Cancer, also known as malignant neoplasm, is a complex and potentially fatal disease characterized by uncontrolled and abnormal cell growth in the body. The main problems with microarray cancer studies are the high cu...
Cancer, also known as malignant neoplasm, is a complex and potentially fatal disease characterized by uncontrolled and abnormal cell growth in the body. The main problems with microarray cancer studies are the high curse of dimensionality and small sample size caused by redundant and irrelevant genes. To deal with the number of features that exceed the amount of data, this research purposed double filtering method Lasso-GA, Lasso is used to select the features based on feature correlation while Genetic Algorithm is used to optimize the most important features with accuracy traditional machine learning as its fitness function. The results show how effective the suggested method is; in breast cancer, the linear SVC model achieves excellent accuracy (0.93), precision (0.94), recall (0.94), and F1 score (0.94), while in lung cancer, the linear SVC, random forest, and logistic regression models perform well (accuracy: 0.95, precision: 0.92, recall: 1, F1 score: 0.95). Logistic regression is the most effective method for bladder cancer, with an accuracy of 0.82, precision of 0.77, recall of 1, and F1 score of 0.87.
In a music scenario, both auditory and visual elements are essential to achieve an outstanding performance. Recent research has focused on the generation of body movements or fingering from audio in music performance....
详细信息
Analyzing the impact of fuel price increases in Indonesia is crucial for making informed strategic decisions. This paper presents a smart fuzzy decision model aimed at predicting changes in household spending in respo...
Analyzing the impact of fuel price increases in Indonesia is crucial for making informed strategic decisions. This paper presents a smart fuzzy decision model aimed at predicting changes in household spending in response to fuel price fluctuations. The model utilizes fuzzy logic as its main method, enabling it to capture the intricate relationships between household spending and fuel prices. In addition, the proposed model incorporates various factors that can potentially influence household spending. By simulating prediction results under different fuel price increments ranging from 0% to 30%, the model provides valuable insights for policymaking concerning fuel pricing and offers strategies to mitigate the impact of fuel price fluctuations on household welfare.
Plasmonic sensors exhibit high sensitivity due to enhanced local fields. But, their detectivity is poor because of their poor Q-factors. Using a plasmonic BIC, we experimentally demonstrate enhanced Q-factors in a pla...
详细信息
Detecting fake news in the digital era is challenging due to the proliferation of misinformation. One of the crucial is-sues in this domain is the inherent class imbalance, where genuine news articles significantly ou...
Detecting fake news in the digital era is challenging due to the proliferation of misinformation. One of the crucial is-sues in this domain is the inherent class imbalance, where genuine news articles significantly outnumber fake ones. This imbalance severely hampers the performance of machine and deep learning models in accurately identifying fake news. Consequently, there is a compelling need to address this problem effectively. In this study, we delve into fake news detection and tackle the critical issue of imbalanced data. We investigate the application of Easy Data Augmentation (EDA) techniques, including back-translation, random insertion, random deletion, and random swap to mitigate the adverse effects of imbalanced data. This study focuses on employing these techniques in conjunction with a deep learning framework, specifically a Bidirectional Long Short-Term Memory (BiLSTM) architecture. The results of the EDA techniques will be systematically compared to see their effectiveness and their impacts on model performance. This study reveals that various EDA techniques, when coupled with a BiLSTM architecture, yield significant improvements in fake news detection. Among the experiments, it shows that Random Insertion, with an impressive accuracy rate of 81.68%, a precision score of 89.38%, and an F1-Score of 87.77% emerges as the most promising technique. The study also highlights the exceptional potential of Back-translation stands out with an 87.16% recall performance.
Addressing multiple criteria and parameter issues in computer modelling presents a significant challenge. Several factors including data types, parameter behaviours, and purposes, must be taken into account to enhance...
Addressing multiple criteria and parameter issues in computer modelling presents a significant challenge. Several factors including data types, parameter behaviours, and purposes, must be taken into account to enhance computer modelling capability; particularly in evaluation cases. Through the utilization of a multi-criteria and method approach, a decision model was effectively developed to assess a case of environmental sustainability level of a building. One method operated in the study is the curve method for handling membership function form in realizing fuzzy logic. This innovative model demonstrates superior performance. It achieves an impressive accuracy rate of 96%, surpassing the previous model that employed a trapezoidal approach to describe fuzzy membership functions hy 1%.
Containerization has become a popular approach in application development in applications development and deployment, many benefits we can get such as improved scalability, portability, and resource efficiency. Contai...
Containerization has become a popular approach in application development in applications development and deployment, many benefits we can get such as improved scalability, portability, and resource efficiency. Container-based applications, utilizing technologies like Docker and Kubernetes, have transformed the packaging, deployment, and management of software from the desktop environment to the cloud platform. In this context, software metrics approach plays a good role in evaluating the characteristics and performance of container-based applications, ensuring that developers and operators are on the same page. This article explores the importance of software metrics in optimizing the software lifecycle of container-based applications, addressing the unique challenges they present, and highlighting the potential benefits of leveraging metrics to improve performance and efficiency. Our finding Performance Metrics and Availability Metrics is the most metrics that the most measure by applications owner, relevant studies and industry practices, this study aims to provide insights and recommendations to effectively measure and optimize region-based software systems.
Rapid progress in the field of artificial intelligence has created opportunities for extensive applications in software development. One area that receives attention is the evaluation of code quality using machine lea...
Rapid progress in the field of artificial intelligence has created opportunities for extensive applications in software development. One area that receives attention is the evaluation of code quality using machine learning techniques. In this investigation, we examined the possible application of machine learning to predict the likelihood of defects in computer code. We employ NASA archival data as case studies. Machine learning models employ neural network algorithms. Our exploration involves partitioning the dataset into training data and test data for performance evaluation. The findings indicate that the Neural Organize technique with resampling yields a high level of accuracy in predicting software defects. Our simulated neural network is capable of identifying intricate patterns in the data and providing precise measurements of the size and intensity of defects. These results have significant implications in the software business, enabling developers to promptly identify possible vulnerabilities and take preventive measures before product release.
This study is related to a system that enables elderly people to communicate interactively with young people who use existing message exchange services by simply speaking to an avatar on a tablet PC, without having to...
详细信息
Applications designed utilizing Microservices Architecture (MSA) provide the desirable trait of good maintainability. To ensure optimal maintainability, it is important to provide services that are suitable and adhere...
Applications designed utilizing Microservices Architecture (MSA) provide the desirable trait of good maintainability. To ensure optimal maintainability, it is important to provide services that are suitable and adhere to prescribed rules. Multiple aspects must be taken into account while designing services to ensure optimal maintainability. The objective of this study is to examine the elements that impact the capacity to sustain and improve maintainability in service design, ultimately resulting in an application that possesses strong maintainability. The Systematic Literature Review (SLR) will be utilized to identify variables and strategies for their enhancement, by examining pertinent publications on the subject. After examining 45 publications, the study discovered 8 elements and 14 solutions that can enhance the highlighted parameters throughout the services design process. The outcomes of this systematic literature review (SLR) are anticipated to give valuable insights to application developers, empowering them to generate service designs that exhibit commendable maintainability for the developed applications.
暂无评论