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Theoretical analysis and comparative study of top 10 optimization algorithms with DMS algorithm

INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2023年第3期17卷 607-620页

作者： Srivani, B. Sandhya, N. Rani, B. Padmaja Jawaharlal Nehru Technol Univ Hyderabad Dept CSE Hyderabad Telangana India VNR Vignana Jyothi Inst Engn & Technol Dept CSE Hyderabad Telangana India

The significance of big data are prone to complication in solving optimization issues. In several scenarios, one requires adapting several contradictory goals and satisfies various criterions. This made the research on multi-objective optimization more vital and has become main topic. This paper presents theoretical analysis and comparative study of top ten optimization algorithms with respect to DMS. The performance analysis and study of optimization algorithms in big data streaming are explicated. Here, the top ten algorithms of optimization based on recency and popularity are considered. In addition, the performance analysis based on Efficiency, Reliability, Quality of solution, and superiority of DMS algorithm over other top 10 algorithms are examined. From analysis, the DMS provides better efficiency as it endeavours less computational effort to generate better solution, due to acquisition of both DA and MS algorithm's benefits and DMS takes less time to process a task. Moreover, the DMS needs less number of iterations in the process of optimization and helps to stop optimization process in local optimum. In addition, the DMS has better reliability as it poses the potential to handle specific level of performance. In addition, the DMS utilizes heuristic information for attaining high reliability. Moreover, the DMS produced high computation accuracy, which reveals its solution quality. From the analysis, it is noted that DMS attained improved outcomes in terms of efficiency, reliability and solution quality in contrast to other top 10 optimization algorithms.

关键词： big data classification spark model optimization big data streaming big data

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Holoentropy based Correlative Naive Bayes classifier and MapReduce model for classifying the big data

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EVOLUTIONARY INTELLIGENCE 2022年第2期15卷 1037-1050页

作者： Banchhor, Chitrakant Srinivasu, N. Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India

big data is the recent imminent technology, which can provide large benefits to the business administration. Owing to such huge volume, it becomes very complicated to ensure effective analysis by the existing techniques. The complications can be related to analyze, capture, sharing, storage, and visualization of the data. To tackle these challenges, a novel classification technique using Holoentropy based Correlative Naive Bayes classifier and MapReduce Model (HCNB-MRM) is proposed. The proposed HCNB, which is designed by combining the Holoentropy function with the correlative based Naive Bayes classifier deals with both high-dimensional data sets as well as extensive datasets to improve the benchmark, and classify the data based on dependent assumption. Therefore, the proposed HCNB-MRM is used to make the process simpler and to choose the best features from big dataset. The proposed HCNB with the MapReduce Model maximizes the performance of big data classification using probability index table, and posterior probability of the testing data samples. The performance of the proposed HCNB-MRM is evaluated using three metrics, such as accuracy, sensitivity, and specificity. From the experimental results, it is analyzed that the proposed HCNB-MRM obtains a high classification accuracy of 93.5965% and 94.3369% for the localization dataset, and skin dataset when compared with the existing techniques.

关键词： big data classification CNB classifier Holoentropy function Correlation function MapReduce model

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Analysis of the MFC Singuliarities of Speech Signals Using big data Methods

Analysis of the MFC Singuliarities of Speech Signals Using B...

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Computing Conference

作者： Skuratovskii, Ruslan, V Osadchyy, Volodymyr Interreg Acad Personnel Management UA-03039 Kiev Ukraine UCF Dept Comp Sci Orlando FL USA

ISBN: (纸本)9783030801267;9783030801250

The role of human speech is intensified by the emotion it conveys. The parameterization of the vector obtained from the sentence divided into the containing emotional-informational part and the informational part is effectively applied. There are several characteristics and singularities of speech that differentiate it among utterances, i.e. various prosodic features like pitch, timbre, loudness and vocal tone which categorize speech into several emotions. They were supplemented by us with a new classification feature of speech, which consists in dividing a sentence into an emotionally charged part of the sentence and a part that carries only informational load. Therefore, the sample speech is changed when it is subjected to various emotional environments. As the identification of the speaker's emotional states can be done based on the Mel scale, MFCC is one such variant to study the emotional aspects of a speaker's utterances. In this work, we implement a model to identify several emotional states from MFCC for two datasets, classify emotions for them on the basis of MFCC features and give the comparison of both. Overall, this work implements the classification model based on dataset minimization that is done by taking the mean of features for the improvement of the classification accuracy rate in different machine learning algorithms.

关键词： big data classification Speech recognition Emotion recognition MFCC Supervised learning Decision trees Mel scale features KNN

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Research on the Large data Intelligent classification Method for Long-Term Health Monitoring of Bridge 1

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3rd European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Hong, Xiaojiang Yu, Mingdong Xichang Univ Dept Civil Xichang Peoples R China Xichang Univ Hydraul Engn Inst Xichang Peoples R China

ISBN: (数字)9783030364052

ISBN: (纸本)9783030364052;9783030364045

In order to improve the intelligent management and information scheduling ability of bridge long-term health monitoring, the real-time data monitoring and automatic collection design of bridge long-term health monitoring are carried out with big data analysis method. A classification method of bridge long-term health monitoring data based on fuzzy correlation feature detection and grid area clustering is proposed. The information fusion and fuzzy chromatography analysis method are used to realize the information fusion of the real-time data of bridge long-term health monitoring, and the adaptive feature extraction of related data is carried out. Excavate the positive correlation characteristic quantity of bridge long-term health monitoring real-time monitoring data flow, carry on the fuzzy clustering and information prediction of bridge long-term health monitoring data flow, and improve the accuracy of bridge long-term health monitoring real-time data monitoring. The simulation results show that the intelligent classification of bridge long-term health monitoring based on this method has high accuracy and low error rate, which improves the real-time performance of bridge monitoring.

关键词： Bridge Long-term health monitoring big data classification

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data classification and Parameter Identification in Power Systems by Manifold Learning

Data Classification and Parameter Identification in Power Sy...

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IEEE Milan PowerTech Conference

作者： Saric, Andrija T. Transtrum, Mark K. Stankovic, Aleksandar M. Fac Tech Sci Dept Power Elect & Commun Engn Novi Sad Serbia Brigham Young Univ Dept Phys & Astron Provo UT 84602 USA Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA

ISBN: (纸本)9781538647226

This paper describes a manifold learning algorithm for big data classification and parameter identification in real-time operation of power systems. We assume a black-box setting, where only SCADA-based measurements at the point of interest are available. data classification is based on diffusion maps, where an improved data-informed metric construction for partition trees is used. data reduction is demonstrated on an hourly measurement tensor example, collected from the power flow solutions calculated for daily load/generation profiles. Parameter identification is performed on the same example, generated via randomly selected input parameters. The proposed method is illustrated on the case of the static part (ZIP) of a detailed WECC load model, connected to a single bus of a real world 441-bus power system.

关键词： big data classification Parameter identification Manifold learning Diffusion maps WECC load model

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Trust and energy aware routing algorithm for Internet of Things networks

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2021年第4期34卷 e2858-e2858页

作者： Mujeeb, Shaik Mohammed Sam, Rachapudy Praveen Madhavi, Kasa Jawaharlal Nehru Technol Univ Anantapur JNTUA Dept Comp Sci & Engn Ananthapuramu 515002 Andhra Pradesh India G Pulla Reddy Engn Coll Autonomous Dept Comp Sci & Engn Kurnool India JNTUA Coll Engn Ananthapuramu Dept Comp Sci & Engn Ananthapuramu India

The expansion in the Internet of Things (IoT) has led to a shift towards smart technologies. IoT focuses on integrating networks to facilitate smooth services to humans. The interface between the mobility patterns and the routing protocols is considered to increase the performance of the network. However, incorporating security in the IoT network has been a major issue that continues to nurture with increasing IoT devices. This article addresses this issue by developing a novel technique, namely energy harvesting trust aware routing algorithm (EHTARA) for initiating a trust-based routing model in the IoT network in the presence of ambient energy sources. The cost metric is newly devised by considering energy, distance, and trust parameters for determining the best path. At the base station, big data classification is performed using the adaptive exponential-Bat (adaptive E-Bat) algorithm based deep belief network (DBN). The training of DBN is performed using the adaptive E-Bat algorithm, which is the combination of adaptive concept, exponential weighted moving average (EWMA), and Bat algorithm (BA). Here, the optimization-based map-reduce framework helps to deal with the imbalanced data by adapting the deep learning in classification. The proposed EHTARA outperformed other methods with a maximal energy of 0.927.

关键词： big data classification energy efficiency Internet of Things routing trust

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Deep SOMs for automated feature extraction and classification from big data streaming 9

Deep SOMs for automated feature extraction and classificatio...

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9th International Conference on Machine Vision (ICMV)

作者： Sakkari, Mohamed Ejbali, Ridha Zaied, Mourad Univ Sfax ENIS Res Grp Intelligent Machines BP1173 Sfax 3038 Tunisia

ISBN: (纸本)9781510611320

In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

关键词： Deep SOMs feature extraction big data classification

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Swarm intelligent based online feature selection (OFS) and weighted entropy frequent pattern mining (WEFPM) algorithm for big data analysis

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第5-Sup期22卷 11791-11803页

作者： Devi, S. Gayathri Sabrigiriraj, M. Coimbatore Inst Engn & Technol Dept Informat Technol Coimbatore 641109 Tamil Nadu India SVS Coll Engn Dept Elect & Commun Engn Coimbatore 642109 Tamil Nadu India

During the past two decades, frequent pattern mining (FPM) has acquired the interests of many researchers: which involves extracting the itemsets from transactions, sequences from big dataset, which occurs frequently and to recognize from the molecular structures, the common sub graph. In this big data era, the unpredictable flow and huge quantity of data brings new challenges in FPM such as space and time complexity. In general, most of the research work focus on recognizing the patterns that occurs frequently, from the set of specific data, where the patterns within every transaction were definitely known a priori. Among these, the users focus only on the small part of this FP. In order to tackle such problems in the current scenario, it is necessary sometimes to select the important features alone, using appropriate FPM algorithms, in order to reduce the complexity level. Themajor objective of thiswork is to improve FPM mining results and improve classification accuracy of big dataset samples. To tackle the first challenge, the levy flight bat algorithm (LFBA) along with online feature selection (OFS) approach is proposed, which is used to filter the low quality features from the big data in an online manner. Subsequently to address the second challenge, a weighted entropy frequent pattern mining (WEFPM) is enforced for FPM, to accomplish better computation time when compared with other methods such as direct discriminative pattern mining (DDPMine) and iterative sampling based frequent itemset mining (ISbFIM), where enumeration of entire feature combinations were completed. So the WEFPM algorithm employed in this paper, targets to identify only the specific frequent patterns which are required by the user. By iterating this procedure, it assures that the acquired frequent patterns can be enumerated by using both the theoretical and empirical research, so that enumeration doesn't proceed into a combinatorial explosion. And also, using the above said LFBA-OFS appro

关键词： big data Online feature selection (OFS) Meta-heuristic algorithm big data classification Multiple Kernel learning support vector machine (MKLSVM) Levy flight bat algorithm (LFBA) Weighted entropy frequent pattern mining (WEFPM)

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A Critical Review on Adverse Effects of Concept Drift over Machine Learning classification Models

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第1期11卷 206-211页

作者： Jameel, Syed Muslim Hashmani, Manzoor Ahmed Alhussain, Hitham Rehman, Mobashar Budiman, Arif Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskandar Perak Malaysia Univ Teknol PETRONAS Dept Comp & Informat Sci Ctr Res Data Sci CERDAS HPC3 Seri Iskandar Perak Malaysia Univ Tunku Abdul Rahman Fac Informat & Commun Technol Petaling Jaya Perak Malaysia Univ Indonesia Fac Comp Sci Depok West Java Indonesia

big data (BD) is participating in the current computing revolution in a big way. Industries and organizations are utilizing their insights for Business Intelligence using Machine Learning Models (ML-Models). Deep Learning Models (DL-Models) have been proven to be a better selection than Shallow Learning Models (SL-Models). However, the dynamic characteristics of BD introduce many critical issues for DL-Models, Concept Drift (CD) is one of them. CD issue frequently appears in Online Supervised Learning environments in which data trends change over time. The problem may even worsen in the BD environment due to veracity and variability factors. Due to the CD issue, the accuracy of classification results degrades in ML-Models, which may make ML-Models not applicable. Therefore, ML-Models need to adapt quickly to changes to maintain the accuracy level of the results. In current solutions, a substantial improvement in accuracy and adaptability is needed to make ML-Models robust in a non-stationary environment. In the existing literature, the consolidated information on this issue is not available. Therefore, in this study, we have carried out a systematic critical literature review to discuss the Concept Drift taxonomy and identify the adverse effects and existing approaches to mitigate CD.

关键词： big data classification machine learning online supervised learning concept drift Adaptive Convolutional Neural Network Extreme Learning Machine (ACNNELM) Meta-Cognitive Online Sequential Extreme Learning Machine (MOSELM) Online Sequential Extreme Learning Machine (OSELM) Real Drift (RD) Virtual Drift (VD) Hybrid Drift (HD) Deep Learning (DL) Shallow Learning (SL) Concept Drift (CD)

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A Parallel and Distributed Radial Basis Function Network for big data Analytics

A Parallel and Distributed Radial Basis Function Network for...

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IEEE Region 10 Conference on Technology, Knowledge, and Society (TENCON)

作者： Kamaruddin, S. K. Ravi, Vadlamani Inst Dev & Res Banking Technol Ctr Excellence Analyt Castle HillsRd 1 Hyderabad 500057 Telangana India Univ Hyderabad SCIS Hyderabad 500046 Telangana India

ISBN: (纸本)9781728118956

Radial Basis Function Network (RBFN) is one of the versatile neural network architectures, in that it can be used to solve regression as well as classification tasks. The proposed work implements RBFN in a parallel and distributed environment of Apache Spark. The new version of RBFN is referred to as PRBFN. The main contribution in this work is embedding of K-Means parallel to (a parallel distributed clustering method) in between input and hidden layer and implementation of parallel least square estimation employing outer product of matrices. The effectiveness of the PRBFN was tested on HEPMASS, HIGGS, ccFraud, and Amazon Movie Review (for sentiment classification) datasets under 10 Fold Cross Validation (10-FCV) setup. It is emphasized that the chief objective is to present a distributed and parallel version of the RBFN to handle big datasets.

关键词： Apache Spark big data classification PRBFN K-Means parallel to Sentiment analysis

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