Depression is a prevalent illness in todays society. It changes and influences our entire method of thought and our emotional, cognitive, and everyday behavioral behaviors. It affected over 264 million people, and the proportion increases every day. Mainly when it lasts for a prolonged time, it becomes a severe issue or health topic. It leads the trustworthy person to also malfunction, and that person commits suicide in his final position. There are several causes for depression, though social networking like Facebook, Twitter, and other networking plays a critical role in getting us more depressed. Most people in Asia use Facebook, Twitter, and various chat applications, and there they express their emotions. That is why our research initiative picks social media. Some work has been done on depression but depression detection on the Bengali community is done very rarely. So it has become a strong demand for today. The social media has intialted a study based on depression, tweets, and numerous chat app responses, and gathered Bengali data and projected depression posts and commentaries. Diverse approaches of machine learning have been used to evaluate these data and forecast depression and for algorithm purpose Support vector machine, Random Forest, Decision Tree, K-Nearest Neighbors, Naive Bayes (Multinomial Naive Bayes), Logistic Regression has been used. The desired results can be obtained by adding those algorithms. Moreover, different algorithms send us different results as trends were common, but ultimately the precision was the same for all algorithms applied to our dataset.
{"title":"Machine Learning Techniques for Depression Analysis on Social Media- Case Study on Bengali Community","authors":"Debasish Bhattacharjee Victor, Jamil Kawsher, Md Shad Labib, Subhenur Latif","doi":"10.1109/ICECA49313.2020.9297436","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297436","url":null,"abstract":"Depression is a prevalent illness in todays society. It changes and influences our entire method of thought and our emotional, cognitive, and everyday behavioral behaviors. It affected over 264 million people, and the proportion increases every day. Mainly when it lasts for a prolonged time, it becomes a severe issue or health topic. It leads the trustworthy person to also malfunction, and that person commits suicide in his final position. There are several causes for depression, though social networking like Facebook, Twitter, and other networking plays a critical role in getting us more depressed. Most people in Asia use Facebook, Twitter, and various chat applications, and there they express their emotions. That is why our research initiative picks social media. Some work has been done on depression but depression detection on the Bengali community is done very rarely. So it has become a strong demand for today. The social media has intialted a study based on depression, tweets, and numerous chat app responses, and gathered Bengali data and projected depression posts and commentaries. Diverse approaches of machine learning have been used to evaluate these data and forecast depression and for algorithm purpose Support vector machine, Random Forest, Decision Tree, K-Nearest Neighbors, Naive Bayes (Multinomial Naive Bayes), Logistic Regression has been used. The desired results can be obtained by adding those algorithms. Moreover, different algorithms send us different results as trends were common, but ultimately the precision was the same for all algorithms applied to our dataset.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128863839","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297478
K. Malathi, R. Kavitha, M. Liza
Normally, the encryption and decryption is done only to convert the text into an encrypted form (i.e.) the confused form of text. In this type of method a hacker may easily hack the text using the public key or private key. So in this paper a new technique called Text to image encryption has been proposed. This will convert the plain text or information into an image format. That image will hide the encrypted text. If the user wants to view the text, first the image is divided into blocks. Each color component will be modified using the secret key. It will be difficult to the hackers to hack the information. This method can be used for large set of databases.
{"title":"Pixel based method for Text to Image Encryption","authors":"K. Malathi, R. Kavitha, M. Liza","doi":"10.1109/ICECA49313.2020.9297478","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297478","url":null,"abstract":"Normally, the encryption and decryption is done only to convert the text into an encrypted form (i.e.) the confused form of text. In this type of method a hacker may easily hack the text using the public key or private key. So in this paper a new technique called Text to image encryption has been proposed. This will convert the plain text or information into an image format. That image will hide the encrypted text. If the user wants to view the text, first the image is divided into blocks. Each color component will be modified using the secret key. It will be difficult to the hackers to hack the information. This method can be used for large set of databases.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128911339","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297581
M. Rahman, Md Saif Kabir, Md Nazaf Rabbi, Mohammad Hashib Sarker, Ishmam Ahmed Chowdhury, Golam Sarowar
DC-DC and AC-DC converters are often used to obtain the craved voltage level. However, the conventional converters are not suitable for high output voltages without depreciating various parameters like conversion efficiency. In this paper, a new Cascaded Buck-Boost Zeta (BBZ) converter topology is proposed. Also, a closed-loop is implemented to improve THD and power factor. This converter’s DC-DC topology can deliver the output voltage as high as 773V along with high conversion efficiency at an 80% duty cycle. The AC-DC topology gives a maximum efficiency of 98.29%. The efficiency levels of both the topology are also relatively high at different duty cycles.
{"title":"Design and Analysis of Cascaded Buck-Boost Zeta (BBZ) Converter for Improved Efficiency at High Output Voltage","authors":"M. Rahman, Md Saif Kabir, Md Nazaf Rabbi, Mohammad Hashib Sarker, Ishmam Ahmed Chowdhury, Golam Sarowar","doi":"10.1109/ICECA49313.2020.9297581","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297581","url":null,"abstract":"DC-DC and AC-DC converters are often used to obtain the craved voltage level. However, the conventional converters are not suitable for high output voltages without depreciating various parameters like conversion efficiency. In this paper, a new Cascaded Buck-Boost Zeta (BBZ) converter topology is proposed. Also, a closed-loop is implemented to improve THD and power factor. This converter’s DC-DC topology can deliver the output voltage as high as 773V along with high conversion efficiency at an 80% duty cycle. The AC-DC topology gives a maximum efficiency of 98.29%. The efficiency levels of both the topology are also relatively high at different duty cycles.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127645527","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297532
Y. Gupta, Tanusha Mittal
Big data analytics is the one which acquire, organise and analyse the huge volume of data with high velocity to find some patterns and useful information. The data sets are so large that it can’t be handled by traditional databases to manage and process the structure and unstructured data. Hence, big data tools i.e. Hadoop, is required due to its high scalability, availability and cluster environment mechanism for analysing large volume of data. MapReduce is one of the important components of Hadoop which is able to handle the unstructured data. But to use MapReduce, high programming skills are needed. Therefore, due to the reason of programming, users are moving towards some other tools i.e. Apache Pig or Apache Cassandra. In these tools, the data is simply analysed by executing the queries or commands. This paper will discuss about the architectural of Apache Pig and Apache Cassandra and afterwards both the technologies regarding some factors are compared to find out which one is better.
{"title":"Comparative Study of Apache Pig & Apache Cassandra in Hadoop Distributed Environment","authors":"Y. Gupta, Tanusha Mittal","doi":"10.1109/ICECA49313.2020.9297532","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297532","url":null,"abstract":"Big data analytics is the one which acquire, organise and analyse the huge volume of data with high velocity to find some patterns and useful information. The data sets are so large that it can’t be handled by traditional databases to manage and process the structure and unstructured data. Hence, big data tools i.e. Hadoop, is required due to its high scalability, availability and cluster environment mechanism for analysing large volume of data. MapReduce is one of the important components of Hadoop which is able to handle the unstructured data. But to use MapReduce, high programming skills are needed. Therefore, due to the reason of programming, users are moving towards some other tools i.e. Apache Pig or Apache Cassandra. In these tools, the data is simply analysed by executing the queries or commands. This paper will discuss about the architectural of Apache Pig and Apache Cassandra and afterwards both the technologies regarding some factors are compared to find out which one is better.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121628167","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297533
V. Dutt, Satya Murthy Sasubilli, Anand Eswararao Yerrapati
A chatbot is a computer program designed to simulate a conversation with human users, especially over the Internet. Ultimately, the chatbot acts like a virtual assistant or interactive agent in a conversations interface to respond to user queries or messages via communication channel like mobile apps, messenger apps or browser-based applications. Chatbots have become more popular nowadays and most of the companies are implementing them wherever they can to reduce the operation cost. In many cases, human resources are utilized to respond to user queries, where the chatbot can do the same job by searching the data in the system so that the human talent can be used for other advanced tasks. As the advancement in the technology, chatbots are also evaluated in a better way such that they can do some other tasks beyond just answering the textual questions. This paper provides a chatbot based solution for users or candidates, who are searching for a job to apply in a company. This solution makes the job searching and applying process easy, where the user can apply for a job in a few taps without visiting the company website or their mobile app.
{"title":"Dynamic Information Retrieval With Chatbots: A Review of Artificial Intelligence Methodology","authors":"V. Dutt, Satya Murthy Sasubilli, Anand Eswararao Yerrapati","doi":"10.1109/ICECA49313.2020.9297533","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297533","url":null,"abstract":"A chatbot is a computer program designed to simulate a conversation with human users, especially over the Internet. Ultimately, the chatbot acts like a virtual assistant or interactive agent in a conversations interface to respond to user queries or messages via communication channel like mobile apps, messenger apps or browser-based applications. Chatbots have become more popular nowadays and most of the companies are implementing them wherever they can to reduce the operation cost. In many cases, human resources are utilized to respond to user queries, where the chatbot can do the same job by searching the data in the system so that the human talent can be used for other advanced tasks. As the advancement in the technology, chatbots are also evaluated in a better way such that they can do some other tasks beyond just answering the textual questions. This paper provides a chatbot based solution for users or candidates, who are searching for a job to apply in a company. This solution makes the job searching and applying process easy, where the user can apply for a job in a few taps without visiting the company website or their mobile app.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115847352","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297598
S. Sahunthala, A. Geetha, L. Parthiban
Nowadays, XML database growth plays a vital role in many real time applications. XML database contains a collection of XML dataset. More analytical functions are applied to XML database by using Xquery. In real world, huge businesses are exchanging the data as XML data model. In general, space and time parameters are considered for Xquery processing in the database. In existing, the analytical operation is analyzed in eXist-DB and BaseX databases with the execution time of ORBDA dataset. In existing system, the prediction analysis operation is not supposed in the dataset. In this paper, Xquery is processed by using Riak database. Riak database produces better execution time than eXist-DB and BaseX. This research has analyzed the prediction operation for ORBDA dataset using machine learning approach. This paper uses various regression techniques to analyze the prediction operation. Machine learning approaches produce better accuracy in prediction. The query processing time is reduced than the existing approach. This research uses ORBDA dataset in demonstration.
{"title":"Analysing Computational Complexity For Prediction Function In Health Record Dataset","authors":"S. Sahunthala, A. Geetha, L. Parthiban","doi":"10.1109/ICECA49313.2020.9297598","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297598","url":null,"abstract":"Nowadays, XML database growth plays a vital role in many real time applications. XML database contains a collection of XML dataset. More analytical functions are applied to XML database by using Xquery. In real world, huge businesses are exchanging the data as XML data model. In general, space and time parameters are considered for Xquery processing in the database. In existing, the analytical operation is analyzed in eXist-DB and BaseX databases with the execution time of ORBDA dataset. In existing system, the prediction analysis operation is not supposed in the dataset. In this paper, Xquery is processed by using Riak database. Riak database produces better execution time than eXist-DB and BaseX. This research has analyzed the prediction operation for ORBDA dataset using machine learning approach. This paper uses various regression techniques to analyze the prediction operation. Machine learning approaches produce better accuracy in prediction. The query processing time is reduced than the existing approach. This research uses ORBDA dataset in demonstration.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121045891","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 role of Electrical Energy Storage (EES) is becoming increasingly important in the proportion of distributed generators continue to increase in the power system. With the deepening of China’s electricity market reform, for promoting investors to construct more EES, it is necessary to study the profit model of it. Therefore, this article analyzes three common profit models that are identified when EES participates in peak-valley arbitrage, peak-shaving, and demand response. On this basis, take an actual energy storage power station as an example to analyze its profitability by current regulations. Results show that the benefit of EES is quite considerable.
{"title":"Analysis and Comparison for The Profit Model of Energy Storage Power Station","authors":"Xuyang Zhang, Fengming Zhang, Chao Chen, Yingtao Sun, Q. Ai, Minyu Chen","doi":"10.1109/ICECA49313.2020.9297527","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297527","url":null,"abstract":"The role of Electrical Energy Storage (EES) is becoming increasingly important in the proportion of distributed generators continue to increase in the power system. With the deepening of China’s electricity market reform, for promoting investors to construct more EES, it is necessary to study the profit model of it. Therefore, this article analyzes three common profit models that are identified when EES participates in peak-valley arbitrage, peak-shaving, and demand response. On this basis, take an actual energy storage power station as an example to analyze its profitability by current regulations. Results show that the benefit of EES is quite considerable.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116415574","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297483
Ram Kumar Madupu, Chiranjeevi Kothapalli, Vasanthi Yarra, S. Harika, C. Z. Basha
Emotion recognition using facial expression is very much necessary these days. Different kinds of emotions reflect a different definitions. Facial emotion recognition plays a major role in driver warning systems, it can also play an important role in shopping malls to predict unusual activity like terrorist attacks, robbery and much more. Predicting the suicidal tendency of a person also can be done using facial emotion recognition. An automatic facial emotion classification system is proposed in this paper using the Convolution Neural Network (CNN) with the features extracted from the Speeded Up Robust Features (SURF). 91% accuracy is achieved with the proposed model which supports tracking human emotion with facial expressions.
{"title":"Automatic Human Emotion Recognition System using Facial Expressions with Convolution Neural Network","authors":"Ram Kumar Madupu, Chiranjeevi Kothapalli, Vasanthi Yarra, S. Harika, C. Z. Basha","doi":"10.1109/ICECA49313.2020.9297483","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297483","url":null,"abstract":"Emotion recognition using facial expression is very much necessary these days. Different kinds of emotions reflect a different definitions. Facial emotion recognition plays a major role in driver warning systems, it can also play an important role in shopping malls to predict unusual activity like terrorist attacks, robbery and much more. Predicting the suicidal tendency of a person also can be done using facial emotion recognition. An automatic facial emotion classification system is proposed in this paper using the Convolution Neural Network (CNN) with the features extracted from the Speeded Up Robust Features (SURF). 91% accuracy is achieved with the proposed model which supports tracking human emotion with facial expressions.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"682 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126688186","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297645
Arushi Singh, M. Jayakumar
With the evolving communication systems, the need for beamforming to improve the gain of the transmitting or receiving antenna has also increased. Beamforming allows to direct the radiated energy with the intended choice of direction efficiently. The main focus of this work is to develop an effective method for beamforming at the receiver side antennas for deploying Line-of-Sight (LOS) communication in Satellite Communication (Satcom) by using machine learning algorithms to detect signals as accurately as possible and to reduce the time taken to steer the beam as well as complexity of operations if a standard beamforming algorithm was used. To implement this, the antenna array weights are pre-calculated for a number of beam directions and kept as a database which are given to a linear regression machine learning model. The signal weights that are calculated for each array element by using their progressive measured phase difference is due to the arriving signal, that are given as input to a linear regression model and the direction of arrival (DOA) of the signal is predicted. The curve fitted linear regression model can be implemented in real-time geostationary satellite communication systems to accurately intercept the signal of interest.
{"title":"Machine Learning based Digital Beamforming for Line-of-Sight optimization in Satcom on the Move Technology","authors":"Arushi Singh, M. Jayakumar","doi":"10.1109/ICECA49313.2020.9297645","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297645","url":null,"abstract":"With the evolving communication systems, the need for beamforming to improve the gain of the transmitting or receiving antenna has also increased. Beamforming allows to direct the radiated energy with the intended choice of direction efficiently. The main focus of this work is to develop an effective method for beamforming at the receiver side antennas for deploying Line-of-Sight (LOS) communication in Satellite Communication (Satcom) by using machine learning algorithms to detect signals as accurately as possible and to reduce the time taken to steer the beam as well as complexity of operations if a standard beamforming algorithm was used. To implement this, the antenna array weights are pre-calculated for a number of beam directions and kept as a database which are given to a linear regression machine learning model. The signal weights that are calculated for each array element by using their progressive measured phase difference is due to the arriving signal, that are given as input to a linear regression model and the direction of arrival (DOA) of the signal is predicted. The curve fitted linear regression model can be implemented in real-time geostationary satellite communication systems to accurately intercept the signal of interest.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127066255","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297543
P. Patel, D. K. Meda
In this proposed work, an improvised analysis in the parameter of a conventional Microstrip Patch Antenna for 5G is reviwed. In this design of the antenna is modified for the better gain and return loss with the best possible result using simulation software (HFSS-19.2). The performance of the antenna has been measured and compared to analyze in terms of gain, the return loss, radiation pattern and bandwidth at 28 GHz operating frequency.
{"title":"An Improvised Analysis in the Parameter of a Conventional Microstrip Patch Antenna for 5G Communication","authors":"P. Patel, D. K. Meda","doi":"10.1109/ICECA49313.2020.9297543","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297543","url":null,"abstract":"In this proposed work, an improvised analysis in the parameter of a conventional Microstrip Patch Antenna for 5G is reviwed. In this design of the antenna is modified for the better gain and return loss with the best possible result using simulation software (HFSS-19.2). The performance of the antenna has been measured and compared to analyze in terms of gain, the return loss, radiation pattern and bandwidth at 28 GHz operating frequency.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124018349","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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