Anomaly detection in sequential signals is gaining prominence, especially with limited training data and timeliness requirements. Fully extracting the data-inside changing information, we propose a novel Wavelet-Enhan...
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there are hundreds of kinds of plants on Earth, and many of them have medicinal or curative properties. Approximately 80% of the global population continues to rely on traditional medicine. In Ayurveda, the use of her...
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With the popularity of GPS-equipped smart devices, spatial crowdsourcing (SC) techniques have attracted growing attention in both academia and industry. In existing trajectory-aware task assignment approaches, tasks a...
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Electrical impedance tomography (EIT) aims to reconstruct the body's internal electrical conductivity distribution from surface voltage measurements. This non-invasive, non-ionizing, and cost-effective technique i...
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作者：
Kumar, G. MuthuHemanand, D.
Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India
Department of Computer Science and Engineering Tamil Nadu Chennai India
The field of artificial intelligence (AI) has seen significant advancements in recent years. These days, artificial intelligence (AI) tools are being utilized by organizations in both the public and commercial sectors...
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ISBN:
(纸本)9798350375237
The field of artificial intelligence (AI) has seen significant advancements in recent years. These days, artificial intelligence (AI) tools are being utilized by organizations in both the public and commercial sectors all over the world. Individuals, organizations, and society as a whole will reap broad and significant advantages as a result of the capabilities of artificial intelligence (AI) both today and in the near future. Nevertheless, these very same technical advancements give rise to significant concerns, such as the question of how to ensure that artificial intelligence technology is built and implemented in a manner that is in accordance with the applicable data privacy laws and standards. The fast development of artificial intelligence presents substantial hurdles in terms of protecting customers' privacy and the confidentiality of their data. The purpose of this essay is to suggest an all-encompassing strategy for the development of a framework to solve these concerns. First, an overview of prior research on security and privacy in artificial intelligence is presented, with an emphasis on both the progress that has been made and the limits that still remain. In the same vein, open research topics and gaps that need to be addressed in order to improve existing frameworks are recognized. Regarding the development of the framework, the topic of data protection in artificial intelligence is discussed. This includes elaborating on the significance of protecting the data that is utilized in artificial intelligence models, as well as elaborating on the policies and practices that are in place to ensure the data's safety and the methods that are utilized to maintain the data's integrity. Additionally, the security of artificial intelligence is investigated, which includes an analysis of the vulnerabilities and dangers that are present in artificial intelligence systems, as well as the presentation of instances of potential assaults and malevolent manipulations,
People with disabilities often face difficulties communicating with the general public, especially regarding sign language. Sign Language Recognition (SLR) is a technology focused on recognizing, interpreting, and tra...
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This paper investigates gender disparity in the computerscience (CS) program at the University of British Columbia Okanagan (UBC-O). Despite the national average of about 30% for women in CS programs, UBC-O shows a m...
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With the rapid development of the Industrial Internet of Things (IIoT), higher requirements have been put forward for hardware devices, and as the core part of hardware devices, the performance of chips is critical. T...
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Menstrual cycle prediction is a critical issue for many women, as it can help them plan their daily activities, and prepare for potential physical and emotional changes. However, current methods for predicting menstru...
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ISBN:
(纸本)9798350370096
Menstrual cycle prediction is a critical issue for many women, as it can help them plan their daily activities, and prepare for potential physical and emotional changes. However, current methods for predicting menstruation often rely on physiological data such as age, cycle length, and ovulation history, which may not be convenient or accurate for some *** paper presents a comparative study of menstruation prediction models using daily social media sentiment data. The underlying hypothesis is that social media posts can reflect the user's emotions and feelings, which may be related to the menstrual cycle. By tracking emotions from social media posts, it may be possible to monitor potential symptoms associated with the menstrual cycle, such as premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD).The proposed models are evaluated using a dataset of social media posts and self-reported menstruation data. The results demonstrate that the proposed models can effectively predict the onset of menstruation and identify potential symptoms of PMS and PMDD. This work has the potential to contribute to the development of new tools and interventions for women's health and *** experimental research utilized a dataset of posts on X from one woman over a 3-year period (2020-2023), covering both normal and abnormal menstrual cycles. The challenge of this research was to transform ordinary text that could be posted on social media but could not be used for prediction into text that could be used for in-depth prediction and analysis. This was achieved through two processes: 1) Sentiment Analysis using the WangchanBERTa model to determine the sentiment of the posts, which achieved an accuracy of 61.7%;2) the Time Series Forecasting process for predicting the date of menstruation using the Random Forest model was the most appropriate if compared to Linear Regression and SVR, which is considered to be the most accurate. Challenge new faces This is bec
This research addresses the pressing issue of breast cancer detection, emphasizing the development and evaluation of deep learning models using two distinct datasets. The first dataset involves histology images, where...
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