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检索条件"任意字段=4th International Conference on Machine Learning and Soft Computing, ICMLSC 2020"

共 563 条记录，以下是221-230 订阅

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A machine learning Approach in Data Perturbation for Privacy-Preserving Data Mining 4th

A Machine Learning Approach in Data Perturbation for Privacy...

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4th international conference on Smart computing and Informatics, SCI 2020

作者： Dansana, Jayanti Singh, Adarsh KIIT Deemed To Be University Bhubaneswar Odisha India

ISBN: (纸本)9789811615016

Data mining is a process where we can extract relevant information or patterns from the collection of data. In this era of big data, every organization aims to handle huge amounts of data and perform data mining techniques in order to extract pieces of information or patterns for various work and decision making. To protect privileged data and leakage of private information, the clients use different privacy-preserving techniques such as perturbation that protects client's data from revealing private information. the job of perturbing data on the client side is a herculean task, and it gets more difficult with the increase in the size of data. In this paper, we proposed a machine learning regression model that has been trained in such a way that it predicts the perturb data from original data and it even contains a comparative study of different regression models and their accuracy in perturbing the data. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Privacy-preserving techniques

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Supervısed Learnıng Algorıthm: A Survey 4th

Supervısed Learnıng Algorıthm: A Survey

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

作者： Kanksha Singh, Harjit Laxmi, Vijay Lovely Professional University Jalandhar India Guru Kashi University Talwandi Sabo India

ISBN: (纸本)9789811636592

the supervised learning algorithm is exactly what in which we've input as well as output variable which is able to be A or maybe B which uses an algorithm to find out the mapping feature from the feedback to the output. supervised algorithms like SVM, decision tree, naive Bayes distinction, etc. the primary motive of this particular paper is actually discussing the supervised learning methods that can help to the distinction of information on real-life information sets. It also details the charts of references within the last second area in which we compared which algorithm is perfect in which situation and exactly why based on particular parameters. the potential range of this paper is to survey on supervised learning algorithms and the comparison between them so that a brand new individual who's wanting to master machine learning could comprehend in layman language. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Supervised learning

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Online Credit Card Fraud Analytics Using machine learning Techniques 4th

Online Credit Card Fraud Analytics Using Machine Learning Te...

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4th international conference on Data Management, Analytics and Innovation, ICDMAI 2020

作者： Kumar, Akshi Anand, Kartik Jha, Simran Gupta, Jayansh Department of Computer Science & Engineering Delhi Technological University DelhiNew Delhi110042 India

ISBN: (纸本)9789811556159

Every year, companies and financial institutions worldwide lose billions due to credit card fraud. With the advancement in electronic commerce and communication technology and the development of modern technology, there has been an increase in the usage of credit cards and also the risks associated with it. Financial fraud detection systems are being implemented everywhere to minimize these losses. In the real world, fraudulent and real transactions are scattered all around and it is extremely difficult to distinguish between them. there is a class imbalance problem due to the minority of fraudulent transactions. the enormity of imbalanced data poses a challenge on how well machine learning techniques can efficiently detect fraud with a high prediction accuracy, and at the same time reduce misclassification costs. In this paper, experiments are performed to compare the most commonly used ML algorithms and investigate their performance when applied to a massive and highly imbalanced dataset. We compare six Supervised machine learning techniques, namely, Logistic Regression, K-Nearest Neighbours, Naive Bayes, Decision Trees, Random Forest and Linear Support Vector machine (SVM). © 2021, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Assessment of Suitability of Metaheuristics and machine learning for Task Scheduling Process: A Review of Aptness in Heavy Task Environments 4th

Assessment of Suitability of Metaheuristics and Machine Lear...

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4th international conference on Smart Trends for computing and Communications, SmartCom 2020

作者： Lohi, Shantanu Anandrao Tiwari, Nandita Department of Computer Science and Engineering RKDF Institute of Science and Technology Sarvepalli RadhaKrishnan University BhopalMP India

ISBN: (纸本)9789811552236

Considering the advanced nature of computational abilities, task scheduling is a measure of success in any multiprocessing environment performing many different tasks in real-time and effective scheduling process and so on. Cost-effective resource scheduling or CERS algorithm has been a crucial algorithm for many cloud deployments over the years. this algorithm is computationally simple, has minimum overheads, and is based on keeping the cloud utilization to the most optimum level. We analyzed that keeping effective task response time is one of the main drawbacks of the CERS algorithm. In this paper, we propose new machine learning-based optimization algorithm that utilizes the concept as proposed by CERS but in process improves it further using an amalgamation of pre-learning and continuous adaptation techniques in order to reduce the mean response time for a given set of tasks. the proposed algorithm will further be compared with the standard CERS implementation, and the results will be evaluated in terms of resource cost and mean response time. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Detection of glaucoma disease using image processing, soft computing and deep learning approaches 4

Detection of glaucoma disease using image processing, soft c...

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4th international conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020

作者： Pandey, Anuradha Patre, Pooja Minj, Jasmine VEC Department of CS and Engg. Lakhanpur C.G. India

ISBN: (数字)9781728154640

ISBN: (纸本)9781728154640

Glaucoma disease becomes a more common eye disease that occurs due to pressure on eye cells. Many image processing based methods have been applied earlier for the detection of glaucoma disease but their accuracy of classification was not up to the mark. the pressure on eye cells increases with the use of mobile phones, video games in the daily life of human beings. In this paper, the three different methods ares shared for the detection of glaucoma disease using image processing techniques, machine learning techniques, and using a convolutional neural network model of deep learning on the Bin Rushed database. the image processing techniques are used for the extraction of features like CDR and RDR, then classification performed using a neural network, support vector machine, decision tree, and K nearest machine learning model. the highest accuracy of 98% got for K nearest neighbor method and the VVG-16 deep learning model accuracy was 99.6%. © 2020 IEEE.

关键词： Image segmentation

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4th IFIP TC 12 international conference on Intelligence Science, ICIS 2020

4th IFIP TC 12 International Conference on Intelligence Scie...

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4th IFIP TC 12 international conference on Intelligence Science, ICIS 2020

ISBN: (纸本)9783030748258

the proceedings contain 27 papers. the special focus in this conference is on Intelligence Science. the topics include: Trilevel Multi-criteria Decision Analysis Based on three-Way Decision;Recurrent Self-evolving Takagi–Sugeno–Kan Fuzzy Neural Network (RST-FNN) Based Type-2 Diabetic Modeling;granulated Tables with Frequency by Discretization and their Application;person Authentication by Gait Data from Smartphone Sensors Using Convolutional Autoencoder;research on Personal Credit Risk Assessment Model Based on Instance-Based Transfer learning;from Texts to Classification Knowledge;ANAS: Sentence Similarity Calculation Based on Automatic Neural Architecture Search;fully Interval Integer Transhipment Problem- A Solution Approach;Novel Image Compression and Deblocking Approach Using BPN and Deep Neural Network Architecture;characterization of Orderly Behavior of Human Crowd in Videos Using Deep learning;some Discussions on Subjectivity of machine and its Function;stability Analysis of Imprecise Prey-Predator Model;comparative Performance Study on Human Activity Recognition with Deep Neural Networks;beam and Ball Plant System Controlling Using Intuitionistic Fuzzy Control;application of Pinching Method to Quantify Sensitivity of Reactivity Coefficients on Power Defect;computation with Democracy: An Intelligent System;Economy and Unemployment Due to COVID19: Secondary Research;optimal Control of Dengue-Chikungunya Co-infection: A Mathematical Study;comparative Analysis of machine learning Algorithms for Categorizing Eye Diseases;hexagon of Intelligence;interactive Granular computing Model for Intelligent Systems;a Framework for the Approximation of Relations;logical Treatment of Incomplete/Uncertain Information Relying on Different Systems of Rough Sets;possibility theory and Possibilistic Logic: Tools for Reasoning Under and About Incomplete Information;similarity-Based Rough Sets with Annotation Using Deep learning.

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A Review on Various Deep learning Techniques for Identification of Plant Diseases 4th

A Review on Various Deep Learning Techniques for Identificat...

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

作者： Sirohi, Akash Malik, Arun Isha Luhach, Ashish Kr. Lovely Professional University Punjab India Department of Computer Science Lovely Professional University Punjab India The PNG University of Technology Lae Papua New Guinea

ISBN: (纸本)9789811636592

Plants are the most important for the living of human beings as well as for our environment. Agriculture plays an important role in the economy of our country, so it becomes important to save the plants from diseases. It is necessary to detect the diseases in an earlier stage to save the plants, which is one of the most difficult things so, In order to detect or identify the diseases in the plants there are some traditional methods which are done by manually becomes very difficult and they require lots amount of time, expertise in plant diseases and has excessive processing time. Nowadays machine learning and deep learning is used widely for this purpose with the help of images. So this paper, perform a survey on the various deep learning techniques or models for the identification of plants diseases. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image processing

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Dual Enhancement for Multi-Label learning with Missing Labels 21

Dual Enhancement for Multi-Label Learning with Missing Label...

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4th international conference on machine learning and machine Intelligence, MLMI 2021

作者： Liu, Shengyuan Wang, Haobo Hu, Tianlei Chen, Ke Key Lab of Intelligent Computing Based Big Data of Zhejiang Province College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9781450384247

the goal of multi-label learning with missing labels (MLML) is assigning each testing instance multiple labels given training instances that have a partial set of labels. the most challenging issue is to complete the missing labels by leveraging input signals and given labels. Existing methods usually complete missing entries by utilizing label correlation, which, however, overlook knowledge from the object itself. To tackle this problem, we propose a dual enhancement framework that enhances missing entries from the label- perspective and instance-perspective. First, we develop a Long Short-Term Memory (LSTM) network that utilizes sequential label correlations to complete missing labels. Second, we expand the training dataset via a MixUp procedure to further enhance the label confidence. Extensive experiments on three multi-label image datasets clearly verify the superiority of our proposed method to the baselines. © 2021 ACM.

关键词： Image recognition

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Fake News Detection Using Passive-Aggressive Classifier and Other machine learning Algorithms 4th

Fake News Detection Using Passive-Aggressive Classifier and ...

引用

4th international conference on computing and Network Communications, CoCoNet 2020

作者： Nagashri, K. Sangeetha, J. Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (纸本)9789813369863

Fake news means false facts generated for deceiving the readers. the generation of fake news has become very easy which can mislead people and cause panic. therefore, fake news detection is gaining prominence in research field. As a solution, this paper aims at finding the best possible algorithms to detect fake news. In this paper, term frequency–inverse document frequency (TFIDF) as well as count vector techniques is used separately for text preprocessing. Six machine learning algorithms namely passive-aggressive classifier (PAC), naive Bayes (NB), random forest (RF), logistic regression (LR), support vector machine (SVM), and stochastic gradient descent (SGD) are compared using evaluation metrics such as accuracy, precision, recall, and F1 score, the results have shown that the TFIDF is a better text preprocessing technique. PAC and SVM algorithms show the best performance for the considered dataset. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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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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