Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and...
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In this paper, we introduce the applications of third-order reduced biquaternion tensors in color video processing. We first develop an algorithm for computing the singular value decomposition (SVD) of a third-order r...
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Medical Image Analysis(MIA)is one of the active research areas in computer vision,where brain tumor detection is the most investigated domain among researchers due to its deadly *** tumor detection in magnetic resonan...
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Medical Image Analysis(MIA)is one of the active research areas in computer vision,where brain tumor detection is the most investigated domain among researchers due to its deadly *** tumor detection in magnetic resonance imaging(MRI)assists radiologists for better analysis about the exact size and location of the ***,the existing systems may not efficiently classify the human brain tumors with significantly higher *** addition,smart and easily implementable approaches are unavailable in 2D and 3D medical images,which is the main problem in detecting the *** this paper,we investigate various deep learning models for the detection and localization of the tumor in MRI.A novel twotier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second ***,in this paper,we introduce a well-annotated dataset comprised of tumor and normal *** experimental results demonstrate the effectiveness of the proposed framework by achieving 97%accuracy using GoogLeNet on the proposed dataset for classification and 83%for localization tasks after finetuning the pre-trained you only look once(YOLO)v3 model.
The recent COVID-19 pandemic caused by the novel coronavirus,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has had a significant impact on human life and the economy around the world.A reverse transcript...
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The recent COVID-19 pandemic caused by the novel coronavirus,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has had a significant impact on human life and the economy around the world.A reverse transcription polymerase chain reaction(RT-PCR)test is used to screen for this disease,but its low sensitivity means that it is not sufficient for early detection and *** RT-PCR is a time-consuming procedure,there is interest in the introduction of automated techniques for *** learning has a key role to play in the field of medical *** most important issue in this area is the choice of key ***,we propose a set of deep learning features based on a system for automated classification of computed tomography(CT)images to identify ***,this method was used to prepare a database of three classes:Pneumonia,COVID19,and *** dataset consisted of 6000 CT images refined by a hybrid contrast stretching *** the next step,two advanced deep learning models(ResNet50 and DarkNet53)were fine-tuned and trained through transfer *** features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization *** each deep model,the Rao-1 algorithm and the PSO algorithm were combined in the hybrid ***,the selected features were merged using the new minimum parallel distance non-redundant(PMDNR)*** final fused vector was finally classified using the extreme machine *** experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%.Comparing the different classification algorithms at the different levels of the features demonstrated the reliability of the proposed framework.
Adversarial attacks can mislead automatic speech recognition (ASR) systems into predicting an arbitrary target text, thus posing a clear security threat. To prevent such attacks, we propose DistriBlock, an efficient d...
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With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as dig...
This research focuses on image classification, grouping images based on shared characteristics. The study aims to identify the highest-potential category within various fields, from high to low potential. By understan...
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ISBN:
(数字)9798331517601
ISBN:
(纸本)9798331517618
This research focuses on image classification, grouping images based on shared characteristics. The study aims to identify the highest-potential category within various fields, from high to low potential. By understanding existing potential, researchers can inform targeted strategies for future development. The methodology combines Logistic Regression in Orange Data Mining to enhance classification accuracy. Hierarchical Clustering is used to analyze 7,777 image results. Findings reveal that Tourism (C3) has the most potential, followed by Culinary (C2) and Culture (C4), while Handcrafts (C1) exhibit the least potential. In conclusion, logistic regression successfully identifies influential aspects of Sukabumi through Instagram images, aiding potential development
Chatbot technology is considered one of the latest artificial intelligence tools used to achieve interaction between the organization and the customer. It relies on natural language processing, as it helps the organiz...
Chatbot technology is considered one of the latest artificial intelligence tools used to achieve interaction between the organization and the customer. It relies on natural language processing, as it helps the organization know the desires and needs of the customer, in addition to collecting customer data and displaying the services provided by the organization. This research proposes an architectural model for supporting chatbots based on the ChatGPT model. It receives customer interaction inputs, understands questions, and responds quickly and appropriately. In addition, this model was designed and implemented using the Voiceflow platform. This model was named EDUBot to build communication elements and components to customize the structure of the virtual conversation and manage the dialogue. This model can be applied to the Educational City institution as a case study to improve customer service and help the institution manage customer relationships more efficiently.
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