Rivista e Edizione

Volume 22 (2022): Edizione 3 (September 2022)

Volume 22 (2022): Edizione 2 (June 2022)

Volume 22 (2022): Edizione 1 (March 2022)

Volume 21 (2021): Edizione 4 (December 2021)

Volume 21 (2021): Edizione 3 (September 2021)

Volume 21 (2021): Edizione 2 (June 2021)

Volume 21 (2021): Edizione 1 (March 2021)

Volume 20 (2020): Edizione 6 (December 2020)
Special Edizione on New Developments in Scalable Computing

Volume 20 (2020): Edizione 5 (December 2020)
Special issue on Innovations in Intelligent Systems and Applications

Volume 20 (2020): Edizione 4 (November 2020)

Volume 20 (2020): Edizione 3 (September 2020)

Volume 20 (2020): Edizione 2 (June 2020)

Volume 20 (2020): Edizione 1 (March 2020)

Volume 19 (2019): Edizione 4 (November 2019)

Volume 19 (2019): Edizione 3 (September 2019)

Volume 19 (2019): Edizione 2 (June 2019)

Volume 19 (2019): Edizione 1 (March 2019)

Volume 18 (2018): Edizione 5 (May 2018)
Special Thematic Edizione on Optimal Codes and Related Topics

Volume 18 (2018): Edizione 4 (November 2018)

Volume 18 (2018): Edizione 3 (September 2018)

Volume 18 (2018): Edizione 2 (June 2018)

Volume 18 (2018): Edizione 1 (March 2018)

Volume 17 (2017): Edizione 5 (December 2017)
Special Edizione With Selected Papers From The Workshop “Two Years Avitohol: Advanced High Performance Computing Applications 2017

Volume 17 (2017): Edizione 4 (November 2017)

Volume 17 (2017): Edizione 3 (September 2017)

Volume 17 (2017): Edizione 2 (June 2017)

Volume 17 (2017): Edizione 1 (March 2017)

Volume 16 (2016): Edizione 6 (December 2016)
Special issue with selection of extended papers from 6th International Conference on Logistic, Informatics and Service Science LISS’2016

Volume 16 (2016): Edizione 5 (October 2016)
Edizione Title: Special Edizione on Application of Advanced Computing and Simulation in Information Systems

Volume 16 (2016): Edizione 4 (December 2016)

Volume 16 (2016): Edizione 3 (September 2016)

Volume 16 (2016): Edizione 2 (June 2016)

Volume 16 (2016): Edizione 1 (March 2016)

Volume 15 (2015): Edizione 7 (December 2015)
Special Edizione on Information Fusion

Volume 15 (2015): Edizione 6 (December 2015)
Special Edizione on Logistics, Informatics and Service Science

Volume 15 (2015): Edizione 5 (April 2015)
Special Edizione on Control in Transportation Systems

Volume 15 (2015): Edizione 4 (November 2015)

Volume 15 (2015): Edizione 3 (September 2015)

Volume 15 (2015): Edizione 2 (June 2015)

Volume 15 (2015): Edizione 1 (March 2015)

Volume 14 (2014): Edizione 5 (December 2014)
Special Edizione

Volume 14 (2014): Edizione 4 (December 2014)

Volume 14 (2014): Edizione 3 (September 2014)

Volume 14 (2014): Edizione 2 (June 2014)

Volume 14 (2014): Edizione 1 (March 2014)

Volume 13 (2013): Edizione Special-Edizione (December 2013)

Volume 13 (2013): Edizione 4 (December 2013)
The publishing of the present issue (Volume 13, No 4, 2013) of the journal “Cybernetics and Information Technologies” is financially supported by FP7 project “Advanced Computing for Innovation” (ACOMIN), grant agreement 316087 of Call FP7 REGPOT-2012-2013-1.

Volume 13 (2013): Edizione 3 (September 2013)

Volume 13 (2013): Edizione 2 (June 2013)

Volume 13 (2013): Edizione 1 (March 2013)

Volume 12 (2012): Edizione 4 (December 2012)

Volume 12 (2012): Edizione 3 (September 2012)

Volume 12 (2012): Edizione 2 (June 2012)

Volume 12 (2012): Edizione 1 (March 2012)

Dettagli della rivista
Formato
Rivista
eISSN
1314-4081
Pubblicato per la prima volta
13 Mar 2012
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

Volume 19 (2019): Edizione 3 (September 2019)

Dettagli della rivista
Formato
Rivista
eISSN
1314-4081
Pubblicato per la prima volta
13 Mar 2012
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

10 Articoli
Accesso libero

Measuring the Significance of Writing Style for Recommending Where to Publish – A Case Study

Pubblicato online: 26 Sep 2019
Pagine: 3 - 15

Astratto

Abstract

Writing style plays a role in publication venue recommendation. However, such finding should be observed further; it is concluded from an arbitrary dataset which contains various topics and writing quality. This paper aims to observe that style's impact in a more controlled environment. A dataset with the same specific topic and writing quality was used and analysed. In our case, the dataset is comprised of papers published on reputable software engineering publication venues with natural language generation as the specific topic. According to our observation, writing style only affects significantly on paper title wherein the impact is proportional to n in n-gram. Moreover, the style’s impact becomes more salient when the venues are grouped per publisher or only a specific publication type is considered.

Parole chiave

  • Recommender system
  • writing style
  • publication venues
  • N-gram language model
Accesso libero

Towards Big Data Analytics in the e-Learning Space

Pubblicato online: 26 Sep 2019
Pagine: 16 - 24

Astratto

Abstract

The issues related to the analysis and management of Big Data, aspects of the security, stability and quality of the data, represent a new research, and engineering challenge. In the present paper, techniques for Big Data storage, search, analysis and management in the area of the virtual e-Learning space and the problems in front of them are considered. A numerical example for explorative analysis of data about the students from Burgas Free University is applied, using instrument for Data Mining of Orange. The analysis is a base for a system for localization of students at risk.

Parole chiave

  • Big Data Analytics
  • Data Mining
  • Map/Reduce
  • e-Learning
  • Orange
Accesso libero

Course Sequence Recommendation with Course Difficulty Index Using Subset Sum Approximation Algorithms

Pubblicato online: 26 Sep 2019
Pagine: 25 - 44

Astratto

Abstract

Choice Based Course Selection (CBCS) allows students to select courses based on their preferred sequence. This preference in selection is normally bounded by constraints set by a university like pre-requisite(s), minimum and maximum number of credits registered per semester. Unplanned course sequence selection affects the performance of the students and may prolong the time to complete the degree. Course Difficulty Index (DI) also contributes to the decline in the performance of the students. To overcome these difficulties, we propose a new Subset Sum Approximation Problem (SSAP) aims to distribute courses to each semester with approximately equal difficulty level using Maximum Prerequisite Weightage (MPW) Algorithm, Difficulty Approximation (DA) algorithm and Adaptive Genetic Algorithm (AGA). The three algorithms have been tested using our university academic dataset and DA algorithm outperforms with 98% accuracy than the MPW and AGA algorithm during course distribution.

Parole chiave

  • Course sequence recommendation
  • Course credits
  • Course difficulty
  • Pre-requisite weight
  • Approximation Algorithm
Accesso libero

Group Decision Making in Evaluation and Ranking of Students by Extended Simple Multi-Attribute Rating Technique

Pubblicato online: 26 Sep 2019
Pagine: 45 - 56

Astratto

Abstract

The paper deals with evaluation and ranking of students taking into account two main criteria of the learning – theoretical knowledge and practical skills. These criteria are divided into several sub-criteria to reflect different aspects of the learning outcomes. To make such complex evaluation the proper utility function based on simple multi-attribute rating technique is proposed. This new utility function includes not only the evaluation score and weighted coefficients for criteria importance, but considers also additional coefficients that indicate how theoretical knowledge and practical skills will take part in the aggregated final assessment. The formulated model is applied for the assessing of the students on web programming. The students are ranked under three different cases where the theoretical knowledge and practical skills take different part in the aggregated assessment. The obtained results demonstrate the applicability of the described approach by providing different ranking depending on the importance of the theoretical and practical aspects.

Parole chiave

  • Students’ assessments
  • ranking
  • web programming
  • group decision making
Accesso libero

Simplifying the Structural Complexity of Software Systems

Pubblicato online: 26 Sep 2019
Pagine: 57 - 73

Astratto

Abstract

Simplification of execution traces is peculiarly important in the case of software comprehension. The objective is to make execution traces in ways that are more tractable and less difficult. However, the simplification process is a difficult task, particularly, in object-oriented contexts. Due to coupling, execution traces of object-oriented systems involve the Spaghetti Architectures phenomenon, which is a very complicated structure of dependencies. Therefore, the simplification process needs a well-established approach to be helpful for software comprehension. Otherwise, the simplified execution traces will be informative as their structures will involve several gaps that lead to a misunderstanding process. This research uses decoupling to guide the simplification of object-oriented execution traces. Specifically, decoupling truthfully can decrease the complexity of execution traces without eliminating the trace components and making numerous gaps in the trace structure. Then, decoupling can solve the problem of the Spaghetti Architectures phenomenon. A controlled experiment was conducted to empirically validate the usefulness and effectivity of the suggested work. There was a significant statistical added value demonstrated in the time required and the accurate solutions of the tasks being solved. More precisely, 25% less time required with a 62% more correct solutions were achieved solving the experiment’s comprehension tasks.

Parole chiave

  • Decoupling
  • execution trace analysis
  • object-oriented
  • software comprehension
  • software maintenance
Accesso libero

Enhanced Evolutionary Computing Assisted Robust SLA-Centric Load Balancing System for Mega Cloud Data Centers

Pubblicato online: 26 Sep 2019
Pagine: 74 - 93

Astratto

Abstract

Considering significance of a robust and Quality of Service (QoS) centric cloud computing, virtualization assisted load-balancing has been found a potential solution. However, assuring optimal Virtual-Machine (VM) migration with minimum violation of Service-Level-Agreement (SLA) and QoS degradation has been the challenge for academia-industries. VM allocation or scheduling being an NP-hard problem has been solved by numerous heuristic approaches such as classical Genetic Algorithm (GA), Ant Colony Optimization (ACO), etc. However they have been found confined due to local minima and convergence issues, especially for Mega Data Centres (MDCs). To alleviate such issues, in this paper an enhanced Evolutionary Computing algorithm named Adaptive Re-sampling GA (ARGA) algorithm has been developed that in conjunction with a stochastic prediction based dynamic load–measurement and Maximum Correlation (MC) assisted VM selection perform optimal load balancing over IaaS MDC infrastructures. The proposed ARGA VM allocation model with dual-level dynamic threshold assisted load estimation and MC based VM selection has exhibited lower SLA violation, performance degradation, downtime and minimum VM migration as compared to classical ACO based load balancing.

Parole chiave

  • Load-balancing
  • virtualization
  • Adaptive Re-sampling Genetic Algorithm (ARGA)
  • SLA provision
Accesso libero

Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

Pubblicato online: 26 Sep 2019
Pagine: 94 - 117

Astratto

Abstract

Efficiently provisioning the resources in a large computing domain like cloud is challenging due to uncertainty in resource demands and computation ability of the cloud resources. Inefficient provisioning of the resources leads to several issues in terms of the drop in Quality of Service (QoS), violation of Service Level Agreement (SLA), over-provisioning of resources, under-provisioning of resources and so on. The main objective of the paper is to formulate optimal resource provisioning policies by efficiently handling the uncertainties in the jobs and resources with the application of Neutrosophic Soft-Set (NSS) and Fuzzy Neutrosophic Soft-Set (FNSS). The performance of the proposed work compared to the existing fuzzy auto scaling work achieves the throughput of 80% with the learning rate of 75% on homogeneous and heterogeneous workloads by considering the RUBiS, RUBBoS, and Olio benchmark applications.

Parole chiave

  • SARSA (State-Action Reward-State-Action)
  • Resource provisioning
  • Uncertainty
  • Soft-set
  • elasticity
  • throughput
  • learning rate
Accesso libero

Mitigating Hotspot Issue in WSN Using Sensor Nodes with Varying Initial Energy Levels and Quantification Algorithm

Pubblicato online: 26 Sep 2019
Pagine: 118 - 136

Astratto

Abstract

A typical Wireless Sensor Network (WSN) uses multihop communication rather than direct transmission. In a multi-hop communication, the sensor node communicates the sensed data to its neighbor node, which is comparatively closer to the sink and the receiving node will forward the data to its neighbor node. This process continues until the data reaches the sink. Due to the multihop communication, the nodes closer to the sink have to transmit and receive more data and control packets compared to other nodes. Hence, the nodes closer to sink may deplete their energy at a faster rate and may die soon. This may create network isolation. This issue is called as the Hotspot problem. In this paper, we are proposing a Quantification algorithm for Sensor Nodes with varying Initial Energy Level to mitigate the Hotspot effect.

Parole chiave

  • WSN
  • hotspot
  • multihop
  • tier
  • SMAC (Sensor MAC)
  • RIMRP (Refined Integrated MAC and Routing Protocol)
Accesso libero

Smart Compact Laser System for Animation Projections

Pubblicato online: 26 Sep 2019
Pagine: 137 - 153

Astratto

Abstract

In this paper, we present the design of a compact laser system for animation projections both indoors and outdoors. Our focus is on the hardware and software aspects of the electronic control of the system from the design phase to the experimental tests and evaluations. The main purpose behind our development efforts is the creation of an affordable laser system for research, entertainment and marketing purposes using contemporary advances in electronics, software development, semiconductor laser diodes and optics. The system is “smart” in the sense that it connects to other devices and the Internet via a WiFi network, so that in addition to doing standalone laser projections, it also has remote control and remote debugging capabilities. Via its two embedded microcontrollers, the system offers an easy integration into existing Internet of Things (IoT) infrastructure. The experimental results are very promising and bring us closer to creating a viable product from both a technological and an economic standpoint.

Parole chiave

  • Laser system
  • Smart electronic control
  • Animation projection
  • Firmware
  • Multimedia embedded system
  • Internet of Things
Accesso libero

Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

Pubblicato online: 26 Sep 2019
Pagine: 154 - 167

Astratto

Abstract

This paper introduces a technique that can efficiently identify symptoms and risk factors for early childhood diseases by using feature reduction, which was developed based on Principal Component Analysis (PCA) method. Previous research using Apriori algorithm for association rule mining only managed to get the frequent item sets, so it could only find the frequent association rules. Other studies used ARIMA algorithm and succeeded in obtaining the rare item sets and the rare association rules. The approach proposed in this study was to obtain all the complete sets including the frequent item sets and rare item sets with feature reduction. A series of experiments with several parameter values were extrapolated to analyze and compare the computing performance and rules produced by Apriori algorithm, ARIMA, and the proposed approach. The experimental results show that the proposed approach could yield more complete rules and better computing performance.

Parole chiave

  • Early childhood diseases
  • PCA
  • Medical record
  • Apriori Algorithm
10 Articoli
Accesso libero

Measuring the Significance of Writing Style for Recommending Where to Publish – A Case Study

Pubblicato online: 26 Sep 2019
Pagine: 3 - 15

Astratto

Abstract

Writing style plays a role in publication venue recommendation. However, such finding should be observed further; it is concluded from an arbitrary dataset which contains various topics and writing quality. This paper aims to observe that style's impact in a more controlled environment. A dataset with the same specific topic and writing quality was used and analysed. In our case, the dataset is comprised of papers published on reputable software engineering publication venues with natural language generation as the specific topic. According to our observation, writing style only affects significantly on paper title wherein the impact is proportional to n in n-gram. Moreover, the style’s impact becomes more salient when the venues are grouped per publisher or only a specific publication type is considered.

Parole chiave

  • Recommender system
  • writing style
  • publication venues
  • N-gram language model
Accesso libero

Towards Big Data Analytics in the e-Learning Space

Pubblicato online: 26 Sep 2019
Pagine: 16 - 24

Astratto

Abstract

The issues related to the analysis and management of Big Data, aspects of the security, stability and quality of the data, represent a new research, and engineering challenge. In the present paper, techniques for Big Data storage, search, analysis and management in the area of the virtual e-Learning space and the problems in front of them are considered. A numerical example for explorative analysis of data about the students from Burgas Free University is applied, using instrument for Data Mining of Orange. The analysis is a base for a system for localization of students at risk.

Parole chiave

  • Big Data Analytics
  • Data Mining
  • Map/Reduce
  • e-Learning
  • Orange
Accesso libero

Course Sequence Recommendation with Course Difficulty Index Using Subset Sum Approximation Algorithms

Pubblicato online: 26 Sep 2019
Pagine: 25 - 44

Astratto

Abstract

Choice Based Course Selection (CBCS) allows students to select courses based on their preferred sequence. This preference in selection is normally bounded by constraints set by a university like pre-requisite(s), minimum and maximum number of credits registered per semester. Unplanned course sequence selection affects the performance of the students and may prolong the time to complete the degree. Course Difficulty Index (DI) also contributes to the decline in the performance of the students. To overcome these difficulties, we propose a new Subset Sum Approximation Problem (SSAP) aims to distribute courses to each semester with approximately equal difficulty level using Maximum Prerequisite Weightage (MPW) Algorithm, Difficulty Approximation (DA) algorithm and Adaptive Genetic Algorithm (AGA). The three algorithms have been tested using our university academic dataset and DA algorithm outperforms with 98% accuracy than the MPW and AGA algorithm during course distribution.

Parole chiave

  • Course sequence recommendation
  • Course credits
  • Course difficulty
  • Pre-requisite weight
  • Approximation Algorithm
Accesso libero

Group Decision Making in Evaluation and Ranking of Students by Extended Simple Multi-Attribute Rating Technique

Pubblicato online: 26 Sep 2019
Pagine: 45 - 56

Astratto

Abstract

The paper deals with evaluation and ranking of students taking into account two main criteria of the learning – theoretical knowledge and practical skills. These criteria are divided into several sub-criteria to reflect different aspects of the learning outcomes. To make such complex evaluation the proper utility function based on simple multi-attribute rating technique is proposed. This new utility function includes not only the evaluation score and weighted coefficients for criteria importance, but considers also additional coefficients that indicate how theoretical knowledge and practical skills will take part in the aggregated final assessment. The formulated model is applied for the assessing of the students on web programming. The students are ranked under three different cases where the theoretical knowledge and practical skills take different part in the aggregated assessment. The obtained results demonstrate the applicability of the described approach by providing different ranking depending on the importance of the theoretical and practical aspects.

Parole chiave

  • Students’ assessments
  • ranking
  • web programming
  • group decision making
Accesso libero

Simplifying the Structural Complexity of Software Systems

Pubblicato online: 26 Sep 2019
Pagine: 57 - 73

Astratto

Abstract

Simplification of execution traces is peculiarly important in the case of software comprehension. The objective is to make execution traces in ways that are more tractable and less difficult. However, the simplification process is a difficult task, particularly, in object-oriented contexts. Due to coupling, execution traces of object-oriented systems involve the Spaghetti Architectures phenomenon, which is a very complicated structure of dependencies. Therefore, the simplification process needs a well-established approach to be helpful for software comprehension. Otherwise, the simplified execution traces will be informative as their structures will involve several gaps that lead to a misunderstanding process. This research uses decoupling to guide the simplification of object-oriented execution traces. Specifically, decoupling truthfully can decrease the complexity of execution traces without eliminating the trace components and making numerous gaps in the trace structure. Then, decoupling can solve the problem of the Spaghetti Architectures phenomenon. A controlled experiment was conducted to empirically validate the usefulness and effectivity of the suggested work. There was a significant statistical added value demonstrated in the time required and the accurate solutions of the tasks being solved. More precisely, 25% less time required with a 62% more correct solutions were achieved solving the experiment’s comprehension tasks.

Parole chiave

  • Decoupling
  • execution trace analysis
  • object-oriented
  • software comprehension
  • software maintenance
Accesso libero

Enhanced Evolutionary Computing Assisted Robust SLA-Centric Load Balancing System for Mega Cloud Data Centers

Pubblicato online: 26 Sep 2019
Pagine: 74 - 93

Astratto

Abstract

Considering significance of a robust and Quality of Service (QoS) centric cloud computing, virtualization assisted load-balancing has been found a potential solution. However, assuring optimal Virtual-Machine (VM) migration with minimum violation of Service-Level-Agreement (SLA) and QoS degradation has been the challenge for academia-industries. VM allocation or scheduling being an NP-hard problem has been solved by numerous heuristic approaches such as classical Genetic Algorithm (GA), Ant Colony Optimization (ACO), etc. However they have been found confined due to local minima and convergence issues, especially for Mega Data Centres (MDCs). To alleviate such issues, in this paper an enhanced Evolutionary Computing algorithm named Adaptive Re-sampling GA (ARGA) algorithm has been developed that in conjunction with a stochastic prediction based dynamic load–measurement and Maximum Correlation (MC) assisted VM selection perform optimal load balancing over IaaS MDC infrastructures. The proposed ARGA VM allocation model with dual-level dynamic threshold assisted load estimation and MC based VM selection has exhibited lower SLA violation, performance degradation, downtime and minimum VM migration as compared to classical ACO based load balancing.

Parole chiave

  • Load-balancing
  • virtualization
  • Adaptive Re-sampling Genetic Algorithm (ARGA)
  • SLA provision
Accesso libero

Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

Pubblicato online: 26 Sep 2019
Pagine: 94 - 117

Astratto

Abstract

Efficiently provisioning the resources in a large computing domain like cloud is challenging due to uncertainty in resource demands and computation ability of the cloud resources. Inefficient provisioning of the resources leads to several issues in terms of the drop in Quality of Service (QoS), violation of Service Level Agreement (SLA), over-provisioning of resources, under-provisioning of resources and so on. The main objective of the paper is to formulate optimal resource provisioning policies by efficiently handling the uncertainties in the jobs and resources with the application of Neutrosophic Soft-Set (NSS) and Fuzzy Neutrosophic Soft-Set (FNSS). The performance of the proposed work compared to the existing fuzzy auto scaling work achieves the throughput of 80% with the learning rate of 75% on homogeneous and heterogeneous workloads by considering the RUBiS, RUBBoS, and Olio benchmark applications.

Parole chiave

  • SARSA (State-Action Reward-State-Action)
  • Resource provisioning
  • Uncertainty
  • Soft-set
  • elasticity
  • throughput
  • learning rate
Accesso libero

Mitigating Hotspot Issue in WSN Using Sensor Nodes with Varying Initial Energy Levels and Quantification Algorithm

Pubblicato online: 26 Sep 2019
Pagine: 118 - 136

Astratto

Abstract

A typical Wireless Sensor Network (WSN) uses multihop communication rather than direct transmission. In a multi-hop communication, the sensor node communicates the sensed data to its neighbor node, which is comparatively closer to the sink and the receiving node will forward the data to its neighbor node. This process continues until the data reaches the sink. Due to the multihop communication, the nodes closer to the sink have to transmit and receive more data and control packets compared to other nodes. Hence, the nodes closer to sink may deplete their energy at a faster rate and may die soon. This may create network isolation. This issue is called as the Hotspot problem. In this paper, we are proposing a Quantification algorithm for Sensor Nodes with varying Initial Energy Level to mitigate the Hotspot effect.

Parole chiave

  • WSN
  • hotspot
  • multihop
  • tier
  • SMAC (Sensor MAC)
  • RIMRP (Refined Integrated MAC and Routing Protocol)
Accesso libero

Smart Compact Laser System for Animation Projections

Pubblicato online: 26 Sep 2019
Pagine: 137 - 153

Astratto

Abstract

In this paper, we present the design of a compact laser system for animation projections both indoors and outdoors. Our focus is on the hardware and software aspects of the electronic control of the system from the design phase to the experimental tests and evaluations. The main purpose behind our development efforts is the creation of an affordable laser system for research, entertainment and marketing purposes using contemporary advances in electronics, software development, semiconductor laser diodes and optics. The system is “smart” in the sense that it connects to other devices and the Internet via a WiFi network, so that in addition to doing standalone laser projections, it also has remote control and remote debugging capabilities. Via its two embedded microcontrollers, the system offers an easy integration into existing Internet of Things (IoT) infrastructure. The experimental results are very promising and bring us closer to creating a viable product from both a technological and an economic standpoint.

Parole chiave

  • Laser system
  • Smart electronic control
  • Animation projection
  • Firmware
  • Multimedia embedded system
  • Internet of Things
Accesso libero

Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

Pubblicato online: 26 Sep 2019
Pagine: 154 - 167

Astratto

Abstract

This paper introduces a technique that can efficiently identify symptoms and risk factors for early childhood diseases by using feature reduction, which was developed based on Principal Component Analysis (PCA) method. Previous research using Apriori algorithm for association rule mining only managed to get the frequent item sets, so it could only find the frequent association rules. Other studies used ARIMA algorithm and succeeded in obtaining the rare item sets and the rare association rules. The approach proposed in this study was to obtain all the complete sets including the frequent item sets and rare item sets with feature reduction. A series of experiments with several parameter values were extrapolated to analyze and compare the computing performance and rules produced by Apriori algorithm, ARIMA, and the proposed approach. The experimental results show that the proposed approach could yield more complete rules and better computing performance.

Parole chiave

  • Early childhood diseases
  • PCA
  • Medical record
  • Apriori Algorithm

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