Digital filter design plays a crucial role in signal processing applications, aiming to enhance, extract, or suppress specific components of a signal. Soft computing techniques have emerged as effective methods for de...
详细信息
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...
详细信息
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 ...
详细信息
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...
详细信息
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,
Unsupervised anomaly detection in cloud computing is crucial for system security and efficiency. However, the challenges posed by large data volumes, low anomaly rates, and diverse anomaly patterns in time series with...
详细信息
ISBN:
(纸本)9798350359329;9798350359312
Unsupervised anomaly detection in cloud computing is crucial for system security and efficiency. However, the challenges posed by large data volumes, low anomaly rates, and diverse anomaly patterns in time series within cloud computing scenarios make it difficult for previous methods to obtain consistent and reliable representations for distinguishing anomalies. To avoid the degradation of model representation ability caused by abnormal sparsity, we propose the Density Transformer, a novel reconstruction-based explicit association modeling model that can amplify the non-trivial correlation of abnormal points with adjacent time points. Specifically, we express the density association by calculating the kernel density estimate at each time point, and the series association by calculating the self-attention at each time point. Then, the model uses an adversarial training strategy to produce a more significant difference in "association discrepancy" between normal points and abnormal points, thereby ensuring robust results in anomaly detection. Our model has been rigorously evaluated on a comprehensive collection of 6 publicly available real-world datasets, and the Density Transformer can achieve up to 46% improvement in F1-score compared to existing methods.
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...
详细信息
This paper introduces an Artificial Neural Network model that integrates advanced deep learning techniques from computer vision and natural language processing domains. The model focuses on automating the captioning p...
详细信息
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 ...
详细信息
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...
详细信息
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 ...
详细信息
暂无评论