This paper focuses on using the cooperative neuro-fuzzy system for the effective and customised selection of entities from large and heterogeneous resources by presenting a general architecture. An experiment is carried out with the fast-moving consumer goods to prove the utility of the architecture. It is observed that most consumers go for the frequent purchase of fast-moving consumer items. Further, various brands, costs, discounts, schemes, quantities, and reviews might make it challenging. Hence, such decisions need to be intelligent and practically feasible in terms of time and effort. The paper discusses neural networks to categorise the entities, type-1 & 2 fuzzy membership functions with rules, training sets, and graphical views of the fuzzy rules and the experiment details. Besides the generic approach and experiment, the paper also discusses the work done so far with their limitations and applications in other domains. At the end, the paper presents the limitations and possible future enhancements.
{"title":"Effective Selection of Entities From Heterogeneous and Large Resources Using a Cooperative Neuro-Fuzzy System","authors":"P. Sajja, Rasendu Mishra","doi":"10.4018/ijsda.302633","DOIUrl":"https://doi.org/10.4018/ijsda.302633","url":null,"abstract":"This paper focuses on using the cooperative neuro-fuzzy system for the effective and customised selection of entities from large and heterogeneous resources by presenting a general architecture. An experiment is carried out with the fast-moving consumer goods to prove the utility of the architecture. It is observed that most consumers go for the frequent purchase of fast-moving consumer items. Further, various brands, costs, discounts, schemes, quantities, and reviews might make it challenging. Hence, such decisions need to be intelligent and practically feasible in terms of time and effort. The paper discusses neural networks to categorise the entities, type-1 & 2 fuzzy membership functions with rules, training sets, and graphical views of the fuzzy rules and the experiment details. Besides the generic approach and experiment, the paper also discusses the work done so far with their limitations and applications in other domains. At the end, the paper presents the limitations and possible future enhancements.","PeriodicalId":44415,"journal":{"name":"International Journal of System Dynamics Applications","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41636824","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}
J. K. Appati, Winfred Yaokumah, E. Owusu, Paul Ammah
One among a lot of public health concerns in rural and tropical areas is the human intestinal parasite. Traditionally, diagnosis of these parasites is by visual analysis of stool specimens, which is usually tedious and time-consuming. In this study, the authors combine techniques in the Laplacian pyramid, Gabor filter, and wavelet to build a feature vector for the discrimination of intestinal worm in a low-resolution image captured with mobile devices. The dimension of the feature vector is reduced using principal component analysis, and the resultant vector is considered as input to the SVM classifier. The proposed framework was applied to the Makerere intestinal dataset. At its preliminary stage, the results demonstrate satisfactory classification with an accuracy rate of 65.22% with possible extension in future work.
{"title":"Primary Mobile Image Analysis of Human Intestinal Worm Detection","authors":"J. K. Appati, Winfred Yaokumah, E. Owusu, Paul Ammah","doi":"10.4018/ijsda.302631","DOIUrl":"https://doi.org/10.4018/ijsda.302631","url":null,"abstract":"One among a lot of public health concerns in rural and tropical areas is the human intestinal parasite. Traditionally, diagnosis of these parasites is by visual analysis of stool specimens, which is usually tedious and time-consuming. In this study, the authors combine techniques in the Laplacian pyramid, Gabor filter, and wavelet to build a feature vector for the discrimination of intestinal worm in a low-resolution image captured with mobile devices. The dimension of the feature vector is reduced using principal component analysis, and the resultant vector is considered as input to the SVM classifier. The proposed framework was applied to the Makerere intestinal dataset. At its preliminary stage, the results demonstrate satisfactory classification with an accuracy rate of 65.22% with possible extension in future work.","PeriodicalId":44415,"journal":{"name":"International Journal of System Dynamics Applications","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42465768","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}
By the second decade of the 21st century, there has been a multi-faceted technological development in the field of networked control system (NCS). This progression in NCS has not only revealed its significant applications in various areas but has also unveiled various difficulties associated with it that hampered the operations of networked control system. Network-induced delays are issues that promote many other issues like packet dropout and brevity in bandwidth utilization. In this research article, network-induced delay has been curtailed by using the harmony between Smith predictor and Markov approach. The error estimation of the Smith predictor controller used for the simulation is carried out through a Markov approach which allows the control of the system to operate smoothly by optimizing the control signal. To implement the proposed method, the authors have simulated a third order system in Matlab/Simulink software.
{"title":"Performance Accretion in Delay Compensation of Networked Control System Using Markov Approach-Based Randomness Estimation in Smith Predictor","authors":"Ratish Kumar, Rajiv Kumar, M. Nigam","doi":"10.4018/ijsda.302634","DOIUrl":"https://doi.org/10.4018/ijsda.302634","url":null,"abstract":"By the second decade of the 21st century, there has been a multi-faceted technological development in the field of networked control system (NCS). This progression in NCS has not only revealed its significant applications in various areas but has also unveiled various difficulties associated with it that hampered the operations of networked control system. Network-induced delays are issues that promote many other issues like packet dropout and brevity in bandwidth utilization. In this research article, network-induced delay has been curtailed by using the harmony between Smith predictor and Markov approach. The error estimation of the Smith predictor controller used for the simulation is carried out through a Markov approach which allows the control of the system to operate smoothly by optimizing the control signal. To implement the proposed method, the authors have simulated a third order system in Matlab/Simulink software.","PeriodicalId":44415,"journal":{"name":"International Journal of System Dynamics Applications","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42935541","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 statistical growth analysis of field crop has become a great challenge in agriculture. Analyzing the growth of crop through automation provides extensive significance to the farmers for getting information about the problem arising in plants due to irregular growth monitoring. The idea behind this work is the importance of mapping with pixel-based clustering technique for growth analysis in terms of height calculation of rice crop (rice variety is MTU-1010). Height measurement plays a vital role in regular assessment for a healthy crop, and the approach proposed in this work achieves 97.58% accuracy of 14 sampled datasets taken from Indira Gandhi Agriculture University of Raipur, Chhattisgarh; a real-time dataset has been prepared. Proposed work is used for analyzing vertical as well as horizontal scaling technique. Vertical mapping provides the height of a single plant whereas horizontal mapping using k-means clustering provides an average height of the whole field. This work uses machine learning, and image processing techniques are used for this work.
{"title":"Statistical Growth Analysis of Rice Plants in Chhattisgarh Region Using Automated Pixel-Based Mapping Technique","authors":"B. Patel, Aakanksha Sharaff, S. Verulkar","doi":"10.4018/ijsda.302632","DOIUrl":"https://doi.org/10.4018/ijsda.302632","url":null,"abstract":"The statistical growth analysis of field crop has become a great challenge in agriculture. Analyzing the growth of crop through automation provides extensive significance to the farmers for getting information about the problem arising in plants due to irregular growth monitoring. The idea behind this work is the importance of mapping with pixel-based clustering technique for growth analysis in terms of height calculation of rice crop (rice variety is MTU-1010). Height measurement plays a vital role in regular assessment for a healthy crop, and the approach proposed in this work achieves 97.58% accuracy of 14 sampled datasets taken from Indira Gandhi Agriculture University of Raipur, Chhattisgarh; a real-time dataset has been prepared. Proposed work is used for analyzing vertical as well as horizontal scaling technique. Vertical mapping provides the height of a single plant whereas horizontal mapping using k-means clustering provides an average height of the whole field. This work uses machine learning, and image processing techniques are used for this work.","PeriodicalId":44415,"journal":{"name":"International Journal of System Dynamics Applications","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49645154","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 : 2020-05-18DOI: 10.21203/rs.3.rs-29370/v1
Poonam Mittal, M. Mangla, N. Sharma, Reena, Suneeta Satpathy, S. Mohanty
During this pandemic outbreak of COVID-19, the whole world is getting severely affected in respect of population health and economy. This novel virus has brought the whole world including the most developed countries to a standstill in a very short span like never before. The prime reason for this unexpected outburst of COVID-19 is lack of effective medicine and lack of proper understanding of the influencing factors. Here, authors aim to find the effect of epidemiological factors that influence its spread using a fuzzy approach. For the same, a total of 9 factors have been considered which are classified into Risk and Preventive factors. This fuzzy model supports to understand and evaluate the impact of these factors on the spread of COVID-19. Also, the model establishes a basis for understanding the effect of risk factors on preventive factors and vice versa. It is worth mentioning that this is the first attempt to analyze the effect of Clinical and Epidemiological factors with respect to COVID-19 using a fuzzy approach.
{"title":"Fuzzy Modelling of Clinical and Epidemiological Factors for COVID-19","authors":"Poonam Mittal, M. Mangla, N. Sharma, Reena, Suneeta Satpathy, S. Mohanty","doi":"10.21203/rs.3.rs-29370/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-29370/v1","url":null,"abstract":"\u0000 During this pandemic outbreak of COVID-19, the whole world is getting severely affected in respect of population health and economy. This novel virus has brought the whole world including the most developed countries to a standstill in a very short span like never before. The prime reason for this unexpected outburst of COVID-19 is lack of effective medicine and lack of proper understanding of the influencing factors. Here, authors aim to find the effect of epidemiological factors that influence its spread using a fuzzy approach. For the same, a total of 9 factors have been considered which are classified into Risk and Preventive factors. This fuzzy model supports to understand and evaluate the impact of these factors on the spread of COVID-19. Also, the model establishes a basis for understanding the effect of risk factors on preventive factors and vice versa. It is worth mentioning that this is the first attempt to analyze the effect of Clinical and Epidemiological factors with respect to COVID-19 using a fuzzy approach.","PeriodicalId":44415,"journal":{"name":"International Journal of System Dynamics Applications","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2020-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48043079","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 : 2019-10-01DOI: 10.4018/ijsda.2019100102
Marc Haddad, Rami Otayek
The adoption of the lean approach has yet to extend to the majority of manufacturers in developing countries where traditional work practices are dominant and cultural resistance to change is high. This research consists of a case study about lean implementation at a clothing manufacturer in a developing country. Production wastes are identified and appropriate lean techniques, namely Total Productive Maintenance, Kanban and Supermarket Pull, are identified to eliminate or reduce them. The potential impacts on the manufacturing system are first assessed using a system dynamics model. The modeling results showed a “getting worse before getting better” behavior as work-in-process increased in the short-term, before a net reduction of 34% on average was achieved over the first 3 months. This result was replicated by a similar trend in the actual lean implementation on the factory floor, showing the usefulness of SD modeling for supporting the sustainability of lean interventions where short-term drawbacks can be deceptive when compared to the long-term benefits of lean.
{"title":"Assessing the Sustainment of a Lean Implementation Using System Dynamics Modeling","authors":"Marc Haddad, Rami Otayek","doi":"10.4018/ijsda.2019100102","DOIUrl":"https://doi.org/10.4018/ijsda.2019100102","url":null,"abstract":"The adoption of the lean approach has yet to extend to the majority of manufacturers in developing countries where traditional work practices are dominant and cultural resistance to change is high. This research consists of a case study about lean implementation at a clothing manufacturer in a developing country. Production wastes are identified and appropriate lean techniques, namely Total Productive Maintenance, Kanban and Supermarket Pull, are identified to eliminate or reduce them. The potential impacts on the manufacturing system are first assessed using a system dynamics model. The modeling results showed a “getting worse before getting better” behavior as work-in-process increased in the short-term, before a net reduction of 34% on average was achieved over the first 3 months. This result was replicated by a similar trend in the actual lean implementation on the factory floor, showing the usefulness of SD modeling for supporting the sustainability of lean interventions where short-term drawbacks can be deceptive when compared to the long-term benefits of lean.","PeriodicalId":44415,"journal":{"name":"International Journal of System Dynamics Applications","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4018/ijsda.2019100102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47375000","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 : 2019-10-01DOI: 10.4018/ijsda.2019100101
Milton M. Herrera, Lina A. Carvajal-Prieto, Mauricio Uriona-Maldonado, F. Ojeda
This article shows that customer value generation has drivers, which could be different according to each stakeholder within the electricity industry, affecting its growth. Each stakeholder has different interests that affect the decision-making process and the customer value perception in the long term, which impacts on profitability. In order to illustrate how to identify and model key performance drivers to evaluate creating value in the electricity utility industry, this study used a simulation with the system dynamics methodology. Through simulation scenarios, this study shows that, the high customer value perception allows the electricity utilities industry to create more value. This is illustrated with the case of some electricity utilities engaged in the generation and distribution in the Colombian electricity market. The results show a new point of view that contributes to marketers and engineers in the analysis of the relationship between the stakeholders and electricity firms.
{"title":"Modeling the Customer Value Generation in the Industry's Supply Chain","authors":"Milton M. Herrera, Lina A. Carvajal-Prieto, Mauricio Uriona-Maldonado, F. Ojeda","doi":"10.4018/ijsda.2019100101","DOIUrl":"https://doi.org/10.4018/ijsda.2019100101","url":null,"abstract":"This article shows that customer value generation has drivers, which could be different according to each stakeholder within the electricity industry, affecting its growth. Each stakeholder has different interests that affect the decision-making process and the customer value perception in the long term, which impacts on profitability. In order to illustrate how to identify and model key performance drivers to evaluate creating value in the electricity utility industry, this study used a simulation with the system dynamics methodology. Through simulation scenarios, this study shows that, the high customer value perception allows the electricity utilities industry to create more value. This is illustrated with the case of some electricity utilities engaged in the generation and distribution in the Colombian electricity market. The results show a new point of view that contributes to marketers and engineers in the analysis of the relationship between the stakeholders and electricity firms.","PeriodicalId":44415,"journal":{"name":"International Journal of System Dynamics Applications","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42532396","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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