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Automatic Number Plate Recognition (ANPR) combines electronic hardware and complex computer vision software algorithms to recognize the characters on vehicle license plate numbers. Many researchers have proposed and implemented ANPR for various applications such as law enforcement and security, access control, border access, tracking stolen vehicle...
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... can rapidly classify a picture into hundreds of predetermined categories and recognise specific objects or faces within photos, as well as read printed text. Figure 3 shows the system flow of the text recognition handling done inside the ANPR CNN mobile app. The device's camera sensor is initialised by CameraSourcePreview class, and it will start to get screening frames from the image sensor. ...
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... can rapidly classify a picture into hundreds of predetermined categories and recognise specific objects or faces within photos, as well as read printed text. Figure 3 shows the system flow of the text recognition handling done inside the ANPR CNN mobile app. The device's camera sensor is initialised by CameraSourcePreview class, and it will start to get screening frames from the image sensor. ...
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... All features which satisfy the weight threshold of Relief F are selected as elements of optimal feature subset 2, termed as M2. Lastly, features of M1 and M2 are combined together as M1 U M2, which reflects the final optimal feature subset [27], [45], [46], [47], [49]. ...
Research in the field of IDS has been going on since long time; however, there exists a number of ways to further improve the efficiency of IDS. This paper investigates the performance of Intrusion detection system using feature reduction and EBPA. The first step involves the reduction in number of features, based on the combination of information gain and correlation. In the next step, error back propagation algorithm (EBPA) is used to train the network and then analyze the performance. EBPA is commonly used due to its ease of use, high accuracy and efficiency. The proposed model is tested over the KDD Cup 99 and NSL-KDD datasets. Results show that the proposed IDS model with reduced feature set outperforms the other models, both in terms of performance metrics and processing time.
Microstrip patch antenna (MPA) plays key role in the wireless communication. The research is continuing going to design and optimization of the antenna for various advance application such as 5G and IOT. Artificial intelligence based techniques such as machine learning is also capable to optimize the parameter values and make prediction model based on the given dataset. This research paper shows the machine learning based techniques to optimize the microstrip patch antenna parameters with the performance improvement in terms of accuracy, Mean Squared Error, and Mean Absolute Error. The antenna optimization process may be greatly accelerated using this data-driven simulation technique. Additionally, the advantages of evolutionary learning and dimensionality reduction methods in antenna performance analysis are discussed. To analyze the antenna bandwidth and improve the performance parameters is the main concern of this work.
