Browsing by Author "Dr. Mohammad Ashrafuzzaman Khan (AZK)"

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    ENHANCING IMAGES FOR COLOR VISION DEFICIENCY (CVD) USING DEEP LEARNING
    (North South University, 2024-04-30) TANEEM AHMED; SIDDHARTHA SANKAR SAHA; MAHBUB MORSHED RIFAT; Dr. Mohammad Ashrafuzzaman Khan (AZK); 2013102042; 2011567042; 2011415042
    In a stunning world full of colors, color vision deficiency is the most common difficulty every human being faces. This problem still has no treatment. The subject of this research is to expose artificial intelligence methods as profound studies that aim at material, tangible visualization through images of color blindness in humanity, hence becoming a hope that technology will eventually fill the gap in color perception. The two AI models are the basis for creating and utilizing the project. The first model is Daltonization, which simulates the perception of color by different kinds of CVD and hence can create image transformations that specifically cater to one's eye problems. Secondly, A CNN-based Autoencoder model trains on various images taken under normal vision color conditions and color vision deficiency (CVD) conditions. Therefore, the aim is to create an autoencoder that will convert any image into a better-colored one that is clearly visible to individuals suffering from CVDs. The triumph of this task is determined by how well it performs in terms of two criteria. Quantitative measurements allow for an analysis of the degree to which the latest picture reproduces original images through methods like SSIM (Structural similarity index). User testing and feedback obtained by people suffering from CVD constitute quality appraisals for verifying the acceptability of changes made to their visual perception. We ensure that data collection and utilization are done per strict ethical principles to avoid violating privacy rights or obtaining participants' permission. The primary objective of using converted images is to adhere to and also maintain established accessibility standards for people with Color Vision Deficiency (CVD), therefore rendering them more manageable. The project focuses on user feedback and iterative design methods aimed at guaranteeing that the produced tools are intuitive and user-friendly for persons with low vision (CVD). These AI models work well on web browsers, mobile devices, or picture-editing tools. This project looks not only at improving the accessibility of images but also examines possible uses for the models in this field within augmented reality and virtual reality, amongst other emerging technologies. By improving how they see things while learning or working with it every day during their hours of relaxation at home, we hope that through our efforts, those suffering from CVD would experience an enhanced quality of life. "The exploration of AI will demonstrate how deep learning could come in handy in solving accessibility issues, hence pushing the field further. A user-friendly tool will be developed through this project, which should integrate its software or applications with existing platforms and workflow easily. The project asserts higher levels of inclusivity in visual design by increasing awareness and promotion. This project combines leading-edge AI tech and user-centered design principles, potentially changing how people with color vision deficiencies see objects.
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    European Football Player Price Prediction Using Machine Learning
    (North South University, 2023) S M Minhazur Rahman; Rifatul Islam Ovi; Ashfe Asade Simon; Anika Shama Siddique; Dr. Mohammad Ashrafuzzaman Khan (AZK); 1821822642; 1821197642; 1911962642; 1911918642
    European Football Player Price Prediction Using Machine Learning In most sports, especially football, most coaches and analysts search for key performance indicators using notational analysis. The prediction of European football player prices is an important task for clubs, agents, and investors in the football industry. Making informed judgments about player transfers, contract negotiations, and investments is made possible by accurate price projection. The opportunity to create data-driven models for player price prediction has arisen in recent years due to the accessibility of enormous volumes of player performance data and market information. There are certain factors that influence player prices including individual statistics, age, position, market value and others. Traditionally predictions are made on the basis of these factors. Machine Learning techniques have been a significant source of advanced opportunities to analyze, predict and visualize player prices. In this paper, we estimate players’ market values using four regression models that were tested on the full set of features—linear regression, XG boost, AdaBoost, SVR, Gradient Boosting, and random forests. The dataset containing 19,240 records of Football Player is attained from European Football League and Country. In addition, we want to analyze the information and identify the key elements influencing the estimation of the player market value. For predicting the market values of the players, random forest performed better than other algorithms. In comparison to the baseline, it has the best accuracy score and the lowest error ratio.