Pub Date : 2021-02-23DOI: 10.36548/JEEA.2020.4.004
G. Ranganathan, Bindhu
There have been many compression standards developed during the past few decades and technological advances has resulted in introducing many methodologies with promising results. As far as PSNR metric is concerned, there is a performance gap between reigning compression standards and learned compression algorithms. Based on research, we experimented using an accurate entropy model on the learned compression algorithms to determine the rate-distortion performance. In this paper, discretized Gaussian Mixture likelihood is proposed to determine the latent code parameters in order to attain a more flexible and accurate model of entropy. Moreover, we have also enhanced the performance of the work by introducing recent attention modules in the network architecture. Simulation results indicate that when compared with the previously existing techniques using high-resolution and Kodak datasets, the proposed work achieves a higher rate of performance. When MS-SSIM is used for optimization, our work generates a more visually pleasant image.
{"title":"Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules","authors":"G. Ranganathan, Bindhu","doi":"10.36548/JEEA.2020.4.004","DOIUrl":"https://doi.org/10.36548/JEEA.2020.4.004","url":null,"abstract":"There have been many compression standards developed during the past few decades and technological advances has resulted in introducing many methodologies with promising results. As far as PSNR metric is concerned, there is a performance gap between reigning compression standards and learned compression algorithms. Based on research, we experimented using an accurate entropy model on the learned compression algorithms to determine the rate-distortion performance. In this paper, discretized Gaussian Mixture likelihood is proposed to determine the latent code parameters in order to attain a more flexible and accurate model of entropy. Moreover, we have also enhanced the performance of the work by introducing recent attention modules in the network architecture. Simulation results indicate that when compared with the previously existing techniques using high-resolution and Kodak datasets, the proposed work achieves a higher rate of performance. When MS-SSIM is used for optimization, our work generates a more visually pleasant image.","PeriodicalId":20643,"journal":{"name":"Proposed for presentation at the 2020 Virtual MRS Fall Meeting & Exhibit held November 27 - December 4, 2020.","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86895705","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 : 2021-02-19DOI: 10.36548//JIIP.2020.4.005
Dr. S. Manoharan
Recently, the application of voice-controlled interfaces plays a major role in many real-time environments such as a car, smart home and mobile phones. In signal processing, the accuracy of speech recognition remains a thought-provoking challenge. The filter designs assist speech recognition systems in terms of improving accuracy by parameter tuning. This task is some degree of form filter’s narrowed specifications which lead to complex nonlinear problems in speech recognition. This research aims to provide analysis on complex nonlinear environment and exploration with recent techniques in the combination of statistical-based design and Support Vector Machine (SVM) based learning techniques. Dynamic Bayes network is a dominant technique related to speech processing characterizing stack co-occurrences. This method is derived from mathematical and statistical formalism. It is also used to predict the word sequences along with the posterior probability method with the help of phonetic word unit recognition. This research involves the complexities of signal processing that it is possible to combine sentences with various types of noises at different signal-to-noise ratios (SNR) along with the measure of comparison between the two techniques.
{"title":"Analysis of Complex Non-Linear Environment Exploration in Speech Recognition by Hybrid Learning Technique","authors":"Dr. S. Manoharan","doi":"10.36548//JIIP.2020.4.005","DOIUrl":"https://doi.org/10.36548//JIIP.2020.4.005","url":null,"abstract":"Recently, the application of voice-controlled interfaces plays a major role in many real-time environments such as a car, smart home and mobile phones. In signal processing, the accuracy of speech recognition remains a thought-provoking challenge. The filter designs assist speech recognition systems in terms of improving accuracy by parameter tuning. This task is some degree of form filter’s narrowed specifications which lead to complex nonlinear problems in speech recognition. This research aims to provide analysis on complex nonlinear environment and exploration with recent techniques in the combination of statistical-based design and Support Vector Machine (SVM) based learning techniques. Dynamic Bayes network is a dominant technique related to speech processing characterizing stack co-occurrences. This method is derived from mathematical and statistical formalism. It is also used to predict the word sequences along with the posterior probability method with the help of phonetic word unit recognition. This research involves the complexities of signal processing that it is possible to combine sentences with various types of noises at different signal-to-noise ratios (SNR) along with the measure of comparison between the two techniques.","PeriodicalId":20643,"journal":{"name":"Proposed for presentation at the 2020 Virtual MRS Fall Meeting & Exhibit held November 27 - December 4, 2020.","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77399020","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 : 2021-01-27DOI: 10.36548/JSCP.2020.4.006
P. Karrupusamy
Predictive maintenance is the way to improve asset management in every manufacturing industry. While handling advance costlier machinery in the industry, the predictive maintenance knowledge will be essential to protect the machinery before gets degradation performance. Recently, the emergence of business in manufacturing industry deals with good systems, regular intervals maintenance process, predictive maintenance (PdM), machine learning (ML) approaches are extensively applied for handling the health standing of business instrumentation. Now the digital transformation towards I4.0, data techniques, processed management and communication networks; it’s doable to gather huge amounts of operational and processes conditions information generated type many items of kit and harvest information for creating an automatic fault detection and diagnosing with the aim to attenuate period of time and increase utilization rate of the parts and increase their remaining helpful lives. The predictive maintenance is inevitable for property good producing in I40. This paper aims to provide a comprehensive review of the recent advancements of metric capacity unit techniques wide applied to PdM for good producing in I4.0 by classifying the analysis consistent with metric capacity unit algorithms, ML class, machinery and instrumentation used device employed in information acquisition, classification of knowledge size and kind, and highlight the key contributions of the researchers and so offers pointers and foundation for additional analysis. In this research paper we constructed a Random Forest model to predict the failure of the various machine in manufacturing industry. It compares the prediction result with Decision Tree (DT) algorithm and proves its superiority in accuracy and precision.
{"title":"Machine Learning Approach to Predictive Maintenance in Manufacturing Industry - A Comparative Study","authors":"P. Karrupusamy","doi":"10.36548/JSCP.2020.4.006","DOIUrl":"https://doi.org/10.36548/JSCP.2020.4.006","url":null,"abstract":"Predictive maintenance is the way to improve asset management in every manufacturing industry. While handling advance costlier machinery in the industry, the predictive maintenance knowledge will be essential to protect the machinery before gets degradation performance. Recently, the emergence of business in manufacturing industry deals with good systems, regular intervals maintenance process, predictive maintenance (PdM), machine learning (ML) approaches are extensively applied for handling the health standing of business instrumentation. Now the digital transformation towards I4.0, data techniques, processed management and communication networks; it’s doable to gather huge amounts of operational and processes conditions information generated type many items of kit and harvest information for creating an automatic fault detection and diagnosing with the aim to attenuate period of time and increase utilization rate of the parts and increase their remaining helpful lives. The predictive maintenance is inevitable for property good producing in I40. This paper aims to provide a comprehensive review of the recent advancements of metric capacity unit techniques wide applied to PdM for good producing in I4.0 by classifying the analysis consistent with metric capacity unit algorithms, ML class, machinery and instrumentation used device employed in information acquisition, classification of knowledge size and kind, and highlight the key contributions of the researchers and so offers pointers and foundation for additional analysis. In this research paper we constructed a Random Forest model to predict the failure of the various machine in manufacturing industry. It compares the prediction result with Decision Tree (DT) algorithm and proves its superiority in accuracy and precision.","PeriodicalId":20643,"journal":{"name":"Proposed for presentation at the 2020 Virtual MRS Fall Meeting & Exhibit held November 27 - December 4, 2020.","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89000868","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 : 2021-01-05DOI: 10.36548/jsws.2020.4.002
S. Smys, W. Haoxiang
Cyber threat is a major issue that has been terrorizing the computing work. A typical cyber-physical system is crucial in ensuring a safe and secure architecture of a sustainable computing ecosystem. Cyber Threat Intelligence (CTI) is a new methodology that is used to address some of the existing cyber threats and ensure a more secure environment for communication. Data credibility and reliability plays a vital role in increasing the potential of a typical CTI and the data collected for this purpose is said to be highly reliable. In this paper, we have introduced a CTI system using blockchain to tackle the issues of sustainability, scalability, privacy and reliability. This novel approach is capable of measuring organizations contributions, reducing network load, creating a reliable dataset and collecting CTI data with multiple feeds. We have testing various parameters to determine the efficiency of the proposed methodology. Experimental results show that when compared to other methodologies, we can save upto 20% of storage space using the proposed methodology.
{"title":"Data Elimination on Repetition using a Blockchain based Cyber Threat Intelligence","authors":"S. Smys, W. Haoxiang","doi":"10.36548/jsws.2020.4.002","DOIUrl":"https://doi.org/10.36548/jsws.2020.4.002","url":null,"abstract":"Cyber threat is a major issue that has been terrorizing the computing work. A typical cyber-physical system is crucial in ensuring a safe and secure architecture of a sustainable computing ecosystem. Cyber Threat Intelligence (CTI) is a new methodology that is used to address some of the existing cyber threats and ensure a more secure environment for communication. Data credibility and reliability plays a vital role in increasing the potential of a typical CTI and the data collected for this purpose is said to be highly reliable. In this paper, we have introduced a CTI system using blockchain to tackle the issues of sustainability, scalability, privacy and reliability. This novel approach is capable of measuring organizations contributions, reducing network load, creating a reliable dataset and collecting CTI data with multiple feeds. We have testing various parameters to determine the efficiency of the proposed methodology. Experimental results show that when compared to other methodologies, we can save upto 20% of storage space using the proposed methodology.","PeriodicalId":20643,"journal":{"name":"Proposed for presentation at the 2020 Virtual MRS Fall Meeting & Exhibit held November 27 - December 4, 2020.","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73830567","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 : 2021-01-05DOI: 10.36548/jsws.2020.4.001
J. Chen, Lu-Tsou Yeh
With the increase in mobile broadband utilization, more spectrum release is recommended by the Federal Communications Commission for spectrum sharing under a three-tire system called Citizens Broadband Radio Service. The standardization, functional and operational necessities of this framework are defined by the Wireless Innovation Forum. If an unavoidable shipborne radar appears on the channel, the channel must be vacated by the lower tier users. The timing constraints on CBRS is also stringent. Wireless stations transmit short beacon frames termed as heartbeat signals. These signals consist of the wireless channel encryption data, Service Set Identifier (SSID) and other credential data. These signals also transmit commands to vacate a channel. The heartbeat interval, timing constraint and domain proxy features are analyzed in this paper. CBSD renunciation and spectrum acquisition is performed with the help of domain proxy based communication. The CBRS-SAS channel allocation algorithm is further investigated. The communication interoperability and network robustness can improved with the introduction of secondary SAS and secondary domain proxy respectively.
{"title":"Optimization of Citizen Broadband Radio Service Frequency Allocation for Dynamic Spectrum Access System","authors":"J. Chen, Lu-Tsou Yeh","doi":"10.36548/jsws.2020.4.001","DOIUrl":"https://doi.org/10.36548/jsws.2020.4.001","url":null,"abstract":"With the increase in mobile broadband utilization, more spectrum release is recommended by the Federal Communications Commission for spectrum sharing under a three-tire system called Citizens Broadband Radio Service. The standardization, functional and operational necessities of this framework are defined by the Wireless Innovation Forum. If an unavoidable shipborne radar appears on the channel, the channel must be vacated by the lower tier users. The timing constraints on CBRS is also stringent. Wireless stations transmit short beacon frames termed as heartbeat signals. These signals consist of the wireless channel encryption data, Service Set Identifier (SSID) and other credential data. These signals also transmit commands to vacate a channel. The heartbeat interval, timing constraint and domain proxy features are analyzed in this paper. CBSD renunciation and spectrum acquisition is performed with the help of domain proxy based communication. The CBRS-SAS channel allocation algorithm is further investigated. The communication interoperability and network robustness can improved with the introduction of secondary SAS and secondary domain proxy respectively.","PeriodicalId":20643,"journal":{"name":"Proposed for presentation at the 2020 Virtual MRS Fall Meeting & Exhibit held November 27 - December 4, 2020.","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79791306","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 : 2020-12-31DOI: 10.46222/ajhtl.19770720-56
S. W. Kamau, B. Kalui
The food service industry has to continuously innovate; however, concerns have been raised regarding the issue of distinctive and customized innovation models. The study aimed at the general objective to investigate how non-technological food service innovation models create value in restaurants. The specific objective was to establish and explore the relationship between non-technological food service innovation models and value creation. The study used a cross-sectional descriptive survey research design which involved those hotel restaurants in Nairobi County that were registered with the Tourism Regulatory Authority (TRA) as at 2016. Multistage stratified sampling—as well as purposeful and random sampling techniques—were used with a sample size of 385 respondents. Data collection instruments included questionnaires, interview guide, and observation-checklist, achieving a response rate of 82.9%. The coded data was analysed using descriptive and inferential statistical data analytical methods. Hypotheses were tested using multinomial regression, t-test and chi-square. Food service innovation model had no significant relationship with value creation in restaurants (p-value of 0.554). The study concludes that there is a need for a systematic procedure/model for developing non-technological food service innovations. To these end the study proposes a new food service innovation model with new variable such as consultation of professionals. This will enable an innovative organizational culture and lead to significant cost savings in the food service industry, among other benefits.
{"title":"Non-Technological Food Service Innovation Models: Towards Building Value Creation in Restaurants within Hotels in Nairobi County, Kenya","authors":"S. W. Kamau, B. Kalui","doi":"10.46222/ajhtl.19770720-56","DOIUrl":"https://doi.org/10.46222/ajhtl.19770720-56","url":null,"abstract":"The food service industry has to continuously innovate; however, concerns have been raised regarding the issue of distinctive and customized innovation models. The study aimed at the general objective to investigate how non-technological food service innovation models create value in restaurants. The specific objective was to establish and explore the relationship between non-technological food service innovation models and value creation. The study used a cross-sectional descriptive survey research design which involved those hotel restaurants in Nairobi County that were registered with the Tourism Regulatory Authority (TRA) as at 2016. Multistage stratified sampling—as well as purposeful and random sampling techniques—were used with a sample size of 385 respondents. Data collection instruments included questionnaires, interview guide, and observation-checklist, achieving a response rate of 82.9%. The coded data was analysed using descriptive and inferential statistical data analytical methods. Hypotheses were tested using multinomial regression, t-test and chi-square. Food service innovation model had no significant relationship with value creation in restaurants (p-value of 0.554). The study concludes that there is a need for a systematic procedure/model for developing non-technological food service innovations. To these end the study proposes a new food service innovation model with new variable such as consultation of professionals. This will enable an innovative organizational culture and lead to significant cost savings in the food service industry, among other benefits.","PeriodicalId":20643,"journal":{"name":"Proposed for presentation at the 2020 Virtual MRS Fall Meeting & Exhibit held November 27 - December 4, 2020.","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91540832","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 : 2020-12-31DOI: 10.46222/ajhtl.19770720-55
Palesa Mpkhine, Ita Geyser
The demographic influences affecting the wellbeing of front of house (FoH) female employees who are employed in hotels. The work engagement subscales, vigour, dedication and absorption were measured against the participants’ age, level of education and marital status. A cross-sectional survey was done from a sample (n = 100) of female participants. A biographical questionnaire and The Utrecht Work Engagement Scale (UWES) were administered. Significant relationships were found on the vigour, dedication and absorption subscales. FoH female employees younger than 35, those with tertiary education and those without life partners displayed higher levels of wellbeing. Therefore work engagement levels vary with regards to age, marital and educational status. Human resource specialists for hotels could measure work engagement and apply it through in-house policies and supportive practices as well as defend these practices regarding their FOH female employees as female employees are the majority of employees within the hospitality industry. The workforce in South Africa is characterized by demographic diversity. The variances of work engagement are imperative as it enhances the guest experience and improves productivity and ultimately increases financial turnover for the hotels who operate in a very competitive market.
{"title":"The Impact of Demographic Influences on Work Engagement for Front of House Female Hotel Employees","authors":"Palesa Mpkhine, Ita Geyser","doi":"10.46222/ajhtl.19770720-55","DOIUrl":"https://doi.org/10.46222/ajhtl.19770720-55","url":null,"abstract":"The demographic influences affecting the wellbeing of front of house (FoH) female employees who are employed in hotels. The work engagement subscales, vigour, dedication and absorption were measured against the participants’ age, level of education and marital status. A cross-sectional survey was done from a sample (n = 100) of female participants. A biographical questionnaire and The Utrecht Work Engagement Scale (UWES) were administered. Significant relationships were found on the vigour, dedication and absorption subscales. FoH female employees younger than 35, those with tertiary education and those without life partners displayed higher levels of wellbeing. Therefore work engagement levels vary with regards to age, marital and educational status. Human resource specialists for hotels could measure work engagement and apply it through in-house policies and supportive practices as well as defend these practices regarding their FOH female employees as female employees are the majority of employees within the hospitality industry. The workforce in South Africa is characterized by demographic diversity. The variances of work engagement are imperative as it enhances the guest experience and improves productivity and ultimately increases financial turnover for the hotels who operate in a very competitive market.","PeriodicalId":20643,"journal":{"name":"Proposed for presentation at the 2020 Virtual MRS Fall Meeting & Exhibit held November 27 - December 4, 2020.","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88590720","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 : 2020-12-31DOI: 10.46222/AJHTL.19770720-81
T. Gumede, A. Nzama
This study aimed to explore the model that can be used to improve local community participation in ecotourism development processes. The study was conducted at the communities adjoining the Oribi Gorge Nature Reserve in KwaZulu-Natal, South Africa. A mixed methods design was adopted by the study during collection and analysis of data. A total of 384 respondents were sampled through convenience sampling technique. Questionnaires were used to collect data through face-to-face surveys. The study found that local communities had not been actively participating in ecotourism development processes, especially those undertaken within the rural setting as a result of different socio-economic factors including lacking necessary skills. This study asserts that this gap could be mitigated through implementation of local community participation improvement model (LCPIM) based on its potential for influencing enactment and/or amendment of policies on ecotourism development
{"title":"Enhancing Community Participation in Ecotourism through a Local Community Participation Improvement Model","authors":"T. Gumede, A. Nzama","doi":"10.46222/AJHTL.19770720-81","DOIUrl":"https://doi.org/10.46222/AJHTL.19770720-81","url":null,"abstract":"This study aimed to explore the model that can be used to improve local community participation in ecotourism development processes. The study was conducted at the communities adjoining the Oribi Gorge Nature Reserve in KwaZulu-Natal, South Africa. A mixed methods design was adopted by the study during collection and analysis of data. A total of 384 respondents were sampled through convenience sampling technique. Questionnaires were used to collect data through face-to-face surveys. The study found that local communities had not been actively participating in ecotourism development processes, especially those undertaken within the rural setting as a result of different socio-economic factors including lacking necessary skills. This study asserts that this gap could be mitigated through implementation of local community participation improvement model (LCPIM) based on its potential for influencing enactment and/or amendment of policies on ecotourism development","PeriodicalId":20643,"journal":{"name":"Proposed for presentation at the 2020 Virtual MRS Fall Meeting & Exhibit held November 27 - December 4, 2020.","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79146840","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 : 2020-12-31DOI: 10.46222/ajhtl.19770720-52
Tshepo Mothoagae, N. Joseph
Research has been conducted on the grading of tourism establishments but little research has been conducted on the implementation of Artificial Intelligence (AI) to increase the number of graded tourism establishments. The objective of this study was to identify variables influencing tourism grading and to use them to construct a Bayesian Model for increasing the number of tourism establishments. Data was collected using an online survey questionnaire developed using the Survey Monkey tool. A total of 87 responses were received from 60 non-graded and 27 graded tourism establishments. The results indicate six factors affecting tourism grading, namely cost of grading, grading benefits, simplicity/complexity of grading application process, government funding, training of prospective grading applicants and computer literacy. The results further indicate grading cost and grading benefits as the most important factors for increasing the number of tourism establishments. The study implies that using this model will assist grading professionals to make informed decisions on initiatives aimed at increasing the number of graded tourism establishments. The study is among the first on implementation of AI to increase tourism grading establishments.
{"title":"The Design of a Bayesian Network Model for Increasing the Number of Graded Tourism Establishments","authors":"Tshepo Mothoagae, N. Joseph","doi":"10.46222/ajhtl.19770720-52","DOIUrl":"https://doi.org/10.46222/ajhtl.19770720-52","url":null,"abstract":"Research has been conducted on the grading of tourism establishments but little research has been conducted on the implementation of Artificial Intelligence (AI) to increase the number of graded tourism establishments. The objective of this study was to identify variables influencing tourism grading and to use them to construct a Bayesian Model for increasing the number of tourism establishments. Data was collected using an online survey questionnaire developed using the Survey Monkey tool. A total of 87 responses were received from 60 non-graded and 27 graded tourism establishments. The results indicate six factors affecting tourism grading, namely cost of grading, grading benefits, simplicity/complexity of grading application process, government funding, training of prospective grading applicants and computer literacy. The results further indicate grading cost and grading benefits as the most important factors for increasing the number of tourism establishments. The study implies that using this model will assist grading professionals to make informed decisions on initiatives aimed at increasing the number of graded tourism establishments. The study is among the first on implementation of AI to increase tourism grading establishments.","PeriodicalId":20643,"journal":{"name":"Proposed for presentation at the 2020 Virtual MRS Fall Meeting & Exhibit held November 27 - December 4, 2020.","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78482135","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 : 2020-12-31DOI: 10.36548/jitdw.2020.4.006
Pallikonda Subhashini, Raqshanda Siddiqua, A. Keerthana, Pamu Pavani
Gaining from books is an unremarkable and latent cycle. The content and images in the books are most certainly not interactive; this prompts the basic barricades to learning looked by students, for example, constraints in comprehending the hypothetical ideas, absence of explanatory, basic reasoning. These detours are overwhelmed by computerized books, however paper-based books are frequently favored over computerized books due to their adaptability and portability. In this paper, we present a remarkable arrangement that utilizes augmented reality to make the learning measure more interactive and fascinating. The application when focused on text or image shows significant 3-dimensional(3D) model or video on the smart phone screen. The application gives some assistance to the students by encouraging them to learn new ideas utilizing graphical guide. Aside from utilization in schooling, it can likewise be utilized in the field of commercial, the travel industry, gaming, medication.
{"title":"Augmented Reality in Education","authors":"Pallikonda Subhashini, Raqshanda Siddiqua, A. Keerthana, Pamu Pavani","doi":"10.36548/jitdw.2020.4.006","DOIUrl":"https://doi.org/10.36548/jitdw.2020.4.006","url":null,"abstract":"Gaining from books is an unremarkable and latent cycle. The content and images in the books are most certainly not interactive; this prompts the basic barricades to learning looked by students, for example, constraints in comprehending the hypothetical ideas, absence of explanatory, basic reasoning. These detours are overwhelmed by computerized books, however paper-based books are frequently favored over computerized books due to their adaptability and portability. In this paper, we present a remarkable arrangement that utilizes augmented reality to make the learning measure more interactive and fascinating. The application when focused on text or image shows significant 3-dimensional(3D) model or video on the smart phone screen. The application gives some assistance to the students by encouraging them to learn new ideas utilizing graphical guide. Aside from utilization in schooling, it can likewise be utilized in the field of commercial, the travel industry, gaming, medication.","PeriodicalId":20643,"journal":{"name":"Proposed for presentation at the 2020 Virtual MRS Fall Meeting & Exhibit held November 27 - December 4, 2020.","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81786428","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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