International Journal of Intelligent Systems and Applications in Engineering最新文献_第8页

Adaptive Neuro-Fuzzy Inferential Approach for the Diagnosis of Prostate Diseases 自适应神经模糊推理法诊断前列腺疾病

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2022-02-08 DOI: 10.5815/ijisa.2022.01.03

Matthew Cobbinah, U. Abdulrahman, Abaido K Emmanuel

{"title":"Adaptive Neuro-Fuzzy Inferential Approach for the Diagnosis of Prostate Diseases","authors":"Matthew Cobbinah, U. Abdulrahman, Abaido K Emmanuel","doi":"10.5815/ijisa.2022.01.03","DOIUrl":"https://doi.org/10.5815/ijisa.2022.01.03","url":null,"abstract":"In this study, Adaptive Neuro-fuzzy Inferential System (ANFIS) is adapted for diagnosing prostate diseases. The system involves generating and tuning a fuzzy inference system to handle the imprecise terms used for describing prostate cases and severity. Several diagnostic variables were used to learn the feature statistics present in a typical data, while the trained model was validated and adapted for testing new prostate cases. A total of 335 data from patients’ records were collected at the Medi Moses Prostate Centre, Kumasi Ghana. The dataset was partitioned into 70% which was used for model training, and the other 30% was utilized in the validation phase. The proposed model was implemented in the MATLAB environment. Evaluation result from the proposed system demonstrated that the system achieved an accurate diagnostic result with an RMSE value of 11%. This indicates that the system has a relatively high accuracy and could be accepted for prostate diagnosis. Furthermore, the model was able to learn well and generalize the features in the data set, making the proposed ANFIS model suitable for new cases. Performance analysis showed that the ANFIS is well suited for handling the crispy values used in prostate diagnosis; thus, it can be extensively employed in other similar areas of medical diagnosis.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89774409","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 Intelligent Ensemble Classification Method For Spam Diagnosis in Social Networks 基于智能集成分类的社交网络垃圾邮件诊断方法

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2022-02-08 DOI: 10.5815/ijisa.2022.01.02

Ali Ahraminezhad, Musa Mojarad, Hassan Arfaeinia

{"title":"An Intelligent Ensemble Classification Method For Spam Diagnosis in Social Networks","authors":"Ali Ahraminezhad, Musa Mojarad, Hassan Arfaeinia","doi":"10.5815/ijisa.2022.01.02","DOIUrl":"https://doi.org/10.5815/ijisa.2022.01.02","url":null,"abstract":"In recent years, the destructive behavior of social networks spammers has seriously threatened the information security of ordinary users. To reduce this threat, many researchers have extracted the behavioral characteristics of spam and obtained good results based on machine learning algorithms to identify them. However, most of these studies use a single classification technique that often works differently for different spam data. In this paper, an intelligent ensemble classification method for social networks spam detection is introduced. The proposed heterogeneous ensemble learning framework is based on stack generalization and uses an evolutionary algorithm to improve the modeling process and reduce complexity. In particular, particle swarm optimization has been used as an evolutionary algorithm to optimize model parameters to reduce model complexity. These parameters include a subset of effective features and a subset of the most appropriate single classification techniques. The SPAM E-mail dataset used in this article contains the correct and effective features in spam prediction. Experimental results show that the proposed algorithm effectively improves the detection rate of spam and performs better than the methods used.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85503358","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

Towards an Intelligent Machine Learning-based Business Approach 迈向基于智能机器学习的商业方法

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2022-02-08 DOI: 10.5815/ijisa.2022.01.01

Mohamed Nazih Omri, Wafa Mribah

{"title":"Towards an Intelligent Machine Learning-based Business Approach","authors":"Mohamed Nazih Omri, Wafa Mribah","doi":"10.5815/ijisa.2022.01.01","DOIUrl":"https://doi.org/10.5815/ijisa.2022.01.01","url":null,"abstract":"With the constant increase of data induced by stakeholders throughout a product life cycle, companies tend to rely on project management tools for guidance. Business intelligence approaches that are project-oriented will help the team communicate better, plan their next steps, have an overview of the current project state and take concrete actions prior to the provided forecasts. The spread of agile working mindsets are making these tools even more useful. It sets a basic understanding of how the project should be running so that the implementation is easy to follow on and easy to use. In this paper, we offer a model that makes project management accessible from different software development tools and different data sources. Our model provide project data analysis to improve aspects: (i) collaboration which includes team communication, team dashboard. It also optimizes document sharing, deadlines and status updates. (ii) planning: allows the tasks described by the software to be used and made visible. It will also involve tracking task time to display any barriers to work that some members might be facing without reporting them. (iii) forecasting to predict future results from behavioral data, which will allow concrete measures to be taken. And (iv) Documentation to involve reports that summarize all relevant project information, such as time spent on tasks and charts that study the status of the project. The experimental study carried out on the various data collections on our model and on the main models that we have studied in the literature, as well as the analysis of the results, which we obtained, clearly show the limits of these studied models and confirms the performance of our model as well as efficiency in terms of precision, recall and robustness.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84099352","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: 3

BERT based Hierarchical Alternating Co-Attention Visual Question Answering using Bottom-Up Features 基于BERT的自底向上特征分层交替共注意视觉问答

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2022-01-01 DOI: 10.17762/ijisae.v10i3s.2427

引用次数: 0

A Framework for Flood Extent Mapping using CNN Transfer Learning 基于CNN迁移学习的洪水范围映射框架

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2022-01-01 DOI: 10.17762/ijisae.v10i3s.2426

引用次数: 0

Damage Detection of Carbon Face Sheet Nomex Sandwich Composites with Image Processing Technique 基于图像处理技术的碳面片Nomex夹层复合材料损伤检测

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-27 DOI: 10.18201/ijisae.2021474023

Ibrahim Demirci

引用次数: 0

TheRobust EEG Based Emotion Recognition using Deep Neural Network 基于深度神经网络的脑电情绪识别

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-26 DOI: 10.18201/ijisae.2021473639

Samad Barri Khojasteh

引用次数: 1

Data Classification of Early-Stage Diabetes Risk Prediction Datasets and Analysis of Algorithm Performance Using Feature Extraction Methods and Machine Learning Techniques 基于特征提取方法和机器学习技术的早期糖尿病风险预测数据集数据分类及算法性能分析

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-26 DOI: 10.18201/ijisae.2021473767

A. Yaşar

引用次数: 4

A Novel Hybrid Model For Automated Analysis Of Cardiotocograms Using Machine Learning Algorithms 使用机器学习算法自动分析心电图的新型混合模型

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-26 DOI: 10.18201/ijisae.2021473716

Emre Avuçlu

引用次数: 1

Analysis of Intentional Noise Insertion Approach on the Copy-Move Forgery Detection in Digital Image 数字图像复制-移动伪造检测中的故意噪声插入方法分析

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-26 DOI: 10.18201/ijisae.2021473644

Serkan Ozbay

引用次数: 0