Machine Learning Aspects and its Applications Towards Different Research Areas

Kumar, Yogesh; Kaur, Komalpreet; Singh, Gurpreet

doi:10.1109/iccakm46823.2020.9051502

Cited by 59 publications

(12 citation statements)

References 20 publications

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“…Fig. 11 shows that the system developed in a prior study [4] and our system have 98% and 98.13% recognition accuracies, respectively. The system from the other study uses a CNN for the recognition of the license plate data, but the major drawback is that it is unable to work with more than one vehicle type; it also cannot be applied to images captured at night.…”

Section: Different Vehicle Typesmentioning

confidence: 91%

“…DL is mainly used for decision-making purposes but can include many other applications. This type of learning is highly beneficial in the fields of image analysis, speech and facial recognition, translation of languages, and emotion recognition [4]. These two learning techniques have been useful for traffic law enforcement and ITS, as these are the driving forces for designing an efficient ALPR system.…”

Section: Introductionmentioning

confidence: 99%

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Automatic License Plate Recognition System for Vehicles Using a CNN

Kumar¹,

Pandian²

2022

Computers, Materials &Amp; Continua

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Automatic License Plate Recognition (ALPR) systems are important in Intelligent Transportation Services (ITS) as they help ensure effective law enforcement and security. These systems play a significant role in border surveillance, ensuring safeguards, and handling vehicle-related crime. The most effective approach for implementing ALPR systems utilizes deep learning via a convolutional neural network (CNN). A CNN works on an input image by assigning significance to various features of the image and differentiating them from each other. CNNs are popular for license plate character recognition. However, little has been reported on the results of these systems with regard to unusual varieties of license plates or their success at night. We present an efficient ALPR system that uses a CNN for character recognition. A combination of pre-processing and morphological operations was applied to enhance input image quality, which aids system efficiency. The system has various features, such as the ability to recognize multi-line, skewed, and multifont license plates. It also works efficiently in night mode and can be used for different vehicle types. An overall accuracy of 98.13% was achieved using the proposed CNN technique.

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Section: Different Vehicle Typesmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Automatic License Plate Recognition System for Vehicles Using a CNN

Kumar¹,

Pandian²

2022

Computers, Materials &Amp; Continua

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“…It allows the use of non-numerical. It uses a combination of one-hot encoding and an advanced mean encoding [24].…”

Section: ) Catboostmentioning

confidence: 99%

Assaying the Statistics of Crime Against Women in India using Provenance and Machine Learning Models

Bhardwaj¹,

Bawa²

2022

IJACSA

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Now-a-days, the surging of crime against women is occurring at a startling rate in India. According to the National Commission for Women, there was a 46% increase in reports of crimes against women in the initial months of the year 2021 in comparison with the same period in 2020. However, to handle this problem, the need of the hour is to fetch relevant and timely information about the various types of crime taking place and make specific predictions based on the existing information to safeguard women from future predictable contingencies. AI and Machine learning mechanisms have become a powerful tool in predicting the crime rate in India under various crime categories by analyzing the crime patterns, crime-centric areas, and the comparative study of various crime categories. Hence, from 2001 to 2019, a women's crime-based dataset from NCRB has been used in this paper, which included various crime sub-categories, for instance; molestation, sexual harassment, rape, kidnapping, dowry deaths, cruelty to family, importation of girls, immortal traffic, sati prevention act, and others. To acquire a better understanding of the data, a framework has been created which makes use of provenance and machine learning algorithms on the dataset, which has been grouped based on several factors such as distribution of cases convicted or reported every year, safest and un-safest states for women in India, etc. Different machine learning algorithms, such as gradient boosting and its many versions, Random forest, and many more, have been used on the dataset. Their performances are evaluated using various metrics such as accuracy, recall, precision, F1 score, and root mean error square.

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“…AI is defined as a tool that is used all over the world in various cities and campuses of education. Some technologies that come under it are the internet, household appliances, search engines, smartphones, and other various applications [11]. A great development has also been seen in the education sector using AI in different areas that are discussed in this section.…”

Section: Applications Of Ai In Educationsmentioning

confidence: 99%