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检索条件"任意字段=2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021"

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Energy Price Forecasting in Python Using machine learning Algorithm 2nd

Energy Price Forecasting in Python Using Machine Learning Al...

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2nd international conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2020

作者： Mohith, P.G.S. Madhava Krishna, P. Velmurugan, A. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9789811587511

One of the primary objectives of shrewd framework is to decrease power top burden and to adjust the hole between power market interest. Clients can participate in the activities of keen matrix, where the vitality cost can be decreased by vitality conservation also, load moving. Right now, estimating is a key pointer of clients exchanging load. For the most part, exact point cost anticipating is normal due to the necessity of economy and industry. As for clients, they are really anxious to know whether the power cost surpasses the particular client characterized edges, which they used to choose to turn the heap on or then again off. Under this situation, clients require the power value characterization. Subsequently, some particular limits dependent on point value estimating calculations are utilized to characterize the power cost. Capacity estimation methods are the essential of point value anticipating calculations, in which the essential procedure of value development is imitated by a value model. In addition, value grouping requires lower precision. Consequently, power cost arrangement turns into a key need in the value determining. In genuine world, the power costs are impacted by a number of components in which demand and supply are the two direct factors. Other than them, the power costs are impacted by physical attributes, for example, fuel value, power necessity, sustainable power source supply, and so forth also, it fluctuates hourly. Power cost anticipating is a huge piece of keen framework since it makes shrewd network cost productive. Since the power value changes often and a lot of keen meters screen the earth, for example, fuel age, wind age, and transmission, continuously, the measure of recorded information is very enormous. © 2021, Springer Nature Singapore Pte Ltd.

关键词： learning algorithms

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Applying Artificial Intelligence to Primary and Secondary School Physical Education

Applying Artificial Intelligence to Primary and Secondary Sc...

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Information Science and Education (ICISE-IE), international conference on

作者： Zhewei He Xiaohong Niu China Mountaineering and Outdoor Sports College Institute of Physical Education. China University of Geosciences Wuhan China

ISBN: (纸本)9781665438308

The physical education curriculum reform has been fruitful since the founding of New China, however, the physical education curriculum reform has long neglected the combination with the latest technology and has not broken through the education approach based on physical education and health curriculum. Since the birth of artificial intelligence education, it has provided a solution to break through the limitation of distance and teachers’ resources, and the combination of artificial intelligence education and the physical education curriculum is an important direction to deepen the physical education curriculum reform. The simplified application of "human-computer learning symbiosis" breaks through the current dilemma of insufficient technological power and AI education theory, and helps the application of AI education in the physical education curriculum. It has the application advantages of human-oriented and personalized teaching; promoting the formation of lifelong sports concepts; cultivating higher-order thinking ability; and promoting the development of the sports industry.

关键词： Symbiosis Industries Economics Information science Education Refining learning (artificial intelligence)

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Case Recommendation Algorithm of Discipline Inspection and Supervision based on Knowledge Graph

Case Recommendation Algorithm of Discipline Inspection and S...

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international conference on Electronics, Communications and Information Technology (CECIT)

作者： Yongjun Wang Jing Gao Junjie Chen Zhijun Shen Haibo Wang College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory HohhotChina

ISBN: (纸本)9781665437585

In view of work problems in the field of disciplinary and supervision such as the large number of cases, the complexity of the content of the documents, the large workload of data compilation, and the low efficiency of conviction and sentencing, intelligent recommendation of similar cases is an important measure to alleviate this situation. In order to improve the accuracy and interpretability of similar case recommendation, based on the constructed laws and regulations knowledge graph of disciplinary inspection and supervision and the disciplinary case dataset, an intelligent recommendation algorithm for similar cases that integrates the characteristics of the legal knowledge graph of disciplinary inspection and supervision is proposed. The method constructs a keyword dictionary of legal elements that map the keywords in the case text with the entities in the knowledge graph, and merges the knowledge features of the matched legal entities with the feature vectors of the original case text so that enhances the representation of legal knowledge characteristics corresponding to disciplinary. Experiments with several machine learning methods and deep learning methods show that the proposed method has a certain improvement in F-score.

关键词： machine learning algorithms Law Text categorization Semantics Inspection Market research Regulation

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DeepMSWeb: A Web-Based Decision Support System via Deep learning for Automatic Detection of MS Lesions

DeepMSWeb: A Web-Based Decision Support System via Deep Lear...

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international Informatics and Software Engineering conference (UBMYK)

作者： Mehmet Süleyman Yildirim Emre Dandil Technology of Sogut Vocational School Bilecik Seyh Edebali University Bilecik Turkey Engineering Bilecik Seyh Edebali University Bilecik Turkey

ISBN: (纸本)9781665407601

Multiple Sclerosis (MS) is a common neurological disorder in recent years. The diagnosis process of the disease starts with the accurate and precise detection of lesions from MR images. In addition, important achievements are achieved with computer aided decision support systems, which are used as an auxiliary secondary tool in the detection of MS. In this study, we present a web-based decision support system (DeepMSWeb) developed via deep learning for the detection of MS lesions on a publicly-available dataset. Mask R-CNN architecture, one of the deep learning models, is used in the infrastructure of DeepMSWeb, and the developed web application has a flexible and user-friendly interface. In addition, experimental studies are carried out with DeepMSWeb on the dataset consisting of MR images for the detection of MS lesions, and the detection accuracy of the application is supported by similarity measurement metrics. Radiologists who have experienced DeepMsWeb are confirmed that DeepMSWeb can be used as a decision support system for the detection of MS lesions. In addition, it is evaluated that DeepMs Web can be used in different screen sizes, is easy to use and is a fast as decision support tool.

关键词： Decision support systems Deep learning Neurological diseases Measurement Multiple sclerosis computer architecture Service-oriented architecture

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Detecting Advance Fee Fraud Using NLP Bag of Word Model

Detecting Advance Fee Fraud Using NLP Bag of Word Model

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international conference on Cyberspace (CYBER)

作者： Muhammad Hamisu Ali Mansour School of Computer Science and Technology University of Bedfordshire Luton Bedfordshire

ISBN: (纸本)9781665444101

Advance Fee Fraud (AFF) is a form of Internet fraud prevalent within the Cybercrimes domain in literature. Evidence shows that huge financial assets are stolen from the global economy as a result of AFF. Consequently, this paper presents a fraudulent email classifier (FEC) that detects and classifies an email as fraudulent or non-fraudulent using Natural Language Process (NLP) model referred to as Bag-of-Words (BoW). The classifier is designed and trained to detect and classify AFF that originate from known sources using Nigeria as a Case study. Dataset is obtained and used for the training while testing the classifier logs. Experimentally, the classifier was trained using various machine learning algorithms with BoW generated as predictors. By selecting the best algorithms, the classifier was tested and found to perform satisfactorily.

关键词： Training machine learning algorithms conferences Natural languages Prediction algorithms Feature extraction Classification algorithms

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Concolic-Based Software Vulnerability Prediction Using Ensemble learning 2nd

Concolic-Based Software Vulnerability Prediction Using Ensem...

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2nd international conference on Biologically Inspired Techniques in Many Criteria Decision Making, BITMDM 2021

作者： Barisal, Swadhin Kumar Kishore, Pushkar Nayak, Gayatri Hira, Ridhy Pratim Kumar, Rohit Kumar, Ritesh Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed To Be University Odisha Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Odisha Rourkela India

ISBN: (纸本)9789811687389

Detecting software vulnerabilities are critical for limiting the damage caused by hostile exploits and program failures. This frequently necessitates the accurate identification of susceptible execution routes. However, as software complexity has increased, it has become notoriously difficult to find such susceptible paths by exploring the entire program execution space. Therefore, concolic testing is used in this paper as one of the ways to deal with this problem. Here, the observations and discoveries are collected from experimenting and implementing concolic testing. First, several trained classifier models like random forest, support vector machine, stochastic gradient descent, and AdaBoost are tested against a test dataset created by randomly selecting 30% of the data from the original dataset. Then, multiple classifier models help predict whether a program is faulty or benign. After testing out several classifier models, an ensemble is done on the top 3 highest accuracy classifiers. Overall, 87% accuracy is achieved with an F1-score of 85.1%. This result indicates that 87% of the program’s labels are accurately detected by our proposed model while higher F1-score represents the proposed model’s balanced detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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application of artificial intelligence in the field of medicine for diagnosis 2

Application of artificial intelligence in the field of medic...

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2nd international conference on Mathematical Sciences, ICMS 2020

作者： Selvan, Mercy Paul Gladence, Mary Mary, A. Viji Amutha Memala, W. Abitha Sravya, M. Navya, M. Dept. of CSE Sathyabama Institute of Science and Technology Chennai India Sathyabama Institute of Science and Technology Chennai India

Because of data mining progress in biomedical and human services networks, precise investigation of clinical data benefits early illness acknowledgment, persistent consideration and network administrations. At the point when the nature of clinical data is inadequate die precision of study is diminished. In addition, various locales display one of a kind appearances of certain territorial maladies, which may brings about debilitating the forecast of illness flare-ups. In the proposed framework, it gives Al calculations to viable forecast of different illness events in sickness visit social orders. It try the adjusted gauge models over genuine medical clinic data gathered. To heat the trouble of inadequate data, it utili/.e an inert factor model to reconstruct the missing data. It probe a territorial interminable sickness of cerebral dead tissue. Utilizing organized and unstructured data from emergency clinic it use machine learning Decision Tree calculation. It predicts likely infections by mining informational indexes and gives recommended specialists and healing arrangements. It will likewise direct the clients by offering lips to carry on with a sound life, some eating routine lips and furthermore value of plants and nourishment things. As tar as wc could possibly know in the territory of clinical large data examination none of the current work concentrated on the two data types Contrasted with a few run of the mill gauge calculations, the estimation precision of our proposed calculation arrives at 94.8% with a union speed which is quicker than that of the Decision tree ailment hazard forecast calculation. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Decision trees

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application of Game Equilibrium Theory and Algorithm in Modern Talent Training Model 2nd

Application of Game Equilibrium Theory and Algorithm in Mode...

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2nd EAI international conference on application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2022

作者： Kang, Kaijie Lanzhou Vocational University of Resources and Environment Gansu Lanzhou730070 China

ISBN: (纸本)9783031239496

With the advent of the information society in the 21st century, colleges and universities, as the training base of various senior talents required by the society, are facing new challenges and development opportunities, and shoulder the important task of how to cultivate comprehensive talents with all-round quality to meet the needs of the information society. To accomplish this historical mission, colleges and universities are required to constantly update and change their ideas in educational consciousness, teaching content, educational technology means and training objectives in order to adapt to the rapid development of information society. Provide scientific and quantitative methods for the training mode of talents. Taking employment as the guidance, this paper analyzes the game between talent training and social demand, and establishes the game model of talent training mode for the well-off group;Finally, the solution algorithm of the model is developed by using the equilibrium theory of game theory. Under the limited conditions of school training and social needs, food and clothing employees will randomly choose the training mode of the minimum value of learning consumption function;The game mode of P*, which ensures the lowest level of income for both sides of the well-off society. Through the example of the algorithm and the analysis of the results of the game model, it shows that the model and the solution algorithm are scientific, feasible and effective, which can be used as a reference for the cultivation of all kinds of talents. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Personnel training

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Determining Mice Sex from Chest X-rays using Deep learning

Determining Mice Sex from Chest X-rays using Deep Learning

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international conference on Cyberspace (CYBER)

作者： Abiodun Ajiboye Kola Babalola Institute of Computer Vision and Machine Learning Lagos Nigeria European Molecular Biology Laboratory European Bioinformatics Institute Cambridgshire UK

ISBN: (纸本)9781665444095;9781665444101

This Following on from work by Babalola et al. It is shown that the sex of mice can be determined from x-ray images of the chest region alone using convolutional neural networks. The anatomical differences that may be responsible for this is further sinvestigated, as it may be useful in determining phenotype changes caused by knocking out genes - hence in understanding genotype-phenotype effects. Our results indicate that the cervical vertebrae may play an important role in the ability of our convolutional neural network to classify the sex of mice correctly using only x-rays of the chest region.

关键词： Deep learning conferences Mice Convolutional neural networks X-ray imaging Biomedical imaging

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LSTM Based Model For Apple Inc Stock Price Forecasting

LSTM Based Model For Apple Inc Stock Price Forecasting

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computer Science and Management Technology (ICCSMT), international conference on

作者： Huaijin Shi Gao Yuan Zhuoran Lu Qian Liang The Chinese University of Hong Kong Shenzhen China Lanzhou University Lanzhou China Xi&#x0027 an Jiaotong University Xi&#x0027 an China Yunnan Normal University School of Economics and Management Yunnan China

ISBN: (纸本)9781665420648

The prediction of stock price is a popular and difficult topic that attracted and confused many investors over a long period of time. Because of the complex transaction market, there are a lot of risks when we do transactions. Until now, there are two schools about the stock market forecasting: fundamental analysis and technical analysis. The topic of this paper is to use the Recurrent Neural Networks to predict the stock price of Apple Inc in the future. In addition, the important unit of our RNN is Long Short-term Memory (LSTM), which introduces the memory cell, replacing traditional artificial neurons in the hidden layer of the network. Our Networks are able to associate memories and input remote in time, which could grasp the structure of data dynamically over time with high prediction capacity. To visualize our results, we draw three figures. We evaluated our model's performance on the dataset provided by the kaggle competition. The results of the experiment show that our method achieves a good performance compared with other machine learning methods. The RMSE of our model is 0.66 and 0.39 smaller than ridge regression and the neural network model respectively.

关键词： Analytical models Recurrent neural networks Computational modeling Neurons Data visualization machine learning Predictive models

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