This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease ...
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This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen
The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, whi...
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Meniscal tears, a prevalent orthopedic condition caused by abrupt knee movements, excessive load, or injury, require an accurate diagnosis for effective treatment. This study investigates the vision transformer (ViT) ...
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The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the Worl...
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The problem of determining the type of grapevine leave (GL) has an important place in the agricultural field and especially in the field of viticulture. It is a foodstuff that is consumed as a table especially in ever...
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Considering the increasing and widespread use of chatbots, it is of great importance to provide methods and tools to address ethical concerns and to make users aware of various aspects of a chatbot, including non-func...
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Ear diseases are defined as pathological conditions that indicate dysfunction or abnormal function of the ear organ, which is part of the auditory system of living organisms that regulates hearing and balance function...
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Ear diseases are defined as pathological conditions that indicate dysfunction or abnormal function of the ear organ, which is part of the auditory system of living organisms that regulates hearing and balance functions. These diseases usually manifest as conditions that affect the internal components of the ear structure and can manifest themselves with symptoms such as hearing loss, ear pain, balance problems, and fluid accumulation in the ear. The accuracy of the diagnosis depends on expert knowledge and subjective opinion. This method is prone to human error. This study presents a novel computer-aided diagnosis system for otoscope images of ear diseases, utilizing a vision transformer-based feature extractor combined with machine learning classifiers to provide accurate second opinions for ENT specialists. For this purpose, a new model based on state-of-the-art vision transformer feature extractor and machine learning models is proposed. In the experimental study, the dataset, comprising 880 eardrum images categorized into four classes (CSOM, earwax, myringosclerosis, and normal), was split into training (70%), validation (10%), and testing (20%) subsets. Each image was preprocessed to 420 × 380 pixels to fit the input dimensions of the models. The vision transformer architecture was utilized for feature extraction, followed by classification using various machine learning algorithms including kNN, SVM, and random forest. As a result, the model using vision transformer feature extractor and k-nearest neighbors (kNN) algorithm achieved 99.00% accuracy. In this study, a deep learning-based and computer-aided diagnosis system, in other words, a computational model, was developed instead of the current human error-prone disease diagnosis method used by ear nose throat (ENT) specialists. The main purpose of the deep learning-based decision support system is to support the diagnosis process where expert knowledge is difficult to access and to provide an alternative opi
In today's software testing community, quality assessment remains critical, with mutation testing standing as a cornerstone technique for evaluating the effectiveness of test cases. This method involves introducin...
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Most of the research conducted in action recognition is mainly focused on general human action recognition, and most of the available datasets support studies in general human action recognition. In more specific cont...
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We study the task of automated house design,which aims to automatically generate 3D houses from user ***,in the automatic system,it is non-trivial due to the intrinsic complexity of house designing:1)the understanding...
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We study the task of automated house design,which aims to automatically generate 3D houses from user ***,in the automatic system,it is non-trivial due to the intrinsic complexity of house designing:1)the understanding of user requirements,where the users can hardly provide high-quality requirements without any professional knowledge;2)the design of house plan,which mainly focuses on how to capture the effective information from user *** address the above issues,we propose an automatic house design framework,called auto-3D-house design(A3HD).Unlike the previous works that consider the user requirements in an unstructured way(e.g.,natural language),we carefully design a structured list that divides the requirements into three parts(i.e.,layout,outline,and style),which focus on the attributes of rooms,the outline of the building,and the style of decoration,*** the processing of architects,we construct a bubble diagram(i.e.,graph)that covers the rooms′attributes and relations under the constraint of *** addition,we take each outline as a combination of points and orders,ensuring that it can represent the outlines with arbitrary ***,we propose a graph feature generation module(GFGM)to capture layout features from the bubble diagrams and an outline feature generation module(OFGM)for outline ***,we render 3D houses according to the given style requirements in a rule-based *** on two benchmark datasets(i.e.,RPLAN and T3HM)demonstrate the effectiveness of our A3HD in terms of both quantitative and qualitative evaluation metrics.
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