Pub Date : 2022-10-08DOI: 10.5815/ijisa.2022.05.03
R. O. Olanrewaju, M. A. Jallow, S. A. Olanrewaju
In this research, alternate random wave sine and cosine for discrete time-varying processes via Autoregressive Integrated Moving Average (ARIMA) in a deterministic manner were developed. The mean and variance of the cosine and sine periodical time-varying wavy functions were derived such that Maclaurin series via full Taylor series expansion was used to rewrite the mean and variance functions. Wavy buoys of sea temperature, significant wave height, and mean wave direction of Belmullet Inner (Berth B) and Belmullet Outer (Berth A) of the Atlantic Ocean based on the west coastal of Ireland were subjected to the random sine and cosine wave functions of ARIMA. Cosine-ARIMA (1, 1, 3) and cosine-ARIMA (0, 1, 1) were the sea temperature inner and outer oceanic climate wave buoys of Berth B and A with time-periods of 8437.5 and 8035.714 respectively. Cosine-ARIMA (5, 1, 0) gave minimum performance for peak direction of inner and outer oceanic climate wave buoys of both Berth B and A, but with different time-periods of 168750 and 56250 respectively. Lastly, cosine-ARIMA (2, 1, 2) and sine-ARIMA (0, 1, 5) put in the ideal generalization for wave height of Berth B and A with the same associated wave time-periods of 56250, that is, it takes 56250 seconds to complete one swaying cycle.
{"title":"An Analysis of the Atlantic Ocean Wave Via Random Cosine and Sine Alternate Wavy ARIMA Functions","authors":"R. O. Olanrewaju, M. A. Jallow, S. A. Olanrewaju","doi":"10.5815/ijisa.2022.05.03","DOIUrl":"https://doi.org/10.5815/ijisa.2022.05.03","url":null,"abstract":"In this research, alternate random wave sine and cosine for discrete time-varying processes via Autoregressive Integrated Moving Average (ARIMA) in a deterministic manner were developed. The mean and variance of the cosine and sine periodical time-varying wavy functions were derived such that Maclaurin series via full Taylor series expansion was used to rewrite the mean and variance functions. Wavy buoys of sea temperature, significant wave height, and mean wave direction of Belmullet Inner (Berth B) and Belmullet Outer (Berth A) of the Atlantic Ocean based on the west coastal of Ireland were subjected to the random sine and cosine wave functions of ARIMA. Cosine-ARIMA (1, 1, 3) and cosine-ARIMA (0, 1, 1) were the sea temperature inner and outer oceanic climate wave buoys of Berth B and A with time-periods of 8437.5 and 8035.714 respectively. Cosine-ARIMA (5, 1, 0) gave minimum performance for peak direction of inner and outer oceanic climate wave buoys of both Berth B and A, but with different time-periods of 168750 and 56250 respectively. Lastly, cosine-ARIMA (2, 1, 2) and sine-ARIMA (0, 1, 5) put in the ideal generalization for wave height of Berth B and A with the same associated wave time-periods of 56250, that is, it takes 56250 seconds to complete one swaying cycle.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73847192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-08DOI: 10.5815/ijisa.2022.05.02
Md. Atik-Uz-Zaman Atik, Abu Osman Al Mahbub
Taking into consideration the lack of circumstantial alertness, automated fault analysis and labor-saving switches, the present-day electrical power grid system has been deteriorating day by day. The backbone technology of this grid system is too ill-fitted to the on-going demand for electricity. Despite the fact that the government of Bangladesh has set a new target of reaching the total power generation to be 40,000 MW by 2030. Hence the infrastructure and corresponding technology of the electrical power sector are required to be modernized to cope with this gigantic target within a short time. Another challenging fact is that the rapid expansion of population and power-intensive industrialization trigger off the carbon emissions that lead to global climate change. Also, the constraints of electricity generation capacity, unidirectional way of communication, failure of power equipment and dropping off conventional sources of energy impose burden on the existing electric power grid. This paper articulates the needfulness of reflection on CO2 emissions or reduction in the electricity bill of the consumer in developing countries by employing a mathematical model and by proposing some fruitful methods to implement AMI for smart grid.
{"title":"Modeling Electricity Bill with the Reflection of CO2 Emissions and Methods of Implementing AMI for Smart Grid in Bangladesh","authors":"Md. Atik-Uz-Zaman Atik, Abu Osman Al Mahbub","doi":"10.5815/ijisa.2022.05.02","DOIUrl":"https://doi.org/10.5815/ijisa.2022.05.02","url":null,"abstract":"Taking into consideration the lack of circumstantial alertness, automated fault analysis and labor-saving switches, the present-day electrical power grid system has been deteriorating day by day. The backbone technology of this grid system is too ill-fitted to the on-going demand for electricity. Despite the fact that the government of Bangladesh has set a new target of reaching the total power generation to be 40,000 MW by 2030. Hence the infrastructure and corresponding technology of the electrical power sector are required to be modernized to cope with this gigantic target within a short time. Another challenging fact is that the rapid expansion of population and power-intensive industrialization trigger off the carbon emissions that lead to global climate change. Also, the constraints of electricity generation capacity, unidirectional way of communication, failure of power equipment and dropping off conventional sources of energy impose burden on the existing electric power grid. This paper articulates the needfulness of reflection on CO2 emissions or reduction in the electricity bill of the consumer in developing countries by employing a mathematical model and by proposing some fruitful methods to implement AMI for smart grid.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"141 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77234200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-08DOI: 10.5815/ijisa.2022.05.01
S. Turgay
Recently, health management systems have some troubles such as insufficient sharing of medical data, security problems of shared information, tampering and leaking of private data with data modeling probes and developing technology. Local learning is performed together with federated learning and differential entropy method to prevent the leakage of medical confidential information, so blockchain-based learning is preferred to completely eliminate the possibility of leakage while in global learning. Qualitative and quantitative analysis of information can be made with information entropy technology for the effective and maximum use of medical data in the local learning process. The blockchain is used the distributed network structure and inherent security features, at the same time information is treated as a whole, not as islands of data. All the way through this work, data sharing between medical systems can be encouraged, access records tampered with, and better support medical research and definitive medical treatment. The M/M/1 queue for the memory pool and M/M/C queue to combine integrated blockchains with a unified learning structure. With the proposed model, the number of transactions per block, mining of each block, learning time, index operations per second, number of memory pools, waiting time in the memory pool, number of unconfirmed transactions in the whole system, total number of transactions were examined. Thanks to this study, the protection of the medical privacy information of the user during the service process and the autonomous management of the patient’s own medical data will benefit the protection of privacy within the scope of medical data sharing. Motivated by this, proposed a blockchain and federated learning-based data management system able to develop in next studies.
{"title":"Blockchain Management and Federated Learning Adaptation on Healthcare Management System","authors":"S. Turgay","doi":"10.5815/ijisa.2022.05.01","DOIUrl":"https://doi.org/10.5815/ijisa.2022.05.01","url":null,"abstract":"Recently, health management systems have some troubles such as insufficient sharing of medical data, security problems of shared information, tampering and leaking of private data with data modeling probes and developing technology. Local learning is performed together with federated learning and differential entropy method to prevent the leakage of medical confidential information, so blockchain-based learning is preferred to completely eliminate the possibility of leakage while in global learning. Qualitative and quantitative analysis of information can be made with information entropy technology for the effective and maximum use of medical data in the local learning process. The blockchain is used the distributed network structure and inherent security features, at the same time information is treated as a whole, not as islands of data. All the way through this work, data sharing between medical systems can be encouraged, access records tampered with, and better support medical research and definitive medical treatment. The M/M/1 queue for the memory pool and M/M/C queue to combine integrated blockchains with a unified learning structure. With the proposed model, the number of transactions per block, mining of each block, learning time, index operations per second, number of memory pools, waiting time in the memory pool, number of unconfirmed transactions in the whole system, total number of transactions were examined. Thanks to this study, the protection of the medical privacy information of the user during the service process and the autonomous management of the patient’s own medical data will benefit the protection of privacy within the scope of medical data sharing. Motivated by this, proposed a blockchain and federated learning-based data management system able to develop in next studies.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82053453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-08DOI: 10.5815/ijisa.2022.05.04
Parneeta Dhaliwal, Suyash Sharma, Lakshay Chauhan
The consumption of wine these days is becoming more common in social gatherings and to monitor the health of individuals it's very important to maintain the quality of the wine. For the assessment of wine quality many methods have been proposed. We have described a technique to pre-process the “Vinho Verde” wine dataset. The dataset consists of red and white wine samples. The wine dataset size has been reduced from a total of 13 attributes to 9 attributes without any loss of performance. This has been validated through various classification techniques like Random Forest Classifier, Decision tree Classifiers, K-Nearest Neighbor Classifier and Artificial Neural Network Classifier. These classifiers have been compared based on two performance metrics of accuracy and RMSE values. Among the three classifiers Random Forest tends to outperform the other two classifiers in various measures for predicting the quality of the wine.
{"title":"Detailed Study of Wine Dataset and its Optimization","authors":"Parneeta Dhaliwal, Suyash Sharma, Lakshay Chauhan","doi":"10.5815/ijisa.2022.05.04","DOIUrl":"https://doi.org/10.5815/ijisa.2022.05.04","url":null,"abstract":"The consumption of wine these days is becoming more common in social gatherings and to monitor the health of individuals it's very important to maintain the quality of the wine. For the assessment of wine quality many methods have been proposed. We have described a technique to pre-process the “Vinho Verde” wine dataset. The dataset consists of red and white wine samples. The wine dataset size has been reduced from a total of 13 attributes to 9 attributes without any loss of performance. This has been validated through various classification techniques like Random Forest Classifier, Decision tree Classifiers, K-Nearest Neighbor Classifier and Artificial Neural Network Classifier. These classifiers have been compared based on two performance metrics of accuracy and RMSE values. Among the three classifiers Random Forest tends to outperform the other two classifiers in various measures for predicting the quality of the wine.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90331832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-08DOI: 10.5815/ijisa.2022.05.05
J. Ojo, Chinedu K. Ijomah, S. B. Akinpelu
The impact of rain-influenced attenuation (RIA) has a more pronounced effect as frequency increases, especially in the tropical zones with heavier rainfall than the temperate zones. The International Telecommunication Union (ITU) has recommended a universal model which may not fit well in this tropical region due to the temperate data used to develop the model. It is therefore necessary to adopt locally measured data to develop a suitable model for each region, as also recommended by ITU recommendation 618-13. The experimental site for this study is at the Department of Physics, Federal University of Technology, Akure, Nigeria (7.299° N, 5.147° E) in the tropical rainforest region of Nigeria. In the present work, the backpropagation neural network (BPNN) of the artificial neural network (ANN) is trained based on time-series rain rates data collected between 2015 and 2019 to predict time-series RIA. Based on four inputs (rain rate, rain heights, elevation angle, and polarization angle), the generated data was subjected to training, validation, and testing. The ANN was further trained using the Levenberg-Marquardt algorithm to fit the inputs and the targets to create a dynamic model for RIA forecasting. Further validation was tested using actual data of rain attenuation from a Ku-band beacon at the site. Subsequently, the RIA model created by the ANN was compared to those generated using the synthetic storm technique, ITU, and the actual rain attenuation obtained from a beacon measurement. The highest rain rate observed was about 225.8 mm/hr with a corresponding rain attenuation of about 61 dB as estimated by the SST model and about 68 dB by the ITU model, while the predicted attenuation by the ANN is 55 dB. This implies that an extra power of 6 dB and 13 dB is added by the SST model and ITU model, respectively, for the downlink signal, to compensate for the rain attenuation link. The results also reveal that during 0.01 percent of an average year that signal may be attenuated, a relatively tiny margin of error between anticipated rain attenuation using ANN and the SST model is exceeded. In general, the new ANN-generated RIA model had the lowest root mean square error, average relative error, and standard deviation at the selected time percentages, according to the model validation. Hence, the new ANN model can predict more effective RIA in the region when compared with the global ITU-R model.
{"title":"Artificial Neural Networks for Earth-Space Link Applications: A Prediction Approach and Intercomparison of Rain-influenced Attenuation Models","authors":"J. Ojo, Chinedu K. Ijomah, S. B. Akinpelu","doi":"10.5815/ijisa.2022.05.05","DOIUrl":"https://doi.org/10.5815/ijisa.2022.05.05","url":null,"abstract":"The impact of rain-influenced attenuation (RIA) has a more pronounced effect as frequency increases, especially in the tropical zones with heavier rainfall than the temperate zones. The International Telecommunication Union (ITU) has recommended a universal model which may not fit well in this tropical region due to the temperate data used to develop the model. It is therefore necessary to adopt locally measured data to develop a suitable model for each region, as also recommended by ITU recommendation 618-13. The experimental site for this study is at the Department of Physics, Federal University of Technology, Akure, Nigeria (7.299° N, 5.147° E) in the tropical rainforest region of Nigeria. In the present work, the backpropagation neural network (BPNN) of the artificial neural network (ANN) is trained based on time-series rain rates data collected between 2015 and 2019 to predict time-series RIA. Based on four inputs (rain rate, rain heights, elevation angle, and polarization angle), the generated data was subjected to training, validation, and testing. The ANN was further trained using the Levenberg-Marquardt algorithm to fit the inputs and the targets to create a dynamic model for RIA forecasting. Further validation was tested using actual data of rain attenuation from a Ku-band beacon at the site. Subsequently, the RIA model created by the ANN was compared to those generated using the synthetic storm technique, ITU, and the actual rain attenuation obtained from a beacon measurement. The highest rain rate observed was about 225.8 mm/hr with a corresponding rain attenuation of about 61 dB as estimated by the SST model and about 68 dB by the ITU model, while the predicted attenuation by the ANN is 55 dB. This implies that an extra power of 6 dB and 13 dB is added by the SST model and ITU model, respectively, for the downlink signal, to compensate for the rain attenuation link. The results also reveal that during 0.01 percent of an average year that signal may be attenuated, a relatively tiny margin of error between anticipated rain attenuation using ANN and the SST model is exceeded. In general, the new ANN-generated RIA model had the lowest root mean square error, average relative error, and standard deviation at the selected time percentages, according to the model validation. Hence, the new ANN model can predict more effective RIA in the region when compared with the global ITU-R model.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81365470","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 : 2022-08-08DOI: 10.5815/ijisa.2022.04.05
P. Ekanayake, L. Wickramasinghe, J. Jayasinghe
This research introduces machine learning models using the Gaussian Process Regression (GPR) depicting the association between paddy yield and weather in Sri Lanka. All major regions in the island with most contribution to the total paddy production were considered in this research. The climatic factors of rainfall, relative humidity, minimum temperature, maximum temperature, average wind speed, evaporation, and sunshine hours were considered as input (independent) variables, while the paddy yield was the output (dependent) variable. The collinearity within each pair of independent and dependent variables was determined using Spearman’s and Pearson’s correlation coefficients. Data sets corresponding to the two main annual paddy cultivation seasons since 2009 were trained in MATLAB to develop crop-weather models. The most appropriate Kernel function was chosen from among four types of Kernels viz. Rational Quadratic, Exponential, Squared Exponential, and Matern 5/2 based on their degree of coherence in modeling. This approach exploits the full potential of GPR in developing highly accurate crop-weather models. The performance of the crop-weather models was measured by the Correlation Coefficient, Mean Absolute Percentage Error, Mean Squared Error, Root Mean Squared Error Ratio, Nash Number and the BIAS. All the GPR-based models proposed in this paper are highly accurate in terms of the aforementioned evaluation metrics. Accordingly, when the climatic data are known or projected, the paddy yield and thereby the harvest of Sri Lanka can be predicted precisely by using the proposed crop-weather models.
{"title":"Development of Crop-Weather Models Using Gaussian Process Regression for the Prediction of Paddy Yield in Sri Lanka","authors":"P. Ekanayake, L. Wickramasinghe, J. Jayasinghe","doi":"10.5815/ijisa.2022.04.05","DOIUrl":"https://doi.org/10.5815/ijisa.2022.04.05","url":null,"abstract":"This research introduces machine learning models using the Gaussian Process Regression (GPR) depicting the association between paddy yield and weather in Sri Lanka. All major regions in the island with most contribution to the total paddy production were considered in this research. The climatic factors of rainfall, relative humidity, minimum temperature, maximum temperature, average wind speed, evaporation, and sunshine hours were considered as input (independent) variables, while the paddy yield was the output (dependent) variable. The collinearity within each pair of independent and dependent variables was determined using Spearman’s and Pearson’s correlation coefficients. Data sets corresponding to the two main annual paddy cultivation seasons since 2009 were trained in MATLAB to develop crop-weather models. The most appropriate Kernel function was chosen from among four types of Kernels viz. Rational Quadratic, Exponential, Squared Exponential, and Matern 5/2 based on their degree of coherence in modeling. This approach exploits the full potential of GPR in developing highly accurate crop-weather models. The performance of the crop-weather models was measured by the Correlation Coefficient, Mean Absolute Percentage Error, Mean Squared Error, Root Mean Squared Error Ratio, Nash Number and the BIAS. All the GPR-based models proposed in this paper are highly accurate in terms of the aforementioned evaluation metrics. Accordingly, when the climatic data are known or projected, the paddy yield and thereby the harvest of Sri Lanka can be predicted precisely by using the proposed crop-weather models.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84009066","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 : 2022-08-08DOI: 10.5815/ijisa.2022.04.01
K. Akyol, Abdulkadir Karacı, M. Tiftikçi
Child abuse is a social and medical problem that has negative effects on the individual development of the child and can lead to mental disorders such as depression and post-traumatic stress disorder in both short and long-term mental health. Therefore, any abuse that the child may encounter should be immediately intervened. This paper presents the design of an integrated embedded warning system that includes an embedded system module, a server-based module, and a mobile-based module as a solution to concerns of ensuring the safety of students in places where there are fewer safety measures. Our solution aims to ensure that the school management team is quickly informed about the adverse situation that primary school students may encounter and able to respond to them. In this context, this system activates the warning status when it correctly detects the phrases 'help me' and 'give it up'. Thus, any negativity that may be encountered in a closed environment is prevented. The embedded warning system detected correctly the phrase "help me" with 80%, and the phrase "give it up" with 75%.
虐待儿童是一个社会和医疗问题，对儿童的个人发展产生负面影响，并可能导致短期和长期精神健康方面的精神障碍，如抑郁症和创伤后应激障碍。因此，孩子可能遇到的任何虐待都应该立即干预。本文设计了一种集成的嵌入式报警系统，该系统包括嵌入式系统模块、基于服务器的模块和基于移动的模块，以解决学生在安全措施较少的地方的安全问题。我们的解决方案旨在确保学校管理团队迅速了解小学生可能遇到的不利情况，并能够作出反应。在这种情况下，当系统正确检测到“help me”和“give it up”这两个短语时，就会激活警告状态。因此，可以防止在封闭环境中可能遇到的任何负面影响。嵌入式预警系统对短语“帮助我”的正确率为80%，对短语“放弃”的正确率为75%。
{"title":"Care4Student: An Embedded Warning System for Preventing Abuse of Primary School Students","authors":"K. Akyol, Abdulkadir Karacı, M. Tiftikçi","doi":"10.5815/ijisa.2022.04.01","DOIUrl":"https://doi.org/10.5815/ijisa.2022.04.01","url":null,"abstract":"Child abuse is a social and medical problem that has negative effects on the individual development of the child and can lead to mental disorders such as depression and post-traumatic stress disorder in both short and long-term mental health. Therefore, any abuse that the child may encounter should be immediately intervened. This paper presents the design of an integrated embedded warning system that includes an embedded system module, a server-based module, and a mobile-based module as a solution to concerns of ensuring the safety of students in places where there are fewer safety measures. Our solution aims to ensure that the school management team is quickly informed about the adverse situation that primary school students may encounter and able to respond to them. In this context, this system activates the warning status when it correctly detects the phrases 'help me' and 'give it up'. Thus, any negativity that may be encountered in a closed environment is prevented. The embedded warning system detected correctly the phrase \"help me\" with 80%, and the phrase \"give it up\" with 75%.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81225755","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 : 2022-08-08DOI: 10.5815/ijisa.2022.04.04
Debadrita Panda, S. Mukhopadhyay, Rajarshi Saha, P. Panigrahi
The behaviour of consumers mostly follows the guidelines derived from marketing theories and models. But under some unavoidable circumstances, the consumers show a complete deviation compared to their existing consumption pattern, purchase behaviour, decision-making and so on. Under similar circumstances, this study aims to capture both urban and rural Bottom of the Pyramid (BoP) consumers’ perceptions of various marketing mixes during the COVID-19 pandemic situation. With a sample size of 378 and 282, the perception towards different marketing mixes has been captured for Pre-COVID and During-COVID periods, respectively. The adopted quantitative analysis indicates a difference in perception towards marketing mix During COVID compared to Pre-COVID. Moreover, the selection of West Bengal, India, as an area of research fulfills the BoP literature’s existing prominent research gap. This study also comes with the potential to assist marketers and the Fast-Moving Consumer Goods (FMCG) industry in framing strategies to target BoP consumers.
{"title":"BoPCOVIPIP: Capturing the Dynamics of Marketing Mix Among Bottom of Pyramid Consumers during COVID-19","authors":"Debadrita Panda, S. Mukhopadhyay, Rajarshi Saha, P. Panigrahi","doi":"10.5815/ijisa.2022.04.04","DOIUrl":"https://doi.org/10.5815/ijisa.2022.04.04","url":null,"abstract":"The behaviour of consumers mostly follows the guidelines derived from marketing theories and models. But under some unavoidable circumstances, the consumers show a complete deviation compared to their existing consumption pattern, purchase behaviour, decision-making and so on. Under similar circumstances, this study aims to capture both urban and rural Bottom of the Pyramid (BoP) consumers’ perceptions of various marketing mixes during the COVID-19 pandemic situation. With a sample size of 378 and 282, the perception towards different marketing mixes has been captured for Pre-COVID and During-COVID periods, respectively. The adopted quantitative analysis indicates a difference in perception towards marketing mix During COVID compared to Pre-COVID. Moreover, the selection of West Bengal, India, as an area of research fulfills the BoP literature’s existing prominent research gap. This study also comes with the potential to assist marketers and the Fast-Moving Consumer Goods (FMCG) industry in framing strategies to target BoP consumers.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74285598","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 : 2022-08-08DOI: 10.5815/ijisa.2022.04.03
M. Bourahla
Web ontologies can contain vague concepts, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption. A concept description can be very exact (crisp concept) or exact (fuzzy concept) if its knowledge is complete, otherwise it is inexact (vague concept) if its knowledge is incomplete. In this paper, we propose a method based on the rough set theory for reasoning on vague ontologies. With this method, the detection of vague concepts will insert into the original ontology new rough vague concepts where their description is defined on approximation spaces to be used by extended Tableau algorithm for automatic reasoning. A prototype of Tableau's extended algorithm is developed and tested on examples where encouraging results are given by this method to demonstrate that unlike other methods, it is possible to answer queries even in the presence of incomplete information.
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Pub Date : 2022-08-08DOI: 10.5815/ijisa.2022.04.02
Y. Singh, Mahendra Tiwari
Diabetes is a life-threatening and long-lasting illness that produces high blood glucose levels. Diabetes may cause various diseases, including liver disease, blindness, amputation, urinary organ infections, etc. This research work aims to introduce a hybrid framework to enhance outcomes predictability and interoperability with reduced ill-posed problems, over-fitting problems, and class imbalance problems for diagnosing diabetes mellitus using data mining techniques. Diabetes may be recognized in many ways. One of these methods is data mining techniques. The use of data mining to medical data has yielded meaningful, significant, and effective results that may improve medical expertise and decision-making. This study suggests a hybrid technique for detecting DM that combines the lasso regression algorithm with the artificial neural network (ANN) classifier algorithm. The Lasso regression technique is used for variable selection and regularization. Because the dataset was shrunk, the computing time was considerably minimized. The ANN classifier received the Lasso regression output as an input and classified patients correctly as diabetic and non-diabetic, i.e., tested positives and negatives. The Pima Indians dataset was used in this experiment, consisting of 768 samples of female participants who are diabetic and non-diabetic. According to experimental observations, the proposed hybrid technique achieved 93% classification accuracy for predicting diabetes mellitus. The experimental results showed that our proposed method had a classification accuracy of 93% for determining whether a patient has diabetes or not. The experimental outcomes demonstrated that a hybrid data-mining approach might assist clinicians in making better diagnoses when identifying diabetes patients.
{"title":"A Novel Hybrid Approach for Detection of Type-2 Diabetes in Women Using Lasso Regression and Artificial Neural Network","authors":"Y. Singh, Mahendra Tiwari","doi":"10.5815/ijisa.2022.04.02","DOIUrl":"https://doi.org/10.5815/ijisa.2022.04.02","url":null,"abstract":"Diabetes is a life-threatening and long-lasting illness that produces high blood glucose levels. Diabetes may cause various diseases, including liver disease, blindness, amputation, urinary organ infections, etc. This research work aims to introduce a hybrid framework to enhance outcomes predictability and interoperability with reduced ill-posed problems, over-fitting problems, and class imbalance problems for diagnosing diabetes mellitus using data mining techniques. Diabetes may be recognized in many ways. One of these methods is data mining techniques. The use of data mining to medical data has yielded meaningful, significant, and effective results that may improve medical expertise and decision-making. This study suggests a hybrid technique for detecting DM that combines the lasso regression algorithm with the artificial neural network (ANN) classifier algorithm. The Lasso regression technique is used for variable selection and regularization. Because the dataset was shrunk, the computing time was considerably minimized. The ANN classifier received the Lasso regression output as an input and classified patients correctly as diabetic and non-diabetic, i.e., tested positives and negatives. The Pima Indians dataset was used in this experiment, consisting of 768 samples of female participants who are diabetic and non-diabetic. According to experimental observations, the proposed hybrid technique achieved 93% classification accuracy for predicting diabetes mellitus. The experimental results showed that our proposed method had a classification accuracy of 93% for determining whether a patient has diabetes or not. The experimental outcomes demonstrated that a hybrid data-mining approach might assist clinicians in making better diagnoses when identifying diabetes patients.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"127 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85118893","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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