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Towards Detecting the Fake Profile in Social Media using Hybrid Model of LSTM and Extreme learning machine

Towards Detecting the Fake Profile in Social Media using Hyb...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Santanu Modak Zameer Ahmed S. Mulla Aditya Verma K. K. Sunalini P M D Ali Khan Prashant Gupta School of Computer Science Swami Vivekananda University West Bengal India R. M. Dhariwal Sinhgad Management School Kondhapuri-Pune Maharashtra India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India School of Computing Mohan Babu University Tirupati Andhra Pradesh India Welingkar Institute of Management Development and Research Mumbai Maharashtra India

the last decade has seen an explosion in the use of online social networks (OSNs), with many people relying on them for the majority of their social contacts. they use OSNs to maintain relationships, disseminate information, plan events, and even do business online. Attackers and imposters looking to steal personal information, distribute fake news, or engage in other forms of bad behavior have taken notice of the explosive expansion of OSNs and the wealth of information about its members. However, academics have begun exploring effective approaches to identify suspicious or fraudulent accounts by analyzing account data and applying classification algorithms. In addition, results from stand-alone categorization algorithms aren’t always accurate, and some of the exploited account attributes actually have a detrimental impact on the final conclusions or have no impact at all. the solution provided makes use of preprocessing methods like tokenization and the removal of stop words. Chi-squared features are used to select and extract features before an LS TM-ELM model is trained. the proposed method outperforms the previous two models significantly.

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Data-Model Intergrowth Makes Better Time Series Prediction 1

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proceedings of the 4th international conference on Neural Computing for Advanced applications, NCAA 2023

作者： Chen, Lixian Liu, Hongda Sun, Chongqi Wang, Yi Hu, Yongheng College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China

ISBN: (数字)9789819958474

ISBN: (纸本)9789819958467

In common prediction problems, the open-loop prediction framework is popular because of its fast prediction process and easy implementation. However, time series prediction is a special prediction problem because of the temporal nature of the time series, in which missing values are not only the target of the time series prediction, but also the flexible decision variables in the training sets of the prediction model, which indirectly control the predicted values, allowing the prediction process to be considered as a closed loop. this paper proves that the closed-loop prediction framework performs better on prediction accuracy than the open-loop framework. In this paper, the missing value prediction of the time series is modeled as a special optimization problem, in which the missing values and prediction models are decision variables constraining each other. the prediction models keep being trained on the training set containing the predicted values through iteration. the objective function is the virtual accuracy of the target dimension prediction model. When the objective function reaches its minimum, the best missing values and prediction models are given, which can solve any time series prediction problem. therefore, the method is a general closed-loop prediction framework for time series prediction. In the experiment, 5 typical machine learning models in the closed-loop framework perform better than ones in the open-loop framework on 4 datasets with different frequency characteristics. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Coverage Prediction of Novel Coronavirus Vaccine Based on Deep learning Time Series Analysis 4

Coverage Prediction of Novel Coronavirus Vaccine Based on De...

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4th international conference on computer Information Science and Artificial Intelligence, CISAI 2021

作者： Hua, Fengrui BNU-HKBU United International College Division of Science and Technology Zhuhai China

ISBN: (纸本)9781665406925

Since the beginning of 2020, COVID-19 has swept the world, bringing many inconveniences and even threats to human life. through medical scientists' constant study, the vaccine was finally developed earlier this year. According to mathematical and medical modelling, if novel coronavirus transmission is counted as three (i.e., one patient can infect three), 70% of the vaccinations will be required for protection to be substantially achieved. To tackle the issue of vaccine coverage prediction, this paper proposed three-time series analysis models, which can be utilized to analyze and predict the COVID-19 Vaccine coverage worldwide with the application of machine learning. For a long time, statistical methods have mostly solved time series prediction problems (AR, AM, ARMA, ARIMA). Mathematicians try to constantly refine these techniques to constrain stationary and non-stationary time series, but the results are often not very good. In this paper, we propose a method based on deep learning, using CNN-LSTM, VAE-LSTM, DeepAR, and other models to analyze and predict the data of vaccine coverage rate. the experimental results demonstrated that the RMSE of LSTM, CNN-LSTM, VAE-LSTM and DeepAR are 9.295522e+07, 1.028151e+07, 1.857031e+06 and 1.961001e+07 separately. © 2021 IEEE.

关键词： COVID-19

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Extracting Behavior Events from Epidemic Daily Reports by Combining Deep learning and Adversarial Training

Extracting Behavior Events from Epidemic Daily Reports by Co...

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computer, Big Data and Artificial Intelligence (ICCBD+AI), international conference on

作者： Shaofu Lin Shiwei Zhou Zhe Xu Jianhui Chen Faculty of Information Technology Beijing University Of Technology Beijing China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing University of Technology Beijing China Beijing Key Laboratory of MRI and Brain Informatics Beijing University Of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Engineering Research Center of Digital Community Ministry of Education Beijing China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

Extracting emergencies from network information has become an important means of public security. However, existing event extraction methods rely on large-scale annotated corpora and are difficult to be effectively applied to extract epidemic behavior events, which is a brand-new task of event extraction. Firstly, an epidemic provenance model is defined to comprehensively model epidemic information in epidemic daily reports. On this basis, we propose an epidemic extraction model of epidemic behavior events, called CBEventMine, that combines deep learning and adversarial training. A relatively simple model structure of BiLSTM+CRF+sigmoid is adopted to achieve fast convergence of the model on small sample data, and an adversarial learning mechanism is added to alleviate the overfitting problem of the model in few-slot learning. the experimental results demonstrate that the proposed model CBEventMine could effectively extract epidemic event than the existing state-of-the-art methods.

关键词： Training Deep learning Epidemics Lightweight structures Data models Adversarial machine learning Public security

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An improved triangular management framework for improve the automotive vehicles using decision based data analysis

An improved triangular management framework for improve the ...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Sanjeev Sharma Prachi Vashist Tanu Sinha Automotive Business Unit Siemens India Pvt Ltd Noida UP India Department of Information Technology Raj Kumar Goel Institute of Technology Ghaziabad UP India Department of Computer and Application ABES Institute of Technology Ghaziabad UP India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

the automotive industry changes quickly due to incredible new technologies and client demands. Automotive manufacturers must constantly push the envelope to improve their vehicles to be competitive. In practice, this is easier said than done — it demands near-impossible management and decision-making throughout all levels of the organization. To handle this, we put forward a novel decision-driven data triangulation framework. Data Collection and Analysis A framework consists of data collection, decision-making, and implementation. Component 1: — Here, data is collected from different sources like customer feedback, market trends and internal processes. machine learning and predictive analytics are all employed to sift out in the data what it is that's valuable. the decision-making component then uses this data to make informed decisions and set direction for improvement. this involves establishing a sense of the areas most important for change and detailing the exact process directing change. the third component is executing these decisions, continually monitoring and iterating to determine a better decision. Fused with sound decision-making and implementation requirements, our framework empowers automotive manufacturers to drive tangible vehicle improvement through data-driven decisions. It helps manufacturers maintain fast development in the automotive market and improves customer satisfaction and loyalty.

关键词： Industries Data analysis Costs Decision making Supply chains machine learning Companies Data collection Market research Automotive engineering

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Spectral-Spatial Deep learning for Multispectral Enhanced Image Classification and Analysis

Spectral-Spatial Deep Learning for Multispectral Enhanced Im...

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international conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Varunraje J. Jagtap Nilesh B. Ahire Sarthak A. Chaudhari Prathamesh S. Shinde P. William Department of Electronics and Computer Engineering Sanjivani College of Engineering Savitribai Phule Pune University Pune India Department of Computer Engineering Sanjivani College of Engineering Savitribai Phule Pune University Pune India Department of Information Technology Sanjivani College of Engineering Savitribai Phule Pune University Pune India

In this study, we present a framework for spectral-spatial feature-based classification (SSFC), which employs deep learning techniques and dimension reduction to simultaneously extract spectral and spatial characteristics. the term “spectral-spatial feature-based classification” is abbreviated as “SSFC.” the acronym SSFC (Spectral-Spatial Feature-Based Categorization) stands for this method of categorization. It is suggested here to apply a method called balanced local discriminant embedding to extract spectral attributes from high-dimensional hyperspectral data sets. these features may be used to better understand the data. During the course of this experiment, multispectral image analysis (385–1050 nm) was used to take a recording of a 20-wavelength reflection of the pellets. Principal component analysis (PCA), support vector machine (SVM), and linear discriminant analysis (LDA) were used in the statistical analysis that was conducted. A novel nonparametric supervised spectral-spatial classification technique for multispectral images is introduced and discussed in this study. the technique was created with the idea of applying it to the world of data imaging. the posterior probability for a multispectral image's unknown pixel will be higher if it possesses digital number (DN) vectors that are comparable to those of other pixels in the training class. this is because distinct kinds of DN vectors are thought to follow various statistical distributions in their own particular manners. the proposed method assumes that the vectors in each class have a Gaussian mixed distribution based on the statistical features of DN vectors. this is done to make the analysis simpler. For better understanding, a nonparametric adaptively Bayesian method has been devised to estimate the most precise values for the distribution model's parameters.

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Compression scenarios for Federated learning in Smart Manufacturing 4

Compression scenarios for Federated Learning in Smart Manufa...

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4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Nasri, Seif Allah El Mesloul Ullah, Ihsan Madden, Michael G Departement of Electronic Badji Mokhtar University Annaba23000 Algeria School of Computer Science University of Galway GalwayH91 TK33 Ireland

Recent advances in Industrial Internet of things (IIoT) and communication technologies have provided new concepts of smart manufacturing and paved the way for the new era of Industry 4.0. the huge amount of generated data from machinery, connected systems, and edge devices can be exploited to extract actionable insights that can enhance the decision-making process in smart manufacturing, targeting to improve the return of investment. Conventionally, smart decisions are obtained from centralised data using predefined machine/deep learning models. To achieve better accuracy, these models require training based on diverse, unbiased, and comprehensive datasets, which may not be feasible in distributed and remote facilities due to the high scalability of smart manufacturing and data privacy concerns. Federated learning stands as a primary solution to these issues by bringing collaborative smart manufacturing without requiring centralised data training rather than model sharing. However, these models with many parameters result in big size, which put pressure on the communication channels due to frequent transfers from server to clients and vice versa and may face communication delays and model corruption. In addition, bigger models create issues for edge devices with limited memory and processing capacity which needs consideration. therefore, effective compression methods are mandatory to ensure Communication-Efficient Federated learning. In this paper, first, we discuss some possible application opportunities of FL in smart manufacturing, especially for the automotive industry but that can be extended for others. Secondly, we highlight FL communication challenge in smart manufacturing for which we present the compression concept and objectives in FL. It is followed by discussing the state-of-the-art compression techniques in FL. thirdly, a special focus is given to NNR and DeepCABAC for their demonstrated high compression gains with a wide variety of deep models. Finall

关键词： Decision making

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Development and application of Robot Automatic Inspection System Technology Based on machine learning 4

Development and Application of Robot Automatic Inspection Sy...

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4th IEEE international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2021

作者： Zhang, Zhimin Yang, Wen Ding, Xinfang Wan, Hailin Yang, Yiping Methanol Production From Coal Branch of Gansu Huating Coal Electricty Co.Ltd. Gansu Pingliang744000 China

ISBN: (纸本)9781665428392

With the development of artificial intelligence technology, computer technology and other related technologies, there are more and more researches on mobile robots. the robot has gone through three stages: from a simple teaching and reproducing robot to a low-level intelligent robot with vision, hearing, touch and certain adaptability, and then to a high-level intelligent robot with various advanced sensors and self-adaptive and self-learning functions. Intelligent voice inspection robot can be effectively used in petrochemical, oilfield, nuclear industry and other industries, especially in areas where people are inconvenient to reach, such as pipelines, tanks and towers. the robot is not taught what actions to take, but it evaluates the current state through a value function to select actions to maximize the cumulative reward in the long-term goal. this paper focuses on the design and implementation of intelligent voice inspection robot system, hoping to play a certain reference role for the further improvement and development of intelligent voice inspection robot system in China. © 2021 IEEE.

关键词： Intelligent robots

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A method of constructing distributed big data analysis model for machine learning based on cloud computing 2021

A method of constructing distributed big data analysis model...

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4th international conference on Information Systems and computer Aided Education, ICISCAE 2021

作者： Li, Jicai Liu, Dan College of Information and Computer Engineering Northeast Forestry University Heilongjiang Harbin150040 China

ISBN: (纸本)9781450390255

there are many big data analysis methods, and it is an effective method to build big data analysis model through machine learning. Big data is characterized by large data scale and long calculation cycle. In order to speed up the calculation speed and shorten the calculation cycle, distributed computing method is one of the effective methods to solve the above problems. With the wide application and rapid development of information technology, cloud computing as a new business computing model has attracted more and more attention. However, the security of cloud computing data storage model lacks reliability. Under the mainstream cloud computing and big data basic environment, building a better model from resource aggregation to analysis and mining, and modeling distributed big data analysis can provide high-reliability, high-security, high-efficiency analysis services for practical analysis and mining applications such as intelligence judgment, information deployment and control, stakeholder analysis and intelligent decision-making. © 2021 ACM.

关键词： Cloud computing

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Research on the application of Speech Recognition in Medical Clinic 4

Research on the Application of Speech Recognition in Medical...

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4th international conference on computer Information Science and Artificial Intelligence, CISAI 2021

作者： Jin, Han Chengdu University of Information and Technology Software Engineering Institute Chengdu China

ISBN: (纸本)9781665406925

In recent years, with the development of deep learning, speech recognition has also been rapidly developed. the wide application of speech recognition facilitates our life and work. At present, clinicians bear a huge burden of clinical consultation work. the application of speech recognition in medical clinic will greatly shorten the time of clinician's diagnosis and treatment information input, effectively improve the work efficiency of the clinician, and reduce the work burden of the clinician. this paper introduces the framework of speech recognition and the common methods of each part, and puts forward solutions to the problems faced by the application of speech recognition in medical clinic. the effectiveness of the proposed method is verified in the clinical speech recognition task of Traditional Chinese medicine. Experimental results show that the proposed method can effectively improve the accuracy of speech recognition model in TCM clinical speech recognition tasks. © 2021 IEEE.

关键词： Speech recognition

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