scholarly journals Application of Data Mining Techniques for the Investigation of Factors Affecting Transportation Enterprises

2021 ◽  
Author(s):  
babak Ejlaly ◽  
Mahdi Yousefi Nejad Attari ◽  
Hannaneh Heidarpour ◽  
Ali Ala
Keyword(s):  
Data Mining ◽  
New Technologies ◽  
Classification Tree ◽  
Rail Transportation ◽  
Effective Parameters ◽  
Factors Affecting ◽  
Data Mining Techniques ◽  
Probabilistic Classification ◽  
Tree Algorithms ◽  
The Cost

<p> Inland transportation, due to the importance of this business and competition between active organizations in this field, applying new technologies in management and making better decisions can be beneficial. We have presented three data mining techniques of clustering, association rules, and classification to investigate the factors affecting the cost and time of road and rail transportation. Using methods based on the K-means algorithm with comparing four clusterings, we have proposed Naive Bayes (probabilistic) classification to determine the total accuracy of transportation percent to 97.91%. Finally, classification tree algorithms such as Bayesian theory and random forest have been used, and the results and output rules have been compared. This article is comprehensive and new to use various effective parameters inland transportation. We will confirm its efficiency by using the criterion(5v)(which we will explain in its place) and then the results in the field. A larger one, called land transit, could be generalized between the two countries. In the end, we have discussed more in methodology and results</p>

scholarly journals Application of Data Mining Techniques for the Investigation of Factors Affecting Transportation Enterprises

2021 ◽  
Author(s):  
babak Ejlaly ◽  
Mahdi Yousefi Nejad Attari ◽  
Hannaneh Heidarpour ◽  
Ali Ala
Keyword(s):  
Data Mining ◽  
New Technologies ◽  
Classification Tree ◽  
Rail Transportation ◽  
Effective Parameters ◽  
Factors Affecting ◽  
Data Mining Techniques ◽  
Probabilistic Classification ◽  
Tree Algorithms ◽  
The Cost

<p> Inland transportation, due to the importance of this business and competition between active organizations in this field, applying new technologies in management and making better decisions can be beneficial. We have presented three data mining techniques of clustering, association rules, and classification to investigate the factors affecting the cost and time of road and rail transportation. Using methods based on the K-means algorithm with comparing four clusterings, we have proposed Naive Bayes (probabilistic) classification to determine the total accuracy of transportation percent to 97.91%. Finally, classification tree algorithms such as Bayesian theory and random forest have been used, and the results and output rules have been compared. This article is comprehensive and new to use various effective parameters inland transportation. We will confirm its efficiency by using the criterion(5v)(which we will explain in its place) and then the results in the field. A larger one, called land transit, could be generalized between the two countries. In the end, we have discussed more in methodology and results</p>

Data Mining

2011 ◽  
pp. 921-926
Author(s):  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
Electronic Commerce ◽  
New Technologies ◽  
Research Area ◽  
Future Research ◽  
Data Mining Techniques ◽  
Wide Range ◽  
Degree Of Confidence ◽  
Common Application ◽  
Existing Data

There is an explosion in the amount of data that organizations generate, collect, and store. Organizations are gradually relying more on new technologies to access, analyze, summarize, and interpret information intelligently. Data mining, therefore, has become a research area with increased importance (Amaratunga & Cabrera, 2004). Data mining is the search for valuable information in large volumes of data (Hand, Mannila, & Smyth, 2001). It can discover hidden relationships, patterns, and interdependencies and generate rules to predict the correlations, which can help the organizations make critical decisions faster or with a greater degree of confidence (Gargano & Ragged, 1999). There is a wide range of data mining techniques, which has been successfully used in many applications. This article is an attempt to provide an overview of existing data mining applications. The article begins by explaining the key tasks that data mining can achieve. It then moves to discuss applications domains that data mining can support. The article identifies three common application domains, including bioinformatics, electronic commerce, and search engines. For each domain, how data mining can enhance the functions will be described. Subsequently, the limitations of current research will be addressed, followed by a discussion of directions for future research.

Farmer's Perception regarding On-farm Water Conservation in Punjab Agriculture

2021 ◽  
pp. 273-281
Keyword(s):  
Water Conservation ◽  
New Technologies ◽  
Media Exposure ◽  
Knowledge Level ◽  
Climatic Zones ◽  
Factors Affecting ◽  
The Cost ◽  
Mass Media Exposure ◽  
On Farm ◽  
Farmer’S Perception

The present paper aimed to explore the farmer's perception regarding on-farm water conservation in Punjab agriculture and outline the critical factors affecting the knowledge and adoption of on-farm water conservation techniques. The study was conducted in Moga, Rupnagar and Sri Muktsar Sahib districts selected randomly representing three agro-climatic zones of Punjab. The study revealed that most of the respondents were literate with farming experience of more than 15 years. The regression analysis applied on the knowledge index, and adoption index concluded that by enhancing the education level, mass media exposure, extension contacts, participation in extension activities, and farming experience, the knowledge level regarding the new technologies may improve, resulted in the effective adoption of that particular technology. It will also help to reduce future constraints in the adoption of the technologies and for increasing the income level of the farmers by decreasing the cost of production.

scholarly journals Prediction on Deposit Subscription of Customer based on Bank Telemarketing using Decision Tree with Entropy Comparison

2020 ◽  
Vol 4 (2) ◽  
pp. 57-66
Author(s):  
Ardytha Luthfiarta ◽  
Junta Zeniarja ◽  
Edi Faisal ◽  
Wibowo Wicaksono
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Credit Card ◽  
Banking System ◽  
Decision Making Process ◽  
Data Mining Techniques ◽  
Related Information ◽  
Customer Information ◽  
Tree Algorithms

Banking system collect enormous amounts of data every day. This data can be in the form of customer information,  transaction  details,  risk profiles,   credit   card   details,   limits   and   collateral    details, compliance  Anti Money Laundering (AML) related information, trade  finance  data,  SWIFT  and  telex  messages. In addition,  Thousands  of decision  are  made in Banking system. For example, banks everyday creates credit decisions,  relationship  start  up,  investment   decisions, AML  and  Illegal  financing  related decision.  To create this decision, comprehensive review on various  reports  and drills  down  tools  provided  by the banking systems is needed.  However, this is a manual process which  is  error  prone  and  time  consuming  due  to  large volume of transactional  and historical  data available. Hence, automatic knowledge mining is needed to ease the decision making process.  This research focuses on data mining techniques to handle the mentioned problem. The technique will focus on classification method using Decision Tree algorithms.  This research provides an overview of the data mining techniques and   procedures will be performed.   It also provides   an insight   into how these techniques can be used in deposit subscription  in banking system to make a decision making process easier and more productive. Keywords - Telemarketing, bank deposit, decision tree, classification, data mining, entropy.

Data Mining Prospects in Mobile Social Networks

2013 ◽  
pp. 145-172 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Zhongshi He ◽  
Huazheng Zhu
Keyword(s):  
Data Mining ◽  
Social Networking ◽  
Large Scale ◽  
New Technologies ◽  
Data Mining Techniques ◽  
Large Scale Data ◽  
Social Technologies ◽  
New Ideas ◽  
Using Data ◽  
Fundamental Insight

Unable to accommodating new technologies, including social technology, mobile devices and computing are other potential problems, which are significant challenges to social-networking service. The very broad range of such social-networking challenges and problems are demanding advanced and dynamic tools. Therefore, in this chapter, we introduced and discussed data mining prospects to overcome the traditional social-networking challenges and problems, which led to optimization of MSNs application and performances. The proposed method infers defining and investigating social-networking problems using data mining techniques and algorithms based on the large-scale data. The approach is also exploring the possible potential of users and systems contexts, which leads to mine the personal contexts such as the users’ locations and situations from the mobile logs. In these sections, we discussed and introduced new ideas on social technologies, data mining techniques and algorithm’s prospects, social technology’s key functional and performances, which include social analysis, security and fraud detections by presenting a brief analysis, and modeling based descriptions. The approach also empirically discussed using the real survey data, which the result showed how data mining vitally significant to explore MSNs performance and its crosscutting impacts. Finally, this chapter provides fundamental insight to researchers and practitioners who need to know data mining prospects and techniques to analyze large, complex and frequently changing data. This chapter is also providing a state-of-the-art of data mining techniques and algorithm’s dynamic prospects.

scholarly journals Exploring Technical Quality Factors That Enhance Mobile Learning Applications Services Using Data Mining Techniques

2021 ◽  
Vol 17 (4) ◽  
pp. 1-23
Author(s):  
Ahmad Abu-Al-Aish
Keyword(s):  
Data Mining ◽  
Student Satisfaction ◽  
Mobile Learning ◽  
Interface Design ◽  
Teaching And Learning ◽  
New Technologies ◽  
Quality Factors ◽  
Factors Affecting ◽  
Technical Factors ◽  
Using Data

Mobile learning (m-learning) has become an increasingly attractive solution for schools and universities that utilize new technologies in their teaching and learning setting. This study investigates the technical factors affecting the development of m-learning applications services from students’ perspectives. It presents a model consisting of twelve technical factors, including content usefulness, scalability, security, functionality, accessibility, interface design, interactivity, reliability, availability, trust, responsiveness, and personalization. To evaluate the model, a questionnaire was designed and distributed to 151 students in Jerash University, Jordan. The results indicate that all technical factors have positive affects on learner satisfaction and overall m-learning applications services, however the data mining analysis revealed that security and scalability factors exert a major impact on student satisfaction with m-learning applications services. This study gives insight for the future of developing and design m-learning applications.

scholarly journals Evaluating the Performance of Engineering’s Students in Mathematic Subject based on Academic Decision-Making Techniques

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 154-165
Author(s):  
Abbas Atwan Mhawes ◽  
Ahmed Yousif Falih Saedi ◽  
Ali Talib Qasim Al-Aqbi ◽  
Lamees Abdalhasan Salman
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Cognitive Skills ◽  
Data Extraction ◽  
Learning System ◽  
Factors Affecting ◽  
Data Mining Techniques ◽  
Sequence Modeling ◽  
Wide Range ◽  
Estimation Sequence

Data mining is characterized as a quest for useful knowledge via large quantities of data. Some basic and most common techniques for data extraction are association rules, grouping, clustering, estimation, sequence modeling. For a wide range of applications, data mining techniques are used. Techniques of data analysis are essential to the preparation and implementation of the administration of the learning system, including behavioral guidance and personal behavior appraisal. The article applies data analytical methods to the role of student classification. Several tests are used for the interpretation of the findings. In keeping with the methodology proposed in the paper, the classification using cognitive skills provides more detailed results than the findings of other study published. Five algorithms were used (J48, Naïve Bayes, Multilayer Perception, K Star and SMO). This essay discusses and measures the application of the various algorithms so that factors affecting the success and failure of students can be identified, student performance can be estimated, and the significant consequences of the mathematics system for the second university year can be identified. However the number of exams can be minimized using data mining techniques. In terms of time and consequences, this shortened analysis plays a key role.

Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

2020 ◽  
pp. 1-26
Author(s):  
Remzi Fiskin ◽  
Erkan Cakir ◽  
Coşkan Sevgili
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Decision Tree ◽  
Influential Factor ◽  
Factors Affecting ◽  
Port Authorities ◽  
Prevention Programmes ◽  
Tree Algorithms ◽  
Using Data

As tugboats interact very closely with ships in restricted waters, the possibility of accidents increases in these operations. Despite the high accident possibility, there is a gap in studies on tugboat accidents. This study aims to analyse accidents involving tugboats using data mining. For this purpose, a tugboat accidents dataset consisting of a total of 496 accident records for the period from 2008 to 2019 was collected. Logistic regression and decision tree algorithms were implemented to the dataset. The results revealed that tugboat propulsion type is the most important and influential factor in the severity of tugboat accidents. The inferences drawn from these results could be beneficial for tugboat operators and port authorities in enhancing their awareness of the factors affecting tugboat accidents. In addition, the outputs of this study can be a reference for management units in developing strategies for preventing tugboat accidents and can also be used in effective planning for practicable prevention programmes and practices.

Applications for Data Mining Techniques in Customer Relationship Management

2011 ◽  
pp. 188-192
Author(s):  
Natalie Clewley ◽  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
New Technologies ◽  
Relationship Management ◽  
Customer Relationship ◽  
Future Research ◽  
Supporting Evidence ◽  
Business Decisions ◽  
Data Mining Techniques ◽  
Hidden Knowledge

With the explosion in the amount of data produced in commercial environments, organizations are faced with the challenge of how to collect, analyze, and manage such large volumes of data. As a consequence, they have to rely upon new technologies to efficiently and automatically manage this process. Data mining is an example of one such technology, which can help to discover hidden knowledge from an organization’s databases with a view to making better business decisions (Changchien & Lu, 2001). Data mining, or knowledge discovery from databases (KDD), is the search for valuable information within large volumes of data (Hand, Mannila & Smyth, 2001), which can then be used to predict, model or identify interrelationships within the data (Urtubia, Perez-Correa, Soto & Pszczolkowski, 2007). By utilizing data mining techniques, organizations can gain the ability to predict future trends in both the markets and customer behaviors. By providing detailed analyses of current markets and customers, data mining gives organizations the opportunity to better meet the needs of its customers. With such significance in mind, this chapter aims to investigate how data mining techniques can be applied in customer relationship management (CRM). This chapter is organized as follows. Firstly, an overview of the main functionalities data mining technologies can provide is given. The following section presents application examples where data mining is commonly applied within the domain, with supporting evidence as to how each enhances CRM processes. Finally, current issues and future research trends are discussed before the main conclusions are presented.

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