Nowadays, the rapid development of social networks has led to the proliferation of social news. However, the spreading of fake news is a critical issue. Fake news is news written to intentionally misinform or deceive readers. News on social networks is short and lacks context. This makes it difficult for detecting fake news based on shared content. In this paper, we propose an ensemble classification model to detect fake news based on exploiting the wisdom of crowds. The social interactions and the user?s credibility are mined to automatically detect fake news on Twitter without considering news content. The proposed method extracts the features from a Twitter dataset and then a voting ensemble classifier comprising three classifiers namely, Support Vector Machine (SVM), Naive Bayes, and Softmax is used to classify news into two categories which are fake and real news. The experiments on real datasets achieved the highest F1 score of 78.8% which was better than the baseline by 6.8%. The proposed method significantly improved the accuracy of fake news detection in comparison to other methods.
{"title":"A framework for fake news detection based on the wisdom of crowds and the ensemble learning model","authors":"Hai Truong, Van Tran","doi":"10.2298/csis230315048t","DOIUrl":"https://doi.org/10.2298/csis230315048t","url":null,"abstract":"Nowadays, the rapid development of social networks has led to the proliferation of social news. However, the spreading of fake news is a critical issue. Fake news is news written to intentionally misinform or deceive readers. News on social networks is short and lacks context. This makes it difficult for detecting fake news based on shared content. In this paper, we propose an ensemble classification model to detect fake news based on exploiting the wisdom of crowds. The social interactions and the user?s credibility are mined to automatically detect fake news on Twitter without considering news content. The proposed method extracts the features from a Twitter dataset and then a voting ensemble classifier comprising three classifiers namely, Support Vector Machine (SVM), Naive Bayes, and Softmax is used to classify news into two categories which are fake and real news. The experiments on real datasets achieved the highest F1 score of 78.8% which was better than the baseline by 6.8%. The proposed method significantly improved the accuracy of fake news detection in comparison to other methods.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work aims to perform the unified management of various departments engaged in smart city construction by big data, establish a synthetic data collection and sharing system, and provide fast and convenient big data services for smart applications in various fields. A new electricity demand prediction model based on back propagation neural network (BPNN) is proposed for China?s electricity industry according to the smart city?s big data characteristics. This model integrates meteorological, geographic, demographic, corporate, and economic information to form a big intelligent database. Moreover, the K-means clustering algorithm mines and analyzes the data to optimize the power consumers? information. The BPNN model is used to extract features for prediction. Users with weak daily correlation obtained by the K-means clustering algorithm only input the historical load of adjacent moments into the BPNN model for prediction. Finally, the electricity market is evaluated by exploring the data correlation in-depth to verify the proposed model?s effectiveness. The results indicate that the K-mean algorithm can significantly improve the segmentation accuracy of power consumers, with a maximum accuracy of 85.25% and average accuracy of 83.72%. The electricity consumption of different regions is separated, and the electricity consumption is classified. The electricity demand prediction model can enhance prediction accuracy, with an average error rate of 3.27%. The model?s training significantly speeds up by adding the momentum factor, and the average error rate is 2.13%. Therefore, the electricity demand prediction model achieves high accuracy and training efficiency. The findings can provide a theoretical and practical foundation for electricity demand prediction, personalized marketing, and the development planning of the power industry.
{"title":"Predicting smart cities' electricity demands using k-means clustering algorithm in smart grid","authors":"Shurui Wang, Aifeng Song, Yufeng Qian","doi":"10.2298/csis220807013w","DOIUrl":"https://doi.org/10.2298/csis220807013w","url":null,"abstract":"This work aims to perform the unified management of various departments engaged in smart city construction by big data, establish a synthetic data collection and sharing system, and provide fast and convenient big data services for smart applications in various fields. A new electricity demand prediction model based on back propagation neural network (BPNN) is proposed for China?s electricity industry according to the smart city?s big data characteristics. This model integrates meteorological, geographic, demographic, corporate, and economic information to form a big intelligent database. Moreover, the K-means clustering algorithm mines and analyzes the data to optimize the power consumers? information. The BPNN model is used to extract features for prediction. Users with weak daily correlation obtained by the K-means clustering algorithm only input the historical load of adjacent moments into the BPNN model for prediction. Finally, the electricity market is evaluated by exploring the data correlation in-depth to verify the proposed model?s effectiveness. The results indicate that the K-mean algorithm can significantly improve the segmentation accuracy of power consumers, with a maximum accuracy of 85.25% and average accuracy of 83.72%. The electricity consumption of different regions is separated, and the electricity consumption is classified. The electricity demand prediction model can enhance prediction accuracy, with an average error rate of 3.27%. The model?s training significantly speeds up by adding the momentum factor, and the average error rate is 2.13%. Therefore, the electricity demand prediction model achieves high accuracy and training efficiency. The findings can provide a theoretical and practical foundation for electricity demand prediction, personalized marketing, and the development planning of the power industry.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"34 1","pages":"657-678"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79424646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Blockchain technology gained popularity thanks to its decentralized and transparent features. However, it suffers from a lack of privacy as it stores data publicly and has difficulty to handle data updates due to its main feature known as immutability. In this paper, we propose a decentralized data storage and access framework that combines blockchain technology with Distributed Hash Table (DHT), a role-based access control model, and multiple encryption mechanisms. Our framework stores metadata and DHT keys on the blockchain, while encrypted data is managed on the DHT, which enables data owners to control their data. It allows authorized actors to store and read their data in a decentralized storage system. We design REST APIs to ensure interoperability over the Web. Concerning data updates, we propose a pointer system that allows data owners to access their update history, which solves the issue of data updates while preserving the benefits of using the blockchain. We illustrate our solution with a wood supply chain use case and propose a traceability algorithm that allows the actors of the wood supply chain to trace the data and verify product origin. Our framework design allows authorized users to access the data and protects data against linking, eavesdropping, spoofing, and modification attacks. Moreover, we provide a proof of-concept implementation, security and privacy analysis, and evaluation for time consumption and scalability. The experimental results demonstrate the feasibility, security, privacy, and scalability of the proposed solution.
{"title":"A framework for privacy-aware and secure decentralized data storage","authors":"S. Aslam, M. Mrissa","doi":"10.2298/csis220110007a","DOIUrl":"https://doi.org/10.2298/csis220110007a","url":null,"abstract":"Blockchain technology gained popularity thanks to its decentralized and transparent features. However, it suffers from a lack of privacy as it stores data publicly and has difficulty to handle data updates due to its main feature known as immutability. In this paper, we propose a decentralized data storage and access framework that combines blockchain technology with Distributed Hash Table (DHT), a role-based access control model, and multiple encryption mechanisms. Our framework stores metadata and DHT keys on the blockchain, while encrypted data is managed on the DHT, which enables data owners to control their data. It allows authorized actors to store and read their data in a decentralized storage system. We design REST APIs to ensure interoperability over the Web. Concerning data updates, we propose a pointer system that allows data owners to access their update history, which solves the issue of data updates while preserving the benefits of using the blockchain. We illustrate our solution with a wood supply chain use case and propose a traceability algorithm that allows the actors of the wood supply chain to trace the data and verify product origin. Our framework design allows authorized users to access the data and protects data against linking, eavesdropping, spoofing, and modification attacks. Moreover, we provide a proof of-concept implementation, security and privacy analysis, and evaluation for time consumption and scalability. The experimental results demonstrate the feasibility, security, privacy, and scalability of the proposed solution.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"1 1","pages":"1235-1261"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88907569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recognizing pedestrian attributes has recently obtained increasing attention due to its great potential in person re-identification, recommendation system, and other applications. Existing methods have achieved good results, but these methods do not fully utilize region information and the correlation between attributes. This paper aims at proposing a robust pedestrian attribute recognition framework. Specifically, we first propose an end-to-end framework for attribute recognition. Secondly, spatial and semantic self-attention mechanism is used for key points localization and bounding boxes generation. Finally, a hierarchical recognition strategy is proposed, the whole region is used for the global attribute recognition, and the relevant regions are used for the local attribute recognition. Experimental results on two pedestrian attribute datasets PETA and RAP show that the mean recognition accuracy reaches 84.63% and 82.70%. The heatmap analysis shows that our method can effectively improve the spatial and the semantic correlation between attributes. Compared with existing methods, it can achieve better recognition effect.
{"title":"Pedestrian attribute recognition based on dual self-attention mechanism","authors":"Zhongkui Fan, Ye-peng Guan","doi":"10.2298/csis220815016f","DOIUrl":"https://doi.org/10.2298/csis220815016f","url":null,"abstract":"Recognizing pedestrian attributes has recently obtained increasing attention due to its great potential in person re-identification, recommendation system, and other applications. Existing methods have achieved good results, but these methods do not fully utilize region information and the correlation between attributes. This paper aims at proposing a robust pedestrian attribute recognition framework. Specifically, we first propose an end-to-end framework for attribute recognition. Secondly, spatial and semantic self-attention mechanism is used for key points localization and bounding boxes generation. Finally, a hierarchical recognition strategy is proposed, the whole region is used for the global attribute recognition, and the relevant regions are used for the local attribute recognition. Experimental results on two pedestrian attribute datasets PETA and RAP show that the mean recognition accuracy reaches 84.63% and 82.70%. The heatmap analysis shows that our method can effectively improve the spatial and the semantic correlation between attributes. Compared with existing methods, it can achieve better recognition effect.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"60 5 1","pages":"793-812"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86799019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual perception principle of watching video is crucial in ensuring video works accurately and effectively grasped by audience. This article proposes an investigation into the efficiency of human visual perception on video clips considering exposure duration. The study focused on the correlation between the video shot duration and the subject?s perception of visual content. The subjects? performances were captured as perceptual scores on the testing videos by watching time-regulated clips and taking questionnaire. The statistical results show that three-second duration for each video shot is necessary for audience to grasp the main visual information. The data also indicate gender differences in perceptual procedure and attention focus. The findings can help for manipulating clip length in video editing, both via AI tools and manually, maintaining perception efficiency as possible in limited duration. This method is significant for its structured experiment involving subjects? quantified performances, which is different from AI methods of unaccountable.
{"title":"The duration threshold of video content observation: An experimental investigation of visual perception efficiency","authors":"Jianping Song, Tianran Tang, Guosheng Hu","doi":"10.2298/csis220919017s","DOIUrl":"https://doi.org/10.2298/csis220919017s","url":null,"abstract":"Visual perception principle of watching video is crucial in ensuring video works accurately and effectively grasped by audience. This article proposes an investigation into the efficiency of human visual perception on video clips considering exposure duration. The study focused on the correlation between the video shot duration and the subject?s perception of visual content. The subjects? performances were captured as perceptual scores on the testing videos by watching time-regulated clips and taking questionnaire. The statistical results show that three-second duration for each video shot is necessary for audience to grasp the main visual information. The data also indicate gender differences in perceptual procedure and attention focus. The findings can help for manipulating clip length in video editing, both via AI tools and manually, maintaining perception efficiency as possible in limited duration. This method is significant for its structured experiment involving subjects? quantified performances, which is different from AI methods of unaccountable.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"57 1","pages":"879-892"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87715073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The industrial smoke scene is complex and diverse, and the cost of labeling a large number of smoke data is too high. Under the existing conditions, it is very challenging to efficiently use a large number of existing scene annotation data and network models to complete the image classification and recognition task in the industrial smoke scene. Traditional deep learn-based networks can be directly and efficiently applied to normal scene classification, but there will be a large loss of accuracy in industrial smoke scene. Therefore, we propose a novel generative adversarial network based on LSTM and convolutional block attention module for industrial smoke image recognition. In this paper, a low-cost data enhancement method is used to effectively reduce the difference in the pixel field of the image. The smoke image is input into the LSTM in generator and encoded as a hidden layer vector. This hidden layer vector is then entered into the discriminator. Meanwhile, a convolutional block attention module is integrated into the discriminator to improve the feature self-extraction ability of the discriminator model, so as to improve the performance of the whole smoke image recognition network. Experiments are carried out on real diversified industrial smoke scene data, and the results show that the proposed method achieves better image classification and recognition effect. In particular, the F scores are all above 89%, which is the best among all the results.
{"title":"Generative adversarial network based on LSTM and convolutional block attention module for industrial smoke image recognition","authors":"Dahai Li, Rui Yang, Su Chen","doi":"10.2298/csis221125027l","DOIUrl":"https://doi.org/10.2298/csis221125027l","url":null,"abstract":"The industrial smoke scene is complex and diverse, and the cost of labeling a large number of smoke data is too high. Under the existing conditions, it is very challenging to efficiently use a large number of existing scene annotation data and network models to complete the image classification and recognition task in the industrial smoke scene. Traditional deep learn-based networks can be directly and efficiently applied to normal scene classification, but there will be a large loss of accuracy in industrial smoke scene. Therefore, we propose a novel generative adversarial network based on LSTM and convolutional block attention module for industrial smoke image recognition. In this paper, a low-cost data enhancement method is used to effectively reduce the difference in the pixel field of the image. The smoke image is input into the LSTM in generator and encoded as a hidden layer vector. This hidden layer vector is then entered into the discriminator. Meanwhile, a convolutional block attention module is integrated into the discriminator to improve the feature self-extraction ability of the discriminator model, so as to improve the performance of the whole smoke image recognition network. Experiments are carried out on real diversified industrial smoke scene data, and the results show that the proposed method achieves better image classification and recognition effect. In particular, the F scores are all above 89%, which is the best among all the results.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"20 1","pages":"1707-1728"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68463696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Konstantinos Varvoutas, Georgia Kougka, A. Gounaris
Business process re-engineering (or optimization) has been attracting a lot of interest, and it is considered as a core element of business process management (BPM). One of its most effective mechanisms is task re-sequencing with a view to decreasing process duration and costs, whereas duration (aka cycle time) can be reduced using task parallelism as well. In this work, we propose a novel combination of these two mechanisms, which is resource allocation-aware. Starting from a solution where a given resource allocation in business processes can drive optimizations in an underlying BPMN diagram, our proposal considers resource allocation and model modifications in a combined manner, where an initially suboptimal resource allocation can lead to better overall process executions. More specifically, the main contribution is twofold: (i) to present a proposal that leverages a variant of representation of processes as Refined Process Structure Trees (RPSTs) with a view to enabling novel resource allocation-driven task re-ordering and parallelisation in a principled manner, and (ii) to introduce a resource allocation paradigm that assigns tasks to resources taking into account the re-sequencing opportunities that can arise. The results show that we can yield improvements in a very high proportion of our experimental cases, while these improvements can reach 45% decrease in cycle time.
{"title":"Systematic exploitation of parallel task execution in business processes","authors":"Konstantinos Varvoutas, Georgia Kougka, A. Gounaris","doi":"10.2298/csis230401057v","DOIUrl":"https://doi.org/10.2298/csis230401057v","url":null,"abstract":"Business process re-engineering (or optimization) has been attracting a lot of interest, and it is considered as a core element of business process management (BPM). One of its most effective mechanisms is task re-sequencing with a view to decreasing process duration and costs, whereas duration (aka cycle time) can be reduced using task parallelism as well. In this work, we propose a novel combination of these two mechanisms, which is resource allocation-aware. Starting from a solution where a given resource allocation in business processes can drive optimizations in an underlying BPMN diagram, our proposal considers resource allocation and model modifications in a combined manner, where an initially suboptimal resource allocation can lead to better overall process executions. More specifically, the main contribution is twofold: (i) to present a proposal that leverages a variant of representation of processes as Refined Process Structure Trees (RPSTs) with a view to enabling novel resource allocation-driven task re-ordering and parallelisation in a principled manner, and (ii) to introduce a resource allocation paradigm that assigns tasks to resources taking into account the re-sequencing opportunities that can arise. The results show that we can yield improvements in a very high proportion of our experimental cases, while these improvements can reach 45% decrease in cycle time.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"20 1","pages":"1661-1685"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68464187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the digital revolution, the amount of data to be processed is growing every day. One of the more common functions used to process these data is classification. However, the results obtained by most existing classifiers are not satisfactory, as they often depend on the number and type of attributes within the datasets. In this paper, a maximum entropy model based on class probability distribution is proposed for classifying data in sparse datasets with fewer attributes and instances. Moreover, a new idea of using Lagrange multipliers is suggested for estimating class probabilities in the process of class label prediction. Experimental analysis indicates that the proposed model has an average accuracy of 89.9% and 86.93% with 17 and 36 datasets. Besides, statistical analysis of the results indicates that the proposed model offers greater classification accuracy for over 50% of datasets with fewer attributes and instances than other competitors.
{"title":"Class probability distribution based maximum entropy model for classification of datasets with sparse instances","authors":"Arumugam Saravanan, Damotharan Anandhi, Marudhachalam Srividya","doi":"10.2298/csis211030001s","DOIUrl":"https://doi.org/10.2298/csis211030001s","url":null,"abstract":"Due to the digital revolution, the amount of data to be processed is growing every day. One of the more common functions used to process these data is classification. However, the results obtained by most existing classifiers are not satisfactory, as they often depend on the number and type of attributes within the datasets. In this paper, a maximum entropy model based on class probability distribution is proposed for classifying data in sparse datasets with fewer attributes and instances. Moreover, a new idea of using Lagrange multipliers is suggested for estimating class probabilities in the process of class label prediction. Experimental analysis indicates that the proposed model has an average accuracy of 89.9% and 86.93% with 17 and 36 datasets. Besides, statistical analysis of the results indicates that the proposed model offers greater classification accuracy for over 50% of datasets with fewer attributes and instances than other competitors.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"130 1","pages":"949-976"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76110877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is necessary to improve the efficiency of meteorological service monitoring in smart cities and refine the prediction of extreme weather in smart cities continuously. Firstly, this paper discusses the weather prediction model of artificial influence under Machine Learning (ML) technology and the weather prediction model under the Decision Tree (DT) algorithm. Through ML technology, meteorological observation systems and meteorological data management platforms are developed. The DT algorithm receives and displays the real meteorological signals of extreme weather. Secondly, Artificial Intelligence (AI) technology stores and manages the data generated in the meteorological detection system. Finally, the lightning monitoring system is used to monitor the meteorological conditions of Shaanxi Province from September to December 2021. In addition, the different meteorological intelligent forecast performance of the intelligent forecast meteorological model is verified and analyzed through the national meteorological forecast results from 2018 to 2019. The results suggest that the ML algorithm can couple bad weather variation with the existing mesoscale regional prediction methods to improve the weather forecast accuracy; the AI system can analyze the laws of cloud layer variation along with the existing data and enhance the operational efficiency of urban weather modification. By comparison, the proposed model outperforms the traditional one by 35.26%, and the maximum, minimum, and average prediction errors are 5.95%, 0.59%, and 3.76%, respectively. This exploration has a specific practical value for improving smart city weather modification operation efficiency.
{"title":"Machine learning-based intelligent weather modification forecast in smart city potential area","authors":"Zengyuan Chao","doi":"10.2298/csis220717018c","DOIUrl":"https://doi.org/10.2298/csis220717018c","url":null,"abstract":"It is necessary to improve the efficiency of meteorological service monitoring in smart cities and refine the prediction of extreme weather in smart cities continuously. Firstly, this paper discusses the weather prediction model of artificial influence under Machine Learning (ML) technology and the weather prediction model under the Decision Tree (DT) algorithm. Through ML technology, meteorological observation systems and meteorological data management platforms are developed. The DT algorithm receives and displays the real meteorological signals of extreme weather. Secondly, Artificial Intelligence (AI) technology stores and manages the data generated in the meteorological detection system. Finally, the lightning monitoring system is used to monitor the meteorological conditions of Shaanxi Province from September to December 2021. In addition, the different meteorological intelligent forecast performance of the intelligent forecast meteorological model is verified and analyzed through the national meteorological forecast results from 2018 to 2019. The results suggest that the ML algorithm can couple bad weather variation with the existing mesoscale regional prediction methods to improve the weather forecast accuracy; the AI system can analyze the laws of cloud layer variation along with the existing data and enhance the operational efficiency of urban weather modification. By comparison, the proposed model outperforms the traditional one by 35.26%, and the maximum, minimum, and average prediction errors are 5.95%, 0.59%, and 3.76%, respectively. This exploration has a specific practical value for improving smart city weather modification operation efficiency.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"20 1","pages":"631-656"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78649132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many problems that were considered complex and unsolvable have started to solve and new technologies have emerged through to the development of GPU technology. Solutions have established for NP-Complete and NP-Hard problems with the acceleration of studies in the field of artificial intelligence, which are very interesting for both mathematicians and computer scientists. The most striking one among such problems is the Traveling Salesman Problem in recent years. This problem has solved by artificial intelligence?s metaheuristic algorithms such as Genetic algorithm and Ant Colony optimization. However, researchers are always looking for a better solution. In this study, it is aimed to design a low-cost and optimized algorithm for Traveling Salesman Problem by using GPU parallelization, Machine Learning, and Artificial Intelligence approaches. In this manner, the proposed algorithm consists of three stages; Cluster the points in the given dataset with K-means clustering, find the shortest path with Ant Colony in each of the clusters, and connect each cluster at the closest point to the other. These three stages were carried out by parallel programming. The most obvious difference of the study from those found in the literature is that it performs all calculations on the GPU by using Elitist Ant Colony Optimization. For the experimental results, examinations were carried out on a wide variety of datasets in TSPLIB and it was seen that the proposed parallel KMeans-Elitist Ant Colony approach increased the performance by 30% compared to its counterparts.
{"title":"Solution for TSP/mTSP with an improved parallel clustering and elitist ACO","authors":"G. Baydogmus","doi":"10.2298/csis220820053b","DOIUrl":"https://doi.org/10.2298/csis220820053b","url":null,"abstract":"Many problems that were considered complex and unsolvable have started to solve and new technologies have emerged through to the development of GPU technology. Solutions have established for NP-Complete and NP-Hard problems with the acceleration of studies in the field of artificial intelligence, which are very interesting for both mathematicians and computer scientists. The most striking one among such problems is the Traveling Salesman Problem in recent years. This problem has solved by artificial intelligence?s metaheuristic algorithms such as Genetic algorithm and Ant Colony optimization. However, researchers are always looking for a better solution. In this study, it is aimed to design a low-cost and optimized algorithm for Traveling Salesman Problem by using GPU parallelization, Machine Learning, and Artificial Intelligence approaches. In this manner, the proposed algorithm consists of three stages; Cluster the points in the given dataset with K-means clustering, find the shortest path with Ant Colony in each of the clusters, and connect each cluster at the closest point to the other. These three stages were carried out by parallel programming. The most obvious difference of the study from those found in the literature is that it performs all calculations on the GPU by using Elitist Ant Colony Optimization. For the experimental results, examinations were carried out on a wide variety of datasets in TSPLIB and it was seen that the proposed parallel KMeans-Elitist Ant Colony approach increased the performance by 30% compared to its counterparts.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"42 1","pages":"195-214"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76926812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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