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the 2nd international conference on soft computing and data mining, scdm-2016

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the 2nd international conference on soft computing and data mining, scdm-2016

ISBN: (纸本)9783319512792

The proceedings contain 64 papers. The special focus in this conference is on soft computing, data mining, Web mining, Services and Security. The topics include: cluster validation analysis on attribute relative of soft-set theory;optimizing weights in Elman recurrent neural networks with wolf search algorithm;optimization of ANFIS using artificial bee colony algorithm for classification of Malaysian SMEs;forecasting of Malaysian oil production and oil consumption using fuzzy time series;a fuzzy TOPSIS with Z-numbers approach for evaluation on accident at the construction site;formation control optimization for odor localization;artificial neural network with hyperbolic tangent activation function to improve the accuracy of COCOMO II model;a study of data imputation using fuzzy C-Means with particle swarm optimization;one-way ANOVA model with fuzzy data for consumer demand;an efficient chicken swarm based back-propagation algorithm;a review on violence video classification using convolutional neural networks;modified Backpropagation algorithm for polycystic ovary syndrome detection based on ultrasound images;an implementation of local regression smoothing on evolving fuzzy algorithm for planting calendar forecasting based on rainfall;chebyshev multilayer perceptron neural network with levenberg Marquardt-back propagation learning for classification tasks;a model for mining association rules from formal concept analysis;an efficient model for mining least patterns from data repository;integration of self-adaptation approach on requirements modeling and variety of approaches in self-adaptation requirements.

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Using data mining Techniques to improving Key Performance Indicators 2

Using Data Mining Techniques to improving Key Performance In...

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2nd international Mobile, Intelligent, and Ubiquitous computing conference, MIUCC 2022

作者： Ahmed, Assem Khalaf Zaied, Abdel Nasser H. Hagag, Riham Helwan University Faculty of Commerce Business Information Systems Department Cairo Egypt Professor of Information Systems Cairo Egypt

ISBN: (纸本)9781665466776

The study provides a framework for improving the most key performance indicators and arranging them according to the degree of impact in order to enhance decision-making, speed and increase accuracy in data analysis, reduce mismanagement and time to take appropriate decisions a timely manner and ensure that the organization is moving towards achieving its goals, through one of the techniques of business intelligence (data mining and Knowledge discovery) and choosing the best technique that can be used to improve the key performance indicators. © 2022 IEEE.

关键词： soft sensors Key performance indicator Neural networks Decision making Standards organizations Predictive models Ubiquitous computing

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2nd international conference on Signal and data Processing, ICSDP 2022

2nd International Conference on Signal and Data Processing, ...

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2nd international conference on Signal and data Processing, ICSDP 2022

ISBN: (纸本)9789819914098

The proceedings contain 48 papers. The special focus in this conference is on Signal and data Processing. The topics include: Process mining-Based Behavioral Modeling of Learners in Self-paced Learning Environment;blockchain Scalability: Solutions, Challenges and Future Possibilities;Solution of Unit Commitment Problems in GAMS Computational Environment;taxonomy and Implications of Machine Learning for Internet of Things: Qualities, Uses and Algorithms;auto Organizer: A Machine Learning-Based Tool for Automatic Organization of Files;twitter Spam Detection Using Different Machine Learning Techniques;on the Role of Perceptual Information in Image Classification;bengali Text Classification Based on Probability Measure;comprehensive Analysis on the Performance of Antenna Design Using Machine Learning Techniques;design and Analysis of Novel Miniaturized Metamaterial Structures for Multiband Applications;hybrid Particle Swarm Optimization Based Deep Learning Model for the Stage Classification of Lung Cancer;Interference Cancellation by Using Viterbi Algorithm for Space Base AIS System;a Stacked Multichannel Feature Map Based U-Net Model for Brain Tumor Segmentation;spatial Attention Gated U-Net Structure for Capsule Endoscopy Image Super-Resolution;feature Engineering and Selection for the Identification of Fake News in Social Media;early Detection of Pathological Myopia in Fundus Images Using Deep Learning;an Investigation on the Extractive Summarization of Kannada Text;rice Plant Leaf Disease Detection—A Comparison of Various Methodologies;combating Fake News with Machine Learning and Deep Learning Methods;Employing soft computing-Based GGA-MLP for Hyperparameter Optimization in COVID-19-Infected Lung Image data Classification;Design of Compact UWB MIMO Antenna with High Isolation Using Square Swirl Shape EBG Structure;forecasting with Fuzzy Time Series and Variation.

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An Ensemble Sentiment Classification System of Twitter data for Airline Services Analysis: Smart Use of soft computing 2

An Ensemble Sentiment Classification System of Twitter Data ...

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2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Pokhriyal, Shubham Gairola, Shivansh Vats, Satvik Rawat, Ramesh Singh Raut, Rajesh B. Sharma, Vikrant Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798350369960

To perform the airline services analysis it is difficult to gather the data by means of questionnaire investigation as it is both labour and time consuming. Using information obtained from Twitter is a useful method for gathering customer feedback and enhancing services. Everyone uses social media today, therefore there are a lot of user perspectives available. Twitter social media data frequently employs confusing phrasing that makes it challenging to distinguish between positive and negative sentiments. More than a billion social media communications need to be properly kept in a database and analysed in order to interpret the data. In this study, the majority vote principle was used to guide the use of an ensemble sentiment classification technique that combined the C4.5 Decision Tree, SVM, Bayesian Network, Naive Bayes and Random Forest algorithms. The results show that the suggested ensemble classifier performs better than both majority voting ensemble classifiers and stand-alone classifiers. According to our findings, the ensemble approach might increase overall mining and sentiment classification performance for other services as well. © 2023 IEEE.

关键词： component formatting insert (key words) style styling

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A Prospective Investigation into the Predictive Modeling of Dementia Disease Risk Using Electronic Medical Records from Maine 2

A Prospective Investigation into the Predictive Modeling of ...

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2nd international conference on Machine Learning, Cloud computing, and Intelligent mining, MLCCIM 2023

作者： Wang, Xiaofang Liu, Modi McElhinney, Doffb T Alfreds, Shaun Han, Shiyuan Zhou, Jin Ling, Xuefeng B Intelligent Computing University of Jinan Shandong Provincial Key Laboratory of Network Based Jinan China Healthcare Business Intelligence Solutions Inc Palo AltoCA United States Stanford University Palo Department of Cardiothoracic Surgery AltoCA United States HealthInfo Net PortlandME United States Stanford University Palo Department of Surgery AltoCA United States

ISBN: (纸本)9798350328530

Ahstract-(1) Background: Currently available dementia risk prediction models primarily estimate the incidence of dementia over medium- to long-term periods (e.g., 5-20 years), and no models have been developed to predict dementia incidence in the short term (e.g., one year). Furthermore, many existing models rely on traditional statistical methods and incorporate variables that are not routinely available in clinical practice, limiting their practical use in non-specialized settings. (2) Objective: The objective was to create and verify a predictive model for identifying individuals in a general population who are at risk of developing new incidents of general dementia and Alzheimer's Disease (AD) within the next year. (3) Methods: This study utilized data from the Maine Health Information Exchange network and included individuals who had visited any care facility between 2014 and 2016 and had no prior history of dementia. To develop the predictive model for incident dementia, an Extreme Gradient Boosting (XGBoost) algorithm was employed and trained using the clinical data of patients from the retrospective cohort of the previous year. The model was then validated in the prospective cohort to predict the risk of incident dementia and AD in the following year. (4) Results: The model was developed using EMR data from over 1.1 million individuals in Maine, and was able to accurately predict the risk of incident dementia and AD within the following year. The AUC for the model was 0.884 for general dementia and 0.897 for AD, which outperformed existing models. By stratifying the population into low-, medium-, and high-risk categories based on predictive scores, healthcare providers can better allocate resources and target interventions for individuals at high risk. The low-risk category had a very low incidence of dementia (0.05%) and AD (0.05%), while the high-risk category had a significantly higher incidence (17.65% for dementia, 19.84% for AD). (5) Conclusions: Our m

关键词： Patient treatment

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Early-Stage Diabetes Prediction using data mining Algorithms 22

Early-Stage Diabetes Prediction using Data Mining Algorithms

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2nd international conference on computing Advancements: Age of computing and Augmented Life, ICCA 2022

作者： Moniruzzaman, Md Zaman, A.G.M. Tasnia, Rifah Biswas, Sutopa Khanam, Mehnur Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

ISBN: (纸本)9781450397346

Diabetes is a very common disease nowadays. If not treated early diabetes can pose a profoundly serious health threat. Much research has been conducted to find out the optimal solution for diabetes detection by applying different data mining algorithms, where the dataset consists of different medicinal attributes. In this study, our aim is to examine whether diabetes can be detected at early-stage by applying different data mining algorithms to the non-medicinal dataset;as well as to investigate whether data normalization techniques can improve the classifiers accuracy. Naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Decision Tree, Random Forest, and Gradient Boosting Classifier (GBC) algorithms are applied to the Early Stage Diabetes Risk Prediction dataset in conjunction with Decimal Point Scaling, Z-Score Normalization, Pareto Scaling, Variable Stability Scaling, Min-Max normalization, Max normalization, Maximum Absolute Scaling, Mean Centered Scaling, soft-max normalization, Power Transformer, Median and Median Absolute Deviation Normalization, Robust Scaling and Log Scaling normalization methods. In this experiment, we discovered that early-stage diabetes detection is possible without any medical diagnosis data. The result shows that GBC performs better compared to other classification algorithms in combination with data normalization and achieved an impressive 99.038% prediction accuracy. © 2022 ACM.

关键词： data mining

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Comprehensive overview on the deployment of machine learning, deep learning, reinforcement learning algorithms in Selfish mining attack in blockchain 2

Comprehensive overview on the deployment of machine learning...

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2nd IEEE Mysore Sub Section international conference, MysuruCon 2022

作者： Jeyasheela Rakkini, M.J. Geetha, K. Sastra Deemed University School of Computing Thanjavur India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Blockchain, a disruptive technology, has many applications in the domain of Finance, banking, real estate, insurance, supply chain, gaming industry with much more plethora of applications in near future. In spite of the decentralized, distributed, transparent, tamper-proof, data- provenance nature of the blockchain, it is subject to a lot of security attacks such as forking attacks and block withholding attacks. One such attack under the forking attack is selfish mining, which targets the reward distribution and also the difficulty adjustment algorithms(DAA). An exhaustive surve is done on the existing approaches to detect selfish mining and also on the profitability of selfish mining attacks. This survey is organized particularly around the aspects of selection or exploration of the shortest branch of the blockchain when a fork occurs. We aim to identify the implications of selecting the shorter branch of the fork in the blockchain, especially after 2016 blocks, where a difficulty adjustment occurs. Our survey focuses on the deployment of machine learning and deep learning, reinforcement methods on mitigating the selfish mining attacks in the blockchain. © 2022 IEEE.

关键词： Blockchain

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An Efficient Detection System For Cardio Vascular Disease Using Machine Learning Algorithms 2

An Efficient Detection System For Cardio Vascular Disease Us...

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2nd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Sarmiladevi, N. Basker, K. Tamilarasu, R. Nivetha, L. Kongunadu College of Engineering and Technology Department of Artificial Intelligence and Data Science Trichy India Cihan University-Duhok Kurdistan Region Department of Artificial Intelligence and Data Science Iraq

ISBN: (纸本)9781665437899

mining and acquiring knowledge technologies have played an increasingly important role in the healthcare system in latest generations, with the objective of transforming all data into an intelligible format. Medical professionals make 12 percent to 13 percent inaccuracies throughout the prognosis process, according to several design dictates. As a result, an automated ailment forecasting system is required for improved health diagnosis, and greater reliability is emphasised for assessment purposes. Furthermore, numerous researchers have emphasised the importance of prediction performance in order to improve the design of a computerized method. By minimising computational burden and factors that add to computational effort, the developed automation methodology is especially suited to predict cardiovascular diseases. The retrieved and chosen information are input into the classifier model in terms of enhancing forecast accuracy. For varying degree of success, the publicly available baseline heart disease database is being explored. Pattern learning techniques are used to examine the characteristics in both linear and non-linear ways. To improve the prediction rate, Linearly Iterated Support Vector Regression (LISVR) and stacked monkey optimization (SMO) are used for classification. This calculations were carried out in the MATLAB 2016b framework, and the analysis is carried out using a variety of available methods. The predicted model's exactness is 98.5 percent, specificity is 98.8 percent, recall is 99.5%, and F -measure is 99.28 percent. In comparison to current procedures, the obtained findings indicate that perhaps the expected LISVR- SMO operates more effectively than previous techniques and provides a good trading. © 2022 IEEE.

关键词： Metadata

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A Comprehensive Review on Machine Learning Models for Medical data Classification 2

A Comprehensive Review on Machine Learning Models for Medica...

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2nd international conference on Smart Electronics and Communication, ICOSEC 2021

作者： Ahmed, Mohammed Zaheer Mahesh, Chitraivel VelTech Rangarajan Dr.Sagunthala RD Institute of Science and Technology CSE Department Tamilnadu Chennai600062 India VelTech Rangarajan Dr.Sagunthala RD Institute of Science and Technology HOD IT Department Tamilnadu Chennai600062 India

ISBN: (纸本)9781665433686

The content of multi-modal medical features representing medical data was researched and many modes of learning were analyzed for the risk assessment of diseases in order to gather information from medical health data and generate intelligent application relevant inquiries. The representation of medical data employs the method of deep learning. To attain model adaptability, the study and extraction of different disease features employ the same procedure. Classification of medical data is an important data mining challenge that has interested various academics throughout the world for a decade. Various data mining algorithms are utilized for health data sorting such as classification, clustering, regression, soft computing and more. Feature selection algorithms play a key part in every challenge in machine learning. The selection of the best method produces an optimal subset of properties, which enhances accuracy and reduces training time. It is also beneficial to remove the useless characteristics for high-dimensional datasets. This paper provides a brief review on medical data classification models to provide a brief assessment of the common feature selection and classification models specifically utilized for medical data classification. Various aspects, such as the medical data sets available, selection of features, choice of classification, problems in the identification and analysis of the key methods of selecting features and detailed mechanisms are represented in this survey paper. © 2021 IEEE.

关键词： Classification (of information)

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Design and Implementation of New Energy Technology Innovation Panoramic Monitoring System Based on Big data mining and Knowledge Graph computing

Design and Implementation of New Energy Technology Innovatio...

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Image Processing and Computer Applications (ICIPCA), IEEE international conference on

作者： Yuzhong Zhou Yuliang Yang Jie Lin Zhengping Lin Jiahao Shi CSG Electric Power Research Institute Guangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper proposes a new energy operation monitoring and analysis system design based on knowledge graph technology. With rich semantic representation ability and flexible structure, this paper constructs an effective carrier for computer cognition of information and knowledge in the physical world. The process of establishing knowledge graph of new energy operation monitoring and analysis is put forward. By studying the data sources of power grid operation and maintenance, the relationship between operation and maintenance is obtained. Then, an operating system health evaluation method based on fuzzy evaluation is proposed. The weights of each index are determined by the method of expert evaluation. The member function of abnormal alarm is constructed by combining the distribution function of triangle and step type. The experiment proves that the algorithm can reflect the actual operation condition well. The paper plays a certain role in promoting the reliability of the operation and maintenance of the new energy operation monitoring system.

关键词： Technological innovation soft sensors Operating systems Semantics Knowledge graphs Power industry Power grids Maintenance Monitoring System analysis and design

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