Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.017681
S. Ranjithkumar, S. Chenthur pandian
{"title":"Automatic License Plate Recognition System for Vehicles Using a CNN","authors":"S. Ranjithkumar, S. Chenthur pandian","doi":"10.32604/cmc.2022.017681","DOIUrl":"https://doi.org/10.32604/cmc.2022.017681","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"77 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74197168","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.2022.021652
A. Siddiqa, Syed Abbas Zilqurnain Naqvi, Muhammad Ahsan, A. Ditta, Hani Alquhayz, M. A. Khan, Muhammad Adnan Khan
{"title":"An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery","authors":"A. Siddiqa, Syed Abbas Zilqurnain Naqvi, Muhammad Ahsan, A. Ditta, Hani Alquhayz, M. A. Khan, Muhammad Adnan Khan","doi":"10.32604/cmc.2022.021652","DOIUrl":"https://doi.org/10.32604/cmc.2022.021652","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79043103","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.2022.019029
Sami Ullah, Nurul Hidayah Mohd Nor, H. Daud, N. Zainuddin, Hadi Fanaee-T, Alamgir Khalil
Space-time disease cluster detection assists in conducting disease surveillance and implementing control strategies. The state-of-the-art method for this kind of problem is the Space-time Scan Statistics (SaTScan) which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson or Gaussian counts. Addressing this problem, an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric solution. However, it is based on the population counts data which are not always available in the least developed countries. In addition, the population counts are difficult to approximate for some surveillance data such as emergency department visits and over-the-counter drug sales, where the catchment area for each hospital/pharmacy is undefined. We extend the population-based Multi-EigenSpot method to approximate the potential disease clusters from the observed/reported disease counts only with no need for the population counts. The proposed adaptation uses an estimator of expected disease count that does not depend on the population counts. The proposed method was evaluated on the real-world dataset and the results were compared with the population-based methods: Multi-EigenSpot and SaTScan. The result shows that the proposed adaptation is effective in approximating the important outputs of the population-based methods.
{"title":"An Eigenspace Method for Detecting Space-Time Disease Clusters with Unknown Population-Data","authors":"Sami Ullah, Nurul Hidayah Mohd Nor, H. Daud, N. Zainuddin, Hadi Fanaee-T, Alamgir Khalil","doi":"10.32604/cmc.2022.019029","DOIUrl":"https://doi.org/10.32604/cmc.2022.019029","url":null,"abstract":"Space-time disease cluster detection assists in conducting disease surveillance and implementing control strategies. The state-of-the-art method for this kind of problem is the Space-time Scan Statistics (SaTScan) which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson or Gaussian counts. Addressing this problem, an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric solution. However, it is based on the population counts data which are not always available in the least developed countries. In addition, the population counts are difficult to approximate for some surveillance data such as emergency department visits and over-the-counter drug sales, where the catchment area for each hospital/pharmacy is undefined. We extend the population-based Multi-EigenSpot method to approximate the potential disease clusters from the observed/reported disease counts only with no need for the population counts. The proposed adaptation uses an estimator of expected disease count that does not depend on the population counts. The proposed method was evaluated on the real-world dataset and the results were compared with the population-based methods: Multi-EigenSpot and SaTScan. The result shows that the proposed adaptation is effective in approximating the important outputs of the population-based methods.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"89 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78985472","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.2022.017772
Muhammad Ibrahim, Muhammad Hanif, Shabir Ahmad, Faisal Jamil, Tayyaba Sehar, Yunjung Lee, Dohyeun Kim
: The Internet of Things (IoT) has been widely adopted in various domains including smart cities, healthcare, smart factories, etc. In the last few years, the fitness industry has been reshaped by the introduction of smart fitness solutions for individuals as well as fitness gyms. The IoT fitness devices collect trainee data that is being used for various decision-making. However, it will face numerous security and privacy issues towards its realization. This work focuses on IoT security, especially DoS/DDoS attacks. In this paper, we have proposed a novel blockchain-enabled protocol (BEP) that uses the notion of a self-exposing node (SEN) approach for securing fitness IoT applications. The blockchain and SDN architectures are employed to enhance IoT security by a highly preventive security monitoring, analysis and response system. The proposed approach helps in detecting the DoS/DDoS attacks on the IoT fitness system and then mitigating the attacks. The BEP is used for handling Blockchain-related activities and SEN could be a sensor or actu-ator node within the fitness IoT system. SEN provides information about the inbound and outbound traffic to the BEP which is used to analyze the DoS/DDoS attacks on the fitness IoT system. The SEN calculates the inbound and outbound traffic features’ entropies and transmits them to the Blockchain in the form of transaction blocks. The BEP picks the whole mined blocks’ transactions and transfers them to the SDN controller node. The controller node correlates the entropies data of SENs and decides about the DoS or DDoS attack. So, there are two decision points, one is SEN, and another is the controller. To evaluate the performance of our proposed system, several experiments are performed and results concerning the entropy values and attack detection rate are obtained. The proposed approach has outperformed the other two approaches concerning the attack detection rate by an increase of 11% and 18% against Approach 1 and Approach 2 respectively.
{"title":"SDN Based DDos Mitigating Approach Using Traffic Entropy for IoT Network","authors":"Muhammad Ibrahim, Muhammad Hanif, Shabir Ahmad, Faisal Jamil, Tayyaba Sehar, Yunjung Lee, Dohyeun Kim","doi":"10.32604/cmc.2022.017772","DOIUrl":"https://doi.org/10.32604/cmc.2022.017772","url":null,"abstract":": The Internet of Things (IoT) has been widely adopted in various domains including smart cities, healthcare, smart factories, etc. In the last few years, the fitness industry has been reshaped by the introduction of smart fitness solutions for individuals as well as fitness gyms. The IoT fitness devices collect trainee data that is being used for various decision-making. However, it will face numerous security and privacy issues towards its realization. This work focuses on IoT security, especially DoS/DDoS attacks. In this paper, we have proposed a novel blockchain-enabled protocol (BEP) that uses the notion of a self-exposing node (SEN) approach for securing fitness IoT applications. The blockchain and SDN architectures are employed to enhance IoT security by a highly preventive security monitoring, analysis and response system. The proposed approach helps in detecting the DoS/DDoS attacks on the IoT fitness system and then mitigating the attacks. The BEP is used for handling Blockchain-related activities and SEN could be a sensor or actu-ator node within the fitness IoT system. SEN provides information about the inbound and outbound traffic to the BEP which is used to analyze the DoS/DDoS attacks on the fitness IoT system. The SEN calculates the inbound and outbound traffic features’ entropies and transmits them to the Blockchain in the form of transaction blocks. The BEP picks the whole mined blocks’ transactions and transfers them to the SDN controller node. The controller node correlates the entropies data of SENs and decides about the DoS or DDoS attack. So, there are two decision points, one is SEN, and another is the controller. To evaluate the performance of our proposed system, several experiments are performed and results concerning the entropy values and attack detection rate are obtained. The proposed approach has outperformed the other two approaches concerning the attack detection rate by an increase of 11% and 18% against Approach 1 and Approach 2 respectively.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"62 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79025382","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.2022.020920
N. Malhotra, M. Bala
{"title":"Intelligent Multilevel Node Authentication in Mobile Computing Using Clone Node","authors":"N. Malhotra, M. Bala","doi":"10.32604/cmc.2022.020920","DOIUrl":"https://doi.org/10.32604/cmc.2022.020920","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"35 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90704047","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.2022.017295
W. El-shafai, Amira A. Mahmoud, El-sayed M. El-Rabaie, Taha E. Taha, Osama F. Zahran, Adel S. El-Fishawy, M. Abd-Elnaby, Fathi E. Abd El-Samie
{"title":"Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning","authors":"W. El-shafai, Amira A. Mahmoud, El-sayed M. El-Rabaie, Taha E. Taha, Osama F. Zahran, Adel S. El-Fishawy, M. Abd-Elnaby, Fathi E. Abd El-Samie","doi":"10.32604/cmc.2022.017295","DOIUrl":"https://doi.org/10.32604/cmc.2022.017295","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"54 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89245328","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.2022.020473
Suruchi Talwani, Khaled Alhazmi, Jimmy Singla, Hasan J. Alyamani, A. Bashir
: Cloud computing promises the advent of a new era of service boosted by means of virtualization technology. The process of virtualization means creation of virtual infrastructure, devices, servers and computing resources needed to deploy an application smoothly. This extensively practiced technology involves selecting an efficient Virtual Machine (VM) to complete the task by transferring applications from Physical Machines (PM) to VM or from VM to VM. The whole process is very challenging not only in terms of computation but also in terms of energy and memory. This research paper presents an energy aware VM allocation and migration approach to meet the challenges faced by the growing number of cloud data centres. Machine Learning (ML) based Artificial Bee Colony (ABC) is used to rank the VM with respect to the load while considering the energy efficiency as a crucial parameter. The most efficient virtual machines are further selected and thus depending on the dynamics of the load and energy, applications are migrated from one VM to another. The simulationanalysis is performed in Matlab and it shows that this research work results in more reduction in energy consumption as compared to existing studies.
{"title":"Allocation and Migration of Virtual Machines Using Machine Learning","authors":"Suruchi Talwani, Khaled Alhazmi, Jimmy Singla, Hasan J. Alyamani, A. Bashir","doi":"10.32604/cmc.2022.020473","DOIUrl":"https://doi.org/10.32604/cmc.2022.020473","url":null,"abstract":": Cloud computing promises the advent of a new era of service boosted by means of virtualization technology. The process of virtualization means creation of virtual infrastructure, devices, servers and computing resources needed to deploy an application smoothly. This extensively practiced technology involves selecting an efficient Virtual Machine (VM) to complete the task by transferring applications from Physical Machines (PM) to VM or from VM to VM. The whole process is very challenging not only in terms of computation but also in terms of energy and memory. This research paper presents an energy aware VM allocation and migration approach to meet the challenges faced by the growing number of cloud data centres. Machine Learning (ML) based Artificial Bee Colony (ABC) is used to rank the VM with respect to the load while considering the energy efficiency as a crucial parameter. The most efficient virtual machines are further selected and thus depending on the dynamics of the load and energy, applications are migrated from one VM to another. The simulationanalysis is performed in Matlab and it shows that this research work results in more reduction in energy consumption as compared to existing studies.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"12 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89659172","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.2022.020523
Olutomilayo Olayemi Petinrin, Faisal Saeed, Xiangtao Li, F. Ghabban, Ka-chun Wong
: Bioactive compounds in plants, which can be synthesized using N-arylation methods such as the Buchwald-Hartwig reaction, are essential in drug discovery for their pharmacological effects. Important descriptors are necessary for the estimation of yields in these reactions. This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation. The algorithms were evaluated based on computational time and the number of selected descriptors. Analyses show that robust performance is obtained with more descriptors, compared to cases where fewer descriptors are selected. The essential descriptor was deduced based on the frequency of occurrence within the 50 extracted data subsets, and better performance was achieved with the voting ensemble than other algorithms with RMSE of 6.4270 and R 2 of 0.9423. The results and deductions from this study can be readily applied in the decision-making process of chemical synthesis by saving the computational cost associated with initial descriptor selection for yield estimation. The ensemble model has also shown robust performance in its yield estimation ability and efficiency.
{"title":"Reactions’ Descriptors Selection and Yield Estimation Using Metaheuristic Algorithms and Voting Ensemble","authors":"Olutomilayo Olayemi Petinrin, Faisal Saeed, Xiangtao Li, F. Ghabban, Ka-chun Wong","doi":"10.32604/cmc.2022.020523","DOIUrl":"https://doi.org/10.32604/cmc.2022.020523","url":null,"abstract":": Bioactive compounds in plants, which can be synthesized using N-arylation methods such as the Buchwald-Hartwig reaction, are essential in drug discovery for their pharmacological effects. Important descriptors are necessary for the estimation of yields in these reactions. This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation. The algorithms were evaluated based on computational time and the number of selected descriptors. Analyses show that robust performance is obtained with more descriptors, compared to cases where fewer descriptors are selected. The essential descriptor was deduced based on the frequency of occurrence within the 50 extracted data subsets, and better performance was achieved with the voting ensemble than other algorithms with RMSE of 6.4270 and R 2 of 0.9423. The results and deductions from this study can be readily applied in the decision-making process of chemical synthesis by saving the computational cost associated with initial descriptor selection for yield estimation. The ensemble model has also shown robust performance in its yield estimation ability and efficiency.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"52 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87910242","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.2022.019046
M. F. U. Rehman, Fawad Ahmed, M. A. Khan, U. Tariq, Sajjad Shaukat Jamal, Jawad Ahmad, I. Hussain
: In recent years, the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits. This in turn has helped in improving the quality and production of vegetables and fruits. Citrus fruits are well known for their taste and nutritional values. They are one of the natural and well known sources of vitamin C and planted worldwide. There are several diseases which severely affect the quality and yield of citrus fruits. In this paper, a new deep learning based technique is proposed for citrus disease classification. Two different pre-trained deep learning models have been used in this work. To increase the size of the citrus dataset used in this paper, image augmentation techniques are used. Moreover, to improve the visual quality of images, hybrid contrast stretching has been adopted. In addition, transfer learning is used to retrain the pre-trained models and the feature set is enriched by using feature fusion. The fused feature set is optimized using a meta-heuristic algorithm, the Whale Optimization Algorithm (WOA). The selected features are used for the classification of six different diseases of citrus plants. The proposed technique attains a classification accuracy of 95.7% with superior results when compared with recent techniques.
{"title":"Classification of Citrus Plant Diseases Using Deep Transfer Learning","authors":"M. F. U. Rehman, Fawad Ahmed, M. A. Khan, U. Tariq, Sajjad Shaukat Jamal, Jawad Ahmad, I. Hussain","doi":"10.32604/cmc.2022.019046","DOIUrl":"https://doi.org/10.32604/cmc.2022.019046","url":null,"abstract":": In recent years, the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits. This in turn has helped in improving the quality and production of vegetables and fruits. Citrus fruits are well known for their taste and nutritional values. They are one of the natural and well known sources of vitamin C and planted worldwide. There are several diseases which severely affect the quality and yield of citrus fruits. In this paper, a new deep learning based technique is proposed for citrus disease classification. Two different pre-trained deep learning models have been used in this work. To increase the size of the citrus dataset used in this paper, image augmentation techniques are used. Moreover, to improve the visual quality of images, hybrid contrast stretching has been adopted. In addition, transfer learning is used to retrain the pre-trained models and the feature set is enriched by using feature fusion. The fused feature set is optimized using a meta-heuristic algorithm, the Whale Optimization Algorithm (WOA). The selected features are used for the classification of six different diseases of citrus plants. The proposed technique attains a classification accuracy of 95.7% with superior results when compared with recent techniques.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"4 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88376229","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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