Pub Date : 2022-11-01DOI: 10.18178/ijmlc.2022.12.6.1122
{"title":"Controllable Question Generation with Semantic Graphs","authors":"","doi":"10.18178/ijmlc.2022.12.6.1122","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.6.1122","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43701746","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 : 2022-11-01DOI: 10.18178/ijmlc.2022.12.6.1121
{"title":"Analysis of Cyber Activities of Potential Business Customers Using Neo4j Graph Databases","authors":"","doi":"10.18178/ijmlc.2022.12.6.1121","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.6.1121","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44026271","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 : 2022-11-01DOI: 10.18178/ijmlc.2022.12.6.1112
{"title":"Forecasting Electricity Consumption in the Philippines Using ARIMA Models","authors":"","doi":"10.18178/ijmlc.2022.12.6.1112","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.6.1112","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42594871","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 : 2022-11-01DOI: 10.18178/ijmlc.2022.12.6.1116
{"title":"A Look-Ahead operator as a Learning Strategy for Solving Bi-objective Scheduling Multiprocessor Tasks on Two Dedicated Processors","authors":"","doi":"10.18178/ijmlc.2022.12.6.1116","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.6.1116","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48053219","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 : 2022-10-05DOI: 10.53759/7669/jmc202202020
Heikku Siltanen, Lars Vlrtanen
Data collecting, caching, analysis, and processing in close proximity to where the data is collected is referred to as "edge intelligence," a group of linked devices and systems. Edge Intelligence aims to improve data processing quality and speed while also safeguarding the data's privacy and security. This area of study, which dates just from 2011, has shown tremendous development in the last five years, despite its relative youth. This paper provides a survey of the architectures of edge intelligence (Data Placement-Based Architectures to Reduce Latency; 2) Orchestration-Based ECAs- IoT. 3) Big Data Analysis-Based Architectures; and 4) Security-Based Architectures) as well as the challenges and solutions for innovative architectures in edge intelligence.
{"title":"Assessment of Innovative Architectures, Challenges and Solutions of Edge Intelligence","authors":"Heikku Siltanen, Lars Vlrtanen","doi":"10.53759/7669/jmc202202020","DOIUrl":"https://doi.org/10.53759/7669/jmc202202020","url":null,"abstract":"Data collecting, caching, analysis, and processing in close proximity to where the data is collected is referred to as \"edge intelligence,\" a group of linked devices and systems. Edge Intelligence aims to improve data processing quality and speed while also safeguarding the data's privacy and security. This area of study, which dates just from 2011, has shown tremendous development in the last five years, despite its relative youth. This paper provides a survey of the architectures of edge intelligence (Data Placement-Based Architectures to Reduce Latency; 2) Orchestration-Based ECAs- IoT. 3) Big Data Analysis-Based Architectures; and 4) Security-Based Architectures) as well as the challenges and solutions for innovative architectures in edge intelligence.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78006836","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 : 2022-10-05DOI: 10.53759/7669/jmc202202019
Minu Balakrishnan
Due to the rise of ubiquitous computing, issues around the discovery of new services have become a hot topic in the academic community. This is because futuristic ubiquitous computing frameworks as well as unsupervised Ad hoc systems will require service discovery. Automatically discovering network services and their characteristics, as well as dynamically advertising their persistence, are both possible with service discovery. Multiple service discovery technologies, including Service Discovery Protocols (SDP) runners, Universal Plug and Play (UPnP), Service Location Protocol (SLP), and Jini, have been suggested. In this paper, we detail the process of service discovery and control using Ubiquitous Remote Manager (URM), a system for remote and autonomous management of home gateways that allows for more system simplicity and finer grained command of the underlying network in a ubiquitous computing setting. Ubiquitous Service Discovery (USD) is a key component of the infrastructure of the smart homes of the future. USD's goal is to locate the most suitable service, given the user's preferences and needs, in a pervasive and extensive setting.
{"title":"An Evaluation of Ubiquitous Service Discovery and Remote Management in Ad-Hoc Networks","authors":"Minu Balakrishnan","doi":"10.53759/7669/jmc202202019","DOIUrl":"https://doi.org/10.53759/7669/jmc202202019","url":null,"abstract":"Due to the rise of ubiquitous computing, issues around the discovery of new services have become a hot topic in the academic community. This is because futuristic ubiquitous computing frameworks as well as unsupervised Ad hoc systems will require service discovery. Automatically discovering network services and their characteristics, as well as dynamically advertising their persistence, are both possible with service discovery. Multiple service discovery technologies, including Service Discovery Protocols (SDP) runners, Universal Plug and Play (UPnP), Service Location Protocol (SLP), and Jini, have been suggested. In this paper, we detail the process of service discovery and control using Ubiquitous Remote Manager (URM), a system for remote and autonomous management of home gateways that allows for more system simplicity and finer grained command of the underlying network in a ubiquitous computing setting. Ubiquitous Service Discovery (USD) is a key component of the infrastructure of the smart homes of the future. USD's goal is to locate the most suitable service, given the user's preferences and needs, in a pervasive and extensive setting.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73724601","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 : 2022-10-05DOI: 10.53759/7669/jmc202202024
Aaron Mike Oquaye, J. Mills
The manufacturing industry has come to regard logistics to enhance their product or service rather than a place to save costs in recent years. It is also well acknowledged that logistics and supply chain management impact firm performance. Despite the expanding importance of logistics, little work has been done to create a theory of logistics' role in the manufacturing industry, and further research into this area is certainly needed. The first part of the literature review presents an empirical review of the concept of the supply chain and logistics in the Ghanaian context. The section touches in the ideologies of both the inbound and outbound logistics. The literature review provides an assessment from performance measurement before presenting an empirical review of what this paper presents. Following that, this paper reviews earlier studies that have thoroughly analyzed supply networks. The data analysis is reported in the results and discussion sections included in this research. Lastly, we conclude with some recommendations after this section.
{"title":"A Case Study of Ghanaian Manufacturing Industry","authors":"Aaron Mike Oquaye, J. Mills","doi":"10.53759/7669/jmc202202024","DOIUrl":"https://doi.org/10.53759/7669/jmc202202024","url":null,"abstract":"The manufacturing industry has come to regard logistics to enhance their product or service rather than a place to save costs in recent years. It is also well acknowledged that logistics and supply chain management impact firm performance. Despite the expanding importance of logistics, little work has been done to create a theory of logistics' role in the manufacturing industry, and further research into this area is certainly needed. The first part of the literature review presents an empirical review of the concept of the supply chain and logistics in the Ghanaian context. The section touches in the ideologies of both the inbound and outbound logistics. The literature review provides an assessment from performance measurement before presenting an empirical review of what this paper presents. Following that, this paper reviews earlier studies that have thoroughly analyzed supply networks. The data analysis is reported in the results and discussion sections included in this research. Lastly, we conclude with some recommendations after this section.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74765571","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 : 2022-10-05DOI: 10.53759/7669/jmc202202021
Vincenzo Anselmi
Cyber-Physical Systems (CPSs) are a new study area that has the potential to bring together the physical and digital worlds. Researchers in this study focus on the ways in which CPSs communicate with one other. Features and requirements for data transfer in CPSs, and also related unsolved issues, are covered in this paper. The IEEE 802.11 b/g protocols were used to construct a CPS solution for ambient sensing (humidity and temperature). Wireless Fidelity (Wi- Fi) sensors that can connect to a preexisting Wireless LAN and servers that provides access to data collected everywhere IEEE 802.11 b/g networks connectivity is available and from any Internet-connected device are required for this method.
{"title":"A Critical Analysis of Advanced Communication in Cyber-Physical Systems","authors":"Vincenzo Anselmi","doi":"10.53759/7669/jmc202202021","DOIUrl":"https://doi.org/10.53759/7669/jmc202202021","url":null,"abstract":"Cyber-Physical Systems (CPSs) are a new study area that has the potential to bring together the physical and digital worlds. Researchers in this study focus on the ways in which CPSs communicate with one other. Features and requirements for data transfer in CPSs, and also related unsolved issues, are covered in this paper. The IEEE 802.11 b/g protocols were used to construct a CPS solution for ambient sensing (humidity and temperature). Wireless Fidelity (Wi- Fi) sensors that can connect to a preexisting Wireless LAN and servers that provides access to data collected everywhere IEEE 802.11 b/g networks connectivity is available and from any Internet-connected device are required for this method.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"107 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90425158","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 : 2022-10-05DOI: 10.53759/7669/jmc202202023
Anna Recchi
The developments in hardware and wireless networks have brought humans to the brink of a new era in which small, wire-free devices will give them access to data at any time and any location and significantly contribute to the building of smart surroundings. Wireless Sensor Network (WSN) sensors collect data on the parameters they are used to detect. However, the performance of these sensors is constrained due to power and bandwidth limitations. In order to get beyond these limitations, they may use Machine Learning (ML) techniques. WSNs have witnessed a steady rise in the use of advanced ML techniques to distribute and improve network performance over the last decade. ML enthuses a plethora of real-world applications that maximize resource use and extend the network's life span. Furthermore, WSN designers have agreed that ML paradigms may be used for a broad range of meaningful tasks, such as localization and data aggregation as well as defect detection and security. This paper presents a survey of the ML models, as well as application in wireless networking and information processing. In addition, this paper evaluates the open challenges and future research directions of ML for WSNs.
{"title":"A Survey of Machine Learning for Information Processing and Networking","authors":"Anna Recchi","doi":"10.53759/7669/jmc202202023","DOIUrl":"https://doi.org/10.53759/7669/jmc202202023","url":null,"abstract":"The developments in hardware and wireless networks have brought humans to the brink of a new era in which small, wire-free devices will give them access to data at any time and any location and significantly contribute to the building of smart surroundings. Wireless Sensor Network (WSN) sensors collect data on the parameters they are used to detect. However, the performance of these sensors is constrained due to power and bandwidth limitations. In order to get beyond these limitations, they may use Machine Learning (ML) techniques. WSNs have witnessed a steady rise in the use of advanced ML techniques to distribute and improve network performance over the last decade. ML enthuses a plethora of real-world applications that maximize resource use and extend the network's life span. Furthermore, WSN designers have agreed that ML paradigms may be used for a broad range of meaningful tasks, such as localization and data aggregation as well as defect detection and security. This paper presents a survey of the ML models, as well as application in wireless networking and information processing. In addition, this paper evaluates the open challenges and future research directions of ML for WSNs.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78991744","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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