International Journal of Advances in Applied Computational Intelligence最新文献

An Intelligent Neutrosophic Model for Evaluation Sustainable Housing Affordability 可持续住房可负担性评价的智能中性模型

International Journal of Advances in Applied Computational Intelligence Pub Date : 1900-01-01 DOI: 10.54216/ijaaci.020205

I. Pustokhina, D. A. Pustokhin

引用次数: 0

Triangular Neutrosophic Multi-Criteria Decision Making AHP Method for Solar Power Site Selection 太阳能电站选址的三角形中性多准则决策AHP方法

International Journal of Advances in Applied Computational Intelligence Pub Date : 1900-01-01 DOI: 10.54216/ijaaci.020201

A. .., R. Almajed

引用次数: 4

Single Valued Neutrosophic Set for Selection of Water Supply in Intelligent Farming 智能农业供水选择的单值嗜中性集

International Journal of Advances in Applied Computational Intelligence Pub Date : 1900-01-01 DOI: 10.54216/ijaaci.020204

Taif Khalid Shakir, A. Masri

引用次数: 1

Cardiovascular Diseases Forecasting using Machine Learning Models 使用机器学习模型预测心血管疾病

International Journal of Advances in Applied Computational Intelligence Pub Date : 1900-01-01 DOI: 10.54216/ijaaci.010204

Heba R. Abdelhady, Mahmoud M. Ismail

引用次数: 2

Linear Regression and K Nearest Neighbors Machine Learning Models for Person Fat Forecasting 线性回归和K近邻机器学习模型用于人体脂肪预测

International Journal of Advances in Applied Computational Intelligence Pub Date : 1900-01-01 DOI: 10.54216/ijaaci.030204

Alshaimaa A. Tantawy

{"title":"Linear Regression and K Nearest Neighbors Machine Learning Models for Person Fat Forecasting","authors":"Alshaimaa A. Tantawy","doi":"10.54216/ijaaci.030204","DOIUrl":"https://doi.org/10.54216/ijaaci.030204","url":null,"abstract":"Predicting a person's person fat percentage is an important part of keeping tabs on their health and fitness. An accurate assessment of person fat allows for the development of individualized programmer for health and wellbeing, the promotion of illness prevention, and the evaluation of the efficacy of weight management initiatives. This study reviews the current state of the art in person fat prediction approaches, which includes the use of machine learning algorithms. Obesity is a chronic condition characterized by high levels of person fat and is linked to several health issues. Since several methods exist for estimating person fat percentage to evaluate obesity, these assessments are usually expensive and need specialized equipment. Therefore, determining obesity and its associated disorders requires an accurate estimate of person fat proportion according to readily available person measures. This paper presented a machine-learning model for forecasting person fat. This problem is a regression, so this paper used two regression models to deal with the regression dataset. This paper used linear regression (LR) and k nearest neighbors (KNN). The two models were applied to real datasets. The dataset has 252 records. The results showed the LR has the highest score than the KNN model.","PeriodicalId":166689,"journal":{"name":"International Journal of Advances in Applied Computational Intelligence","volume":"59 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":"122361143","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

Computational Intelligence Approach for Biometric Gait Identification 生物特征步态识别的计算智能方法

International Journal of Advances in Applied Computational Intelligence Pub Date : 1900-01-01 DOI: 10.54216/ijaaci.020105

Hadeer Mahmoud, A. Abdelhafeez

引用次数: 0

Intelligent systems and AI techniques: Recent advances and Future directions 智能系统和人工智能技术:最新进展和未来方向

International Journal of Advances in Applied Computational Intelligence Pub Date : 1900-01-01 DOI: 10.54216/ijaaci.010202

Ismail Eyad Samara

引用次数: 1

Unveiling the Power of Convolutional Networks: Applied Computational Intelligence for Arrhythmia Detection from ECG Signals 揭示卷积网络的力量:应用计算智能从心电信号检测心律失常

International Journal of Advances in Applied Computational Intelligence Pub Date : 1900-01-01 DOI: 10.54216/ijaaci.010205

Alber Aziz, Hoda K. Mohamed, A. Abdelhafeez

{"title":"Unveiling the Power of Convolutional Networks: Applied Computational Intelligence for Arrhythmia Detection from ECG Signals","authors":"Alber Aziz, Hoda K. Mohamed, A. Abdelhafeez","doi":"10.54216/ijaaci.010205","DOIUrl":"https://doi.org/10.54216/ijaaci.010205","url":null,"abstract":"Arrhythmias are a significant cause of morbidity and mortality worldwide, necessitating accurate and timely detection for effective clinical intervention. Electrocardiogram (ECG) signals serve as invaluable sources of information for diagnosing arrhythmias, but their analysis is complex and demanding. Recent advancements in computational intelligence, particularly Convolutional Networks (CNNs), have demonstrated remarkable capabilities in various signal-processing tasks. In this paper, we unveil the power of CNNs by applying computational intelligence techniques to detect arrhythmias from ECG signals. The proposed methodology involves preprocessing the ECG signals to enhance their quality and remove noise interference. Subsequently, CNN architectures are developed and trained using a large dataset of annotated ECG recordings. The network's structure is optimized to effectively capture the discriminative features present in the ECG signals that characterize diverse types of arrhythmias. Through an extensive evaluation process, the performance of the CNN models is assessed using confusion matrices. Experimental results demonstrate the effectiveness of the applied computational intelligence approach in arrhythmia detection. The CNN model achieves outstanding performance, exhibiting robustness against noise and variations in ECG recording conditions, highlighting its potential for real-world applications.","PeriodicalId":166689,"journal":{"name":"International Journal of Advances in Applied Computational Intelligence","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":"116378463","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: 1

Enhancing Rice Crop Health through Computational Intelligence-Based Disease Detection 基于计算智能的病害检测提高水稻作物健康

International Journal of Advances in Applied Computational Intelligence Pub Date : 1900-01-01 DOI: 10.54216/ijaaci.030102

Nouran Ajabnoor, A. Salamai

{"title":"Enhancing Rice Crop Health through Computational Intelligence-Based Disease Detection","authors":"Nouran Ajabnoor, A. Salamai","doi":"10.54216/ijaaci.030102","DOIUrl":"https://doi.org/10.54216/ijaaci.030102","url":null,"abstract":"Rice is one of the most important staple crops worldwide, and rice plant diseases are a significant threat to global food security. Early detection and accurate classification of these diseases are crucial for effective disease management and prevention of crop losses. In this paper, we propose a novel computational intelligence-based technique for rice disease detection and classification. Our proposed method is composed of a residual network-based feature extractor followed by a Light Gradient Boosting Machine (LGBM) classifier. We use a publicly available rice leaf dataset to evaluate the performance of our proposed method. The results demonstrate that our proposed method achieves high accuracy, sensitivity, and specificity in identifying diseased rice plants, outperforming existing state-of-the-art methods. We also compare our proposed method against other methods using different performance metrics, showing its superior performance. The proposed method provides a promising approach to enhance rice crop health management and can be adapted and customized for other crops and agricultural settings. The proposed computational intelligence-based technique for rice disease detection and classification has significant implications for improving crop productivity and ensuring food security.","PeriodicalId":166689,"journal":{"name":"International Journal of Advances in Applied Computational Intelligence","volume":"224 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":"121863695","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 Framework of Convolutional Neural Network (CNN) and Attention CNN for Early Diagnosis of Alzheimer's Disease 卷积神经网络(CNN)的深度学习框架及其对阿尔茨海默病早期诊断的关注

International Journal of Advances in Applied Computational Intelligence Pub Date : 1900-01-01 DOI: 10.54216/ijaaci.030201

Hoda K. Mohamed, A. Abdelhafeez, Nariman A. Khalil

{"title":"Deep Learning Framework of Convolutional Neural Network (CNN) and Attention CNN for Early Diagnosis of Alzheimer's Disease","authors":"Hoda K. Mohamed, A. Abdelhafeez, Nariman A. Khalil","doi":"10.54216/ijaaci.030201","DOIUrl":"https://doi.org/10.54216/ijaaci.030201","url":null,"abstract":"One of the biggest killers in the industrialized world is Alzheimer's disease (AD). Although computer-aided techniques have shown promising outcomes in laboratory experiments, they have yet to be used in a clinical setting. Recently, deep neural networks have gained traction, particularly for image processing tasks. There has been a dramatic increase in the number of publications written on the topic of identifying AD using deep learning since 2017. It has been observed that deep networks are more efficient than standard machine learning methods for detecting AD. It remains difficult to identify AD because distinguishing between comparable brain signals during categorization needs an extremely discriminative depiction of features. This paper proposed a deep neural network method for prediction the AD. Low-level computer vision has been a hotspot for research into deep convolutional neural networks (CNNs). Studies often focus on enhancing performance through the use of very deep CNNs. Yet, as one goes deeper, the effect of the shallow layers on the deeper ones gradually diminishes. Prompted by reality. This paper compared with the CNN and attention CNN models. The proposed model applied in the AD dataset which contains 5121 images for the train set. The results showed the attention CNN model is better than the CNN model in accuracy, precision, recall, loss, and AUC.","PeriodicalId":166689,"journal":{"name":"International Journal of Advances in Applied Computational Intelligence","volume":"98 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":"117165074","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