Often, operators of machines, including unmanned ground vehicles (UGVs) or working machines, are forced to work in unfavourable conditions, e.g. high temperatures continuously for a long period of time. This has a huge impact on their concentration, which usually determines the success of many tasks entrusted to them. Electroencephalography (EEG) allows the study of the electrical activity of the brain. It allows determining, for example, whether the operator is able to focus on the realization of his tasks. The main goal of the article was to develop an algorithm for determining the state of brain activity by analysing the EEG signal. For this purpose, methods of EEG signal acquisition were described, basic types of brain waves were discussed, and exemplary states of brain activity were recorded. Particular attention was paid to technical aspects related to signal analysis. The LabVIEW environment was used to implement the created algorithm. The results of the research showing the operation of the developed EEG signal analyser were also presented.
{"title":"EEG Signal Analysis for Monitoring Concentration of Operators","authors":"Ł. Rykała","doi":"10.14313/2cnmqm79","DOIUrl":"https://doi.org/10.14313/2cnmqm79","url":null,"abstract":"Often, operators of machines, including unmanned ground vehicles (UGVs) or working machines, are forced to work in unfavourable conditions, e.g. high temperatures continuously for a long period of time. This has a huge impact on their concentration, which usually determines the success of many tasks entrusted to them. Electroencephalography (EEG) allows the study of the electrical activity of the brain. It allows determining, for example, whether the operator is able to focus on the realization of his tasks. The main goal of the article was to develop an algorithm for determining the state of brain activity by analysing the EEG signal. For this purpose, methods of EEG signal acquisition were described, basic types of brain waves were discussed, and exemplary states of brain activity were recorded. Particular attention was paid to technical aspects related to signal analysis. The LabVIEW environment was used to implement the created algorithm. The results of the research showing the operation of the developed EEG signal analyser were also presented.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"12 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951835","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}
Henni Mansour Abdelwaheb, Kacimi Abderrahmane, Belaidi Aek
Teleoperation robotic systems control, which enables humans to perform activities in remote situations, has become an extremely challenging field in recent decades. In this paper, a Model Free Proportional-Derivative Sliding Mode Controller (MFPDSMC) is devoted to the synchronization problem of teleoperation systems subject to actuator dynamics, time-varying delay, model uncertainty, and input interaction forces. For the first time, the teleoperation model used in this study combines actuator dynamics and manipulator models into a single equation, which improves model accuracy and brings it closer to the actual system than in prior studies. Further, the proposed control approach, called Free, involves the simple measurement of inputs and outputs to enhance the system's performance without relying on any knowledge from the mathematical model. In addition, our strategy includes a Sliding Mode term with the MFPD term to increase system stability and attain excellent performance against external disturbances. Finally, using the Lyapunov function under specified conditions, asymptotic stability is established, and simulation results are compared and provided to demonstrate the efficacy of the proposed strategy.
{"title":"Model-Free Sliding Mode Control for a Nonlinear Teleoperation System with Actuator Dynamics","authors":"Henni Mansour Abdelwaheb, Kacimi Abderrahmane, Belaidi Aek","doi":"10.14313/v5snhs97","DOIUrl":"https://doi.org/10.14313/v5snhs97","url":null,"abstract":"Teleoperation robotic systems control, which enables humans to perform activities in remote situations, has become an extremely challenging field in recent decades. In this paper, a Model Free Proportional-Derivative Sliding Mode Controller (MFPDSMC) is devoted to the synchronization problem of teleoperation systems subject to actuator dynamics, time-varying delay, model uncertainty, and input interaction forces. For the first time, the teleoperation model used in this study combines actuator dynamics and manipulator models into a single equation, which improves model accuracy and brings it closer to the actual system than in prior studies. Further, the proposed control approach, called Free, involves the simple measurement of inputs and outputs to enhance the system's performance without relying on any knowledge from the mathematical model. In addition, our strategy includes a Sliding Mode term with the MFPD term to increase system stability and attain excellent performance against external disturbances. Finally, using the Lyapunov function under specified conditions, asymptotic stability is established, and simulation results are compared and provided to demonstrate the efficacy of the proposed strategy.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"44 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951832","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}
Miguel-Angel Ortega-Palacios, Amparo Dora Palomino-Merino, Fernando Reyes-Cortes
The study of humanoid robots is still a challenge for the scientific community, although there are several related works in this area, several limitations have been found in the literature that drive the need to develop an inverse kinematic modeling of biped robots. This paper presents a research proposal for the Bioloid Premium robot. The objective is to propose a complete solution to the inverse kinematics model for a 18 DOF (Degrees Of Freedom) biped robot. This model will serve as a starting point to obtain the dynamic model of the robot in a subsequent work. The proposed methodology can be extended to other biped robots.
{"title":"Inverse Kinematics Model For a 18 Degrees of Freedom Robot","authors":"Miguel-Angel Ortega-Palacios, Amparo Dora Palomino-Merino, Fernando Reyes-Cortes","doi":"10.14313/t4yf9254","DOIUrl":"https://doi.org/10.14313/t4yf9254","url":null,"abstract":"The study of humanoid robots is still a challenge for the scientific community, although there are several related works in this area, several limitations have been found in the literature that drive the need to develop an inverse kinematic modeling of biped robots. This paper presents a research proposal for the Bioloid Premium robot. The objective is to propose a complete solution to the inverse kinematics model for a 18 DOF (Degrees Of Freedom) biped robot. This model will serve as a starting point to obtain the dynamic model of the robot in a subsequent work. The proposed methodology can be extended to other biped robots.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"5 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138949079","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}
Sentiment Analysis is a useful tool in several social and business contexts. Aspect Sentiment Classification is a subtask in Sentiment Analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different Deep Learning models have been proposed to solve Aspect Sentiment Classification focus on a specific domain such as restaurant,hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The Continual Learning approach with neural networks has been used to solve aspect classification in multiple domains. However, avoid low aspect classification performance in Continual Learning is challenging. As a consequence, potential neural networkweight shifts in the learning process in different domains or datasets.In this paper, a novel Aspect Sentiment Classification approach is proposed. Our approach combines a Transformer Deep Learning technique with a Continual Learning algorithm in different domains. The input layer used is the pre‐trained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 .
{"title":"A Model of Continual and Deep Learning for Aspect Based in Sentiment Analysis","authors":"Dionis López Ramos, Fernando J. Artigas Fuentes","doi":"10.14313/vs1zaw06","DOIUrl":"https://doi.org/10.14313/vs1zaw06","url":null,"abstract":"Sentiment Analysis is a useful tool in several social and business contexts. Aspect Sentiment Classification is a subtask in Sentiment Analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different Deep Learning models have been proposed to solve Aspect Sentiment Classification focus on a specific domain such as restaurant,hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The Continual Learning approach with neural networks has been used to solve aspect classification in multiple domains. However, avoid low aspect classification performance in Continual Learning is challenging. As a consequence, potential neural networkweight shifts in the learning process in different domains or datasets.In this paper, a novel Aspect Sentiment Classification approach is proposed. Our approach combines a Transformer Deep Learning technique with a Continual Learning algorithm in different domains. The input layer used is the pre‐trained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 .","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"136 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138953307","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 : 2023-12-01DOI: 10.14313/jamris-1-2023-8
Abir Nasry, Abderrahmane Ezzahout, F. Omary
Abstract As security is one of the basic human needs, we need security systems that can prevent crimes from happening. In general, surveillance videos are used to observe the environment and human behavior in a given location. However, surveillance videos can only be used to record images or videos, without additional information. Therefore, more advanced cameras are needed to obtain other additional information such as the position and movement of people. This research extracted this information from surveillance video footage using a person tracking, detection, and identification algorithm. The framework for these is based on deep learning algorithms, a popular branch of artificial intelligence. In the field of video surveillance, person tracking is considered a challenging task. Many computer vision, machine learning, and deep learning techniques have been developed in recent years. The majority of these techniques are based on frontal view images or video sequences. In this work, we will compare some previous work related to the same topic.
{"title":"People Tracking in Video Surveillance Systems Based on Artificial Intelligence","authors":"Abir Nasry, Abderrahmane Ezzahout, F. Omary","doi":"10.14313/jamris-1-2023-8","DOIUrl":"https://doi.org/10.14313/jamris-1-2023-8","url":null,"abstract":"Abstract As security is one of the basic human needs, we need security systems that can prevent crimes from happening. In general, surveillance videos are used to observe the environment and human behavior in a given location. However, surveillance videos can only be used to record images or videos, without additional information. Therefore, more advanced cameras are needed to obtain other additional information such as the position and movement of people. This research extracted this information from surveillance video footage using a person tracking, detection, and identification algorithm. The framework for these is based on deep learning algorithms, a popular branch of artificial intelligence. In the field of video surveillance, person tracking is considered a challenging task. Many computer vision, machine learning, and deep learning techniques have been developed in recent years. The majority of these techniques are based on frontal view images or video sequences. In this work, we will compare some previous work related to the same topic.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"13 1-2","pages":"59 - 68"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139191949","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}
Abstract This article presents a model based on machine learning for the selection of the characteristics that most influence the low industrial yield of cane sugar production in Cuba. The set of data used in this work corresponds to a period of ten years of sugar harvests from 2010 to 2019. A process of understanding the business and of understanding and preparing the data is carried out. The accuracy of six rule learning algorithms is evaluated: CONJUNCTIVERULE, DECISIONTABLE, RIDOR, FURIA, PART and JRIP. The results obtained allow us to identify: R417, R379, R378, R419a, R410, R613, R1427 and R380, as the indicators that most influence low industrial performance.
{"title":"Feature Selection for the Low Industrial Yield of Cane Sugar Production Based on Rule Learning Algorithms","authors":"Yohan Gil Rodríguez, Raisa Socorro Llanes, Alejandro Rosete, Lisandra Bravo Ilisástigui","doi":"10.14313/jamris-1-2023-2","DOIUrl":"https://doi.org/10.14313/jamris-1-2023-2","url":null,"abstract":"Abstract This article presents a model based on machine learning for the selection of the characteristics that most influence the low industrial yield of cane sugar production in Cuba. The set of data used in this work corresponds to a period of ten years of sugar harvests from 2010 to 2019. A process of understanding the business and of understanding and preparing the data is carried out. The accuracy of six rule learning algorithms is evaluated: CONJUNCTIVERULE, DECISIONTABLE, RIDOR, FURIA, PART and JRIP. The results obtained allow us to identify: R417, R379, R378, R419a, R410, R613, R1427 and R380, as the indicators that most influence low industrial performance.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"38 2","pages":"13 - 21"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139193179","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 : 2023-12-01DOI: 10.14313/jamris-1-2023-7
Swapnil Soner, R. Litoriya, Ravi Khatri, Ali Asgar Hussain, Shreyas Pagrey, Sunil Kumar Kushwaha
Abstract The Covid 19 (coronavirus) pandemic has become one of the most lethal health crises worldwide. This virus gets transmitted from a person by respiratory droplets when they sneeze or when they speak. According to leading and well-known scientists, wearing face masks and maintaining six feet of social distance are the most substantial protections to limit the virus’s spread. In the proposed model we have used the Convolutional Neural Network (CNN) algorithm of Deep Learning (DL) to ensure efficient real-time mask detection. We have divided the system into two parts—1. Train Face Mask Detector 2. Apply Face Mask Detector—for better understanding. This is a realtime application that is used to discover or detect the person who is wearing a mask at the proper position or not, with the help of camera detection. The system has achieved an accuracy of 99% after being trained with the dataset, which contains around 1376 images of width and height 224×224 and also gives the alarm beep message after the detection of no mask or improper mask usage in a public place.
{"title":"Real-Time Face Mask Detection in Mass Gatherings to Reduce Covid-19 Spread","authors":"Swapnil Soner, R. Litoriya, Ravi Khatri, Ali Asgar Hussain, Shreyas Pagrey, Sunil Kumar Kushwaha","doi":"10.14313/jamris-1-2023-7","DOIUrl":"https://doi.org/10.14313/jamris-1-2023-7","url":null,"abstract":"Abstract The Covid 19 (coronavirus) pandemic has become one of the most lethal health crises worldwide. This virus gets transmitted from a person by respiratory droplets when they sneeze or when they speak. According to leading and well-known scientists, wearing face masks and maintaining six feet of social distance are the most substantial protections to limit the virus’s spread. In the proposed model we have used the Convolutional Neural Network (CNN) algorithm of Deep Learning (DL) to ensure efficient real-time mask detection. We have divided the system into two parts—1. Train Face Mask Detector 2. Apply Face Mask Detector—for better understanding. This is a realtime application that is used to discover or detect the person who is wearing a mask at the proper position or not, with the help of camera detection. The system has achieved an accuracy of 99% after being trained with the dataset, which contains around 1376 images of width and height 224×224 and also gives the alarm beep message after the detection of no mask or improper mask usage in a public place.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"47 9","pages":"51 - 58"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139191999","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 : 2023-12-01DOI: 10.14313/jamris-1-2023-5
Marcin Kujawa, R. Piotrowski
Abstract The article presents an approach to data anonymization with the use of generally available tools. The focus is put on the practical aspects of using open-source tools in conjunction with programming libraries provided by suppliers of industrial control systems. This universal approach shows the possibilities of using various operating systems as a platform for process data anonymization. An additional advantage of the described approach is the ease of integration with various types of advanced data analysis tools based both on the out-of-the-box approach (e.g., business intelligence tools) as well as customized solutions. The discussed case describes the anonymization of data for the needs of sensitive analysis by a wider group of recipients during the construction of a predictive model used to support decisions.
{"title":"Automated Anonymization of Sensitive Data on Production Unit","authors":"Marcin Kujawa, R. Piotrowski","doi":"10.14313/jamris-1-2023-5","DOIUrl":"https://doi.org/10.14313/jamris-1-2023-5","url":null,"abstract":"Abstract The article presents an approach to data anonymization with the use of generally available tools. The focus is put on the practical aspects of using open-source tools in conjunction with programming libraries provided by suppliers of industrial control systems. This universal approach shows the possibilities of using various operating systems as a platform for process data anonymization. An additional advantage of the described approach is the ease of integration with various types of advanced data analysis tools based both on the out-of-the-box approach (e.g., business intelligence tools) as well as customized solutions. The discussed case describes the anonymization of data for the needs of sensitive analysis by a wider group of recipients during the construction of a predictive model used to support decisions.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"17 4","pages":"40 - 44"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139190487","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 : 2023-12-01DOI: 10.14313/jamris-1-2023-6
Sheetal Bawane, D. K. Panda
Abstract This paper presents the design of a parabolic reflector fed through a patch antenna array feed to enhance its directivity and radiation properties. Adaptive beam formers steer and alter an array’s beam pattern to increase signal reception and minimize interference. Weight selection is a critical difficulty in achieving low SLL and beam width. Low Side Lobe Level [SLL]and narrow beam reduce antenna radiation and reception. Adjusting the weights reduces SLL and tilts the nulls. Adaptive beam formers are successful signal processors if their array output converges to the required signal. Smart antenna weights can be determined using any window function. Half Power Beam Width and SLL could be used to explore different algorithms. Both must be low for excellent smart antenna performance. In noisy settings, ACLMS and CLMS create narrow beams and side lobes. AANGD offers more control than CLMS and ACLMS. The blend of CLMS and ACLMS is more effective at signal convergence than CLMS and AANGD. It presents an alternative to the conventionally used horn-based feed network for C-band applications such as satellite communication. Broadside radiation patterns and 4x4 circular patch antenna arrays are used in the proposed design. 1400 aperture illumination is provided by the array’s feed parabolic reflector, whose F/D ratio is 0.36. The proposed design’s efficacy is assessed using simulation analysis.
{"title":"Hybrid Adaptive Beamforming Approach for Antenna Array Fed Parabolic Reflector for C-Band Applications","authors":"Sheetal Bawane, D. K. Panda","doi":"10.14313/jamris-1-2023-6","DOIUrl":"https://doi.org/10.14313/jamris-1-2023-6","url":null,"abstract":"Abstract This paper presents the design of a parabolic reflector fed through a patch antenna array feed to enhance its directivity and radiation properties. Adaptive beam formers steer and alter an array’s beam pattern to increase signal reception and minimize interference. Weight selection is a critical difficulty in achieving low SLL and beam width. Low Side Lobe Level [SLL]and narrow beam reduce antenna radiation and reception. Adjusting the weights reduces SLL and tilts the nulls. Adaptive beam formers are successful signal processors if their array output converges to the required signal. Smart antenna weights can be determined using any window function. Half Power Beam Width and SLL could be used to explore different algorithms. Both must be low for excellent smart antenna performance. In noisy settings, ACLMS and CLMS create narrow beams and side lobes. AANGD offers more control than CLMS and ACLMS. The blend of CLMS and ACLMS is more effective at signal convergence than CLMS and AANGD. It presents an alternative to the conventionally used horn-based feed network for C-band applications such as satellite communication. Broadside radiation patterns and 4x4 circular patch antenna arrays are used in the proposed design. 1400 aperture illumination is provided by the array’s feed parabolic reflector, whose F/D ratio is 0.36. The proposed design’s efficacy is assessed using simulation analysis.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"39 5","pages":"45 - 50"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139192830","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}
Biomedicine is still working to solve the problem of detecting brain tumours, one of the biggest problems in the profession today. With improved technology or instrument, early diagnosis of brain cancers is feasible. Classifying brain tumour kinds using patent brain pictures enables automation in automated procedures. Furthermore, the suggested new method is utilised to tell the difference between brain tumours and other brain diseases. To split the tumour and other brain areas, the input picture is first pre-processed. After this, the pictures are divided into different colours and levels, and then they are run through the Gray Level Co-Occurrence and SURF extraction methods to uncover the important details in the photographs. Using genetic optimization, the retrieved characteristics are made smaller. For training and testing tumour classification, the cut-down characteristics are used using an advanced learning technique. The technique's accuracy, error, sensitivity, and specificity are all evaluated alongside the current method. The method has a 90%+ accuracy rate, with less than 2% inaccuracy for all kinds of cancers. Finally, the specificity and sensitivity of every kind are above 90% and 50% correspondingly. Using a genetic algorithm to support the approach is more efficient, since the method it uses has both higher accuracy and specificity than the other techniques.
{"title":"Automatic Detection of Brain Tumors Using Genetic Algorithms with Multiple Stages in Magnetic Resonance Images","authors":"Karthik Annam, Sunil Kumar G, Ashok Babu P, Narsaiah Domala","doi":"10.14313/jamris/4-2022/31","DOIUrl":"https://doi.org/10.14313/jamris/4-2022/31","url":null,"abstract":"Biomedicine is still working to solve the problem of detecting brain tumours, one of the biggest problems in the profession today. With improved technology or instrument, early diagnosis of brain cancers is feasible. Classifying brain tumour kinds using patent brain pictures enables automation in automated procedures. Furthermore, the suggested new method is utilised to tell the difference between brain tumours and other brain diseases. To split the tumour and other brain areas, the input picture is first pre-processed. After this, the pictures are divided into different colours and levels, and then they are run through the Gray Level Co-Occurrence and SURF extraction methods to uncover the important details in the photographs. Using genetic optimization, the retrieved characteristics are made smaller. For training and testing tumour classification, the cut-down characteristics are used using an advanced learning technique. The technique's accuracy, error, sensitivity, and specificity are all evaluated alongside the current method. The method has a 90%+ accuracy rate, with less than 2% inaccuracy for all kinds of cancers. Finally, the specificity and sensitivity of every kind are above 90% and 50% correspondingly. Using a genetic algorithm to support the approach is more efficient, since the method it uses has both higher accuracy and specificity than the other techniques.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135830061","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：