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检索条件"任意字段=2020 2nd International Conference on Applied Machine Learning and Data Science, ICAMLDS2020"

共 178 条记录，以下是171-180 订阅

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Wind Speed Time Series Prediction Using a Single Dendritic Neuron Model

Wind Speed Time Series Prediction Using a Single Dendritic N...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Zhenyu Song Tianle Zhou Xuemei Yan Cheng Tang Junkai Ji College of Computer Science and Technology Taizhou University Taizhou China School of Computer Engineering Jiangsu University of Technology Changzhou China Faculty of Engineering University of Toyama Toyama Japan College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728196381

ISBN: (纸本)9781728196398

The chaotic and intrinsic complexity of wind speed time series calls for an appropriate model to accurately predict the moving tendency. In this study, we proposed a single dendritic neuron model (S-DNM) using a back-propagation algorithm to accomplish wind speed forecasting. First, based on mutual information method and false nearest neighbor method, the time delay and embedding dimension are calculated. Second, the phase space is reconstructed by time delay and embedding dimension, and the characteristics of wind speed time series are analyzed. Then, the maximum Lyapunov exponent is applied to confirm the chaotic properties of the wind speed time series. Finally, using wind speed data from Sotavento located in Galicia, Spain, the performance of the forecasting method is evaluated for short-term horizons (1 hour ahead). Experimental results show that the proposed S-DNM performed better than the traditional ELMAN model and classic multi-layered perceptron network. Thus, it is concluded that the proposed model is suitable for wind speed forecasting.

关键词： Wind speed Delay effects Time series analysis Neurons Predictive models Prediction algorithms Forecasting

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Anomaly-based IDS to Detect Attack Using Various Artificial Intelligence & machine learning Algorithms: A Review

Anomaly-based IDS to Detect Attack Using Various Artificial ...

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international conference on data, Engineering and Applications (IDEA)

作者： Alka Mishra Pradeep Yadav Computer Science ITM GOI Gwalior India

ISBN: (数字)9781728157184

ISBN: (纸本)9781728157191

Cyber-attacks are becoming more complex & increasing tasks in accurate intrusion detection (ID). Failure to avoid intrusion can reduce the reliability of security services, for example, integrity, Privacy & availability of data. The rapid proliferation of computer networks (CNs) has reformed the perception of network security. Easily accessible circumstances affect computer networks from many threats by hackers. Threats to a network are many & hypothetically devastating. Researchers have recognized an Intrusion Detection System (IDS) up to identifying attacks into a wide variety of environments. Several approaches to intrusion detection, usually identified as Signature-based Intrusion Detection Systems (SIDS) & Anomaly-based Intrusion Detection Systems (AIDS), were proposed in the literature to address computer safety hazards. This survey paper grants a review of current IDS, complete analysis of prominent new works & generally utilized dataset to evaluation determinations. It also introduces avoidance techniques utilized by attackers to avoid detection. This paper delivers a description of AIDS for attack detection. IDS is an applied research area in artificial intelligence (AI) that uses multiple machine learning algorithms.

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Centroid Sort: a Clustering-based Technique for Accelerating Sorting Algorithms

Centroid Sort: a Clustering-based Technique for Accelerating...

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international Multidisciplinary Information Technology and Engineering conference (IMITEC)

作者： Peter Olukanmi Peter Popoola Michael Olusanya School of Natural and Applied Sciences Sol Plaatje University Kimberley South Africa School of Mathematics Computer Science and Statistics University of KwaZulu-Natal South Africa

ISBN: (数字)9781728195209

ISBN: (纸本)9781728195216

Sorting does not only occupy a central place in computer science; it also constitutes a building block in machine learning algorithms. This paper draws a useful connection between sorting and clustering, leading to a general approach for accelerating sorting algorithms. Basically, clustering is presented as an effective way to implement the well-known divide-and-conquer strategy. The key idea is that unless an algorithm takes (sub-)linear time, which is an impossible average complexity for sorting, if a k-means-type clustering is applied to the input data, the sum of the individual sorting times will be less than the time for the original input, and sorted versions of the partitions only need be concatenated. Quadratic time, which is common among well-known algorithms, can be accelerated up to k times. Although any clustering algorithm can be used, to minimize overhead, we propose a simplified one-pass k-means-type algorithm comprising a single iteration of Lloyds standard k-means algorithm. We sample k items as partition prototypes; then each item in the data is grouped with its nearest prototype. As proof of concept, we present experiments involving 3 well-known quadratic-time algorithms, namely Insertion Sort, Bubble Sort and Selection Sort.

关键词： machine learning algorithms Clustering algorithms Prototypes Partitioning algorithms Acceleration Time complexity Sorting

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Analysis of Social Media data to Classify and Detect Frequent Issues Using machine learning Approach

Analysis of Social Media Data to Classify and Detect Frequen...

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Advanced Information and Communication Technology (ICAICT), international conference on

作者： Pankaj Bhowmik Md. Sohrawordi U.A. Md. Ehsan Ali Md. Najmul Hasan Prodip Kumar Roy Hajee Mohammad Danesh Science and Technology University (HSTU) Dinajpur Bangladesh

ISBN: (数字)9780738123233

ISBN: (纸本)9781665422406

Social Media (SM) is becoming the next-level journalism as it reflects all the viral contents, social affairs, current conditions of our surroundings. SM site Facebook has achieved the utmost popularity in Bangladesh. Nowadays, people prefer to share their views about a problem on SM rather than any other platform. Considering these consequences, this study (Bangladesh perspective) proposed a system which will analyze Facebook data and will classify and detect the problems people are facing most frequently at a particular time. The problems are categorized into 12 major classes addressing the socio-economic aspects of Bangladesh. The dataset, contains public posts (and comments) from Facebook, is manually collected and labeled with the classes. Performing efficient data preparation, feature vectors are constructed with TF-IDF weights then Chi-square is applied to select the most correlated features. Four machine learning algorithms e.g., Logistic Regression, Support Vector machine, Multinomial Naive Bayes, Random Forest are employed as supervised classifiers. Besides, hyperparameter tuning of these classifier algorithms is carried out with Grid Search and the best candidate model is selected applying 10-folds cross validation. Finally, the selected `Logistic Regression' model is built with securing classification accuracy of over 93%. The classification report illustrates important information about problems outgrowth.

关键词： Support vector machines Vocabulary machine learning algorithms Social networking (online) Feature extraction Task analysis Tuning

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A Parallel Adaptive DBSCAN Algorithm Based on k-Dimensional Tree Partition

A Parallel Adaptive DBSCAN Algorithm Based on k-Dimensional ...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Xin Lu Yu Wang Jiao Yuan Xun Wang Kun Fu Ke Yang School of software University of Electronic Science and Technology of China Chengdu China China Railway Eryuan Engineering Group Co. Ltd Chengdu China

ISBN: (数字)9781728196381

ISBN: (纸本)9781728196398

The existing parallel DBSCAN (density based spatial clustering of applications with noise) algorithm needs to determine the parameter settings manually, and the datasets will be repeatedly accessed in the process of data partitioning and data merging, which reduces the efficiency of the algorithm excuting. Therefore, this paper proposes a parallel adaptive DBSCAN algorithm based on k-dimensional tree partition. It divides the dataset into several balanced data partitions by using k-dimensional tree, and carries out parallel computing in spark distributed computing framework, thus increasing the concurrent processing ability of the algorithm program and improving the I/O access speed. In addition, the improved adaptive DBSCAN parameter method is applied to each data partition for clustering analysis to obtain local clusters, which solves the random problem of manual setting parameters in the clustering process, and ensures the data quality of clustering mining. At the same time of creating local clusters, this algorithm also puts the mapping relationship between data points and adjacent points into the HashMap data structure of the master node, and uses it to merge local clusters into whole clusters, which can reduce the time cost of data merging. The experimental results show that the proposed algorithm can save about 18% running time compared with RDD-DBSCAN algorithm without reducing the clustering quality. With the increase of the number of cluster nodes, the running efficiency of the algorithm can be further improved, so it is suitable for processing massive data clustering analysis.

关键词： machine learning algorithms Merging Clustering algorithms data structures Partitioning algorithms Sparks data mining

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Sentiment Classification of Movie Reviews by Supervised machine learning Approaches Using Ensemble learning & Voted Algorithm

Sentiment Classification of Movie Reviews by Supervised Mach...

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international conference on data, Engineering and Applications (IDEA)

作者： Rubeena Parveen Neelesh Shrivastava Pradeep Tripathi Vindhya Institute of Technology & Science Satna M.P. India

ISBN: (数字)9781728157184

ISBN: (纸本)9781728157191

In this paper Authors explained their experimental works. In this work we choose movies review from corpora community. corpora contain numbers of dataset from where we selected movies review. In this dataset we have two categories negative & positive each category contains 1000 files. In our file we have movies review by different reviewer. we took. txt file extension. Here we are finding sentiment polarity against those movies. we extend some more in comparison to our base papers. we apply numbers of classification algorithm like NaiveBayes, SkLearn, Support Vector machine. We applied multiple classifier like MultinomialNB, GaussianNB, BernoulliNB and many more. we select different features (in count or numbers) to understand Accuracy Level of different Algorithms. Here Authors used Ensemble learning where we combined number of classification algorithms and try voted algorithm to find best accuracy.

关键词： Sentiment Analysis StopWord Tokens Features Training & Testing data Model or classifier Na?ve Bayes SVM SKlearn voted Algorithm Ensemble learning Cinema token Support Vector Network Films Sentiment analysis Sorting algorithm dataset algorithms

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Algorithms for Automatic Identification and Analysis of Sri Lankan Anopheles Mosquito Species

Algorithms for Automatic Identification and Analysis of Sri ...

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Advancements in Computing (ICAC), 2019 international conference on

作者： Vigneshwaran Palanisamy Vaikunthavasan Thiruchenthooran Sinnathamby Noble Surendran Nagulan Ratnarajah Vavuniya Campus of the University of Jaffna Sri Lanka University of Jaffna Sri Lanka Faculty of Applied Science Vavuniya Campus of the University of Jaffna Sri Lanka

ISBN: (数字)9781728184128

ISBN: (纸本)9781728184135

Microscopic digital image processing algorithms are presented here to automatically detect primary morphological features of Sri Lankan anopheline mosquitoes, as an essential step towards the development of automated identification and analysis of various species of anopheline mosquitoes. Mosquitoes that belong to genus Anopheles spread the causative pathogen of malaria. Perfect and speedy species identification is crucial in any surveillance and control strategies. Currently, morphological taxonomic keys are used to identify various species. Two or more primary morphological characteristics, such as a number of dark spots of wings and pale bands of legs, are used in each step of the hierarchical key. To achieve the automatic detection of the primary morphological features, image processing algorithms performed at three levels. At the pre-processing level, methods work with raw, possibly noisy pixel values, with noise reduction and smoothing. In the mid-level, algorithms are utilized pre-processing results for further means with background removing and spots/bands segmentation. At the final level, techniques try to extract the semantics of spots/bands and counting the spots/bands from the information provided. Thirty samples of anopheline mosquitoes' wings and legs microscopic images were analysed with satisfactory results.

关键词： Legged locomotion machine learning algorithms Microscopy Veins Surveillance Feature extraction data mining

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Application of machine learning Techniques In Forecasting Groundwater Levels in the Grootfontein Aquifer

Application of Machine Learning Techniques In Forecasting Gr...

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international Multidisciplinary Information Technology and Engineering conference (IMITEC)

作者： Yolanda Kanyama Ritesh Ajoodha Helen Seyler ndivhuwo Makondo Hlanganani Tutu School of Computer Science and Applied Mathematics The University of the Witwatersrand IBM Research-Africa Johannesburg South Africa Delta-H Groundwater Systems Pty Ltd Pretoria South Africa IBM Research-Africa Computer Science and Applied Mathematics The University of the Witwatersrand Johannesburg South Africa Molecular Sciences Institute School of Chemistry The University of the Witwatersrand Johannesburg South Africa

ISBN: (数字)9781728195209

ISBN: (纸本)9781728195216

In this paper, we attempt to provide a data driven solution to model groundwater levels in the Grootfontein Aquifer in the North West Province of South Africa by testing several predictive models. Groundwater plays a crucial role in supplying water to a significant part of the population for agricultural, industrial, environmental and/or domestic use. Recent advancements in data analytics, and the analysis of large data sets has allowed the production of powerful predictive models. Five different data driven techniques namely, support vector regression, gradient boosting trees, decision trees, random forest regression and multilayer feed-forward neural network techniques were applied to predict groundwater levels. Modelling was carried out for four boreholes located in the Grootfontein dolomite aquifer considering discharge, rainfall and temperature as model inputs. Five site specific models were developed for each borehole. Model performance was evaluated using coefficient of determination and root mean squared error. Comparison of goodness of fit revealed that data driven methods can indeed capture the trend of water level fluctuations in the aquifer sufficiently with the GB algorithm performing better than other algorithms in both the training and verification stages. Whilst the models performed adequately when predicting groundwater level on a monthly basis for 36 months, further investigation is needed towards determining their efficacy in longer term projections to assist in the decision making process of sustainable groundwater use. This paper provides the following contributions: (a) a ranking of the attributes according to their mutual information (MI); (b) a reference for model selection; and (c) a predictive model to forecast groundwater levels in the Grootfontein aquifer.

关键词： Training Vegetation Predictive models Prediction algorithms Discharges (electric) Forecasting Testing

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