The pandemic across the globe has constrained the change from a conventional face to face to e-learning platforms. The most challenging task during online learning is to be aware and support the emotional side of students. In existing environments, the emotion of the listener consideration is lagging. This can be provided by capturing the emotions of the listener through facial expressions. In general, the most common facial expressions are happy, sad, anger, fear, disgust, neutral and surprise. This knowledge can be used to classify different listeners. Hence in this article, we proposed a novel approach to identify an emotion based learner category in the development of Intelligent Adaptive E-Learning Environment by using Convolution Neural Network. The major work is composed of emotion detection model and learner categorization. The emotion detection model is trained by using a standard FER2013 dataset and it is extended with live streams of learners. The results of emotion detection model are extended to categorize the learners by fusing emotions and comprehend as Active, Evaluative, Passive and Non-Listener. The proposed model is trained using 100 epochs and achieved an accuracy of 94.44% in the training phase. This knowledge helps to interpret learner’s participation in e-learning environment.
{"title":"An Approach for Learner Categorization Based on Emotions in Intelligent Adaptive E-Learning Environment","authors":"Madhubala Myneni, Haritha Akkineni, Chennupalli Srinivasulu","doi":"10.13052/jmm1550-4646.18611","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.18611","url":null,"abstract":"The pandemic across the globe has constrained the change from a conventional face to face to e-learning platforms. The most challenging task during online learning is to be aware and support the emotional side of students. In existing environments, the emotion of the listener consideration is lagging. This can be provided by capturing the emotions of the listener through facial expressions. In general, the most common facial expressions are happy, sad, anger, fear, disgust, neutral and surprise. This knowledge can be used to classify different listeners. Hence in this article, we proposed a novel approach to identify an emotion based learner category in the development of Intelligent Adaptive E-Learning Environment by using Convolution Neural Network. The major work is composed of emotion detection model and learner categorization. The emotion detection model is trained by using a standard FER2013 dataset and it is extended with live streams of learners. The results of emotion detection model are extended to categorize the learners by fusing emotions and comprehend as Active, Evaluative, Passive and Non-Listener. The proposed model is trained using 100 epochs and achieved an accuracy of 94.44% in the training phase. This knowledge helps to interpret learner’s participation in e-learning environment.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123371236","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 : 2022-07-18DOI: 10.13052/jmm1550-4646.1864
M. Lambay, S.Pakkir Mohideen, Dr. S. Pakkir Mohideen
Recommendations are useful suggestions used by people from all walks of life. However, the usage of recommender systems plays a vital role in modern applications. They are found in different domains such as E-commerce. Concerning the health care industry, recommendations play a very crucial role. This industry has significance as it is linked to the lives of people and their well-being. Human health depends on the diet followed. Keeping this fact in mind, in this paper, we investigated healthy diet recommendations. The recommender systems that are existing in healthcare focused a little in this area. From the literature, it is understood that most of the frameworks on health recommendations are theoretical in nature. As food decides health, it is to be given paramount importance. In this paper, we proposed a hybrid mechanism based on Artificial Intelligence (AI) for big data analytics. Particularly we used Machine Learning (ML) for generating healthy diet recommendations. The proposed system is known as Hybrid Recommender System (HRS). It involves a hybrid approach with Natural Language Processing (NLP) and machine learning. An algorithm named Intelligent Recommender for Healthy Diet (IR-HD) is proposed to analyze data and provide healthy diet recommendations. IR-HD could generate recommendations on a healthy diet and outperform existing models. Python data science platform is used to implement the recommender system. The results of experiments showed that the system is capable of providing quality recommendations and it has performance improvement over the state of the art.
{"title":"A Hybrid Approach Based Diet Recommendation System Using ML and Big Data Analytics","authors":"M. Lambay, S.Pakkir Mohideen, Dr. S. Pakkir Mohideen","doi":"10.13052/jmm1550-4646.1864","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1864","url":null,"abstract":"Recommendations are useful suggestions used by people from all walks of life. However, the usage of recommender systems plays a vital role in modern applications. They are found in different domains such as E-commerce. Concerning the health care industry, recommendations play a very crucial role. This industry has significance as it is linked to the lives of people and their well-being. Human health depends on the diet followed. Keeping this fact in mind, in this paper, we investigated healthy diet recommendations. The recommender systems that are existing in healthcare focused a little in this area. From the literature, it is understood that most of the frameworks on health recommendations are theoretical in nature. As food decides health, it is to be given paramount importance. In this paper, we proposed a hybrid mechanism based on Artificial Intelligence (AI) for big data analytics. Particularly we used Machine Learning (ML) for generating healthy diet recommendations. The proposed system is known as Hybrid Recommender System (HRS). It involves a hybrid approach with Natural Language Processing (NLP) and machine learning. An algorithm named Intelligent Recommender for Healthy Diet (IR-HD) is proposed to analyze data and provide healthy diet recommendations. IR-HD could generate recommendations on a healthy diet and outperform existing models. Python data science platform is used to implement the recommender system. The results of experiments showed that the system is capable of providing quality recommendations and it has performance improvement over the state of the art.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129056830","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 : 2022-07-18DOI: 10.13052/jmm1550-4646.18614
Youngsik Kim
Soft 3D models such as Slime need to use a variety of different animations in the same situation in order to show a natural dynamic appearance. Metaball can express soft objects well with a small amount of data, but it requires a lot of computation time for real-time rendering. Because of this, it is difficult to find models using Metaball in 3D games. This paper developed a 3D slime game character using Metaball by applying ray marching technique in Unreal Engine 4. In addition, 3D games including slime characters and general fixed game character models were produced and the performance of it were evaluated. Even if the number of slime characters is changed from 0 to 40, it has been verified that the rendering speed is maintained at 30 FPS (Frames Per Second) or more, so that the game can be played.
{"title":"Design and Performance Evaluation of Soft 3D Models using Metaball in Unreal Engine 4","authors":"Youngsik Kim","doi":"10.13052/jmm1550-4646.18614","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.18614","url":null,"abstract":"Soft 3D models such as Slime need to use a variety of different animations in the same situation in order to show a natural dynamic appearance. Metaball can express soft objects well with a small amount of data, but it requires a lot of computation time for real-time rendering. Because of this, it is difficult to find models using Metaball in 3D games. This paper developed a 3D slime game character using Metaball by applying ray marching technique in Unreal Engine 4. In addition, 3D games including slime characters and general fixed game character models were produced and the performance of it were evaluated. Even if the number of slime characters is changed from 0 to 40, it has been verified that the rendering speed is maintained at 30 FPS (Frames Per Second) or more, so that the game can be played.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"33 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113976270","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 : 2022-07-18DOI: 10.13052/jmm1550-4646.18617
Ronnasak Wongverawatanakul, A. Leelasantitham
Strategic IT demand plays a crucial role in the success of any business. This process requires an in-depth understanding of the organizational strategy level by senior management and management at various levels. This abstract describes the importance of IT Demand Management to the strategic planning of organizations’ information technology innovations where management at all levels are involved in thinking, analyzing, and deciding every important IT investment that it can contribute to business success. This abstract presents a conceptual model, the relationship between EO and TO strategies, and ITDM processes to meet the needs of Optimization of OP that aims to become Innovation Organizations. This result is obtained through a survey study of 50 companies. Samples were management at different levels as the survey can confirm that the ITDM process is an important part of what management needs to know, understand and it can be used as a decision-making tool in organizations’strategic planning as well as it can create a process to become good governance. It can be able to drive and support the IT needs of customers and organizations for the success of the business in the future, truly. Moreover, this concept can be applied in several business areas or industries such as Banking, Energy, Mobile technology, or Telecommunication business.
{"title":"Strategic IT Demand Management for Business and Innovation Organization","authors":"Ronnasak Wongverawatanakul, A. Leelasantitham","doi":"10.13052/jmm1550-4646.18617","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.18617","url":null,"abstract":"Strategic IT demand plays a crucial role in the success of any business. This process requires an in-depth understanding of the organizational strategy level by senior management and management at various levels. This abstract describes the importance of IT Demand Management to the strategic planning of organizations’ information technology innovations where management at all levels are involved in thinking, analyzing, and deciding every important IT investment that it can contribute to business success. This abstract presents a conceptual model, the relationship between EO and TO strategies, and ITDM processes to meet the needs of Optimization of OP that aims to become Innovation Organizations. This result is obtained through a survey study of 50 companies. Samples were management at different levels as the survey can confirm that the ITDM process is an important part of what management needs to know, understand and it can be used as a decision-making tool in organizations’strategic planning as well as it can create a process to become good governance. It can be able to drive and support the IT needs of customers and organizations for the success of the business in the future, truly. Moreover, this concept can be applied in several business areas or industries such as Banking, Energy, Mobile technology, or Telecommunication business.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"153 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131346552","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 : 2022-07-18DOI: 10.13052/jmm1550-4646.1866
Paulo Victor de Magalhães Rozatto, André Luiz Cunha de Oliveira, Rodrigo L. S. Silva
The purpose of this paper is to present OrBI, a framework for developing hands-free user interfaces for interacting with low-cost Virtual Reality systems through mobile devices. The proposed implementation was developed and used in various Virtual Reality apps. To assess OrBI’s precision and ease to use, we submit the framework to a quantitative and qualitative evaluation. The findings indicate that the proposed interface model is easy to use and have a suitable precision. The findings also showed that the framework might be challenging for older people to use and, in some cases, tiresome.
{"title":"OrBI - A Hands-free Virtual Reality User Interface for Mobile Devices","authors":"Paulo Victor de Magalhães Rozatto, André Luiz Cunha de Oliveira, Rodrigo L. S. Silva","doi":"10.13052/jmm1550-4646.1866","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1866","url":null,"abstract":"The purpose of this paper is to present OrBI, a framework for developing hands-free user interfaces for interacting with low-cost Virtual Reality systems through mobile devices. The proposed implementation was developed and used in various Virtual Reality apps. To assess OrBI’s precision and ease to use, we submit the framework to a quantitative and qualitative evaluation. The findings indicate that the proposed interface model is easy to use and have a suitable precision. The findings also showed that the framework might be challenging for older people to use and, in some cases, tiresome.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130468271","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 : 2022-07-18DOI: 10.13052/jmm1550-4646.1865
Zhong Wang, K. Foo, Steven Yan, V. A. González, Nasser Giacaman
Virtual reality (VR) technology is quickly becoming more accessible to the general public due to the availability and capabilities of modern smartphone devices. However, such mobile devices are not as powerful as high-end desktop systems where VR is mostly established. Running demanding VR apps leads to performance issues such as lag, excessive heat, and fast battery drainage. To avoid these problems, software factors must be optimised. The user evaluation (N=51)(N=51) involved presenting multiple VR scenes (with varying frame rates), requiring participants to judge which scenes felt smooth; the results indicate that anything below 50 FPS was tolerable at best and nauseating at worst. To also measure the performance impact of various software settings, benchmarks were conducted on different smartphones. The results highlight the effects when varying the number of displayed on-screen objects, as well as outlining which settings should be avoid when specifically targeting mobile VR platforms. Heat and battery life were found to be non-issues at recommended performance levels. The proposed work established valuable guidelines which can be helpful for real time applications in development time reduction and complexity simplification from graphical and refresh rate optimization perspectives.
{"title":"Refresh Rate and Graphical Benchmarks for Mobile VR Application Development","authors":"Zhong Wang, K. Foo, Steven Yan, V. A. González, Nasser Giacaman","doi":"10.13052/jmm1550-4646.1865","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1865","url":null,"abstract":"\u0000\u0000\u0000Virtual reality (VR) technology is quickly becoming more accessible to the general public due to the availability and capabilities of modern smartphone devices. However, such mobile devices are not as powerful as high-end desktop systems where VR is mostly established. Running demanding VR apps leads to performance issues such as lag, excessive heat, and fast battery drainage. To avoid these problems, software factors must be optimised. The user evaluation (N=51)(N=51) involved presenting multiple VR scenes (with varying frame rates), requiring participants to judge which scenes felt smooth; the results indicate that anything below 50 FPS was tolerable at best and nauseating at worst. To also measure the performance impact of various software settings, benchmarks were conducted on different smartphones. The results highlight the effects when varying the number of displayed on-screen objects, as well as outlining which settings should be avoid when specifically targeting mobile VR platforms. Heat and battery life were found to be non-issues at recommended performance levels. The proposed work established valuable guidelines which can be helpful for real time applications in development time reduction and complexity simplification from graphical and refresh rate optimization perspectives.\u0000\u0000\u0000","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131417224","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 : 2022-07-18DOI: 10.13052/jmm1550-4646.1867
Mohammed Elamine Moumene, Khadidja Benkedadra, Fatima Zohra Berras
The detection of human skin color has been studied extensively during the past two decades. It is an essential task for various computer vision applications such as biometric authentication, face/hands tracking and gesture analysis. New machine learning methods are effective for skin color detection. However, they are not suitable for real time applications since they are computationally heavy. A lightweight approach for skin color detection consists of using segmentation rules extracted by an investigation on skin color distribution. The kin appearance varies with diversity of image types, acquisition parameters and scene illumination. There are no general segmentation rules that provide effective skin segmentation for different scene conditions. In this paper we present a real-time skin color detector which adapts itself according to tracked human parts. First, initial thresholds are calculated using two popular skin datasets. Those thresholds can also be calculated quickly using small training sets. The proposed skin color detector showed comparable skin segmentation to DeepLabV3++ application and an improvement in term of F1 measure when compared to methods that relies on static rules.
{"title":"Real Time Skin Color Detection Based on Adaptive HSV Thresholding","authors":"Mohammed Elamine Moumene, Khadidja Benkedadra, Fatima Zohra Berras","doi":"10.13052/jmm1550-4646.1867","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1867","url":null,"abstract":"The detection of human skin color has been studied extensively during the past two decades. It is an essential task for various computer vision applications such as biometric authentication, face/hands tracking and gesture analysis. New machine learning methods are effective for skin color detection. However, they are not suitable for real time applications since they are computationally heavy. A lightweight approach for skin color detection consists of using segmentation rules extracted by an investigation on skin color distribution. The kin appearance varies with diversity of image types, acquisition parameters and scene illumination. There are no general segmentation rules that provide effective skin segmentation for different scene conditions. In this paper we present a real-time skin color detector which adapts itself according to tracked human parts. First, initial thresholds are calculated using two popular skin datasets. Those thresholds can also be calculated quickly using small training sets. The proposed skin color detector showed comparable skin segmentation to DeepLabV3++ application and an improvement in term of F1 measure when compared to methods that relies on static rules.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130660087","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 : 2022-07-18DOI: 10.13052/jmm1550-4646.18619
Shin Kim, Kyoungro Yoon
Breast cancer is a fatal disease affecting women, and early detection and proper treatment are crucial. Classifying medical images correctly is the first and most important step in the cancer diagnosis stage. Deep learning-based classification methods in various domains demonstrate advances in accuracy. However, as deep learning improves, the layers of neural networks get deeper, raising challenges, such as overfitting and gradient vanishing. For instance, a medical image is simpler than an ordinary one, making it vulnerable to overfitting issues. We present breast histopathological classification methods with two deep neural networks, Xception and LightXception with aid of voting schemes over split images. Most deep neural networks classify thousands classes of images, but the breast histopathological image classes are far fewer than those of other image classification tasks. Because the BreakHis dataset is relatively simpler than typical image datasets, such as ImageNet, applying the conventional highly deep neural networks may suffer from the aforementioned overfitting or gradient vanishing problems. Additionally, highly deep neural networks require more resources, leading to high computational costs. Consequently, we propose a new network; LightXception by cutting off layers at the bottom of the Xception network and reducing the number of channels of convolution filters. LightXception has only about 35% of parameters compared to those of the original Xception network with minimal expense on performance. Based on images with 100X magnification factor, the performance comparisons for Xception vs. LightXception are 97.42% vs. 97.31% on classification accuracy, 97.42% vs. 97.42% on recall, and 99.26% vs. 98.67% of precision.
{"title":"Deep and Lightweight Neural Network for Histopathological Image Classification","authors":"Shin Kim, Kyoungro Yoon","doi":"10.13052/jmm1550-4646.18619","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.18619","url":null,"abstract":"\u0000\u0000\u0000Breast cancer is a fatal disease affecting women, and early detection and proper treatment are crucial. Classifying medical images correctly is the first and most important step in the cancer diagnosis stage. Deep learning-based classification methods in various domains demonstrate advances in accuracy.\u0000However, as deep learning improves, the layers of neural networks get deeper, raising challenges, such as overfitting and gradient vanishing. For instance, a medical image is simpler than an ordinary one, making it vulnerable to overfitting issues.\u0000We present breast histopathological classification methods with two deep neural networks, Xception and LightXception with aid of voting schemes over split images. Most deep neural networks classify thousands classes of images, but the breast histopathological image classes are far fewer than those of other image classification tasks. Because the BreakHis dataset is relatively simpler than typical image datasets, such as ImageNet, applying the conventional highly deep neural networks may suffer from the aforementioned overfitting or gradient vanishing problems. Additionally, highly deep neural networks require more resources, leading to high computational costs. Consequently, we propose a new network; LightXception by cutting off layers at the bottom of the Xception network and reducing the number of channels of convolution filters. LightXception has only about 35% of parameters compared to those of the original Xception network with minimal expense on performance. Based on images with 100X magnification factor, the performance comparisons for Xception vs. LightXception are 97.42% vs. 97.31% on classification accuracy, 97.42% vs. 97.42% on recall, and 99.26% vs. 98.67% of precision.\u0000\u0000\u0000","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133519425","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 : 2022-07-18DOI: 10.13052/jmm1550-4646.18616
Thammavich Wongsamerchue, A. Leelasantitham
Thailand’s current energy trading system is an Enhanced Single Buyer (ESB), a market monopoly by a single buyer. It will produce and distribute electricity to service providers in each area, enabling them to distribute services to consumers. In terms of the consumer aspect, it is necessary to purchase electricity from only one seller, and it is not possible to choose the manufacturer independently. Since the price mechanism is not competitive, the market price is mainly determined by a single buyer. Meanwhile, alternative energy power generation technology has progressed. Anyone can become a power producer using wind power or solar energy. People can easily produce electricity to use in their households. Besides, residual energy from use will be sold only to the ESB. However, there is a selling restriction because there is only one buyer. Importantly, Blockchain technology can be applied to enable independent electricity trading. In other words, called peer-to-peer (P2P) trading, the Thai government has policies to promote P2P trading. However, there are not many systems supporting P2P energy trading since P2P trading is still in the beginning stages of the Pilot Project. In this study, the researchers have presented a P2P Power Trading Model using Blockchain technology. This research presents a system with efficiency and simplicity. Also, there are other technology highlights such as IoT, Lora, and Electronic Double Auction. The researcher has designed, implemented, and tested it for actual electrical power trading. It can prove to be traded according to the designed test cases. Importantly, we are truly confident that this research will benefit those interested in developing real-world applications. This research can also be used as an alternative to the traditional power purchase and sale system.
{"title":"An Electronic Double Auction of Prepaid Electricity Trading Using Blockchain Technology","authors":"Thammavich Wongsamerchue, A. Leelasantitham","doi":"10.13052/jmm1550-4646.18616","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.18616","url":null,"abstract":"Thailand’s current energy trading system is an Enhanced Single Buyer (ESB), a market monopoly by a single buyer. It will produce and distribute electricity to service providers in each area, enabling them to distribute services to consumers. In terms of the consumer aspect, it is necessary to purchase electricity from only one seller, and it is not possible to choose the manufacturer independently. Since the price mechanism is not competitive, the market price is mainly determined by a single buyer. Meanwhile, alternative energy power generation technology has progressed. Anyone can become a power producer using wind power or solar energy. People can easily produce electricity to use in their households. Besides, residual energy from use will be sold only to the ESB. However, there is a selling restriction because there is only one buyer. Importantly, Blockchain technology can be applied to enable independent electricity trading. In other words, called peer-to-peer (P2P) trading, the Thai government has policies to promote P2P trading. However, there are not many systems supporting P2P energy trading since P2P trading is still in the beginning stages of the Pilot Project. In this study, the researchers have presented a P2P Power Trading Model using Blockchain technology. This research presents a system with efficiency and simplicity. Also, there are other technology highlights such as IoT, Lora, and Electronic Double Auction. The researcher has designed, implemented, and tested it for actual electrical power trading. It can prove to be traded according to the designed test cases. Importantly, we are truly confident that this research will benefit those interested in developing real-world applications. This research can also be used as an alternative to the traditional power purchase and sale system.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124034815","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 : 2022-07-18DOI: 10.13052/jmm1550-4646.18612
M. Al-Shabi
Over the recent decades, incorporating Vehicular Ad-hoc Network (VANET) into Cloud computing plays a vital role, since it provides a reliable safety journey to vehicular drivers, passengers, etc. However, attaining security and emergency message dissemination is still major bottleneck in VANET combined Cloud, due to the dynamic nature of vehicles and wireless communication. Our major intention is to provide high level security in VANET-Cloud environment. In addition to it, we also reduce delay in emergency dissemination. Our proposed Delay aware Emergency Message Dissemination and Data Retrieval in secure (DEMD22RS) VANET-Cloud is composed of four sequential processes: Authentication, Clustering, Data Retrieval and Data dissemination. In regard to maintaining security for both Road Side Unit (RSU) and Vehicles, we propose Hash based Credential Authentication Scheme (HCAS) that affords authentication using Secure Hash Algorithm-3 (SHA-3) and Elliptic Curve Points (ECP). To sustain a stable cluster, Firm Aware Clustering Scheme (FACS) is pursued where Stud Krill Herd (SKH) algorithm is exploited. In the data retrieval process, cloud provides requested information to the RSU in encrypted form using the Twofish algorithm. RSU discover the path to deliver received data through executing Artificial Neural Network (ANN) algorithm. In order to diminish delay in emergency message dissemination, best disseminator is selected by cluster head using Fuzzy-Topsis (FT) algorithm. Our DEMD22RS VANET-Cloud network is implemented in Network Simulator 3 tool. Finally, the evaluation of DEMD22RS work performance is achieved by computing consequent metrics that are Throughput, Packet Delivery Ratio, Transmission delay, Average delay, Key generation time, Encryption time and Decryption time.
{"title":"An Enhance the Performance of Mining Vehicular and Machinery Security Systems Using Artificial Intelligence in VANET Cloud Computing","authors":"M. Al-Shabi","doi":"10.13052/jmm1550-4646.18612","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.18612","url":null,"abstract":"Over the recent decades, incorporating Vehicular Ad-hoc Network (VANET) into Cloud computing plays a vital role, since it provides a reliable safety journey to vehicular drivers, passengers, etc. However, attaining security and emergency message dissemination is still major bottleneck in VANET combined Cloud, due to the dynamic nature of vehicles and wireless communication. Our major intention is to provide high level security in VANET-Cloud environment. In addition to it, we also reduce delay in emergency dissemination. Our proposed Delay aware Emergency Message Dissemination and Data Retrieval in secure (DEMD22RS) VANET-Cloud is composed of four sequential processes: Authentication, Clustering, Data Retrieval and Data dissemination. In regard to maintaining security for both Road Side Unit (RSU) and Vehicles, we propose Hash based Credential Authentication Scheme (HCAS) that affords authentication using Secure Hash Algorithm-3 (SHA-3) and Elliptic Curve Points (ECP). To sustain a stable cluster, Firm Aware Clustering Scheme (FACS) is pursued where Stud Krill Herd (SKH) algorithm is exploited. In the data retrieval process, cloud provides requested information to the RSU in encrypted form using the Twofish algorithm. RSU discover the path to deliver received data through executing Artificial Neural Network (ANN) algorithm. In order to diminish delay in emergency message dissemination, best disseminator is selected by cluster head using Fuzzy-Topsis (FT) algorithm. Our DEMD22RS VANET-Cloud network is implemented in Network Simulator 3 tool. Finally, the evaluation of DEMD22RS work performance is achieved by computing consequent metrics that are Throughput, Packet Delivery Ratio, Transmission delay, Average delay, Key generation time, Encryption time and Decryption time.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114381303","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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