In the fast-moving world, it is noticed that every industry is developing gradually, but recently it is identified that the use of AI has become the talk of own. Therefore, this study is focused on gathering data regarding the AI on how it has transformed the entire world's corporate sector. The essential application AI in the business world helps the business to perform better in the corporate sector. In this paper, the critical role of artificial intelligence is to grow business in different sectors and also address its ethical and unethical issues. The paper has all the initial background and comprehensive literature regarding AI and machine learning. It is discovered how the technological world has been striving to take their business on to new heights, which requires updated technological changes in internal business activities. Companies can now effortlessly interact with their customers in making their application accessible for the end-users through implementing AI and machine learning. Companies are getting higher profitability and enhancing their performance and achieving economic advantages by integrated AI. Moreover, their technological developments will take human jobs in the future, so, it is suggested that humans should work on their skills and competencies so that they can deal with unemployment.
Recognizing Urdu text in natural images is more challenging as compared to other languages, such as English, due to the cursive nature of Urdu script. However, Urdu scene text has not received enough attention from both industry and academia due to the lack of the dataset of Urdu text. We propose a largescale Urdu Scene Text Dataset (USTD) to address this problem, which is designed for Urdu scene text detection and recognition. The proposed dataset contains 29674 text annotations (17877 Urdu and 11797 English), 749725 characters in 6389 images. It covers a wide variety of text images with both Nastaleeq and Naskh writing styles, taken from different streets and roads of Pakistan. The vast diversity of this dataset makes it a benchmark to work on and train robust neural networks for the detection and recognition of cursive text. Besides, baseline results are also provided with several state-of-the-art networks, including TextBoxes++, Seglink, DB(ResNet-50) and EAST for text localization and Convolutional Recurrent Neural Network (CRNN) for text recognition. To further evaluate the performance of these models, we have used the most popular evaluation matrices of precision, recall, and F-measure. Our experimental outputs reveal that an end-to-end combination of DB(ResNet-50) and CRNN provides the best results with precision, recall, and F-measure of 0.7526, 0.5974, and 0.6660, respectively.
In this paper, we present a simple design of a feed network for the antenna to achieve a lower side lobe level. Side Lobe Levels (SLL) are critical issues in the detection of an object. Higher side lobe levels can increase the false detection of objects in an autonomous vehicle system. The array is designed and simulated for four different frequencies, one at a time to make it a scalable design. The chosen frequencies are 10 GHz, 15 GHz, 20 GHz, and 24 GHz. The feed network design consists of eight patch elements with an equal power divider and CST studio software is used for simulations. From simulation results, it can be observed that VSWR is equal to 1.26, 1.16, 1.62, and 1.05 at respective frequencies. So, the radiation efficiency can be achieved as -1.14 dB, -0.92 dB, -0.46 dB, -0.41 dB. The results substantiate that proposed design can reduce the SLL more than -24.5 dB in the elevation plane and also it is greater than 14.4 dB at all the aforementioned frequencies.
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