Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.018270
Awais Khan, M. A. Khan, M. Javed, Majed Alhaisoni, U. Tariq, S. Kadry, Jung-In Choi, Yunyoung Nam
Human gait recognition (HGR) has received a lot of attention in the last decade as an alternative biometric technique. The main challenges in gait recognition are the change in in-person view angle and covariant factors. The major covariant factors are walking while carrying a bag and walking while wearing a coat. Deep learning is a new machine learning technique that is gaining popularity. Many techniques for HGR based on deep learning are presented in the literature. The requirement of an efficient framework is always required for correct and quick gait recognition.We proposed a fully automated deep learning and improved ant colony optimization (IACO) framework for HGR using video sequences in this work. The proposed framework consists of four primary steps. In the first step, the database is normalized in a video frame. In the second step, two pre-trained models named ResNet101 and InceptionV3 are selected andmodified according to the dataset’s nature. After that, we trained both modified models using transfer learning and extracted the features. The IACO algorithm is used to improve the extracted features. IACO is used to select the best features, which are then passed to the Cubic SVM for final classification. The cubic SVM employs a multiclass method. The experiment was carried out on three angles (0, 18, and 180) of the CASIA B dataset, and the accuracy was 95.2, 93.9, and 98.2 percent, respectively. A comparison with existing techniques is also performed, and the proposed method outperforms in terms of accuracy and computational time.
{"title":"Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization","authors":"Awais Khan, M. A. Khan, M. Javed, Majed Alhaisoni, U. Tariq, S. Kadry, Jung-In Choi, Yunyoung Nam","doi":"10.32604/cmc.2022.018270","DOIUrl":"https://doi.org/10.32604/cmc.2022.018270","url":null,"abstract":"Human gait recognition (HGR) has received a lot of attention in the last decade as an alternative biometric technique. The main challenges in gait recognition are the change in in-person view angle and covariant factors. The major covariant factors are walking while carrying a bag and walking while wearing a coat. Deep learning is a new machine learning technique that is gaining popularity. Many techniques for HGR based on deep learning are presented in the literature. The requirement of an efficient framework is always required for correct and quick gait recognition.We proposed a fully automated deep learning and improved ant colony optimization (IACO) framework for HGR using video sequences in this work. The proposed framework consists of four primary steps. In the first step, the database is normalized in a video frame. In the second step, two pre-trained models named ResNet101 and InceptionV3 are selected andmodified according to the dataset’s nature. After that, we trained both modified models using transfer learning and extracted the features. The IACO algorithm is used to improve the extracted features. IACO is used to select the best features, which are then passed to the Cubic SVM for final classification. The cubic SVM employs a multiclass method. The experiment was carried out on three angles (0, 18, and 180) of the CASIA B dataset, and the accuracy was 95.2, 93.9, and 98.2 percent, respectively. A comparison with existing techniques is also performed, and the proposed method outperforms in terms of accuracy and computational time.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81975660","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.2022.019369
Hyun Ahn, Kyunghee Sun, Kwanghoon Pio Kim
: Time series forecasting has become an important aspect of data analysis and has many real-world applications. However, undesirable missing values are often encountered, which may adversely affect many forecasting tasks. In this study, we evaluateand compare the effects of imputationmethods for estimating missing values in a time series. Our approach does not include a simulation to generate pseudo-missing data, but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom. In an experiment, therefore, several time series forecasting models are trained using different training datasets prepared using each imputation method. Subsequently, the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting models. The results obtained from a total of four experimental cases show that the k -nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.
{"title":"Comparison of Missing Data Imputation Methods in Time Series Forecasting","authors":"Hyun Ahn, Kyunghee Sun, Kwanghoon Pio Kim","doi":"10.32604/cmc.2022.019369","DOIUrl":"https://doi.org/10.32604/cmc.2022.019369","url":null,"abstract":": Time series forecasting has become an important aspect of data analysis and has many real-world applications. However, undesirable missing values are often encountered, which may adversely affect many forecasting tasks. In this study, we evaluateand compare the effects of imputationmethods for estimating missing values in a time series. Our approach does not include a simulation to generate pseudo-missing data, but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom. In an experiment, therefore, several time series forecasting models are trained using different training datasets prepared using each imputation method. Subsequently, the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting models. The results obtained from a total of four experimental cases show that the k -nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"38 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84160349","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.2022.020777
Rao Muhammad Asif, M. Shakir, J. Nebhen, Ateeq Ur Rehman, M. Shafiq, Jin-Ghoo Choi
: 5G technology can greatly improve spectral efficiency (SE) and throughput of wireless communications.In this regard, multipleinput multiple output (MIMO) technology has become the most influential technology using huge antennas and user equipment (UE). However, the use of MIMO in 5G wireless technology will increase circuit power consumption and reduce energy efficiency (EE). In this regard, this article proposes an optimal solution for weighing SE and throughput tradeoff with energy efficiency. The research work is based on the Wyner model of uplink (UL) and downlink (DL) transmission under the multi-cell model scenario. The SE-EE trade-off is carried out by optimizing the choice of antenna and UEs, while the approximation method based on the logarithmic function is used for optimization. In this paper, we analyzed the combination of UL and DL power consumption models and precoding schemes for all actual circuit power consumption models to optimize the trade-off between EE and throughput. The simulation results show that the SE-EE trade-off has been significantly improved by developing UL and DL transmission models with the approximation method based on logarithmic functions. It is also recognized that the throughput-EE trade-off can be improved by knowing the total actual power consumed by the entire network.
{"title":"Energy Efficiency Trade-off with Spectral Efficiency in MIMO Systems","authors":"Rao Muhammad Asif, M. Shakir, J. Nebhen, Ateeq Ur Rehman, M. Shafiq, Jin-Ghoo Choi","doi":"10.32604/cmc.2022.020777","DOIUrl":"https://doi.org/10.32604/cmc.2022.020777","url":null,"abstract":": 5G technology can greatly improve spectral efficiency (SE) and throughput of wireless communications.In this regard, multipleinput multiple output (MIMO) technology has become the most influential technology using huge antennas and user equipment (UE). However, the use of MIMO in 5G wireless technology will increase circuit power consumption and reduce energy efficiency (EE). In this regard, this article proposes an optimal solution for weighing SE and throughput tradeoff with energy efficiency. The research work is based on the Wyner model of uplink (UL) and downlink (DL) transmission under the multi-cell model scenario. The SE-EE trade-off is carried out by optimizing the choice of antenna and UEs, while the approximation method based on the logarithmic function is used for optimization. In this paper, we analyzed the combination of UL and DL power consumption models and precoding schemes for all actual circuit power consumption models to optimize the trade-off between EE and throughput. The simulation results show that the SE-EE trade-off has been significantly improved by developing UL and DL transmission models with the approximation method based on logarithmic functions. It is also recognized that the throughput-EE trade-off can be improved by knowing the total actual power consumed by the entire network.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"34 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81069882","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.2022.019890
Chia-Nan Wang, Shao-Dong Syu, C. Chou, Viet Tinh Nguyen, Dang Van Thuy Cuc
: Agriculture is a key facilitator of economic prosperity and nourishes the huge global population. To achieve sustainable agriculture, several factors should be considered, such as increasing nutrient and water efficiency and/or improving soil health and quality. Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effec-tiveness of crop yields. Fertilizer supplies most of the necessary nutrients for plants, and it is estimated that at least 30%–50% of crop yields is attributable to commercial fertilizer nutrient inputs. Fertilizer is always a major concern in achieving sustainable and efficient agriculture. Applying reasonable and cus-tomized fertilizers will require a significant increase in the number of formulae, involving increasing costs and the accurate forecasting of the right time to apply the suitable formulae. An alternative solution is given by two-stage production planning under stochastic demand, which divides a planning schedule into two stages. The primary stage has non-existing demand information, the inputs of which are the proportion of raw materials needed for producing fertilizer products, the cost for purchasing pays attention to maximizing total profit based on information from customer demand, as well as being informed regarding concerns about system cost at Stage 2.
{"title":"Two-Stage Production Planning Under Stochastic Demand: Case Study of Fertilizer Manufacturing","authors":"Chia-Nan Wang, Shao-Dong Syu, C. Chou, Viet Tinh Nguyen, Dang Van Thuy Cuc","doi":"10.32604/cmc.2022.019890","DOIUrl":"https://doi.org/10.32604/cmc.2022.019890","url":null,"abstract":": Agriculture is a key facilitator of economic prosperity and nourishes the huge global population. To achieve sustainable agriculture, several factors should be considered, such as increasing nutrient and water efficiency and/or improving soil health and quality. Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effec-tiveness of crop yields. Fertilizer supplies most of the necessary nutrients for plants, and it is estimated that at least 30%–50% of crop yields is attributable to commercial fertilizer nutrient inputs. Fertilizer is always a major concern in achieving sustainable and efficient agriculture. Applying reasonable and cus-tomized fertilizers will require a significant increase in the number of formulae, involving increasing costs and the accurate forecasting of the right time to apply the suitable formulae. An alternative solution is given by two-stage production planning under stochastic demand, which divides a planning schedule into two stages. The primary stage has non-existing demand information, the inputs of which are the proportion of raw materials needed for producing fertilizer products, the cost for purchasing pays attention to maximizing total profit based on information from customer demand, as well as being informed regarding concerns about system cost at Stage 2.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"31 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":"78135328","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.2022.019544
M. Tariq Mahmood
: Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type, scenarios and level of blurriness. In this paper, we propose an effective method for blur detection and segmentation based on transfer learning concept. The proposed method consists of two separate steps. In the first step, genetic programming (GP) model is developed that quantify the amount of blur for each pixel in the image. The GP model method uses the multi-resolution features of the image and it provides an improved blur map. In the second phase, the blur map is segmented into blurred and non-blurred regions by using an adaptive threshold. A model based on support vector machine (SVM) is developed to compute adaptive threshold for the input blur map. The performance of the proposed method is evaluated using two different datasets and compared with various state-of-the-art methods. The comparativeanalysis reveals that the proposed method performs better against the state-of-the-art techniques.
{"title":"Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold","authors":"M. Tariq Mahmood","doi":"10.32604/cmc.2022.019544","DOIUrl":"https://doi.org/10.32604/cmc.2022.019544","url":null,"abstract":": Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type, scenarios and level of blurriness. In this paper, we propose an effective method for blur detection and segmentation based on transfer learning concept. The proposed method consists of two separate steps. In the first step, genetic programming (GP) model is developed that quantify the amount of blur for each pixel in the image. The GP model method uses the multi-resolution features of the image and it provides an improved blur map. In the second phase, the blur map is segmented into blurred and non-blurred regions by using an adaptive threshold. A model based on support vector machine (SVM) is developed to compute adaptive threshold for the input blur map. The performance of the proposed method is evaluated using two different datasets and compared with various state-of-the-art methods. The comparativeanalysis reveals that the proposed method performs better against the state-of-the-art techniques.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"78 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78214048","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.2022.020954
Ibrahim M. Mehedi, Mohd Heidir Mohd Shah, Soon Xin Ng, Abdulah Jeza Aljohani, M. El-Hajjar, M. Moinuddin
: This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion (AGDI) to track the position of a Linear Flexible Joint Cart (LFJC) system along with vibration suppression of the flexible joint. The proposed AGDI control law will be comprised of two control elements. The baseline (continuous) control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control objec-tives. The control law is realized by inverting the prescribed dynamics using dynamically scaled Moore-Penrose generalized inversion. To boost the robust attributes against system nonlinearities, parametric uncertainties and external perturbations, a discontinuous control law will be augmented which is based on the concept of sliding mode principle. In discontinuous control law, the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering reduction. The closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is guaranteed. Rigorous computer simulations followed by experimental investigation will be performed on Quanser’s LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.
{"title":"Position Control of Flexible Joint Carts Using Adaptive Generalized Dynamics Inversion","authors":"Ibrahim M. Mehedi, Mohd Heidir Mohd Shah, Soon Xin Ng, Abdulah Jeza Aljohani, M. El-Hajjar, M. Moinuddin","doi":"10.32604/cmc.2022.020954","DOIUrl":"https://doi.org/10.32604/cmc.2022.020954","url":null,"abstract":": This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion (AGDI) to track the position of a Linear Flexible Joint Cart (LFJC) system along with vibration suppression of the flexible joint. The proposed AGDI control law will be comprised of two control elements. The baseline (continuous) control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control objec-tives. The control law is realized by inverting the prescribed dynamics using dynamically scaled Moore-Penrose generalized inversion. To boost the robust attributes against system nonlinearities, parametric uncertainties and external perturbations, a discontinuous control law will be augmented which is based on the concept of sliding mode principle. In discontinuous control law, the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering reduction. The closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is guaranteed. Rigorous computer simulations followed by experimental investigation will be performed on Quanser’s LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"50 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78271574","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.2022.021671
Ahmed R. Abas, Ibrahim Elhenawy, Mahinda Zidan, Mahmoud Othman
: Due to the widespread usage of social media in our recent daily lifestyles, sentiment analysis becomes an important field in pattern recognition and Natural Language Processing (NLP). In this field, users’feedback data on a specific issue are evaluated and analyzed. Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research. Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature. Emotions describe a state of mind of distinct behaviors, feelings, thoughts and experiences. The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text. This model is formed by a combination of the Bidirectional Encoder Representations from Transformer (BERT) and the Convolutional Neural networks (CNN) for textual classification. This model embraces the BERT to train the word semantic representation language model. According to the word context, the semantic vector is dynamically generated and then placed into the CNN to predict the output. Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets. The BERT-CNN model achieves an accuracy of 94.7% and an F1-score of 94% for semeval2019 task3 dataset and an accuracy of 75.8% and an F1-score of 76% for ISEAR dataset.
{"title":"BERT-CNN: A Deep Learning Model for Detecting Emotions from Text","authors":"Ahmed R. Abas, Ibrahim Elhenawy, Mahinda Zidan, Mahmoud Othman","doi":"10.32604/cmc.2022.021671","DOIUrl":"https://doi.org/10.32604/cmc.2022.021671","url":null,"abstract":": Due to the widespread usage of social media in our recent daily lifestyles, sentiment analysis becomes an important field in pattern recognition and Natural Language Processing (NLP). In this field, users’feedback data on a specific issue are evaluated and analyzed. Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research. Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature. Emotions describe a state of mind of distinct behaviors, feelings, thoughts and experiences. The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text. This model is formed by a combination of the Bidirectional Encoder Representations from Transformer (BERT) and the Convolutional Neural networks (CNN) for textual classification. This model embraces the BERT to train the word semantic representation language model. According to the word context, the semantic vector is dynamically generated and then placed into the CNN to predict the output. Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets. The BERT-CNN model achieves an accuracy of 94.7% and an F1-score of 94% for semeval2019 task3 dataset and an accuracy of 75.8% and an F1-score of 76% for ISEAR dataset.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"36 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74297171","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.2022.022668
A. Arokiaraj Jovith, Mahantesh Mathapati, M. Sundarrajan, N. Gnanasankaran, S. Kadry, Maytham N. Meqdad, Shabnam Mohamed Aslam
{"title":"Two-Tier Clustering with Routing Protocol for IoT Assisted WSN","authors":"A. Arokiaraj Jovith, Mahantesh Mathapati, M. Sundarrajan, N. Gnanasankaran, S. Kadry, Maytham N. Meqdad, Shabnam Mohamed Aslam","doi":"10.32604/cmc.2022.022668","DOIUrl":"https://doi.org/10.32604/cmc.2022.022668","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"75 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74077627","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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