Article
Chemistry, Analytical
Halima Hamid N. Alrashedy, Atheer Fahad Almansour, Dina M. Ibrahim, Mohammad Ali A. Hammoudeh
Summary: This paper proposes a framework called BrainGAN for generating and classifying brain MRI images using GAN architectures and deep learning models. The ResNet152V2 model outperforms other models in terms of accuracy, precision, recall, AUC, and loss when tested on brain MRI images generated by DCGAN architecture.
Article
Neurosciences
Xiuzhi Zhao, Wenting Chen, Weicheng Xie, Linlin Shen
Summary: This study proposes a Style Attention based Global-local Aware GAN to generate personalized caricatures. It integrates the facial characteristics of a subject through a landmark-based warp controller for personalized shape exaggeration and uses a style-attention module for appropriate fusion of facial features and caricature style. The results indicate that the proposed method can preserve the identity of input photos and generate caricatures close to those drawn by real artists.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Ammar Ul Hassan, Irfanullah Memon, Jaeyoung Choi
Summary: Designing and generating novel fonts manually is a laborious process. Recent advancements in generative adversarial networks (GANs) have improved font generation, however, current methods still have limitations. To address these limitations, we propose a font generation method that employs a conditional font GAN (CFGAN) to generate an infinite number of font styles with real-time photorealistic results.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Zhentan Zheng, Jianyi Liu, Nanning Zheng
Summary: This paper proposes a novel network method called Patch Permutation GAN (P-2-GAN) that can learn stroke style from a single style image efficiently. Patch permutation is used to generate multiple training samples, and a patch discriminator that can process both patch-wise images and natural images is designed. A local texture descriptor based criterion is also proposed to evaluate the style transfer quality quantitatively. Experimental results demonstrate that our method can produce finer quality re-renderings with improved computational efficiency compared to state-of-the-art methods.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Artificial Intelligence
Guo Pu, Yifang Men, Yiming Mao, Yuning Jiang, Wei-Ying Ma, Zhouhui Lian
Summary: This paper proposes Attribute-Decomposed GAN (ADGAN) and its enhanced version (ADGAN++) for controllable image synthesis, which can produce realistic images with desired attributes provided in various source inputs. The core ideas of the proposed ADGAN and ADGAN++ are both to embed component attributes into the latent space as independent codes and thus achieve flexible and continuous control of attributes via mixing and interpolation operations in explicit style representations. The major difference between them is that ADGAN processes all component attributes simultaneously while ADGAN++ utilizes a serial encoding strategy.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jin Zhao, Feifei Lee, Chunyan Hu, Hongliu Yu, Qiu Chen
Summary: In this paper, a lightweight domain-attention generative adversarial network (LDA-GAN) is proposed for unpaired image-to-image translation. By introducing an improved domain-attention module and a novel separable-residual block, the generator can focus more on important object regions and retain depth and spatial information, resulting in more realistic images.
Article
Computer Science, Artificial Intelligence
Qiang-Lin Yuan, Han-Ling Zhang
Summary: This paper proposes a GAN-based makeup transfer framework called RAMT-GAN, aiming to achieve realistic and accurate makeup style transfer. By introducing identity preservation loss and background invariant loss, the model can synthesize makeup faces with accurate reference style while maintaining identity and background information.
IMAGE AND VISION COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
JaeWon Kim, KyoHoon Jin, SooJin Jang, ShinJin Kang, YoungBin Kim
Summary: Generating game effect sprites using generative adversarial networks (GANs) can be a cost-effective and efficient approach to game development, especially when there is limited available open-source game sprite data. The proposed Game Effect Sprite Generative Adversarial Network (GESGAN) is capable of producing style-translated images for various shapes of object images and drawing styles in near real-time.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Zhen Xu, Si Wu, Qianfen Jiao, Hau-San Wong
Summary: This paper introduces a GAN-based generative model for accurately extracting and transferring makeup styles from reference facial images to target faces. The proposed model utilizes target-aware makeup style encoding and verification, and improves the accuracy and fidelity of makeup transfer through encoding the difference map and learning style consistency.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Automation & Control Systems
Weining Wang, Yifan Li, Huan Ye, Fenghua Ye, Xiangmin Xu
Summary: This study proposes an innovative generative adversarial network for ink painting style transfer, addressing the asymmetry between photographs and ink paintings. The network utilizes generators of differing capabilities and introduces unique loss functions to improve image quality and model optimization speed. A Chinese bird ink painting dataset is also built to validate the model's effectiveness.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Haipeng Deng, Qiuxia Wu, Han Huang, Xiaowei Yang, Zhiyong Wang
Summary: The unsupervised image-to-image translation aims to learn a mapping that translates images from one domain to another. Current GAN models require expensive operations and suffer from high computational costs. To address this, we propose using involution, a lightweight operator, to enhance the GAN structure and introduce a novel loss term to evaluate perceptual similarity distance.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
Yezhi Shu, Ran Yi, Mengfei Xia, Zipeng Ye, Wang Zhao, Yang Chen, Yu-Kun Lai, Yong-Jin Liu
Summary: In this article, a novel multi-style generative adversarial network (GAN) architecture called MS-CartoonGAN is proposed, which can transform photos into multiple cartoon styles. The shared network architecture exploits the common characteristics of cartoon styles, achieving better cartoonization and being more efficient than single-style cartoonization methods.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Computer Science, Software Engineering
Yue Yu, Ding Li, Benyuan Li, Nengli Li
Summary: Image generation has always been an important direction in computer vision research. The proposed multi-style image generative network efficiently generates high-quality images with different artistic styles based on semantic images. It has faster result generation speed compared to the current state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
J. Mercy Faustina, V Akash, Anmol Gupta, V Divya, Takasi Manoj, N. Sadagopan, B. Sivaselvan
Summary: This paper extensively studies neural style transfer algorithms and customizes them for Indian art styles. It analyzes various methods, from the seminal work of Gatys et al using CNNs to the state-of-the-art image-to-image translation models using GANs. Based on the results, the paper proposes a more suitable approach and custom architecture for Indian art styles, especially Tanjore paintings, and presents evaluation methods, including a proposed metric, to assess the model's results.
NETWORK-COMPUTATION IN NEURAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Md. Alamin Talukder, Md. Manowarul Islam, Md. Ashraf Uddin, Arnisha Akhter, Md. Alamgir Jalil Pramanik, Sunil Aryal, Muhammad Ali Abdulllah Almoyad, Khondokar Fida Hasan, Mohammad Ali Moni
Summary: Brain tumors are fatal and devastating, reducing life expectancy significantly. Accurate diagnosis is crucial for treatment plans. Manual analysis of MRI data is challenging and time-consuming, calling for a reliable deep learning model.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Bitanu Chatterjee, Trinav Bhattacharyya, Kushal Kanti Ghosh, Agneet Chatterjee, Ram Sarkar
Summary: This article presents a framework for maximizing influence propagation in a social network, which includes community detection and the utilization of the Shuffled Frog Leaping algorithm. Experimental results show that our method performs well compared to other algorithms.
Article
Computer Science, Artificial Intelligence
Erik Cuevas, Hector Escobar, Ram Sarkar, Heba F. Eid
Summary: This paper proposes a new population initialization method for metaheuristic algorithms, where the initial set of candidate solutions is obtained through the sampling of the objective function. The method aims to find initial solutions that are close to the prominent values of the objective function, and these initial points represent promising regions of the search space. The proposed approach shows faster convergence and improved quality of solutions compared to other similar approaches.
APPLIED INTELLIGENCE
(2023)
Article
Engineering, Biomedical
Aleksandr M. Sinitca, Airat R. Kayumov, Pavel Zelenikhin, Andrey G. Porfiriev, Dmitrii I. Kaplun, Mikhail I. Bogachev
Summary: The proposed method effectively segments biomedical images based on local edge density and achieves high accuracy without the need for preliminary learning or tuning. The algorithm's segmentation and quantification capabilities are validated in various biomedical microscopic images, showcasing its potential for efficient image analysis in the field.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Rishav Pramanik, Payel Pramanik, Ram Sarkar
Summary: Breast cancer is a leading cause of premature death among women globally, but early detection and diagnosis can save lives. Hence, computer scientists are working to develop reliable models to tackle this disease. A proposed lightweight model combines transfer learning-based deep learning (DL) with feature selection to detect abnormalities in breast thermograms. This model performs well in detecting and differentiating malignant and healthy breasts.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Konudula Anjali Rao, Abhishek Kumar, Dmitrii Kaplun, Sujit Kumar Patel, Neetesh Purohit
Summary: This paper develops a mathematical model for the efficient realization of a generalized M x M polyphase parallel finite impulse response filter structure. The proposed structure takes advantage of the coefficient symmetry property of linear-phase FIR filters without the need for pre/post circuit blocks. The reduction of delay elements is also utilized to improve resource usage. The results show that the proposed structure is more efficient compared to traditional structures and resolves issues faced by fast FIR algorithms for higher prime values of M.
IET CIRCUITS DEVICES & SYSTEMS
(2023)
Article
Computer Science, Information Systems
Sagnik Ganguly, Sanmit Mandal, Samir Malakar, Ram Sarkar
Summary: This paper introduces a new copy-move image forgery detection technique which relies on a texture feature descriptor called Local Tetra Pattern (LTrP) for block level image comparison used to localize tampered region(s). Experimental results demonstrate that the proposed technique has been able to detect the forged regions with higher accuracy as compared to many state-of-the-art copy-move forgery detection methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Biomedical
Mikhail I. Bogachev, Asya I. Lyanova, Aleksandr M. Sinitca, Svetlana A. Pyko, Nikita S. Pyko, Alexander Kuzmenko, Sergey A. Romanov, Olga I. Brikova, Margarita Tsygankova, Dmitry Y. Ivkin, Sergey Okovityi, Veronika A. Prikhodko, Dmitrii I. Kaplun, Yuri I. Sysoev, Airat R. Kayumov
Summary: Rapid advancement in computer vision technologies allows for quantitative assessment of animal behavior using a scalable model based on fractional Brownian motion. This model provides a more explicit characterization of behavioral patterns and can also estimate scalar metrics commonly used in behavioral analysis.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Information Systems
S. k Mohiuddin, Samir Malakar, Ram Sarkar
Summary: Video forgery has become more common due to the easy availability of tools. This study proposes an ensemble based method to detect duplicate frames in a video. By extracting different types of features and applying lexicographical sorting, the method achieves high detection accuracy and outperforms state-of-the-art methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Sk Mohiuddin, Samir Malakar, Munish Kumar, Ram Sarkar
Summary: Video plays a critical role in conveying authenticity in various fields such as surveillance, medicine, journalism, and social media. However, the trust in videos is diminishing due to the ease of video forgery using accessible editing tools. This article comprehensively discusses the initiatives and recent trends in video forgery detection research worldwide.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Avirup Bhattacharyya, Avigyan Bhattacharya, Sourajit Maity, Pawan Kumar Singh, Ram Sarkar
Summary: Designing an automatic vehicle detection system that caters to the requirements of the traffic management system is important. This research develops a still image database, JUVDsi v1, for designing an automated traffic management system in India. The database addresses the shortcomings of existing databases and is evaluated using state-of-the-art deep learning architectures.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Rishav Pramanik, Bihan Banerjee, George Efimenko, Dmitrii Kaplun, Ram Sarkar
Summary: We have observed the healing process of our society from the impact of COVID-19. The prevention of future pandemics requires appropriate protocols and methodologies to efficiently deal with outbreaks. This paper proposes an ensemble learning-based framework for the early detection of Monkeypox virus from skin lesion images. The framework achieves high accuracy, precision, recall, and F1 scores, making it effective in identifying the presence of Monkeypox.
Article
Computer Science, Interdisciplinary Applications
Ritam Guha, Kushal Kanti Ghosh, Suman Kumar Bera, Ram Sarkar, Seyedali Mirjalili
Summary: This paper proposes a binary adaptation of Equilibrium Optimizer (EO) called Discrete EO (DEO) for solving binary optimization problems. DEOSA algorithm, combining DEO with Simulated Annealing (SA) as a local search procedure, is applied to various datasets and outperforms other algorithms. The scalability and robustness of DEOSA are also tested on high-dimensional Microarray datasets and Knapsack problems, showing its superiority.
JOURNAL OF COMPUTATIONAL SCIENCE
(2023)
Article
Computer Science, Hardware & Architecture
Peter Boyvalenkov, Pavel Lyakhov, Natalia Semyonova, Maria Valueva, Georgi Boyvalenkov, Dmitrii Minenkov, Dmitrii Kaplun
Summary: The paper proposes an algorithm for finding Residue Number Systems (RNS) with six modules (6-tuples) with the Sum of Quotients equal to 2 for some positive integer. It is shown that there are exactly thirteen such 6-tuples with specific conditions and three of them are investigated. The hypothesis is that such RNS allow efficient hardware implementations of non-modular operations.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Computer Science, Information Systems
Nandkishor Joshi, Abhishek Kumar, Dmitrii Minenkov, Dmitrii Kaplun, S. C. Sharma
Summary: This paper introduces the application of cognitive radio networks in effective radio spectrum utilization and proposes a fuzzy-based optimization framework for the 802.11 (DCF) MAC protocol, which can improve the throughput and reduce the delay of cognitive radio AdHoc networks.