With easy access and aggressive data growth via social media networks, it has become challenging to differentiate between falsified and real information. Due to the ease with which information can be communicated, the...
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In the frameworks of Indian stock market, where multiple socio-economic factors impact the stock price movements, this paper focuses on designing an extensive approach to predict stock prices by utilizing sentiment an...
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This study emphasizes the potential of chatbots in revolutionizing healthcare, particularly in the context of infectious disease management. While hospitals have long been the primary source of medical check-ups, diag...
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Air pollution is an issue of great concern. PM2.5 is the most dangerous pollutant out of all the pollutants. A large number of missing values is present in multivariate pollution data. This makes the prediction of PM2...
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In recent years,exploring the relationship between community structure and node centrality in complex networks has gained significant attention from researchers,given its fundamental theoretical significance and pract...
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In recent years,exploring the relationship between community structure and node centrality in complex networks has gained significant attention from researchers,given its fundamental theoretical significance and practical *** address the impact of network communities on target nodes and effectively identify highly influential nodes with strong propagation capabilities,this paper proposes a novel influential spreaders identification algorithm based on density entropy and community structure(DECS).The proposed method initially integrates a community detection algorithm to obtain the community partition results of the *** then comprehensively considers the internal and external density entropies and degree centrality of the target node to evaluate its *** validation is conducted on eight networks of varying sizes through susceptible–infected–recovered(SIR)propagation experiments and network static attack *** experimental results demonstrate that the proposed method outperforms five other node centrality methods under the same comparative conditions,particularly in terms of information spreading capability,thereby enhancing the accurate identification of critical nodes in networks.
Diabetes Mellitus is one of the most severe diseases,and many studies have been conducted to anticipate *** research aimed to develop an intelligent mobile application based on machine learning to determine the diabet...
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Diabetes Mellitus is one of the most severe diseases,and many studies have been conducted to anticipate *** research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic,pre-diabetic,or non-diabetic without the assistance of any physician or medical *** study’s methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture *** Diabetes Prediction Approach uses a novel approach,Light Gradient Boosting Machine(LightGBM),to ensure a faster *** Proposed System ArchitectureDesign has been combined into sevenmodules;the Answering Question Module is a natural language processing Chabot that can answer all kinds of questions related to *** Doctor Consultation Module ensures free treatment related to *** this research,90%accuracy was obtained by performing K-fold cross-validation on top of the K nearest neighbor’s algorithm(KNN)&*** evaluate the model’s performance,Receiver Operating Characteristics(ROC)Curve and Area under the ROC Curve(AUC)were applied with a value of 0.948 and 0.936,*** manuscript presents some exploratory data analysis,including a correlation matrix and survey ***,the proposed solution can be adjustable in the daily activities of a diabetic patient.
In recent days,Deep Learning(DL)techniques have become an emerging transformation in the field of machine learning,artificial intelligence,computer vision,and so ***,researchers and industries have been highly endorse...
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In recent days,Deep Learning(DL)techniques have become an emerging transformation in the field of machine learning,artificial intelligence,computer vision,and so ***,researchers and industries have been highly endorsed in the medical field,predicting and controlling diverse diseases at specific *** tumor prediction is a vital chore in analyzing and treating liver *** paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks(CNN)and a depth-based variant search algorithm with advanced attention mechanisms(CNN-DS-AM).The proposed work aims to improve accuracy and robustness in diagnosing and treating liver *** anticipated model is assessed on a Computed Tomography(CT)scan dataset containing both benign and malignant liver *** proposed approach achieved high accuracy in predicting liver tumors,outperforming other state-of-the-art ***,advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver *** results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor *** can assist radiologists in their diagnosis and treatment *** proposed system achieved a high accuracy of 95.5%in predicting liver tumors,outperforming other state-of-the-art methods.
Endocrine tumors are malignant tumors that get up inside the endocrine system, a network of glands and organs responsible for producing hormones that affect the physiological procedures of the frame. Although endocrin...
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ISBN:
(纸本)9798350383348
Endocrine tumors are malignant tumors that get up inside the endocrine system, a network of glands and organs responsible for producing hormones that affect the physiological procedures of the frame. Although endocrine tumors are usually unusual, early detection is critical for successful treatment. Due to the lack of reliable medical markers for endocrine tumors, early detection is mainly predicated on imaging and laboratory tests. However, these checks may be luxurious and can be hard to interpret. In recent years, time collection analysis (TSA) has been gaining popularity as a powerful device for the early detection of unclassified endocrine tumors. Time series analysis is a form of statistical evaluation that applies mathematical fashions to datasets by converting data values over a hard and fast time frame. It's miles used to discover trends in the facts, making it possible to discover abnormal behavior that could indicate contamination. This method has been shown to have a high degree of accuracy. It may provide insight into, in any other case, unclassified endocrine tumors, making an allowance for the set-off detection and remedy. In this paper, we discuss the capacity of time collection evaluation inside the early detection of unclassified endocrine tumors, alongside the demanding situations and opportunities associated with its use. Time series evaluation has been established to be a powerful tool for the early detection of unclassified endocrine tumors. By presenting perception into patterns and developments in temporal information, this method allows studies to pinpoint regions of molecular change from which ability biomarkers can be diagnosed. The technique is predicated on measuring and analyzing adjustments inside the gene expression levels of regulation networks over time. This method has been used to become aware of adjustments within the expression of pathways related to G protein-coupled receptors and pathways associated with endocrinology. Moreove
Fruit and Vegetable Recognition with Calorie Estimation based on Mobilenetv2 is a pioneering research endeavor aimed at leveraging deep learning techniques to enhance dietary monitoring and health management. Building...
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A key component of managing natural resources is the use and cover of the land. Maps of environmental changes are created using it in order to monitor ecosystems. For forestry, urban planning and agriculture, automati...
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