Pub Date : 2022-02-12DOI: 10.1109/AISP53593.2022.9760640
P. Vaidya, S. Chauhan, V. Jaiswal
The drug-like molecule which could treat multiple diseases is commercially more viable and can act on multiple biological pathways. Such drug candidates can also be more important in the treatment of complex diseases like cancer. Traditional methods are not focused on the development of such drugs, but computational method can be developed to predict multiple disease potential of drug-like molecules. Computational methods have been extremely successful in drug discovery through prediction of drug potential of the drug-like molecules such as toxicity, physiological effects, binding energy and binding pose with the receptor. Computational methods to predict multiple disease potential of the drug-like molecules are not worked out so far in spite of the high importance of such drugs and it can also expedite the drug repurposing. Hence, information of approved drugs used for the treatment of single and multiple diseases was included to develop the machine learning-based model for the prediction of multiple disease potential of the drug-like molecules. Molecular descriptors were used as the features and optimally selected for support vector machine-based prediction models. The fairly high accuracy of developed method justifies the importance of selected method and approach. The developed method is expected to expedite the drug discovery process through the prediction of multi-drug potential of drug-like molecules.
{"title":"Prediction of Multi Class Drugs: A Perspective for Designing Drug with Many Uses","authors":"P. Vaidya, S. Chauhan, V. Jaiswal","doi":"10.1109/AISP53593.2022.9760640","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760640","url":null,"abstract":"The drug-like molecule which could treat multiple diseases is commercially more viable and can act on multiple biological pathways. Such drug candidates can also be more important in the treatment of complex diseases like cancer. Traditional methods are not focused on the development of such drugs, but computational method can be developed to predict multiple disease potential of drug-like molecules. Computational methods have been extremely successful in drug discovery through prediction of drug potential of the drug-like molecules such as toxicity, physiological effects, binding energy and binding pose with the receptor. Computational methods to predict multiple disease potential of the drug-like molecules are not worked out so far in spite of the high importance of such drugs and it can also expedite the drug repurposing. Hence, information of approved drugs used for the treatment of single and multiple diseases was included to develop the machine learning-based model for the prediction of multiple disease potential of the drug-like molecules. Molecular descriptors were used as the features and optimally selected for support vector machine-based prediction models. The fairly high accuracy of developed method justifies the importance of selected method and approach. The developed method is expected to expedite the drug discovery process through the prediction of multi-drug potential of drug-like molecules.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82356600","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760604
Samarpita Hatua, D. Ray, Sahadeb Shit, D. Das, Sayanti Hazra
Industry 4.0 offers a radical transformation to increase cost-effective, flexible, and efficient production of higher-quality fully automated systems by collecting and analyzing data across machines. From the last few decades, power industry has started to focus on real-time systems instead of using static methodology in periodical boiler inspection. The power plant undergoes sudden break down due to cracks and foreign bodies causing huge economic loss to the plant as well as the country. To avoid such unforeseen breakdown, most of the power plants has adopted inspection and monitoring system as a regular solution. Visual inspection is one of the most popular techniques for such inspections using a tiny camera with high-power LEDs (Known as Borescope). But it has several limitations for circumferential (360°) and longitudinal (2000mm) coverage and also equidistance inspection from the center of the header is not possible using a conventional Borescope. A specific Digital Video Recorder (DVR) used for the inspection and monitoring is not sufficient to resolve multipurpose requirements such as position of the foreign body and crack, feature of magnification, and more important is data log including plant information and crack details with images. A real-time inspection module has been developed integrated with robotic (AI) based on computer vision to make the inspection dynamic and fully automated.
{"title":"Development of an Inspection Software towards Detection and Location of Cracks and Foreign Objects in Boiler header or Pipes","authors":"Samarpita Hatua, D. Ray, Sahadeb Shit, D. Das, Sayanti Hazra","doi":"10.1109/AISP53593.2022.9760604","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760604","url":null,"abstract":"Industry 4.0 offers a radical transformation to increase cost-effective, flexible, and efficient production of higher-quality fully automated systems by collecting and analyzing data across machines. From the last few decades, power industry has started to focus on real-time systems instead of using static methodology in periodical boiler inspection. The power plant undergoes sudden break down due to cracks and foreign bodies causing huge economic loss to the plant as well as the country. To avoid such unforeseen breakdown, most of the power plants has adopted inspection and monitoring system as a regular solution. Visual inspection is one of the most popular techniques for such inspections using a tiny camera with high-power LEDs (Known as Borescope). But it has several limitations for circumferential (360°) and longitudinal (2000mm) coverage and also equidistance inspection from the center of the header is not possible using a conventional Borescope. A specific Digital Video Recorder (DVR) used for the inspection and monitoring is not sufficient to resolve multipurpose requirements such as position of the foreign body and crack, feature of magnification, and more important is data log including plant information and crack details with images. A real-time inspection module has been developed integrated with robotic (AI) based on computer vision to make the inspection dynamic and fully automated.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79444570","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760663
A. Patil, Rama Bansidhar Dan, N. Priya
Flower Species recognition has been a major field in image processing. Recognition fails many times the reason behind this is lack of knowledge about medicinal flower among the normal ones. Vision based technique has been used to create automated system which helps even common man to identify flowers around them. The main goal is to extract certain features from the input image by applying different techniques like machine learning and computer vision in order to classify image. In this paper, it is analyzed that flowers recognition has given success rate using image processing.
{"title":"Flower Identification System Using Vision Based Technique","authors":"A. Patil, Rama Bansidhar Dan, N. Priya","doi":"10.1109/AISP53593.2022.9760663","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760663","url":null,"abstract":"Flower Species recognition has been a major field in image processing. Recognition fails many times the reason behind this is lack of knowledge about medicinal flower among the normal ones. Vision based technique has been used to create automated system which helps even common man to identify flowers around them. The main goal is to extract certain features from the input image by applying different techniques like machine learning and computer vision in order to classify image. In this paper, it is analyzed that flowers recognition has given success rate using image processing.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79691247","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760674
V. Cp, S. Kalaivanan, R. Karthik, A. Sanjana
Due to the quick increase of IoT devices, they lack the authentication standards and administration needed to keep user data secure. Hackers could cause significant infrastructure harm by infiltrating a wide spectrum of IoT devices. Blockchain use in IoT technology guarantees trust and authentication across all IoT elements, resulting in IoT security. Blockchain is a decentralized, distributed, and shared database that enables the creation of decentralized apps. Traceability, openness, immutability, and fault tolerance are some of the qualities of this technology that help maintain data privacy in IoT scenarios and thus create a safe environment. We look at a potential strategy for securely controlling IoT devices,i.e., devices connected to the internet using smart contracts on the blockchain in this study. This paper demonstrates how the proposed system comprising of a blockchain and smart contracts work efficiently in concurrence to avoid tampering by unauthorized parties. We have employed web3 library to control the linked devices by implementing Ethereum nodes (second most popular blockchain) on Raspberry Pi simulations and node.js.
{"title":"Blockchain-based IoT Device Security","authors":"V. Cp, S. Kalaivanan, R. Karthik, A. Sanjana","doi":"10.1109/AISP53593.2022.9760674","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760674","url":null,"abstract":"Due to the quick increase of IoT devices, they lack the authentication standards and administration needed to keep user data secure. Hackers could cause significant infrastructure harm by infiltrating a wide spectrum of IoT devices. Blockchain use in IoT technology guarantees trust and authentication across all IoT elements, resulting in IoT security. Blockchain is a decentralized, distributed, and shared database that enables the creation of decentralized apps. Traceability, openness, immutability, and fault tolerance are some of the qualities of this technology that help maintain data privacy in IoT scenarios and thus create a safe environment. We look at a potential strategy for securely controlling IoT devices,i.e., devices connected to the internet using smart contracts on the blockchain in this study. This paper demonstrates how the proposed system comprising of a blockchain and smart contracts work efficiently in concurrence to avoid tampering by unauthorized parties. We have employed web3 library to control the linked devices by implementing Ethereum nodes (second most popular blockchain) on Raspberry Pi simulations and node.js.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73187454","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760559
Kaustubha H. Shedbalkar, D. More
The solar power forecasting is important factor that provides support to planning terms of power distribution organizations. The time based forecasting is feasible due to dependable outcome of solar power generation on weather status. The weather status itself is prediction method involving approach which is becoming considerably accurate these days. The power generation outcome is the multiple parameter regression model. This paper shows the experimental outcome of solar power generation forecasting with linear, ridge and Bayesian regression models. The best performing Bayesian model is compared with other existing methods in which Bayesian model outperforms in terms of mean square error for 15 minutes time interval data in batch processing approach.
{"title":"Bayesian Regression for Solar Power Forecasting","authors":"Kaustubha H. Shedbalkar, D. More","doi":"10.1109/AISP53593.2022.9760559","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760559","url":null,"abstract":"The solar power forecasting is important factor that provides support to planning terms of power distribution organizations. The time based forecasting is feasible due to dependable outcome of solar power generation on weather status. The weather status itself is prediction method involving approach which is becoming considerably accurate these days. The power generation outcome is the multiple parameter regression model. This paper shows the experimental outcome of solar power generation forecasting with linear, ridge and Bayesian regression models. The best performing Bayesian model is compared with other existing methods in which Bayesian model outperforms in terms of mean square error for 15 minutes time interval data in batch processing approach.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75176514","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760529
Naga Chandrika Gandikota, Gurumurthy Komanapalli
In this paper, a second order sinusoidal oscillator (SO) has been presented using Current Feedback Operational Amplifiers (CFOA) as active element. It uses five resistors and two capacitors. It exhibits complete independent tuning between frequency of oscillation and condition of oscillation through resistors. The sensitivity analysis has been carried out and it is observed that it exhibits low FO sensitivity to various circuit parameters. PSPICE simulations are used to check the efficacy of the proposed circuit and the simulation results are in close proximity with theoretical calculations. The observed total harmonic distortion (THD) is lower than 2.5%.
{"title":"CFOA Based Second Order Low Frequency Sensitive Sinusoidal Oscillator","authors":"Naga Chandrika Gandikota, Gurumurthy Komanapalli","doi":"10.1109/AISP53593.2022.9760529","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760529","url":null,"abstract":"In this paper, a second order sinusoidal oscillator (SO) has been presented using Current Feedback Operational Amplifiers (CFOA) as active element. It uses five resistors and two capacitors. It exhibits complete independent tuning between frequency of oscillation and condition of oscillation through resistors. The sensitivity analysis has been carried out and it is observed that it exhibits low FO sensitivity to various circuit parameters. PSPICE simulations are used to check the efficacy of the proposed circuit and the simulation results are in close proximity with theoretical calculations. The observed total harmonic distortion (THD) is lower than 2.5%.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87867518","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760652
N. Kumar, Nidhi Gupta
Emotion recognition is the process to study about the emotions in a human being. This is a research field where one can understand and recognize the feelings of human and ability of expression which varies from each other at great extent. Several methods have been developed to study emotions such as facial expression, speech method, textual method and EEG signal. In this study work, we have reviewed several methods to find an efficiency of emotions up to accurate observations. Several papers on emotion recognition from the year 2007 to 2021 are been explored in this paper to observe the accuracy 95.20% using electroencephalogram (EEG) signal and 95% using EEG signals with statistical features and neural network. The average accuracy ranges in between 63% to 73% using EEG signal and facial expressions, both.
{"title":"A Comprehensive Study on Machine Learning Approaches for Emotion Recognition","authors":"N. Kumar, Nidhi Gupta","doi":"10.1109/AISP53593.2022.9760652","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760652","url":null,"abstract":"Emotion recognition is the process to study about the emotions in a human being. This is a research field where one can understand and recognize the feelings of human and ability of expression which varies from each other at great extent. Several methods have been developed to study emotions such as facial expression, speech method, textual method and EEG signal. In this study work, we have reviewed several methods to find an efficiency of emotions up to accurate observations. Several papers on emotion recognition from the year 2007 to 2021 are been explored in this paper to observe the accuracy 95.20% using electroencephalogram (EEG) signal and 95% using EEG signals with statistical features and neural network. The average accuracy ranges in between 63% to 73% using EEG signal and facial expressions, both.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77238237","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760650
U. Pattapu, Suneel Miriyala
In this paper, a simple structure based hexagonal microstrip-patch antenna has designed for wireless energy transmission. In this proposed structure, spurious harmonic suppression has achieved by using H shaped slot, defected ground structure (DGS) and open stub. Spurious frequencies has suppressed up to fourth harmonics. Designed antenna is compact size, operating frequency is 5.8 GHz and 10 dB return loss impedance bandwidth of 5.48-6.08 GHz (10.38%) simulated gain of 3.8 dB and radiation efficiency of more than 79% have also been achieved from the designed structure. Because of its fruitful properties, this antenna is well suited for wireless energy transfer applications.
{"title":"A 5.80 GHz Harmonic Suppression Antenna for Wireless Energy Transfer Application","authors":"U. Pattapu, Suneel Miriyala","doi":"10.1109/AISP53593.2022.9760650","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760650","url":null,"abstract":"In this paper, a simple structure based hexagonal microstrip-patch antenna has designed for wireless energy transmission. In this proposed structure, spurious harmonic suppression has achieved by using H shaped slot, defected ground structure (DGS) and open stub. Spurious frequencies has suppressed up to fourth harmonics. Designed antenna is compact size, operating frequency is 5.8 GHz and 10 dB return loss impedance bandwidth of 5.48-6.08 GHz (10.38%) simulated gain of 3.8 dB and radiation efficiency of more than 79% have also been achieved from the designed structure. Because of its fruitful properties, this antenna is well suited for wireless energy transfer applications.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72851311","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760547
Reva Nagi, S. S. Tripathy
Reliable and accurate identification of disease is required for protecting the plant from pathogens and obviating the yield loss. The advent of computer vision and image processing techniques has encouraged contribution in disease identification systems in plants. This paper proposes a fuzzy feature extraction technique and Probabilistic Neural Network (PNN) for the identification of grapevine diseases using leaf images. The color features are extracted using fuzzy color histogram. Then, the extracted features are fed to a PNN classifier for grapevine disease classification. The proposed technique achieves a maximum recognition accuracy of 95.54% on the test dataset. On comparing the proposed system with upcoming deep learning techniques, the former is found to be more efficient for small training data.
{"title":"Disease identification in grapevine leaf images using fuzzy-PNN","authors":"Reva Nagi, S. S. Tripathy","doi":"10.1109/AISP53593.2022.9760547","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760547","url":null,"abstract":"Reliable and accurate identification of disease is required for protecting the plant from pathogens and obviating the yield loss. The advent of computer vision and image processing techniques has encouraged contribution in disease identification systems in plants. This paper proposes a fuzzy feature extraction technique and Probabilistic Neural Network (PNN) for the identification of grapevine diseases using leaf images. The color features are extracted using fuzzy color histogram. Then, the extracted features are fed to a PNN classifier for grapevine disease classification. The proposed technique achieves a maximum recognition accuracy of 95.54% on the test dataset. On comparing the proposed system with upcoming deep learning techniques, the former is found to be more efficient for small training data.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87289311","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760673
D. K. Rout, Pihu Ranjan, Disha Mukherjee, Sananda Kumar, Deepa Das
Internet-of-Things (IoT) is technology that promises to connect every thing. IoT for human health monitoring is particularly of interest as it promises to revolutionize health care. And that is possible with the integration of wireless body area network (BAN) with IoT. Radio channel play an important role in designing efficient transceivers for IoT capable BAN sensors. Recently, multiple researchers have worked on modeling the radio propagation in and around the human body. Such measurements not only help understand the signal propagation, which is the obvious outcome, they also help researchers design efficient and optimized transceivers. Thus, in this paper, we measure the path loss in a body surface to external scenario for the 900 MHz band in indoor scenarios and model it into a simple pathloss model. The results in the article have been compiled from more than 15000 measurements in typical real-life scenario.
{"title":"Radio Propagation Modeling for Body Surface to External Communication Scenario","authors":"D. K. Rout, Pihu Ranjan, Disha Mukherjee, Sananda Kumar, Deepa Das","doi":"10.1109/AISP53593.2022.9760673","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760673","url":null,"abstract":"Internet-of-Things (IoT) is technology that promises to connect every thing. IoT for human health monitoring is particularly of interest as it promises to revolutionize health care. And that is possible with the integration of wireless body area network (BAN) with IoT. Radio channel play an important role in designing efficient transceivers for IoT capable BAN sensors. Recently, multiple researchers have worked on modeling the radio propagation in and around the human body. Such measurements not only help understand the signal propagation, which is the obvious outcome, they also help researchers design efficient and optimized transceivers. Thus, in this paper, we measure the path loss in a body surface to external scenario for the 900 MHz band in indoor scenarios and model it into a simple pathloss model. The results in the article have been compiled from more than 15000 measurements in typical real-life scenario.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90838962","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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