Journal of Intelligent Systems and Internet of Things最新文献_第7页

Smart Security Area (SSA) for Radar system technology 智能安全区域(SSA)雷达系统技术

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.080203

Shamiza Hussein, Eslam Hesham

引用次数: 0

Chaotic Butterfly Optimization with Optimal Multi-key Image Encryption Technique for Wireless Sensor Networks 基于最优多密钥图像加密技术的无线传感器网络混沌蝴蝶优化

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.010203

Disheng Zheng, Kai Liang

{"title":"Chaotic Butterfly Optimization with Optimal Multi-key Image Encryption Technique for Wireless Sensor Networks","authors":"Disheng Zheng, Kai Liang","doi":"10.54216/jisiot.010203","DOIUrl":"https://doi.org/10.54216/jisiot.010203","url":null,"abstract":"Wireless sensor network (WSN) comprises a set of sensor nodes, mainly used for data collection and tracking process. The imaging sensors in WSN captures the images from the target environment, which needs to be securely transmitted to the base station (BS). Since data transmission in WSN takes place through wireless links, security is a major challenging issue involved in the design of WSN. Image encryption is a commonly available solution to securely transmit the images to destination without comprising security. Therefore, this study designs a novel Chaotic Butterfly Optimization with Optimal Multi-key Image Encryption (CBO-OMKIE) technique for WSN. The goal of the CBO-OMKIE technique is to securely encrypt the images in WSN. The proposed CBO-OMKIE technique involves the design of multi-key based image encryption technique to accomplish security in WSN. In addition, the CBO algorithm is applied to determine the optimal keys involved in the encryption process and it helps for improving the security level to a maximum extent. The performance validation of the CBO-OMKIE technique takes place using benchmark test images and the outcomes were examined under several aspects. The simulation outcome pointed out the enhanced security analysis of the CBO-OMKIE technique over the other techniques.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"133 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":"114753386","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

Intelligent Asset Tracking System for Logistics Industry using IoT and Big Data 基于物联网和大数据的物流行业智能资产跟踪系统

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.000104

S. Krit

{"title":"Intelligent Asset Tracking System for Logistics Industry using IoT and Big Data","authors":"S. Krit","doi":"10.54216/jisiot.000104","DOIUrl":"https://doi.org/10.54216/jisiot.000104","url":null,"abstract":"The logistics industry is a complex and dynamic ecosystem that requires efficient and reliable asset tracking systems (IATS) to optimize operations and reduce costs. To address these challenges, an IATS is proposed in this paper that leverages the power of IoT and big data technologies to collect real-time data on the location, condition, and status of assets such as trucks, containers, and shipments. The system is designed to provide end-to-end visibility and control of assets throughout the logistics value chain. It uses a combination of RFID, GPS, and other tracking technologies to collect data on asset location, temperature, humidity, vibration, and other relevant parameters. The data is then transmitted to a cloud-based platform for storage, processing, and analysis using big data analytics and machine learning algorithms. The platform enables logistics companies to monitor and manage their assets in real-time, optimize routes and schedules, and improve delivery times. It also provides machine learning tools for predictive modeling of asset price movement, enabling companies to identify potential price changes before they occur and minimize loss. The efficiency and effectiveness of our system were shown through simulation studies using data from real-world assets; as a result, it is an attractive option for the tracking and management of assets in real-world logistic businesses.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","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":"129788950","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

A New Method for Web Service Recommendation Based on QoS Prediction 基于QoS预测的Web服务推荐新方法

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.000101

M. A. Salam

引用次数: 0

Artificial Neural Network Based Approach for Food Recognition Using Various Filters 基于人工神经网络的各种滤波器食物识别方法

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.070205

Upma .., Praveen Gupta, Chaur Singh Rajpoot

{"title":"Artificial Neural Network Based Approach for Food Recognition Using Various Filters","authors":"Upma .., Praveen Gupta, Chaur Singh Rajpoot","doi":"10.54216/jisiot.070205","DOIUrl":"https://doi.org/10.54216/jisiot.070205","url":null,"abstract":"Food image recognition system has various applications now a day. In this paper, we have used a machine learning supervised approach and Support Vector Machine to classify different food images. SVM has been classified to detect and recognize food images with the least modification. By applying various filters like a texture filter, a segmentation method, clustering, and a SVM approach we have achieved more accuracy than other machine learning approaches with manually extracting features. Sustenance is an indivisible piece of people groups lives. we tend to apply a convolution neural network (CNN) to the undertakings of analyst work and perceiving sustenance pictures. Clarification for the wide decent variety of styles of nourishment, and picture acknowledgment of sustenance things are typically unpleasant difficulties. Nevertheless, profound learning has been demonstrated starting late to be a genuinely extreme picture acknowledgment framework, and CNN could be a dynamic approach to managing profound learning. CNN showed on a very basic level higher precision than did old-fashioned help vector-machine-based courses with carefully assembled decisions. For sustenance picture disclosure, CNN likewise demonstrated fundamentally count higher precision than a standard technique. Generally higher precision than standard techniques.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"7 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":"133745999","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

Survey of Artificial Intelligence of Things for Smart Buildings: A closer outlook 面向智能建筑的物联网人工智能研究综述

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.080206

A. Sleem, Ibrahim Elhenawy

{"title":"Survey of Artificial Intelligence of Things for Smart Buildings: A closer outlook","authors":"A. Sleem, Ibrahim Elhenawy","doi":"10.54216/jisiot.080206","DOIUrl":"https://doi.org/10.54216/jisiot.080206","url":null,"abstract":"Artificial Intelligence of Things (AIoT) is a term used to describe the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies. AIoT combines the capabilities of AI algorithms with the data generated by IoT devices to enable real-time decision-making and automation of various processes. Smart buildings refers to a type of building that utilizes advanced technologies to improve its efficiency, performance, and functionality of indoor tasks in a way that provide a safe and comfortable environment for occupants. This paper provides an overview of the research literature on AIoT technologies that is contribute to the development of smart buildings and their functionality. We discuss the benefits of AIoT empowered smart buildings, which include reduced energy consumption and costs, improved occupant comfort and productivity, and increased safety and security. we also discusses the challenges associated with the deployment of AIoT in smart buildings, including data privacy and security concerns, interoperability issues, and the need for specialized expertise. Further, we discuss the promising areas of future research that pave the way for further research on AIoT empowered smart buildings. We concludes our work with a discussion of the potential for AIoT empowered smart buildings to contribute to the sustainability of cities and improve the quality of life for their occupants.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"3 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":"131440664","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

Modeling of Multiple Share Creation with Optimal Signcryption Technique for Digital Image Security 基于最优签名加密技术的数字图像安全多共享创建建模

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.000103

A. R. W. Sait, I. Pustokhina, M. Ilayaraja

引用次数: 8

A Proposed Optimization Model for Water Quality Prediction in Internet of Things Environment 一种物联网环境下水质预测优化模型

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.060203

Gopal Chaudhary, Puneet Singh Lamba, Deepali Virmani

引用次数: 0

Intelligent Differential Evolution based Feature Selection with Deep Neural Network for Intrusion Detection in Wireless Sensor Networks 基于智能差分进化特征选择的深度神经网络无线传感器网络入侵检测

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.000204

I. M. El-Hasnony

{"title":"Intelligent Differential Evolution based Feature Selection with Deep Neural Network for Intrusion Detection in Wireless Sensor Networks","authors":"I. M. El-Hasnony","doi":"10.54216/jisiot.000204","DOIUrl":"https://doi.org/10.54216/jisiot.000204","url":null,"abstract":"Wireless sensor network (WSN) is mainly utilized for data gathering and surveillance applications. As WSN is majorly deployed in harsh and hostile environments, security remains a critical issue which needs to be resolved. An intrusion detection system (IDS) is one of the proficient ways used to determine the presence of abnormal behaviors (i.e. intrusions) in the network. Earlier studies have focused on the design of machine learning (ML) and deep learning (ML) models to design IDS. With this motivation, this paper presents an intelligent differential evolution based feature selection with deep neural network (IDEFS-DNN) for intrusion detection in WSN. The proposed IDEFS-DNN model aims to select optimum set of features and classify the intrusions in the network. In addition, the IDEFS-DNN technique involves the design of IDEFS technique to choose a subset of optimum features. Moreover, the chosen features are fed into the DNN technique for classification purposes. The usage of IDEFS technique helps to reduce the complexity and increase the classifier outcome. In order to portray the improved performance of the IDEFS-DNN technique, wide ranging experiments take place on benchmark datasets and the results are inspected under varying aspects. The simulation results ensured the enhanced intrusion detection performance of the IDEFS-DNN technique over the other IDS models.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","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":"131094957","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: 7

Recurrent Model for Automatic Detection Cardiac Arrhythmia on the Internet of Healthcare Things 基于医疗物联网的心律失常自动检测循环模型

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.020104

Waleed Abd Elkhalik

引用次数: 0