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检索条件"丛书名=Learning and Analytics in Intelligent Systems"

共 229 条记录，以下是71-80 订阅

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Multi-Verse Optimization of Multilayer Perceptrons (MV-MLPs) for Efficient Modeling and Forecasting of Crude Oil Prices Data

Multi-Verse Optimization of Multilayer Perceptrons (MV-MLPs)...

引用

International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Nayak, Sarat Chandra Dash, Ch Sanjeev Kumar Mishra, Bijan Bihari Dehuri, Satchidananda CMR Coll Engn & Technol Dept Comp Sci & Engn Hyderabad 501401 India Silicon Inst Technol Dept Comp Sci Bhubaneswar India Fakir Mohan Univ Dept Informat & Commun Technol Balasore 756019 Odisha India

ISBN: (纸本)9783030390334;9783030390327

Arbitrary changes in crude oil prices makes its forecasting quite difficult. Multilayer neural networks are found to be effective in predicting such cured oil prices. Crafting optimal neural network architecture requires numerous trial and error methods. This article presents a hybrid model based on multi-verse optimization (MVO) of multilayer perceptron (MLP), termed as (MV-MLP), where a universe/individual of MVO represents a potential MLP in the universe of discourse. A set of such universes forms a population and the best universe, i.e. optimal MLP is selected through a search process. The search process starts with a random population, gradually moves toward the global optimum and the optimal MLP is obtained at fly rather fixing it earlier. The proposed MV-MLP is evaluated on forecasting the crude oil prices and the predictability performance is established through comparative study with other trained models trained. Experimental results and comparative study suggests the superiority of MV-MLP based forecasting.

关键词： Crude oil price forecasting Multi-verse optimizer Multilayer perceptron Artificial neural network Evolutionary optimization algorithm

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A Survey of the Different Itemset Representation for Candidate Generation

A Survey of the Different Itemset Representation for Candida...

引用

International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Kharkongor, Carynthia Nath, Bhabesh Tezpur Univ Tezpur 784028 Assam India

ISBN: (纸本)9783030390334;9783030390327

Itemset representation is a pivotal part in association rule mining. The itemset representation is a way of how the itemset in the dataset are stored in the memory. There are different data structures used for storing such itemsets. Some of the common data structures used are linked list, array. The efficiency of the association rule mining algorithm largely depends on the way the itemsets are stored. The execution time and memory consumption of the itemsets plays a vital role for determining the performance of the mining algorithms. In this paper, a study of the data structures used for itemset representation is discussed. The different data structures are being tested on the different datasets for generation of candidate itemsets. The performance of the different data structures in candidate generation process is analysed.

关键词： Itemset Candidate Data structure Frequent itemset Array

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A Systematic Frame Work Using Machine learning Approaches in Supply Chain Forecasting

A Systematic Frame Work Using Machine Learning Approaches in...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Prahathish, K. Naren, J. Vithya, G. Akhil, S. Kumar, K. Dinesh Gupta, S. Sai Krishna Mohan SASTRA Deemed Univ Sch Comp Thanjavur India KL Univ Sch Comp Vijayawada Andhra Pradesh India

ISBN: (纸本)9783030390334;9783030390327

Forecasting is an important study in the field of Supply Chain and Logistics for Operations Management. Based on a study a systematic framework has been worked and has been proposed for the same. Artificial Neural Network has been into this field and has been utilized for an efficient way to forecast and reduce errors marginally. The purpose of such a systematic approach using the proposed architecture is to reduce inventory holdings which shall largely account for important decision-making policies in the future.

关键词： Supply chain Forecast Artificial Neural Network Logistics Support Vector Machine Bullwhip Effect Inventory

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Operations on Picture Fuzzy Numbers and Their Application in Multi-criteria Group Decision Making Problems

Operations on Picture Fuzzy Numbers and Their Application in...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Dutta, Palash Bora, Rajdeep Dash, Satya Ranjan Dibrugarh Univ Dept Math Dibrugarh 786004 Assam India Kakoan Coll Dept Math Jorhat 785107 Assam India KIIT Deemed Univ Sch Comp Applicat Bhubaneswar India

ISBN: (纸本)9783030390334;9783030390327

Uncertainty is an unavoidable component of our life and fuzzy set theory (FST) is generally explored to deal with it. However, in some complex situations FST is not capable to play crucial rule. In such situations Picture fuzzy set (PFS) come into the picture which is the direct extension of FST and Intuitionistic fuzzy set (IFS). Although different studies on FST and IFS have been done including their algebraic structure, however these studies are found to be inappropriate to deal with picture fuzzy situations. In this regard, this present paper presents the basic arithmetic operations on PFSs along with numerical examples. Finally application of PFSs in multi-criteria group decision making is performed through a case study.

关键词： Intuitionistic fuzzy set Picture fuzzy set Triangular picture fuzzy set

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Extractive Odia Text Summarization System: An OCR Based Approach

Extractive Odia Text Summarization System: An OCR Based Appr...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Pattnaik, Priyanka Mallick, Debasish Kumar Parida, Shantipriya Dash, Satya Ranjan KIIT Univ Sch Comp Engn Bhubaneswar Odisha India Idiap Res Inst Martigny Switzerland

ISBN: (纸本)9783030390334;9783030390327

Automatic text summarization is considered as a challenging task in natural language processing field. In the case of multilingual scenario particularly for the low-resource, morphologically complex languages the availability of summarization data set is rare and difficult to construct. In this work, we propose a novel technique to extract Odia text from the image files using optical character recognition (OCR) and summarize the obtained text using extractive summarization techniques. Also, we performed a manual evaluation to measure the quality of summaries to validate our techniques. The proposed approach is found suitable for generating summarized Odia text and the same technique can also extend to other low-resource languages for extractive summarization system.

关键词： Optical character recognition Text summarization Natural language processing

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Classification of Arrhythmia Using Artificial Neural Network with Grey Wolf Optimization

Classification of Arrhythmia Using Artificial Neural Network...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Mohapatra, Saumendra Kumar Sahoo, Sipra Mohanty, Mihir Narayan Siksha O Anusandhan Deemed Be Univ Dept Comp Sci & Engn ITER Bhubaneswar Odisha India Siksha O Anusandhan Deemed Be Univ Dept Elect & Commun Engn ITER Bhubaneswar Odisha India

ISBN: (纸本)9783030390334;9783030390327

Research on biomedical signal analysis is growing day-by-day. Accurate classification is an essential and challenging task. Authors in this work have tried to obtain better accuracy in the work cardiac signal classification using artificial neural network (ANN) classifier. Weights of the neural network are optimized using Grey Wolf Optimization (GWO) algorithm. The proposed optimized model is utilized for arrhythmia classification. Data collected from UCI repository for the said purpose. The performance is compared for both ANN and ANN with GWO. 93.38% classification accuracy is obtained by using ANN-GWO classifier. As compare to earlier work it is found that the proposed approach is much better in terms of accuracy.

关键词： ECG Arrhythmia ANN Optimization GWO

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Machine learning Models for Stock Prediction Using Real-Time Streaming Data

Machine Learning Models for Stock Prediction Using Real-Time...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Jena, Monalisa Behera, Ranjan Kumar Rath, Santanu Kumar Fakir Mohan Univ Dept Informat & Commun Technol Balasore Odisha India Veer Surendra Sai Univ Technol Dept Comp Sci & Engn Burla India Natl Inst Technol Rourkela Dept Comp Sci & Engn Rourkela India

ISBN: (纸本)9783030390334;9783030390327

In recent years stock prediction has attracted a lot of attention to the researchers in financial sectors. Apart from the static log data, streaming data has also been proven to be a perennial source of data analysis collected in real-time, which basically deals with the continuous flow of data carrying information from sources like websites, mobile phone applications, server logs, social websites, trading floors, etc. The classifying model made out of historical data can be relentlessly honed to give even more accurate results since its outcome is always compared to the next tick of the clock. In this study, an attempt is made to develop machine learning models to predict the potential prices of a company's stock which helps in making financial decisions. Spark streaming has been considered for the processing of humongous data and data ingestion tools like NodeJS have been further used for analysis. Earlier researches are made on the same concept but the present goal of the study is to develop such a model that is scalable, fault-tolerant and has a lower latency. The model rests on a distributed computing architecture called the Lambda Architecture which helps in attaining the goals as intended. Upon analysis, it is found that prediction of stock values is more accurate when support vector regression is applied. The historical stock values are considered as supervised datasets for training the models.

关键词： Stream processing Support vector regression NodeJS Decision Tree Stock prediction

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Epidemiology of Breast Cancer (BC) and Its Early Identification via Evolving Machine learning Classification Tools (MLCT)-A Study

Epidemiology of Breast Cancer (BC) and Its Early Identificat...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Maurya, Rajesh Kumar Yadav, Sanjay Kumar Tewari, Pragya Sam Higginbottom Univ Agr Technol & Sci SHUATS Dept Comp Sci & Informat Technol Allahabad Uttar Pradesh India ABES Engn Coll Dept Comp Applicat Ghaziabad UP India

ISBN: (纸本)9783030390334;9783030390327

Now a day Breast cancer (BC) is very common and terrific disease in women, most detected and second leading cause of the ladies' demise from the worldwide. Big number of people is passing their life or poor survival rate is because of this disease every year. Females are at high risk of BC, so it became quite essential and necessary for doctors to choose for an exact and suitable treatment for avoidance and remedy of cancer patients. So the basic motive is to find the cancer cells very correctly. Forecasting and categorization of BC using an effective and correct model of machine learning (ML) is essential for creation a new type of BC prognostic and diagnostic policies that really give a reduction push to the sufferer. Diversified technology, including Bayesian classifiers, Artificial Neural Networks and Decision Trees have been commonly applied in cancerous tumor. Undoubtedly methods used for Machine learning may increase our understanding about breast cancer prediction and progression. It is important to consider these approaches in daily clinical practice. Neural networks are now a day's very key and popular field in computational biology, chiefly in the area of radiology, oncology, cardiology and urology. In this study, we had summarized numerous ML techniques which could be used as an important tool by surgeons for timely detection, and prediction of cancerous cells has been studied and introduced.

关键词： Cancer statistics in India Algorithms for cancer detection Types of ML techniques Bioinformatics Open source data for research Risk factor Performance

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Hyperspectral Image Classification Using Stochastic Gradient Descent Based Support Vector Machine

Hyperspectral Image Classification Using Stochastic Gradient...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Sampurnima, Pattem Satapathy, Sandeep Kumar Mishra, Shruti Mallick, Pradeep Kumar KL Univ Dept CSE Vijayawada Andhra Pradesh India VIT AP Univ Sch CSE Amaravati 522237 Andhra Pradesh India Kalinga Inst Ind Technol KIIT Deemed Be Univ Sch Comp Engn Bhubaneswar India

ISBN: (纸本)9783030390334;9783030390327

In the recent days the Hyperspectral images are most popularly used for remote sensing. Hyperspectral imaging has many applications including resource management, mineral exploration, agriculture, environmental monitoring and other tasks for earth observation. Earlier these images are very rarely available. But with recent appearance of Airborne Hyperspectral Imaging System, hyperspectral images entered into main stream of remote sensing. In this work we have considered few officially and publicly available hyperspectral image data. As the image contains spectral, spatial and temporal resolutions in the image, so to classify several regions in the images we have considered the powerful machine learning technique that is Support Vector Machine (SVM) optimized with Stochastic Gradient Descent (SGD) for image classification task.

关键词： Remote sensing

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A Survey on Ant Colony Optimization for Solving Some of the Selected NP-Hard Problem

A Survey on Ant Colony Optimization for Solving Some of the ...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Mandal, Akshaya Kumar Dehuri, Satchidananda Fakir Mohan Univ Dept Informat & Commun Technol Balasore 756019 Odisha India

ISBN: (纸本)9783030390334;9783030390327

This paper analyses various ant colony optimization (ACO) based techniques for solving some of the selected intractable problems. ACO is one of the popularly used techniques in the field of meta-heuristic techniques that gave acceptable solutions to intractable problems like Travelling Salesperson (TS), Subset Selection (SS), Minimum Vertex Cover (MVC), and 0/1 Knapsack in tolerable amount of time. We have reviewed literature on the usage of aforesaid meta-heuristic algorithms for solving the intractable problems like TS, SS, MVC, and 0/1-Knapsack. A review of several ACO for NP-Hard problems with different instances shows that ACO algorithm demonstrates significant effectiveness and robustness in solving intractable problems.

关键词： Ant system Ant colony optimization Travelling salesman problem Subset selection problem Minimum Vertex Cover

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