Research on Anomaly Monitoring Algorithm of Uncertain Large Data Flow Based on Artificial Intelligence

2019 ◽  
pp. 109-117
Author(s):  
Shuang-cheng Jia ◽  
Feng-ping Yang
Keyword(s):  
Artificial Intelligence ◽  
Data Flow ◽  
Large Data ◽  
Monitoring Algorithm

scholarly journals Research on Quantitative Remote Sensing Monitoring Algorithm of Air Pollution Based on Artificial Intelligence

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Yun Liu ◽  
Yuqin Jing ◽  
Yinan Lu
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Air Pollution ◽  
Transmission Model ◽  
Atmospheric Radiation ◽  
Remote Sensing Monitoring ◽  
Quantitative Remote Sensing ◽  
Monitoring Accuracy ◽  
Radiation Transmission ◽  
Monitoring Algorithm

When the current algorithm is used for quantitative remote sensing monitoring of air pollution, it takes a long time to monitor the air pollution data, and the obtained range coefficient is small. The error between the monitoring result and the actual result is large, and the monitoring efficiency is low, the monitoring range is small, and the monitoring accuracy rate is low. An artificial intelligence-based quantitative monitoring algorithm for air pollution is proposed. The basic theory of atmospheric radiation transmission is analyzed by atmospheric radiation transfer equation, Beer–Bouguer–Lambert law, parallel plane atmospheric radiation theory, atmospheric radiation transmission model, and electromagnetic radiation transmission model. Quantitative remote sensing monitoring of air pollution provides relevant information. The simultaneous equations are constructed on the basis of multiband satellite remote sensing data through pixel information, and the aerosol turbidity of the atmosphere is calculated by the equation decomposition of the pixel information. The quantitative remote sensing monitoring of air pollution is realized according to the calculated aerosol turbidity. The experimental results show that the proposed algorithm has high monitoring efficiency, wide monitoring range, and high monitoring accuracy.

Emerging Pharmacotherapy and Health Care Needs of Patients in the Age of Artificial Intelligence and Digitalization

2020 ◽  
Vol 54 (10) ◽  
pp. 1038-1046
Author(s):  
Barbara J. Zarowitz
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Ethnic Diversity ◽  
Health Care Professionals ◽  
Large Data ◽  
The United States ◽  
Wearable Devices ◽  
Data Sets ◽  
Care Needs ◽  
Clinician Training

Advances in the application of artificial intelligence, digitization, technology, iCloud computing, and wearable devices in health care predict an exciting future for health care professionals and our patients. Projections suggest an older, generally healthier, better-informed but financially less secure patient population of wider cultural and ethnic diversity that live throughout the United States. A pragmatic yet structured approach is recommended to prepare health care professionals and patients for emerging pharmacotherapy needs. Clinician training should include genomics, cloud computing, use of large data sets, implementation science, and cultural competence. Patients will need support for wearable devices and reassurance regarding digital medicine.

scholarly journals An artificial-intelligence technique for qualitatively deriving enzyme kinetic mechanisms from initial-velocity measurements and its application to hexokinase

1989 ◽  
Vol 264 (1) ◽  
pp. 175-184 ◽  
Author(s):  
L Garfinkel ◽  
D M Cohen ◽  
V W Soo ◽  
D Garfinkel ◽  
C A Kulikowski
Keyword(s):  
Artificial Intelligence ◽  
Experimental Data ◽  
Ionic Strength ◽  
Initial Velocity ◽  
Large Data ◽  
Data Sets ◽  
Enzyme Kinetic ◽  
Experimental Conditions ◽  
Enzyme Preparations ◽  
Kinetic Mechanisms

We have developed a computer method based on artificial-intelligence techniques for qualitatively analysing steady-state initial-velocity enzyme kinetic data. We have applied our system to experiments on hexokinase from a variety of sources: yeast, ascites and muscle. Our system accepts qualitative stylized descriptions of experimental data, infers constraints from the observed data behaviour and then compares the experimentally inferred constraints with corresponding theoretical model-based constraints. It is desirable to have large data sets which include the results of a variety of experiments. Human intervention is needed to interpret non-kinetic information, differences in conditions, etc. Different strategies were used by the several experimenters whose data was studied to formulate mechanisms for their enzyme preparations, including different methods (product inhibitors or alternate substrates), different experimental protocols (monitoring enzyme activity differently), or different experimental conditions (temperature, pH or ionic strength). The different ordered and rapid-equilibrium mechanisms proposed by these experimenters were generally consistent with their data. On comparing the constraints derived from the several experimental data sets, they are found to be in much less disagreement than the mechanisms published, and some of the disagreement can be ascribed to different experimental conditions (especially ionic strength).

scholarly journals Tweaking Business Planning With Artificial Intelligence

2021 ◽  
Vol 2 (4) ◽  
pp. 1-22
Author(s):  
Jing Rui Chen ◽  
P. S. Joseph Ng
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analysis ◽  
Learning Experience ◽  
Compulsory Education ◽  
Large Data ◽  
Data Sets ◽  
Chinese Market ◽  
Artificial Intelligence Technology ◽  
Education Support

Griffith AI&BD is a technology company that uses big data platform and artificial intelligence technology to produce products for schools. The company focuses on primary and secondary school education support and data analysis assistance system and campus ARTIFICIAL intelligence products for the compulsory education stage in the Chinese market. Through big data, machine learning and data mining, scattered on campus and distributed systems enable anyone to sign up to join the huge data processing grid, and access learning support big data analysis and matching after helping students expand their knowledge in a variety of disciplines and learning and promotion. Improve the learning process based on large data sets of students, and combine ai technology to develop AI electronic devices. To provide schools with the best learning experience to survive in a competitive world.

scholarly journals Computational analysis of transition temperatures (Tts) of proteins fused to elastin-like polypeptide (ELP): deep fake evaluation of proteins, linkers, and trailers features

2021 ◽  
Author(s):  
Mohammad Davoud Ghafari ◽  
Iraj Rasooli ◽  
Khosro Khajeh ◽  
Bahareh Dabirmanesh ◽  
Mohammadreza Ghafari ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Phase Transition ◽  
Transition Temperature ◽  
Computational Analysis ◽  
Large Data ◽  
High Accuracy ◽  
Sequence Length ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Protein Feature

The phase transition temperature (Tt) prediction of the Elastin-like polypeptides (ELPs) is not trivial because it is related to complex sets of variables such as composition, sequence length, hydrophobic characterization, hydrophilic characterization, the sequence order in the fused proteins, linkers and trailer constructs. In this paper, two unique quantitative models are presented for the prediction of the Tt of a family of ELPs that could be fused to different proteins, linkers, and trailers. The lack of need to use multiple software, peptide information, such as PDB file, as well as knowing the second and third structures of proteins are the advantages of this model besides its high accuracy and speed. One of our models could predict the Tt values of the fused ELPs by entering the protein, linker, and trailer features with R2=99%. Also, another model is able to predict the Tt value by entering the fused protein feature with R2=96%. For more reliability, our method is enriched by Artificial Intelligence (AI) to generate similar proteins. In this regard, Generative Adversarial Network (GAN) is our AI method to create fake proteins and similar values. The experimental results show that our strategy for prediction of Tt is reliable in large data.

scholarly journals Innovation and opportunity: review of the UK’s national AI strategy

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Emre Kazim ◽  
Denise Almeida ◽  
Nigel Kingsman ◽  
Charles Kerrigan ◽  
Adriano Koshiyama ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Industrial Policy ◽  
Data Protection ◽  
Data Flow ◽  
Regulatory System ◽  
Legal Liability ◽  
Step Change ◽  
Clear Signal ◽  
The Uk

AbstractThe publication of the UK’s National Artificial Intelligence (AI) Strategy represents a step-change in the national industrial, policy, regulatory, and geo-strategic agenda. Although there is a multiplicity of threads to explore this text can be read primarily as a ‘signalling’ document. Indeed, we read the National AI Strategy as a vision for innovation and opportunity, underpinned by a trust framework that has innovation and opportunity at the forefront. We provide an overview of the structure of the document and offer an emphasised commentary on various standouts. Our main takeaways are: Innovation First: a clear signal is that innovation is at the forefront of UK’s data priorities. Alternative Ecosystem of Trust: the UK’s regulatory-market norms becoming the preferred ecosystem is dependent upon the regulatory system and delivery frameworks required. Defence, Security and Risk: security and risk are discussed in terms of utilisation of AI and governance. Revision of Data Protection: the signal is that the UK is indeed seeking to position itself as less stringent regarding data protection and necessary documentation. EU Disalignment—Atlanticism?: questions are raised regarding a step back in terms of data protection rights. We conclude with further notes on data flow continuity, the feasibility of a sector approach to regulation, legal liability, and the lack of a method of engagement for stakeholders. Whilst the strategy sends important signals for innovation, achieving ethical innovation is a harder challenge and will require a carefully evolved framework built with appropriate expertise.

scholarly journals Recent Advances in Blockchain and Artificial Intelligence Integration: Feasibility Analysis, Research Issues, Applications, Challenges, and Future Work

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhonghua Zhang ◽  
Xifei Song ◽  
Lei Liu ◽  
Jie Yin ◽  
Yu Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Research Work ◽  
Three Dimensional ◽  
Large Data ◽  
Point Of View ◽  
Point System ◽  
Research Issues ◽  
Artificial Intelligence Technology ◽  
Point To Point ◽  
Future Work

Blockchain constructs a distributed point-to-point system, which is a secure and verifiable mechanism for decentralized transaction validation and is widely used in financial economy, Internet of Things, large data, cloud computing, and edge computing. On the other hand, artificial intelligence technology is gradually promoting the intelligent development of various industries. As two promising technologies today, there is a natural advantage in the convergence between blockchain and artificial intelligence technologies. Blockchain makes artificial intelligence more autonomous and credible, and artificial intelligence can prompt blockchain toward intelligence. In this paper, we analyze the combination of blockchain and artificial intelligence from a more comprehensive and three-dimensional point of view. We first introduce the background of artificial intelligence and the concept, characteristics, and key technologies of blockchain and subsequently analyze the feasibility of combining blockchain with artificial intelligence. Next, we summarize the research work on the convergence of blockchain and artificial intelligence in home and overseas within this category. After that, we list some related application scenarios about the convergence of both technologies and also point out existing problems and challenges. Finally, we discuss the future work.

scholarly journals Artificial intelligence in luminal endoscopy

2020 ◽  
Vol 13 ◽  
pp. 263177452093522
Author(s):  
Shraddha Gulati ◽  
Andrew Emmanuel ◽  
Mehul Patel ◽  
Sophie Williams ◽  
Amyn Haji ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Large Scale ◽  
Randomised Trial ◽  
Improve Patient Care ◽  
Large Data ◽  
Detection Rates ◽  
Diagnostic Endoscopy ◽  
Adenoma Detection ◽  
Intelligence Studies

Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett’s, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice.

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