Browsing by Author "Rakibul Hasan"

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    Open Access
    A CRITICAL ANALYSIS OF THE BANGLADESH SECURITIES AND EXCHANGE COMMISSION QUALIFIED INVESTOR OFFER BY SMALL CAPITAL COMPANIES, 2018
    (North South University, 2019-12) Mirza M. Ferdous; Hasan Al Mamun; Md. Golam Rabbani; Rakibul Hasan; Mehnaz Ahmed Khan; Kazi Tasnim Wahid; Jashim Uddin Ahmed, PhD
    2018 marked a turning point in the history of the Dhaka Stock Exchange (DSE) as it finalized the sale of 25 percent of its ordinary shares to a Chinese consortium which is comprised of the Shanghai Stock Exchange (SSE) and the Shenzhen Stock Exchange (SZSE). The USD 125 million Dollar transaction promised improvements to the DSE, both for issuers as well as existing and prospective investors. The f irst evidence of change was introduced with the DSE’s declaration to create a “small cap board” for companies with paid up capital of BDT 50 million to 300 million. This decision effectively creates a completely new pathway for small businesses to raise funds through capital markets. The primary market for the small cap companies, while heavily restricted to the general public, is expected to provide investors with access to securities with higher growth potential than those listed on the main board. This paper proposes a critical analysis of the small cap board both from the issuer’s and the holder’s perspective. It looks at the opportunities of the small cap board, the impact of its restrictions, and also compares it to successful small cap boards in other countries
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    Open Access
    Photo-To-Cartoon Translation with Generative Adversarial Network
    (North South University, 2022) Istiaque Ahmed; Kazi Md. Ifthekhar Uddin; Rakibul Hasan; Riasat Khan; 1812420042; 1811019042; 1811194042
    Cartoons are a popular art form in our daily lives, and the ability to automatically create cartoon graphics from photos is highly desired. Cartoon images have a more vibrant and lively appearance than traditional naive pictures. This study aims to explain the process of translating real-world photos into cartoon-like images. While converting pictures to cartoons, there were a few difficulties, including fine hair edges, mismatched colors, and texture concerns. Photos were converted to cartoon-style images using generative adversarial networks (GAN). Various neural network-based GAN networks, DCGAN, CycleGAN, and AnimeGAN, have been applied in this work for cartoon conversion. Among them, CycleGAN performs better in transforming actual photographs into colorful, eye-catching cartoons. This project's approach is based on learning-based methodologies, which have lately gained popularity for stylizing images in artistic forms like painting. The results may be used to convert real-world photographs to high-quality cartoon graphics quickly. This project provides a web API that contains training weights derived from the models outlined below. Based on that API, we created a web app that converts real-world images into high-quality cartoon graphics for various cartoon styles. In these experiments, it outperforms state-of-the-art approaches to producing high-quality cartoon graphics from real-world photos. Numerical results show that the CycleGAN approach has the lowest training time per epoch and requires the minimum number of trainable parameters.