The heart is perhaps one of the most vital organs supporting human life, but it is more at risk in our contemporary world with its rapid pace of life. The lifestyles in modem times and exert an immense burden on cardi...
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Generative AI is transforming the way humans interact with robots by integrating language-driven comprehension with embodied execution. While recent research leveraging large language models (LLMs) to enhance communic...
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The rapid growth of smart cities, healthcare monitoring, and environmental sensing relies heavily on the real-time data processing capabilities of Wireless Sensor Networks (WSNs). However, these networks face signific...
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Video object segmentation aims to extract 2D object masks by segmenting video frames into multiple objects, which is crucial in various practical applications such as medical imaging, etc.. However, traditional video ...
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Air pollution is a significant obstacle to achieving urban sustainability and public health, particularly in rapidly developing regions. Accurate air quality prediction is essential for proactive pollution management ...
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Vehicle detection and counting using OpenCV is a crucial aspect of modern traffic management systems. This mini project aims to develop a robust solution for detecting vehicles in real-time video streams and accuratel...
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Based on a polyurethane high-pressure foaming device, the process of synthesizing polyurethane from isocyanate (-NCO) and polyether polyol (-OH) under the catalytic effect of various additives was simulated. Using the...
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The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This articl...
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ISBN:
(数字)9798331523923
ISBN:
(纸本)9798331523930
The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. Data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD.
Toxic comments are comments which are disrespectful, unreasonable and infuriating that make the reading uncomfortable. Sometimes these comments are inappropriate and not good for the public. These comments make daily ...
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The growing complexity and number of cyber threats call for sophisticated detection approaches that provide both high performance and interpretability. The proposed hybrid AI approach (AE-RF-CNN-LSTM) is a promising h...
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ISBN:
(数字)9798331543891
ISBN:
(纸本)9798331543907
The growing complexity and number of cyber threats call for sophisticated detection approaches that provide both high performance and interpretability. The proposed hybrid AI approach (AE-RF-CNN-LSTM) is a promising hybrid up-to-date improving SEO framework for cybersecurity. All these models play a steady role in performance improvement. Autoencoders are trained to sense patterns from normal data to assist in anomaly detection, random forests help with the robustness of the model with overfitting reduction, and CNN-LSTMs assist greatly in recognizing the complex temporal dependencies in network traffic data. Experimental results show that this approach achieves substantially improved accuracy nearest neighbor anomaly detection and surpasses every single model in terms of accuracy. This hybrid framework, thus, helps to detect known and unknown cyber threats, leading to a huge decrease in false positive and false negative rates. Furthermore, integrating explainable AI (XAI) methods, including SHAP (SHapley Additive explanations) and LIME (Local Interpretable Model-Agnostic Explanations), to enhance decision interpretability. The methods enable security analysts to comprehend the most relevant features of making predictions and, therefore, trust the model and correct further cybersecurity actions. This not only allows for the improvement of defense mechanisms against cyberattacks but also builds confidence in AI-driven security solutions by providing interpretability and assurance of performance.
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