By the MAXSAT problem, we are given a set V of m variables and a collection C of C clauses over V. We will seek a truth assignment to maximize the number of satisfied clauses. This problem is NP-hard even for its rest...
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Digital image analysis is an effective, least time-consuming, minimal human interventional, optimal resource-oriented technique used in the last few decades. computerized image analysis has been of great aid to young ...
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Fake reviews are a significant challenge for online consumer and social media platforms, as they mislead consumers and disrupt fair market competition. Traditional centralized detection methods face numerous limitatio...
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Due to the growing population of patients, in accordance to the general population growth rate, it remains imperative that healthcare deliveries are not only timely but also of high quality. It should ideally be desig...
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ISBN:
(数字)9798331533038
ISBN:
(纸本)9798331533045
Due to the growing population of patients, in accordance to the general population growth rate, it remains imperative that healthcare deliveries are not only timely but also of high quality. It should ideally be designed to track the patient’s activities, behaviors, timetable, and general well-being. Therefore, this paper aims to describe the methods of monitoring patients and detecting their illnesses using machine learning. Subsequently, with the help of different angled camera views, mounted on different positions, we would like to design a health monitoring and surveillance system that will help identify possible medical complications in the long run. Physiological and behavioral patterns are examined by using machine learning algorithms, which would then determine the presence of certain health conditions. In our approach, we prioritize accuracy and real-time to apply the results in real cases. Initial outcomes indicate how it will improve the quality of patient care by obtaining timely medical information while relieving medical personnel’s workload. The present investigation opens the avenue for the development of more intricate, individualized, and preventive gerontological healthcare models. For this purpose, we are using two well-known datasets, ETRIActivity3D and NTU-RGB-D. The first dataset is for the patient care daily life activities and the second is for the medical condition. The data is well-preprocessed to reduce the computational cost and to focus on the main subject of the data. Then a robust Multi-feature descriptors system has been used to get the gradient values of the features. These values are processed by using feature matching and optimization. In the end, the classifier gives us an accuracy of $87.5 \%$ and $89.5 \%$ in ETRI and NTU-RGB-D datasets respectively.
Healthcare sector is a broad field offering diverse facilities which includes diagnosis of medical issues, offering advanced treatments and surgeries to ensure the wellness of a patient. Due to the ever increasing hea...
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Spam has increased as a result of the widespread use of email-based communication, making reliable and effective categorization techniques necessary to detect and filter undesirable information. Our paper presents a S...
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ISBN:
(数字)9798331537579
ISBN:
(纸本)9798331537586
Spam has increased as a result of the widespread use of email-based communication, making reliable and effective categorization techniques necessary to detect and filter undesirable information. Our paper presents a Semantic Graph Neural Network (SGNN) method that reframes email categorization as a graph-based problem. Instead of using conventional numerical embeddings, emails are represented as semantic graphs. This approach makes use of the relational and structural information included in email content, enabling a more sophisticated and contextually sensitive classification procedure. Our tests, which were carried out on a number of popular public datasets, show that SGNN routinely beats state-of the-art deep learning models and attains greater accuracy, especially in the difficult field of spam categorization. These findings highlight SGNN's promise as a practical and scalable approach to email spam detection in the real world, providing enhanced classification accuracy without the hassle of embedding layers.
DGL is a language for specifying and generating random data for testing and simulation. Output can take many forms and can be placed in files, mysql databases and C++ internal variables. In many cases, data files cont...
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In mobile healthcare and remote diagnosis, nucleus segmentation is a critical step for pathological analysis, diagnosis, and classification, requiring real-time processing and high accuracy. However, variations in nuc...
In mobile healthcare and remote diagnosis, nucleus segmentation is a critical step for pathological analysis, diagnosis, and classification, requiring real-time processing and high accuracy. However, variations in nucleus size, blurred contours, uneven staining, cell clustering, and overlapping cells hinder precise segmentation. Additionally, existing deep learning models often prioritize accuracy at the cost of increased complexity, making them unsuitable for resource-limited edge devices and real-world deployment. To address the aforementioned issues, we propose an edge-aware dual branch network for nucleus instance segmentation. The network simultaneously predicts target information and target contours. Within the network, we propose a context fusion block (CF-block) that effectively extracts and merges contextual information from the network. Additionally, we introduce a post-processing method that combines the target information and target contours to distinguish overlapping nuclei and generate an instance segmentation image. Extensive quantitative evaluations are conducted to assess the performance of our method. Experimental results demonstrate the superior performance of the proposed method compared to state-of-the-art approaches on the BNS, MoNuSeg, and CPM-17 datasets.
Among many prediction algorithms, grey system theory has been widely used for prediction, decision-making and evaluation in many fields such as economy, science and so on. Taking the national cotton yield from 2000 to...
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With the surge of e-commerce growth in India, it has become very necessary for brands to understand the sentiments of customers so that they can improve their offerings and remain in competition. In this study, the tr...
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