Journal of Intelligent Systems and Internet of Things最新文献_第4页

Sustainable Management for the Architectural Heritage in Intelligent Cities using MCDM methods 基于MCDM方法的智慧城市建筑遗产可持续管理

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.060104

H. K. Tripathy, S. A. Ajagbe, E. El-Kenawy

引用次数: 0

Intelligent Model for Customer Churn Prediction using Deep Learning Optimization Algorithms 基于深度学习优化算法的客户流失预测智能模型

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.080104

A. Abualkishik, R. .., William Thompson

{"title":"Intelligent Model for Customer Churn Prediction using Deep Learning Optimization Algorithms","authors":"A. Abualkishik, R. .., William Thompson","doi":"10.54216/jisiot.080104","DOIUrl":"https://doi.org/10.54216/jisiot.080104","url":null,"abstract":"Business intelligence (BI) mentions to the technical and procedural structure which gathers, supplies, and examines the data formed by company action. BI is a wide term that includes descriptive analytics, procedure analysis, data mining, and performance benchmarking. Customer churn is a general problem across businesses from several sectors. Companies are working always for improving their supposed quality by way of providing timely and quality service to its customer. Customer churn is developed most initial challenges which several firms were facing currently. Many churn prediction techniques and methods were presented before in literature for predicting customer churn from the domains like telecom, finance, banking, and so on. Researchers are also working on customer churn prediction (CCP) from e-commerce utilizing data mining and machine learning (ML) approaches. This manuscript focuses on the development of Stacked Deep Learning with Wind Driven Optimization based Business Intelligence for Customer Churn Prediction model. The proposed model is considered an intelligent system that applies golden sine algorithm (GSA) based feature selection approach to derive a set of features. In addition, the stacked gated recurrent unit (SGRU) model is applied for the prediction of customer churns.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"20 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":"116849757","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

Intelligent Traffic Management using IoT and Machine Learning 使用物联网和机器学习的智能交通管理

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.080201

Reem Atassi, Aditi Sharma

{"title":"Intelligent Traffic Management using IoT and Machine Learning","authors":"Reem Atassi, Aditi Sharma","doi":"10.54216/jisiot.080201","DOIUrl":"https://doi.org/10.54216/jisiot.080201","url":null,"abstract":"The continuous improvements in the Internet of Things (IoTs) and machine learning (ML) make them the key enabling technologies for intelligent traffic management (ITM).The ability to accurately predict network traffic has been demonstrated as crucial for effective network management and strategic planning. Proactive management of future congestion incidents requires access to reliable long-term forecasting models. Conventional prediction methods often fail to completely capture the spatiotemporal features of the traffic flows because of the complexity of the interdependence between the flows. To this end, we proposed to improve the management of traffic with a novel framework for the predictive modeling of traffic flows. The proposed formwork introduces an improved graph network to capture the positional information in traffic follows. It is also capable of precisely capturing temporal dynamics using an improved bidirectional learning module. An attention mechanism is presented to capture the interactions among spatial and temporal patterns to further empower the predictive power of the model. Proof-of-concept experimentations are conducted on the PeMSD7 dataset, and the results (MAE: 0.197, MSE: 0.13, RMSE: 0.36, ) demonstrate the efficiency of our model over the state-of-the-art.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"70 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":"127325212","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 multi-criteria decision-based approach for sustainable growth of the energy sector: the case study of India and Vietnam 能源部门可持续增长的智能多标准决策方法:印度和越南的案例研究

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.060105

Vishal Srivastava, Saurabh Bhardwaj, Gopal Chaudhary

{"title":"An intelligent multi-criteria decision-based approach for sustainable growth of the energy sector: the case study of India and Vietnam","authors":"Vishal Srivastava, Saurabh Bhardwaj, Gopal Chaudhary","doi":"10.54216/jisiot.060105","DOIUrl":"https://doi.org/10.54216/jisiot.060105","url":null,"abstract":"Traditional and sustainable forms of energy are examined from a variety of angles, including economics, technology, society, ecology, politics, and flexibility, to ensure that Vietnam’s energy industry continues to expand sustainably. These sources have been evaluated and assessed using an extended spherical fuzzy MCDM method. Thermal, gas, nuclear, solar, wind, biomass, and hydro energy alternatives are employed in the decision-making model. The weights of evaluation criteria are determined using the spherical fuzzy AHP model, and renewable power choices are prioritized using the WPM model. We used six main criteria, twenty-six sub-criteria, and seven alternatives. In Vietnam, solar power was found to be the most suitable, followed by wind and hydropower, followed by hydro, followed by Biomass. The worst alternative is thermal. After that, fourteen situations were built, taking into consideration the first five renewable technologies (solar, wind, hydro, biomass, and gas power), in assessing the ideal energy mix scenario for the slow gestation of Vietnam's energy sector. Solar, wind, and hydro energy growth in a pass shipping scenario.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"51 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":"127445608","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

The Application of Fuzzy Collaborative Intelligence to Detect COVID-19 Minor Symptoms 模糊协同智能在新型冠状病毒轻微症状检测中的应用

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.050205

A. Abualkishik

引用次数: 1

Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier 基于自适应学习算法的多MLP分类器自闭症谱系诊断

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.020201

F. Safara

引用次数: 4

An advanced optimization technique for integrating IoT and cloud computing on manufacturing performance for supply chain management 将物联网和云计算集成到供应链管理制造绩效的先进优化技术

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.070203

A. M. AbdelMouty

引用次数: 0

Securing Wireless Sensor Networks Against DoS attacks in Industrial 4.0 保护无线传感器网络免受工业4.0中的DoS攻击

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.080106

Ossama H. Embarak, Raed Abu Zitar

{"title":"Securing Wireless Sensor Networks Against DoS attacks in Industrial 4.0","authors":"Ossama H. Embarak, Raed Abu Zitar","doi":"10.54216/jisiot.080106","DOIUrl":"https://doi.org/10.54216/jisiot.080106","url":null,"abstract":"Wireless Sensor Networks (WSNs) play a vital role in Industrial 4.0 by facilitating significant data collection for monitoring and control purposes. However, their distributed and resource-constrained nature makes WSNs vulnerable to Denial-of-Service (DoS) attacks, which can impede their normal operation and jeopardize their functionality. To address this issue, we propose a new machine learning (ML) approach that enhances the security of WSNs against DoS attacks in Industrial 4.0. Our approach incorporates a spatial learning unit, which captures the positional information in WSN traffic flows, and a temporal learning unit which captures time interdependency features within periods of traffic flows. To evaluate the proposed approach, we tested it on a publicly available dataset. The results demonstrate that it achieves a high detection rate while maintaining a low false alarm rate. Moreover, our Intrusion Detection System (IDS) exhibits good scalability and robustness against various DoS attacks. Our approach provides a reliable and effective solution to secure WSNs in Industrial 4.0 against DoS attacks and can be further developed and tested in various real-world scenarios.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"103 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113997778","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

Trust Aware Moth Flame Optimization based Secure Clustering for Wireless Sensor Networks 基于信任感知蛾焰优化的无线传感器网络安全聚类

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.050202

A. R. W. Sait, M. Ilayaraja

引用次数: 4

A Survey on Flower pollination algorithm 花卉授粉算法综述

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.020101

Safaa .., Ibrahim Elhenawy

引用次数: 0