Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558890
Hatice Altınok, Serdar Kurdal, Murtaza Mehdi Yucal
Bu çalışmada çagrı merkezlerinde kullanılan otomatik konuşma tanıma sisteminde Türkçe ve yabancı kökenli sözeükleri taniyabilen bir yapıdan bahsedilmektedir. Çagrı merkezlerinin farkli sektör ve alanlara hizmet vermesinden kaynaklı Türkçe kökenli konuşmalarin yanı sıra birçok yabancı kökenli kelimelerin geçtigi konuşmalara da sahiptir. Sadece Türkçe için geliştirilen konuşma tanıma sistemleri bu merkezlerin ihtiyaeını tam anlamıyla karşılayamadigi için, Türkçe ve yabancı kelimelerin bir arada yer aldığı hibrit bir yapı oluşturulmuştur. Bu yapıda seslere karşılık gelen metinler okunuldugu gibi yazılmak yerine, okunuşun düzgün söylendigi varsayilarak yazilmiştir. Böylelikle metine çevrilen konuşmanın anlamlandırılmasındaki geçen zaman azalmıştir. Söyleyiş sözlügünde düzgün söylemlerin karşısına okunuş varyasyonları eklenerek degişik agızdaki konuşmalarin ve yabancı kökenli kelimelerin tanınması saglanmıştır. Gerçekleştirilen çalişma sonueunda uygulanan yöntemler sayesinde öneeki sisteme göre başarım oramnin arttığı gözlemlenmiştir.
{"title":"Turkish Speech Recognition in Call Centers","authors":"Hatice Altınok, Serdar Kurdal, Murtaza Mehdi Yucal","doi":"10.1109/UBMK52708.2021.9558890","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558890","url":null,"abstract":"Bu çalışmada çagrı merkezlerinde kullanılan otomatik konuşma tanıma sisteminde Türkçe ve yabancı kökenli sözeükleri taniyabilen bir yapıdan bahsedilmektedir. Çagrı merkezlerinin farkli sektör ve alanlara hizmet vermesinden kaynaklı Türkçe kökenli konuşmalarin yanı sıra birçok yabancı kökenli kelimelerin geçtigi konuşmalara da sahiptir. Sadece Türkçe için geliştirilen konuşma tanıma sistemleri bu merkezlerin ihtiyaeını tam anlamıyla karşılayamadigi için, Türkçe ve yabancı kelimelerin bir arada yer aldığı hibrit bir yapı oluşturulmuştur. Bu yapıda seslere karşılık gelen metinler okunuldugu gibi yazılmak yerine, okunuşun düzgün söylendigi varsayilarak yazilmiştir. Böylelikle metine çevrilen konuşmanın anlamlandırılmasındaki geçen zaman azalmıştir. Söyleyiş sözlügünde düzgün söylemlerin karşısına okunuş varyasyonları eklenerek degişik agızdaki konuşmalarin ve yabancı kökenli kelimelerin tanınması saglanmıştır. Gerçekleştirilen çalişma sonueunda uygulanan yöntemler sayesinde öneeki sisteme göre başarım oramnin arttığı gözlemlenmiştir.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115816111","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558884
L. Cherikbayeva, A. Yerimbetova, Elmira Daiyrbayeva
This paper proposes the study of cluster analysis methods for solving the problem of pattern recognition, including group solution methods. The study selected methods for solving the problem of cluster analysis based on a group solution with incomplete training information, investigated and developed models of group solutions based on existing known algorithms. The novelty of the work consists in a combination of algorithms for collective cluster analysis and nuclear classification methods. Numerical experiments on test problems and a real hyperspectral image demonstrate the effectiveness of the proposed method, including in the presence of noisy data.
{"title":"Research of Cluster Analysis Methods for Group Solutions of the Pattern Recognition Problem","authors":"L. Cherikbayeva, A. Yerimbetova, Elmira Daiyrbayeva","doi":"10.1109/UBMK52708.2021.9558884","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558884","url":null,"abstract":"This paper proposes the study of cluster analysis methods for solving the problem of pattern recognition, including group solution methods. The study selected methods for solving the problem of cluster analysis based on a group solution with incomplete training information, investigated and developed models of group solutions based on existing known algorithms. The novelty of the work consists in a combination of algorithms for collective cluster analysis and nuclear classification methods. Numerical experiments on test problems and a real hyperspectral image demonstrate the effectiveness of the proposed method, including in the presence of noisy data.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121353783","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558904
Kağan Küçük, Fatih Kahraman, M. Kamasak, E. Adali
Instant exchange rates offered to customers are the most critical issues in the banking industry. It is very important for both the bank and the customer that the offers given are at the appropriate level. In this study, approximately 5 months of data were used and estimation models were designed for the estimation of the currency offers given to the customers. The study was conducted over 18 different currencies. In the study, dependent variables were determined as customer segment, instant exchange rate, day information, time information and volatility value. The independent variable is the exchange rate margin. The training was carried out with daily data and using RF, GBM, ANN, DNN and CNN algorithms. Random search algorithm was used to find the hyperparameters of the algorithms and the results of the model training were compared. The models with the lowest error values were selected to be used in the estimation phase. Mean Square Error (MSE) and Mean Absolute Error (MAE) functions were used to measure performance. It has been observed that artificial neural networks and convolutional neural network algorithms reveal better results than other algorithms according to the trainings carried out on three different models. Estimation time for 18 currencies is about 3 seconds.
{"title":"Learning and Predicting Asset Management","authors":"Kağan Küçük, Fatih Kahraman, M. Kamasak, E. Adali","doi":"10.1109/UBMK52708.2021.9558904","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558904","url":null,"abstract":"Instant exchange rates offered to customers are the most critical issues in the banking industry. It is very important for both the bank and the customer that the offers given are at the appropriate level. In this study, approximately 5 months of data were used and estimation models were designed for the estimation of the currency offers given to the customers. The study was conducted over 18 different currencies. In the study, dependent variables were determined as customer segment, instant exchange rate, day information, time information and volatility value. The independent variable is the exchange rate margin. The training was carried out with daily data and using RF, GBM, ANN, DNN and CNN algorithms. Random search algorithm was used to find the hyperparameters of the algorithms and the results of the model training were compared. The models with the lowest error values were selected to be used in the estimation phase. Mean Square Error (MSE) and Mean Absolute Error (MAE) functions were used to measure performance. It has been observed that artificial neural networks and convolutional neural network algorithms reveal better results than other algorithms according to the trainings carried out on three different models. Estimation time for 18 currencies is about 3 seconds.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126780242","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558982
Furkan Eren, Zeynep Gündoğar
Medical images are crucial data sources for diseases that can not be diagnosed easily. X-rays, one of the medical images, have high resolution. Processing high-resolution images leads to a few problems such as difficulties in data storage, computational load, and the time required to process high-dimensional data. It is vital to be able to diagnose diseases fast and accurately. In this study, a data set consisting of lung X-rays of patients with and without COVID-19 symptoms was taken into consideration. Disease diagnosis from these images can be summarized in two steps as preprocessing and classification. The preprocessing step covers the feature extraction process and for this the recently developed decomposition-based method, Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR), is proposed as a feature extraction method. The classification of images is the second step where the methods of Random Forests and Support Vector Machines are applied. Also, the X-ray images have been reduced by 99,9% with TMEMPR and with several state-of-the-art feature extraction methods such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT). The results are examined with regard to different feature extraction methods and it is observed that a higher accuracy rate is achieved when the TMEMPR method is used.
{"title":"Classification of Covid-19 X-ray Images Using Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR)","authors":"Furkan Eren, Zeynep Gündoğar","doi":"10.1109/UBMK52708.2021.9558982","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558982","url":null,"abstract":"Medical images are crucial data sources for diseases that can not be diagnosed easily. X-rays, one of the medical images, have high resolution. Processing high-resolution images leads to a few problems such as difficulties in data storage, computational load, and the time required to process high-dimensional data. It is vital to be able to diagnose diseases fast and accurately. In this study, a data set consisting of lung X-rays of patients with and without COVID-19 symptoms was taken into consideration. Disease diagnosis from these images can be summarized in two steps as preprocessing and classification. The preprocessing step covers the feature extraction process and for this the recently developed decomposition-based method, Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR), is proposed as a feature extraction method. The classification of images is the second step where the methods of Random Forests and Support Vector Machines are applied. Also, the X-ray images have been reduced by 99,9% with TMEMPR and with several state-of-the-art feature extraction methods such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT). The results are examined with regard to different feature extraction methods and it is observed that a higher accuracy rate is achieved when the TMEMPR method is used.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126951108","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9559035
Murat Aslanyürek, A. Mesut
In this study, Static Dictionary Compression (SDC) method, which is an approach developed to compress short texts, is proposed. The word-based static dictionaries used in this approach were obtained from clusters formed as a result of running a clustering method repeatedly until certain criteria are met. Short text is compressed with the dictionary that has the largest number of words in common with it. It has been shown by tests conducted with datasets containing short texts in 6 different languages that the proposed method compresses better than the general purpose compression methods Gzip, Bzip2, Zstd and PPMd. In the tests made with the data set containing only English short texts, it has been shown that the SDC method can compress better than the smza, shoco and b64pack methods used to compress short texts, and Brotli, which gives good results in short texts because it uses a static dictionary.
{"title":"A Static Dictionary-Based Approach To Compressing Short Texts","authors":"Murat Aslanyürek, A. Mesut","doi":"10.1109/UBMK52708.2021.9559035","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9559035","url":null,"abstract":"In this study, Static Dictionary Compression (SDC) method, which is an approach developed to compress short texts, is proposed. The word-based static dictionaries used in this approach were obtained from clusters formed as a result of running a clustering method repeatedly until certain criteria are met. Short text is compressed with the dictionary that has the largest number of words in common with it. It has been shown by tests conducted with datasets containing short texts in 6 different languages that the proposed method compresses better than the general purpose compression methods Gzip, Bzip2, Zstd and PPMd. In the tests made with the data set containing only English short texts, it has been shown that the SDC method can compress better than the smza, shoco and b64pack methods used to compress short texts, and Brotli, which gives good results in short texts because it uses a static dictionary.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116533791","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558920
Nazli Ece Uykur, Begum Mutlu, E. Sezer
Software fault protection can be first achieved by fault prediction. The earlier the fault prediction can be done in the software development life-cycle, the lower the damage and repair costs caused by the defects that will occur. Machine learning is one well-known method for the decision-making part of automatic software fault prediction. However, the applicability of machine learning methods is low due to the lack of data in the early stages of development processes. In this study, the data needed in the design of rule-base was obtained from counterpart projects, and the fault prediction problem was evaluated by the fuzzy rule-based systems’ point of view since these systems have portability utility which allows rule transfer between different problems with similar goals in the same domain. Briefly, this study aims to show that early-stage fault prediction is possible with the portability characteristics of fuzzy systems sourced from the inter-project rule transfer. Several experiments have been performed by using the software metrics datasets of 5 software projects to support this idea. Fuzzy systems obtained from several combinations of these datasets were evaluated by their prediction accuracy. The results show that more accurate rules can be obtained from previously completed software projects, and the use of rule bases gathered from those projects’ software metrics repositories can be transfered to predict the faulty modules of the current software project.
{"title":"Early Stage Fault Prediction via Inter-Project Rule Transfer","authors":"Nazli Ece Uykur, Begum Mutlu, E. Sezer","doi":"10.1109/UBMK52708.2021.9558920","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558920","url":null,"abstract":"Software fault protection can be first achieved by fault prediction. The earlier the fault prediction can be done in the software development life-cycle, the lower the damage and repair costs caused by the defects that will occur. Machine learning is one well-known method for the decision-making part of automatic software fault prediction. However, the applicability of machine learning methods is low due to the lack of data in the early stages of development processes. In this study, the data needed in the design of rule-base was obtained from counterpart projects, and the fault prediction problem was evaluated by the fuzzy rule-based systems’ point of view since these systems have portability utility which allows rule transfer between different problems with similar goals in the same domain. Briefly, this study aims to show that early-stage fault prediction is possible with the portability characteristics of fuzzy systems sourced from the inter-project rule transfer. Several experiments have been performed by using the software metrics datasets of 5 software projects to support this idea. Fuzzy systems obtained from several combinations of these datasets were evaluated by their prediction accuracy. The results show that more accurate rules can be obtained from previously completed software projects, and the use of rule bases gathered from those projects’ software metrics repositories can be transfered to predict the faulty modules of the current software project.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114437004","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9559023
Khamroyeva Shahlo Mirdjanovna
In this article, we discuss the development of a model of the Uzbek language FST (finite state transducer) in the creation of a morphological analyzer of the Uzbek language. There are key factors for automatic morphological analysis, such as stem, base, prefix, suffix, spelling rules. To do this, you need to create a database of word-formers in Uzbek (pre-/post-stem), lexical and syntactic suffixes, particles in the form of suffixes.
{"title":"Finite State Machine Model for Uzbek Language Morphological Analyzer","authors":"Khamroyeva Shahlo Mirdjanovna","doi":"10.1109/UBMK52708.2021.9559023","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9559023","url":null,"abstract":"In this article, we discuss the development of a model of the Uzbek language FST (finite state transducer) in the creation of a morphological analyzer of the Uzbek language. There are key factors for automatic morphological analysis, such as stem, base, prefix, suffix, spelling rules. To do this, you need to create a database of word-formers in Uzbek (pre-/post-stem), lexical and syntactic suffixes, particles in the form of suffixes.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117333555","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558975
Hidayet Takçi, Saliha Yeşilyurt
Autism is a generalized pervasive developmental disorder that can be characterized by language and communication disorders. Screening tests are often used to diagnose such a disorder; however, they are usually time-consuming and costly tests. In recent years, machine learning methods have been frequently utilized for this purpose due to their performance and efficiency. This paper employs the most eight prominent machine learning algorithms and presents an empirical evaluation of their performances in diagnosing autism disorder on four different benchmark datasets, which are up-to-date and originate from the QCHAT, AQ-10-child, and AQ-10-adult screening tests. In doing so, we also utilize precision, sensitivity, specificity, and classification accuracy metrics to scrutinize their performances. According to the experimental results, the best outcomes are obtained with C-SVC, a classifier based on a support vector machine. More importantly, in terms of C-SVC performance metrics even lead to 100% in all datasets. Multivariate logistic regression has been taken second place. On the other hand, the lowest results are obtained with the C4.5 algorithm, a decision tree-based algorithm.
{"title":"Diagnosing Autism Spectrum Disorder Using Machine Learning Techniques","authors":"Hidayet Takçi, Saliha Yeşilyurt","doi":"10.1109/UBMK52708.2021.9558975","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558975","url":null,"abstract":"Autism is a generalized pervasive developmental disorder that can be characterized by language and communication disorders. Screening tests are often used to diagnose such a disorder; however, they are usually time-consuming and costly tests. In recent years, machine learning methods have been frequently utilized for this purpose due to their performance and efficiency. This paper employs the most eight prominent machine learning algorithms and presents an empirical evaluation of their performances in diagnosing autism disorder on four different benchmark datasets, which are up-to-date and originate from the QCHAT, AQ-10-child, and AQ-10-adult screening tests. In doing so, we also utilize precision, sensitivity, specificity, and classification accuracy metrics to scrutinize their performances. According to the experimental results, the best outcomes are obtained with C-SVC, a classifier based on a support vector machine. More importantly, in terms of C-SVC performance metrics even lead to 100% in all datasets. Multivariate logistic regression has been taken second place. On the other hand, the lowest results are obtained with the C4.5 algorithm, a decision tree-based algorithm.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116311617","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558955
G. Yilmaz, G. Akar
In order to provide improved multimedia services to the end users, developing objective models efficiently predicting 3 Dimensional (3D) video Quality of Experience (QoE) can currently be considered as one of the most significant research areas. Nevertheless, there is currently no model standardized and widely utilized by the researchers due to its efficient and reliable assessment of the 3D video quality. Therefore, highly exploited 2 Dimensional (2D) video quality assessment models such as Structural SIMilarity Index (SSIM) are preferred for the 3D video quality evaluation. However, providing efficiency and reliability for the 3D video quality assessment using the 2D video quality assessment models can only be ensured if they include 3D video related features effecting Human Visual System (HVS). Under the light of these information, the SSIM model is improved for the 3D video quality assessment using perceptually significant feature, contrast and motion characteristics having impact on the HVS in this study. The results obtained by utilizing the improved SSIM model clearly present that the model is quite competent to provide enhanced multimedia services to the end users.
{"title":"3D Video Quality Evaluation Based on SSIM Model Improvement","authors":"G. Yilmaz, G. Akar","doi":"10.1109/UBMK52708.2021.9558955","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558955","url":null,"abstract":"In order to provide improved multimedia services to the end users, developing objective models efficiently predicting 3 Dimensional (3D) video Quality of Experience (QoE) can currently be considered as one of the most significant research areas. Nevertheless, there is currently no model standardized and widely utilized by the researchers due to its efficient and reliable assessment of the 3D video quality. Therefore, highly exploited 2 Dimensional (2D) video quality assessment models such as Structural SIMilarity Index (SSIM) are preferred for the 3D video quality evaluation. However, providing efficiency and reliability for the 3D video quality assessment using the 2D video quality assessment models can only be ensured if they include 3D video related features effecting Human Visual System (HVS). Under the light of these information, the SSIM model is improved for the 3D video quality assessment using perceptually significant feature, contrast and motion characteristics having impact on the HVS in this study. The results obtained by utilizing the improved SSIM model clearly present that the model is quite competent to provide enhanced multimedia services to the end users.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125724234","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9559020
Ümit Emre Köse, R. Samet
Nowadays, many studies are done on the detection of malicious software. Static, Dynamic and Hybrid analysis methods are used to collect data for malware detection. With these methods, data is created by reading the information in the file without running the malicious software, or by examining the places it affects such as changes on the network at runtime, api calls. With the advancement of today’s technology, these data are combined with Machine learning algorithms or architectures of Deep Learning to detect malware. Detection of malicious software On the data set containing malicious software, it was detected by using CNN and ANN neural networks. While close to 10,000 datasets showed a success rate of close to 99%, datasets close to 50,000 achieved close to 97% success. In our study, a success rate of 98.1% was achieved for nearly 50,000 data sets. Among the studies researched, malware detection was made with higher accuracy than the studies using data sets closest to 50,000.
{"title":"Detection of Malware with Deep Learning Method","authors":"Ümit Emre Köse, R. Samet","doi":"10.1109/UBMK52708.2021.9559020","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9559020","url":null,"abstract":"Nowadays, many studies are done on the detection of malicious software. Static, Dynamic and Hybrid analysis methods are used to collect data for malware detection. With these methods, data is created by reading the information in the file without running the malicious software, or by examining the places it affects such as changes on the network at runtime, api calls. With the advancement of today’s technology, these data are combined with Machine learning algorithms or architectures of Deep Learning to detect malware. Detection of malicious software On the data set containing malicious software, it was detected by using CNN and ANN neural networks. While close to 10,000 datasets showed a success rate of close to 99%, datasets close to 50,000 achieved close to 97% success. In our study, a success rate of 98.1% was achieved for nearly 50,000 data sets. Among the studies researched, malware detection was made with higher accuracy than the studies using data sets closest to 50,000.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125770817","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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