Journal of Artificial Intelligence and Metaheuristics最新文献

New Approach of Estimating Sarcasm based on the percentage of happiness of facial Expression using Fuzzy Inference System 基于面部表情快乐度百分比的模糊推理系统估计讽刺的新方法

Journal of Artificial Intelligence and Metaheuristics Pub Date : 2022-11-14 DOI: 10.54216/jaim.010104

Louloua M. Al-Saedi, M. Gaata, M. Abotaleb, H. Alkattan

引用次数: 3

Improving the Regression of Communities and Crime Using Ensemble of Machine Learning Models 利用机器学习模型集成改进社区和犯罪的回归

Journal of Artificial Intelligence and Metaheuristics Pub Date : 1900-01-01 DOI: 10.54216/jaim.010103

Hamzah A. Alsayadi, Nima Khodadadi, Sunil Kumar

引用次数: 6

Automatic Speech Recognition for Qur’an Verses using Traditional Technique 基于传统技术的古兰经经文语音自动识别

Journal of Artificial Intelligence and Metaheuristics Pub Date : 1900-01-01 DOI: 10.54216/jaim.010202

Hamzah A. Alsayadi, Mohammed Hadwan

引用次数: 0

Bridging the Gap: An Explainable Methodology for Customer Churn Prediction in Supply Chain Management 弥合差距:供应链管理中客户流失预测的可解释方法

Journal of Artificial Intelligence and Metaheuristics Pub Date : 1900-01-01 DOI: 10.54216/jaim.040102

Adel Oubelaid, A. Ibrahim, Ahmed M. Elshewey

{"title":"Bridging the Gap: An Explainable Methodology for Customer Churn Prediction in Supply Chain Management","authors":"Adel Oubelaid, A. Ibrahim, Ahmed M. Elshewey","doi":"10.54216/jaim.040102","DOIUrl":"https://doi.org/10.54216/jaim.040102","url":null,"abstract":"Customer churn prediction is a critical task for businesses aiming to retain their valuable customers. Nevertheless, the lack of transparency and interpretability in machine learning models hinders their implementation in real-world applications. In this paper, we introduce a novel methodology for customer churn prediction in supply chain management that addresses the need for explainability. Our approach take advantage of XGBoost as the underlying predictive model. We recognize the importance of not only accurately predicting churn but also providing actionable insights into the key factors driving customer attrition. To achieve this, we employ Local Interpretable Model-agnostic Explanations (LIME), a state-of-the-art technique for generating intuitive and understandable explanations. By utilizing LIME to the predictions made by XGBoost, we enable decision-makers to gain intuition into the decision process of the model and the reasons behind churn predictions. Through a comprehensive case study on customer churn data, we demonstrate the success of our explainable ML approach. Our methodology not only achieves high prediction accuracy but also offers interpretable explanations that highlight the underlying drivers of customer churn. These insights supply valuable management for decision-making processes within supply chain management.","PeriodicalId":147150,"journal":{"name":"Journal of Artificial Intelligence and Metaheuristics","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122782400","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

Solar Tracking System Using pixel identification algorithm 太阳跟踪系统采用像素识别算法

Journal of Artificial Intelligence and Metaheuristics Pub Date : 1900-01-01 DOI: 10.54216/jaim.030103

N. Behdad, Sunil Kumar

引用次数: 0

Tapping into Knowledge: Ontological Data Mining Approach for Detecting Cardiovascular Disease Risk Causes Among Diabetes Patients 利用知识:本体论数据挖掘方法检测糖尿病患者心血管疾病风险原因

Journal of Artificial Intelligence and Metaheuristics Pub Date : 1900-01-01 DOI: 10.54216/jaim.040101

H. Alkattan, S. K. Towfek, M. Shams

{"title":"Tapping into Knowledge: Ontological Data Mining Approach for Detecting Cardiovascular Disease Risk Causes Among Diabetes Patients","authors":"H. Alkattan, S. K. Towfek, M. Shams","doi":"10.54216/jaim.040101","DOIUrl":"https://doi.org/10.54216/jaim.040101","url":null,"abstract":"The prevalence of cardiovascular disease (CVD) is a serious public health issue, and it is of particular concern for people with diabetes because of the increased risk of cardiovascular problems that these people experience. In this study, we suggest a novel method of Ontological Data Mining (ODM) for identifying the origins of CVD risk in diabetic patients. We want to improve the readability and precision of prediction models by incorporating domain knowledge and semantic linkages into the data mining process. In this work, we examine a large dataset consisting of 70,000 patient records with 11 attributes, all of which are derived through a thorough clinical history and physical examination. As part of our methodology, we use decision trees, support vector machines (SVMs), and gradient boosting (GB). The distribution patterns of critical variables with respect to CVD outcomes can be better understood through the use of visual representations such as box plots, distributional plots, and pie charts. Finding significant connections and causal relationships between risk factors and CVD outcomes is made possible by the suggested ODM method. Our research has promising implications for bettering the treatment of patients with diabetes, facilitating targeted interventions, and enhancing risk assessment and preventative methods for cardiovascular disease.","PeriodicalId":147150,"journal":{"name":"Journal of Artificial Intelligence and Metaheuristics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121990923","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: 2

Weather Forecasting over Iraq Using Machine Learning 利用机器学习对伊拉克进行天气预报

Journal of Artificial Intelligence and Metaheuristics Pub Date : 1900-01-01 DOI: 10.54216/jaim.020204

Israa Jasim .., Bashar Al-Nuaimi, Ther Intisar Baker

引用次数: 3

Navigating the Storm: Cutting-Edge Risk Mitigation and Analysis for Volatile Markets 在风暴中航行:波动市场的前沿风险缓解和分析

Journal of Artificial Intelligence and Metaheuristics Pub Date : 1900-01-01 DOI: 10.54216/jaim.040204

S. K. Towfek

引用次数: 0

Energy Efficiency Modeling Using Whale Optimization Algorithm and Ensemble Model 基于鲸鱼优化算法和集成模型的能源效率建模

Journal of Artificial Intelligence and Metaheuristics Pub Date : 1900-01-01 DOI: 10.54216/jaim.020103

A. Oubelaid, M. Shams, Mostafa Salaheldin Abdelsalam Abotaleb

{"title":"Energy Efficiency Modeling Using Whale Optimization Algorithm and Ensemble Model","authors":"A. Oubelaid, M. Shams, Mostafa Salaheldin Abdelsalam Abotaleb","doi":"10.54216/jaim.020103","DOIUrl":"https://doi.org/10.54216/jaim.020103","url":null,"abstract":"machinery enterprises can benefit greatly from including energy efficiency models into their energy management and conservation efforts. Due to a lack of theoretical formulations, this paper integrates machining parameters and configuration parameters into energy efficiency models, with ML methods applied to increase generality. A three-year data set from a manufacturing facility serves as the basis for a comparison examination of two scenarios, with an emphasis on evaluating forecast precision, stability, and computing efficiency. To estimate future energy efficiency in Scenario 1, only cross-sectional data is utilized, completely discounting the wear and tear on spindle motors and cutting tools. In this study, we use five error measures to compare and contrast three classic ML algorithms: artificial neural networks, support vector regression, and Gaussian process regression. In Case 2, we build the a voting ensemble model in a more realistic setting, taking into account the dynamic characteristics of the aging spindle motor and tool wear. It is clear from the comparison that all of the Case 1 models experience performance erosion, but the proposed voting ensemble model is able to produce a sustainable increase in accuracy.","PeriodicalId":147150,"journal":{"name":"Journal of Artificial Intelligence and Metaheuristics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129030269","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: 4

Data Mining Techniques in Predictive Medicine: An Application in hemodynamic prediction for abdominal aortic aneurysm disease 预测医学中的数据挖掘技术:在腹主动脉瘤疾病血流动力学预测中的应用

Journal of Artificial Intelligence and Metaheuristics Pub Date : 1900-01-01 DOI: 10.54216/jaim.050103

D. S. Khafaga, A. Ibrahim, S. K. Towfek, Nima Khodadadi

{"title":"Data Mining Techniques in Predictive Medicine: An Application in hemodynamic prediction for abdominal aortic aneurysm disease","authors":"D. S. Khafaga, A. Ibrahim, S. K. Towfek, Nima Khodadadi","doi":"10.54216/jaim.050103","DOIUrl":"https://doi.org/10.54216/jaim.050103","url":null,"abstract":"Due to its potential to enhance patient outcomes and ease individualized therapy, predictive medicine has received considerable interest in recent years. In this article we examine the use of data mining in predictive medicine, with a particular emphasis on hemodynamic prediction for abdominal aortic aneurysm (AAA) disease. In AAA, the abdominal aortic wall becomes weakened and may rupture, putting the patient's life in danger. Clinical decision making and treatment planning for AAA rely heavily on accurate hemodynamic prediction. For developing these predictive models for hemodynamic assessment, we use the well-known data mining techniques of Random Forest (RF) and AdaBoost. To capture complicated interactions, the RF approach employs a collection of decision trees, while AdaBoost iteratively improves the model by giving more weight to examples that were incorrectly classified. The experimental evidence shows that these methods are effective in providing reliable estimates of the hemodynamics of AAA. This research adds to the expanding field of predictive medicine by providing new understanding of the potential of data mining methods to improve the quality of care for patients with AAA illness.","PeriodicalId":147150,"journal":{"name":"Journal of Artificial Intelligence and Metaheuristics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130974666","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