{"title":"A hybrid approach of homomorphic encryption and differential privacy for privacy preserving classification","authors":"Ezgi Zorarpacı, S. A. Özel","doi":"10.18100/ijamec.801157","DOIUrl":"https://doi.org/10.18100/ijamec.801157","url":null,"abstract":"","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128019795","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}
Temperature forecasting is a progressive and time series analysis process to forecast the state of the temperature for a certain location in coming time. Nowadays, agriculture and manufacturing sectors are mostly dependent on temperature so forecasting is important to be precise because temperature warnings can save life and property. In this work, the Prophet Forecasting Model is used for Myitkyina's annual temperature forecasting using historical (2010 to 2017) time series data. Myitkyina is the capital city of the northernmost state (Kachin) in Myanmar, located 1480 kilometers from Yangon. Prophet is a modular regression model for time series predictions with high accuracy by using simple interpretable parameters that consider the effect of custom seasonality and holidays. In this study, the temperature forecasting model is proposed by using weather dataset provided by an International institution, National Oceanic and Atmospheric Administration (NOAA). This work implements the multi-step univariate time series prediction model and compares the forecasted value against the actual data. Such findings check that the proposed forecasting model provides an efficient and accurate prediction for temperature in Myitkyina.
{"title":"Time Series Prediction Based on Facebook Prophet: A Case Study, Temperature Forecasting in Myintkyina","authors":"Z. Oo, S. Phyu","doi":"10.18100/ijamec.816894","DOIUrl":"https://doi.org/10.18100/ijamec.816894","url":null,"abstract":"Temperature forecasting is a progressive and time series analysis process to forecast the state of the temperature for a certain location in coming time. Nowadays, agriculture and manufacturing sectors are mostly dependent on temperature so forecasting is important to be precise because temperature warnings can save life and property. In this work, the Prophet Forecasting Model is used for Myitkyina's annual temperature forecasting using historical (2010 to 2017) time series data. Myitkyina is the capital city of the northernmost state (Kachin) in Myanmar, located 1480 kilometers from Yangon. Prophet is a modular regression model for time series predictions with high accuracy by using simple interpretable parameters that consider the effect of custom seasonality and holidays. In this study, the temperature forecasting model is proposed by using weather dataset provided by an International institution, National Oceanic and Atmospheric Administration (NOAA). This work implements the multi-step univariate time series prediction model and compares the forecasted value against the actual data. Such findings check that the proposed forecasting model provides an efficient and accurate prediction for temperature in Myitkyina.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132258672","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}
Pınar Kırcı, Metin Yücel Namli, Murat Ergin, Feyyaz Avci
version of this paper was presented at 9th International Conference on Advanced Technologies (ICAT'20), 10-12 August 2020, Istanbul, Turkey with the title of “Smart Home System for Making Easier The Living of The Elderly”.
{"title":"Smart Home System for Making Easier the Living of The Elderly","authors":"Pınar Kırcı, Metin Yücel Namli, Murat Ergin, Feyyaz Avci","doi":"10.18100/ijamec.800606","DOIUrl":"https://doi.org/10.18100/ijamec.800606","url":null,"abstract":"version of this paper was presented at 9th International Conference on Advanced Technologies (ICAT'20), 10-12 August 2020, Istanbul, Turkey with the title of “Smart Home System for Making Easier The Living of The Elderly”.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116120496","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}
In the course of the outbreak of coronovirus (Covid-19), which emerged in Wuhan, China at the end of 2019, and then spread all over the world, the biggest assistants in the fight against this virus were the technologies which used. Today, the areas where artificial intelligence is applied and the developments in the focus of artificial intelligence lead the technology. With Industry 4.0, there is no need for manpower to meet especially unqualified workforce in many business sectors. The idea of doing things by machines has begun to cause serious changes in the world. In order for the work to be done by the machines, importance has been given to the development of the decision making capabilities of the machines. The decision-making ability of the machines is based on previous periods. The lack of necessary computer hardware parts in testing the hypotheses made in the previous periods caused. It has not been applied in the past due to the high time and cost of hypotheses developed. Today, as a result of the rapid growth of technology, hardware elements with high processing capability can now be obtained at affordable prices. As a result of the acceleration of the developed hardware elements, many methods that took a long time in the past have reached the level that everyone can apply. We observe that what needs to be done for digital transformation in our country has been tested in many sectors. The most basic element for digital transformation is artificial intelligence technology. This is an indication that artificial intelligence technologies have started to be used in many areas of our lives. Accordingly, the use of artificial intelligence technologies in different areas, especially in medicine, played an important role in combating the epidemic during the coronavirus (Covid-19) epidemic process. In this study, the concept of artificial intelligence and the usage areas of artificial intelligence techniques are discussed in the literature section. Then, the applications developed using artificial intelligence technologies during the coronavirus (Covid-19) epidemic process were evaluated and the adequacy of the applications developed by analysing in the method section was discussed. This is an open access article under the CC BY-SA 4.0 license. (https://creativecommons.org/licenses/by-sa/4.0/)
{"title":"Analysis of Artificial Intelligence Technologies Used In The Covid-19 Outbreak Process","authors":"Fatih Ilkbahar, Eylul Sungu","doi":"10.18100/ijamec.800910","DOIUrl":"https://doi.org/10.18100/ijamec.800910","url":null,"abstract":"In the course of the outbreak of coronovirus (Covid-19), which emerged in Wuhan, China at the end of 2019, and then spread all over the world, the biggest assistants in the fight against this virus were the technologies which used. Today, the areas where artificial intelligence is applied and the developments in the focus of artificial intelligence lead the technology. With Industry 4.0, there is no need for manpower to meet especially unqualified workforce in many business sectors. The idea of doing things by machines has begun to cause serious changes in the world. In order for the work to be done by the machines, importance has been given to the development of the decision making capabilities of the machines. The decision-making ability of the machines is based on previous periods. The lack of necessary computer hardware parts in testing the hypotheses made in the previous periods caused. It has not been applied in the past due to the high time and cost of hypotheses developed. Today, as a result of the rapid growth of technology, hardware elements with high processing capability can now be obtained at affordable prices. As a result of the acceleration of the developed hardware elements, many methods that took a long time in the past have reached the level that everyone can apply. We observe that what needs to be done for digital transformation in our country has been tested in many sectors. The most basic element for digital transformation is artificial intelligence technology. This is an indication that artificial intelligence technologies have started to be used in many areas of our lives. Accordingly, the use of artificial intelligence technologies in different areas, especially in medicine, played an important role in combating the epidemic during the coronavirus (Covid-19) epidemic process. In this study, the concept of artificial intelligence and the usage areas of artificial intelligence techniques are discussed in the literature section. Then, the applications developed using artificial intelligence technologies during the coronavirus (Covid-19) epidemic process were evaluated and the adequacy of the applications developed by analysing in the method section was discussed. This is an open access article under the CC BY-SA 4.0 license. (https://creativecommons.org/licenses/by-sa/4.0/)","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121892435","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}
version of this paper was presented at 9th International Conference on Advanced Technologies (ICAT'20), 10-12 August 2020, Istanbul, Turkey with the title of “A Comparison of Convolutional and Recurrent Network Algorithms On Human Activity Recognition”.
{"title":"Human activity recognition and classification using of convolutional neural networks and recurrent neural networks","authors":"Mohammed Albaba, Alhakam Qassab, Arif Yilmaz","doi":"10.18100/ijamec.803105","DOIUrl":"https://doi.org/10.18100/ijamec.803105","url":null,"abstract":"version of this paper was presented at 9th International Conference on Advanced Technologies (ICAT'20), 10-12 August 2020, Istanbul, Turkey with the title of “A Comparison of Convolutional and Recurrent Network Algorithms On Human Activity Recognition”.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121704950","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}
version of this paper was presented at 9th International Conference on Advanced Technologies (ICAT'20), 10-12 August 2020, Istanbul, Turkey with the title of “Lung Disease Classification using Machine Learning Algorithms”.
{"title":"Lung disease classification using machine learning algorithms","authors":"Murat Aykanat, Ozkan Kilic, B. Kurt, S. Saryal","doi":"10.18100/ijamec.799363","DOIUrl":"https://doi.org/10.18100/ijamec.799363","url":null,"abstract":"version of this paper was presented at 9th International Conference on Advanced Technologies (ICAT'20), 10-12 August 2020, Istanbul, Turkey with the title of “Lung Disease Classification using Machine Learning Algorithms”.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131042479","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}
In today's conditions, it is difficult to obtain information quickly and efficiently due to the size of the data. There are various text documents on the internet and a good extraction algorithm is essential to have the most relevant information from them. Long texts can be boring sometimes. So, readers are eager to get the main idea of the text or any useful information. For this reason, the importance of automatic summarization systems is understood. Text summarization systems can be considered as abstractive summarization or extractive summarization. While abstractive systems produce a summary with new sentences, extractive systems make a selection of sentences from the text used and combine them and present them as a summary. Creating a successful summarization algorithm increases in direct proportion to the success of applying text mining techniques. Text summary systems provide a summary of the text to the user by scoring words and sentences in the main text using various methods and combining high ranked sentences as a result of the process. In this context, many scoring methods have been used. In our study, news data sets are used. The algorithm used is based on extraction and has been evaluated using a task-independent method. After evaluation, the two highest scores taken are ROUGE-1 with 0.68 score and ROUGE-S with 0.54 score. Through all evaluation steps, Precision, Recall and F-Measure values are also specified to see the steps clearly. This is an open access article under the CC BY-SA 4.0 license. (https://creativecommons.org/licenses/by-sa/4.0/)
{"title":"Extractive Text Summarization System for News Texts","authors":"Fahrettin Horasan, Burhan Bilen","doi":"10.18100/ijamec.800905","DOIUrl":"https://doi.org/10.18100/ijamec.800905","url":null,"abstract":"In today's conditions, it is difficult to obtain information quickly and efficiently due to the size of the data. There are various text documents on the internet and a good extraction algorithm is essential to have the most relevant information from them. Long texts can be boring sometimes. So, readers are eager to get the main idea of the text or any useful information. For this reason, the importance of automatic summarization systems is understood. Text summarization systems can be considered as abstractive summarization or extractive summarization. While abstractive systems produce a summary with new sentences, extractive systems make a selection of sentences from the text used and combine them and present them as a summary. Creating a successful summarization algorithm increases in direct proportion to the success of applying text mining techniques. Text summary systems provide a summary of the text to the user by scoring words and sentences in the main text using various methods and combining high ranked sentences as a result of the process. In this context, many scoring methods have been used. In our study, news data sets are used. The algorithm used is based on extraction and has been evaluated using a task-independent method. After evaluation, the two highest scores taken are ROUGE-1 with 0.68 score and ROUGE-S with 0.54 score. Through all evaluation steps, Precision, Recall and F-Measure values are also specified to see the steps clearly. This is an open access article under the CC BY-SA 4.0 license. (https://creativecommons.org/licenses/by-sa/4.0/)","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126491696","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}
In this study, a liking estimation system based on electroencephalogram (EEG) signals is developed for neuromarketing applications. The determination of the degree of appreciation of a product by consumers has become an important research topic using machine learning methods. Biological data is recorded while viewing product pictures or videos, then processed by signal processing methods. In this study, 32 channel EEG signals are recorded from subjects who watched two different car advertisement videos and the liking status is determined. After watching the advertisement videos, the participants were asked to vote for the rating of the different images (front view, dashboard, side view, rear view, taillight, logo and grille) of the products. The signals corresponding to these different video regions from the EEG recordings were segmented and analyzed by the Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD). The statistical features were extracted from Intrinsic Mode Functions (IMF) and the liking status classifications were performed. The classification performance of EMD- and EEMD-based methods are 93.4% and 97.8% respectively on Brand1, and 93.5% and 97.4% respectively on Brand2. In addition, the classification accuracy on both brands combined are 85.1% and 85.7% respectively. The promising results obtained using Support Vector Machines (SVM) show that the proposed EEG-based method may be used in neuromarketing studies.
{"title":"Detection of Consumer Preferences Using EEG Signals","authors":"Burak Ceylan, Serkan Tuzun, A. Akan","doi":"10.18100/ijamec.802214","DOIUrl":"https://doi.org/10.18100/ijamec.802214","url":null,"abstract":"In this study, a liking estimation system based on electroencephalogram (EEG) signals is developed for neuromarketing applications. The determination of the degree of appreciation of a product by consumers has become an important research topic using machine learning methods. Biological data is recorded while viewing product pictures or videos, then processed by signal processing methods. In this study, 32 channel EEG signals are recorded from subjects who watched two different car advertisement videos and the liking status is determined. After watching the advertisement videos, the participants were asked to vote for the rating of the different images (front view, dashboard, side view, rear view, taillight, logo and grille) of the products. The signals corresponding to these different video regions from the EEG recordings were segmented and analyzed by the Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD). The statistical features were extracted from Intrinsic Mode Functions (IMF) and the liking status classifications were performed. The classification performance of EMD- and EEMD-based methods are 93.4% and 97.8% respectively on Brand1, and 93.5% and 97.4% respectively on Brand2. In addition, the classification accuracy on both brands combined are 85.1% and 85.7% respectively. The promising results obtained using Support Vector Machines (SVM) show that the proposed EEG-based method may be used in neuromarketing studies.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117108568","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}
This paper presents a multi-criteria evaluation model applied to the parameterization of the MRP method. Existing optimization approaches that address this problem tend to adopt a means of simulation. A simulated solution is characterized by a pair (parameters, performance indicators). In the context of the evaluation of solutions, the work of Barth, Damand et al. (2003) propose a heuristic approach to extracting knowledge from a solution set. The approach is based on the definition of a multi-criteria solution comparison function. The objective of this paper is to present the detailed modeling of this comparison function. Ultimately, this result contributes to the formalization of a multicriteria optimization problem. A problem solving strategy is proposed.
{"title":"Multi-criteria evaluation model: an application of MRP parameterization","authors":"D. Damand","doi":"10.18100/ijamec.817719","DOIUrl":"https://doi.org/10.18100/ijamec.817719","url":null,"abstract":"This paper presents a multi-criteria evaluation model applied to the parameterization of the MRP method. Existing optimization approaches that address this problem tend to adopt a means of simulation. A simulated solution is characterized by a pair (parameters, performance indicators). In the context of the evaluation of solutions, the work of Barth, Damand et al. (2003) propose a heuristic approach to extracting knowledge from a solution set. The approach is based on the definition of a multi-criteria solution comparison function. The objective of this paper is to present the detailed modeling of this comparison function. Ultimately, this result contributes to the formalization of a multicriteria optimization problem. A problem solving strategy is proposed.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125507260","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}
{"title":"Comparison and Evaluation of Cross Platform Mobile Application Development Tools","authors":"Mehmet Işitan, M. Koklu","doi":"10.18100/ijamec.832673","DOIUrl":"https://doi.org/10.18100/ijamec.832673","url":null,"abstract":"ABSTRACT","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114738527","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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