Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776836
Srinjoy Ganguly, Sai Nandan Morapakula, Luis Miguel Pozo Coronado
Sentiment classification is one of the best use cases of classical natural language processing (NLP). We witness its power in various domains such as banking, business, and the marketing industry. We already know how classical AI and machine learning can change and improve technology. Quantum natural language processing (QNLP) is a young and gradually emerging technology that can provide a quantum advantage for NLP tasks. In this paper, we show the first application of QNLP for sentiment analysis and achieve perfect test set accuracy for three different kinds of simulations and decent accuracy for experiments run on a noisy quantum device. We utilize the lambeq QNLP toolkit and t|ket > by Cambridge Quantum (Quantinuum) to produce the results.
情感分类是经典自然语言处理(NLP)的最佳用例之一。我们在银行、商业和营销行业等各个领域见证了它的力量。我们已经知道经典的人工智能和机器学习如何改变和改进技术。量子自然语言处理(Quantum natural language processing, QNLP)是一项新兴的技术，可以为自然语言处理任务提供量子优势。在本文中，我们展示了QNLP在情感分析中的首次应用，并在三种不同类型的模拟中实现了完美的测试集准确性，并在噪声量子设备上运行的实验中实现了不错的准确性。我们利用lambeq QNLP工具包和剑桥量子(Quantum)的t|ket >来产生结果。
{"title":"Quantum Natural Language Processing Based Sentiment Analysis Using Lambeq Toolkit","authors":"Srinjoy Ganguly, Sai Nandan Morapakula, Luis Miguel Pozo Coronado","doi":"10.1109/ICPC2T53885.2022.9776836","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776836","url":null,"abstract":"Sentiment classification is one of the best use cases of classical natural language processing (NLP). We witness its power in various domains such as banking, business, and the marketing industry. We already know how classical AI and machine learning can change and improve technology. Quantum natural language processing (QNLP) is a young and gradually emerging technology that can provide a quantum advantage for NLP tasks. In this paper, we show the first application of QNLP for sentiment analysis and achieve perfect test set accuracy for three different kinds of simulations and decent accuracy for experiments run on a noisy quantum device. We utilize the lambeq QNLP toolkit and t|ket > by Cambridge Quantum (Quantinuum) to produce the results.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132102807","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 : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9777008
Alimurtaza Merchant, Naveen Shenoy, Abhinav Bharali, M. A. Kumar
The Open University (OU), one of the largest public research universities, provides a wide range of data from its distance learning courses. Hence, the Open University Learning Analytics Dataset (OULAD) allows predicting student academic performance in online learning programs. The dataset consists of demographic features such as gender, disability, education level, and behavioural features, which depict engagement levels of students in courses. This paper predicts student academic performance in online learning programs using machine learning and statistical values. We train multi-class classifiers on the preprocessed dataset after feature selection and removing noisy data. Decision Tree, Random Forest, Gradient Boosting and KNN classifiers are trained on both demographic data alone and including virtual learning environment (VLE) data with it. Each classifier shows greater accuracy with the VLE data included. All classifiers achieve accuracies above 92%, with gradient boosting achieving the maximum accuracy of 97.5%.
{"title":"Predicting Students' Academic Performance in Virtual Learning Environment Using Machine Learning","authors":"Alimurtaza Merchant, Naveen Shenoy, Abhinav Bharali, M. A. Kumar","doi":"10.1109/ICPC2T53885.2022.9777008","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9777008","url":null,"abstract":"The Open University (OU), one of the largest public research universities, provides a wide range of data from its distance learning courses. Hence, the Open University Learning Analytics Dataset (OULAD) allows predicting student academic performance in online learning programs. The dataset consists of demographic features such as gender, disability, education level, and behavioural features, which depict engagement levels of students in courses. This paper predicts student academic performance in online learning programs using machine learning and statistical values. We train multi-class classifiers on the preprocessed dataset after feature selection and removing noisy data. Decision Tree, Random Forest, Gradient Boosting and KNN classifiers are trained on both demographic data alone and including virtual learning environment (VLE) data with it. Each classifier shows greater accuracy with the VLE data included. All classifiers achieve accuracies above 92%, with gradient boosting achieving the maximum accuracy of 97.5%.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124330465","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 : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9777076
P. Krishna, V. Meena, Vinay Singh
Load frequency control (LFC) in an interconnected power system is advancing towards a crucial path due to the modern penetration of highly uncertain renewable energy sources in power systems. The modern LFC systems should be capable of handling complex regulation problems with a high degree of renewable source diversification to assure the generation-load balance. In this contribution, a rule-based fuzzy PI controller is implemented in a four-area interconnected power system with penetration of renewable energy sources like wind energy, hydro-gen aqua electrolyzer - fuel cell (HAE-FC), photo-voltaic (PV) system, and geothermal energy sources separately in each area along with a conventional energy source. The frequency response of four-area interconnected power system is obtained employing a rule-based fuzzy PI controller for load disturbances and is compared with the conventional PI controller. The simulation results demonstrate the superiority of the fuzzy PI controller over the conventional PI controller.
{"title":"Load Frequency Control in Four-area Interconnected Power System Using Fuzzy PI Controller With Penetration of Renewable Energies","authors":"P. Krishna, V. Meena, Vinay Singh","doi":"10.1109/ICPC2T53885.2022.9777076","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9777076","url":null,"abstract":"Load frequency control (LFC) in an interconnected power system is advancing towards a crucial path due to the modern penetration of highly uncertain renewable energy sources in power systems. The modern LFC systems should be capable of handling complex regulation problems with a high degree of renewable source diversification to assure the generation-load balance. In this contribution, a rule-based fuzzy PI controller is implemented in a four-area interconnected power system with penetration of renewable energy sources like wind energy, hydro-gen aqua electrolyzer - fuel cell (HAE-FC), photo-voltaic (PV) system, and geothermal energy sources separately in each area along with a conventional energy source. The frequency response of four-area interconnected power system is obtained employing a rule-based fuzzy PI controller for load disturbances and is compared with the conventional PI controller. The simulation results demonstrate the superiority of the fuzzy PI controller over the conventional PI controller.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133809887","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 : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776845
Archika Malhotra, Aditi Singh
The Very Large Scale Integration (VLSI) industry has started adapting the Artificial Intelligence (AI) techniques in design automation as it provides the opportunity to transform the whole chip design methodology. It has been seen that in System-On-Chip (SoC), in order to add ML algorithms to increase its efficiency, there is a need to reduce the existing power consumption of the hardware as well. Hence, this makes AI an integral part of the VLSI industry. With this in mind, an extensive review has been conducted on various aspects of AI in the field of VLSI. This paper throws light on how AI has marked its way on various subfields of VLSI, namely, analog, digital and physical design. We have also taken into account the recent machine learning and deep learning techniques incorporated in VLSI.
{"title":"Implementation of AI in the field of VLSI: A Review","authors":"Archika Malhotra, Aditi Singh","doi":"10.1109/ICPC2T53885.2022.9776845","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776845","url":null,"abstract":"The Very Large Scale Integration (VLSI) industry has started adapting the Artificial Intelligence (AI) techniques in design automation as it provides the opportunity to transform the whole chip design methodology. It has been seen that in System-On-Chip (SoC), in order to add ML algorithms to increase its efficiency, there is a need to reduce the existing power consumption of the hardware as well. Hence, this makes AI an integral part of the VLSI industry. With this in mind, an extensive review has been conducted on various aspects of AI in the field of VLSI. This paper throws light on how AI has marked its way on various subfields of VLSI, namely, analog, digital and physical design. We have also taken into account the recent machine learning and deep learning techniques incorporated in VLSI.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122566640","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 : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9777020
D. Saxena, Ashutosh Kumar Singh
This paper proposes a Virtual Machine (VM) failure prediction based intelligent cloud resource management (FP-IRM) model that estimates failure of VMs proactively and assorts all the available resources effectively. Specifically, a novel ensemble predictor is developed to determine any resource (CPU, storage) congestion prior to occurrence in real-time. Accordingly, the VM migration process is triggered proactively to proficiently manage the VM failures by reason of insufficient physical resources. FP-IRM model is implemented and evaluated by using a real-world benchmark Google Cluster VM traces dataset. The experimental simulation and comparison with state-of-the-arts confirms the influential performance of the proposed model which has reduced the number of active servers up to 51.2 % and an improved resource utilization up to 24.3 % over the comparative approaches.
{"title":"VM Failure Prediction based Intelligent Resource Management Model for Cloud Environments","authors":"D. Saxena, Ashutosh Kumar Singh","doi":"10.1109/ICPC2T53885.2022.9777020","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9777020","url":null,"abstract":"This paper proposes a Virtual Machine (VM) failure prediction based intelligent cloud resource management (FP-IRM) model that estimates failure of VMs proactively and assorts all the available resources effectively. Specifically, a novel ensemble predictor is developed to determine any resource (CPU, storage) congestion prior to occurrence in real-time. Accordingly, the VM migration process is triggered proactively to proficiently manage the VM failures by reason of insufficient physical resources. FP-IRM model is implemented and evaluated by using a real-world benchmark Google Cluster VM traces dataset. The experimental simulation and comparison with state-of-the-arts confirms the influential performance of the proposed model which has reduced the number of active servers up to 51.2 % and an improved resource utilization up to 24.3 % over the comparative approaches.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123877557","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 : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9777078
P. Sujatha, R.S. Dhivya
The main goal of this research is to analyze the employee performance associated with organization's growth. The research starts with collecting the employee information and then a deep exploratory data analysis has been conducted on the collected data to identify the significance contribution towards the organization. Finally, ensemble learning framework designed to identify the potential of employees to the organization. To the best of our knowledge, this is the first work which consists of all the aspects of employee performance has been included in the dataset. Also, we have used the real time employee dataset from reputed MNC Company from Chennai. The results show that the proposed Extreme gradient boosting ensemble learning algorithm gives better results.
{"title":"Ensemble Learning Framework to Predict the Employee Performance","authors":"P. Sujatha, R.S. Dhivya","doi":"10.1109/ICPC2T53885.2022.9777078","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9777078","url":null,"abstract":"The main goal of this research is to analyze the employee performance associated with organization's growth. The research starts with collecting the employee information and then a deep exploratory data analysis has been conducted on the collected data to identify the significance contribution towards the organization. Finally, ensemble learning framework designed to identify the potential of employees to the organization. To the best of our knowledge, this is the first work which consists of all the aspects of employee performance has been included in the dataset. Also, we have used the real time employee dataset from reputed MNC Company from Chennai. The results show that the proposed Extreme gradient boosting ensemble learning algorithm gives better results.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124113379","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 : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776871
P. Salodkar, Shraddha Meshram, Shouvik Dey
Sustainable electrical systems emphasize energy conservation and environmental protection. One of the ways to achieve this is by using an efficient fuel cell system. Designing a fuel cell should start with a set of objectives. In this literature, a review of fuel cells has been done from a broad perspective. One of the objectives to design a fuel cell lies with the basic working mechanism and voltage-current characteristics. It is followed by understanding the architectural configurations to prepare a stack system. In order to develop a hybrid model, various control strategies must be adopted. Crisp modeling is also required to simulate the system in an artificial environment, which aids in improving the operating parameters of the model. Basic modeling parameters are reviewed in this paper. This can further be elaborated on to complex systems. Few major applications in predominant sectors are also briefed.
{"title":"Review of Hydrogen Fuel Cell as an emergent field in Green Technology","authors":"P. Salodkar, Shraddha Meshram, Shouvik Dey","doi":"10.1109/ICPC2T53885.2022.9776871","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776871","url":null,"abstract":"Sustainable electrical systems emphasize energy conservation and environmental protection. One of the ways to achieve this is by using an efficient fuel cell system. Designing a fuel cell should start with a set of objectives. In this literature, a review of fuel cells has been done from a broad perspective. One of the objectives to design a fuel cell lies with the basic working mechanism and voltage-current characteristics. It is followed by understanding the architectural configurations to prepare a stack system. In order to develop a hybrid model, various control strategies must be adopted. Crisp modeling is also required to simulate the system in an artificial environment, which aids in improving the operating parameters of the model. Basic modeling parameters are reviewed in this paper. This can further be elaborated on to complex systems. Few major applications in predominant sectors are also briefed.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128470829","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 : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776966
Sweta Gupta, K. Gupta, P. Shukla, M. Shrivas
Voting is a well-proven method of democratizing human societies and achieving consensus to avoid any conflicts. The unbiased trusted third parties are involved to conduct and administer the voting. However, in the past, various incidents of misconduct and biases were reported in the voting process. The blockchain eliminates the need for any trusted third party by using consensus algorithms and smart contracts, hence is the best fit to conduct voting as blockchain provides tamperproof, transparent, secure, and auditable voting solutions. Blockchain uses public-private key cryptography to ensure end-to-end encryption and uses hash functions and hashes to tamperproof voting ledgers. Quantum computing is going to be a major threat to the blockchain as current public-private key cryptography and hash functions are being used in almost all blockchains and Distributed Ledger Technologies (DLTs) belonging to the Pre-quantum cryptography era. This research article critically reviews the advancement and current state of blockchain-based online voting systems, their features, and challenges along with advancements in quantum computing, post-quantum cryptography. This research article proposes a system design of a voting system on the blockchain using post-quantum cryptography along with systematic and critical views and conclusions towards quantum-resistant blockchain for a future online voting system in the post-quantum cryptography era.
{"title":"Blockchain-based Voting System Powered by Post-Quantum Cryptography (BBVSP-PQC)","authors":"Sweta Gupta, K. Gupta, P. Shukla, M. Shrivas","doi":"10.1109/ICPC2T53885.2022.9776966","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776966","url":null,"abstract":"Voting is a well-proven method of democratizing human societies and achieving consensus to avoid any conflicts. The unbiased trusted third parties are involved to conduct and administer the voting. However, in the past, various incidents of misconduct and biases were reported in the voting process. The blockchain eliminates the need for any trusted third party by using consensus algorithms and smart contracts, hence is the best fit to conduct voting as blockchain provides tamperproof, transparent, secure, and auditable voting solutions. Blockchain uses public-private key cryptography to ensure end-to-end encryption and uses hash functions and hashes to tamperproof voting ledgers. Quantum computing is going to be a major threat to the blockchain as current public-private key cryptography and hash functions are being used in almost all blockchains and Distributed Ledger Technologies (DLTs) belonging to the Pre-quantum cryptography era. This research article critically reviews the advancement and current state of blockchain-based online voting systems, their features, and challenges along with advancements in quantum computing, post-quantum cryptography. This research article proposes a system design of a voting system on the blockchain using post-quantum cryptography along with systematic and critical views and conclusions towards quantum-resistant blockchain for a future online voting system in the post-quantum cryptography era.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116131532","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 : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9777007
Jennalyn N. Mindoro, M. A. Malbog, Marte D. Nipas, Julie Ann B. Susa, Aimee G. Acoba, Joshua S. Gulmatico
The use of sentiment analysis of the customers' insight on the product delivery services would be an excellent opportunity to evaluate the customers' emotions in the courier delivery services. The result can link with the review to substitute for the product's performance or customer satisfaction. The primary steps in the study are data collection, data pre-processing, and sentiment analysis. The emotional data were analyzed using the VADER algorithm. A chatbot was used as an intermediary tool to collect emotional datasets. The compound score was calculated by adding the valence scores of each word in the lexicon, then adjusting them according to the guidelines and normalizing them. The negative, neutral, and positive scores represent the customer's sentiment. The test results achieved 93.33% sentiment accuracy based on the computed sentiment polarities and compound scores. The output evaluates that the system is effective based on the outcome and can be considered an innovative approach in analyzing the customers' perception of the product and services available in the market.
{"title":"Sentiment Analysis in Customer Experience in Philippine Courier Delivery Services using VADER Algorithm Thru Chatbot Interviews","authors":"Jennalyn N. Mindoro, M. A. Malbog, Marte D. Nipas, Julie Ann B. Susa, Aimee G. Acoba, Joshua S. Gulmatico","doi":"10.1109/ICPC2T53885.2022.9777007","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9777007","url":null,"abstract":"The use of sentiment analysis of the customers' insight on the product delivery services would be an excellent opportunity to evaluate the customers' emotions in the courier delivery services. The result can link with the review to substitute for the product's performance or customer satisfaction. The primary steps in the study are data collection, data pre-processing, and sentiment analysis. The emotional data were analyzed using the VADER algorithm. A chatbot was used as an intermediary tool to collect emotional datasets. The compound score was calculated by adding the valence scores of each word in the lexicon, then adjusting them according to the guidelines and normalizing them. The negative, neutral, and positive scores represent the customer's sentiment. The test results achieved 93.33% sentiment accuracy based on the computed sentiment polarities and compound scores. The output evaluates that the system is effective based on the outcome and can be considered an innovative approach in analyzing the customers' perception of the product and services available in the market.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"39 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114093172","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 : 2022-03-01DOI: 10.1109/icpc2t53885.2022.9776739
{"title":"Second International Conference on Power, Control and Computing Technologies (ICPC2T-2022)","authors":"","doi":"10.1109/icpc2t53885.2022.9776739","DOIUrl":"https://doi.org/10.1109/icpc2t53885.2022.9776739","url":null,"abstract":"","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114854803","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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