Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.019328
Zelin Deng, Bo Zhou, Pei He, Jian Huang, O. Alfarraj, Amr M. Tolba
: Image captioning aims to generate a corresponding description of an image. In recent years, neural encoder-decoder models have been the dominant approaches, in which the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are used to translate an image into a natural language description. Among these approaches, the visual attention mechanisms are widely used to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. However, most conventional visual attention mechanisms are based on high-level image features, ignoring the effects of other image features, and giving insufficient consideration to the relative positions between image features. In this work, we propose a Position-Aware Transformer model with image-feature attention and position-aware attention mechanisms for the above problems. The image-feature attention firstly extracts multi-level features by using Feature Pyramid Network (FPN), then utilizes the scaled-dot-product to fuse these features, which enables our model to detect objects of different scales in the image more effectively without increasing parameters. In the position-aware attention mechanism, the relative positions between image features are obtained at first, afterwards the relative positions are incorporated into the originalimage features to generate captions more accurately. Experiments are carried out on the MSCOCO dataset and our approach achieves competitive BLEU-4, METEOR, ROUGE-L, CIDEr scores compared with some state-of-the-art approaches, demonstrating the effectiveness of our approach.
{"title":"A Position-Aware Transformer for Image Captioning","authors":"Zelin Deng, Bo Zhou, Pei He, Jian Huang, O. Alfarraj, Amr M. Tolba","doi":"10.32604/cmc.2022.019328","DOIUrl":"https://doi.org/10.32604/cmc.2022.019328","url":null,"abstract":": Image captioning aims to generate a corresponding description of an image. In recent years, neural encoder-decoder models have been the dominant approaches, in which the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are used to translate an image into a natural language description. Among these approaches, the visual attention mechanisms are widely used to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. However, most conventional visual attention mechanisms are based on high-level image features, ignoring the effects of other image features, and giving insufficient consideration to the relative positions between image features. In this work, we propose a Position-Aware Transformer model with image-feature attention and position-aware attention mechanisms for the above problems. The image-feature attention firstly extracts multi-level features by using Feature Pyramid Network (FPN), then utilizes the scaled-dot-product to fuse these features, which enables our model to detect objects of different scales in the image more effectively without increasing parameters. In the position-aware attention mechanism, the relative positions between image features are obtained at first, afterwards the relative positions are incorporated into the originalimage features to generate captions more accurately. Experiments are carried out on the MSCOCO dataset and our approach achieves competitive BLEU-4, METEOR, ROUGE-L, CIDEr scores compared with some state-of-the-art approaches, demonstrating the effectiveness of our approach.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"56 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90912146","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 : 2022-01-01DOI: 10.32604/cmc.2022.019921
Khuram Nawaz Khayam, Zahid Mehmood, Hassan Nazeer Chaudhry, M. Usman Ashraf, U. Tariq, Mohammed Nawaf Altouri, Khalid Alsubhi
{"title":"FPGA Implementation of Deep Leaning Model for Video Analytics","authors":"Khuram Nawaz Khayam, Zahid Mehmood, Hassan Nazeer Chaudhry, M. Usman Ashraf, U. Tariq, Mohammed Nawaf Altouri, Khalid Alsubhi","doi":"10.32604/cmc.2022.019921","DOIUrl":"https://doi.org/10.32604/cmc.2022.019921","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90983208","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 paper investigates the use of multi-agent deep Q-network (MADQN) to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning (MARL) approach. The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions, particularly rainfall. MADQN is based on deep Q-network (DQN), which is an integration of the traditional reinforcement learning (RL) and the newly emerging deep learning (DL) approaches. MADQN enables traffic light controllers to learn, exchange knowledge with neighboring agents, and select optimal joint actions in a collaborative manner. A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in Malaysia. Investigation is also performed using a grid traffic network (GTN) to understand that the proposed scheme is effective in a traditional traffic network. Our proposed scheme is evaluated using two simulation tools, namely Matlab and Simulation of Urban Mobility (SUMO). Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30% in the simulations.
{"title":"Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control","authors":"Faizan Rasheed, Kok-Lim Alvin Yau, Rafidah Md Noor, Yung-Wey Chong","doi":"10.32604/cmc.2022.022952","DOIUrl":"https://doi.org/10.32604/cmc.2022.022952","url":null,"abstract":": This paper investigates the use of multi-agent deep Q-network (MADQN) to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning (MARL) approach. The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions, particularly rainfall. MADQN is based on deep Q-network (DQN), which is an integration of the traditional reinforcement learning (RL) and the newly emerging deep learning (DL) approaches. MADQN enables traffic light controllers to learn, exchange knowledge with neighboring agents, and select optimal joint actions in a collaborative manner. A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in Malaysia. Investigation is also performed using a grid traffic network (GTN) to understand that the proposed scheme is effective in a traditional traffic network. Our proposed scheme is evaluated using two simulation tools, namely Matlab and Simulation of Urban Mobility (SUMO). Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30% in the simulations.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88795923","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 : 2022-01-01DOI: 10.32604/cmc.2022.023195
W. Yu, Jintao Cui, Yang Gao, M. Zhu, L. Shao, Yanbo Shen, Xiaozhao Zhang, Chen Guo, Hanxiaoya Zhang
Land desertification is a widely concerned ecological environment problem. Studying the evolution trend of desertification types is of great significance to prevent and control land desertification. In this study, we applied the decision tree classification method, to study the land area and temporal and spatial change law of different types of desertification in the North Bank of Qinghai Lake area from 1987 to 2014, based on the current land use situation and TM remote sensing image data of Haiyan County, Qinghai Province, The results show that the area of mild desertification land and moderate desertification land in the study area has decreased, while the area of severe desertification land and extreme desertification land has increased significantly in the past 30 years. The area of desertification land decreased by 4.02 km2, of which the area of mild and moderate desertification land decreased by 39.73 km2 and 36.8 km2 respectively, and the area of severe and extreme desertification land increased by 32.78 km2 and 39.73 km2 respectively. As for the mutual transformation relationship, the transformation from severe desertification land to extreme desertification land is the main, and the junction of severe desertification land and extreme desertification land is the sensitive area of transformation. In the north shore of Qinghai Lake, the sandy land tends to expand eastward. The research provides reference basis for local land desertification monitoring, and has a great guidance for local effective land desertification and soil and water conservation.
{"title":"Evolution of Desertification Types on the North Shore of Qinghai Lake","authors":"W. Yu, Jintao Cui, Yang Gao, M. Zhu, L. Shao, Yanbo Shen, Xiaozhao Zhang, Chen Guo, Hanxiaoya Zhang","doi":"10.32604/cmc.2022.023195","DOIUrl":"https://doi.org/10.32604/cmc.2022.023195","url":null,"abstract":"Land desertification is a widely concerned ecological environment problem. Studying the evolution trend of desertification types is of great significance to prevent and control land desertification. In this study, we applied the decision tree classification method, to study the land area and temporal and spatial change law of different types of desertification in the North Bank of Qinghai Lake area from 1987 to 2014, based on the current land use situation and TM remote sensing image data of Haiyan County, Qinghai Province, The results show that the area of mild desertification land and moderate desertification land in the study area has decreased, while the area of severe desertification land and extreme desertification land has increased significantly in the past 30 years. The area of desertification land decreased by 4.02 km2, of which the area of mild and moderate desertification land decreased by 39.73 km2 and 36.8 km2 respectively, and the area of severe and extreme desertification land increased by 32.78 km2 and 39.73 km2 respectively. As for the mutual transformation relationship, the transformation from severe desertification land to extreme desertification land is the main, and the junction of severe desertification land and extreme desertification land is the sensitive area of transformation. In the north shore of Qinghai Lake, the sandy land tends to expand eastward. The research provides reference basis for local land desertification monitoring, and has a great guidance for local effective land desertification and soil and water conservation.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"5 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87530029","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 : 2022-01-01DOI: 10.32604/cmc.2022.020847
R. Manjula Devi, M. Premkumar, Pradeep Jangir, Mohamed Abdelghany Elkotb, Rajvikram Madurai Elavarasan, Kottakkaran Sooppy Nisar
: Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost, gains, energy, mass, and so on. In order to solve optimization problems, metaheuristic algorithms are essential. Most of these techniques are influenced by collective knowledge and natural foraging. There is no such thing as the best or worst algorithm; instead, there are more effective algorithms for certain problems. Therefore, in this paper, a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization (RKO) algorithm, called Improved Runge-Kutta Optimization (IRKO) algorithm, is suggested for solving optimization problems. The IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO version. The performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization problems. The outcomes of IRKO are compared with seven state-of-the-art algorithms, including the basic RKO algorithm. Compared to other algorithms, the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization problems. The runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems, including real-world optimization problems.
{"title":"IRKO: An Improved Runge-Kutta Optimization Algorithm for Global Optimization Problems","authors":"R. Manjula Devi, M. Premkumar, Pradeep Jangir, Mohamed Abdelghany Elkotb, Rajvikram Madurai Elavarasan, Kottakkaran Sooppy Nisar","doi":"10.32604/cmc.2022.020847","DOIUrl":"https://doi.org/10.32604/cmc.2022.020847","url":null,"abstract":": Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost, gains, energy, mass, and so on. In order to solve optimization problems, metaheuristic algorithms are essential. Most of these techniques are influenced by collective knowledge and natural foraging. There is no such thing as the best or worst algorithm; instead, there are more effective algorithms for certain problems. Therefore, in this paper, a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization (RKO) algorithm, called Improved Runge-Kutta Optimization (IRKO) algorithm, is suggested for solving optimization problems. The IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO version. The performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization problems. The outcomes of IRKO are compared with seven state-of-the-art algorithms, including the basic RKO algorithm. Compared to other algorithms, the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization problems. The runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems, including real-world optimization problems.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"5 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88856930","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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