Day by day cardio vascular disease death cases increasing. It mainly affects the human heart and blood vessels and it is difficult to diagnosis it. In this paper various machine learning algorithm is used to predict t...
Day by day cardio vascular disease death cases increasing. It mainly affects the human heart and blood vessels and it is difficult to diagnosis it. In this paper various machine learning algorithm is used to predict the cardio vascular disease. The data set of 14 attributes is used for prediction. The irrelevant features are handled using Boruta algorithm of 100 iterations. The proposed work uses Adaboosting with different hyper parameter and the multiple decision trees are built for the wrongly classified feature. At finally the Adaboosting algorithm achieve 93.74% accuracy in predicting the disease.
Training and fine-tuning large language models (LLMs) with hundreds of billions to trillions of parameters requires tens of thousands of GPUs, and a highly scalable software stack. In this work, we present a novel fou...
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Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an...
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Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-aided diagnosis of diabetic retinopathy.
This study analyzes the influence of the COVID-19 pandemic on impulse buying on the marketplace platform in Jakarta. This research is motivated because the COVID-19 pandemic period lasted quite a long time. Therefore,...
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The Quantum Approximate Optimization Algorithm (QAOA) has enjoyed increasing attention in noisy intermediate-scale quantum computing due to its application to combinatorial optimization problems. Because combinatorial...
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The K-means algorithm, one of the most well-known clustering techniques, has been widely employed to solve a variety of problems. In contrast, the k-means clustering algorithm has numerous restrictions. For instance, ...
The K-means algorithm, one of the most well-known clustering techniques, has been widely employed to solve a variety of problems. In contrast, the k-means clustering algorithm has numerous restrictions. For instance, the difficulty of dealing with voluminous data, the sensitivity of the outlier, and the random selection of the initial centroid. In this paper, a parallel K-means clustering algorithm is proposed that improves the performance of sequential K-means clustering algorithms by removing outliers from the data before clustering, dividing the data into smaller sections among the threads, and selecting the initial centroid with care. Our primary parallelization tool was OpenMP, which was implemented using the C programming language on 234,296 records. This experiment was conducted using sequential and parallel source code, with modifications made to enhance the parallel functionality. The improved parallel execution resulted in a significant reduction in execution time relative to sequential algorithms. The proposed algorithm source code is also available on GitHub for the community.
The vibrations generated by rock blasting are a serious and hazardous outcome of these activities,causing harmful effects on the surrounding environment as well as the nearby *** the local ecology and human communitie...
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The vibrations generated by rock blasting are a serious and hazardous outcome of these activities,causing harmful effects on the surrounding environment as well as the nearby *** the local ecology and human communities suffer from the consequences of these *** the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity(PPV)and frequency,which are essential parameters for measuring vibration *** prediction of vibration occurrence is critically ***,this study employs five machine learning models for predicting the PPV and frequency resulting from quarry *** work compares five machine learning models(XGBoost,Catboost,Bagging,Gradient Boosting,and Random Forest Regression)to choose the most efficient performance *** performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase,there was a strong correlation observed between the actual and the predicted *** analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R^(2)=0.983,MSE=0.000078,RMSE=0.008,NRMSE=0.019,MAD=0.004,MAPE=35.197 in the PPV prediction,and R^(2)=0.975,MSE=0.000243,RMSE=0.015,NRMSE=0.031,MAD=0.008,MAPE=37.281 for the frequency *** study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration,and *** the context of the mining and civil industry,the application of this study offers significant potential for enhancing safety protocols and optimizing operational *** employing machine learning models,this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.
The rapidly increasing popularity of micromobility devices, such as e-scooters and bicycles, in urban environments underscores the critical need for advanced trajectory prediction models to enhance road safety and fac...
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ISBN:
(数字)9798331505929
ISBN:
(纸本)9798331505936
The rapidly increasing popularity of micromobility devices, such as e-scooters and bicycles, in urban environments underscores the critical need for advanced trajectory prediction models to enhance road safety and facilitate seamless integration into intelligent transportation systems. This study introduces a novel approach to predicting the movements of bicyclists and e-scooter riders by leveraging a unique dataset that combines bird's-eye and egocentric views, collected from diverse urban settings across the United States. Employing state-of-the-art deep learning techniques, our model significantly outperforms traditional linear and polynomial regression models in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE), demonstrating superior accuracy in forecasting the trajectories of vulnerable road users. Furthermore, our approach incorporates cognitive annotations to predict crossing intentions, enriching the model's predictive capabilities. The findings highlight the potential of our method to contribute to the development of proactive safety measures and collision avoidance systems, ultimately fostering safer urban mobility landscapes for all road users.
With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most ...
With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, resulting in a lack of energy measuring tools for other programming languages such as R. To address this gap, we propose “RJoules”, a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of RJoules by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. The demonstration of the tool is available at https://***/yMKFuvAM-DE and related artifacts at https://***/RJoules/.
Intersections are essential road infrastructures for traffic in modern metropolises. However, they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence of traffic coordination me...
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