As internet use in communication networks has grown, fake news has become a big problem. The misleading heading of the news loses the trust of the reader. Many techniques have emerged, but they fail because fraudsters...
详细信息
The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A hi...
详细信息
The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.
There has been a tremendous increase in social media text-based opinions and reviews as a result of the quick development of social media. It emphasizes those users on platforms like social media often express their v...
详细信息
This article proposes a novel idea for creating a sentiment-based stock market index forecasting model by amalgamating price and sentiment data hidden in the price pattern itself. The state-of-the-art methodologies us...
详细信息
Antenna optimization using machine learning is a rapidly evolving field that leverages the power of artificial intelligence to design and improve antenna systems. Antenna optimization is a process of modifying antenna...
详细信息
With the profound use of digital contents in education and social media, multimedia content have become a prevalent means of communication and with such rapid increase, information security is still a major concern. T...
详细信息
Suicide is a significant public health issue that devastates individuals and society. Early warning systems are crucial in preventing suicide. The purpose of this research is to create a deep learning model to identif...
详细信息
In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge ...
详细信息
In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic *** have extensive studies on malicious traffic detection with single challenge,but the detection of complex traffic has not been widely *** adaptive random forests(QARF) is proposed to detect traffic streams with concept drift,imbalance and lack of labeled *** is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling *** achieves querying a small number of instances from unlabeled traffic streams to obtain effective *** conduct experiments using the NSL-KDD dataset to evaluate the performance of *** is compared with other state-of-the-art *** experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD *** performs better than other state-of-the-art methods in comparisons.
A major obstacle to the realization of novel inorganic materials with desirable properties is efficient materials discovery over both the materials property and synthesis *** this work,we propose and compare two novel...
详细信息
A major obstacle to the realization of novel inorganic materials with desirable properties is efficient materials discovery over both the materials property and synthesis *** this work,we propose and compare two novel reinforcement learning(RL)approaches to inverse inorganic oxide materials design to target promising compounds using specified property and synthesis *** models successfully learn chemical guidelines such as negative formation energy,charge neutrality,and electronegativity balance while maintaining high chemical diversity and *** demonstrate multi-objective RL algorithms that can generate novel compounds with both desirable materials properties(band gap,formation energy,bulk modulus,shear modulus)and synthesis objectives(low sintering and calcination temperatures).We apply template-based crystal structure prediction to suggest feasible crystal structure matches for target inorganic compositions identified by our machine learning(ML)algorithms to highlight the plausibility of the identified target *** analyze the benefits and drawbacks of the ML approaches tested in this work in the context of accelerated inorganic materials *** work isolates and evaluates the effects of different RL methodologies to suggest promising,valid compounds of interest by exploring the chemical design space for materials discovery.
Machine learning has been massively utilized to construct data-driven solutions for predicting the lifetime of rechargeable batteries in recent years, which project the physical measurements obtained during the early ...
详细信息
Machine learning has been massively utilized to construct data-driven solutions for predicting the lifetime of rechargeable batteries in recent years, which project the physical measurements obtained during the early charging/discharging cycles to the remaining useful lifetime. While most existing techniques train the prediction model through minimizing the prediction error only, the errors associated with the physical measurements can also induce negative impact to the prediction accuracy. Although total-least-squares(TLS) regression has been applied to address this issue, it relies on the unrealistic assumption that the distributions of measurement errors on all input variables are equivalent, and cannot appropriately capture the practical characteristics of battery degradation. In order to tackle this challenge, this work intends to model the variations along different input dimensions, thereby improving the accuracy and robustness of battery lifetime prediction. In specific, we propose an innovative EM-TLS framework that enhances the TLS-based prediction to accommodate dimension-variate errors, while simultaneously investigating the distributions of them using expectation-maximization(EM). Experiments have been conducted to validate the proposed method based on the data of commercial Lithium-Ion batteries, where it reduces the prediction error by up to 29.9 % compared with conventional TLS. This demonstrates the immense potential of the proposed method for advancing the R&D of rechargeable batteries.
暂无评论