P. Sharma

Bio: P. Sharma is an academic researcher. The author has contributed to research in topics: MNIST database & Computer science. The author has an hindex of 1, co-authored 5 publications receiving 6 citations.

NFT artwork generation using oscillatory activation functions in GANs

Abstract: The concept of digital ownership is not new, and has been widely used in gaming contexts to allow players to customize their experiences via profile pictures, skins, upgrades and add-ons. In this paper we propose a novel model for NFT generation which uses Oscillatory activation function instead of other mainstream activation functions. Here, we are using a combination of GCU and ReLU to train the model and the subsequently use for the prediction. We have used the Bored Apes Yacht Club Dataset[4] available here. This dataset contains 10,000 images of famous NFTs from Bored Apes Yacht Club. NFTs will accelerate the growth of the cryptocurrency space outside of finance, and will bring novel ideas and approaches from new sets of creators, artists, collectors of digital items, developers and more. Preprint. Under review.

Image Enhancement using ESRGAN for CNN based X-Ray Classification

Abstract: There is tremendous amount of computational power in artificial intelligence models like computing variety of complex mathematical calculations and recognizing objects. In the past six to seven years, the amount of computing power used by record-breaking AI models doubled frequently in the time span of months. An interesting way in which these models learn and progress is through deep learning. Deep learning is an intelligent machine’s way in which machines learn without being supervised by us and grants them the power to recognize speech, translate, and even make or take data-driven decisions. Machines consider this as a studying method, inspired by the architecture of the human brain and how we learn. An important deep learning method where we train the machines on information that is unlabeled is called unsupervised learning. A strong part of neural networks that are utilized for unsupervised learning is Generative Adversarial Networks. When it comes to applications on images quality improvement, Super Resolution GAN (SRGAN) have a key role to play in it. It was proposed by researchers at Twitter. The motive of this GAN is to super resolute the fine textures of the image by upscaling it. In order to enhance the images further, ESRGAN is used. As the name suggests, ESRGAN is an implementation of SRGAN and uses some added components of SRGAN.

Continuous control using Deep Q Learning with Oscillatory Activation functions

Abstract: The capacity of reinforcement learning (RL) to learn from the interaction between the environment and agent provides an optimal control strategy. DQN is a deep neural network structure used for estimation of Q-value of the Q-learning method. The CartPole game is essentially a game in which a stick is attached to a cart and the cart moves along a friction-less track. The goal here is to make the cart move left or right to keep the pole from falling. The system is controlled by applying a force of +1 or -1 to the cart. The pendulum starts upright, and the goal is to prevent it from falling over. A reward of +1 is provided for every timestep that the pole remains upright. The episode ends when the pole is more than 15 degrees from vertical, or the cart moves more than 2.4 units from the center. In this paper, we explore the CartPole game and the effect oscillatory activation functions have on this.

Evaluation of Oscillatory Activation Functions on MNIST dataset using GANs

Abstract: MNIST is essentially a database of handwritten digits and thus, having a dataset such as this with over 70,000 images makes it a great source to perform experimentation on. Thus, in this particular paper, we are working on exploring the use and output of the oscillatory activation functions on the MNIST dataset by generating a GAN to generate these kind of handwritten digits.The code has been written essentially in Pytorch and we have used a number of oscillatory activation functions and viewed the results. A GAN is a network in which there are 2 neural networks, i.e. the generator and discriminator which are pitted against each other. This is essentially done to enable the generator to generate a fake image and the discriminator to classify that image as real or fake. The training is done accordingly. Thus, viewing the results of oscialltory activation functions in GANs might open us up to new possibilites and help us understand whether the oscillatory activation functions would yield better results or not in the case of GANs.

Brain tumor based mri image enhancement using entropy and clahe based intuitionistic fuzzy method with deep learning

Abstract: The inner area of the human brain is where abnormal brain cells gather when they become a mass. These are known as brain tumors, and based on the location and size of the tumor, they can produce a wide range of symptoms. Accurate segmentation and classification of brain tumors are critical for effective diagnosis and treatment planning. In this paper, we present a novel approach for the segmentation and classification of brain tumors using Entropy and CLAHE Based Intuitionistic Fuzzy Method with Deep Learning. Entropy and CLAHE (Contrast Limited Adaptive Histogram Equalization) based Intuitionistic Fuzzy Method with Deep Learning is a technique that combines several image processing and machine learning algorithms to enhance the quality of images. By applying entropy-based techniques to an image, we can identify and highlight the most significant features or patterns in the image. Our study provides a thorough evaluation of the proposed technique and its performance compared to other methods, showing its effectiveness and potential for use in real-world applications. Our method separates the tumor regions from the healthy tissue and provides accurate results in comparison with traditional methods. The results of this study demonstrate the potential of this approach to improve the diagnosis and treatment of brain tumors and provide a foundation for future research in this field. The proposed technique holds significant promise for improving the prognosis and quality of life for patients with brain tumors.

A Distributed Authenticity Verification Scheme Using Deep Learning for NFT Market

Abstract: With the recent proliferation of blockchains, identifying security risks to them has become an important issue. Among the various types of cyberattacks against blockchains, the blockchain poisoning attack involves the storing of malicious data in the blockchain to compromise it. One scenario is an attack that distributes forgeries of digital content traded and managed using Non-Fungible Token (NFT) on the blockchain. Currently, concomitant with the growing interest in NFT-based content trading, blockchain poisoning attacks on NFT trading and their effects have also increased. In this study, we examined the issues that may lead to attacks in the process from generation to distribution of digital content using NFT from the viewpoint of flexibility and interoperability of the content. Consequently, we discovered that there are two types of attack risks in NFT trading using malicious content: fake attacks and reuse attacks. As a countermeasure against these attacks, we propose a method for verifying the authenticity of the content itself using a decentralized scheme. The proposed method ensures the confidentiality of contents by using deep learning as an irreversible transformation operation in the distributed scheme and for privacy protection.

Brain tumor based mri image enhancement using entropy and clahe based intuitionistic fuzzy method with deep learning

Abstract: The inner area of the human brain is where abnormal brain cells gather when they become a mass. These are known as brain tumors, and based on the location and size of the tumor, they can produce a wide range of symptoms. Accurate segmentation and classification of brain tumors are critical for effective diagnosis and treatment planning. In this paper, we present a novel approach for the segmentation and classification of brain tumors using Entropy and CLAHE Based Intuitionistic Fuzzy Method with Deep Learning. Entropy and CLAHE (Contrast Limited Adaptive Histogram Equalization) based Intuitionistic Fuzzy Method with Deep Learning is a technique that combines several image processing and machine learning algorithms to enhance the quality of images. By applying entropy-based techniques to an image, we can identify and highlight the most significant features or patterns in the image. Our study provides a thorough evaluation of the proposed technique and its performance compared to other methods, showing its effectiveness and potential for use in real-world applications. Our method separates the tumor regions from the healthy tissue and provides accurate results in comparison with traditional methods. The results of this study demonstrate the potential of this approach to improve the diagnosis and treatment of brain tumors and provide a foundation for future research in this field. The proposed technique holds significant promise for improving the prognosis and quality of life for patients with brain tumors.

Characteristics of Blockchain-based Digital Asset Datasets: A Systematic Review

Abstract: The widespread use of two types of digital assets derived from blockchain technology, namely cryptocurrencies and NFTs, is inextricably linked to the technology's high level of popularity. The transparency of the blockchain allows for quick access to a wealth of data. This work generates an in-depth review of online datasets for both sorts of digital assets. This study provides a thorough analysis of the platforms employed and the characteristics of the datasets. A meticulous analysis of 45 datasets and the 88 papers that used them reveals the use of more than seven different types of platforms and three different types of data characteristics across all datasets. Datasets with graph-based data type generally have low sparsity, while ordered data type can have various resolutions. Datasets with unstructured data type have specific properties to meet particular needs and can have high dimensionality.