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Comparison of Machine Learning classification and Clustering algorithms for TV Commercials Detection

IEEE ACCESS 2023年 11卷 116741-116751页

作者： Abdelfattah, Eman Joshi, Shreehar Sacred Heart Univ Sch Comp Sci & Engn Fairfield CT 06825 USA Ramapo Coll Sch Theoret & Appl Sci Mahwah NJ 07430 USA Boring Co Las Vegas NV 89169 USA

One of the essential aspects of broadcast monitoring is to detect and consequently extract commercial blocks in telecast news videos. The research carried out until now have based their work almost entirely on preconceived characteristics that are associated with a channel. With the advertisers constantly looking to work around the existing policies, the reliance on the nature of channels during an advertisement does not suffice. The other approach towards identifying a commercial is by frequentist approach. However, it is often the case that sponsored programs and other programs share similar time in any specified hour, rendering the frequentist approach almost useless in the process. As such, this paper uses machine learning based approach which is more generic and can employ inherent differences that commercials have over their non-commercial counterparts for classifying and clustering commercials in the news videos. The datasets which contain 90 hours of recordings from five different news channels from US, England and India have been used to train and test nine different classifiers - K Neighbors, Support Vector Machine, Decision Tree, Random Forests, Ada Boost, Gradient Boost, Gaussian NB, Linear Discriminant Analysis, and Quadratic Discriminant Analysis - and five different clustering algorithms - K Means, Agglomerative, Birch, Mini-Batch K Means, and Gaussian Mixture. Our results show that the Random Forests outperforms all the other classifiers used with respect to F1 score and median time to train and test on each of these datasets that consists of features of shots extracted from 18 hours of video. Similarly, Mini Batch K Means was found to perform the best for forming clusters of news and commercials.

关键词： TV Videos Feature extraction Clustering algorithms classification algorithms Machine learning algorithms Machine learning Advertising TV commercial detection machine learning classification clustering

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Examining the performance of classification algorithms for imbalanced data sets in web author identification

Examining the performance of classification algorithms for i...

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Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT), Conference of

作者： Alisa A. Vorobeva ITMO University St. Petersburg Russia

ISBN: (纸本)9781509025008

Individuals, criminals or even terrorist organizations can use web-communication for criminal purposes; to avoid the prosecution they try to hide their identity. To increase level of safety in Web we have to improve the author (or web-user) identification and authentication procedures. In field of web author identification the situation of imbalanced data sets appears rather frequent, when number of one author's texts significantly exceeds the number of other's. This is common situation for the modern web: social networks, blogs, emails etc. Author identification task is some sort of classification task. To develop methods, technics and tools for web author identification we have to examine the performance of classification algorithms for imbalanced data sets. In this work several modern classification algorithms were tested on data sets with various levels of class imbalance and different number of available webpost The best accuracy in all experiments was achieved with Random Forest algorithm.

关键词： classification algorithms Training Support vector machines Blogs Text categorization Logistics Authentication

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Synthesis of classification algorithms of the Quasi-Deterministic Signals with Random Parameters

Synthesis of Classification Algorithms of the Quasi-Determin...

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Microwave Conference (RMC), All-Russian

作者： Vladimir Mikhaylovich Artyushenko Vladimir Ivanovich Volovach Information technology and management systems department Technological University Korolev city Russian Federation Informational and electronic service department Volga Region State University of Service Togliatty city Russian Federation Voronezh State University Voronezh city Russian Federation

ISBN: (数字)9798331540944

ISBN: (纸本)9798331540951

The problem of synthesis of the algorithms for classifying quasi-deterministic signals with random parameters is considered. The need to solve such a problem is associated with the widespread occurrence of signal processing cases where the average likelihood ratio is not expressed through elementary functions. It is shown that the process of calculating the statistics of the average likelihood ratio can be represented by a sequence of elementary transformations of input influence samples. Expressions are found for determining asymptotically sufficient statistics when solving radio engineering problems, including possible simplifications for certain conditions. The corresponding block diagrams of computers have been obtained, allowing them to work when both the noise environment and the parameters of useful signals change. An approximate expression for the unconditional likelihood ratio for a wide class of probability distributions of input values and random parameters of signals is found. classification algorithms are given and their quality is assessed.

关键词： Computers Computational modeling Noise Integral equations Signal processing algorithms Probabilistic logic Probability distribution Mathematical models classification algorithms Structural engineering

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How to effectively utilize information to design hyperspectral target detection and classification algorithms

How to effectively utilize information to design hyperspectr...

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IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data

作者： Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

ISBN: (纸本)0780383508

Hyperspectral imagery offers a means of uncovering enormous spectral information that can be used for various applications in data exploitation. How effectively such information is used affects the way image analysis algorithms are designed. In this paper, we take up this issue and focus on algorithms designed and developed for target detection and classification in hyperspectral imagery. In order to effectively characterize the information available before and after the data are processed, the a priori information and a posteriori information are used in accordance with how the information is obtained. A piece of information is referred to as a priori information if it is provided by known knowledge before data are processed. On the other hand, a piece of information is referred to as a posteriori information if it is unknown a priori, but can be obtained directly from the data in an unsupervised fashion during the course of data processing. Since a priori information is known beforehand, it can be further decomposed into two types of information, desired and undesired a priori information. The desired a priori information is the knowledge that will assist, improve and enhance data analysis, whereas the undesired a priori information is the knowledge that hinders, interferes or destructs analysis during data processing. This paper investigates how these three types of information play their roles in design and development of several hyperspectral target detection and classification algorithms. Experiments are also conducted to validate their utility.

关键词： Algorithm design and analysis Object detection classification algorithms Hyperspectral imaging Data processing Data analysis Hyperspectral sensors Image analysis Information analysis Interference constraints

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The effect of training data on hyperspectral classification algorithms

The effect of training data on hyperspectral classification ...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Okan Bilge Özdemir Yasemin Yardımcı Çetin Enformatik Enstitüsü Orta Doğu Teknik Üniversitesi Ankara Turkey

In this study, the performance of different hyperspectral classification algorithms with the same training set is investigated. In addition, the effect of the dimension and sampling strategy for the training set selection is demonstrated. Support Vector Machines (SVM), K- Nearest Neighbor (K-NN) and Maximum Likelihood (ML) methods are used. The contribution of using spatial information with spectral information is observed. Meanshift segmentation and window weighting methods are used for spatial information. High resolution Pavia University hyperspectral data and Indian Pines data are used in this study.

关键词： Hyperspectral imaging Support vector machines Kernel classification algorithms Training

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Comparative Analysis of Results of Modern classification algorithms Usage for Determining the Type of Physical Activity Based on Integrated Sensors Data

Comparative Analysis of Results of Modern Classification Alg...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： A. V. Agranovskii A. P. Silukov Saint-Petersburg State University of Aerospace Instrumentation Saint Petersburg Russia

The article considers the usage of various classification algorithms to process data from integrated sensors of a modern smartphone to obtain user's physical activity. To compare classification algorithms, we use modeling based on a dataset from an experiment that has 561 significant characteristics for each type of activity with a total number of records equal to 10299. The analysis of data from integrated sensors is performed by several classification algorithms: decision trees, Random Forest algorithm, logistic regression, support vector machine with a linear function and a kernel function. In addition to pure (without modification) ones, algorithms with preliminary optimization of hyperparameters using random search and cross-validation are used. The evaluation metrics are: Accuracy, Precision, Recall, F-measure, Mean Absolute Error and Root Mean Square Error. The estimated metrics and confusion matrices obtained using specially written python scripts using the sklearn library are provided for each algorithm. Analysis of the presented results shows that the best classification results both with preliminary optimization of hyperparameters and cross-validation, and without preliminary tuning, are provided by the use of linear support vector machine algorithm.

关键词： Measurement Support vector machine classification Sensor phenomena and characterization classification algorithms Telecommunications Root mean square Regression tree analysis

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Review of Brain Tumor Segmentation, Detection and classification algorithms in fMRI Images

Review of Brain Tumor Segmentation, Detection and Classifica...

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International Conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES)

作者： Tom Philip Pries Roshan Jahan Preetam Suman Universität Osnabrück Osnabrück Germany Integral University Lucknow India

ISBN: (纸本)9781538642559;9781538642542

Nowadays Brain tumor detection in early stage is necessary because many people died due to unawareness of having a brain tumor. On the other side influence of machine learning becomes larger and larger in our lifes and our society, artificial intelligence might also start playing an important role in medical diagnosis and support of doctors and surgeons. This paper is focused on review of those papers which include segmentation, detection and classification of brain tumors. The common procedure for an algorithm which aims to classify brain tumors on fMRI or MRI scans is: Preprocessing the image for example by removing noise, then segmenting the image, which yields the region which might be a brain tumor, and finally classifying features such as inten-sity, shape and texture of this region. Many machine learning approaches towards brain tumor detection have already been made. However, these approaches, even though yielding good results, are not used yet. Therefore this research topic remains important and still requires attention.

关键词： Tumors Feature extraction Image segmentation classification algorithms Functional magnetic resonance imaging Support vector machines Clustering algorithms

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Polynomial classification algorithms for Markov decision processes

Polynomial classification algorithms for Markov decision pro...

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IEEE Conference on Decision and Control

作者： Eugene A. Feinberg Fenghsu Yang Department of Applied Math and Statistics State University of New York Stony Brook NY USA

ISBN: (纸本)9781424431236

The unichain classification problem detects whether an MDP with finite states and actions is unichain or not under all deterministic policies. This problem has been proven to be NP-hard. This paper provides polynomial algorithms for this problem while there exists a state in an MDP, which is either recurrent under all deterministic policies or absorbing under some action.

关键词： Polynomials classification algorithms State-space methods Stochastic processes Statistics Inventory control

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Comparative Study on Data Mining classification algorithms for Predicting Road Traffic Accident Severity

Comparative Study on Data Mining Classification Algorithms f...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Tadesse Kebede Bahiru Dheeraj Kumar Singh Engdaw Ayalew Tessfaw Dept. of Information Technology Parul University Vadodara Gujarat India

According to World Health Organization report the number of deaths by road traffic accident is more than 1.25 million people and every year with non-fatal accidents affecting more than 20-50 million people. Several factors are contributed on the occurrence of road traffic accident. In this study, data mining classification techniques applied to establish models (classifiers) to identify accident factors and to predict traffic accident severity using previously recorded traffic data. Using WEKA (Waikato Environment for Knowledge Analysis) data mining decision tree (J48, ID3 and CART) and Naïve Bayes classifiers are built to model the severity of injury. The classification performance of all these algorithms is compared based on their results. The experimental result shows that the accuracy of J48 classifier is higher than others.

关键词： Accidents classification algorithms Roads Decision trees Prediction algorithms Data mining Training

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Prediction of protein – peptide binding residues using classification algorithms

Prediction of protein – peptide binding residues using clas...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Shima Shafiee Abdolhossein Fathi Fardin Abdali Mohammadi RAZI UNIVERSITY Kermanshah Iran

ISBN: (数字)9781728195742

ISBN: (纸本)9781728195759

Peptide-binding proteins prediction is important in understanding biological interaction, protein performance analysis, cellular processes, drug design, and even cancer prediction, so using experimental predictive methods, despite their operational capabilities, has limitations such as being costly and need to spend more time, differences between unrecognized protein structures and sequences, so design and development of computational systems for maintenance, optimal models for representing biological knowledge, management and the analysis of big biological data is so important that the authors used machine learning-based techniques such as Support Vector Machine (SVM),Random Forest (RF),Decision Tree (C4.5), Decision Tree (ID3),Gradient Boosting classifiers, which evaluated experimental results to optimize Support Vector Machine(SVM) classifier (Radial Basis Function kernel) with significant evaluation parameters such as accuracy(ACC) is equal to 0.7401 and 0.7599 for 10-fold cross validation and independent test set and also specificity (SPE) is equal to 0.7966 and 0.8088 for 10-fold cross validation and independent test set (respectively) by using various Structure-Based and Sequence-Based features.

关键词： Proteins Support vector machine classification Vegetation Predictive models Prediction algorithms classification algorithms Performance analysis

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