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检索条件"任意字段=5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022"

共 261 条记录，以下是151-160 订阅

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Application of Duality Properties of Renyi Entropy for Parameter Tuning in an Unsupervised machine learning Task 5th

Application of Duality Properties of Renyi Entropy for Para...

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5th international conference on applied Informatics, ICAI 2022

作者： Koltcov, Sergei Laboratory for Social and Cognitive Informatics National Research University Higher School of Economics 55/2 Sedova Street St. Petersburg192148 Russia

ISBN: (纸本)9783031196461

this work demonstrates the possibility of applying the duality properties of a statistical collection of texts to determine the optimal number of topics/clusters. In a series of numerical experiments on text data, it was demonstrated that Renyi entropy of topic models, expressed in Sq form (based on the escort distribution), as a function of the number of topics, is the most effective in terms of determining the optimal number of topics. At the same time, S2-Q and S1/q forms are not suitable for determining the number of topics. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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Development of an Intent-Based Network Incorporating machine learning for Service Assurance of E-Commerce Online Stores 1

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5th international conference on machine learning for Networking, MLN 2022

作者： Hurtado, Remigio Torres, Mario Pintado, Bryan Muñoz, Arantxa Universidad Politécnica Salesiana Calle Vieja 12-30 y Elia Liut Cuenca Ecuador

ISBN: (数字)9783031361838

ISBN: (纸本)9783031361821

Managing the large number of network devices is a major challenge for organizations. the management of these devices requires meticulous care since manual configurations of traditional networks often lead to errors. Manual configurations result in inefficient troubleshooting. Instead of worrying about the development of new services, the organizations’ staff is exhausted by the arduous task of maintaining network devices. Specifically, one of the organizations’ core services is their e-commerce service available through their website, through which they present and sell products. Companies that have had a great growth require optimizing the use of CPU, memory, bandwidth resources, among others, in order to provide an available and scalable service for the large number of customers. Due to this, this paper proposes: 1) an Intent Based Network (IBN) based on Cisco’s IBN architecture applied to online stores in e-commerce, 2) a scalable and efficient method that uses Neural Networks (NN) for resource prediction in order to adapt to the large number of customers and the growing demand in this era of Big data. the IBN architecture provides automation, flexibility and unlike Software Defined Networks (SDN) includes the integration of artificial intelligence and data mining techniques in order to analyze future trends and problems for network service assurance. the neural network predicts the appropriate bandwidth value based on the trend of the number of users that the online store server will consume and other resources such as CPU, memory and storage usage. this value is automatically configured in the network, so that the service always has a high availability. We have created a dataset by collecting data from the network we have developed. We have defined a base neural network model and subsequently optimized this model by minimizing the Mean Absolute Error (MAE). We have shown that the optimized neural network outperforms other machine learning methods such as Random

关键词： Websites

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Packet Loss Prediction Using Artificial Intelligence Unified with Big data analytics, Internet of things and Cloud Computing Technologies 5

Packet Loss Prediction Using Artificial Intelligence Unified...

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5th international conference on Information Systems and Computer Networks, ISCON 2021

作者： Chauhan, Rishabh Kumar, Sunil Amity School of Engineering and Technology Amity University Noida India

ISBN: (纸本)9781665403412

Big data analytics, Artificial Intelligence and cloud computing all together has emerged with an ultimate goal of automating and changing human life by providing their services. these incredibly strong technologies have huge potential by working together, making human life simpler and advanced. To increase the popularity of any of these services, Quality of Service metrics are needed to be defined clear. One of those quality metrics is packet loss or packet delivery, which is the main research idea of this paper. With advancement in Intelligent Network there exists a scope to predict packet loss, by analyzing the recorded network traffic and processing said data under certain machine learning algorithms to create a model to either predict packet loss or tell which variable is responsible for packet loss. this paper includes the study of packet loss behavior of networks. the analytics techniques applied successfully by analyzing big network traffic data, processing of data, using AI and machine learning classifier 'XGBoost' and hence designed a model to predict Packet loss which is a QoS metric with an accuracy of 90 percent. the model is personalized to work on WireShark data. © 2021 IEEE.

关键词： Quality of service

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data-Driven Disaster Management in a Smart City 5th

Data-Driven Disaster Management in a Smart City

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5th EAI international conference on Intelligent Transport Systems (INTSYS)

作者： Goncalves, Sandra P. Ferreira, Joao C. Madureira, Ana Inst Univ Lisboa ISCTE IUL ISTAR Lisbon Portugal Politecn Porto 2ISRC Inst Super Engn Porto Porto Portugal

ISBN: (纸本)9783030976033;9783030976026

Disasters, both natural and man-made, are extreme and complex events with consequences that translate into a loss of life and/or destruction of properties. the advances in IT and Big data analysis represent an opportunity for the development of resilient environments once the application of analytical methods allows extracting information from a significant amount of data, optimizing the decision-making processes. this research aims to apply the CRISP-DM methodology to extract information about incidents that occurred in the city of Lisbon with emphasis on occurrences that affected buildings, constituting a tool to assist in the management of the city. through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the area most affected by occurrences. Finally, machine learning models were applied to the data and the predictive model Random Forest obtained the best result with an accuracy of 58%.

关键词： Disaster management data mining machine learning Smart city

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Detecting Spam Email with machine learning Optimized with Harris Hawks optimizer (HHO) Algorithm

Detecting Spam Email with Machine Learning Optimized with Ha...

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13th international conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th international conference on Emerging data and Industry 4.0, EDI40 2022

作者： Mashaleh, Ashraf S. Binti Ibrahim, Noor Farizah Al-Betar, Mohammed Azmi Mustafa, Hossam M.J. Yaseen, Qussai M. School of Computer Sciences Universiti Sains Malaysia Penang Malaysia Computer Center Al-Balqa Applied University Al-Salt Jordan Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Al-Huson Jordan IS and CS Department Faculty of Computer Science and Informatics Amman Arab University Amman Jordan Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Email spam has been a big issue in recent years. As the percentage of internet users grows, so does the number of spam emails. Technologies are being used for illegitimate and immoral activities, such as phishing and robbery. As a consequence, it is essential to identify fraudulent spammers by employing machine learning techniques. this paper presents a novel spam classification technique that integrates the Harris Hawks optimizer (HHO) algorithm with the k-Nearest Neighbors algorithm (k-NN). the Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm motivated by Harris' Hawks' cooperative relations and surprises pounce pursue technique in nature. According to empirical results on the dataset, the suggested model can handle high-dimensional data (Spambase). the suggested model's spam detection accuracy is compared to the numerous algorithms, including the Binary Dragonfly Algorithm (BDA), Equilibrium Optimizer (EO), Teaching-learning-based Optimization, Seagull Optimization Algorithm (SO), and Marine Predators Algorithm (MPA). We found that the proposed technique trumps the other spam detection techniques investigated in this study in terms of classification accuracy. the experimental results showed that the proposed technique accuracy reaches %94.3. © 2022 Elsevier B.V.. All rights reserved.

关键词： email spam detection Harris Hawks optimizer (HHO) k-NN machine learning

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Comparing representations of biological data learned with different AI paradigms, augmenting and cropping strategies 5

Comparing representations of biological data learned with di...

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5th international conference on Medical Imaging with Deep learning (MIDL)

作者： Dmitrenko, Andrei Masiero, Mauro M. Zamboni, Nicola Swiss Fed Inst Technol Life Sci Zurich PhD Program Syst Biol Zurich Switzerland Swiss Fed Inst Technol Inst Mol Syst Biol Zurich Switzerland

Recent advances in computer vision and robotics enabled automated large-scale biological image analysis. Various machine learning approaches have been successfully applied to phenotypic profiling. However, it remains unclear how they compare in terms of biological feature extraction. In this study, we propose a simple CNN architecture and implement 4 different representation learning approaches. We train 16 deep learning setups on the 770k cancer cell images dataset under identical conditions, using different augmenting and cropping strategies. We compare the learned representations by evaluating multiple metrics for each of three downstream tasks: i) distance-based similarity analysis of known drugs, ii) classification of drugs versus controls, iii) clustering within cell lines. We also compare training times and memory usage. Among all tested setups, multi-crops and random augmentations generally improved performance across tasks, as expected. Strikingly, self-supervised (implicit contrastive learning) models showed competitive performance being up to 11 times faster to train. Self-supervised regularized learning required the most of memory and computation to deliver arguably the most informative features. We observe that no single combination of augmenting and cropping strategies consistently results in top performance across tasks and recommend prospective research directions.

关键词： Representation learning self-supervised learning regularized learning comparison memory constraints cancer research microscopy imaging.

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learning analytics and machine learning

Learning Analytics and Machine Learning

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international conference on Signal Processing and Information Security (ICSPIS)

作者： Aisha Almaazmi Elham Karmastaji Shadi Atallah Hamzah Ali Alkhazaleh Wathiq Manoor College of Engineering and IT University of Dubai Academic City 14143 Dubai UAE

ISBN: (纸本)9781665492669

this paper integrate the state-of-the-art processes, practices, and challenges of applying learning analytics and data modeling methods in learning and teaching contexts. A large amount of research employed analytics techniques to support the learners and their learning environments to improve learning outcomes and performance. this study aims to analyze learning Analysts in data science, especially how they can be used for better learning processes. this study uses a secondary research design to collect data from various online databases. these databases include Google Scholar and ProQuest. Only research published in 10 years is considered to ensure the recency of the extracted insights and information. the research outcome is presented as a taxonomy for learning analytics and the associated machine learning application in the education domain.

关键词： Electronic learning databases Education Taxonomy Information security machine learning data science

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Traffic noise prediction using machine learning and monte carlo data augmentation: A case study on the Patiala city in India 5

Traffic noise prediction using machine learning and monte ca...

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5th international conference on applied Physics, Simulation and Computing, APSAC 2021

作者： Singh, Daljeet Kaler, Priyal Lyall, Ishita Singh, Aekamjot Pannu, H.S. Mechanical Engineering Department Thapar Institute of Engineering and Technology Patiala147004 India Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala147004 India

Traffic noise pollution is a serious problem in the modern urban areas especially to design new architecture of smart cities, highways, hospitals, schools for an efficient and healthy environment. To analyse this aspect, we have proposed a machine learning based prediction of sound pressure level on an original dataset collected in Patiala city in India. Vehicular traffic and sound pressure level data was collected on different sites in the city. A total of 502 data samples on the identified sites were obtained for the study. Further this data is augmented using Monte Carlo simulation to 10 times of its initial size and the Artificial Neural Networks (ANN) have been trained and compared with other machine learning methods for the vehicular traffic noise prediction. the input parameters in the model are traffic volume Q, percentage of heavy vehicles P and the average speed of vehicles V and the output parameter is the equivalent continuous sound pressure level, Leq dB(A). the experimental results show ANN which is trained on the augmented data using Monte Carlo simulations outperforms other advanced methods making it an effective measure for vehicular traffic noise prediction to develop a healthy environment which is free of noise pollution. © 2022 Published under licence by IOP Publishing Ltd.

关键词： Noise pollution

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Development of a Variety of Fast machine learning Model for ECG-based Arrhythmia Classifier 5

Development of a Variety of Fast Machine Learning Model for ...

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5th IEEE international conference on Artificial Intelligence and Knowledge Engineering, AIKE 2022

作者： Zhang, Gengjia Shin, Siho Jung, Jaehyo Li, Meina Kim, Youn Tae Ai Healthcare Research Center Department of It Fusion Technology Chosun University 309 Pilmun-daero Dong-gu Gwangju61452 Korea Republic of College of Instrumentation and Electrical Engineering Jilin University Changchun130061 China

ISBN: (数字)9781665471206

ISBN: (纸本)9781665471206

Although deep learning has been proving its capability in various fields, training and testing by learning a large amount of data and deep neural networks remain time consuming. To address this issue, a high-performance GPU and CPU, SSD storage, and a large amount of RAM is required, which is expensive. We propose a new classifier algorithm by feature point extraction that can be trained and tested quickly. the performance of the proposed algorithm was verified by classifying heart diseases by applying the MIT-BIH arrhythmia data set. First, the noise was removed by Wavelet transform, and feature points were extracted using root mean square (RMS), crest factor, margin factor, form factor, kurtosis, and pulse factor. then, the performance was compared using various classification algorithms. the two feature extraction methods are compared to evaluate the accuracy of each algorithm, the execution time of the model during training, and the memory usage. Our proposed algorithm is applied to various health care systems such as heart disease and depression, and it is predicted that it will be able to help users toward health care at low cost. © 2022 IEEE.

关键词： Electrocardiograms

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Investigation of Automatic Part-of-Speech Tagging using CRF, HMM and LSTM on Misspelled and Edited Texts 5

Investigation of Automatic Part-of-Speech Tagging using CRF,...

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5th Artificial Intelligence and Cloud Computing conference, AICCC 2022

作者： Aydinov, Farhad Huseynov, Igbal Sayadzada, Sofiya Rustamov, Samir Data Science University of Glasgow United Kingdom Computational Linguistics and Language Technologies Charles University Prague Czech Republic Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark School of Information Technology and Engineering ADA University Baku Azerbaijan Institute of Control Systems BakuVarna Bulgaria Azerbaijan

ISBN: (纸本)9781450398749

Part-of-speech tagging is the process of assigning words in a given text to appropriate parts-of speech in order to reduce the disambiguation which may arise depending on the contextual usage of the words. In this paper, the problem of word sense disambiguation in Azerbaijani language is addressed by applying part of speech tagging on two varying data corpora, misspelled, and edited (clean) text using 3 different machine learning algorithms: Hidden Markov Model, Long Short-Term Memory, and Conditional Random Fields. the comparative analysis on the outcomes of the mentioned algorithms and their accuracy scores were analysed in the paper. the misspelled dataset for the experiments is provided by Unibank from their chatbot dialogues while the clean textual data was retrieved from the books and newspapers in Azerbaijani. the experiments showed that the Bidirectional LSTM has the highest accuracy scores for both edited (98.2%) and noisy (96.2%) data corpora. Suggested models can be used in the application of algorithms focuses on part of speech tags and syntactic structure of Azerbaijani language which is an agglutinative language belonging to Turkic languages family, thus enabling the research to be further investigated in other agglutinative languages with similar grammatical structure. © 2022 ACM.

关键词： Hidden Markov models

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