Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674090
Chang Zeyu
While 5G is being deployed commercially worldwide, scientists have carried out research for 6G as well as WIFI 6G bands, and LIFI are also tested. Both advantages and disadvantages of these three wireless communication methods are focused and the respective application scenarios are described as well as the difficulties and challenges to be overcome. An integrated network system of space and earth is proposed to provide users with ubiquitous wireless network connection. Technologies and challenges required by three communication methods are sorted out and the way they can be combined and applied are analyzed through extensive research and analysis.
{"title":"6G, LIFI and WIFI Wireless Systems: Challenges, Development and Prospects","authors":"Chang Zeyu","doi":"10.1109/ICCWAMTIP53232.2021.9674090","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674090","url":null,"abstract":"While 5G is being deployed commercially worldwide, scientists have carried out research for 6G as well as WIFI 6G bands, and LIFI are also tested. Both advantages and disadvantages of these three wireless communication methods are focused and the respective application scenarios are described as well as the difficulties and challenges to be overcome. An integrated network system of space and earth is proposed to provide users with ubiquitous wireless network connection. Technologies and challenges required by three communication methods are sorted out and the way they can be combined and applied are analyzed through extensive research and analysis.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130183822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674124
Muhammad Hanif Tunio, Liao Jianping, Muhammad Hassaan Farooq Butt, Imran Memon
The Rice crop is considered one of the most widely grown crops in Asia and it is susceptible to various types of illnesses at different stages of production. Food safety and production can be affected by rice plant diseases, as well as a significant decline in the quality and quantity of agricultural goods. Plant diseases can potentially prevent grain harvesting entirely in severe circumstances. As a result, automation of identification and diagnosis of plant disease is widely needed in the agriculture field. Many approaches for doing this problem have been offered with deep learning rising as the preferred method because of its excellent achievement. In this proposed research, we used Hybrid deep CNN transfer learning with rice plant images or the classification and identification of various rice diseases, we employed Transfer Learning to generate our deep learning model using Rice_Leaf_Dataset from a secondary source. The proposed model is 90.8% accurate, Experiments show that the proposed approach is viable, and it can be used to detect plant diseases efficiently and outperformed.
{"title":"Identification and Classification of Rice Plant Disease Using Hybrid Transfer Learning","authors":"Muhammad Hanif Tunio, Liao Jianping, Muhammad Hassaan Farooq Butt, Imran Memon","doi":"10.1109/ICCWAMTIP53232.2021.9674124","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674124","url":null,"abstract":"The Rice crop is considered one of the most widely grown crops in Asia and it is susceptible to various types of illnesses at different stages of production. Food safety and production can be affected by rice plant diseases, as well as a significant decline in the quality and quantity of agricultural goods. Plant diseases can potentially prevent grain harvesting entirely in severe circumstances. As a result, automation of identification and diagnosis of plant disease is widely needed in the agriculture field. Many approaches for doing this problem have been offered with deep learning rising as the preferred method because of its excellent achievement. In this proposed research, we used Hybrid deep CNN transfer learning with rice plant images or the classification and identification of various rice diseases, we employed Transfer Learning to generate our deep learning model using Rice_Leaf_Dataset from a secondary source. The proposed model is 90.8% accurate, Experiments show that the proposed approach is viable, and it can be used to detect plant diseases efficiently and outperformed.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130765672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674080
H. Monday, J. Li, G. Nneji, A. Z. Yutra, Bona D. Lemessa, Saifun Nahar, E. James, A. Haq
The electrical system's dependability, security, and efficiency are all improved through smart grid technologies. Its dependence on digital communication technology, on the other hand, introduces new risks and vulnerabilities that should be examined for the purpose to providing effective and trustworthy service delivery. This study presents a method for the detection of distributed denial of service (DDoS) attacks on smart grid infrastructure. Continuous wavelet transform (CWT) is used in the suggested approach to convert one-dimensional traffic data to two-dimensional time-frequency domain scalogram as the input to the wavelet convolutional neural network (WavCovNet) to detect anomalous behavior in the data by distinguishing attack features from normal patterns. Our results demonstrate that the proposed approach detects DDoS attacks with a high rate of detection and with a very low rate of false alarm.
{"title":"The Capability of Wavelet Convolutional Neural Network for Detecting Cyber Attack of Distributed Denial of Service in Smart Grid","authors":"H. Monday, J. Li, G. Nneji, A. Z. Yutra, Bona D. Lemessa, Saifun Nahar, E. James, A. Haq","doi":"10.1109/ICCWAMTIP53232.2021.9674080","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674080","url":null,"abstract":"The electrical system's dependability, security, and efficiency are all improved through smart grid technologies. Its dependence on digital communication technology, on the other hand, introduces new risks and vulnerabilities that should be examined for the purpose to providing effective and trustworthy service delivery. This study presents a method for the detection of distributed denial of service (DDoS) attacks on smart grid infrastructure. Continuous wavelet transform (CWT) is used in the suggested approach to convert one-dimensional traffic data to two-dimensional time-frequency domain scalogram as the input to the wavelet convolutional neural network (WavCovNet) to detect anomalous behavior in the data by distinguishing attack features from normal patterns. Our results demonstrate that the proposed approach detects DDoS attacks with a high rate of detection and with a very low rate of false alarm.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130248723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674162
Muhammad Hassaan Farooq Butt, J. Li, Tehreem Saboor, M. Arslan, Muhammad Adnan Farooq Butt
On the Internet, every different day, kinds of attacks are deployed on innocent users. Among all, phishing is the most severe attack in which users lose their credentials or private information and their financial status quickly. The attacker uses their credibility or sensitive information to harm the target or victim. The attacker is clever and uses different strategies to fetch user-sensitive information. The existing techniques fail to overcome these issues to some extent. This work focuses on discovering the essential features that help to differentiate the legitimate and illegitimate URLs. We applied a deep learning technique on the benchmark datasets to identify the pattern of phishing URLs. We used gradient boosted decision trees algorithm to train our model and applied the regular deeply connected neural network layers in various sequences and Adam optimizer. The most found patterns will help the system to detect phishing URLs and avoid phishing. We consider the accuracy, Ff-score, and Root Mean Square Error (RMSE) as our evaluation metrics for model evaluation. The results show that the trained model can achieve an approximately 92% accuracy and 94% f-score.
{"title":"Intelligent Phishing Url Detection: A Solution Based On Deep Learning Framework","authors":"Muhammad Hassaan Farooq Butt, J. Li, Tehreem Saboor, M. Arslan, Muhammad Adnan Farooq Butt","doi":"10.1109/ICCWAMTIP53232.2021.9674162","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674162","url":null,"abstract":"On the Internet, every different day, kinds of attacks are deployed on innocent users. Among all, phishing is the most severe attack in which users lose their credentials or private information and their financial status quickly. The attacker uses their credibility or sensitive information to harm the target or victim. The attacker is clever and uses different strategies to fetch user-sensitive information. The existing techniques fail to overcome these issues to some extent. This work focuses on discovering the essential features that help to differentiate the legitimate and illegitimate URLs. We applied a deep learning technique on the benchmark datasets to identify the pattern of phishing URLs. We used gradient boosted decision trees algorithm to train our model and applied the regular deeply connected neural network layers in various sequences and Adam optimizer. The most found patterns will help the system to detect phishing URLs and avoid phishing. We consider the accuracy, Ff-score, and Root Mean Square Error (RMSE) as our evaluation metrics for model evaluation. The results show that the trained model can achieve an approximately 92% accuracy and 94% f-score.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127191361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674132
Dai Xuanhui, Chen Juan, Wen Quan
Network pruning is widely used for compressing large neural networks to save computational resources. In traditional pruning methods, predefined hyperparameters are often required to determine the network structure of the target small network. However, too many hyperparameters are often undesirable. Therefore, we use the transformable architecture search (TAS) method to dynamically search the network structure of each layer when pruning the network width. In the TAS method, the channels number of the pruned network in each layer is represented by a learnable probability distribution. By minimizing computation cost, the probability distribution can be calculated and used to get the width configuration of the target pruned network. Then, the depth of the network was compressed based on the model get in the previous step. The method for compressing depth is block-wise intermediate representation training. This method is based on the hint training, where the network depth is compressed by comparing the intermediate representation of each layer of two equally wide teacher and student models. In the experiments, about 0.4% improvement over the existing method was viewed for the ResNet network on both CIFAR10 and CIFAR100 datasets.
{"title":"Network Pruning Based On Architecture Search and Intermediate Representation","authors":"Dai Xuanhui, Chen Juan, Wen Quan","doi":"10.1109/ICCWAMTIP53232.2021.9674132","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674132","url":null,"abstract":"Network pruning is widely used for compressing large neural networks to save computational resources. In traditional pruning methods, predefined hyperparameters are often required to determine the network structure of the target small network. However, too many hyperparameters are often undesirable. Therefore, we use the transformable architecture search (TAS) method to dynamically search the network structure of each layer when pruning the network width. In the TAS method, the channels number of the pruned network in each layer is represented by a learnable probability distribution. By minimizing computation cost, the probability distribution can be calculated and used to get the width configuration of the target pruned network. Then, the depth of the network was compressed based on the model get in the previous step. The method for compressing depth is block-wise intermediate representation training. This method is based on the hint training, where the network depth is compressed by comparing the intermediate representation of each layer of two equally wide teacher and student models. In the experiments, about 0.4% improvement over the existing method was viewed for the ResNet network on both CIFAR10 and CIFAR100 datasets.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131427718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674075
Di Liu, Zhizhao Feng, Zhao Du
Traditional network fault detection methods need to collect data for training, which has data security problems. In recent years, as people pay more and more attention to data privacy, how to ensure data security has become more and more important. At the same time, because the network fault detection needs to meet certain real-time requirements, how to improve the detection speed is also an urgent problem to be solved. Based on the above two problems, this paper proposes a network fault detection algorithm DNFD-SRU based on federated learning and SRU. Federated learning can train the model on the premise of ensuring data security, and SRU has faster training speed.
{"title":"DNFD-SRU: A Distributed Network Fault Detection Method Based on SRU","authors":"Di Liu, Zhizhao Feng, Zhao Du","doi":"10.1109/ICCWAMTIP53232.2021.9674075","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674075","url":null,"abstract":"Traditional network fault detection methods need to collect data for training, which has data security problems. In recent years, as people pay more and more attention to data privacy, how to ensure data security has become more and more important. At the same time, because the network fault detection needs to meet certain real-time requirements, how to improve the detection speed is also an urgent problem to be solved. Based on the above two problems, this paper proposes a network fault detection algorithm DNFD-SRU based on federated learning and SRU. Federated learning can train the model on the premise of ensuring data security, and SRU has faster training speed.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129870320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674140
Zhang Jianfang, Zhang Yaqi, Suyan Chen
The relationship between blockchain and emergency supply chain coordination is analyzed, and the utility model of emergency supply chain coordination is established. Applying the characteristics of blockchain technology, the emergency logistics activity process is recorded on the blockchain. When the emergency logistics activity changes, the emergency supply chain members can record the emergency logistics activity changes to ensure that the emergency logistics activity process is open and transparent. Combined with the blockchain smart contract algorithm of emergency supply chain logistics activities, a blockchain-based emergency supply chain synergy model is constructed.
{"title":"Research On Collaborative Management Model of Emergency Supply Chain Based On Blockchain","authors":"Zhang Jianfang, Zhang Yaqi, Suyan Chen","doi":"10.1109/ICCWAMTIP53232.2021.9674140","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674140","url":null,"abstract":"The relationship between blockchain and emergency supply chain coordination is analyzed, and the utility model of emergency supply chain coordination is established. Applying the characteristics of blockchain technology, the emergency logistics activity process is recorded on the blockchain. When the emergency logistics activity changes, the emergency supply chain members can record the emergency logistics activity changes to ensure that the emergency logistics activity process is open and transparent. Combined with the blockchain smart contract algorithm of emergency supply chain logistics activities, a blockchain-based emergency supply chain synergy model is constructed.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131692849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674150
Yuan Gao, Jianping Li, Yue Zhou, Fei Xiao, He Liu
This paper mainly completes the binary classification of RCV1 text data set by logistic regression. Based on the established logistic regression model, the performance and characteristics of three numerical optimization algorithms–random gradient descent, Mini-Batch random gradient and L-BFGS are studied.
{"title":"Optimization Methods For Large-Scale Machine Learning","authors":"Yuan Gao, Jianping Li, Yue Zhou, Fei Xiao, He Liu","doi":"10.1109/ICCWAMTIP53232.2021.9674150","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674150","url":null,"abstract":"This paper mainly completes the binary classification of RCV1 text data set by logistic regression. Based on the established logistic regression model, the performance and characteristics of three numerical optimization algorithms–random gradient descent, Mini-Batch random gradient and L-BFGS are studied.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129185305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674077
Chen Xi, Qin Kaiyu, Luo Xuan, Huo Huansong, Gou Rui, Li Rui, Wang Jingbo, Chen Bin
This paper focuses on the cooperative motion control problem for a dual-UAV system to locate a target under compound constraints and limited maneuverability. A pair of optimal observation points is calculated based on the triangulation method, so that the positioning problem can be accomplished by a cooperative formation tracking control of the UAVs. Theoretically, the paper achieves the target state estimation and prediction based on the Extended Kalman Filter method, and introduces a distributed motion control algorithm of dual-UAV for cooperative target positioning based on the optimal observation points and artificial potential field. The proposed method satisfies the constraint conditions and improves the target positioning accuracy when performing target location task. Finally, numerical simulation results verify the effectiveness of the scheme.
{"title":"Distributed Motion Control of UAVs for Cooperative Target Location Under Compound Constraints","authors":"Chen Xi, Qin Kaiyu, Luo Xuan, Huo Huansong, Gou Rui, Li Rui, Wang Jingbo, Chen Bin","doi":"10.1109/ICCWAMTIP53232.2021.9674077","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674077","url":null,"abstract":"This paper focuses on the cooperative motion control problem for a dual-UAV system to locate a target under compound constraints and limited maneuverability. A pair of optimal observation points is calculated based on the triangulation method, so that the positioning problem can be accomplished by a cooperative formation tracking control of the UAVs. Theoretically, the paper achieves the target state estimation and prediction based on the Extended Kalman Filter method, and introduces a distributed motion control algorithm of dual-UAV for cooperative target positioning based on the optimal observation points and artificial potential field. The proposed method satisfies the constraint conditions and improves the target positioning accuracy when performing target location task. Finally, numerical simulation results verify the effectiveness of the scheme.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124369586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674126
R. U. Khan, S. Hussain, Amin Ul Haq, M. Asif, M. Yousaf, Aimel Zafar, Sultan Almakdi, Jianping Li, Muhammad Anwar Malghani
The current epidemic situation due to COVID-19 is a public health disaster worldwide. Forecasting play's, a crucial role in determining the pandemic's hypothetical situation and economic situation. It provides the base for authorities, public health officials, management teams, and other stakeholders to plan for future preventive actions in their companies, citizens, and governments. This paper proposes Auto-Regressive Integrated Moving Average mathematical modeling in integration with Box-Jenkins' model-building approach examining the variation in pandemic severity through the Loess smoothed curves to forecast the COVID-19 pandemic situation. The time-plot and forecasting results show Chinese resilience to pact with pandemic situation effectively whereas India was severely affected by the pandemic. The future forecast for India shows the worst situation by the end of 2021. Pakistan and Bangladesh are the least affected among the specified countries while decline in weekly death cases has been observed in Iran till the end of 2021. We observed the Case Fatality Ratio (CFR) of 2.08% globally.
{"title":"Forecasting Time Series COVID-19 Statistical Data with Auto-Regressive Integrated Moving Average and Box-Jenkins' Models","authors":"R. U. Khan, S. Hussain, Amin Ul Haq, M. Asif, M. Yousaf, Aimel Zafar, Sultan Almakdi, Jianping Li, Muhammad Anwar Malghani","doi":"10.1109/ICCWAMTIP53232.2021.9674126","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674126","url":null,"abstract":"The current epidemic situation due to COVID-19 is a public health disaster worldwide. Forecasting play's, a crucial role in determining the pandemic's hypothetical situation and economic situation. It provides the base for authorities, public health officials, management teams, and other stakeholders to plan for future preventive actions in their companies, citizens, and governments. This paper proposes Auto-Regressive Integrated Moving Average mathematical modeling in integration with Box-Jenkins' model-building approach examining the variation in pandemic severity through the Loess smoothed curves to forecast the COVID-19 pandemic situation. The time-plot and forecasting results show Chinese resilience to pact with pandemic situation effectively whereas India was severely affected by the pandemic. The future forecast for India shows the worst situation by the end of 2021. Pakistan and Bangladesh are the least affected among the specified countries while decline in weekly death cases has been observed in Iran till the end of 2021. We observed the Case Fatality Ratio (CFR) of 2.08% globally.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122145173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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