Pub Date : 2022-01-01DOI: 10.32604/cmc.2022.022694
N. S. Ishak, F. C. Seman, N. Zainal, N. A. Awang
: Sugar is important in daily food intake since it is used as food preservative and sweetener. Therefore, is important to analyze the influence of sugar on the spectroscopic properties of the sample. Terahertz spectroscopy is proven to be useful and an efficient method for sugar detection as well as for future food quality industry. However, the lack of detection sensitivity in Terahertz Spectroscopy has prevented it from being used in a widespread spectroscopic analysis technology. In this paper, Frequency Selective Surface (FSS) using the Terahertz Spectroscopy Time Domain Spectrum (THz-TDS) which operates at terahertz frequency range has been demonstrated for application of sugar detection. The FSS is designed with a circle slot structure and has been optimized in line with the molecular resonance of glucose and fructose at different level concentration at 1.98 THz and 1.80 THz, respectively. Transmission magnitude of glucose and sucrose is inversely proportional with the level of sugar concentrations. The realization of the FSS structure is using electron beam lithography and wet etching technique. Results show that the FSS performance for glucose and sucrose reveal fair shifts in measured transmission magnitude from its original in CST by approximately 30%. The use of fabricated FSS with circle structure indicates that the concentration can be improved averagely at 25% for glucose and 13% for sucrose. Thus, it shows that the FSS circle structure combined with THz-TDS has the potential to become an alternative method for food sensing technology in the future.
{"title":"Detection of Low Sugar Concentration Solution Using Frequency Selective Surface (FSS)","authors":"N. S. Ishak, F. C. Seman, N. Zainal, N. A. Awang","doi":"10.32604/cmc.2022.022694","DOIUrl":"https://doi.org/10.32604/cmc.2022.022694","url":null,"abstract":": Sugar is important in daily food intake since it is used as food preservative and sweetener. Therefore, is important to analyze the influence of sugar on the spectroscopic properties of the sample. Terahertz spectroscopy is proven to be useful and an efficient method for sugar detection as well as for future food quality industry. However, the lack of detection sensitivity in Terahertz Spectroscopy has prevented it from being used in a widespread spectroscopic analysis technology. In this paper, Frequency Selective Surface (FSS) using the Terahertz Spectroscopy Time Domain Spectrum (THz-TDS) which operates at terahertz frequency range has been demonstrated for application of sugar detection. The FSS is designed with a circle slot structure and has been optimized in line with the molecular resonance of glucose and fructose at different level concentration at 1.98 THz and 1.80 THz, respectively. Transmission magnitude of glucose and sucrose is inversely proportional with the level of sugar concentrations. The realization of the FSS structure is using electron beam lithography and wet etching technique. Results show that the FSS performance for glucose and sucrose reveal fair shifts in measured transmission magnitude from its original in CST by approximately 30%. The use of fabricated FSS with circle structure indicates that the concentration can be improved averagely at 25% for glucose and 13% for sucrose. Thus, it shows that the FSS circle structure combined with THz-TDS has the potential to become an alternative method for food sensing technology in the future.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"163 3 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83672636","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.2022.020016
Tallat Jabeen, Ishrat Jabeen, Humaira Ashraf, Noor Zaman Jhanjhi, M. Humayun, Mehedi Masud, S. Aljahdali
COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in 2019. It affects the whole world through person-to-person communication. This virus spreads by the droplets of coughs and sneezing, which are quickly falling over the surface. Therefore, anyone can get easily affected by breathing in the vicinity of the COVID-19 patient. Currently, vaccine for the disease is under clinical investigation in different pharmaceutical companies. Until now, multiple medical companies have delivered health monitoring kits. However, a wireless body area network (WBAN) is a healthcare system that consists of nano sensors used to detect the real-time health condition of the patient. The proposed approach delineates is to fill a gap between recent technology trends and healthcare structure. If COVID-19 affected patient is monitored through WBAN sensors and network, a physician or a doctor can guide the patient at the right time with the correct possible decision. This scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein, a Monte Carlo algorithm guided protocol is developed to probe a secured cipher output. Security cipher helps to avoid wireless network issues like packet loss, network attacks, network interference, and routing problems. Monte Carlo based covid-19 detection technique gives 90% better results in terms of time complexity, performance, and efficiency. Results indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity, performance, and efficiency and thus, is advocated as a significant application for lessening hospital expenses.
{"title":"A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare","authors":"Tallat Jabeen, Ishrat Jabeen, Humaira Ashraf, Noor Zaman Jhanjhi, M. Humayun, Mehedi Masud, S. Aljahdali","doi":"10.32604/cmc.2022.020016","DOIUrl":"https://doi.org/10.32604/cmc.2022.020016","url":null,"abstract":"COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in 2019. It affects the whole world through person-to-person communication. This virus spreads by the droplets of coughs and sneezing, which are quickly falling over the surface. Therefore, anyone can get easily affected by breathing in the vicinity of the COVID-19 patient. Currently, vaccine for the disease is under clinical investigation in different pharmaceutical companies. Until now, multiple medical companies have delivered health monitoring kits. However, a wireless body area network (WBAN) is a healthcare system that consists of nano sensors used to detect the real-time health condition of the patient. The proposed approach delineates is to fill a gap between recent technology trends and healthcare structure. If COVID-19 affected patient is monitored through WBAN sensors and network, a physician or a doctor can guide the patient at the right time with the correct possible decision. This scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein, a Monte Carlo algorithm guided protocol is developed to probe a secured cipher output. Security cipher helps to avoid wireless network issues like packet loss, network attacks, network interference, and routing problems. Monte Carlo based covid-19 detection technique gives 90% better results in terms of time complexity, performance, and efficiency. Results indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity, performance, and efficiency and thus, is advocated as a significant application for lessening hospital expenses.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"PP 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84271092","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.2022.018262
Fatemeh Ahmadi Zeidabadi, Sajjad Amiri Doumari, Mohammad Dehghani, Z. Montazeri, Pavel Trojovsk� Gaurav Dhiman
{"title":"Parametric Study of Hip Fracture Risk Using QCT-Based Finite Element Analysis","authors":"Fatemeh Ahmadi Zeidabadi, Sajjad Amiri Doumari, Mohammad Dehghani, Z. Montazeri, Pavel Trojovsk� Gaurav Dhiman","doi":"10.32604/cmc.2022.018262","DOIUrl":"https://doi.org/10.32604/cmc.2022.018262","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"22 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89973778","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.2022.019416
Tehreem Aslam, A. Maqbool, M. Akhtar, Alina Mirza, Muhammad Anees Khan, Wazir Zada Khan, Shadab Alam
{"title":"Blockchain Based Enhanced ERP Transaction Integrity Architecture and PoET Consensus","authors":"Tehreem Aslam, A. Maqbool, M. Akhtar, Alina Mirza, Muhammad Anees Khan, Wazir Zada Khan, Shadab Alam","doi":"10.32604/cmc.2022.019416","DOIUrl":"https://doi.org/10.32604/cmc.2022.019416","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"87 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90391120","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.2022.020098
A. Zaka, R. Jabeen, Kanwal Iqbal Khan
{"title":"Optimization of Reliability–Redundancy Allocation Problems: A Review of the Evolutionary Algorithms","authors":"A. Zaka, R. Jabeen, Kanwal Iqbal Khan","doi":"10.32604/cmc.2022.020098","DOIUrl":"https://doi.org/10.32604/cmc.2022.020098","url":null,"abstract":"","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"21 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90503650","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.2022.019884
Walaa K. Gad, Anas Alokla, Waleed Nazih, M. Aref, A. M. Salem
: Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language. Pseudo-code explains and describes the content of the code without using syntax or programming language technologies. However, writing Pseudo-code to each code instruction is laborious. Recently, neural machine translation is used to generate textual descriptions for the source code. In this paper, a novel deep learning-based transformer (DLBT) model is proposed for automatic Pseudo-code generation from the source code. The proposed model uses deep learning which is based on Neural Machine Translation (NMT) to work as a language translator. The DLBT is based on the transformer which is an encoder-decoder structure. There are three major components: tokenizer and embeddings, transformer, and post-processing. Each code line is tokenized to dense vector. Then transformer captures the relatedness between the source code and the matching Pseudo-code without the need of Recurrent Neural Network (RNN). At the post-processing step, the generated Pseudo-code is optimized. The proposed model is assessed using a real Python dataset, which contains more than 18,800 lines of a source code written in Python. The experiments show promising performance results compared with other machine translation methods such as Recurrent Neural Network (RNN). The proposed DLBT records 47.32, 68. 49 accuracy and BLEU performance measures, respectively.
{"title":"DLBT: Deep Learning-Based Transformer to Generate Pseudo-Code from Source Code","authors":"Walaa K. Gad, Anas Alokla, Waleed Nazih, M. Aref, A. M. Salem","doi":"10.32604/cmc.2022.019884","DOIUrl":"https://doi.org/10.32604/cmc.2022.019884","url":null,"abstract":": Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language. Pseudo-code explains and describes the content of the code without using syntax or programming language technologies. However, writing Pseudo-code to each code instruction is laborious. Recently, neural machine translation is used to generate textual descriptions for the source code. In this paper, a novel deep learning-based transformer (DLBT) model is proposed for automatic Pseudo-code generation from the source code. The proposed model uses deep learning which is based on Neural Machine Translation (NMT) to work as a language translator. The DLBT is based on the transformer which is an encoder-decoder structure. There are three major components: tokenizer and embeddings, transformer, and post-processing. Each code line is tokenized to dense vector. Then transformer captures the relatedness between the source code and the matching Pseudo-code without the need of Recurrent Neural Network (RNN). At the post-processing step, the generated Pseudo-code is optimized. The proposed model is assessed using a real Python dataset, which contains more than 18,800 lines of a source code written in Python. The experiments show promising performance results compared with other machine translation methods such as Recurrent Neural Network (RNN). The proposed DLBT records 47.32, 68. 49 accuracy and BLEU performance measures, respectively.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"2016 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86287296","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.2022.020713
M. Waly, M. Sikkandar, M. Aboamer, S. Kadry, O. Thinnukool
: Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women’s mortality rate. Proper screening of pap smear images is essential to assist the earlier identificationand diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification (IDCNN-CDC) model using biomedical pap smear images. The proposed IDCNN-CDC model involves four major processes such as preprocessing, segmentation, feature extraction, and classification. Initially, the Gaussian filter (GF) technique is applied to enhance data through noise removal process in the Pap smear image. The Tsallis entropy technique with the dragonfly optimization (TE-DFO) algorithm determines the segmentation of an image to identify the diseased portions properly. The cell images are fed into the DL based SqueezeNet model to extract deep-learned features. Finally,the extracted features from SqueezeNet are applied to the weighted extreme learning machine (ELM) classification model to detect and classify the cervix cells. For experimental validation, the Herlev database is employed. The database was developed at Herlev University Hospital (Den-mark). The experimental outcomes make sure that higher performance of the proposed technique interms of sensitivity, specificity, accuracy, and F-Score.
{"title":"Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model","authors":"M. Waly, M. Sikkandar, M. Aboamer, S. Kadry, O. Thinnukool","doi":"10.32604/cmc.2022.020713","DOIUrl":"https://doi.org/10.32604/cmc.2022.020713","url":null,"abstract":": Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women’s mortality rate. Proper screening of pap smear images is essential to assist the earlier identificationand diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification (IDCNN-CDC) model using biomedical pap smear images. The proposed IDCNN-CDC model involves four major processes such as preprocessing, segmentation, feature extraction, and classification. Initially, the Gaussian filter (GF) technique is applied to enhance data through noise removal process in the Pap smear image. The Tsallis entropy technique with the dragonfly optimization (TE-DFO) algorithm determines the segmentation of an image to identify the diseased portions properly. The cell images are fed into the DL based SqueezeNet model to extract deep-learned features. Finally,the extracted features from SqueezeNet are applied to the weighted extreme learning machine (ELM) classification model to detect and classify the cervix cells. For experimental validation, the Herlev database is employed. The database was developed at Herlev University Hospital (Den-mark). The experimental outcomes make sure that higher performance of the proposed technique interms of sensitivity, specificity, accuracy, and F-Score.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"63 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86507203","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.2022.017114
Harshita Patel, D. Rajput, O. Stan, L. Miclea
Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the ‘existing algorithm modification solution’ to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods. The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems. Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data. The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers. Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.
{"title":"A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data","authors":"Harshita Patel, D. Rajput, O. Stan, L. Miclea","doi":"10.32604/cmc.2022.017114","DOIUrl":"https://doi.org/10.32604/cmc.2022.017114","url":null,"abstract":"Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the ‘existing algorithm modification solution’ to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods. The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems. Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data. The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers. Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"26 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86644718","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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