The goal of educational institutions should be to find effective ways to provide new and efficient learning opportunities based on their environment, student characteristics, teacher preparation, economic crisis, and ...
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Cloud computing has emerged as a transformative technology, offering on-demand access to scalable computing resources. However, ensuring optimal performance while managing costs remains a challenging task. This resear...
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ISBN:
(纸本)9798350382471
Cloud computing has emerged as a transformative technology, offering on-demand access to scalable computing resources. However, ensuring optimal performance while managing costs remains a challenging task. This research focuses on evaluating and analyzing the performance of various meta-heuristic techniques in cloud computing environments, with a specific emphasis on execution time and cost as critical performance metrics. The study begins by introducing the concept of meta-heuristic techniques, which are intelligent optimization algorithms that have gained prominence in solving complex, dynamic, and computationally expensive problems. These techniques, such as Base Genetic Algorithms, teachinglearning base optimization, Elephant herding optimization, and Harmony search optimizer have the potential to optimize resource allocation in cloud computing, improving average execution time of tasks, and resource cost efficiency. The analysis of the results demonstrates the effectiveness of meta-heuristic techniques in reducing execution time and managing costs. The findings also highlight the trade-offs between different meta-heuristic approaches, helping cloud administrators make informed decisions when selecting the most suitable technique for specific use cases. In conclusion, this research contributes to the understanding of how meta-heuristic techniques can enhance the performance of cloud computing systems by optimizing resource allocation. By utilizing execution time and cost as key performance metrics, it provides valuable insights for cloud service providers and organizations seeking to improve the efficiency and cost-effectiveness of their cloud-based services. Ultimately, this work aims to facilitate the development of more efficient and cost-conscious cloud computing solutions in an increasingly dynamic and competitive digital landscape. Results shows the performance indicators with four key optimizer in which Elephant herding optimizer outperforms the base GA,
This paper explores deep learning-based algorithms for electrocardiogram (ECG) signal processing and their applications in cardiac health monitoring. Initially, we provide an overview of the fundamental principles of ...
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Neuromorphic computing is a cutting-edge field of research that focuses on designing and developing computer systems and hardware architectures inspired by the structure and functioning of the human brain. The main ob...
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This is a comparative study of teachinglearning-Based Optimization (TLBO) as a human-based algorithm against other types of metaheuristic algorithm: single-agent finite impulse response optimizer (SAFIRO), simulated ...
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This research study introduces an innovative solar-based ironing system that integrates renewable energy with advanced deep-learning techniques to optimize energy consumption in household ironing operations. The syste...
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This paper presents a parameter-efficient prompt tuning method, named PPT, to adapt a large multi-modal model for 3D point cloud understanding. Existing strategies are quite expensive in computation and storage, and d...
ISBN:
(纸本)9798350384581;9798350384574
This paper presents a parameter-efficient prompt tuning method, named PPT, to adapt a large multi-modal model for 3D point cloud understanding. Existing strategies are quite expensive in computation and storage, and depend on time-consuming prompt engineering. We address the problems from three aspects. Firstly, a PromptLearner module is devised to replace hand-crafted prompts with learnable contexts to automate the prompt tuning process. Then, we lock the pre-trained backbone instead of adopting the full fine-tuning paradigm to substantially improve the parameter efficiency. Finally, a lightweight PointAdapter module is arranged near target tasks to enhance prompt tuning for 3D point cloud understanding. Comprehensive experiments are conducted to demonstrate the superior parameter and data efficiency of the proposed method. Meanwhile, we obtain new records on 4 public datasets and multiple 3D tasks, i.e., point cloud recognition, few-shot learning, and part segmentation. The implementation is available at https://***/auniquesun/PPT.
Monkeypox is a recently emerged disease outbreak that has impacted the health of many individuals. This study focuses on developing a skin lesion-based classification system for monkeypox disease. Many recent works su...
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Malignant soft tissue tumor are rare cancers and uncommon diseases. Ideally, rare cancers should be diagnosed by a pathologist who specializes in that organ, but there is a chronic shortage of pathologists in Japan. P...
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The degree of secured currency is one of the key areas that makes its contribution in solving the problem of economic insecurity, highlights the most important. Even as it can be quite strained to actually identify wh...
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