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.2022.019709
S. Ben Hamida, H. Mrabet, Sana Belguith, Adeeb M. Alhomoud, A. Jemai
{"title":"Towards Securing Machine Learning Models Against Membership Inference Attacks","authors":"S. Ben Hamida, H. Mrabet, Sana Belguith, Adeeb M. Alhomoud, A. Jemai","doi":"10.32604/cmc.2022.019709","DOIUrl":"https://doi.org/10.32604/cmc.2022.019709","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"58 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74055719","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.2022.021998
Hatim G. Zaini
: This paper gives and analyses data-driven prediction models for the energy usage of appliances. Data utilized include readings of temperature and humidity sensors from a wireless network. The building envelope is meant to minimize energy demand or the energy required to power the house independent of the appliance and mechanical system efficiency. Approx-imating a mapping function between the input variables and the continuous output variable is the work of regression. The paper discusses the forecasting framework FOPF (Feature Optimization Prediction Framework), which includes feature selection optimization: by removing non-predictive parameters to choose the best-selected feature hybrid optimization technique has been approached. k-nearest neighbors (KNN) Ensemble Prediction Models for the data of the energy use of appliances have been tested against some bases machine learning algorithms. The comparison study showed the powerful, best accuracy and lowest error of KNN with RMSE = 0.0078. Finally, the suggested ensemble model’s performance is assessed using a one-way analysis of variance (ANOVA) test and the Wilcoxon Signed Rank Test. (Two-tailed P-value: 0.0001).
{"title":"Forecasting of Appliances House in a Low-Energy Depend on Grey Wolf Optimizer","authors":"Hatim G. Zaini","doi":"10.32604/cmc.2022.021998","DOIUrl":"https://doi.org/10.32604/cmc.2022.021998","url":null,"abstract":": This paper gives and analyses data-driven prediction models for the energy usage of appliances. Data utilized include readings of temperature and humidity sensors from a wireless network. The building envelope is meant to minimize energy demand or the energy required to power the house independent of the appliance and mechanical system efficiency. Approx-imating a mapping function between the input variables and the continuous output variable is the work of regression. The paper discusses the forecasting framework FOPF (Feature Optimization Prediction Framework), which includes feature selection optimization: by removing non-predictive parameters to choose the best-selected feature hybrid optimization technique has been approached. k-nearest neighbors (KNN) Ensemble Prediction Models for the data of the energy use of appliances have been tested against some bases machine learning algorithms. The comparison study showed the powerful, best accuracy and lowest error of KNN with RMSE = 0.0078. Finally, the suggested ensemble model’s performance is assessed using a one-way analysis of variance (ANOVA) test and the Wilcoxon Signed Rank Test. (Two-tailed P-value: 0.0001).","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"27 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76460496","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.2022.018131
A. Ibrahim, M. Hassaballah, Abdelmgeid A. Ali, Yunyoung Nam
Social networking sites in the most modernized world are flooded with large data volumes. Extracting the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they write. The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale. In a very short period of time, tweets indicate unpredicted increase of coronavirus. They reflect people’s opinions and thoughts with regard to coronavirus and its impact on society. The research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa;due to their shortness and sparsity. In this paper, a hierarchical twitter sentiment model (HTSM) is proposed to show people’s opinions in short texts. The proposed HTSM has two main features as follows: constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width, as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner (VADER) sentiment analysis. The tweets for each extracted important aspect can be categorized as follows: strongly positive, positive, neutral, strongly negative, or negative. The quality of the proposed model is validated by applying it to a popular product and a widespread topic. The results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.
{"title":"COVID19 Outbreak: A Hierarchical Framework for User Sentiment Analysis","authors":"A. Ibrahim, M. Hassaballah, Abdelmgeid A. Ali, Yunyoung Nam","doi":"10.32604/cmc.2022.018131","DOIUrl":"https://doi.org/10.32604/cmc.2022.018131","url":null,"abstract":"Social networking sites in the most modernized world are flooded with large data volumes. Extracting the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they write. The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale. In a very short period of time, tweets indicate unpredicted increase of coronavirus. They reflect people’s opinions and thoughts with regard to coronavirus and its impact on society. The research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa;due to their shortness and sparsity. In this paper, a hierarchical twitter sentiment model (HTSM) is proposed to show people’s opinions in short texts. The proposed HTSM has two main features as follows: constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width, as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner (VADER) sentiment analysis. The tweets for each extracted important aspect can be categorized as follows: strongly positive, positive, neutral, strongly negative, or negative. The quality of the proposed model is validated by applying it to a popular product and a widespread topic. The results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"875 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72662088","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.2022.018917
Amina Bibi, Muhamamd Attique Khan, M. Younus Javed, U. Tariq, Byeong-Gwon Kang, Yun-Seong Nam, Reham R. Mostafa, Rasha H. Sakr
{"title":"Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework","authors":"Amina Bibi, Muhamamd Attique Khan, M. Younus Javed, U. Tariq, Byeong-Gwon Kang, Yun-Seong Nam, Reham R. Mostafa, Rasha H. Sakr","doi":"10.32604/cmc.2022.018917","DOIUrl":"https://doi.org/10.32604/cmc.2022.018917","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"46 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80009224","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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