Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.006
Muhammad Sohaib, Samabia Tehseen
The rapid growth of online media over different social media platforms or over the internet along with many benefits have some negative effects as well. Deep learning has many positive applications like medical, animations and cybersecurity etc. But over the past few years, it is observed that it is been used for negative aspects as well such as defaming, black-mailing and creating privacy concerns for the general public. Deepfake is common terminology used for facial forgery of a person in media like images or videos.The advancement in the forgery creation area have challenged the researchers to create and develop advance forgery detection systems capable to detect facial forgeries. Proposed forgery detection system works on the CNN-LSTM model in which we first extracted faces from the frames using MTCNN then performed spatial feature extraction using pretrained Xception network and then used LSTM for temporal feature extraction. At the end classification is performed to predict the video as real or fake. The system is capable to detect low quality videos. The current system has shown good accuracy results for detecting real or fake videos on the Google deepfake AI dataset.
{"title":"Forgery detection of low quality deepfake videos","authors":"Muhammad Sohaib, Samabia Tehseen","doi":"10.14311/nnw.2023.33.006","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.006","url":null,"abstract":"The rapid growth of online media over different social media platforms or over the internet along with many benefits have some negative effects as well. Deep learning has many positive applications like medical, animations and cybersecurity etc. But over the past few years, it is observed that it is been used for negative aspects as well such as defaming, black-mailing and creating privacy concerns for the general public. Deepfake is common terminology used for facial forgery of a person in media like images or videos.The advancement in the forgery creation area have challenged the researchers to create and develop advance forgery detection systems capable to detect facial forgeries. Proposed forgery detection system works on the CNN-LSTM model in which we first extracted faces from the frames using MTCNN then performed spatial feature extraction using pretrained Xception network and then used LSTM for temporal feature extraction. At the end classification is performed to predict the video as real or fake. The system is capable to detect low quality videos. The current system has shown good accuracy results for detecting real or fake videos on the Google deepfake AI dataset.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67126267","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}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.007
Xiliang Zhang, Na Zhao, Qinyuan Lv, Zhenyu Ma, Qin Qin, Weifei Gan, Jianfeng Bai, Ling Gan
Most existing methods of garbage classification utilize transfer learning to acquire acceptable performance. They focus on some smaller categories. For example, the number of the dataset is small or the number of categories is few. However, they are hardly implemented on small devices, such as a smart phone or a Raspberry Pi, because of the huge number of parameters. Moreover, those approaches have insufficient generalization capability. Based on the aforementioned reasons, a promising cascade approach is proposed. It has better performance than transfer learning in classifying garbage in a large scale. In addition, it requires less parameters and training time. So it is more suitable to a potential application, such as deployment on a small device. Several commonly used backbones of convolutional neural networks are investigated in this study. Two different tasks, that is, the target domain being the same as the source domain and the former being different from the latter, are conducted besides. Results indicate with ResNet101 as the backbone, our algorithm outperforms other existing approaches. The innovation is that, as far as we know, this study is the first work combining a pre-trained convolutional neural network as a feature extractor with extreme learning machine to classify garbage. Furthermore, the training time and the number of trainable parameters is significantly shorter and less, respectively.
{"title":"Garbage classification based on a cascade neural network","authors":"Xiliang Zhang, Na Zhao, Qinyuan Lv, Zhenyu Ma, Qin Qin, Weifei Gan, Jianfeng Bai, Ling Gan","doi":"10.14311/nnw.2023.33.007","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.007","url":null,"abstract":"Most existing methods of garbage classification utilize transfer learning to acquire acceptable performance. They focus on some smaller categories. For example, the number of the dataset is small or the number of categories is few. However, they are hardly implemented on small devices, such as a smart phone or a Raspberry Pi, because of the huge number of parameters. Moreover, those approaches have insufficient generalization capability. Based on the aforementioned reasons, a promising cascade approach is proposed. It has better performance than transfer learning in classifying garbage in a large scale. In addition, it requires less parameters and training time. So it is more suitable to a potential application, such as deployment on a small device. Several commonly used backbones of convolutional neural networks are investigated in this study. Two different tasks, that is, the target domain being the same as the source domain and the former being different from the latter, are conducted besides. Results indicate with ResNet101 as the backbone, our algorithm outperforms other existing approaches. The innovation is that, as far as we know, this study is the first work combining a pre-trained convolutional neural network as a feature extractor with extreme learning machine to classify garbage. Furthermore, the training time and the number of trainable parameters is significantly shorter and less, respectively.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67126327","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}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.010
K. Borna, Reza Ghanbari
Predicting cloud workload is a problematic issue for cloud providers. Recent research has led us to a significant improvement in workload prediction. Although self-adaptive systems have an imperative impact on lowering the number of cloud resources, those still have to be more accurate, detailed and accelerated. A new self-adaptive technique based on a deep learning model to optimize and decrease the use of cloud resources is proposed. It is also demonstrated how to prognosticate incoming workload and how to manage available resources. The PlanetLab dataset in this research is used. The obtained results have been compared to other relevant designs. According to these comparisons with the state-of-theart deep learning methods, our proposed model encompasses a better prediction efficiency and enhances productivity by 5%.
{"title":"A self-adaptive deep learning-based model to predict cloud workload","authors":"K. Borna, Reza Ghanbari","doi":"10.14311/nnw.2023.33.010","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.010","url":null,"abstract":"Predicting cloud workload is a problematic issue for cloud providers. Recent research has led us to a significant improvement in workload prediction. Although self-adaptive systems have an imperative impact on lowering the number of cloud resources, those still have to be more accurate, detailed and accelerated. A new self-adaptive technique based on a deep learning model to optimize and decrease the use of cloud resources is proposed. It is also demonstrated how to prognosticate incoming workload and how to manage available resources. The PlanetLab dataset in this research is used. The obtained results have been compared to other relevant designs. According to these comparisons with the state-of-theart deep learning methods, our proposed model encompasses a better prediction efficiency and enhances productivity by 5%.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67126027","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}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.003
Zhengze Xu, Wenjun Zhang
Previous studies are mainly focused on the works that depth image is treated as flat image, and then depth data tends to be mapped as gray values during the convolution processing and features extraction. To address this issue, an approach of 3D CNN hand pose estimation with end-to-end hierarchical model and physical constraints is proposed. After reconstruction of 3D space structure of hand from depth image, 3D model is converted into voxel grid for further hand pose estimation by 3D CNN. The 3D CNN method makes improvements by embedding end-to-end hierarchical model and constraints algorithm into the networks, resulting to train at fast convergence rate and avoid unrealistic hand pose. According to the experimental results, it reaches 87.98% of mean accuracy and 8.82 mm of mean absolute error (MAE) for all 21 joints within 24 ms at the inference time, which consistently outperforms several well-known gesture recognition algorithms.
{"title":"3D CNN hand pose estimation with end-to-end hierarchical model and physical constraints from depth images","authors":"Zhengze Xu, Wenjun Zhang","doi":"10.14311/nnw.2023.33.003","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.003","url":null,"abstract":"Previous studies are mainly focused on the works that depth image is treated as flat image, and then depth data tends to be mapped as gray values during the convolution processing and features extraction. To address this issue, an approach of 3D CNN hand pose estimation with end-to-end hierarchical model and physical constraints is proposed. After reconstruction of 3D space structure of hand from depth image, 3D model is converted into voxel grid for further hand pose estimation by 3D CNN. The 3D CNN method makes improvements by embedding end-to-end hierarchical model and constraints algorithm into the networks, resulting to train at fast convergence rate and avoid unrealistic hand pose. According to the experimental results, it reaches 87.98% of mean accuracy and 8.82 mm of mean absolute error (MAE) for all 21 joints within 24 ms at the inference time, which consistently outperforms several well-known gesture recognition algorithms.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67126134","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}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.004
Masaya Yumoto, M. Hagiwara
In this paper, we propose new methods for estimating the relative reliability of prediction and rejection methods for selective classification for spiking neural networks (SNNs). We also optimize and improve the efficiency of the RC curve, which represents the relationship between risk and coverage in selective classification. Efficiency here means greater coverage for risk and less risk for coverage in the RC curve. We use the model internal representation when time series data is input to SNN, rank the prediction results that are the output, and reject them at an arbitrary rate. We propose multiple methods based on the characteristics of datasets and the architecture of models. Multiple methods, such as a simple method with discrete coverage and a method with continuous and flexible coverage, yielded results that exceeded the performance of the existing method, softmax response.
{"title":"Selective classification considering time series characteristics for spiking neural networks","authors":"Masaya Yumoto, M. Hagiwara","doi":"10.14311/nnw.2023.33.004","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.004","url":null,"abstract":"In this paper, we propose new methods for estimating the relative reliability of prediction and rejection methods for selective classification for spiking neural networks (SNNs). We also optimize and improve the efficiency of the RC curve, which represents the relationship between risk and coverage in selective classification. Efficiency here means greater coverage for risk and less risk for coverage in the RC curve. We use the model internal representation when time series data is input to SNN, rank the prediction results that are the output, and reject them at an arbitrary rate. We propose multiple methods based on the characteristics of datasets and the architecture of models. Multiple methods, such as a simple method with discrete coverage and a method with continuous and flexible coverage, yielded results that exceeded the performance of the existing method, softmax response.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67126176","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}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.005
E. Akkur, Fuat Türk, Osman Erogul
Many women around the world die due to breast cancer. If breast cancer is treated in the early phase, mortality rates may significantly be reduced. Quite a number of approaches have been proposed to help in the early detection of breast cancer. A novel hybrid feature selection model is suggested in this study. This novel hybrid model aims to build an efficient feature selection method and successfully classify breast lesions. A combination of relief and binary Harris hawk optimization (BHHO) hybrid model is used for feature selection. Then, k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR) and naive Bayes (NB) methods are preferred for the classification task. The suggested hybrid model is tested by three different breast cancer datasets which are Wisconsin diagnostic breast cancer dataset (WDBC), Wisconsin breast cancer dataset (WBCD) and mammographic breast cancer dataset (MBCD). According to the experimental results, the relief and BHHO hybrid model improves the performance of all classification algorithms in all three datasets. For WDBC, relief-BHO-SVM model shows the highest classification rates with an of accuracy of 98.77%, precision of 97.17%, recall of 99.52%, F1-score of 98.33%, specificity of 99.72% and balanced accuracy of 99.62%. For WBCD, relief-BHO-SVM model achieves of accuracy of 99.28%, precision of 98.76%, recall of 99.17%, F1-score of 98.96%, specificity of 99.56% and balanced accuracy of 99.36%. Relief-BHO-SVM model performs the best with an accuracy of 97.44%, precision of 97.41%, recall of 98.26%, F1-score of 97.84%, specificity of 97.47% and balanced accuracy of 97.86% for MBCD. Furthermore, the relief-BHO-SVM model has achieved better results than other known approaches. Compared with recent studies on breast cancer classification, the suggested hybrid method has achieved quite good results.
{"title":"Breast cancer classification using a novel hybrid feature selection approach","authors":"E. Akkur, Fuat Türk, Osman Erogul","doi":"10.14311/nnw.2023.33.005","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.005","url":null,"abstract":"Many women around the world die due to breast cancer. If breast cancer is treated in the early phase, mortality rates may significantly be reduced. Quite a number of approaches have been proposed to help in the early detection of breast cancer. A novel hybrid feature selection model is suggested in this study. This novel hybrid model aims to build an efficient feature selection method and successfully classify breast lesions. A combination of relief and binary Harris hawk optimization (BHHO) hybrid model is used for feature selection. Then, k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR) and naive Bayes (NB) methods are preferred for the classification task. The suggested hybrid model is tested by three different breast cancer datasets which are Wisconsin diagnostic breast cancer dataset (WDBC), Wisconsin breast cancer dataset (WBCD) and mammographic breast cancer dataset (MBCD). According to the experimental results, the relief and BHHO hybrid model improves the performance of all classification algorithms in all three datasets. For WDBC, relief-BHO-SVM model shows the highest classification rates with an of accuracy of 98.77%, precision of 97.17%, recall of 99.52%, F1-score of 98.33%, specificity of 99.72% and balanced accuracy of 99.62%. For WBCD, relief-BHO-SVM model achieves of accuracy of 99.28%, precision of 98.76%, recall of 99.17%, F1-score of 98.96%, specificity of 99.56% and balanced accuracy of 99.36%. Relief-BHO-SVM model performs the best with an accuracy of 97.44%, precision of 97.41%, recall of 98.26%, F1-score of 97.84%, specificity of 97.47% and balanced accuracy of 97.86% for MBCD. Furthermore, the relief-BHO-SVM model has achieved better results than other known approaches. Compared with recent studies on breast cancer classification, the suggested hybrid method has achieved quite good results.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67126247","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}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.015
Faizal Makhrus
Time series forecasting using multilayer feed-forward neural networks (FNN) is potential to give high accuracy. Several factors influence the accuracy. One of them is the choice of activation functions (AFs). There are several AFs commonly used in FNN with their specific characteristics, such as bounded type AFs. They include sigmoid, softsign, arctan, and tanh. This paper investigates the effect of the amplitude in the bounded AFs on the FNNs accuracy. The theoretical investigations use simplified FNN models: linear equation and linear combination. The results show that the higher amplitudes give higher accuracy than typical amplitudes in softsign, arctan, and tanh AFs. However, in sigmoid AF, the amplitude changes do not influence the accuracy. These theoretical results are supported by experiments using the FNN model for time series prediction of 10 foreign exchanges from different continents compared to the US dollar. Based on the experiments, the optimum amplitude of the AFs should be high, that is greater or equal to 100 times of the maximum input values to the FNN, and the accuracy gains up to 310 times.
{"title":"The effect of amplitude modification in S-shaped activation functions on neural network regression","authors":"Faizal Makhrus","doi":"10.14311/nnw.2023.33.015","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.015","url":null,"abstract":"Time series forecasting using multilayer feed-forward neural networks (FNN) is potential to give high accuracy. Several factors influence the accuracy. One of them is the choice of activation functions (AFs). There are several AFs commonly used in FNN with their specific characteristics, such as bounded type AFs. They include sigmoid, softsign, arctan, and tanh. This paper investigates the effect of the amplitude in the bounded AFs on the FNNs accuracy. The theoretical investigations use simplified FNN models: linear equation and linear combination. The results show that the higher amplitudes give higher accuracy than typical amplitudes in softsign, arctan, and tanh AFs. However, in sigmoid AF, the amplitude changes do not influence the accuracy. These theoretical results are supported by experiments using the FNN model for time series prediction of 10 foreign exchanges from different continents compared to the US dollar. Based on the experiments, the optimum amplitude of the AFs should be high, that is greater or equal to 100 times of the maximum input values to the FNN, and the accuracy gains up to 310 times.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 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":"135650380","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}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.022
Zuzana Běinová
Today's quick development of artificial intelligence (AI) brings us to the questions that have until recently been the domain of philosophy or even sciencefiction. When can be a system considered an intelligent one? What is a consciousness and where it comes from? Can systems gain consciousness? It is necessary to have in mind, that although the development seems to be a revolutionary one, the progress is successive, today's technologies did not emerge from thin air, they are firmly built on previous findings. As now some wild thoughts and theories where the AI development leads to have arisen, it is time to look back at the background theories and summarize, what do we know on the topics of intelligence, consciousness, where they come from and what are different viewpoints on these topics. This paper combines the findings from different areas and present overview of different attitudes on systems consciousness and emphasizes the role of systems sciences in helping the knowledge in this area.
{"title":"Reflection on systemic aspects of consciousness","authors":"Zuzana Běinová","doi":"10.14311/nnw.2023.33.022","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.022","url":null,"abstract":"Today's quick development of artificial intelligence (AI) brings us to the questions that have until recently been the domain of philosophy or even sciencefiction. When can be a system considered an intelligent one? What is a consciousness and where it comes from? Can systems gain consciousness? It is necessary to have in mind, that although the development seems to be a revolutionary one, the progress is successive, today's technologies did not emerge from thin air, they are firmly built on previous findings. As now some wild thoughts and theories where the AI development leads to have arisen, it is time to look back at the background theories and summarize, what do we know on the topics of intelligence, consciousness, where they come from and what are different viewpoints on these topics. This paper combines the findings from different areas and present overview of different attitudes on systems consciousness and emphasizes the role of systems sciences in helping the knowledge in this area.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"4 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":"135659617","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}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.021
František Kekula, Pavel Hrubeš
Night-time light (NTL) radiance has a great potential in analyses of dynamic changes in patterns of human activities, and socio-economic and demographic factors. However, most of those analyses are focused on factors at global scales such as the population size, gross domestic product, electric power consumption, fossil fuel carbon dioxide emission etc. In this study we investigate the relationships between three urban lighting indicators and monthly averaged NTL radiance obtained from NASAs Black Marble monthly NTL composites for 4 study areas in the Czech Republic at local scale. The Pearson correlation analysis was used to identify a strength of the correlations between the indicators and radiance at near-nadir for two different snow conditions. The results from the correlation show that radiance has a strong positive correlation with the number of streetlighting points and their total nominal power, while for the average mast height there were observed moderate correlation coefficients. However, the areas with larger scales have higher correlation coefficients. Moreover, we found that the correlation coefficients are higher for snow-covered condition radiances. Generalized linear (GL) regression analysis was used to examine an association between the radiance and selected indicators. Owing to the excess zeros and overdispersion in the data, the zero-inflated regression performs better than the GL regression. Results from the regression analysis evince a statistically significant relationship between the radiance and selected indicators.
{"title":"An empirical study of relationships between urban lighting indicators and night-time light radiance","authors":"František Kekula, Pavel Hrubeš","doi":"10.14311/nnw.2023.33.021","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.021","url":null,"abstract":"Night-time light (NTL) radiance has a great potential in analyses of dynamic changes in patterns of human activities, and socio-economic and demographic factors. However, most of those analyses are focused on factors at global scales such as the population size, gross domestic product, electric power consumption, fossil fuel carbon dioxide emission etc. In this study we investigate the relationships between three urban lighting indicators and monthly averaged NTL radiance obtained from NASAs Black Marble monthly NTL composites for 4 study areas in the Czech Republic at local scale. The Pearson correlation analysis was used to identify a strength of the correlations between the indicators and radiance at near-nadir for two different snow conditions. The results from the correlation show that radiance has a strong positive correlation with the number of streetlighting points and their total nominal power, while for the average mast height there were observed moderate correlation coefficients. However, the areas with larger scales have higher correlation coefficients. Moreover, we found that the correlation coefficients are higher for snow-covered condition radiances. Generalized linear (GL) regression analysis was used to examine an association between the radiance and selected indicators. Owing to the excess zeros and overdispersion in the data, the zero-inflated regression performs better than the GL regression. Results from the regression analysis evince a statistically significant relationship between the radiance and selected indicators.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"68 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":"135659620","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}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.017
Evženie Uglickich, Ivan Nagy
The development of traffic state prediction algorithms embedded in intelligent transportation systems is of great importance for improving traffic conditions for drivers and pedestrians. Despite the large number of prediction methods, existing limitations still confirm the need to find a systematic solution and its adaptation to specific traffic data. This paper focuses on the relationship between traffic flow states in different urban locations, where these states are identified as clusters of traffic counts. Extending the recursive Bayesian mixture estimation theory to the Poisson mixtures, the paper uses the mixture pointers to construct the traffic state prediction model. Using the predictive model, the cluster at the target urban location is predicted based on the traffic counts measured in real time at the explanatory urban location. The main contributions of this study are: (i) recursive identification and prediction of the traffic state at each time instant, (ii) straightforward Poisson mixture initialization, and (iii) systematic theoretical background of the prediction approach. Results of testing the prediction algorithm on real traffic counts are presented.
{"title":"Using Poisson proximity-based weights for traffic flow state prediction","authors":"Evženie Uglickich, Ivan Nagy","doi":"10.14311/nnw.2023.33.017","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.017","url":null,"abstract":"The development of traffic state prediction algorithms embedded in intelligent transportation systems is of great importance for improving traffic conditions for drivers and pedestrians. Despite the large number of prediction methods, existing limitations still confirm the need to find a systematic solution and its adaptation to specific traffic data. This paper focuses on the relationship between traffic flow states in different urban locations, where these states are identified as clusters of traffic counts. Extending the recursive Bayesian mixture estimation theory to the Poisson mixtures, the paper uses the mixture pointers to construct the traffic state prediction model. Using the predictive model, the cluster at the target urban location is predicted based on the traffic counts measured in real time at the explanatory urban location. The main contributions of this study are: (i) recursive identification and prediction of the traffic state at each time instant, (ii) straightforward Poisson mixture initialization, and (iii) systematic theoretical background of the prediction approach. Results of testing the prediction algorithm on real traffic counts are presented.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"17 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":"135650395","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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