Task scheduling, which is important in cloud computing, is one of the most challenging issues in this area. Hence, an efficient and reliable task scheduling approach is needed to produce more efficient resource employ...
详细信息
Globally, one of the leading causes of mortality is heart disease, necessitating timely along with accurate detection to enhance the results for patients. In the following paper, a generalized approach to heart diseas...
详细信息
ISBN:
(纸本)9798350365405
Globally, one of the leading causes of mortality is heart disease, necessitating timely along with accurate detection to enhance the results for patients. In the following paper, a generalized approach to heart disease detection using deep learning is introduced with the view to finding means of utilizing the most cutting-edge machine learning techniques to improve cardiovascular healthcare. The main focus of academic inquiry is directed at utilizing deep learning algorithms to analyze a multitude and variety of medical data, including electrocardiogram images, in order to discover the most effective and precise technique of early heart disease detection and diagnosis. Thus, in this study, a wide range of highly popular state-of-the-art inside deep learning architectures were considered, including but not limited to the neural networks with convolutions. Moreover, the scholars had carefully analyzed and compared above-mentioned architectures using their own method to determine the most suitable one for the detection of cardiac conditions. In the current study, the author applied ECG and electrocardiogram images as their dataset had more varied electrocardiogram images. The dataset's diversity enables the models to capture intricate patterns and achieve robust generalization, essential for real-world deployment. These experimental results demonstrate promising potential for improving heart disease diagnosis and risk assessment, showcasing significantly enhanced performance compared to conventional methods. The study emphasizes the importance of early diagnosis of cardiac disorders in improving patient outcomes and treatment planning. In conclusion, this work provides a thorough investigation of a broad deep learning method for detecting heart disease. By combining diverse medical data, advanced deep learning architectures, and model training techniques, the research showcases the potential of artificial intelligence in revolutionizing cardiovascular healthcare. To an
An authenticated manager must reinforce huge applications and operating systems, keeping information in the cloud while resisting potentially unreliable service providers. This article explores the presence of multipl...
详细信息
The Telecare Medical Information System (TMIS) faces challenges in securely exchanging sensitive health information between TMIS nodes. A Mutual Authenticated Key Agreement (MAKA) scheme is used to eliminate security ...
详细信息
The proliferation of cooking videos on the internet these days necessitates the conversion of these lengthy video contents into concise text recipes. Many online platforms now have a large number of cooking videos, in...
详细信息
The proliferation of cooking videos on the internet these days necessitates the conversion of these lengthy video contents into concise text recipes. Many online platforms now have a large number of cooking videos, in which, there is a challenge for viewers to extract comprehensive recipes from lengthy visual content. Effective summary is necessary in order to translate the abundance of culinary knowledge found in videos into text recipes that are easy to read and follow. This will make the cooking process easier for individuals who are searching for precise step by step cooking instructions. Such a system satisfies the needs of a broad spectrum of learners while also improving accessibility and user simplicity. As there is a growing need for easy-to-follow recipes made from cooking videos, researchers are looking on the process of automated summarization using advanced techniques. One such approach is presented in our work, which combines simple image-based models, audio processing, and GPT-based models to create a system that makes it easier to turn long culinary videos into in-depth recipe texts. A systematic workflow is adopted in order to achieve the objective. Initially, Focus is given for frame summary generation which employs a combination of two convolutional neural networks and a GPT-based model. A pre-trained CNN model called Inception-V3 is fine-tuned with food image dataset for dish recognition and another custom-made CNN is built with ingredient images for ingredient recognition. Then a GPT based model is used to combine the results produced by the two CNN models which will give us the frame summary in the desired format. Subsequently, Audio summary generation is tackled by performing Speech-to-text functionality in python. A GPT-based model is then used to generate a summary of the resulting textual representation of audio in our desired format. Finally, to refine the summaries obtained from visual and auditory content, Another GPT-based model is used
In recent decades, brain tumors have been regarded as a severe illness that causes significant damage to the health of the individual, and finally it results to death. Hence, the Brain Tumor Segmentation and Classific...
详细信息
In recent decades, brain tumors have been regarded as a severe illness that causes significant damage to the health of the individual, and finally it results to death. Hence, the Brain Tumor Segmentation and Classification (BTSC) has gained more attention among researcher communities. BTSC is the process of finding brain tumor tissues and classifying the tissues based on the tumor types. Manual tumor segmentation from is prone to error and a time-consuming task. A precise and fast BTSC model is developed in this manuscript based on a transfer learning-based Convolutional Neural Networks (CNN) model. The utilization of a variant of CNN is because of its superiority in distinct tasks. In the initial phase, the Magnetic Resonance Imaging (MRI) brain images are acquired from the Brain Tumor Image Segmentation Challenge (BRATS) 2019, 2020 and 2021 databases. Then the image augmentation is performed on the gathered images by using zoom-in, rotation, zoom-out, flipping, scaling, and shifting methods that effectively reduce overfitting issues in the classification model. The augmented images are segmented using the layers of the Visual-Geometry-Group (VGG-19) model. Then feature extraction using An Attribute Aware Attention (AWA) methodology is carried out on the segmented images following the segmentation block in the VGG-19 model. The crucial features are then selected using the attribute category reciprocal attention phase. These features are inputted to the Model Agnostic Concept Extractor (MACE) to generate the relevance score between the features for assisting in the final classification process. The obtained relevance scores from the MACE are provided to the max-pooling layer of the VGG-19 model. Then, the final classified output is obtained from the modified VGG-19 architecture. The implemented Relevance score with the AWA-based VGG-19 model is used to classify the tumor as the whole tumor, enhanced tumor, and tumor core. In the classification section, the proposed
Preservation theorems provide a direct correspondence between the syntactic structure of first-order sentences and the closure properties of their respective classes of models. A line of work has explored preservation...
详细信息
Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality ***,in prac...
详细信息
Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality ***,in practice apart from these quality criteria,there require other aspects of coupling and cohesion quality criteria such as lexical and changed-history in designing the modules of the software ***,consideration of limited aspects of software information in the SBSR may generate a sub-optimal modularization ***,such modularization can be good from the quality metrics perspective but may not be acceptable to the *** produce a remodularization solution acceptable from both quality metrics and developers’perspectives,this paper exploited more dimensions of software information to define the quality criteria as modularization ***,these objectives are simultaneously optimized using a tailored manyobjective artificial bee colony(MaABC)to produce a remodularization *** assess the effectiveness of the proposed approach,we applied it over five software *** obtained remodularization solutions are evaluated with the software quality metrics and developers view of *** demonstrate that the proposed software remodularization is an effective approach for generating good quality modularization solutions.
This paper aims to introduce a new approach to MOO and preference-based Multi objective Decision Making founded on AIS principles. AIS originates from vertebrate immune systems in which "fish defend themselves ag...
详细信息
The integration of vision and language has propelled the advancement of artificial intelligence systems. Visual Question Answering (VQA) stands at the nexus of computer vision and natural language processing, enabling...
详细信息
暂无评论