Application development is a very important aspect when it comes to business. from small-scale companies to Fortune Companies everyone has an application through which they are running their business whether it is a w...
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Healthcare industry generates a vast amount of data, the majority of which is sophisticated and massive in size. This information, however, is not "extracted" in order to uncover hidden facts for effective d...
Healthcare industry generates a vast amount of data, the majority of which is sophisticated and massive in size. This information, however, is not "extracted" in order to uncover hidden facts for effective decision-making. Diagnosing heart disease, a noncommunicable disease, is one of the more difficult problems in medicine because it entails the patient's past health history. An accurate and effective automated system can be quite beneficial in detecting cardiac problems. Modern data mining techniques may be able to solve this issue. In the healthcare industry, various information-extracting technologies such as association rule mining, classification, and clustering are used to forecast cardiac disease. In order to address this issue, the research investigates the use of machine learning methods for cardiac disease prediction, including Naive Bayes (NB), logarithmic regression (LR), Support Vector Machine (SVM), the Decision Tree (DT), Random Forests (RF), and the k-nearest-neighbor algorithm (KNN). The Random Forest ensemble technique outperforms with 99% accuracy.
Cardiopulmonary resuscitation (CPR) assistance glove represents a valuable tool for monitoring chest compressions during CPR, which is crucial for effective cardiac arrest response. By incorporating an MPU6050 acceler...
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This paper explores the utilization of a novel transformer-based architecture for end-to-end learning in predicting steering angles in self-driving scenarios while leveraging a novel robust image processing pipeline t...
This paper explores the utilization of a novel transformer-based architecture for end-to-end learning in predicting steering angles in self-driving scenarios while leveraging a novel robust image processing pipeline to handle diverse environmental situations. Our approach relies solely on visual perception as the input to generate control commands. We trained and evaluated our methodology using a proprietary dataset from a self-driving car simulator consisting of image frames paired with their corresponding steering angles. The presented methodology is robust against overfitting, and it shows superior performance in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE) compared to previous methods.
Significant progress has been made in developing systems that can automatically recognize human activities, largely thanks to deep learning models and sensor data. However, high performance and comprehensibility are o...
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"Artificial lung" is a device that simulates breathing process of occupants in a room. This allows you to safely test, e.g., the impact of HVAC systems on the spread of pathogens. The paper describes the con...
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With society's increasing data production and the corresponding demand for systems that are capable of utilizing them, the big data domain has gained significant importance. However, besides the systems' actua...
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This article aims to introduce the generalized stochastic perturbed Schrödinger–Hirota equation, which incorporates multiplicative white noise in the Itô sense. The study focuses on investigating the stocha...
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In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial...
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The impact of hero selection on game outcomes is well-recognized, with input from respected streamers and commentators who emphasize the significance of hero choices. These insights, supported by substantial followers...
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ISBN:
(数字)9798350383591
ISBN:
(纸本)9798350383607
The impact of hero selection on game outcomes is well-recognized, with input from respected streamers and commentators who emphasize the significance of hero choices. These insights, supported by substantial followers and views, underscore the importance of astute hero selection. However, hero selection is not straightforward due to factors like player proficiency and team strategy. While extensive game data allows us to gauge hero performance, practical challenges exist. To address these, a method for real-time outcome prediction based on hero selection is introduced. This approach includes an interface for hero selection, displays sections for hero information, and identifies both winning and losing heroes. The method also calculates and displays the financial advantage in-game, a crucial factor, and adapts to evolving patches, ensuring its reliability and accessibility to players.
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