Pub Date : 2022-03-22DOI: 10.1109/IC_ASET53395.2022.9765871
Sami Briouza, H. Gritli, N. Khraief, S. Belghith, Dilbag Singh
To use surface electromyography (sEMG) signals for therapy and rehabilitation purposes, we first need to tackle a fundamental problem which is the pattern recognition of these signals. Recently, Machine Learning (ML) techniques have drawn a lot of attention from researchers working on sEMG pattern recognition, and the usage of these techniques showed a lot of potentials and proved to be a viable option. For this work, we adopt the random forest classifier, as an ML technique, for the classification of the sEMG signals for the rehabilitation of upper limbs. Furthermore, to be able to test its performance, we considered and tested different combinations of five different time-domain features, namely MAV, WL, ZC, SSC, and finally RMS. Thus, and via experimental results on the adopted dataset, we show how the choice of features influences the quality of classification.
{"title":"Classification of sEMG Biomedical Signals for Upper-Limb Rehabilitation Using the Random Forest Method","authors":"Sami Briouza, H. Gritli, N. Khraief, S. Belghith, Dilbag Singh","doi":"10.1109/IC_ASET53395.2022.9765871","DOIUrl":"https://doi.org/10.1109/IC_ASET53395.2022.9765871","url":null,"abstract":"To use surface electromyography (sEMG) signals for therapy and rehabilitation purposes, we first need to tackle a fundamental problem which is the pattern recognition of these signals. Recently, Machine Learning (ML) techniques have drawn a lot of attention from researchers working on sEMG pattern recognition, and the usage of these techniques showed a lot of potentials and proved to be a viable option. For this work, we adopt the random forest classifier, as an ML technique, for the classification of the sEMG signals for the rehabilitation of upper limbs. Furthermore, to be able to test its performance, we considered and tested different combinations of five different time-domain features, namely MAV, WL, ZC, SSC, and finally RMS. Thus, and via experimental results on the adopted dataset, we show how the choice of features influences the quality of classification.","PeriodicalId":6874,"journal":{"name":"2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"118 1","pages":"161-166"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87929392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-22DOI: 10.1109/IC_ASET53395.2022.9765886
Kadria Ezzine, M. Frikha, J. Di Martino
Much existing voice conversion (VC) systems are attractive owing to their high performance in terms of voice quality and speaker similarity. Nevertheless, without parallel training data, some generated waveform trajectories are not yet smooth, leading to degraded sound quality and mispronunciation issues in the converted speech. To address these shortcomings, this paper proposes a non-parallel VC system based on an auto-regressive model, Phonetic PosteriorGrams (PPGs), and an LPCnet vocoder to generate high-quality converted speech. The proposed auto-regressive structure makes our system able to produce the next step outputs from the previous step acoustic features. Further, the use of PPGs aims to convert any unknown source speaker into a specific target speaker due to their speaker-independent properties. We evaluate the effectiveness of our system by performing any-to-one conversion pairs between native English speakers. Objective and subjective measures show that our method outperforms the best non-parallel VC method of Voice Conversion Challenge 2018 in terms of naturalness and speaker similarity.
{"title":"Non-Parallel Voice Conversion System Using An Auto-Regressive Model","authors":"Kadria Ezzine, M. Frikha, J. Di Martino","doi":"10.1109/IC_ASET53395.2022.9765886","DOIUrl":"https://doi.org/10.1109/IC_ASET53395.2022.9765886","url":null,"abstract":"Much existing voice conversion (VC) systems are attractive owing to their high performance in terms of voice quality and speaker similarity. Nevertheless, without parallel training data, some generated waveform trajectories are not yet smooth, leading to degraded sound quality and mispronunciation issues in the converted speech. To address these shortcomings, this paper proposes a non-parallel VC system based on an auto-regressive model, Phonetic PosteriorGrams (PPGs), and an LPCnet vocoder to generate high-quality converted speech. The proposed auto-regressive structure makes our system able to produce the next step outputs from the previous step acoustic features. Further, the use of PPGs aims to convert any unknown source speaker into a specific target speaker due to their speaker-independent properties. We evaluate the effectiveness of our system by performing any-to-one conversion pairs between native English speakers. Objective and subjective measures show that our method outperforms the best non-parallel VC method of Voice Conversion Challenge 2018 in terms of naturalness and speaker similarity.","PeriodicalId":6874,"journal":{"name":"2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"61 1","pages":"500-504"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91543559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-22DOI: 10.1109/IC_ASET53395.2022.9765892
Nadia Jmour, S. Masmoudi, A. Abdelkrim
Artificial intelligence is used in different fields. This paper describes a new opportunity to connect artificial intelligence to cognitive social psychology. We touch two important psychology paradigms: emotional and cognitive dissonance. Our hypotheses are proved using the new video based emotions analysis system (VEMOS) by testing labeled videos samples filmed by an actor according to specific scenarios and situations proposed by an expert on cognitive psychology describing the two paradigms. First, our approach has shown relevant results that confirm the notion of emotional and cognitive dissonance and the reliability of the intelligent system VEMOS on recognizing emotions efficiently. Then, we successfully add a new method of measuring a score of the individual emotional dissonance as well as his degree of spontaneity and degree of control.
{"title":"Emotional and cognitive dissonance revealed using VEMOS emotion analysis system","authors":"Nadia Jmour, S. Masmoudi, A. Abdelkrim","doi":"10.1109/IC_ASET53395.2022.9765892","DOIUrl":"https://doi.org/10.1109/IC_ASET53395.2022.9765892","url":null,"abstract":"Artificial intelligence is used in different fields. This paper describes a new opportunity to connect artificial intelligence to cognitive social psychology. We touch two important psychology paradigms: emotional and cognitive dissonance. Our hypotheses are proved using the new video based emotions analysis system (VEMOS) by testing labeled videos samples filmed by an actor according to specific scenarios and situations proposed by an expert on cognitive psychology describing the two paradigms. First, our approach has shown relevant results that confirm the notion of emotional and cognitive dissonance and the reliability of the intelligent system VEMOS on recognizing emotions efficiently. Then, we successfully add a new method of measuring a score of the individual emotional dissonance as well as his degree of spontaneity and degree of control.","PeriodicalId":6874,"journal":{"name":"2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"49 1","pages":"115-120"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76252991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-22DOI: 10.1109/IC_ASET53395.2022.9765933
C. Zaoui, Helmi Abrougui, Mohamed Amine Meftah, Saber Hachicha, Ahmed Moulhi, Habib Dallagi
This paper deals with the modeling and control of an autonomous arm with two degrees of freedom. The arm mechanical design was firstly proposed and described. It is designed for ship inspection and cleaning operations. The proposed control technique was developed using sliding mode control combined with feedback linearization approach. Simulation results were carried out to show the effectiveness of the proposed control law.
{"title":"Mechanical Design and Control of an Arm with Two Degrees of Freedom for Inspection and Cleaning Operations","authors":"C. Zaoui, Helmi Abrougui, Mohamed Amine Meftah, Saber Hachicha, Ahmed Moulhi, Habib Dallagi","doi":"10.1109/IC_ASET53395.2022.9765933","DOIUrl":"https://doi.org/10.1109/IC_ASET53395.2022.9765933","url":null,"abstract":"This paper deals with the modeling and control of an autonomous arm with two degrees of freedom. The arm mechanical design was firstly proposed and described. It is designed for ship inspection and cleaning operations. The proposed control technique was developed using sliding mode control combined with feedback linearization approach. Simulation results were carried out to show the effectiveness of the proposed control law.","PeriodicalId":6874,"journal":{"name":"2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"239 1","pages":"532-537"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73044981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-22DOI: 10.1109/IC_ASET53395.2022.9765879
Aymen Chaaira, Rabiaa Gamoudi, L. Sbita
Modern power systems, such as DC-AC inverters, have improved in efficiency. They employ simple control methods. During the switching process, however, they cause harmonic difficulties. This has an effect on the overall system's performances. To reduce these undesired harmonic distortions, unipolar Selective Harmonic Elimination SHEPWM was used in this work. At optimal switching angles, it cancels out odd harmonics. Its goal is to get rid of the first 100 low-order harmonics. By solving non-linear transcendental equations in MATLAB, the fsolve function was utilized to calculate the optimal 50 angles for the SHEPWM. The resulting SHEPWM control signal is implemented in LTspice. It operates a single-phase pure sine wave inverter. Finally, at the inverter output, an LC low-pass filter is used to improve the response and obtain a better sinusoidal AC waveform with lower THD.
{"title":"Unipolar SHEPWM for Pure Sine Wave Single-Phase Inverter","authors":"Aymen Chaaira, Rabiaa Gamoudi, L. Sbita","doi":"10.1109/IC_ASET53395.2022.9765879","DOIUrl":"https://doi.org/10.1109/IC_ASET53395.2022.9765879","url":null,"abstract":"Modern power systems, such as DC-AC inverters, have improved in efficiency. They employ simple control methods. During the switching process, however, they cause harmonic difficulties. This has an effect on the overall system's performances. To reduce these undesired harmonic distortions, unipolar Selective Harmonic Elimination SHEPWM was used in this work. At optimal switching angles, it cancels out odd harmonics. Its goal is to get rid of the first 100 low-order harmonics. By solving non-linear transcendental equations in MATLAB, the fsolve function was utilized to calculate the optimal 50 angles for the SHEPWM. The resulting SHEPWM control signal is implemented in LTspice. It operates a single-phase pure sine wave inverter. Finally, at the inverter output, an LC low-pass filter is used to improve the response and obtain a better sinusoidal AC waveform with lower THD.","PeriodicalId":6874,"journal":{"name":"2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"25 1","pages":"274-278"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74649496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-22DOI: 10.1109/IC_ASET53395.2022.9765898
Souha Ayadi, Z. Lachiri
Visual emotion recognition is a very large field. It plays a very important role in different domains such as security, robotics, and medical tasks. The visual tasks could be either image or video. Unlike the image processing, the difficulty of video processing is always a challenge due to changes in information over time variation. Significant performance improvements when applying deep learning algorithms to video processing. This paper presents a deep neural network based on ResNet50 model. The latter is conducted on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) due to the variance of the nature of the data exists which is speech and song. The choice of ResNet model is based on the ability of facing different problems such as of vanishing gradients, the performing stability offered by this model, the ability of CNN for feature extraction which is considered to be the base architecture for ResNet, and the ability of improving the accuracy results and minimizing the loss. The achieved results are 57.73% for song and 55.52% for speech. Results shows that the Resnet50 model is suitable for both speech and song while maintaining performance stability.
{"title":"Deep Neural Network for visual Emotion Recognition based on ResNet50 using Song-Speech characteristics","authors":"Souha Ayadi, Z. Lachiri","doi":"10.1109/IC_ASET53395.2022.9765898","DOIUrl":"https://doi.org/10.1109/IC_ASET53395.2022.9765898","url":null,"abstract":"Visual emotion recognition is a very large field. It plays a very important role in different domains such as security, robotics, and medical tasks. The visual tasks could be either image or video. Unlike the image processing, the difficulty of video processing is always a challenge due to changes in information over time variation. Significant performance improvements when applying deep learning algorithms to video processing. This paper presents a deep neural network based on ResNet50 model. The latter is conducted on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) due to the variance of the nature of the data exists which is speech and song. The choice of ResNet model is based on the ability of facing different problems such as of vanishing gradients, the performing stability offered by this model, the ability of CNN for feature extraction which is considered to be the base architecture for ResNet, and the ability of improving the accuracy results and minimizing the loss. The achieved results are 57.73% for song and 55.52% for speech. Results shows that the Resnet50 model is suitable for both speech and song while maintaining performance stability.","PeriodicalId":6874,"journal":{"name":"2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"3 1","pages":"363-368"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75736918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-22DOI: 10.1109/IC_ASET53395.2022.9765928
Arom Moreno-Ortiz, Daniela Sánchez-Orozco, Luis López-Estrada, C. Tutivén, Y. Vidal, Marcelo Fajardo-Pruna
This paper studies the mathematical model required to implement an intelligent control system for the autonomous sailboat SenSailor Drone. This work presents the required equations and defines the hardware configuration and interactions between sensors and actuators in the system to be mounted. The proposed model was developed in Python, and it is feasible to interact with open source tools of machine learning. The generated trajectories will be used as input trajectories to train a Neural Network that identifies a plant model and gives an optimal controller for the desired behaviors.
{"title":"Modelling of an Intelligent Control Strategy for an Autonomous Sailboat - SenSailor","authors":"Arom Moreno-Ortiz, Daniela Sánchez-Orozco, Luis López-Estrada, C. Tutivén, Y. Vidal, Marcelo Fajardo-Pruna","doi":"10.1109/IC_ASET53395.2022.9765928","DOIUrl":"https://doi.org/10.1109/IC_ASET53395.2022.9765928","url":null,"abstract":"This paper studies the mathematical model required to implement an intelligent control system for the autonomous sailboat SenSailor Drone. This work presents the required equations and defines the hardware configuration and interactions between sensors and actuators in the system to be mounted. The proposed model was developed in Python, and it is feasible to interact with open source tools of machine learning. The generated trajectories will be used as input trajectories to train a Neural Network that identifies a plant model and gives an optimal controller for the desired behaviors.","PeriodicalId":6874,"journal":{"name":"2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"4 1","pages":"34-38"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75562987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-22DOI: 10.1109/IC_ASET53395.2022.9765931
Boncho Nikov
This paper presents a novel design methodology of a Continuous-time ΣΔ-Modulator operating at a limit cycle. The methodology employs both the describing functions linearization method and the out-of-band gain theory to find the parameters of the loop filter. As a result of the unification a single mathematical description of the loop requirements is obtained as a system of inequalities. Each of the solutions of the inequalities represents a possible modulator. An example is provided to demonstrate the design process.
{"title":"Design of Continuous-Time ΣΔ-modulator With Single-Bit Quantizer With Hysteresis Operating at Limit Cycle","authors":"Boncho Nikov","doi":"10.1109/IC_ASET53395.2022.9765931","DOIUrl":"https://doi.org/10.1109/IC_ASET53395.2022.9765931","url":null,"abstract":"This paper presents a novel design methodology of a Continuous-time ΣΔ-Modulator operating at a limit cycle. The methodology employs both the describing functions linearization method and the out-of-band gain theory to find the parameters of the loop filter. As a result of the unification a single mathematical description of the loop requirements is obtained as a system of inequalities. Each of the solutions of the inequalities represents a possible modulator. An example is provided to demonstrate the design process.","PeriodicalId":6874,"journal":{"name":"2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"13 1","pages":"12-16"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89576395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-22DOI: 10.1109/IC_ASET53395.2022.9765905
Touka Hafsia, H. Tlijani, K. Nouri
Visual informations is very rich to control and pursue mobile robots, like wheeled mobile robots, underwater robots and aerospace mobile robots, etc. These are considered as mobile robots that move in different spaces, their main problem in this case is navigation especially in unknown environments. In effect, this navigation is possible only by the localisation and orientation of the robot by using different embedded sensors. We have the camera which is a necessary sensor in this work, it is an embedded instrument that gives very rich visual information as a sensor complementing the other sensors. In this context, the recognition of objects from visual informations is a main function among the functions very useful in image processing tasks due to its varied applications in the field of robotics. Based on the analysis of this informations and the determination of image features like color or shape or object primitives (points, lines, edges, etc.) or some other features. What interest us in this paper, various feature extraction techniques and classification of point and edge detection are discussed which are required for object recognition showing advantages and disadvantages of the selected algorithms. So, points and edge detection refers to the process of identifying and detecting sharp discontinuities in an image. In this work, we try to develop a novel algorithm using the work on the existing to create a novel detector.
{"title":"A comparative analysis of techniques for extracting features from the object in image processing","authors":"Touka Hafsia, H. Tlijani, K. Nouri","doi":"10.1109/IC_ASET53395.2022.9765905","DOIUrl":"https://doi.org/10.1109/IC_ASET53395.2022.9765905","url":null,"abstract":"Visual informations is very rich to control and pursue mobile robots, like wheeled mobile robots, underwater robots and aerospace mobile robots, etc. These are considered as mobile robots that move in different spaces, their main problem in this case is navigation especially in unknown environments. In effect, this navigation is possible only by the localisation and orientation of the robot by using different embedded sensors. We have the camera which is a necessary sensor in this work, it is an embedded instrument that gives very rich visual information as a sensor complementing the other sensors. In this context, the recognition of objects from visual informations is a main function among the functions very useful in image processing tasks due to its varied applications in the field of robotics. Based on the analysis of this informations and the determination of image features like color or shape or object primitives (points, lines, edges, etc.) or some other features. What interest us in this paper, various feature extraction techniques and classification of point and edge detection are discussed which are required for object recognition showing advantages and disadvantages of the selected algorithms. So, points and edge detection refers to the process of identifying and detecting sharp discontinuities in an image. In this work, we try to develop a novel algorithm using the work on the existing to create a novel detector.","PeriodicalId":6874,"journal":{"name":"2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"321 1","pages":"340-344"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77459383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-22DOI: 10.1109/IC_ASET53395.2022.9765949
M. Rekik, Marwa Grami, L. Krichen
The developed work in this paper focuses on the optimal integration of rechargeable electric vehicles into the smart grid. Indeed, an optimization control is proposed to improve the dynamics and the response of these vehicles by adjusting all the parameters of their regulators during participation in the both concepts: vehicles to grid and grid to vehicles. The suggested approach is performed using the Online multi-objective Particle-Swarm-Optimization (PSO) algorithm. Simulation results obtained by "Matlab Simulink" will be presented to show the feasibility of this studied approach.
{"title":"An Optimal Power Control Strategy For A Plug In Electric Vehicle Based On Online Multi-Objective Particle Swarm Optimization","authors":"M. Rekik, Marwa Grami, L. Krichen","doi":"10.1109/IC_ASET53395.2022.9765949","DOIUrl":"https://doi.org/10.1109/IC_ASET53395.2022.9765949","url":null,"abstract":"The developed work in this paper focuses on the optimal integration of rechargeable electric vehicles into the smart grid. Indeed, an optimization control is proposed to improve the dynamics and the response of these vehicles by adjusting all the parameters of their regulators during participation in the both concepts: vehicles to grid and grid to vehicles. The suggested approach is performed using the Online multi-objective Particle-Swarm-Optimization (PSO) algorithm. Simulation results obtained by \"Matlab Simulink\" will be presented to show the feasibility of this studied approach.","PeriodicalId":6874,"journal":{"name":"2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"40 1","pages":"538-543"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80353683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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