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Clustering Stability via Concept-based Nonnegative Matrix Factorization 2019

Clustering Stability via Concept-based Nonnegative Matrix Fa...

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3rd international conference on machine learning and soft computing (icmlsc)

作者： Nghia Duong-Trung Minh-Hoang Nguyen Nguyen, Hanh T. H. Can Tho Univ Technol Can Tho Vietnam FPT Univ Can Tho Vietnam Univ Trento Dept Informat Engn & Comp Sci Trento Italy Informat Syst & Machine Learning Lab Hildesheim Germany

ISBN: (纸本)9781450366120

One of the most important contributions of topic modeling is to accurately and the ectively discover and classify documents in a collection of texts by a number of clusters/topics. However, finding an appropriate number of topics is a particularly challenging model selection question. In this context, we introduce a new unsupervised conceptual stability framework to access the validity of a clustering solution. We integrate the proposed framework into nonnegative matrix factorization (NMF) to guide the selection of desired number of topics. Our model provides a exible way to enhance the interpretation of NMF for the effective clustering solutions. The work presented in this paper crosses the bridge between stability-based validation of clustering solutions and NMF in the context of unsupervised learning. We perform a thorough evaluation of our approach over a wide range of real-world datasets and compare it to current state-of-the-art which are two NMF-based approaches and four Latent Dirichlet Allocation (LDA) based models. the quantitative experimental results show that integrating such conceptual stability analysis into NMF can lead to significant improvements in the document clustering and information retrieval the ectiveness.

关键词： Concept Discovery Conceptual Stability Topic Modeling Unsupervised Document Clustering Nonnegative Matrix Factorization

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A Recent Research on Malware Detection Using machine learning Algorithm: Current Challenges and Future Works

A Recent Research on Malware Detection Using Machine Learnin...

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7th international conference on Advances in Visual Informatics, IVIC 2021

作者： Gorment, Nor Zakiah Selamat, Ali Krejcar, Ondrej Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia College of Computing and Informatics Universiti Tenaga Nasional Jalan IKRAM-UNITEN Kajang Selangor43000 Malaysia Faculty of Informatics and Management Universiti Hradec Kralove Rokitanskeho 62 Hradec Kralove50003 Czech Republic

ISBN: (纸本)9783030902346

Each year, malware issues remain one of the cybersecurity concerns since malware’s complexity is constantly changing as the innovation rapidly grows. As a result, malware attacks have affected everyday life from various mediums and ways. Therefore, a machine learning algorithm is one of the essential solutions in the security of computer systems to detect malware regarding the ability of machine learning algorithms to keep up with the evolution of malware. This paper is devoted to reviewing the most up-to-date research works from 2017 to 2021 on malware detection where machine learning algorithm including K-Means, Decision Tree, Meta-Heuristic, Naïve Bayes, Neuro-fuzzy, Bayesian, Gaussian, Support Vector machine (SVM), K-Nearest Neighbour (KNN) and n-Grams was discovered using a systematic literature review. This paper aims at the following: (1) it describes each machine learning algorithm, (2) for each algorithm;it shows the performance of malware detection, and (3) we present the challenges and limitations of the algorithm during research processes. © 2021, Springer Nature Switzerland AG.

关键词： Support vector machines

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21st Century learning Skills Predictive Model Using PART Algorithm 2019

21st Century Learning Skills Predictive Model Using PART Alg...

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3rd international conference on machine learning and soft computing (icmlsc)

作者： Aguinaldo, Betchie E. Isabela State Univ Cauayan Isabela Philippines

ISBN: (纸本)9781450366120

The skillset of a graduate is the key to success to perform successfully in their chosen career path, thus, academic institution continuously develops strategic ways to develop and prepare the 21st century learning skills of the student before they completed the higher education studies. This paper presents the 21st Century learning Predictive Model in Programming Logic Formulation using PART classifier algorithm. It also aims to determine the significant attributes in the development of dataset for predictive model and generate a predictive model for 21st Century learning Skills using PART classifier algorithm. Six (6) standardized questionnaire of 21st Century learning Skills of one hundred eight (180) students were coded and used as a dataset of the study. The result of the customized assessment exam in programming logic formulation was used as response attributes of the datasets. As a result of the study, five (5) rules were generated using PART classifier algorithm. Among the 21st Century learning skills, Communication is the strongest predicting attributes of successful performance of students in programming logic formulation.

关键词： Data Mining Predictive Model Classifier Algorithm Employability

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Load Balancing for machine learning Platform in Heterogeneous Distribute computing Environment 20

Load Balancing for Machine Learning Platform in Heterogeneou...

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2020 Research in Adaptive and Convergent Systems, RACS 2020

作者： Kim, Younggwan Lee, Jusuk Kim, Ajung Hong, Jiman Soongsil University Seoul Korea Republic of

ISBN: (纸本)9781450380256

With the recent rapid development of computing power, interest in machine learning research on large data sets is increasing significantly. The machine learning is used in a wide variety of fields, from information retrieval, data mining, and speech recognition to human-computer interaction and application development by non-experts using machine learning platforms. However, there is not enough research on load balancing for distributed systems composed of heterogeneous servers with different performances and architectures that process machine learning tasks. Therefore, in this paper, we propose level hashing-based load balancing applicable to heterogeneous machine learning platforms. The proposed load balancing technique improves the execution time of all machine learning tasks in a machine learning platform by considering the characteristics of machine learning tasks and computing resources of each server. © 2020 ACM.

关键词： machine learning

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Fault Detection in Biological Methanation Process Using machine learning: A Comparative Study of Different Algorithms 18th

Fault Detection in Biological Methanation Process Using Mac...

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Proceedings of the 18th international conference on soft computing Models in Industrial and Environmental Applications, SOCO 2023

作者： Acosta-Pavas, Juan C. Griol, David Callejas, Zoraida Corrales, David Camilo Robles-Rodríguez, Carlos E. Morchain, Jérôme Aceves-Lara, César A. TBI Université de Toulouse CNRS INRAE INSA Toulouse France University of Granada CITIC-UGR Department Software Engineering Periodista Daniel Saucedo Aranda sn Granada18071 Spain TWB 135 Avenue de Rangueil Toulouse31077 France

ISBN: (纸本)9783031425288

In this paper we present a study that evaluates different machine learning models for fault detection based on the optimal operation of the biological methanation process. The optimal operation has been obtained from a multi-objective dynamic optimization based on an extended model of the anaerobic digestion model (ADM1 ME). Two datasets have been generated for the ADM1 ME model by generating disturbances on the inlet liquid flow rate (dataset 1) and the inlet gas flow rate (dataset 2). Variations of ±10, ±15, and ±20 % of both optimal inlets have been assumed. These datasets have been used to train several algorithms: decision tree CART, Random Forest (RF), Gaussian Naive Bayes (GNB), k-Nearest Neighbors (KNN), Quadratic Discriminant Analysis (QDA), and Support Vector machine (SVM). In dataset 1, CART, RF, and Radial Basis Function (RBF) SVM have achieved accuracies higher than 0.90 and 0.85 in the training and test, respectively. In dataset 2, accuracies higher than 0.90 and 0.87 have been obtained for the RF, QDA, and RBF SVM models in the training and test, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Statistical tests

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TRRS: Temporal Recurrent Recommender System based on Time-sync Comments 2019

TRRS: Temporal Recurrent Recommender System based on Time-sy...

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3rd international conference on machine learning and soft computing (icmlsc)

作者： Ren, Hao Wang, Dong Shanghai Jiao Tong Univ Sch Software Engn Shanghai Peoples R China

ISBN: (纸本)9781450366120

Recent years has witnessed great emerge of online video websites, including the exploded number of videos and users. As a result, there appears a lot of personlized recommender systems. However there remain some challenging problems to tackle such as cold start problem, which scientists have made use of all kinds of sideinformation, e.g. gender, age or comments, to release. Currently a new type of video comments, called TSCs (TSC), plays a more and more important role in video watching activity. In this paper we utilize TSC to recommend videos for users. We developed a deep nueral network model called Temporal Recurrent Recommder System (TRRS) which combine multi-layers neural network to extract feature for users and videos. The first layer convert TSC to embeddings, then RNN layer analyze each comment from user or video, and fianlly the merge layer combine all output from prior layer and produce the feature. We use the feature from the network for users and videos to make personlized recommendation.

关键词： Recommendation Time-sync Comments Deep learning

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Water Salinity Assessment Using Remotely Sensed Images—A Comprehensive Survey

Water Salinity Assessment Using Remotely Sensed Images—A Co...

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international conference on Computer Vision and machine Intelligence, CVMI 2022

作者： Priyadarshini, R. Sudhakara, B. Sowmya Kamath, S. Bhattacharjee, Shrutilipi Pruthviraj, U. Gangadharan, K.V. Department of Information Technology National Institute of Technology Karnataka Mangalore Surathkal575025 India Department of Water and Ocean Engineering National Institute of Technology Karnataka Mangalore Surathkal575025 India Department of Mechanical Engineering National Institute of Technology Karnataka Mangalore Surathkal575025 India

ISBN: (纸本)9789811978661

In the past few years, the problem of growing salinity in river estuaries has directly impacted living and health conditions, as well as agricultural activities globally, especially for those rivers which are the sources of daily water consumption for the surrounding community. Key contributing factors include hazardous industrial wastes, residential and urban wastewater, fish hatchery, hospital sewage, and high tidal levels. Conventional survey and sampling-based approaches for water quality assessment are often difficult to undertake on a large-scale basis and are also labor and cost-intensive. On the other hand, remote sensing-based techniques can be a good alternative to cost-prohibitive traditional practices. In this article, an attempt is made to comprehensively assess various approaches, datasets, and models for determining water salinity using remote sensing-based approaches and in situ observations. Our work revealed that remote sensing techniques coupled with other techniques for estimating the salinity of water offer a clear advantage over traditional practices and also is very cost-effective. We also highlight several observations and gaps that can be beneficial for the research community to contribute further in this significant research domain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Empirical Comparison of Area under ROC curve (AUC) and Mathew Correlation Coefficient (MCC) for Evaluating machine learning Algorithms on Imbalanced Datasets for Binary Classification 2019

Empirical Comparison of Area under ROC curve (AUC) and Mathe...

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3rd international conference on machine learning and soft computing (icmlsc)

作者： Halimu, Chongomweru Kasem, Asem Newaz, S. H. Shah Univ Teknol Brunei Sch Comp & Informat Jalan Tungku Link BE-1410 Gadong Brunei

ISBN: (纸本)9781450366120

A common challenge encountered when trying to perform classifications and comparing classifiers is selecting a suitable performance metric. This is particularly important when the data has class-imbalance problems. Area under the Receiver Operating Characteristic Curve (AUC) has been commonly used by the machine learning community in such situations, and recently researchers are starting to use Matthew Correlation Coefficient (MCC), especially in biomedical research. However, there is no empirical study that has been conducted to compare the suitability of the two metrics. In this paper, the aim of this study is to provide insights about how AUC and MCC are compared to each other when used with classical machine learning algorithms over a range of imbalanced datasets. In our study, we utilize an earlier-proposed criteria for comparing metrics based on the degree of consistency and degree of Discriminancy to compare AUC against MCC. We carry out experiments using four machine learning algorithms on 54 imbalanced datasets, with imbalance ratios ranging from 1% to 10%. The results demonstrate that both AUC and MCC are statistically consistent with each other;however AUC is more discriminating than MCC. The same observation is noticed when evaluated on 23 balanced datasets. This suggests AUC to be a better measure than MCC in evaluating and comparing classification algorithms.

关键词： machine learning Matthew Correlation Coefficient Area Under Curve Consistency Discriminancy Indifferency Support Vector machines Decision Tree Random Forest K-Nearest Neighbors

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Inception-v3-Based Recommender System for Crops 2019

Inception-v3-Based Recommender System for Crops

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3rd international conference on machine learning and soft computing (icmlsc)

作者： Guidang, Excel Philip B. Abra State Inst Sci & Technol Lagangilang Abra Philippines

ISBN: (纸本)9781450366120

Inception-v3 model is an image classifier that is commonly used in predictive modelling using an image as an input. Specifically, it achieved the following objectives a) Identify the major crops grown in the Philippines;b) Identify the soil requirements of crops;c) Classify Soil Texture images using Inception-v3;and d) Develop precision crop framing procedure based on Inception-v3. A threshold was set to 60%. The label having highest score that passes the preset threshold was used as a basis in the recommender system. The Inception-v3 model recognizes very well the images are that are clear and recognizable. Inception-v3 is an excellent tool in developing an image-based recommender system. However, the big challenge for authors who are also planning to do similar system lies on how to train the inception-v3 moel without over or under fitting. What can be done to solve thi issue is to play or manipulate the learning rate and the number of epochs. These are said to be the deterministic parameters to fine tune the model.

关键词： Recommender Sytrastem Transfer learning Inceptiion-v3

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LSTM-NB: DoS Attack Detection On SDN With P4 Programmable Dataplane

LSTM-NB: DoS Attack Detection On SDN With P4 Programmable Da...

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Advanced Creative Networks and Intelligent Systems (ICACNIS), international conference on

作者： Sya Raihan Heggi Parman Sukarno Satria Akbar Mugitama School of Computing Telkom University Bandung Indonesia

This paper proposes LSTM-NB, a combination of Long Short-Term Memory (LSTM) and Naive Bayes (NB) algorithms to tackle Denial of Service (DoS) attacks on Programming Protocol-independent Packet Processors (P4) language-based software Defined Network (SDN). The implementation of SDN is becoming more popular. However, there are critical aspects of the SDN architecture, one of which is that it is vulnerable to DoS attacks that can cause the network to lose the availability principle of the CIA Triangle. There are a number of works have been proposed to overcome this vulnerability, however, the threat is still exist. The proposed technique achieves an accuracy of 88% on SDN-DL Dataset, 98% on NSL-KDD, and 96% on CICIDS2017 with FNR score between 1-2%. In addition, we compare our proposed technique with other machine-learning and deep-learning methods. Through extensive experimental evaluation, we conclude that our proposed approach exhibits a strong potential for DoS detection in the SDN environments.

关键词： Deep learning Program processors Computational modeling software algorithms Computer architecture Programming Data models

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