In recent years,enhancement of underwater images is a challenging task,which is gaining priority since the human eye cannot perceive images under *** significant details underwater are not clearly captured using the c...
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In recent years,enhancement of underwater images is a challenging task,which is gaining priority since the human eye cannot perceive images under *** significant details underwater are not clearly captured using the conventional image acquisition techniques,and also they are ***,the quality of the imageprocessingalgorithms can be enhanced in the absence of costly and reliable acquisition *** algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color *** the proposed model,the authors used a deep learning model for underwater image ***,the original image is pre-processed by the white balance algorithm for colour correction and the contrast of the image is improved using the contrast enhancement ***,the pre-processed image is given to the MIRNet for *** is a deep learning framework that can be used to enhance the low-light level *** enhanced image quality is measured using peak signal-to-noise ratio(PSNR),root mean square error(RMSE),and structural similarity index(SSIM)parameters.
Early diagnosis of potentially malignant disorders, such as oral epithelial dysplasia (OED), is the most reliable way to prevent oral cancer. Computational algorithms have been used as a tool to aid specialists in thi...
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ISBN:
(纸本)9798350312249
Early diagnosis of potentially malignant disorders, such as oral epithelial dysplasia (OED), is the most reliable way to prevent oral cancer. Computational algorithms have been used as a tool to aid specialists in this process. In recent years, CNN-based methods have gained more attention due to their improved results in nuclei segmentation tasks. Despite these relevant results, achieving high segmentation accuracy remains a challenging task. In this paper, we propose an ensemble of segmentation models to improve the performance of nuclei segmentation in OED histopathology images. The proposed ensemble consists of four CNN segmentation models, which were combined using three ensemble strategies: simple averaging, weighted averaging and majority voting, achieved accuracy of 90.69%, 90.70% and 88.49%, respectively, when applied to OED images. The model's performance was also evaluated on three publicly available datasets and achieved comparable performance to state-of-the-art segmentation methods. These values indicate that the proposed ensemble methods can be used in medical image analysis applications.
Sign language has long been a fundamental mode of communication for deaf and mute individuals, serving as a crucial tool for inclusivity and interaction. Nonetheless, communication barriers persist as many individuals...
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ISBN:
(数字)9798350361186
ISBN:
(纸本)9798350361193
Sign language has long been a fundamental mode of communication for deaf and mute individuals, serving as a crucial tool for inclusivity and interaction. Nonetheless, communication barriers persist as many individuals outside of these communities struggle to comprehend and utilize sign language effectively. A ground-breaking system known as hand gesture recognition using imageprocessing to audio conversion has emerged to address this issue. This innovative system aims to develop an application capable of translating sign language and hand gestures into text and audio, thereby facilitating communication between deaf and mute individuals and the wider society. This system enables the detection and classification of hand gestures by employing computer vision techniques, such as CNN algorithms for imageprocessing and the MediaPipe framework for hand gesture identification in real-time video streams. Subsequently, the system utilizes audio signals to provide immediate feedback by converting the detected gestures into corresponding sounds. The feature extraction CNN algorithm is implemented in Python, while the execution takes place on a Raspberry Pi connected to an external camera utilizing OpenCv libraries. Through this comprehensive approach, the system endeavours to bridge the communication gap and enhance the inclusion of deaf and mute individuals in various social settings.
In the modern-era of technology, there is an immense boom of e-commerce business and e-commerce powered SAAS. It includes building robust services and good exchange with the internet. Therefore, the Recommendation Sys...
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ISBN:
(数字)9798350367171
ISBN:
(纸本)9798350367188
In the modern-era of technology, there is an immense boom of e-commerce business and e-commerce powered SAAS. It includes building robust services and good exchange with the internet. Therefore, the Recommendation systems can reorganize and restructure the overload of information based on the filtering and bifurcation methods. This research study will illustrate a comprehensive Literature Survey regarding the published articles in the field of e-commerce recommendation systems, which will help the researchers and Software Engineers to do future work. This study has provided varieties of traditional techniques to build such recommendation systems and the challenges faced during creating such recommendation systems. This study has analyzed different recommendation algorithms and a potentially improved model is proposed. Building the Recommendation system is a challenging task, as it involves the problems of data sparsity and scalability requirements. Recommendation systems are crucial for E-commerce business, it provides personalized recommendations to the customer. Software Engineers and researchers can play a vital role in building and improving such recommendation systems by understanding its traditional techniques, challenges and future directions.
This paper describes an intelligent video surveillance system for human behavior and provides a brief overview of systems with similar functional characteristics. We present a collection of methods and algorithms for ...
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This paper describes an intelligent video surveillance system for human behavior and provides a brief overview of systems with similar functional characteristics. We present a collection of methods and algorithms for automatic recognition of human images, their identification, and analysis of behavior based on motor activity using a cascade of neural networks. A technology for capturing a person's image and tracking their movements between video cameras is proposed.
The demand for smarter devices has pushed embedded systems to their limits, particularly in computationally intensive tasks like image recognition and natural language processing. Hardware accelerators have emerged as...
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ISBN:
(数字)9798331528539
ISBN:
(纸本)9798331528546
The demand for smarter devices has pushed embedded systems to their limits, particularly in computationally intensive tasks like image recognition and natural language processing. Hardware accelerators have emerged as a solution, offering significant performance and energy efficiency gains. However, traditional accelerators tied to specific processor architectures suffer from limitations like reduced flexibility and vendor lock-in. This project proposes a hardware accelerator for the K-Nearest Neighbor (kNN) algorithm, leveraging the open-source RISC-v architecture. RISC-v addresses the computational complexity, performance bottlenecks, and energy efficiency challenges of existing kNN implementations. The accelerator is designed with key features such as a customized instruction set, parallelism, pipelining, reduced power consumption, and optimized area utilization. The project aims for efficient use of flip-flops (FFs) and look-up tables (LUTs), ensuring optimal resource *** utilizing RISC-v's flexibility, this project aims to create an efficient hardware accelerator for kNN, enabling real-time processing of large datasets, minimizing energy use, and ensuring scalability across different applications.
The regular detection of pavement cracks is critical for life and property security. However, existing deep learning-based methods of crack detection face difficulties in terms of data acquisition and defect counting....
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The regular detection of pavement cracks is critical for life and property security. However, existing deep learning-based methods of crack detection face difficulties in terms of data acquisition and defect counting. An automatic intelligent detection and tracking system for pavement cracks is proposed. Our system is formed of a pavement crack generative adversarial network (PCGAN) and a crack detection and tracking network called YOLO-MF. First, PCGAN is used to generate realistic crack images, to address the problem of the small number of available images. Next, YOLO-MF is developed based on an improved YOLO v3 modified by an acceleration algorithm and median flow (MF) algorithm to count the number of cracks. In a counting loop, our improved YOLO v3 detects cracks and the MF algorithm tracks the cracks detected in a video. This improved algorithm achieves the best accuracy of 98.47% and F1 score of 0.958 among other algorithms, and the precision-recall curve was close to the top right. A tiny model was developed and an acceleration algorithm was applied, which improved the detection speed by factors of five and six, respectively. In on-site measurement, three cracks were detected and tracked, and the total count was correct. Finally, the system was embedded in an intelligent device consisting of a calculating module, an automated unmanned aerial vehicle, and other components.
Lung segmentation is a process of detection and identification of lung cancer and pneumonia with the help of imageprocessing techniques. Deep learning algorithms can be incorporated to build the computer-aided diagno...
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Forest fires pose a significant threat to ecosystems and wildlife, exacerbating global warming and environmental degradation. The contemporary challenge lies in the difficulty of timely detection by modern organizatio...
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ISBN:
(数字)9798350375190
ISBN:
(纸本)9798350375206
Forest fires pose a significant threat to ecosystems and wildlife, exacerbating global warming and environmental degradation. The contemporary challenge lies in the difficulty of timely detection by modern organizations. In response, the development of an automated forest fire detection system becomes imperative. The system involves the integration of various technologies, including Arduino microcontrollers, cameras, smoke detectors, flame detectors, and imageprocessingalgorithms. When a fire is detected, the system promptly sends alert messages to nearby forest departments, facilitating rapid response and mitigation efforts. This research work explores the design, implementation, and functionality of such an automated forest fire detection system, emphasizing the role of each component in enhancing the overall efficacy of early fire detection. The integration of Arduino technology, coupled with advanced sensors and imageprocessing, exemplifies a multidimensional approach to addressing the pressing issue of forest fires.
At present, vehicle tracking has been realized in many fields. The applications of vehicle tracking include Advanced Driver Assisted System and live broadcast of automobile events. After reading the paper ' Intell...
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