Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088416
Saswati Sahoo, Sushruta Mishra
Nowadays, the number of brain tumor cases among people is increasing globally across the world due to several reasons such as obesity, overweight, excess levels of stress in life, exposure to ionizing radiation, and many more. In previous years, many investigators have provided a range of solutions and effective tools for the identification and categorization of brain tumors. Nevertheless, the existing developed models for brain tumor identification and categorization have diverse limitations such as minimal accuracy and precision values. In this paper, the authors developed a novel model for the comparative analysis of the Progressive Growing-Generative Adversarial Network (PGGAN) with other data augmentation techniques for brain tumor classification. Because of the availability of finite datasets, the brain tumor classification algorithm along with the convolutional neural networks (CNNs) must be enhanced to be more competent for brain tumor classification and identification in real-time diagnosis. The outcome of the proposed model demonstrates that PGGAN delivers higher accuracy, as well as precision, and the Recall with the F1 score is 99.22%, 98.11%, 98.66%, and 97.45%, respectively. In the future, the developed model performance could be measured with other data augmentation techniques for larger datasets for performance constraints computations for further study and implementation of the model for real-time diagnosis of the patients.
{"title":"A Comparative Analysis of PGGAN with Other Data Augmentation Technique for Brain Tumor Classification","authors":"Saswati Sahoo, Sushruta Mishra","doi":"10.1109/ASSIC55218.2022.10088416","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088416","url":null,"abstract":"Nowadays, the number of brain tumor cases among people is increasing globally across the world due to several reasons such as obesity, overweight, excess levels of stress in life, exposure to ionizing radiation, and many more. In previous years, many investigators have provided a range of solutions and effective tools for the identification and categorization of brain tumors. Nevertheless, the existing developed models for brain tumor identification and categorization have diverse limitations such as minimal accuracy and precision values. In this paper, the authors developed a novel model for the comparative analysis of the Progressive Growing-Generative Adversarial Network (PGGAN) with other data augmentation techniques for brain tumor classification. Because of the availability of finite datasets, the brain tumor classification algorithm along with the convolutional neural networks (CNNs) must be enhanced to be more competent for brain tumor classification and identification in real-time diagnosis. The outcome of the proposed model demonstrates that PGGAN delivers higher accuracy, as well as precision, and the Recall with the F1 score is 99.22%, 98.11%, 98.66%, and 97.45%, respectively. In the future, the developed model performance could be measured with other data augmentation techniques for larger datasets for performance constraints computations for further study and implementation of the model for real-time diagnosis of the patients.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"38 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122122355","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088328
D. D. Priya, A. Kiran, P. Purushotham
Internet of Things devices collect and share data (IoT). Internet connections and emerging technologies like IoT offer privacy and security challenges, and this trend is anticipated to develop quickly. Internet of Things intrusions are everywhere. Businesses are investing more to detect these threats. Institutes choose accurate testing and verification procedures. In recent years, IoT utilisation has increasingly risen in healthcare. Where IoT applications gained popular among technologists. IoT devices' energy limits and scalability raise privacy and security problems. Experts struggle to make IoT devices more safe and private. This paper provides a machine-learning-based IDS for IoT network threats (ML-IDS). This study aims to implement ML-supervised IDS for IoT. We're going with a centralised, lightweight IDS. Here, we compare seven popular categorization techniques on three data sets. The decision tree algorithm shows the best intrusion detection results.
{"title":"Lightweight Intrusion Detection System(L-IDS) for the Internet of Things","authors":"D. D. Priya, A. Kiran, P. Purushotham","doi":"10.1109/ASSIC55218.2022.10088328","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088328","url":null,"abstract":"Internet of Things devices collect and share data (IoT). Internet connections and emerging technologies like IoT offer privacy and security challenges, and this trend is anticipated to develop quickly. Internet of Things intrusions are everywhere. Businesses are investing more to detect these threats. Institutes choose accurate testing and verification procedures. In recent years, IoT utilisation has increasingly risen in healthcare. Where IoT applications gained popular among technologists. IoT devices' energy limits and scalability raise privacy and security problems. Experts struggle to make IoT devices more safe and private. This paper provides a machine-learning-based IDS for IoT network threats (ML-IDS). This study aims to implement ML-supervised IDS for IoT. We're going with a centralised, lightweight IDS. Here, we compare seven popular categorization techniques on three data sets. The decision tree algorithm shows the best intrusion detection results.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115786627","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088387
Nrushingh Charan Mahapatra, Prachet Bhuyan
The basic objective of the study is to establish the reinforcement learning technique in the decoding of imagined speech neural signals. The purpose of imagined speech neural computational studies is to give people who are unable to communicate due to physical or neurological limitations of speech generation alternative natural communication pathways. The advanced human-computer interface based on imagined speech decoding based on measurable neural activity could enable natural interactions and significantly improve quality of life, especially for people with few communication alternatives. Recent advances in signal processing and reinforcement learning based on deep learning algorithms have enabled high-quality imagined speech decoding from noninvasively recorded neural activity. Most of the prior research focused on the supervised classification of collected signals, with no naturalistic feedback-based training of imagined speech models for brain-computer interfaces. We employ deep reinforcement learning in this study to create an imagined speech decoder artificial agent based on the deep Q-network (DQN), so that the artificial agent could indeed learn effective policies directly from multidimensional neural electroencephalography (EEG) signal inputs adopting end-to-end reinforcement learning. We show that the artificial agent, supplied only with neural signals and rewards as inputs, was able to decode the imagined speech neural signals efficiently with 81.6947% overall accuracy.
{"title":"Decoding of Imagined Speech Neural EEG Signals Using Deep Reinforcement Learning Technique","authors":"Nrushingh Charan Mahapatra, Prachet Bhuyan","doi":"10.1109/ASSIC55218.2022.10088387","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088387","url":null,"abstract":"The basic objective of the study is to establish the reinforcement learning technique in the decoding of imagined speech neural signals. The purpose of imagined speech neural computational studies is to give people who are unable to communicate due to physical or neurological limitations of speech generation alternative natural communication pathways. The advanced human-computer interface based on imagined speech decoding based on measurable neural activity could enable natural interactions and significantly improve quality of life, especially for people with few communication alternatives. Recent advances in signal processing and reinforcement learning based on deep learning algorithms have enabled high-quality imagined speech decoding from noninvasively recorded neural activity. Most of the prior research focused on the supervised classification of collected signals, with no naturalistic feedback-based training of imagined speech models for brain-computer interfaces. We employ deep reinforcement learning in this study to create an imagined speech decoder artificial agent based on the deep Q-network (DQN), so that the artificial agent could indeed learn effective policies directly from multidimensional neural electroencephalography (EEG) signal inputs adopting end-to-end reinforcement learning. We show that the artificial agent, supplied only with neural signals and rewards as inputs, was able to decode the imagined speech neural signals efficiently with 81.6947% overall accuracy.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121824391","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088392
Rupsa Rani Sahu, A. Raut, S. Samantaray
In the era of Artificial Intelligence the old paradigm of oral healthcare has got augmented with automation. Combining thinking abilities of human mind with the cutting edge technology of machine learning can aid the clinicians meet the growing needs and ensure cordial patient-doctor partnership. Advanced software and computing tools are being used to identify problem areas with lesser reporting time and appropriate clinical decision support system to track clinical outcomes. The perceptive abilities of machine learning is directly proportional to information obtained from patients, images, material applications and treatments done. The specialized algorithms are able to predict unexpected complications likely to be encountered and under-diagnosis of rare pathologies that otherwise might be missed due to limitations of clinicians expertise in that area. Today it is essential to embrace machine learning programmes to evolve age old working practices for greater performance and better outcomes by bridging the existing gap between diagnosis and treatment planning. The paper discusses and acknowledges the performance and futuristic applications of machine learning in various subareas of oral health and research.
{"title":"Technological Empowerment: Applications of Machine Learning in Oral Healthcare","authors":"Rupsa Rani Sahu, A. Raut, S. Samantaray","doi":"10.1109/ASSIC55218.2022.10088392","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088392","url":null,"abstract":"In the era of Artificial Intelligence the old paradigm of oral healthcare has got augmented with automation. Combining thinking abilities of human mind with the cutting edge technology of machine learning can aid the clinicians meet the growing needs and ensure cordial patient-doctor partnership. Advanced software and computing tools are being used to identify problem areas with lesser reporting time and appropriate clinical decision support system to track clinical outcomes. The perceptive abilities of machine learning is directly proportional to information obtained from patients, images, material applications and treatments done. The specialized algorithms are able to predict unexpected complications likely to be encountered and under-diagnosis of rare pathologies that otherwise might be missed due to limitations of clinicians expertise in that area. Today it is essential to embrace machine learning programmes to evolve age old working practices for greater performance and better outcomes by bridging the existing gap between diagnosis and treatment planning. The paper discusses and acknowledges the performance and futuristic applications of machine learning in various subareas of oral health and research.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"149 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113999005","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088358
D. K. Mohanty, Poulami Das Gupta, Raya Dey, Sharanya Pattnaik
Fashion classification is a domain which finds its applications in various fields like e-commerce platforms, social media and criminal identification with clothing similarity or dissimilarity. In this paper, we have used a modified version of convolutional neural network for classification and encompassing the identification of clothing items. Within the fashion classification category, we mainly concentrate on the multi-class classification of different types of apparels. The modified convolution neural network is applied on fashion classification data which reduces over-fitting. Here we have compared the accuracy of the CNN models and have achieved train accuracy and test accuracy of around 93% and 90% respectively which are better than previous works done by other researchers.
{"title":"Modified Convolutional Neural Network for Fashion Classification","authors":"D. K. Mohanty, Poulami Das Gupta, Raya Dey, Sharanya Pattnaik","doi":"10.1109/ASSIC55218.2022.10088358","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088358","url":null,"abstract":"Fashion classification is a domain which finds its applications in various fields like e-commerce platforms, social media and criminal identification with clothing similarity or dissimilarity. In this paper, we have used a modified version of convolutional neural network for classification and encompassing the identification of clothing items. Within the fashion classification category, we mainly concentrate on the multi-class classification of different types of apparels. The modified convolution neural network is applied on fashion classification data which reduces over-fitting. Here we have compared the accuracy of the CNN models and have achieved train accuracy and test accuracy of around 93% and 90% respectively which are better than previous works done by other researchers.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114286266","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088369
G.karthik Reddy, G. Kaushik, Rajan Singh, Raju Naik, B. Ravi, Bingi Sainath
Globally, atmospheric carbon dioxide (CO2) concentration is rising due to rising carbon-based fuel consumption and ongoing deforestation. As carbon dioxide levels grow due to the warming trend, the atmosphere's temperature is predicted to climb. Increased fatigue, headaches, and tinnitus are just a few health issues that high CO2 concentrations in the atmosphere can cause. The electrical activities of the brain, the heart, and the lungs have all been demonstrated to change significantly after a brief exposure to 0.1 percent CO2. Continuous measurements of the atmospheric CO2 content have recently been shown to help evaluate the ventilation conditions in buildings or rooms. Additionally, it prevents the development of the severe acute respiratory syndrome coronavirus 2 (Severe acute respiratory). The coronavirus, known as a powerful acute respiratory, can make people ill. This has grown to be a significant concern in emergency medicine.
{"title":"Smart Device for CO2 Measuring and Supplementing with O2 Using IOT","authors":"G.karthik Reddy, G. Kaushik, Rajan Singh, Raju Naik, B. Ravi, Bingi Sainath","doi":"10.1109/ASSIC55218.2022.10088369","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088369","url":null,"abstract":"Globally, atmospheric carbon dioxide (CO2) concentration is rising due to rising carbon-based fuel consumption and ongoing deforestation. As carbon dioxide levels grow due to the warming trend, the atmosphere's temperature is predicted to climb. Increased fatigue, headaches, and tinnitus are just a few health issues that high CO2 concentrations in the atmosphere can cause. The electrical activities of the brain, the heart, and the lungs have all been demonstrated to change significantly after a brief exposure to 0.1 percent CO2. Continuous measurements of the atmospheric CO2 content have recently been shown to help evaluate the ventilation conditions in buildings or rooms. Additionally, it prevents the development of the severe acute respiratory syndrome coronavirus 2 (Severe acute respiratory). The coronavirus, known as a powerful acute respiratory, can make people ill. This has grown to be a significant concern in emergency medicine.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123744203","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088391
Deepak Sharma, Anurag Saxena, Z. Ali, Neeraj Yadav
In wireless power transfer, does not require the flow of electrons in any material like conductor. In this process electrical energy is originated from the transmission line. It uses a wearable antenna like textile or cloths. The most important applications of the wearable antenna are Wi-Fi or WLAN. on simulating the design the S11 result of textile antenna gives one resonant frequency at 5.24 GHz. The thickness of the textile material is 1mm with dielectric constant 1.7. The transfer of electrical energy wirelessly is too difficult But with the help of receiver antenna makes it possible. Circuit with the feedback upon the frequency of the input voltage are also known as filters. Band pass filter is used in this because it passes the particular frequency range. After that bridge rectifier is utilized which converts radio frequency signal (AC) into DC signal.
{"title":"Rectification of Electrical Energy Using Band Pass Filter","authors":"Deepak Sharma, Anurag Saxena, Z. Ali, Neeraj Yadav","doi":"10.1109/ASSIC55218.2022.10088391","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088391","url":null,"abstract":"In wireless power transfer, does not require the flow of electrons in any material like conductor. In this process electrical energy is originated from the transmission line. It uses a wearable antenna like textile or cloths. The most important applications of the wearable antenna are Wi-Fi or WLAN. on simulating the design the S11 result of textile antenna gives one resonant frequency at 5.24 GHz. The thickness of the textile material is 1mm with dielectric constant 1.7. The transfer of electrical energy wirelessly is too difficult But with the help of receiver antenna makes it possible. Circuit with the feedback upon the frequency of the input voltage are also known as filters. Band pass filter is used in this because it passes the particular frequency range. After that bridge rectifier is utilized which converts radio frequency signal (AC) into DC signal.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125027409","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088412
Uday Bhanu Ghosh, Rohan Sharma
This work proposes to channelize the conventional longitudinal behavior of sound which diverges in every direction after its generation to a beam like behavior seen in transverse waves, which is already conceived in a device named as parametric speaker. On top of this, using technologies such as AI, self-learning, and decision-making capabilities can be endorsed in the hardware-based device, exhausting its full capabilities. The goal of the proposed work is to provide an invisible screening and privacy to the individuals in public and semi-public areas associated with verbal mode of communication. The underlying work focuses on using the unconventional behavior of sound, possessing beam like properties produced through Artificial means by interference and Superposition principle, applicable to sound which is constructed by Parametric Speaker also known as directional Speaker. The following work in this paper attempts to showcase the possibilities of using and harnessing this specific sound properties with sophisticated technologies such as ML and Deep Learning to create semi private areas in public spaces which can not only help people within different age groups (mostly elderly population) but can also help people who are handicapped (such as visually impared people who can only receive information through auditory channels), the scope of the proposed work is not only limited to impared people but, also to general population, with the objective to increase the conventional information transfer system into personalized data delivery service through voice and speech.
{"title":"Machine Intelligence Enabled Parametric Speaker: An Invention Towards Experience Sound","authors":"Uday Bhanu Ghosh, Rohan Sharma","doi":"10.1109/ASSIC55218.2022.10088412","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088412","url":null,"abstract":"This work proposes to channelize the conventional longitudinal behavior of sound which diverges in every direction after its generation to a beam like behavior seen in transverse waves, which is already conceived in a device named as parametric speaker. On top of this, using technologies such as AI, self-learning, and decision-making capabilities can be endorsed in the hardware-based device, exhausting its full capabilities. The goal of the proposed work is to provide an invisible screening and privacy to the individuals in public and semi-public areas associated with verbal mode of communication. The underlying work focuses on using the unconventional behavior of sound, possessing beam like properties produced through Artificial means by interference and Superposition principle, applicable to sound which is constructed by Parametric Speaker also known as directional Speaker. The following work in this paper attempts to showcase the possibilities of using and harnessing this specific sound properties with sophisticated technologies such as ML and Deep Learning to create semi private areas in public spaces which can not only help people within different age groups (mostly elderly population) but can also help people who are handicapped (such as visually impared people who can only receive information through auditory channels), the scope of the proposed work is not only limited to impared people but, also to general population, with the objective to increase the conventional information transfer system into personalized data delivery service through voice and speech.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129659952","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}
The human species has advanced to the point where the twenty-first century marks the start of previously unimaginable achievements. By observing at a person using a camera, picture, or video, the aforementioned technologies can be utilized to establish their age and gender. Our project will walk you through the entire process, including the many approaches and algorithms that can be used, which one is the most accurate, and how everything works together. It will also highlight its importance and how it may be implemented to better our daily life. The major purpose of this project is to develop a detector gender and age that can determine a person's gender and age based on their performance Keeping track of the projected numbers andcarrying out the calculations Analyzing stored data aids in determining model accuracy.
{"title":"Gender and Age Prediction Using Deep Learning","authors":"Gvr Priyanka, Kalie Nishi Latha, Punukollu Surya Prakash, Katamaneni Madhavi","doi":"10.1109/ASSIC55218.2022.10088411","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088411","url":null,"abstract":"The human species has advanced to the point where the twenty-first century marks the start of previously unimaginable achievements. By observing at a person using a camera, picture, or video, the aforementioned technologies can be utilized to establish their age and gender. Our project will walk you through the entire process, including the many approaches and algorithms that can be used, which one is the most accurate, and how everything works together. It will also highlight its importance and how it may be implemented to better our daily life. The major purpose of this project is to develop a detector gender and age that can determine a person's gender and age based on their performance Keeping track of the projected numbers andcarrying out the calculations Analyzing stored data aids in determining model accuracy.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131001012","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}
Nowadays microwave communities are working on the design and development of the dual band, triple band & wide band antennas with partial ground. The customers prefer compact devices for WLAN, Bluetooth, PCS, and WiMAX applications, which are frequently used in tablets, medical instruments, smart phones, portable laptops and handheld electronic gadgets. The proposed antenna is consisting of a partial ground and slotted circular patch with line feed. The Triple bandwidth of proposed antenna is 45.77%, 69.20% and 12.69% suitable for triple band application.
{"title":"Semi Circle Slotted Triangular Shape Antenna Using Flexible Material","authors":"Deepak Sharma, Vinod Kumar Singh, Anupam Vyas, Neetendra Kumar, Rajesh Kumar Dwivedi","doi":"10.1109/ASSIC55218.2022.10088293","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088293","url":null,"abstract":"Nowadays microwave communities are working on the design and development of the dual band, triple band & wide band antennas with partial ground. The customers prefer compact devices for WLAN, Bluetooth, PCS, and WiMAX applications, which are frequently used in tablets, medical instruments, smart phones, portable laptops and handheld electronic gadgets. The proposed antenna is consisting of a partial ground and slotted circular patch with line feed. The Triple bandwidth of proposed antenna is 45.77%, 69.20% and 12.69% suitable for triple band application.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129136068","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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