确定性和随机服务提供商模型

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1304-1311

Sumarlin Sumarlin, Muhammad Zarlis, Suherman Suherman, Syahril Efendi

{"title":"Ubiquitous-cloud-inspired deterministic and stochastic service provider models with mixed-integer-programming","authors":"Sumarlin Sumarlin, Muhammad Zarlis, Suherman Suherman, Syahril Efendi","doi":"10.11591/ijai.v13.i2.pp1304-1311","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1304-1311","url":null,"abstract":"The ubiquitous computing system is a paradigm shift from personal computing to physical integration. This study focuses on the deterministic and stochastic service provider model to provide sub-services to computing nodes to minimize rejection values. This deterministic service provider model aims to reduce the cost of sending data from one place to another by considering the processing capacity at each node and the demand for each sub-service. At the same time, stochastic service provider aims to optimize service provision in a stochastic environment where parameters such as demand and capacity may change randomly. The novelties of this research are the deterministic and stochastic service provider models and algorithms with mixed integer programming (MIP). The test results show that the solution found meets all the constraints and the smallest objective function value. Stochastic modeling minimizes denial of service problems during wireless sensor network (WSN) distribution. The model resented is the ability of wireless sensors to establish connections between distributed computing nodes. Stochastic modeling minimizes denial of service problems during WSN distribution.","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":"141235220","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

Deep learning for audio signal-based tempo classification scenarios 基于音频信号的节奏分类场景深度学习

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1687-1701

Muljono Muljono, Pulung Nurtantio Andono, Sari Ayu Wulandari, Harun Al Azies, Muhammad Naufal

{"title":"Deep learning for audio signal-based tempo classification scenarios","authors":"Muljono Muljono, Pulung Nurtantio Andono, Sari Ayu Wulandari, Harun Al Azies, Muhammad Naufal","doi":"10.11591/ijai.v13.i2.pp1687-1701","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1687-1701","url":null,"abstract":"This article explains how to determine the tempo of the kendhang, an Indonesian traditional melodic instrument. This research presents novelty as technological research related to gamelan instruments, which has rarely been achieved thus far, through the introduction of kendhang tempo types through the sounds produced, with the hope of creating an automatic system that can recognize the kendhang tempo during a gamelan performance. The testing in this work will categorize the tempo of kendhang into three categories: slow, medium, and fast, utilizing one of the two scenario models proposed, mel frequency cepstral coefficients (MFCC) and convolutional neural network (CNN) in the first scenario, and mel spectrogram and CNN in the second. Kendhang's original audio data, which was captured in real time and later enhanced, makes up the data set. The model 1 scenario, which entails feature extraction using MFCC and classification using the CNN classification approach, is the best scenario in this research, based on the experimental results. When compared to the other suggested modeling scenarios, model 1 has a level of 97%, an average accuracy, and a gain value of 96.67%, making it a solid assistant in terms of kendhang's good tempo recognition accuracy.","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":"141231857","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