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Culture and arts are integral parts of human society and evolution. We find ancient cave paintings which prove even our early ancestors were thinking similar to modern humans. Any civilized society creates arts and monuments to reflect their beliefs and ideas. Today's vibrant culture is tomorrow's archeology due to the natural or human-made destruc...
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... results are given in Table 1 and 2. It is observed that all three lightweight image classification models are competitive to one another. However, EfficientNetB0 provides a better result for both the Experiment-I datasets and the Kaggle arts dataset with 5-class classification problem. ...Similar publications
The widespread adoption of the home office weakens corporate networks , as it extends its perimeter to homes and ineffective security policies designed for different operating environments. In this context, wireless network routers serve as enablers of access to critical services. However, identifying the software artifacts and possible vulnerabili...
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... This covers ways to assess the ML predictions' internal coherence and to investigate the factors that influence the presence or absence of archaeological sites in a landscape. This is essential in places where archaeological sites are difficult to access [127] . Two artificial intelligence approaches are introduced [128] over two areas of interest in the image processing field. ...
Undeniably, Deep Learning (DL) has rapidly eroded traditional machine learning in Remote Sensing (RS) and geoscience domains with applications such as scene understanding, material identification, extreme weather detection, oil spill identification, among many others. Traditional machine learning algorithms are given less and less attention in the era of big data. Recently, a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer vision. The number of relevant articles has nearly doubled every year since 2015. Advances in remote sensing technology, as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale, have opened up the possibilities for a wide range of modern applications. However, there are some challenges related to the availability of annotated data, the complex nature of data, and model parameterization, which strongly impact performance. In this article, a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures (vintage Convolutional Neural Network, CNN; Fully Convolutional Networks, FCN; encoder-decoder, recurrent networks; attention models, and generative adversarial models). The characteristics, capabilities, and limitations of current DL models were examined, and potential research directions were discussed.
... This covers ways to assess the ML predictions' internal coherence and to investigate the factors that influence the presence or absence of archaeological sites in a landscape. This is essential in places where archaeological sites are difficult to access [127] . Two artificial intelligence approaches are introduced [128] over two areas of interest in the image processing field. ...
... This process is called "Transfer Learning" [14]. Finally, the best feature selection technique has been combined with a Random Forest classifier and hyperparameter optimizer for the best possible performance on the used dataset [15], [16]. ...
Road transportation is the backbone of the economic
activities of any nation. Millions of commuters use national and
state highways and local municipal roads for daily operations.
Road maintenance is an indispensable part of the concerned
authorities for hassle-free and safe transportation. Poor maintenance of the road and its potholes could cause fatal injuries to the
commuters. As the length of these nationwide road networks is
overwhelming, the modern-day machine learning-based approach
can efficiently detect and classify such a variety of road cracks
and send information to the appropriate parties. This paper
introduces a straightforward machine learning-guided pipeline
that can successfully classify the different types of cracks on
the road. It is also compared with state-of-the-art Convolutional
Neural Network (CNN) models. Our proposed machine learning
approach and different feature selection techniques provide
consistent and competitive results in a short training time. It is
observed that Orthogonal Matching Pursuit (OMP) outperforms
other used feature selectors in terms of selected feature subset
size, classification performance, and training time. The final,
conclusive result is as high as ∼ 97% accuracy obtained from
OMP feature selection and Optuna-based tuned Random Forest
Classifier
