Methods for machine learning, or ML, are becoming more accessible, and consumer-generated data is on the rise, both of which are transforming marketing strategies. Researchers and marketers still have a long way to go before they fully grasp the myriad ways in which ML applications might help businesses gain and keep an edge in the marketplace. This study systematically evaluates the academic and corporate literature to present a taxonomy of marketing use cases based on machine learning. The authors have discovered 11 common use cases that fall into four distinct groups that reflect the core areas of leverage for machine learning in marketing: shopper fundamentals, consuming experience, decisions, and financial impact. The literature highlights practical implications for researchers and marketers by discussing the taxonomy's found repeating patterns and providing an analytical structure for analyzing it and extension.
机器学习（ML）的方法越来越容易获得，消费者生成的数据也在不断增加，这两者都在改变着营销策略。研究人员和营销人员要想完全掌握 ML 应用可能帮助企业获得并保持市场优势的各种方法，还有很长的路要走。本研究系统地评估了学术和企业文献，提出了基于机器学习的营销用例分类法。作者发现了 11 种常见的使用案例，它们分为四个不同的组别，反映了机器学习在营销中的核心应用领域：购物者基础知识、消费体验、决策和财务影响。文献通过讨论分类法发现的重复模式，并提供了分析和扩展分类法的分析结构，突出了对研究人员和营销人员的实际意义。
{"title":"Analysis and Implications of Adopting AI and Machine Learning in Marketing, Servicing, and Communications Technology","authors":"Priyal J. Borole","doi":"10.4018/ijaiml.338379","DOIUrl":"https://doi.org/10.4018/ijaiml.338379","url":null,"abstract":"Methods for machine learning, or ML, are becoming more accessible, and consumer-generated data is on the rise, both of which are transforming marketing strategies. Researchers and marketers still have a long way to go before they fully grasp the myriad ways in which ML applications might help businesses gain and keep an edge in the marketplace. This study systematically evaluates the academic and corporate literature to present a taxonomy of marketing use cases based on machine learning. The authors have discovered 11 common use cases that fall into four distinct groups that reflect the core areas of leverage for machine learning in marketing: shopper fundamentals, consuming experience, decisions, and financial impact. The literature highlights practical implications for researchers and marketers by discussing the taxonomy's found repeating patterns and providing an analytical structure for analyzing it and extension.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"311 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140448115","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}
The purpose is to illustrate how artificial intelligence (AI) technologies have been used for detection and analysis of COVID-19 and other infectious diseases such as breast, lung, and skin cancers; heart disease; and others. Specifically, the use of neural networks (NN) and machine learning (ML) are described along with which countries are creating these techniques and how these are being used for COVID-19 or other disease diagnosis and detection. Illustrations of multi-layer convolutional neural networks (CNN), recurrent neural networks (RNN), and deep neural networks (DNN) are provided to show how these are used for COVID-19 or other disease detection and prediction. A summary of big data analytics for COVID-19 and some available COVID-19 open-source data sets and repositories and their characteristics for research and analysis is also provided. An example is also shown for artificial intelligence (AI) and neural network (NN) applications using real-time COVID-19 data.
{"title":"Survey of Recent Applications of Artificial Intelligence for Detection and Analysis of COVID-19 and Other Infectious Diseases","authors":"R. Segall, Vidhya Sankarasubbu","doi":"10.4018/ijaiml.313574","DOIUrl":"https://doi.org/10.4018/ijaiml.313574","url":null,"abstract":"The purpose is to illustrate how artificial intelligence (AI) technologies have been used for detection and analysis of COVID-19 and other infectious diseases such as breast, lung, and skin cancers; heart disease; and others. Specifically, the use of neural networks (NN) and machine learning (ML) are described along with which countries are creating these techniques and how these are being used for COVID-19 or other disease diagnosis and detection. Illustrations of multi-layer convolutional neural networks (CNN), recurrent neural networks (RNN), and deep neural networks (DNN) are provided to show how these are used for COVID-19 or other disease detection and prediction. A summary of big data analytics for COVID-19 and some available COVID-19 open-source data sets and repositories and their characteristics for research and analysis is also provided. An example is also shown for artificial intelligence (AI) and neural network (NN) applications using real-time COVID-19 data.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126072768","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}
This paper proposes a hybrid text classification model that combines 1D CNN with a single Bidirectional Fast GRU (BiFaGRU) termed as CNN-BiFaGRU. Single convolution layer captures features through a kernel applying 128 filters which are slide over these embeds to find convolutions and drop entire 1D feature maps by using Spatial Dropout, combined vectors using Max-Pooling layer. Then, the Bidirectional CUDNNGRU block to extract temporal features, results of this layer is normalize by the Batch Normalization layer and transmitted to the Fully Connected Layer. The output layer produces the final classification results. Precision/loss score was used as the main criterion on five different datasets (WebKb, R8, R52, AG-News, and 20 NG) to assess the performance of the proposed model. The results indicate that the precision score of the classifier on WebKb, R8, and R52 data sets significantly improved from 90% up to 97% compared to the best result achieved by other methods such as LSTM and Bi-LSTM. Thus, the proposed model shows higher precision and lower loss scores than other methods.
{"title":"Boosting Convolutional Neural Networks Using a Bidirectional Fast Gated Recurrent Unit for Text Categorization","authors":"Assia Belherazem, R. Tlemsani","doi":"10.4018/ijaiml.308815","DOIUrl":"https://doi.org/10.4018/ijaiml.308815","url":null,"abstract":"This paper proposes a hybrid text classification model that combines 1D CNN with a single Bidirectional Fast GRU (BiFaGRU) termed as CNN-BiFaGRU. Single convolution layer captures features through a kernel applying 128 filters which are slide over these embeds to find convolutions and drop entire 1D feature maps by using Spatial Dropout, combined vectors using Max-Pooling layer. Then, the Bidirectional CUDNNGRU block to extract temporal features, results of this layer is normalize by the Batch Normalization layer and transmitted to the Fully Connected Layer. The output layer produces the final classification results. Precision/loss score was used as the main criterion on five different datasets (WebKb, R8, R52, AG-News, and 20 NG) to assess the performance of the proposed model. The results indicate that the precision score of the classifier on WebKb, R8, and R52 data sets significantly improved from 90% up to 97% compared to the best result achieved by other methods such as LSTM and Bi-LSTM. Thus, the proposed model shows higher precision and lower loss scores than other methods.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114702781","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}
This article provides an overview with examples of what Neural Networks (NN), Machine Learning (ML), and Artificial Intelligence (AI) and Data Analytics are, and with their applications in business, urban and biomedical situations. The characteristics of 29 types of neural networks are provided including their distinctive graphical illustrations. A survey of current open-source software (OSS) for neural networks, neural network software available for free trial download for limited time use, open-source software (OSS) for Machine Learning (ML), and open-source software (OSS) for Data Analytics tools are provided. Characteristics of Artificial Intelligence (AI) technologies for Machine Learning available as open-source are discussed. Illustrations of applications of Neural Networks, Machine Learning, and Artificial Intelligence are presented as used in the daily operations of a large international-based software company for optimal configuration of their Helix Data Capacity system and other.
{"title":"Using Open-Source Software for Business, Urban, and Other Applications of Deep Neural Networks, Machine Learning, and Data Analytics Tools","authors":"R. Segall, Vidhya Sankarasubbu","doi":"10.4018/ijaiml.307905","DOIUrl":"https://doi.org/10.4018/ijaiml.307905","url":null,"abstract":"This article provides an overview with examples of what Neural Networks (NN), Machine Learning (ML), and Artificial Intelligence (AI) and Data Analytics are, and with their applications in business, urban and biomedical situations. The characteristics of 29 types of neural networks are provided including their distinctive graphical illustrations. A survey of current open-source software (OSS) for neural networks, neural network software available for free trial download for limited time use, open-source software (OSS) for Machine Learning (ML), and open-source software (OSS) for Data Analytics tools are provided. Characteristics of Artificial Intelligence (AI) technologies for Machine Learning available as open-source are discussed. Illustrations of applications of Neural Networks, Machine Learning, and Artificial Intelligence are presented as used in the daily operations of a large international-based software company for optimal configuration of their Helix Data Capacity system and other.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123713908","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}
Pub Date : 2021-07-01DOI: 10.4018/ijaiml.20210701oa14
Soumia Djaghbellou, Abderraouf Bouziane, A. Attia, Z. Akhtar
The optical character recognition (OCR) system is still an active research field in pattern recognition. Such systems can identify, recognize and distinguish electronically between characters and texts, printed or handwritten. They can also do a transformation of such data type into machine-processable form to facilitate the interaction between user and machine in various applications. In this paper, we present the global structure of an OCR system, with its types (on-line and off-line), categories (printed and handwritten) and its main steps. We also focused on off-line handwritten Arabic character recognition and provided a list of the main datasets publicly available. This paper also presents a survey of the works that have been carried out over recent years. Finally, some open issues and potential research directions have been highlighted
{"title":"A Survey on Arabic Handwritten Script Recognition Systems","authors":"Soumia Djaghbellou, Abderraouf Bouziane, A. Attia, Z. Akhtar","doi":"10.4018/ijaiml.20210701oa14","DOIUrl":"https://doi.org/10.4018/ijaiml.20210701oa14","url":null,"abstract":"The optical character recognition (OCR) system is still an active research field in pattern recognition. Such systems can identify, recognize and distinguish electronically between characters and texts, printed or handwritten. They can also do a transformation of such data type into machine-processable form to facilitate the interaction between user and machine in various applications. In this paper, we present the global structure of an OCR system, with its types (on-line and off-line), categories (printed and handwritten) and its main steps. We also focused on off-line handwritten Arabic character recognition and provided a list of the main datasets publicly available. This paper also presents a survey of the works that have been carried out over recent years. Finally, some open issues and potential research directions have been highlighted","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130473940","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}
Pub Date : 2021-07-01DOI: 10.4018/IJAIML.20210701.OA2
V. Shanmugarajeshwari, M. Ilayaraja
Information is stored in various domains like finance, banking, hospital, education, etc. Nowadays, data stored in medical databases are growing rapidly. The proposed approach entails three parts comparable to preprocessing, attribute selection, and classification C5.0 algorithms. This work aims to design a machine-based diagnostic approach using various techniques. These algorithms improve the efficiency of mining risk factors of chronic kidney diseases, but there are also have some shortcomings. To overcome these issues and improve an effectual clinical decision support system exhausting classification methods over a large volume of the dataset for making better decisions and predictions, this paper presents grouping classification assembly through consuming the C5.0 algorithm, pointing towards assembling time to acquire great accuracy to identify an early diagnosis of chronic kidney disease patients with risk level by analyzing the chronic kidney disease dataset.
{"title":"Intelligent Prediction Techniques for Chronic Kidney Disease Data Analysis","authors":"V. Shanmugarajeshwari, M. Ilayaraja","doi":"10.4018/IJAIML.20210701.OA2","DOIUrl":"https://doi.org/10.4018/IJAIML.20210701.OA2","url":null,"abstract":"Information is stored in various domains like finance, banking, hospital, education, etc. Nowadays, data stored in medical databases are growing rapidly. The proposed approach entails three parts comparable to preprocessing, attribute selection, and classification C5.0 algorithms. This work aims to design a machine-based diagnostic approach using various techniques. These algorithms improve the efficiency of mining risk factors of chronic kidney diseases, but there are also have some shortcomings. To overcome these issues and improve an effectual clinical decision support system exhausting classification methods over a large volume of the dataset for making better decisions and predictions, this paper presents grouping classification assembly through consuming the C5.0 algorithm, pointing towards assembling time to acquire great accuracy to identify an early diagnosis of chronic kidney disease patients with risk level by analyzing the chronic kidney disease dataset.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"8 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132849919","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}
Pub Date : 2021-07-01DOI: 10.4018/IJAIML.20210701.OA5
Manas Malik, Nirbhay Bagmar
An auction-based cloud model is followed in the spot pricing mechanism, where the spot instances charge changes with time. The user is bound to pay for the time that is initially initiated. If the user terminates before the sessional hourly completion, then the customer will be billed on the entire hourly session. In case Amazon terminates the instance then the customer would not be billed for the partial hour. When the current spot price reduces to bid price without any notification the cloud provider terminates the spot instance, it is a big disadvantage to the time of the availability factor, which is highly important. Therefore, it is crucial for the bidder to forecast before engaging the bids for spot prices. This paper represents a technique to analyze and predict the spot prices for instances using machine learning. It also discusses implementation, explored factors in detail, and outcomes on numerous instances of Amazon Elastic Compute Cloud (EC2). This technique reduces efforts and errors for forecasting prices.
{"title":"Forecasting Price of Amazon Spot Instances Using Machine Learning","authors":"Manas Malik, Nirbhay Bagmar","doi":"10.4018/IJAIML.20210701.OA5","DOIUrl":"https://doi.org/10.4018/IJAIML.20210701.OA5","url":null,"abstract":"An auction-based cloud model is followed in the spot pricing mechanism, where the spot instances charge changes with time. The user is bound to pay for the time that is initially initiated. If the user terminates before the sessional hourly completion, then the customer will be billed on the entire hourly session. In case Amazon terminates the instance then the customer would not be billed for the partial hour. When the current spot price reduces to bid price without any notification the cloud provider terminates the spot instance, it is a big disadvantage to the time of the availability factor, which is highly important. Therefore, it is crucial for the bidder to forecast before engaging the bids for spot prices. This paper represents a technique to analyze and predict the spot prices for instances using machine learning. It also discusses implementation, explored factors in detail, and outcomes on numerous instances of Amazon Elastic Compute Cloud (EC2). This technique reduces efforts and errors for forecasting prices.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121435768","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}
Pub Date : 2021-07-01DOI: 10.4018/IJAIML.20210701.OA3
Nida Meddouri, Hela Khoufi, Mondher Maddouri
Knowledge discovery data (KDD) is a research theme evolving to exploit a large data set collected every day from various fields of computing applications. The underlying idea is to extract hidden knowledge from a data set. It includes several tasks that form a process, such as data mining. Classification and clustering are data mining techniques. Several approaches were proposed in classification such as induction of decision trees, Bayes net, support vector machine, and formal concept analysis (FCA). The choice of FCA could be explained by its ability to extract hidden knowledge. Recently, researchers have been interested in the ensemble methods (sequential/parallel) to combine a set of classifiers. The combination of classifiers is made by a vote technique. There has been little focus on FCA in the context of ensemble learning. This paper presents a new approach to building a single part of the lattice with best possible concepts. This approach is based on parallel ensemble learning. It improves the state-of-the-art methods based on FCA since it handles more voluminous data.
{"title":"DFC: A Performant Dagging Approach of Classification Based on Formal Concept","authors":"Nida Meddouri, Hela Khoufi, Mondher Maddouri","doi":"10.4018/IJAIML.20210701.OA3","DOIUrl":"https://doi.org/10.4018/IJAIML.20210701.OA3","url":null,"abstract":"Knowledge discovery data (KDD) is a research theme evolving to exploit a large data set collected every day from various fields of computing applications. The underlying idea is to extract hidden knowledge from a data set. It includes several tasks that form a process, such as data mining. Classification and clustering are data mining techniques. Several approaches were proposed in classification such as induction of decision trees, Bayes net, support vector machine, and formal concept analysis (FCA). The choice of FCA could be explained by its ability to extract hidden knowledge. Recently, researchers have been interested in the ensemble methods (sequential/parallel) to combine a set of classifiers. The combination of classifiers is made by a vote technique. There has been little focus on FCA in the context of ensemble learning. This paper presents a new approach to building a single part of the lattice with best possible concepts. This approach is based on parallel ensemble learning. It improves the state-of-the-art methods based on FCA since it handles more voluminous data.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117187867","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}
Darryl Hond, H. Asgari, Daniel Jeffery, Mike Newman
The specification and verification of algorithms is vital for safety-critical autonomous systems which incorporate deep learning elements. We propose an integrated process for verifying artificial neural network (ANN) classifiers. This process consists of an off-line verification and an on-line performance prediction phase. The process is intended to verify ANN classifier generalisation performance, and to this end makes use of dataset dissimilarity measures. We introduce a novel measure for quantifying the dissimilarity between the dataset used to train a classification algorithm, and the test dataset used to evaluate and verify classifier performance. A system-level requirement could specify the permitted form of the functional relationship between classifier performance and a dissimilarity measure; such a requirement could be verified by dynamic testing. Experimental results, obtained using publicly available datasets, suggest that the measures have relevance to real-world practice for both quantifying dataset dissimilarity, and specifying and verifying classifier performance.
{"title":"An Integrated Process for Verifying Deep Learning Classifiers Using Dataset Dissimilarity Measures","authors":"Darryl Hond, H. Asgari, Daniel Jeffery, Mike Newman","doi":"10.4018/ijaiml.289536","DOIUrl":"https://doi.org/10.4018/ijaiml.289536","url":null,"abstract":"The specification and verification of algorithms is vital for safety-critical autonomous systems which incorporate deep learning elements. We propose an integrated process for verifying artificial neural network (ANN) classifiers. This process consists of an off-line verification and an on-line performance prediction phase. The process is intended to verify ANN classifier generalisation performance, and to this end makes use of dataset dissimilarity measures. We introduce a novel measure for quantifying the dissimilarity between the dataset used to train a classification algorithm, and the test dataset used to evaluate and verify classifier performance. A system-level requirement could specify the permitted form of the functional relationship between classifier performance and a dissimilarity measure; such a requirement could be verified by dynamic testing. Experimental results, obtained using publicly available datasets, suggest that the measures have relevance to real-world practice for both quantifying dataset dissimilarity, and specifying and verifying classifier performance.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126476536","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}
Sidna Jeddou, Amine Baïna, Najid Abdallah, H. E. Alami
The advent of the Internet of Things (IoT) augurs new cutting-edge applications in modern life such as smart cities and smart grids. These applications require protocols more efficient for ensuring the reliability of data communications in the IoT networks. Many works state that IoT cannot meet their demands without application protocols improvement with Artificial Intelligence (AI) as IoT are expected to generate unprecedented traffic giving IoT researchers access to data that can help in studying and analyzing the demands and develop application protocols conceptions to meet the requirement of IoT applications. In literature, several works introduced AI in some layers of the TCP/IP model including wireless communication and routing. In this article, an evaluation of application protocols HTTP, MQTT, DDS, XMPP, AMQP, and CoAP has been presented; and subsequently, the power consumption prediction of MQTT and COAP based on the linear regression model is analyzed, in order to enhance data communications in IoT applications.
{"title":"Power Consumption prediction of IoT application Protocols Based on Linear Regression","authors":"Sidna Jeddou, Amine Baïna, Najid Abdallah, H. E. Alami","doi":"10.4018/ijaiml.287585","DOIUrl":"https://doi.org/10.4018/ijaiml.287585","url":null,"abstract":"The advent of the Internet of Things (IoT) augurs new cutting-edge applications in modern life such as smart cities and smart grids. These applications require protocols more efficient for ensuring the reliability of data communications in the IoT networks. Many works state that IoT cannot meet their demands without application protocols improvement with Artificial Intelligence (AI) as IoT are expected to generate unprecedented traffic giving IoT researchers access to data that can help in studying and analyzing the demands and develop application protocols conceptions to meet the requirement of IoT applications. In literature, several works introduced AI in some layers of the TCP/IP model including wireless communication and routing. In this article, an evaluation of application protocols HTTP, MQTT, DDS, XMPP, AMQP, and CoAP has been presented; and subsequently, the power consumption prediction of MQTT and COAP based on the linear regression model is analyzed, in order to enhance data communications in IoT applications.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121215732","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}
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