With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after ...With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.展开更多
Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the inside.There are many algorithms designed to protect the cloud serv...Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the inside.There are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server attacks.Still,the attackers have designed even better mechanisms to break these security algorithms.Cloud cryptography is the best data protection algorithm that exchanges data between authentic users.In this article,one symmetric cryptography algorithm will be designed to secure cloud server data,used to send and receive cloud server data securely.A double encryption algorithm will be implemented to send data in a secure format.First,the XOR function will be applied to plain text,and then salt technique will be used.Finally,a reversing mechanism will be implemented on that data to provide more data security.To decrypt data,the cipher text will be reversed,salt will be removed,andXORwill be implemented.At the end of the paper,the proposed algorithm will be compared with other algorithms,and it will conclude how much better the existing algorithm is than other algorithms.展开更多
Cloud computing is one of the most attractive and cost-saving models,which provides online services to end-users.Cloud computing allows the user to access data directly from any node.But nowadays,cloud security is one...Cloud computing is one of the most attractive and cost-saving models,which provides online services to end-users.Cloud computing allows the user to access data directly from any node.But nowadays,cloud security is one of the biggest issues that arise.Different types of malware are wreaking havoc on the clouds.Attacks on the cloud server are happening from both internal and external sides.This paper has developed a tool to prevent the cloud server from spamming attacks.When an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)classification.Cloudflare will block those IP addresses that the attacker will use and prevent spamming attacks.However,the KNN classifiers will determine which area the spammer belongs to.At the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.展开更多
Personality distinguishes individuals’ patterns of feeling, thinking,and behaving. Predicting personality from small video series is an excitingresearch area in computer vision. The majority of the existing research ...Personality distinguishes individuals’ patterns of feeling, thinking,and behaving. Predicting personality from small video series is an excitingresearch area in computer vision. The majority of the existing research concludespreliminary results to get immense knowledge from visual and Audio(sound) modality. To overcome the deficiency, we proposed the Deep BimodalFusion (DBF) approach to predict five traits of personality-agreeableness,extraversion, openness, conscientiousness and neuroticism. In the proposedframework, regarding visual modality, the modified convolution neural networks(CNN), more specifically Descriptor Aggregator Model (DAN) areused to attain significant visual modality. The proposed model extracts audiorepresentations for greater efficiency to construct the long short-termmemory(LSTM) for the audio modality. Moreover, employing modality-based neuralnetworks allows this framework to independently determine the traits beforecombining them with weighted fusion to achieve a conclusive prediction of thegiven traits. The proposed approach attains the optimal mean accuracy score,which is 0.9183. It is achieved based on the average of five personality traitsand is thus better than previously proposed frameworks.展开更多
File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most ...File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user files.Currently,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic modeling.However,one major drawback of current topic modeling approaches is better results.They rely on specific language types and domain similarity of the data.In this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the files.The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.展开更多
The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network...The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and discretion.Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities.An intrusion detection system controls the flow of network traffic with the help of computer systems.Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic.For this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network intrusion.The Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks.The proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.展开更多
In the present work,a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide.Four different machine learning algorithm...In the present work,a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide.Four different machine learning algorithms of radial basis function,multi-layer perceptron(MLP),artificial neural networks(ANN),least squares support vector machine(LSSVM)and adaptive neuro-fuzzy inference system(ANFIS)are used to model the solubility of different acids in carbon dioxide based on the temperature,pressure,hydrogen number,carbon number,molecular weight,and the dissociation constant of acid.To evaluate the proposed models,different graphical and statistical analyses,along with novel sensitivity analysis,are carried out.The present study proposes an efficient tool for acid solubility estimation in supercritical carbon dioxide,which can be highly beneficial for engineers and chemists to predict operational conditions in industries.展开更多
In the process of eliminating variables in a symbolic polynomial system,the extraneous factors are referred to the unwanted parameters of resulting polynomial.This paper aims at reducing the number of these factors vi...In the process of eliminating variables in a symbolic polynomial system,the extraneous factors are referred to the unwanted parameters of resulting polynomial.This paper aims at reducing the number of these factors via optimizing the size of Dixon matrix.An optimal configuration of Dixon matrix would lead to the enhancement of the process of computing the resultant which uses for solving polynomial systems.To do so,an optimization algorithm along with a number of new polynomials is introduced to replace the polynomials and implement a complexity analysis.Moreover,the monomial multipliers are optimally positioned to multiply each of the polynomials.Furthermore,through practical implementation and considering standard and mechanical examples the efficiency of the method is evaluated.展开更多
Glycyrrhiza glabra,Mint,Cuminum cyminum,Lavender and Arctium medicinal are considered as edible plants with therapeutic properties and as medicinal plants in Iran.After extraction process of medicinal plants,residual ...Glycyrrhiza glabra,Mint,Cuminum cyminum,Lavender and Arctium medicinal are considered as edible plants with therapeutic properties and as medicinal plants in Iran.After extraction process of medicinal plants,residual wastes are not suitable for animal feed and are considered as waste and as an environmental threat.At present there is no proper management of waste of these plants and they are burned or buried.The present study discusses the possibility of biogas production from Glycyrrhiza Glabra Waste(GGW),Mentha Waste(MW),Cuminum Cyminum Waste(CCW),Lavender Waste(LW)and Arctium Waste(AW).250 g of these plants with TS of 10%were digested in the batch type reactors at the temperature of 35℃.The highest biogas production rate were observed to be 13611 mL and 13471 mL for CCW and GGW(10%TS),respectively.While the maximum methane was related to GGW with a value of 9041 mL(10%TS).The highest specific biogas and methane production were related to CCW with value of 247.4 mL.(g.VS)-1 and 65.1 mL.(g.VS)-1,respectively.As an important result,it was obvious that in lignocellulose materials,it cannot be concluded that the materials with similar ratio of C/N has the similar digestion and biogas production ability.展开更多
During the last few years we have witnessed impressive developments in the area of stochastic local search techniques for intelligent optimization and Reactive Search Optimization. In order to handle the complexity, i...During the last few years we have witnessed impressive developments in the area of stochastic local search techniques for intelligent optimization and Reactive Search Optimization. In order to handle the complexity, in the framework of stochastic local search optimization, learning and optimization has been deeply interconnected through interaction with the decision maker via the visualization approach of the online graphs. Consequently a number of complex optimization problems, in particular multiobjective optimization problems, arising in widely different contexts have been effectively treated within the general framework of RSO. In solving real-life multiobjective optimization problems often most emphasis are spent on finding the complete Pareto-optimal set and less on decision-making. However the com-plete task of multiobjective optimization is considered as a combined task of optimization and decision-making. In this paper, we suggest an interactive procedure which will involve the decision-maker in the optimization process helping to choose a single solution at the end. Our proposed method works on the basis of Reactive Search Optimization (RSO) algorithms and available software architecture packages. The procedure is further compared with the excising novel method of Interactive Multiobjective Optimization and Decision-Making, using Evolutionary method (I-MODE). In order to evaluate the effectiveness of both methods the well-known study case of welded beam design problem is reconsidered.展开更多
The way towards generating a website front end involves a designersettling on an idea for what kind of layout they want the website to have, thenproceeding to plan and implement each aspect one by one until they havec...The way towards generating a website front end involves a designersettling on an idea for what kind of layout they want the website to have, thenproceeding to plan and implement each aspect one by one until they haveconverted what they initially laid out into its Html front end form, this processcan take a considerable time, especially considering the first draft of the designis traditionally never the final one. This process can take up a large amountof resource real estate, and as we have laid out in this paper, by using a Modelconsisting of various Neural Networks trained on a custom dataset. It can beautomated into assisting designers, allowing them to focus on the other morecomplicated parts of the system they are designing by quickly generating whatwould rather be straightforward busywork. Over the past 20 years, the boomin how much the internet is used and the sheer volume of pages on it demands ahigh level of work and time to create them. For the efficiency of the process, weproposed a multi-model-based architecture on image captioning, consisting ofConvolutional neural network (CNN) and Long short-term memory (LSTM)models. Our proposed approach trained on our custom-made database can beautomated into assisting designers, allowing them to focus on the other morecomplicated part of the system. We trained our model in several batches overa custom-made dataset consisting of over 6300 files and were finally able toachieve a Bilingual Evaluation Understudy (BLEU) score for a batch of 50hand-drawn images at 87.86%.展开更多
Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace.Similarly,the field of health informatics is also considered as an extremely important field.This work observes the...Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace.Similarly,the field of health informatics is also considered as an extremely important field.This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis.The developed system has two front ends,the first dedicated for the user to perform the photographing of the trace report.Once the photographing is complete,mobile computing is used to extract the signal.Once the signal is extracted,it is uploaded into the server and further analysis is performed on the signal in the cloud.Once this is done,the second interface,intended for the use of the physician,can download and view the trace from the cloud.The data is securely held using a password-based authentication method.The system presented here is one of the first attempts at delivering the total solution,and after further upgrades,it will be possible to deploy the system in a commercial setting.展开更多
Emotion detection from the text is a challenging problem in the text analytics.The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention ...Emotion detection from the text is a challenging problem in the text analytics.The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions.However,most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets,resulting in performance degradation.To overcome this issue,this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset.The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision,recall ad f-measure.Finally,a classifier with the best performance is recommended for the emotion classification.展开更多
Scour depth around bridge piers plays a vital role in the safety and stability of the bridges.The former approaches used in the prediction of scour depth are based on regression models or black box models in which the...Scour depth around bridge piers plays a vital role in the safety and stability of the bridges.The former approaches used in the prediction of scour depth are based on regression models or black box models in which the first one lacks enough accuracy while the later one does not provide a clear mathematical expression to easily employ it for other situations or cases.Therefore,this paper aims to develop new equations using particle swarm optimization as a metaheuristic approach to predict scour depth around bridge piers.To improve the efficiency of the proposed model,individual equations are derived for laboratory and field data.Moreover,sensitivity analysis is conducted to achieve the most effective parameters in the estimation of scour depth for both experimental and filed data sets.Comparing the results of the proposed model with those of existing regression-based equations reveal the superiority of the proposed method in terms of accuracy and uncertainty.Moreover,the ratio of pier width to flow depth and ratio of d50 (mean particle diameter)to flow depth for the laboratory and field data were recognized as the most effective parameters,respectively.The derived equations can be used as a suitable proxy to estimate scour depth in both experimental and prototype scales.展开更多
Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels.In this study,at first,a series of experimental te...Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels.In this study,at first,a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels.The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions.A set of,data mining and machine learning algorithms including Random Forest(RF),M5P,Random Committee,KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel.Results indicated among these five models;RF method indicated the most precise results with the highest R2 value of 0.9.Finally,the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method(SKM)and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution.The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.展开更多
文摘With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.
文摘Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the inside.There are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server attacks.Still,the attackers have designed even better mechanisms to break these security algorithms.Cloud cryptography is the best data protection algorithm that exchanges data between authentic users.In this article,one symmetric cryptography algorithm will be designed to secure cloud server data,used to send and receive cloud server data securely.A double encryption algorithm will be implemented to send data in a secure format.First,the XOR function will be applied to plain text,and then salt technique will be used.Finally,a reversing mechanism will be implemented on that data to provide more data security.To decrypt data,the cipher text will be reversed,salt will be removed,andXORwill be implemented.At the end of the paper,the proposed algorithm will be compared with other algorithms,and it will conclude how much better the existing algorithm is than other algorithms.
文摘Cloud computing is one of the most attractive and cost-saving models,which provides online services to end-users.Cloud computing allows the user to access data directly from any node.But nowadays,cloud security is one of the biggest issues that arise.Different types of malware are wreaking havoc on the clouds.Attacks on the cloud server are happening from both internal and external sides.This paper has developed a tool to prevent the cloud server from spamming attacks.When an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)classification.Cloudflare will block those IP addresses that the attacker will use and prevent spamming attacks.However,the KNN classifiers will determine which area the spammer belongs to.At the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.
文摘Personality distinguishes individuals’ patterns of feeling, thinking,and behaving. Predicting personality from small video series is an excitingresearch area in computer vision. The majority of the existing research concludespreliminary results to get immense knowledge from visual and Audio(sound) modality. To overcome the deficiency, we proposed the Deep BimodalFusion (DBF) approach to predict five traits of personality-agreeableness,extraversion, openness, conscientiousness and neuroticism. In the proposedframework, regarding visual modality, the modified convolution neural networks(CNN), more specifically Descriptor Aggregator Model (DAN) areused to attain significant visual modality. The proposed model extracts audiorepresentations for greater efficiency to construct the long short-termmemory(LSTM) for the audio modality. Moreover, employing modality-based neuralnetworks allows this framework to independently determine the traits beforecombining them with weighted fusion to achieve a conclusive prediction of thegiven traits. The proposed approach attains the optimal mean accuracy score,which is 0.9183. It is achieved based on the average of five personality traitsand is thus better than previously proposed frameworks.
文摘File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user files.Currently,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic modeling.However,one major drawback of current topic modeling approaches is better results.They rely on specific language types and domain similarity of the data.In this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the files.The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.
文摘The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and discretion.Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities.An intrusion detection system controls the flow of network traffic with the help of computer systems.Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic.For this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network intrusion.The Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks.The proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.
基金This research is sponsored by the Project:“Support of research and development activities of the J.Selye University in the field of Digital Slovakia and creative industry”of the Research&Innovation Operational Programme(ITMS code:NFP313010T504)co-funded by the European Regional Development Fund.
文摘In the present work,a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide.Four different machine learning algorithms of radial basis function,multi-layer perceptron(MLP),artificial neural networks(ANN),least squares support vector machine(LSSVM)and adaptive neuro-fuzzy inference system(ANFIS)are used to model the solubility of different acids in carbon dioxide based on the temperature,pressure,hydrogen number,carbon number,molecular weight,and the dissociation constant of acid.To evaluate the proposed models,different graphical and statistical analyses,along with novel sensitivity analysis,are carried out.The present study proposes an efficient tool for acid solubility estimation in supercritical carbon dioxide,which can be highly beneficial for engineers and chemists to predict operational conditions in industries.
文摘In the process of eliminating variables in a symbolic polynomial system,the extraneous factors are referred to the unwanted parameters of resulting polynomial.This paper aims at reducing the number of these factors via optimizing the size of Dixon matrix.An optimal configuration of Dixon matrix would lead to the enhancement of the process of computing the resultant which uses for solving polynomial systems.To do so,an optimization algorithm along with a number of new polynomials is introduced to replace the polynomials and implement a complexity analysis.Moreover,the monomial multipliers are optimally positioned to multiply each of the polynomials.Furthermore,through practical implementation and considering standard and mechanical examples the efficiency of the method is evaluated.
文摘Glycyrrhiza glabra,Mint,Cuminum cyminum,Lavender and Arctium medicinal are considered as edible plants with therapeutic properties and as medicinal plants in Iran.After extraction process of medicinal plants,residual wastes are not suitable for animal feed and are considered as waste and as an environmental threat.At present there is no proper management of waste of these plants and they are burned or buried.The present study discusses the possibility of biogas production from Glycyrrhiza Glabra Waste(GGW),Mentha Waste(MW),Cuminum Cyminum Waste(CCW),Lavender Waste(LW)and Arctium Waste(AW).250 g of these plants with TS of 10%were digested in the batch type reactors at the temperature of 35℃.The highest biogas production rate were observed to be 13611 mL and 13471 mL for CCW and GGW(10%TS),respectively.While the maximum methane was related to GGW with a value of 9041 mL(10%TS).The highest specific biogas and methane production were related to CCW with value of 247.4 mL.(g.VS)-1 and 65.1 mL.(g.VS)-1,respectively.As an important result,it was obvious that in lignocellulose materials,it cannot be concluded that the materials with similar ratio of C/N has the similar digestion and biogas production ability.
文摘During the last few years we have witnessed impressive developments in the area of stochastic local search techniques for intelligent optimization and Reactive Search Optimization. In order to handle the complexity, in the framework of stochastic local search optimization, learning and optimization has been deeply interconnected through interaction with the decision maker via the visualization approach of the online graphs. Consequently a number of complex optimization problems, in particular multiobjective optimization problems, arising in widely different contexts have been effectively treated within the general framework of RSO. In solving real-life multiobjective optimization problems often most emphasis are spent on finding the complete Pareto-optimal set and less on decision-making. However the com-plete task of multiobjective optimization is considered as a combined task of optimization and decision-making. In this paper, we suggest an interactive procedure which will involve the decision-maker in the optimization process helping to choose a single solution at the end. Our proposed method works on the basis of Reactive Search Optimization (RSO) algorithms and available software architecture packages. The procedure is further compared with the excising novel method of Interactive Multiobjective Optimization and Decision-Making, using Evolutionary method (I-MODE). In order to evaluate the effectiveness of both methods the well-known study case of welded beam design problem is reconsidered.
文摘The way towards generating a website front end involves a designersettling on an idea for what kind of layout they want the website to have, thenproceeding to plan and implement each aspect one by one until they haveconverted what they initially laid out into its Html front end form, this processcan take a considerable time, especially considering the first draft of the designis traditionally never the final one. This process can take up a large amountof resource real estate, and as we have laid out in this paper, by using a Modelconsisting of various Neural Networks trained on a custom dataset. It can beautomated into assisting designers, allowing them to focus on the other morecomplicated parts of the system they are designing by quickly generating whatwould rather be straightforward busywork. Over the past 20 years, the boomin how much the internet is used and the sheer volume of pages on it demands ahigh level of work and time to create them. For the efficiency of the process, weproposed a multi-model-based architecture on image captioning, consisting ofConvolutional neural network (CNN) and Long short-term memory (LSTM)models. Our proposed approach trained on our custom-made database can beautomated into assisting designers, allowing them to focus on the other morecomplicated part of the system. We trained our model in several batches overa custom-made dataset consisting of over 6300 files and were finally able toachieve a Bilingual Evaluation Understudy (BLEU) score for a batch of 50hand-drawn images at 87.86%.
文摘Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace.Similarly,the field of health informatics is also considered as an extremely important field.This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis.The developed system has two front ends,the first dedicated for the user to perform the photographing of the trace report.Once the photographing is complete,mobile computing is used to extract the signal.Once the signal is extracted,it is uploaded into the server and further analysis is performed on the signal in the cloud.Once this is done,the second interface,intended for the use of the physician,can download and view the trace from the cloud.The data is securely held using a password-based authentication method.The system presented here is one of the first attempts at delivering the total solution,and after further upgrades,it will be possible to deploy the system in a commercial setting.
基金This work has partially been sponsored by the Hungarian National Scientific Fund under contract OTKA 129374the Research&Development Operational Program for the project“Modernization and Improvement of Technical Infrastructure for Research and Development of J.Selye University in the Fields of Nanotechnology and Intelligent Space”,ITMS 26210120042,co-funded by the European Regional Development Fund.
文摘Emotion detection from the text is a challenging problem in the text analytics.The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions.However,most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets,resulting in performance degradation.To overcome this issue,this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset.The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision,recall ad f-measure.Finally,a classifier with the best performance is recommended for the emotion classification.
基金This research was supported by the Hungarian State and the European Union under the EFOP-3.6.1-16-2016-00010 project and the 2017-1.3.1-VKE-2017-00025 project.
文摘Scour depth around bridge piers plays a vital role in the safety and stability of the bridges.The former approaches used in the prediction of scour depth are based on regression models or black box models in which the first one lacks enough accuracy while the later one does not provide a clear mathematical expression to easily employ it for other situations or cases.Therefore,this paper aims to develop new equations using particle swarm optimization as a metaheuristic approach to predict scour depth around bridge piers.To improve the efficiency of the proposed model,individual equations are derived for laboratory and field data.Moreover,sensitivity analysis is conducted to achieve the most effective parameters in the estimation of scour depth for both experimental and filed data sets.Comparing the results of the proposed model with those of existing regression-based equations reveal the superiority of the proposed method in terms of accuracy and uncertainty.Moreover,the ratio of pier width to flow depth and ratio of d50 (mean particle diameter)to flow depth for the laboratory and field data were recognized as the most effective parameters,respectively.The derived equations can be used as a suitable proxy to estimate scour depth in both experimental and prototype scales.
文摘Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels.In this study,at first,a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels.The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions.A set of,data mining and machine learning algorithms including Random Forest(RF),M5P,Random Committee,KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel.Results indicated among these five models;RF method indicated the most precise results with the highest R2 value of 0.9.Finally,the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method(SKM)and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution.The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.
