Self Supervised Representation Learning (SSRepL) can capture meaningful and robust representations of the Attention Deficit Hyperactivity Disorder (ADHD) data and have the potential to improve the model's performa...
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While utilizing gas heating systems, there is a substantial danger of hypoxia (sleep death). The high levels of carbon monoxide in the space stifle the flow of blood to the brain, which might cause hemorrhage and subs...
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ISBN:
(纸本)9798350384659
While utilizing gas heating systems, there is a substantial danger of hypoxia (sleep death). The high levels of carbon monoxide in the space stifle the flow of blood to the brain, which might cause hemorrhage and subsequently result in demise. To attempt to address the aforementioned issue, an entirely novel learning technique is developed in which Learning algorithms is going to be used in this endeavor to deliver an automated space heating effect. Whenever a room's requirements are met, the heating in that space is going to be turned on. (room, temperature, oxygen level, Carbon dioxide level, water vapours, foreign gases, humidity). The space is going to stay toasty until the atmospheric oxygen level drops below its limited threshold, at which point the heating system will turn itself to its power-saving mode. Additionally, the individual using it will have the ability to able to furnish the machine with every one of these variables in accordance to their preferences. Whenever there are more people in the room, the heating system will turn itself off. The warmth in the environment is unlikely to trigger a response on the skin of the person using it if the level of moisture in the space falls. The heating system won't turn on if it detects any noxious gas or smoke in the space. If the carbon monoxide level rises to the optimum level, the heating system will automatically turn itself down and switch to power-saving function to use a lesser amount of energy. individuals who have the condition This important development will make it possible for patients with respiratory issues to adjust their warming according to their oxygen in their blood levels. If the individual's arterial oxygen saturation suddenly lowers, the heating system will have to be halted immediately. Suffering the help of this groundbreaking advancement, individuals who have respiratory illnesses will eventually be able to modify their heating according to the amount of oxygenation that exists in their
Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless networks, especially when operating at the millimeter-wave (mm-wave) and terahertz (THz) frequency bands. However, ...
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Heart failure is now widely spread throughout the *** disease affects approximately 48%of the *** is too expensive and also difficult to cure the *** research paper represents machine learning models to predict heart ...
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Heart failure is now widely spread throughout the *** disease affects approximately 48%of the *** is too expensive and also difficult to cure the *** research paper represents machine learning models to predict heart *** fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for *** supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best *** boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data *** and Scikit-learns are used for *** Flow and Keras,along with Python,are used for ANN model *** DT and RF algorithms achieved the highest accuracy of 95%among the ***,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.
Large Language Models (LLMs) are extensive aggregations of human language, designed to understand and generate sophisticated text. LLMs are becoming ubiquitous in a range of applications, from social media to code gen...
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With the increasing integration of automated systems in modern aircraft, the security of avionics systems has become a critical concern. This research aims to conduct a vulnerability analysis of TCAS-related systems, ...
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In this paper, we propose a restoration method of time-varying graph signals, i.e., signals on a graph whose signal values change over time, using deep algorithm unrolling. Deep algorithm unrolling is a method that le...
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This paper aims to evaluate the performance of several supervised machine learning methods as to determine the best algorithm for detecting explosives with multispectral imagery. Ocean Thin Films SpectroCam with 8 int...
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In this article, we propose to study a novel research problem to boost group performance, that is, social-aware diversity-optimized group extraction (SDGE), which takes into consideration the two important factors: 1)...
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We develop a technique to design efficiently computable estimators for sparse linear regression in the simultaneous presence of two adversaries: oblivious and adaptive. Consider the model y∗ = X∗β∗ + η where X∗ is a...
We develop a technique to design efficiently computable estimators for sparse linear regression in the simultaneous presence of two adversaries: oblivious and adaptive. Consider the model y∗ = X∗β∗ + η where X∗ is an n × d random design matrix, β∗ ∈ Rd is a k-sparse vector, and the noise η is independent of X∗ and chosen by the oblivious adversary. Apart from the independence of X∗, we only require a small fraction entries of η to have magnitude at most 1. The adaptive adversary is allowed to arbitrarily corrupt an Ε-fraction of the samples (X1∗, y1∗), ..., (Xn∗, yn∗ ). Given the Ε-corrupted samples (X1, y1), ..., (Xn, yn), the goal is to estimate β∗. We assume that the rows of X∗ are iid samples from some d-dimensional distribution D with zero mean and (unknown) covariance matrix Σ with bounded condition number. We design several robust algorithms that outperform the state of the art even in the special case of Gaussian noise η ∼ N(0, 1)n. In particular, we provide a polynomial-time algorithm that with high probability recovers β∗ up to error O(√Ε) as long as n ≥ O∼ (k2/Ε), only assuming some bounds on the third and the fourth moments of D. In addition, prior to this work, even in the special case of Gaussian design D = N(0, Σ) and noise η ∼ N(0, 1), no polynomial time algorithm was known to achieve error o(√Ε) in the sparse setting n 2. We show that under some assumptions on the fourth and the eighth moments of D, there is a polynomial-time algorithm that achieves error o(√Ε) as long as n ≥ O∼ (k4/Ε3). For Gaussian distribution D = N(0, Σ), this algorithm achieves error O(Ε3/4). Moreover, our algorithm achieves error o(√Ε) for all log-concave distributions if Ε ≤ 1/polylog(d). Our algorithms are based on the filtering of the covariates that uses sum-of-squares relaxations, and weighted Huber loss minimization with 1 regularizer. We provide a novel analysis of weighted penalized Huber loss that is suitable for heavy-tailed designs in the presence of two adversaries
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