The operation of machine literacy algorithms for disfigurement identification and opinion within power systems is presented in this exploration composition. In light of the added complexity of moment's power grids...
详细信息
The operation of machine literacy algorithms for disfigurement identification and opinion within power systems is presented in this exploration composition. In light of the added complexity of moment's power grids, it's of the utmost significance to develop fault discovery and opinion styles that are both effective and effective. As a result of their capacity to assay big datasets and honor patterns, machine literacy ways present several openings that hold great pledges. Several different machinelearning algorithms, including as neural networks, decision trees, support vector machines, and clustering approaches, are delved in this study. The purpose of the disquisition is to estimate the utility of these algorithms in relating and diagnosing blights in power systems. Through the use of empirical evaluation and case studies, the exploration reveals that these algorithms are applicable in the process of perfecting the trustability and effectiveness of power system operations. This exploration donates to the advancement of fault identification and opinion approaches in power engineering, hence paving the way for further exploration.
This study focuses on enhancing DoS attack detection in IoT systems through a machinelearning approach that combines class balancing, feature selection, and optimized classifiers. Utilizing the Edge IIoT dataset, we ...
详细信息
Kidney disease encompasses various abnormalities in renal function, ranging from subtle damage to severe conditions such as excessive cell expansion, impaired blood filtration, and the deposition of crystalline minera...
详细信息
This study uses machinelearning models as potent analytical tools to look into the issue of inventory cost and profit prediction in the automobile industry. Throughout a ten-year dataset from 2012 to 2023, the study ...
详细信息
Software variability engineering benefits from machinelearning (ML) to learn e.g., variability-aware performance models, explore variants of interest and minimize their energy impact. As the number of applications of...
详细信息
ISBN:
(纸本)9798400700019
Software variability engineering benefits from machinelearning (ML) to learn e.g., variability-aware performance models, explore variants of interest and minimize their energy impact. As the number of applications of combining variability with ML grows, we would like to reflect on what is the core to the configuration process in software variability and inference in ML: feature engineering. These disciplines previously managed features explicitly, easing graceful combinations. Now, deep learning techniques derive automatically obscure but efficient features from data. Shall we give up explicit feature management in variability-intensive systems to embrace machinelearningadvances?
The impact of fake news in the present-day world is very high. It has the potential to alter anything irrespective of the profile. Not all fake news is dangerous but some may even ruin the lives of many. There are a l...
详细信息
A predictive model refers to a mathematical or computational model that is designed to make predictions or forecasts based on input data. These models are used in various fields such as finance, healthcare, marketing,...
详细信息
Assessing short answers in educational settings is challenging due to the need for scalability and accuracy, which led to the field of Automatic Short Answer Grading (ASAG). Traditional machinelearning models, such a...
详细信息
ISBN:
(纸本)9798400707018
Assessing short answers in educational settings is challenging due to the need for scalability and accuracy, which led to the field of Automatic Short Answer Grading (ASAG). Traditional machinelearning models, such as ensemble and embeddings, have been widely researched in ASAG, but they often suffer from generalizability issues. Recently, Large Language Models (LLMs) emerged as an alternative to optimize ASAG systems. However, previous research has failed to present a comprehensive analysis of LLMs' performance powered by prompt engineering strategies and compare its capabilities to traditional models. This study presents a comparative analysis between traditional machinelearning models and GPT-4 in the context of ASAG. We investigated the effectiveness of different models and text representation techniques and explored prompt engineering strategies for LLMs. The results indicate that traditional machinelearning models outperform LLMs. However, GPT-4 showed promising capabilities, especially when configured with optimized prompt components, such as few-shot examples and clear instructions. This study contributes to the literature by providing a detailed evaluation of LLM performance compared to traditional machinelearning models in a multilingual ASAG context, offering insights for developing more efficient automatic grading systems.
The revolutionary potential of emotional chatbots in improving human health is explored in this research. We research the operation of various chatbot kinds in order to reveal important qualities necessary for the dev...
详细信息
Colorectal cancer is one of the most common malignant tumors globally. This study aims to construct a classification model to predict the development of colorectal cancer, and to explore the important influential vari...
详细信息
ISBN:
(纸本)9798350375084;9798350375077
Colorectal cancer is one of the most common malignant tumors globally. This study aims to construct a classification model to predict the development of colorectal cancer, and to explore the important influential variables and their interactions that affect the carcinogenesis of colorectal cancer, in order to provide more in-depth scientific evidence for the analysis, treatment and prevention of colorectal cancer. The results show that the model constructed in this study through machinelearning algorithms can effectively classify and predict the development of colorectal cancer. The model has proven to be effective, convenient for practical application, and highly applicable.
暂无评论