Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation

2010 ◽  
Vol 50 (2) ◽  
pp. 205-216 ◽  
Author(s):  
Hanna Geppert ◽  
Martin Vogt ◽  
Jürgen Bajorath
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Virtual Screening ◽  
Current Trends ◽  
Mining Methods ◽  
And Performance

A review for using swarm intelligence in architectural engineering

2021 ◽  
pp. 147807712110390
Author(s):  
Randa Khalil ◽  
Ahmed El-Kordy ◽  
Hesham Sobh
Keyword(s):  
Performance Evaluation ◽  
Swarm Intelligence ◽  
Objective Functions ◽  
Form Generation ◽  
Architectural Engineering ◽  
Current Trends ◽  
Swarm Algorithms ◽  
The Social ◽  
Occupant Comfort ◽  
And Performance

Swarm intelligence algorithms are natural-inspired computational methods that mimic the social interaction between creatures to solve certain problems. Swarmative computational architecture (SCA) is a novel nomenclature proposed by the authors to present the use of various swarm algorithms in solving architectural problems. It includes three main aspects: form generation/adaptation, performance evaluation, and optimization. This study provides a systematic review and comparative analysis for the major publications within the review scope. The correspondence between dynamic subjects and the objective functions for the optimization process is presented. Particularly, dynamic subjects such as building formation parameters and objective functions such as occupant comfort and energy consumption. The main results and criteria are categorized into the design approach, case study, form generation/adaptation, and performance evaluation/optimization. Finally, this review presents the current trends and highlights the gaps in the use of swarm algorithms to solve architectural engineering problems.

scholarly journals A review on performance analysis of data mining methods in IoT

2020 ◽  
Vol 7 (73) ◽  
pp. 193-200
Author(s):  
Ashutosh Kumar Dubey ◽  
Dimple Kapoor ◽  
Vijaita Kashyap
Keyword(s):  
Systematic Review ◽  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Performance Analysis ◽  
System Component ◽  
Different Types ◽  
Communication Capability ◽  
Mining Methods ◽  
And Performance

IoT is capable and helpful in interdisciplinary areas along with the convergence of multiple technologies and platforms. This study adheres the adaptation of data mining technologies along with big data and cloud computing with the IoT system with detailed discussion. This paper supports and provide systematic review and analysis based on the computational parameters and performance analysis. The analysis and discussion are based on the communication capability, system component communication, aspects of data mining, big data and cloud computing in IoT. Different types of transmission and communication barriers have also been discussed and analyze. Finally, based on the study and analysis a framework has been suggested for the smooth functioning of the IoT protocols.

scholarly journals Efficient Anonymization Algorithm for Multiple Sensitive Attributes

2019 ◽  
Vol 9 (1) ◽  
pp. 4961-4963
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Information Loss ◽  
The Internet ◽  
Medical Applications ◽  
Huge Number ◽  
Sensitive Data ◽  
Privacy Preserving Data Mining ◽  
Privacy Model ◽  
Mining Methods

The data of medical applications over the internet contains sensitive data. There exist several methods that provide privacy for these data. Most of the privacy-preserving data mining methods make the assumption of the separation of quasi-identifiers (QID) from multiple sensitive attributes. But in reality, the attributes in a dataset possess both the features of QIDs and sensitive data. In this paper privacy model namely (vi…vj)-diversity is proposed. The proposed anonymization algorithm works for databases containing numerous sensitive QIDs. The real dataset is used for performance evaluation. Our system reduced the information loss for even huge number of attributes and the values of sensitive QID’s are protected.

scholarly journals Stratification-Based Outlier Detection over the Deep Web

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Xuefeng Xian ◽  
Pengpeng Zhao ◽  
Victor S. Sheng ◽  
Ligang Fang ◽  
Caidong Gu ◽  
...  
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Outlier Detection ◽  
Deep Web ◽  
Mining Method ◽  
Query Interface ◽  
Data Source ◽  
Mining Methods ◽  
Pilot Sample ◽  
Primary Contribution

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

scholarly journals Performance Evaluation for Competency of Bank Telemarketing Prediction using Data Mining Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 5766-5774
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Performance Evaluation ◽  
Banking Industry ◽  
Decision Making Process ◽  
Helpful Tool ◽  
Data Mining Approach ◽  
Mining Methods ◽  
Using Data ◽  
Sensitivity Specificity

In today's market there is cut throat competition in the banks and struggling hard to gain competitive advantage over each other. The banking industry has undergone tremendous changes in the way business conducted. They realizes the needs and techniques of data mining which is helpful tool to gather, store, capture data and convert into knowledge. The application of data mining enhances the performance of telemarketing process in banking industry. It also provide an insight how these techniques effectively used in banking industry to make the decision making process easier and productive. This work describes a data mining approach to extract valuable knowledge and information from a bank telemarketing campaign data. At this time, the potential of five data mining methods was explored for forecasting of term deposit subscription. The presentation of these techniques was evaluated on fourteen different classifier parameters. The overall better performance achieved by J48 decision tree which reported 91.2% correctly classified with sensitivity, specificity and lowest error rate of 53.8, 95.9 and 8.8 % respectively

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