Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. this review article aims to explore the current and potential future roles of ma...
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Platform data mining is an important branch of data analysis. Traditional methods such as clustering have achieved satisfactory performance. To overcome the shortcomings of traditional algorithms in mathematical optim...
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Face and Human walking style is plays important role to recognize a person. this paper is a brief assessment of various machine learningalgorithms for solving multi-biometric recognition, their limitations and their ...
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Early detection of vehicle accidents is essential for immediate response and to decrease the severity of injuries while vehicle accidents occur. Among the advanced algorithms YOLOv6, YOLOv7, YOLOv8, and YOLOv9 are com...
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ISBN:
(纸本)9798350354140;9798350354133
Early detection of vehicle accidents is essential for immediate response and to decrease the severity of injuries while vehicle accidents occur. Among the advanced algorithms YOLOv6, YOLOv7, YOLOv8, and YOLOv9 are compared in this article to determine their performance in detecting vehicle accidents that are categorized as "severity" or "moderate." the study emphasizes how important deep learning is to enhancing accident detection systems. Out of all the models evaluated, YOLOv8m has the highest precision (0.979), and the highest recall (0.927) for YOLOv8n. Furthermore, YOLOv9c performs admirably in mAP50, receiving a score of 0.977, indicating its resilience in identifying accidents under a variety of circumstances. YOLOv9e is noteworthy for its better mAP5095, which reaches a high of 0.941, demonstrating its ability to localize accident incidents accurately. these results provide valuable insight into how well particular algorithms perform in real-world situations. through a close examination of their performance measures, this research aids in the advancement of more effective accident detection systems, which in turn improves emergency response operations and lessens the effects of accidents on people and communities. this comparison research emphasizes how crucial it is to use state-of-the-art technology to increase accident detection accuracy and enable timely and efficient response.
this study explores the use of Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) machine learning models in metaheuristic algorithms, with a focus on a modified General Variable Neighb...
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ISBN:
(纸本)9783031629112;9783031629129
this study explores the use of Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) machine learning models in metaheuristic algorithms, with a focus on a modified General Variable Neighborhood Search (GVNS) for the Capacitated Vehicle Routing Problem (CVRP). We analyze the historical chain of actions in GVNS to demonstrate the predictive potential of these models for guiding future heuristic applications or parameter settings in metaheuristics such as Genetic algorithms (GA) or Simulated Annealing (SA). this "optimizing the optimizer" approach reveals that, the history of actions in metaheuristics provides valuable insights for predicting and enhancing heuristic selections. Our preliminary findings suggest that machine learning models, using historical data, offer a pathway to more intelligent and data-driven optimization strategies in complex scenarios, marking a significant advancement in the field of combinatorial optimization.
this research addresses the challenges in the early identification of lung cancer by proposing an automated system using various Machine learning (ML) and image processing techniques. this study has utilized a collect...
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the exponential growth of data driven by the internet has necessitated effective extraction of insights, with big data and machine learning standing as pivotal tools. this paper aims to provide insights into the evolv...
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In order to reduce the roughness of the milling, a optimization algorithm is designed. Firstly, the deep-hole milling parameters of nickel-based alloy were preprocessed, and then the unqualified parameters of nickel-b...
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the liver is a vital organ responsible for numerous essential functions in the body. thus, liver disorders can have severe consequences on overall health and well-being. Early diagnosis and treatment of liver disorder...
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ISBN:
(纸本)9783031774256;9783031774263
the liver is a vital organ responsible for numerous essential functions in the body. thus, liver disorders can have severe consequences on overall health and well-being. Early diagnosis and treatment of liver disorders are crucial to prevent complications such as cirrhosis, liver failure and liver cancer. In this work, a data analysis system aims to identify the most important features in defining liver disease and categorize sick patients according to the severity of the disease. the Indian Liver Patient Dataset was evaluated using a pre-processing data analysis method that considered the Z-score, the correlation, and the Recursive Feature Elimination. After identifying the most important characteristics of the patients, the Fuzzy c-means algorithm was used to classify them based on the severity of the disease. the results of the proposed methodology proved to be effective in creating a decision support system, since it was possible to identify four levels of severity among the patients.
Recommender systems are beneficial in suggesting items to users by knowing their preferences and, therefore, effectively managing the vast amount of available information. Regarding the classical systems that focus on...
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ISBN:
(纸本)9783031686498;9783031686504
Recommender systems are beneficial in suggesting items to users by knowing their preferences and, therefore, effectively managing the vast amount of available information. Regarding the classical systems that focus on accuracy, the needs of their users have changed so much that many sometimes-conflicting performance measures now have to be taken into account. Recent research has enhanced the applicability of multi-objective evolutionary algorithms in recommender systems, balancing indicators such as accuracy with other essential ones. this survey provides a listing of recent works that applied MOEAs to the problem of recommender systems and pays special attention to critical areas, such as methodological approaches, goals, datasets, and evaluation strategies. this analysis, beyond the state-of-the-art synthesis, helps in the determination of the problems that are linked to the use of MOEAs and the prospects of the development of future research. the exploration targets aiding progress and innovation in this dynamic field.
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