Pub Date : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10200801
Aruru Sai Kumar, K. N. Rao, A. Sujith, T. Dhanuja, M. Venkata, Sai Vinay
Static Random Access Memory (SRAM) is a critical component of digital circuits as it is used for high-speed data storage and retrieval. The 6T SRAM cell is a popular type of SRAM cell, which is widely used in various electronic devices such as microprocessors, DSP, and FPGA applications. In this paper, we present a detailed analysis of the 6T SRAM cell. We discuss the working principle of the 6T SRAM cell, its design considerations, and performance analysis.The primary objective of the study is to develop a memory array that consumes minimal power, has low leakage, and is compact in size. The array has a 1024-bit capacity, and read and write operations’ power requirements have been extensively investigated. The power consumption during read and write operations of proposed 1KB SRAM array structure is 50.46 µW and 410 µW, respectively. The paper highlights the importance of power dissipation in CMOS-based SRAM arrays and compares the performance attributes of the proposed array with those of previous works. The operation of a 45 nm 6T SRAM memory cell was validated using the Cadence Virtuoso tool.
{"title":"Design and Implementation of 1KB SRAM array in 45 nm Technology for Low-Power Applications","authors":"Aruru Sai Kumar, K. N. Rao, A. Sujith, T. Dhanuja, M. Venkata, Sai Vinay","doi":"10.1109/ACCESS57397.2023.10200801","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200801","url":null,"abstract":"Static Random Access Memory (SRAM) is a critical component of digital circuits as it is used for high-speed data storage and retrieval. The 6T SRAM cell is a popular type of SRAM cell, which is widely used in various electronic devices such as microprocessors, DSP, and FPGA applications. In this paper, we present a detailed analysis of the 6T SRAM cell. We discuss the working principle of the 6T SRAM cell, its design considerations, and performance analysis.The primary objective of the study is to develop a memory array that consumes minimal power, has low leakage, and is compact in size. The array has a 1024-bit capacity, and read and write operations’ power requirements have been extensively investigated. The power consumption during read and write operations of proposed 1KB SRAM array structure is 50.46 µW and 410 µW, respectively. The paper highlights the importance of power dissipation in CMOS-based SRAM arrays and compares the performance attributes of the proposed array with those of previous works. The operation of a 45 nm 6T SRAM memory cell was validated using the Cadence Virtuoso tool.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115229876","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10200241
S. Mahanty, Ajay Kumar, R. Choudhary, Amit Prakash, Ajay Yadav, Raj Ranjan
The design of an all-optical sequential circuit is one of the significant features of high-speed and fast-switching communication systems. Combinational and sequential logic circuits are both included in a real-world digital system. Thus, we cannot undervalue the significance of sequential logic circuits. Implementation of all-optical sequential circuits includes some great returns e.g. compact design, signal security, low electromagnetic interference, and larger bandwidth, etc. Here this paper displays the efficient application of micro-ring resonators to implement shift registers in the optical domain. The projected design is based on the Delay flip-flop which works on the principle of MRR switching activity. The necessary MATLAB simulated output of the suggested design is included in the study.
{"title":"New All Optical Shift Register using Nonlinear Structure","authors":"S. Mahanty, Ajay Kumar, R. Choudhary, Amit Prakash, Ajay Yadav, Raj Ranjan","doi":"10.1109/ACCESS57397.2023.10200241","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200241","url":null,"abstract":"The design of an all-optical sequential circuit is one of the significant features of high-speed and fast-switching communication systems. Combinational and sequential logic circuits are both included in a real-world digital system. Thus, we cannot undervalue the significance of sequential logic circuits. Implementation of all-optical sequential circuits includes some great returns e.g. compact design, signal security, low electromagnetic interference, and larger bandwidth, etc. Here this paper displays the efficient application of micro-ring resonators to implement shift registers in the optical domain. The projected design is based on the Delay flip-flop which works on the principle of MRR switching activity. The necessary MATLAB simulated output of the suggested design is included in the study.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126062369","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10200336
Pearlsy P V, D. Sankar
In the digitization of Malayalam handwritten documents, recognition of handwritten characters is a difficult task. This is due to the non availability of a labeled benchmark Malayalam handwritten character dataset. The state of the art technique using deep convolutional neural networks demands large amount of labeled dataset. Therefore, this paper aims to develop a pre-trained convolutional neural network (CNN) model for recognizing Malayalam handwritten characters using small sized dataset. Two approaches namely transfer learning and fine tuning of pre-trained Deep Convolutional Neural Network (DCNN) architecture ResNet50 are used to develop models for recognizing Malayalam handwritten characters. Model design is optimized by varying parameters like learning rate, batch size and optimization algorithm. From the experiments, it is found that highest testing accuracy of 78.05% is obtained for the model using fine tuning approach when it is trained with a batch size of 16 using RMSProp optimization algorithm and a learning rate of 0.000001. A testing accuracy of 78.05% is obtained with ResNet50 for binary images even though ResNet50 is pre-trained using colour images.
{"title":"Malayalam Handwritten Character Recognition using Transfer Learning and Fine Tuning of Deep Convolutional Neural Networks","authors":"Pearlsy P V, D. Sankar","doi":"10.1109/ACCESS57397.2023.10200336","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200336","url":null,"abstract":"In the digitization of Malayalam handwritten documents, recognition of handwritten characters is a difficult task. This is due to the non availability of a labeled benchmark Malayalam handwritten character dataset. The state of the art technique using deep convolutional neural networks demands large amount of labeled dataset. Therefore, this paper aims to develop a pre-trained convolutional neural network (CNN) model for recognizing Malayalam handwritten characters using small sized dataset. Two approaches namely transfer learning and fine tuning of pre-trained Deep Convolutional Neural Network (DCNN) architecture ResNet50 are used to develop models for recognizing Malayalam handwritten characters. Model design is optimized by varying parameters like learning rate, batch size and optimization algorithm. From the experiments, it is found that highest testing accuracy of 78.05% is obtained for the model using fine tuning approach when it is trained with a batch size of 16 using RMSProp optimization algorithm and a learning rate of 0.000001. A testing accuracy of 78.05% is obtained with ResNet50 for binary images even though ResNet50 is pre-trained using colour images.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114630379","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10200999
A. S, V. D, Shahul Hameed T A
Metamaterials which possess low refractive indices are used to enhance the directivity of the antenna or its gain. They are structured from artificially made recurring patterns or these are obtained from dielectric engravings in various layers that have sub-wavelength measurements. Here a new, concise, high gain, highly directive, metasurface-based antenna structure has been proposed that consists of a fractal-patterned patch consisting of a regular organization of square patches. The Metasurface layer is structured above the FR4 substrate by periodic unit cells wherein the unit cell is built using a couple of C-shape structured patches at the center being encircled by a pair of L-shape structured patches at the outer surface. The antenna’s gain is increased by inserting a metasurface which acts as a superstrate to the patch. A 1.6 mm broad FR4 substrate sheet separates the antenna’s base layer from its main radiating portion. The antenna’s radiation pattern is extremely directed and has exceptional impedance matching. At a working frequency of 10.44GHz, a gain of 8.33 dB has been achieved, which is a very high gain. The antenna structure that was created is extremely effective and can be utilized for things like satellite communication, maritime vessel traffic control, defense tracking, and is used in the hospital sector.
{"title":"Gain Enhanced X-Band Antenna using Novel Metasurface","authors":"A. S, V. D, Shahul Hameed T A","doi":"10.1109/ACCESS57397.2023.10200999","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200999","url":null,"abstract":"Metamaterials which possess low refractive indices are used to enhance the directivity of the antenna or its gain. They are structured from artificially made recurring patterns or these are obtained from dielectric engravings in various layers that have sub-wavelength measurements. Here a new, concise, high gain, highly directive, metasurface-based antenna structure has been proposed that consists of a fractal-patterned patch consisting of a regular organization of square patches. The Metasurface layer is structured above the FR4 substrate by periodic unit cells wherein the unit cell is built using a couple of C-shape structured patches at the center being encircled by a pair of L-shape structured patches at the outer surface. The antenna’s gain is increased by inserting a metasurface which acts as a superstrate to the patch. A 1.6 mm broad FR4 substrate sheet separates the antenna’s base layer from its main radiating portion. The antenna’s radiation pattern is extremely directed and has exceptional impedance matching. At a working frequency of 10.44GHz, a gain of 8.33 dB has been achieved, which is a very high gain. The antenna structure that was created is extremely effective and can be utilized for things like satellite communication, maritime vessel traffic control, defense tracking, and is used in the hospital sector.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126450925","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}
Many people now-a-days are facing amputation in their early 20’s or 30’s, mainly because of lack of awareness regarding quality measures to be used while mining process in coal mines or in various industries. Different types of amputees has to tackle different day-to-day problems. Majority of amputees lies under people who have lost their arms or legs or sometimes both during military conflicts. There are various types of prosthesis available in market across globe for physically challenged people who have lost their arm or legs, to give them a support system for uplifting their spirit.
{"title":"A Review On Current Technological Advancements In Prosthetic Arms","authors":"Devika Ghadage, Rutu Bagde, Swati Jha, Mohini Dhadi, Chaitali Barhate","doi":"10.1109/ACCESS57397.2023.10200952","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200952","url":null,"abstract":"Many people now-a-days are facing amputation in their early 20’s or 30’s, mainly because of lack of awareness regarding quality measures to be used while mining process in coal mines or in various industries. Different types of amputees has to tackle different day-to-day problems. Majority of amputees lies under people who have lost their arms or legs or sometimes both during military conflicts. There are various types of prosthesis available in market across globe for physically challenged people who have lost their arm or legs, to give them a support system for uplifting their spirit.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132832681","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10200912
S. Guruprasad, Rio D’Souza G. L.
Evolutionary-based algorithms emerged due to their flexibility and effectiveness in solving different varieties of problems. Optimization-based techniques are used in finding solutions that involve multiple conflicting objectives. Parallel evolutionary-based algorithms are used to overcome the time-consuming job of finding solutions to these types of problems. In this paper, we present a parallel genetic programming-based model that runs parallelly and obtains solutions in a minimal amount of time. The model also allows the user to select the best set of objectives based on the requirements of the users. An island model is used which runs the operations on different islands parallelly. This not only decreases the execution time of the process but also increases the diversity of the population. The results obtained in different islands are fed to an ensemble classifier to get the required result. The model was trained and tested using the state-of-the-art ISCX-2012 and CICIDS2017 datasets. In our work, we have mainly focused on detecting the attacks in a system in a short duration of time. The model developed gave significant performance improvement compared to the results obtained using the normal CPU implementation.
{"title":"Parallel Model to Detect Attacks Using Evolutionary Based Technique","authors":"S. Guruprasad, Rio D’Souza G. L.","doi":"10.1109/ACCESS57397.2023.10200912","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200912","url":null,"abstract":"Evolutionary-based algorithms emerged due to their flexibility and effectiveness in solving different varieties of problems. Optimization-based techniques are used in finding solutions that involve multiple conflicting objectives. Parallel evolutionary-based algorithms are used to overcome the time-consuming job of finding solutions to these types of problems. In this paper, we present a parallel genetic programming-based model that runs parallelly and obtains solutions in a minimal amount of time. The model also allows the user to select the best set of objectives based on the requirements of the users. An island model is used which runs the operations on different islands parallelly. This not only decreases the execution time of the process but also increases the diversity of the population. The results obtained in different islands are fed to an ensemble classifier to get the required result. The model was trained and tested using the state-of-the-art ISCX-2012 and CICIDS2017 datasets. In our work, we have mainly focused on detecting the attacks in a system in a short duration of time. The model developed gave significant performance improvement compared to the results obtained using the normal CPU implementation.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124270869","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10200493
Nishant Santosh Paradkar
In today’s world, all of us are dependent on emails. Emails are a very efficient and fast way of sending a message to someone. But malicious users often use it to send fraudulent emails with fake links that steal user credentials like credit card details, login-id, passwords, etc. These emails are called phishing emails. These emails constitute identity fraud as the emails are interpreted to be from banks or other multinational companies. Many existing solutions require the user to check for grammar errors, check the email-id, or avoid clicking any links. But all these actions require human involvement. In this paper, I have implemented and compared current Machine Learning and Deep Learning techniques used with Natural Language Processing to detect phishing emails and achieved an accuracy of 98%.
{"title":"Phishing Email’s Detection Using Machine Learning and Deep Learning","authors":"Nishant Santosh Paradkar","doi":"10.1109/ACCESS57397.2023.10200493","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200493","url":null,"abstract":"In today’s world, all of us are dependent on emails. Emails are a very efficient and fast way of sending a message to someone. But malicious users often use it to send fraudulent emails with fake links that steal user credentials like credit card details, login-id, passwords, etc. These emails are called phishing emails. These emails constitute identity fraud as the emails are interpreted to be from banks or other multinational companies. Many existing solutions require the user to check for grammar errors, check the email-id, or avoid clicking any links. But all these actions require human involvement. In this paper, I have implemented and compared current Machine Learning and Deep Learning techniques used with Natural Language Processing to detect phishing emails and achieved an accuracy of 98%.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117276812","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10200245
Arya Paul, Sona Paul, Manikandan A. R, Katharin P Jose, Sabarinath M.S
Nowadays the surveillance systems are widely used to find out the suspicious events that have occurred. In conventional systems, there are a lot of limitations such as storage, bandwidth, cost, the short lifespan of hardware devices, loading issues, etc. We developed an intelligent surveillance system using deep learning in which the video footage of suspicious events is extracted. Transfer learning, a part of machine learning, is used for face detection which involves the reuse of a pre-trained model on new data. The abnormal activity detection is done using a multi person MoveNet Light model and the face detection is done using VGG16. The suspicious objects found in the frame (gun, mask) are identified using corner detection. This system offers less bandwidth, high security, effective storage, and reduced load-balancing issues. In this paper, we detailed the face detection, object detection and anomaly detection used in our system.
{"title":"Integrated Intelligent Surveillance System Using Deep Learning","authors":"Arya Paul, Sona Paul, Manikandan A. R, Katharin P Jose, Sabarinath M.S","doi":"10.1109/ACCESS57397.2023.10200245","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200245","url":null,"abstract":"Nowadays the surveillance systems are widely used to find out the suspicious events that have occurred. In conventional systems, there are a lot of limitations such as storage, bandwidth, cost, the short lifespan of hardware devices, loading issues, etc. We developed an intelligent surveillance system using deep learning in which the video footage of suspicious events is extracted. Transfer learning, a part of machine learning, is used for face detection which involves the reuse of a pre-trained model on new data. The abnormal activity detection is done using a multi person MoveNet Light model and the face detection is done using VGG16. The suspicious objects found in the frame (gun, mask) are identified using corner detection. This system offers less bandwidth, high security, effective storage, and reduced load-balancing issues. In this paper, we detailed the face detection, object detection and anomaly detection used in our system.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130147939","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10199549
Rishabh Sharma, V. Kukreja, Prince Sood, Abhishek Bhattacharjee
Apple diseases cause significant economic losses to the fruit industry every year. Accurate and timely diagnosis of apple diseases is crucial to prevent the disease’s spread and ensure the production of healthy crops. This study presents a novel hybrid model, combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, for multi-class classification of apple diseases. The model was trained and evaluated on a dataset of images of apple leaves exhibiting different severity degrees of black rot disease. The results of the experiments showed that the hybrid model outperformed traditional single-model approaches, achieving an accuracy of 99.02% in the initial severity degree classification of the disease. This demonstrates the potential of combining CNNs and LSTMs to achieve high accuracy in complex image classification tasks, particularly in the field of plant disease diagnosis. The proposed model provides a valuable tool for apple farmers, researchers, and extension workers in the early detection and management of apple diseases.
{"title":"Classifying the Severity of Apple Black Rot Disease with Deep Learning: A Dual CNN and LSTM Approach","authors":"Rishabh Sharma, V. Kukreja, Prince Sood, Abhishek Bhattacharjee","doi":"10.1109/ACCESS57397.2023.10199549","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10199549","url":null,"abstract":"Apple diseases cause significant economic losses to the fruit industry every year. Accurate and timely diagnosis of apple diseases is crucial to prevent the disease’s spread and ensure the production of healthy crops. This study presents a novel hybrid model, combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, for multi-class classification of apple diseases. The model was trained and evaluated on a dataset of images of apple leaves exhibiting different severity degrees of black rot disease. The results of the experiments showed that the hybrid model outperformed traditional single-model approaches, achieving an accuracy of 99.02% in the initial severity degree classification of the disease. This demonstrates the potential of combining CNNs and LSTMs to achieve high accuracy in complex image classification tasks, particularly in the field of plant disease diagnosis. The proposed model provides a valuable tool for apple farmers, researchers, and extension workers in the early detection and management of apple diseases.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"64 7-8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131044710","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10199278
Soni Singh, S. Mittal, Sunaina Singh
The global community is now seriously threatened by the COVID-19 pandemic. The government of every nation must pay close attention to the analysis of this disease to take the required actions to lessen the impact of this worldwide epidemic. This research focused on the disease outbreak in the Indian region through July 21st, 2021, and evaluated the incidence and mortality. Machine learning techniques, such as the ARIMA model, are applied to perform the prediction analysis on collected data from the World Health Organization (WHO) official portal for India between January 20, 2020, and July 21, 2021. Mean Square Error (MSE), a measure of model performance, was used to assess performance, and it came in between 2170.636098 and 46.839689. In the four weeks of test data, the Expected instances are estimated to be between 192K and 230K, which is fairly similar to the actual figures. The government and physicians will be able to make future strategies with the aid of this study.
{"title":"Analysis and Forecasting of COVID-19 Pandemic Using ARIMA Model","authors":"Soni Singh, S. Mittal, Sunaina Singh","doi":"10.1109/ACCESS57397.2023.10199278","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10199278","url":null,"abstract":"The global community is now seriously threatened by the COVID-19 pandemic. The government of every nation must pay close attention to the analysis of this disease to take the required actions to lessen the impact of this worldwide epidemic. This research focused on the disease outbreak in the Indian region through July 21st, 2021, and evaluated the incidence and mortality. Machine learning techniques, such as the ARIMA model, are applied to perform the prediction analysis on collected data from the World Health Organization (WHO) official portal for India between January 20, 2020, and July 21, 2021. Mean Square Error (MSE), a measure of model performance, was used to assess performance, and it came in between 2170.636098 and 46.839689. In the four weeks of test data, the Expected instances are estimated to be between 192K and 230K, which is fairly similar to the actual figures. The government and physicians will be able to make future strategies with the aid of this study.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130764024","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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