Pub Date : 2022-01-01DOI: 10.32604/iasc.2022.022586
A. Vasuki, V. Ponnusamy
A good wireless device in a system needs high spectral efficiency. NonOrthogonal Multiple Access (NOMA) is a technique used to enhance spectral efficiency, thereby allowing users to share information at the same time and same frequency. The information of the user is super-positioned either in the power or code domain. However, interference cancellation in NOMA aided system is challenging as it determines the reliability of the system in terms of Bit Error Rate (BER). BER is an essential performance parameter for any wireless network. Intelligent Reflecting Surfaces (IRS) enhances the BER of the users by controlling the electromagnetic wave propagation of a given channel. IRS is able to boost the Signal to Noise Ratio (SNR) at the receiver by introducing a phase shift in the incoming signal utilizing cost-effective reflecting materials. This paper evaluates users’ error rate performance by utilizing IRS in NOMA. The error probability expression of users is derived under Rayleigh and Rician fading channel. The accuracy of derived analytical expressions is then validated via simulations. Impact of power allocation factor, coherent and random phase shifting of IRS is evaluated for the proposed IRS-NOMA system.
{"title":"Error Rate Analysis of Intelligent Reflecting Surfaces Aided Non-Orthogonal Multiple Access System","authors":"A. Vasuki, V. Ponnusamy","doi":"10.32604/iasc.2022.022586","DOIUrl":"https://doi.org/10.32604/iasc.2022.022586","url":null,"abstract":"A good wireless device in a system needs high spectral efficiency. NonOrthogonal Multiple Access (NOMA) is a technique used to enhance spectral efficiency, thereby allowing users to share information at the same time and same frequency. The information of the user is super-positioned either in the power or code domain. However, interference cancellation in NOMA aided system is challenging as it determines the reliability of the system in terms of Bit Error Rate (BER). BER is an essential performance parameter for any wireless network. Intelligent Reflecting Surfaces (IRS) enhances the BER of the users by controlling the electromagnetic wave propagation of a given channel. IRS is able to boost the Signal to Noise Ratio (SNR) at the receiver by introducing a phase shift in the incoming signal utilizing cost-effective reflecting materials. This paper evaluates users’ error rate performance by utilizing IRS in NOMA. The error probability expression of users is derived under Rayleigh and Rician fading channel. The accuracy of derived analytical expressions is then validated via simulations. Impact of power allocation factor, coherent and random phase shifting of IRS is evaluated for the proposed IRS-NOMA system.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"Suppl 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90229921","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/iasc.2022.023890
V. Vijayal, K. Krishnamoorthi
{"title":"Integrated Renewable Smart Grid System Using Fuzzy Based Intelligent Controller","authors":"V. Vijayal, K. Krishnamoorthi","doi":"10.32604/iasc.2022.023890","DOIUrl":"https://doi.org/10.32604/iasc.2022.023890","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"1982 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90297431","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/iasc.2022.022104
Nahla H. Barakat, Ahmed H. ElSabbagh
Social media recently became convenient platforms for different groups with common concerns to share their experiences, including Adverse Drug Reactions (ADRs). In this paper, we propose a two stage intelligent algorithm which we call “Simi_to_Prob”, that utilizes social media forums; for ranking ADRs, and evaluating the ADRs prevalence considering different age and gender groups as its first stage. In the second stage, ADRs are predicted utilizing a different data set from the Food and Drug Administration (FDA). In particular, Natural Language Processing (NLP) is used on social media to extract ranked lists of ADRs, which are then validated using novel intrinsic evaluation methods. In the second stage, feature engineering is used to extend the input feature space, then a two stage supervised machine learning method is used to predict future ADRs incidences. Our results show correct ranked list of ADRs for three antihypertensive drugs, where high Spearman’s rank correlation coefficients (rs) of of 0.7458, 0.6678 and 0.5929 were obtained between SIDER database for drug ADRs, and our obtained lists from social media. Furthermore, Relatedness between ADRs and age and gender groups achieved high area under the ROC curve (AUC) reaching 0.959. The second stage results showed high AUCs of 0.96 and 0.99 for the prediction of future ADRs probabilities. The proposed algorithm shows that mining social media can provide reliable source of information, and additional features that can be used to boost supervised machine learning methods’ performance in different domains including Pharmacovigilance research.
{"title":"From Similarities to Probabilities: Feature Engineering for Predicting Drugs’ Adverse Reactions","authors":"Nahla H. Barakat, Ahmed H. ElSabbagh","doi":"10.32604/iasc.2022.022104","DOIUrl":"https://doi.org/10.32604/iasc.2022.022104","url":null,"abstract":"Social media recently became convenient platforms for different groups with common concerns to share their experiences, including Adverse Drug Reactions (ADRs). In this paper, we propose a two stage intelligent algorithm which we call “Simi_to_Prob”, that utilizes social media forums; for ranking ADRs, and evaluating the ADRs prevalence considering different age and gender groups as its first stage. In the second stage, ADRs are predicted utilizing a different data set from the Food and Drug Administration (FDA). In particular, Natural Language Processing (NLP) is used on social media to extract ranked lists of ADRs, which are then validated using novel intrinsic evaluation methods. In the second stage, feature engineering is used to extend the input feature space, then a two stage supervised machine learning method is used to predict future ADRs incidences. Our results show correct ranked list of ADRs for three antihypertensive drugs, where high Spearman’s rank correlation coefficients (rs) of of 0.7458, 0.6678 and 0.5929 were obtained between SIDER database for drug ADRs, and our obtained lists from social media. Furthermore, Relatedness between ADRs and age and gender groups achieved high area under the ROC curve (AUC) reaching 0.959. The second stage results showed high AUCs of 0.96 and 0.99 for the prediction of future ADRs probabilities. The proposed algorithm shows that mining social media can provide reliable source of information, and additional features that can be used to boost supervised machine learning methods’ performance in different domains including Pharmacovigilance research.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"68 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90765935","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/iasc.2022.023666
S. Beatrice, Janaki Meena
Discovering the information about several disorders prevailing in brain and neurology is by no means a new scientific technique. A neurological disorder of any human being can be analyzed using EEG (Electroencephalography) signal from the electrode’s output. Epilepsy (spontaneous recurrent seizure) detection is usually carried out by the physicians using a visual scanning of the signals produced by EEG, which is onerous and may be inaccurate. EEG signal is often used to determine epilepsy, for its merits, such as non-invasive, portable, and economical, can exhibit superior temporal tenacity. This paper surveys the existing artifact removal methods. It puts a new-fangled mode forward to confiscate artifacts and hauls informative derived values from EEG to automate Epilepsy detection. The automated Epilepsy detection has to precisely indicate and detect the neural abnormality of the brain. This indication and detection process necessitates a proficient approach for the prompt removal of artifacts of the EEG signals. An effective artifact removal of EEG signals can alone enable the useful features of the original signals for further processing. Once the original signals excluding the noise is obtained, a delicate strategy for extracting the features of the signals, becomes mandatory in order to accomplish robust classification of the signal. Then an expert classification technique is implemented to aid the automated analysis process to correctly distinguish the EEG signal features.
{"title":"Overhauled Approach to Effectuate the Amelioration in EEG Analysis","authors":"S. Beatrice, Janaki Meena","doi":"10.32604/iasc.2022.023666","DOIUrl":"https://doi.org/10.32604/iasc.2022.023666","url":null,"abstract":"Discovering the information about several disorders prevailing in brain and neurology is by no means a new scientific technique. A neurological disorder of any human being can be analyzed using EEG (Electroencephalography) signal from the electrode’s output. Epilepsy (spontaneous recurrent seizure) detection is usually carried out by the physicians using a visual scanning of the signals produced by EEG, which is onerous and may be inaccurate. EEG signal is often used to determine epilepsy, for its merits, such as non-invasive, portable, and economical, can exhibit superior temporal tenacity. This paper surveys the existing artifact removal methods. It puts a new-fangled mode forward to confiscate artifacts and hauls informative derived values from EEG to automate Epilepsy detection. The automated Epilepsy detection has to precisely indicate and detect the neural abnormality of the brain. This indication and detection process necessitates a proficient approach for the prompt removal of artifacts of the EEG signals. An effective artifact removal of EEG signals can alone enable the useful features of the original signals for further processing. Once the original signals excluding the noise is obtained, a delicate strategy for extracting the features of the signals, becomes mandatory in order to accomplish robust classification of the signal. Then an expert classification technique is implemented to aid the automated analysis process to correctly distinguish the EEG signal features.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"29 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91115284","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/iasc.2022.019767
Saima Akram, A. Nawaz, Muhammad Bilal Riaz, M. Rehman
In this article, the maximum possible numbers of periodic solutions for the quartic differential equation are calculated. In this regard, for the first time in the literature, we developed new formulae to determine the maximum number of periodic solutions greater than eight for the quartic equation. To obtain the maximum number of periodic solutions, we used a systematic procedure of bifurcation analysis. We used computer algebra Maple 18 to solve lengthy calculations that appeared in the formulae of focal values as integrations. The newly developed formulae were applied to a variety of polynomials with algebraic and homogeneous trigonometric coefficients of various degrees. We were able to validate our newly developed formulae by obtaining maximum multiplicity nine in the class C4,1 using algebraic coefficients. Whereas the maximum number of periodic solutions for the classes C4,4; C5,1; C5,5; C6,1; C6:6; C7,1 is eight. Additionally, the stability of limit cycles belonging to the aforementioned classes with algebraic coefficients is briefly discussed. Hence, we conclude from the above-stated facts that our new results are a credible, authentic and pleasant addition to the literature.
{"title":"Periodic Solutions for Two Dimensional Quartic Non-Autonomous Differential Equation","authors":"Saima Akram, A. Nawaz, Muhammad Bilal Riaz, M. Rehman","doi":"10.32604/iasc.2022.019767","DOIUrl":"https://doi.org/10.32604/iasc.2022.019767","url":null,"abstract":"In this article, the maximum possible numbers of periodic solutions for the quartic differential equation are calculated. In this regard, for the first time in the literature, we developed new formulae to determine the maximum number of periodic solutions greater than eight for the quartic equation. To obtain the maximum number of periodic solutions, we used a systematic procedure of bifurcation analysis. We used computer algebra Maple 18 to solve lengthy calculations that appeared in the formulae of focal values as integrations. The newly developed formulae were applied to a variety of polynomials with algebraic and homogeneous trigonometric coefficients of various degrees. We were able to validate our newly developed formulae by obtaining maximum multiplicity nine in the class C4,1 using algebraic coefficients. Whereas the maximum number of periodic solutions for the classes C4,4; C5,1; C5,5; C6,1; C6:6; C7,1 is eight. Additionally, the stability of limit cycles belonging to the aforementioned classes with algebraic coefficients is briefly discussed. Hence, we conclude from the above-stated facts that our new results are a credible, authentic and pleasant addition to the literature.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88503108","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/iasc.2022.021752
Seyoung Kim, Jeonghoon Choi, Kwang Ryel Ryu
Scheduling heat treatment jobs in a hot press forging factory involves forming batches of multiple workpieces for the given furnaces, determining the start time of heating each batch, and sorting out the order of cooling the heated workpieces. Among these, forming batches is particularly difficult because of the various constraints that must be satisfied. This paper proposes an optimization method based on an evolutionary algorithm to search for a heat treatment schedule of maximum productivity with minimum energy cost, satisfying various constraints imposed on the batches. Our method encodes a candidate solution as a permutation of heat treatment jobs and decodes it such that the jobs are grouped into batches satisfying all constraints. Each candidate schedule is evaluated by simulating the heating and cooling processes using cost models for processing time and energy consumption, which are learned from historical process data. Simulation experiments reveal that the schedules built using the proposed method achieve higher productivity with lower energy costs than those built by human experts.
{"title":"Optimization of Heat Treatment Scheduling for Hot Press Forging Using Data-Driven Models","authors":"Seyoung Kim, Jeonghoon Choi, Kwang Ryel Ryu","doi":"10.32604/iasc.2022.021752","DOIUrl":"https://doi.org/10.32604/iasc.2022.021752","url":null,"abstract":"Scheduling heat treatment jobs in a hot press forging factory involves forming batches of multiple workpieces for the given furnaces, determining the start time of heating each batch, and sorting out the order of cooling the heated workpieces. Among these, forming batches is particularly difficult because of the various constraints that must be satisfied. This paper proposes an optimization method based on an evolutionary algorithm to search for a heat treatment schedule of maximum productivity with minimum energy cost, satisfying various constraints imposed on the batches. Our method encodes a candidate solution as a permutation of heat treatment jobs and decodes it such that the jobs are grouped into batches satisfying all constraints. Each candidate schedule is evaluated by simulating the heating and cooling processes using cost models for processing time and energy consumption, which are learned from historical process data. Simulation experiments reveal that the schedules built using the proposed method achieve higher productivity with lower energy costs than those built by human experts.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"4 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88568216","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/iasc.2022.022720
N. Kanagavalli, S. Baghavathi Priya
Recent developments of the World Wide Web (WWW) and social networking (Twitter, Instagram, etc.) paves way for data sharing which has never been observed in the human history before. A major security issue in this network is the creation of fake accounts. In addition, the automatic classification of the text article as true or fake is also a crucial process. The ineffectiveness of humans in distinguishing the true and false information exposes the fake news as a risk to credibility, democracy, logical truth, and journalism in government sectors. Besides, the automatic fake news or rumors from the social networking sites is a major research area in the field of social media analytics. With this motivation, this paper develops a new reliable deep learning (DL) based fake account and fake news detection (RDL-FAFND) model for the social networking sites. The goal of the RDL-FAFND model is to resolve the major problems involved in the social media platforms namely fake accounts, fake news/rumor identification. The presented RDL-FAFND model detects the fake account by the use of a parameter tuned deep stacked Auto encoder (DSAE) using the krill herd (KH) optimization algorithm for detecting the fake social networking accounts. Besides, the presented RDL-FAFND model involves an ensemble of the machine learning (ML) models with different linguistic features (EML-LF) for categorizing the text as true or fake. An extensive set of experiments have been carried out for highlighting the superior performance of the RDL-FAFND model. A detailed comparative results analysis has stated that the presented RDL-FAFND model is considerably better than the existing methods.
{"title":"Social Networks Fake Account and Fake News Identification with Reliable Deep Learning","authors":"N. Kanagavalli, S. Baghavathi Priya","doi":"10.32604/iasc.2022.022720","DOIUrl":"https://doi.org/10.32604/iasc.2022.022720","url":null,"abstract":"Recent developments of the World Wide Web (WWW) and social networking (Twitter, Instagram, etc.) paves way for data sharing which has never been observed in the human history before. A major security issue in this network is the creation of fake accounts. In addition, the automatic classification of the text article as true or fake is also a crucial process. The ineffectiveness of humans in distinguishing the true and false information exposes the fake news as a risk to credibility, democracy, logical truth, and journalism in government sectors. Besides, the automatic fake news or rumors from the social networking sites is a major research area in the field of social media analytics. With this motivation, this paper develops a new reliable deep learning (DL) based fake account and fake news detection (RDL-FAFND) model for the social networking sites. The goal of the RDL-FAFND model is to resolve the major problems involved in the social media platforms namely fake accounts, fake news/rumor identification. The presented RDL-FAFND model detects the fake account by the use of a parameter tuned deep stacked Auto encoder (DSAE) using the krill herd (KH) optimization algorithm for detecting the fake social networking accounts. Besides, the presented RDL-FAFND model involves an ensemble of the machine learning (ML) models with different linguistic features (EML-LF) for categorizing the text as true or fake. An extensive set of experiments have been carried out for highlighting the superior performance of the RDL-FAFND model. A detailed comparative results analysis has stated that the presented RDL-FAFND model is considerably better than the existing methods.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"124 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88638358","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/iasc.2022.020849
Y. Lee, D. Pae, Dae-Ki Hong, M. Lim, Tae-Koo Kang
With the advancement of human-computer interaction and artificial intelligence, emotion recognition has received significant research attention. The most commonly used technique for emotion recognition is EEG, which is directly associated with the central nervous system and contains strong emotional features. However, there are some disadvantages to using EEG signals. They require high dimensionality, diverse and complex processing procedures which make real-time computation difficult. In addition, there are problems in data acquisition and interpretation due to body movement or reduced concentration of the experimenter. In this paper, we used photoplethysmography (PPG) and electromyography (EMG) to record signals. Firstly, we segmented the emotion data into 10-pulses during preprocessing to identify emotions with short period signals. These segmented data were input to the proposed bimodal stacked sparse auto-encoder model. To enhance recognition performance, we adopted a bimodal structure to extract shared PPG and EMG representations. This approach provided more detailed arousal-valence mapping compared with the current high/low binary classification. We created a dataset of PPG and EMG signals, called the emotion dataset dividing into four classes to help understand emotion levels. We achieved high performance of 80.18% and 75.86% for arousal and valence, respectively, despite more class classification. Experimental results validated that the proposed method significantly enhanced emotion recognition performance.
{"title":"Emotion Recognition with Short-Period Physiological Signals Using Bimodal Sparse Autoencoders","authors":"Y. Lee, D. Pae, Dae-Ki Hong, M. Lim, Tae-Koo Kang","doi":"10.32604/iasc.2022.020849","DOIUrl":"https://doi.org/10.32604/iasc.2022.020849","url":null,"abstract":"With the advancement of human-computer interaction and artificial intelligence, emotion recognition has received significant research attention. The most commonly used technique for emotion recognition is EEG, which is directly associated with the central nervous system and contains strong emotional features. However, there are some disadvantages to using EEG signals. They require high dimensionality, diverse and complex processing procedures which make real-time computation difficult. In addition, there are problems in data acquisition and interpretation due to body movement or reduced concentration of the experimenter. In this paper, we used photoplethysmography (PPG) and electromyography (EMG) to record signals. Firstly, we segmented the emotion data into 10-pulses during preprocessing to identify emotions with short period signals. These segmented data were input to the proposed bimodal stacked sparse auto-encoder model. To enhance recognition performance, we adopted a bimodal structure to extract shared PPG and EMG representations. This approach provided more detailed arousal-valence mapping compared with the current high/low binary classification. We created a dataset of PPG and EMG signals, called the emotion dataset dividing into four classes to help understand emotion levels. We achieved high performance of 80.18% and 75.86% for arousal and valence, respectively, despite more class classification. Experimental results validated that the proposed method significantly enhanced emotion recognition performance.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"3 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88859530","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/iasc.2022.022359
Hanan A. Hosni Mahmoud, Amal H. Alharbi, Norah S. Alghamdi
People are required to wear masks in many countries, now a days with the Covid-19 pandemic. Automated mask detection is very crucial to help identify people who do not wear masks. Other important applications is for surveillance issues to be able to detect concealed faces that might be a safety threat. However, automated mask wearing detection might be difficult in complex scenes such as hospitals and shopping malls where many people are at present. In this paper, we present analysis of several detection techniques and their performances. We are facing different face sizes and orientation, therefore, we propose one technique to detect faces of different sizes and orientations. In this research, we propose a framework to incorporate two deep learning procedures to develop a technique for mask-wearing recognition especially in complex scenes and various resolution images. A regional convolutional neural network (R-CNN) is used to detect regions of faces, which is further enhanced by introducing a different size face detection even for smaller targets. We combined that by an algorithm that can detect faces even in low resolution images. We propose a mask-wearing detection algorithms in complex situations under different resolution and face sizes. We use a convolutional neural network (CNN) to detect the presence of the mask around the detected face. Experimental results prove our process enhances the precision and recall for the combined detection algorithm. The proposed technique achieves Precision of 94.5%, and is better than other techniques under comparison.
{"title":"A Framework for Mask-Wearing Recognition in Complex Scenes for Different Face Sizes","authors":"Hanan A. Hosni Mahmoud, Amal H. Alharbi, Norah S. Alghamdi","doi":"10.32604/iasc.2022.022359","DOIUrl":"https://doi.org/10.32604/iasc.2022.022359","url":null,"abstract":"People are required to wear masks in many countries, now a days with the Covid-19 pandemic. Automated mask detection is very crucial to help identify people who do not wear masks. Other important applications is for surveillance issues to be able to detect concealed faces that might be a safety threat. However, automated mask wearing detection might be difficult in complex scenes such as hospitals and shopping malls where many people are at present. In this paper, we present analysis of several detection techniques and their performances. We are facing different face sizes and orientation, therefore, we propose one technique to detect faces of different sizes and orientations. In this research, we propose a framework to incorporate two deep learning procedures to develop a technique for mask-wearing recognition especially in complex scenes and various resolution images. A regional convolutional neural network (R-CNN) is used to detect regions of faces, which is further enhanced by introducing a different size face detection even for smaller targets. We combined that by an algorithm that can detect faces even in low resolution images. We propose a mask-wearing detection algorithms in complex situations under different resolution and face sizes. We use a convolutional neural network (CNN) to detect the presence of the mask around the detected face. Experimental results prove our process enhances the precision and recall for the combined detection algorithm. The proposed technique achieves Precision of 94.5%, and is better than other techniques under comparison.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"72 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89493553","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/iasc.2022.023344
R. Punithavathi, R. Thanga Selvi, R. Latha, G. Kadiravan, V. Srikanth, Neeraj Kumar Shukla
Wireless sensor networks comprise a set of autonomous sensor nodes, commonly used for data gathering and tracking applications. Node localization and intrusion detection are considered as the major design issue in WSN. Therefore, this paper presents a new multi-objective manta ray foraging optimization (MRFO) based node localization with intrusion detection (MOMRFO-NLID) technique for WSN. The goal of the MOMRFO-NLID technique is to optimally localize the unknown nodes and determine the existence of intrusions in the network. The MOMRFO-NLID technique encompasses two major stages namely MRFO based localization of nodes and optimal Siamese Neural Network (OSNN) based intrusion detection. The OSNN technique involves the hyperparameter tuning of the traditional SNN using the MRFO algorithm and consequently increases the detection rate. In order to assess the enhanced performance of the MOMRFONLID technique, a series of simulations take place and the results reported superior performance compared to existing techniques interms of distinct evaluation parameters.
{"title":"Robust Node Localization with Intrusion Detection for Wireless Sensor Networks","authors":"R. Punithavathi, R. Thanga Selvi, R. Latha, G. Kadiravan, V. Srikanth, Neeraj Kumar Shukla","doi":"10.32604/iasc.2022.023344","DOIUrl":"https://doi.org/10.32604/iasc.2022.023344","url":null,"abstract":"Wireless sensor networks comprise a set of autonomous sensor nodes, commonly used for data gathering and tracking applications. Node localization and intrusion detection are considered as the major design issue in WSN. Therefore, this paper presents a new multi-objective manta ray foraging optimization (MRFO) based node localization with intrusion detection (MOMRFO-NLID) technique for WSN. The goal of the MOMRFO-NLID technique is to optimally localize the unknown nodes and determine the existence of intrusions in the network. The MOMRFO-NLID technique encompasses two major stages namely MRFO based localization of nodes and optimal Siamese Neural Network (OSNN) based intrusion detection. The OSNN technique involves the hyperparameter tuning of the traditional SNN using the MRFO algorithm and consequently increases the detection rate. In order to assess the enhanced performance of the MOMRFONLID technique, a series of simulations take place and the results reported superior performance compared to existing techniques interms of distinct evaluation parameters.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"21 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73429864","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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