Flood is a disaster that causes problems. It causes problems such as destruction of property and disruption of civil activities. Predicting floods is a solution to prevent the devastating disaster and is crucial for m...
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In our body, billions of cells are divided each day to form a new cell;it's just the replacement of the dead cells. Cellstogether form tissues, which in turn form organs. In a few cases, the cells are divided more...
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Public transportation plays a crucial role in supporting urban mobility in big cities such as Jakarta. This condition can lead to overcrowding issue at bus stops, especially during working hours, such as in the mornin...
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The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered ...
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Every year, a massive number of films are released. Because of the vast investments made in the film industry, antic-ipating a film's success and minimizing uncertainty early in the film-making process will have a...
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Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query *** key objective is to learn discriminative fine-grai...
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Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query *** key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered,and dissimilar images are separated in the low embedding *** works primarily focused on defining local structure loss functions like triplet loss,pairwise loss,***,training via these approaches takes a long training time,and they have poor ***,representations learned through it tend to tighten up in the embedded space and lose generalizability to unseen *** paper proposes a noise-assisted representation learning method for fine-grained image retrieval to mitigate these *** the proposed work,class manifold learning is performed in which positive pairs are created with noise insertion operation instead of tightening class *** other instances are treated as negatives within the same *** a loss function is defined to penalize when the distance between instances of the same class becomes too small relative to the noise pair in that class in embedded *** proposed approach is validated on CARS-196 and CUB-200 datasets and achieved better retrieval results(85.38%recall@1 for CARS-196%and 70.13%recall@1 for CUB-200)compared to other existing methods.
Global health has been greatly influenced by the COVID-19 pandemic, especially in low- and middle-income nations like Nigeria. Despite the catastrophic effects of the pandemic, little is known about how sociodemograph...
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Proper customer service is the key to increasing customer retention and brand credibility in financial banks. This can be achieved by helping solve customers' issues through their complaints. This study explores t...
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Oil Palm is a prominent and primary plantation commodity in Indonesia, with extensive and expansive plantations making traditional management methods in abnormality assessment impractical and resource intensive. This ...
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Video data is an asset that may be used in various settings, such as a live broadcast on a personal blog or a security camera at a manufacturing facility. Both of these examples are examples of how video data can be u...
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Video data is an asset that may be used in various settings, such as a live broadcast on a personal blog or a security camera at a manufacturing facility. Both of these examples are examples of how video data can be used. It is becoming increasingly common practice across a wide range of applications to use a machine learning appliance as a tool for processing video. Recent years have seen significant advancements made in the field of machine learning in computer vision. These advancements have been achieved. The presentation of humans is approached or even surpassed in areas such as item identification, object categorization, and image segmentation. Despite this, challenging difficulties exist, such as identifying human emotions. This study aims to recognize human emotions by analyzing still images and motion pictures taken from motion pictures using numerous machine learning procedures. To accomplish this, neural networks constructed based on Generative Adversarial Networks (GAN) were used to classify each face picture obtained from a frame into one of the seven categories of facial emotions we chose. To communicate feelings, videos are mined for informative aspects such as audio, single, and multiple video frames. During this process stage, separate instances of the OpenSMILE and Inception-ResNet-v2 models extract feature vectors from the audio and frames. After that, numerous classification models are trained using stochastic gradient descent with the impetus approach (SGDMA). The findings from each of the pictures are compiled into a table, and from that, it is determined which facial expression was seen on-screen the most often throughout the film. The classification of audio feature vectors is accomplished with the application of GAN-SGDMA. The Inception-ResNet-v2 algorithm is utilized to recognize feelings conveyed by still photographs. The findings of several experiments suggest that the presented distributed model GAN-SGDMA could significantly boost the sp
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