Natural language processing (NLP) is an area of research and study that makes it possible for computers to comprehend human language by utilising software engineering concepts from computerscience and artificial inte...
详细信息
The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and...
详细信息
Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text cont...
详细信息
Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text containing text strokes with their hazy backgrounds. Text in the real world uses a variety of font kinds, some of which are difficult to localize due to their chaotic shapes, varied shading degrees, and orientation *** text erasing may include the subtasks of text detection as well as text inpainting. Both subtasks require a large amount of data to be successful;but, existing approaches were limited by insufficient real-world data for scene-text elimination. Eventhough the existing works produced considerable performance improvement in scene text removal, they often leave many text remains like text strokes, thus producinglow-quality visual outcomes. Therefore, this paper proposes an automatic text inpainting and video quality elevation model by using the Improved Convolutional Network-based ***, the video samples are collected from the diverse datasets and then converted into frames. Next, the frames are deblurred using an enhanced Convolutional Neural Network (CNN) model that has three convolutional layers for accurately localizing the texts in frames. Subsequently, the texts are detected by utilizing the CLARA-based VGG-16 network. Afterward, the text strokes are removed using a convolutional Encoder and decoder network to eliminate the presence of text on complex backgrounds and textures. Here, the coordinates of text in the deblurred frames are used to crop out the text stroke regions. So, the texts are in-painted, and then, the text in-painted regions are pasted back to their original positions in the frames. Furthermore, the video quality is elevated with the help of the DenseNet-centric Enhancement network. The experimental outcomes demonstrate that the proposed model effectively removed scene texts and enhanced the video qu
A lot of research shows that there could be several reasons why the duality of agricultural products has been reduced. Plant diseases make up one of the most important components of this quality. Therefore, the reduct...
详细信息
The paper presents a combinatorial algorithm to find the straight skeleton of the inner isothetic cover of a digital object imposed on a uniform background grid. The isothetic polygon (orthogonal polygon) tightly insc...
详细信息
This paper demonstrates the novel approach of sub-micron-thick InGaAs broadband photodetectors(PDs)designed for high-resolution imaging from the visible to short-wavelength infrared(SWIR)*** approaches encounter chall...
详细信息
This paper demonstrates the novel approach of sub-micron-thick InGaAs broadband photodetectors(PDs)designed for high-resolution imaging from the visible to short-wavelength infrared(SWIR)*** approaches encounter challenges such as low resolution and crosstalk issues caused by a thick absorption layer(AL).Therefore,we propose a guided-mode resonance(GMR)structure to enhance the quantum efficiency(QE)of the InGaAs PDs in the SWIR region with only sub-micron-thick *** TiOx/Au-based GMR structure compensates for the reduced AL thickness,achieving a remarkably high QE(>70%)from 400 to 1700 nm with only a 0.98μm AL InGaAs PD(defined as 1μm AL PD).This represents a reduction in thickness by at least 2.5 times compared to previous results while maintaining a high ***,the rapid transit time is highly expected to result in decreased electrical *** effectiveness of the GMR structure is evident in its ability to sustain QE even with a reduced AL thickness,simultaneously enhancing the transit *** breakthrough offers a viable solution for high-resolution and low-noise broadband image sensors.
Earthquake damage prediction is crucial for ensuring the safety of building occupants and preventing substantial financial losses. Because it enables robust structural design, financial readiness, and well-timed expen...
详细信息
Earthquake damage prediction is crucial for ensuring the safety of building occupants and preventing substantial financial losses. Because it enables robust structural design, financial readiness, and well-timed expenditures in preventive measures, anticipating seismic impacts promotes sustainability and long-term building. Machine learning (ML) have transformed building damage prediction, providing efficient methodologies for assessing structural vulnerabilities and risks. ML analyzes multifaceted datasets, handling complex spatial and temporal data, enhancing accuracy in forecasting damage probabilities and enabling proactive monitoring for timely interventions. However, ensemble machine learning and the fine-tuning of such algorithms with the hyperparameter optimization with the earthquake damage prediction have not been explored in the literature yet. Hyperparameter optimization in machine learning enhances model performance and generalization capacity. Skillful adjustment of hyperparameters significantly improves predictive accuracy, resilience, and training convergence, ensuring optimal model performance across diverse datasets and real-world scenarios. This study focuses on improving earthquake damage prediction accuracy through an extensive analysis of the earthquake dataset on ensemble machine learning with hyperparameter tuning. Utilizing various hyperparameter tuning algorithms and examining five ensemble machine learning algorithms, combined with six distinct hyperparameter tuning techniques, significantly enhanced accuracy. The paper’s main contributions include exploring novel hyperparameter tuning algorithms for earthquake damage prediction and filling a knowledge gap in the field. The thorough dataset analysis revealed a scarcity of existing literature, suggesting opportunities for further research. The study underscores the critical role of hyperparameter analysis in machine learning and proposes potential applications beyond earthquake prediction,
Recommender systems are techniques designed to enhance user experience in various domains. They suggest relevant items to users based on their behavior and preferences (Linyuan et al. Feb 2012). These systems are bein...
详细信息
In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary *** tactical...
详细信息
In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary *** tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and *** in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and *** present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile *** on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each *** analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.
Cervical Cancer is the second most common disease among Indian women aged 15 to 44 and is caused by aberrant cell proliferation in the cervix. So, its early detection is very crucial. Many screening measures like Pap ...
Cervical Cancer is the second most common disease among Indian women aged 15 to 44 and is caused by aberrant cell proliferation in the cervix. So, its early detection is very crucial. Many screening measures like Pap smears, HPV tests, and Colposcopies are supported in this scenario. Inception + Support Vector Machine, Ensemble method), K-nearest neighbor, Bagging Decision Tree, Logistic Regression, and Convolutional Neural Networks Machine Learning techniques are the fundamental blocks of the system for cervical cancer cell identification and classification presented in this paper. In addition to the standard classification algorithms used to identify cervical Cancer, cell segmentation and feature extraction methods are typically required. Also, these proposed models need a massive dataset to avoid overfitting and poor generalization problems. After integrating the cell images into these models to obtain deep-learning features, an Extreme Learning Machine–based classifier classifies the given or input images. These techniques are also used for transfer learning and fine-tuning. These proposed models use the Inception deep learning model, which diagnoses cervical Cancer in multiple classes with a precision of approximately 99.1% and gives accuracies based on the input dataset.
暂无评论