Pub Date : 2022-05-26DOI: 10.1109/TEECCON54414.2022.9854840
Sanjay Kumar G, Leela Grace U, Ramu Srikakulapu, Poojasree M, Latha V, Charan Kumar G
This paper provides different challenges for Electric Vehicle parking (EVP) in parking lots (PL) along with different parking architectures for proper placing of Electric vehicle (EV) in the PL that would result in Optimised charging conditions for EV in the PL along with the considerations of PL Space optimization, Time Optimization with the help of angled parking slot to adjust the parking slots to certain angle for fast parking and unparking of vehicles, Driver Preferences regarding location of slot in the parking lot that could result in drivers satisfaction keeping Initial cost concerns for power electronic circuitry in mind. This study also includes engineering extensions for existing PL designs of Conventional fuel Vehicles (CFV) that could make the parking lot feasible for EV charging in the parking lots. In this study different designs are proposed like spiral parking lots which could enable vehicles to park on different flowers, also include the case study of using Pythagorean theorem for creating few more spaces in the parking lot, mobile EV charger also proposed to establish temporary PLs in demand prone areas.
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Pub Date : 2022-05-26DOI: 10.1109/TEECCON54414.2022.9854821
Prarthana Dutta, Naresh Babu Muppalaneni
The growing trends and demands for Artificial Intelligence in various domains due to their excellent performance and generalization ability are known to all. These decisions affect the population in general as they usually deal with sensitive tasks in various fields such as healthcare, education, transportation, etc. Hence, understanding these learned representations would add more descriptive knowledge to better interpret the decisions with the ground truth. The European General Data Protection Regulation reserves the right to receive an explanation against a model producing an automated decision. Understanding the decisions would validate the model behavior, ensure trust, and deal with the risk associated with the model. Upon analyzing the relevant features, we can decide whether the model predictions could be trusted or not in the future. We can further try to reduce the misclassification rate by rectifying the features (of the misclassified instances) if needed. In this way, we can peek into the black-box and gain insight into a model’s prediction, thus understanding the learned representations. In pursuit of this objective, a common approach would be to devise an explanatory model that would explain the predictions made by a model and further analyze those predictions with the ground truth information. We initiated a case study on a diabetes risk prediction dataset by understanding local predictions made by five different Machine Learning models and trying to provide explanations for the misclassified instances.
{"title":"Explaining Machine Learning Predictions: A Case Study","authors":"Prarthana Dutta, Naresh Babu Muppalaneni","doi":"10.1109/TEECCON54414.2022.9854821","DOIUrl":"https://doi.org/10.1109/TEECCON54414.2022.9854821","url":null,"abstract":"The growing trends and demands for Artificial Intelligence in various domains due to their excellent performance and generalization ability are known to all. These decisions affect the population in general as they usually deal with sensitive tasks in various fields such as healthcare, education, transportation, etc. Hence, understanding these learned representations would add more descriptive knowledge to better interpret the decisions with the ground truth. The European General Data Protection Regulation reserves the right to receive an explanation against a model producing an automated decision. Understanding the decisions would validate the model behavior, ensure trust, and deal with the risk associated with the model. Upon analyzing the relevant features, we can decide whether the model predictions could be trusted or not in the future. We can further try to reduce the misclassification rate by rectifying the features (of the misclassified instances) if needed. In this way, we can peek into the black-box and gain insight into a model’s prediction, thus understanding the learned representations. In pursuit of this objective, a common approach would be to devise an explanatory model that would explain the predictions made by a model and further analyze those predictions with the ground truth information. We initiated a case study on a diabetes risk prediction dataset by understanding local predictions made by five different Machine Learning models and trying to provide explanations for the misclassified instances.","PeriodicalId":251455,"journal":{"name":"2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON)","volume":"11 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114043617","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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