Journal of Intelligent Systems and Internet of Things最新文献

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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
{"title":"Sustainable Management for the Architectural Heritage in Intelligent Cities using MCDM methods","authors":"H. K. Tripathy, S. A. Ajagbe, E. El-Kenawy","doi":"10.54216/jisiot.060104","DOIUrl":"https://doi.org/10.54216/jisiot.060104","url":null,"abstract":"The success of sustainable management of the heritage building in an intelligent city is a difficult multi-criteria decision-making (MCDM) issue including the coexistence of conflicting elements. There is an issue with incomplete decision information utilization and information loss throughout the decision-making process, and the interaction difficulty in a fuzzy environment is easy to miss. This paper provides a hybrid MCDM framework that combines the spherical fuzzy analytical hierarchy process (SF-AHP). The SF-AHP is used to assess the significance levels of building heritage. To use the stage MCDM model, a thorough set of assessment criteria based on the notion of sustainable development has been identified via literature research and expert interviews. To assess the efficacy of the suggested strategy, an application is done in this paper. Using the decision framework, the building heritage in intelligent cities has been identified. The suggested technique may be utilized to achieve management of the building heritage in intelligent cities.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"47 10 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":"122429819","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 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
{"title":"The Application of Fuzzy Collaborative Intelligence to Detect COVID-19 Minor Symptoms","authors":"A. Abualkishik","doi":"10.54216/jisiot.050205","DOIUrl":"https://doi.org/10.54216/jisiot.050205","url":null,"abstract":"Coronavirus Illness 2019 (COVID-19), a rare disease carried by a coronavirus known as a novel coronavirus, is now posing a danger to the whole planet. Despite the rising number of cases, there is no commercially available vaccination for COVID-19. The moderate symptoms of COVID-19 illness, on the other hand, may be treated with a variety of antiviral treatments. Even yet, selecting the optimum antiviral medication to manage the moderate symptom of COVID-19 is a difficult and ambiguous option. Selecting a drug might be challenging. Fuzzy collaborative intelligence (FCI) was presented in this research as a solution to solve the difficulty of evaluating the appropriateness of a drug selection. In the FCI method, the fuzzy inverse of column sum, partial consensus fuzzy intersection, and fuzzy procedure for order preference by similarity to the ideal solution. To show the practicality and usefulness of the created approach in real-world applications, a case study of medication choice for COVID-19 illness is being investigated.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","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":"125739510","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
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
{"title":"Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier","authors":"F. Safara","doi":"10.54216/jisiot.020201","DOIUrl":"https://doi.org/10.54216/jisiot.020201","url":null,"abstract":"A medical condition that causes disability and early neurological and cognitive condition is autism spectrum disorder (ASD). Gene expression and environment have an impact on this medical condition. Development of diagnostic instruments and skills improved the autism recognition and increased the society awareness about it. To cope with this disorder collaboration between families, service providers, and autistic individuals is a necessity. Early diagnosis of ASD could help in lessening stress, increase adaptation, and support welfare in healthcare systems. Therefore, a large body of research is attempting to provide an intelligent medical diagnostic system to identify and diagnose ASD in early stages using machine learning methods. In this paper, several multilayer perceptron neural network is proposed for ASD detection in healthcare systems. The learning rate is adaptively tuned to achieve the best results. The results show that the approach proposed in this study achieved 99.6% accuracy, which indicates the superiority of the proposed method in identifying and detecting autism disorder in comparison with similar previous methods.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"2013 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":"127348605","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
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
{"title":"An advanced optimization technique for integrating IoT and cloud computing on manufacturing performance for supply chain management","authors":"A. M. AbdelMouty","doi":"10.54216/jisiot.070203","DOIUrl":"https://doi.org/10.54216/jisiot.070203","url":null,"abstract":"The discipline of Supply Chain Management (SCM) is getting more difficult to master. It is necessary to address information silos on the demand and production frontiers of goods in order to execute the de-coupling factor in the preferences of customers who are engaged in a supply chain to optimize business performance, which in today's world has become a difficulty. The so-called Amazon Effect has, once again, compelled competitors to rethink their approaches to achieving maximum efficiency. The Analytic Hierarchy Process (AHP), which is part of the Multi-Criteria Decision Making (MCDM) Approaches, has been used to offer the preferences of clients of various criteria versus various features (products). AHP is used to compute the weights of criteria, then rank the various alternatives. The AHP method is used to build the pairwise comparison between criteria to check the importance of these criteria. The AHP method checks the consistency of the experts to ensure all data is consistent.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"31 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":"129559462","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
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
{"title":"Trust Aware Moth Flame Optimization based Secure Clustering for Wireless Sensor Networks","authors":"A. R. W. Sait, M. Ilayaraja","doi":"10.54216/jisiot.050202","DOIUrl":"https://doi.org/10.54216/jisiot.050202","url":null,"abstract":"Wireless sensor networks (WSN) encompass numerous sensor nodes deployed in the physical environment to sense parameters and transmit to the base station (BS). Since the nodes in WSN communicate via a wireless channel, security remains a significant issue that needs to be resolved. The choice of cluster heads (CHs) is critical to achieving secure data transmission in WSN. In this aspect, this article presents a novel trust-aware mothflame optimization-based secure clustering (TAMFO-SC) technique for WSN. The goal of the TAMFO-SC technique is to determine the trust level of the nodes and determine the secure CHs. The proposed TAMFO-SC technique initially determines the nodes' trust level, and the node with maximum trust factor can be chosen as CHs. In addition, the TAMFO-SC technique derives a fitness function using two parameters, namely residual energy and trust level. The inclusion of trust level in the CH selection process helps to accomplish security in WSN. A comprehensive experimental analysis exhibits the promising performance of the TAMFO-SC technique over the other compared methods.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","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":"114941380","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
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
{"title":"A Survey on Flower pollination algorithm","authors":"Safaa .., Ibrahim Elhenawy","doi":"10.54216/jisiot.020101","DOIUrl":"https://doi.org/10.54216/jisiot.020101","url":null,"abstract":"Flower pollination algorithm (FPA) is a metaheuristic algorithm that proceeds its representation from flowers' proliferation role in plants. The optimal plant reproduction strategy involves the survival of the fittest as well as the optimal reproduction of plants in terms of numbers. These factors represent the fundamentals of the FPA and are optimization-oriented. Yang developed the FPA in 2012, which has since shown superiority to other metaheuristic algorithms in solving various real-world problems, such as power and energy, signal and image processing, communications, structural design, clustering and feature selection, global function optimization, computer gaming, and wireless sensor networking. Recently, many variants of FPA have been developed by modification, hybridization, and parameter-tuning to cope with the complex nature of optimization problems this paper provides a survey of FPA and its applications.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"24 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":"116830590","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
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