Emotional analysis of product reviews is a hot spot in current datamining research. Whether it is in academic or economic fields, text emotional analysis of e-commerce product reviews has great research value. This a...
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CAD (Coronary Artery Diseases) and CHF (Chronic Heart Failure) are two main forms of heart disease that lead to heart attacks. This disease is increasing the number of casualties worldwide. The heart is an organ that ...
CAD (Coronary Artery Diseases) and CHF (Chronic Heart Failure) are two main forms of heart disease that lead to heart attacks. This disease is increasing the number of casualties worldwide. The heart is an organ that circulates filtered blood to each part of the body. The normal working of the heart is affected due to various conditions like Obesity, Hypertension, lifestyle etc. The large volume of heart diseases is avertable, but they continue to increase for the reason that protective measures are insufficient. To cure these life-threatening diseases, we need a more reliable and accurate system. machinelearning procedures have been employed on numerous therapeutic datasets to industrialise the investigation of medical data. Recently, many researchers have started applying a number of machinelearning techniques to assist the medical community and experts in the detection of heart-related *** technique like Support Vector machine,Artificial Neural Network and Random Forest are examples of datamining and machinelearning approaches that are used to forecast heart disorders. This can help clinician to decide on the diagnosis and forecast of cardiac illness. The prime intention behind writing this research paper is to use machinelearning procedures to forecast a patient's heart condition.
An e-commerce customer churn prediction system was designed based on datamining techniques. Following relevant theories, datamining, machinelearning, and customer relationship management were applied for model cons...
An e-commerce customer churn prediction system was designed based on datamining techniques. Following relevant theories, datamining, machinelearning, and customer relationship management were applied for model construction. A decision tree model and the random forest model were used to build the system. The decision tree model is a decision-making model based on a tree structure, using the construction of a tree for classification or regression tasks. Random forest is an ensemble learning method that improves prediction accuracy and stability by building multiple decision trees and averaging their results. In the implementation of the system, data were preprocessed for data cleaning, handling missing values, and addressing outliers to ensure data quality and integrity. Correlation analysis and principal component analysis were conducted to identify features that significantly impacted customer churn prediction. Finally, continuous variables were determined for model construction and prediction. Using datamining techniques, an effective system was constructed. The system helps e-commerce businesses understand and manage customer relationships and enhance customer satisfaction and loyalty.
E-learning plays an increasingly important role in modern education. Behavioral data from online education platforms make it possible to mine college students' academic performance. The existing datamining models...
E-learning plays an increasingly important role in modern education. Behavioral data from online education platforms make it possible to mine college students' academic performance. The existing datamining models pay little attention to the weight of features in academic performance prediction, and most of them simply treat all features equally. However, each characteristic has a different influence on the predicted outcome. Taking account of this fact, we proposed a datamining model for forecasting undergraduates' academic performance based on multi-feature and support vector machine (SVM). First, the original data was extracted from the e-learning platform and processed. Then, each feature was measured its correlationship with academic performance according to Pearson’s rule and given a weight. Finally, the datamining model was constructed based on the weighted muti-feature and SVM. Simulation of MATLAB shows that the proposed method has higher Accuracy and Recall on forecasting academic performance of undergraduates.
Wireless Sensor Networks (WSNs) have revolutionized data collection, especially in Human Activity recognition (HAR). Multisensor datasets are crucial for a comprehensive understanding of human behavior, enabling more ...
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ISBN:
(数字)9798350372977
ISBN:
(纸本)9798350372984
Wireless Sensor Networks (WSNs) have revolutionized data collection, especially in Human Activity recognition (HAR). Multisensor datasets are crucial for a comprehensive understanding of human behavior, enabling more advanced classification techniques. This study explores the essential role of machinelearning in categorizing activities, especially given the abundance of available multi-sensor data from WSN. The research utilizes information fusion as a pivotal mechanism to boost the accuracy of activity classifications. Employing Support Vector machine (SVM) and Decision Tree (DT) algorithms, the project utilizes advanced data fusion techniques, specifically Kalman Filter (KF) and Covariance Intersection (CI), to optimize information extraction from the provided data. The study encompasses six experiments, including applying SVM and DT on raw data, SVM and DT on data fused by CI, and SVM and DT on data fused by KF. The results of these experiments reveal a significant improvement in the accuracy of SVM and DT classification when incorporating CI and KF. This emphasizes the effectiveness of information fusion techniques in refining the outcomes of human activity recognition systems, showcasing their vital role in enhancing the reliability and precision of activity classifications. This research not only contributes to the field of HAR but also establishes a foundation for further advancements in real-world applications where precise activity classification holds utmost importance.
The recognition of handwritten numbers is a significant challenge in the field of computer vision, as it finds applications in various domains including banking, medical diagnostics, and handwriting recognition. In re...
The recognition of handwritten numbers is a significant challenge in the field of computer vision, as it finds applications in various domains including banking, medical diagnostics, and handwriting recognition. In recent years, there has been a notable enhancement in our technological capabilities due to the progress made in internet accessibility and technology. Deep learning algorithms have emerged as valuable computational tools for addressing several challenges, including the task of digit recognition. The utilization of convolutional neural networks in the task of digit recognition is driven by their ability to acquire hierarchical representations from picture data. This scholarly article provides a comprehensive examination of the application of Convolutional Neural Networks (CNNs) in the field of machinelearning for the purpose of digit recognition. This research examines the architectural components of Convolutional Neural Networks (CNNs), the training procedures employed, and the various methodologies employed for the purpose of digit recognition. The paper also provides an overview of the different datasets used for training and testing CNNs for digit recognition and a discussion of the results obtained from various studies.
DDOS attacks are currently among of the most frequent online crimes. Its prevalence does not, however, make it any simpler to deal with. The resources of the impacted server, such as broadband and buffering size, are ...
DDOS attacks are currently among of the most frequent online crimes. Its prevalence does not, however, make it any simpler to deal with. The resources of the impacted server, such as broadband and buffering size, are lowered down as a result of being unable to give resources to legitimate clients. In this research study, evaluation of many models would be carried out and the one which has the most accuracy would be determined. To identify assaults and typical situations, numerous datasets will be used to train and test the machinelearning algorithms. This study will use weak datamining platforms, and the outcomes of those platforms will be compared and studied.
The datamining task of the classification algorithm is mainly to classify the data and classify them into each known category. As a classification algorithm, SVM has many unique advantages in solving small sample, no...
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This project introduces a Smart Attendance System that utilises Face Verification Technology and the Internet of Things (IoT) to provide a scalable, safe, and efficient way to track attendance in a range of contexts. ...
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ISBN:
(数字)9798331538965
ISBN:
(纸本)9798331538972
This project introduces a Smart Attendance System that utilises Face Verification Technology and the Internet of Things (IoT) to provide a scalable, safe, and efficient way to track attendance in a range of contexts. This creative method automates the attendance process without requiring face-to-face interaction by using IoT-enabled high-resolution cameras and sophisticated facial recognition algorithms to record and validate people's identities. This Smart Attendance System leverages advanced technologies like the Internet of Things (IoT) and Face Verification to provide an efficient, touchless, and secure attendance solution. By utilizing IoT-enabled high-resolution cameras and cutting-edge facial recognition algorithms, it accurately records and authenticates individuals in real-time. The system can be further enhanced with mobile app integration, offering features such as instant notifications for attendance updates, automated absence alerts, and remote access to attendance data, making it ideal for various environments like educational institutions, offices, and large-scale events
Understanding long-term (e.g., one year) traffic characteristics of cellular base stations (BSs) is of great value to network operators. However, there are rarely modeling results about them. In this paper, we charact...
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ISBN:
(数字)9781510652118
ISBN:
(纸本)9781510652118;9781510652101
Understanding long-term (e.g., one year) traffic characteristics of cellular base stations (BSs) is of great value to network operators. However, there are rarely modeling results about them. In this paper, we characterize the long-term (i.e., one year) traffic patterns of thousands of BSs in a large-scale cellular network of China. We first find that the traffic distribution among BSs is highly skewed and BSs' traffic varies dramatically in a year. In order to cluster meaningful BS traffic patterns, we use a new clustering method, in which a BS's monthly traffic is represented by its rank in the BS's traffic time series. In this method, we find that the thousands of BSs have six typical traffic patterns, and the patterns are interpretable: they are clearly related to two important events in China: 1) Spring Festival when a lot of people return hometown to reunion with family, 2) Double 11 Shopping Festival when a lot of people shop online. They are also related to the BSs' geographic location and address information. Our measurement and analysis results provide useful information for cellular network providers to understand and plan their networks, and our clustering method can be applied in similar traffic patternmining problems.
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