scholarly journals An efficient logistic regression and ant colony optimization-based object-oriented quality prediction

2020 ◽  
Vol 5 (18) ◽  
pp. 11-18
Author(s):  
Jitendrea Kumar Saha ◽  
Kailash Patidar ◽  
Rishi Kushwah ◽  
Gaurav Saxena
Keyword(s):  
Logistic Regression ◽  
Ant Colony Optimization ◽  
Dynamic Behavior ◽  
Clustering Algorithm ◽  
Object Oriented ◽  
Ant Colony ◽  
Complete Analysis ◽  
Quality Prediction ◽  
Chi Square ◽  
Class Labels

In this paper an efficient logistic regression and ant colony optimization-based object-oriented quality prediction has been presented. The dataset has been considered based on the object-oriented codes. The code considered here are completely equipped with object-oriented features for the complete analysis. The major parameters considered for the experimentation are class, object, inheritance and dynamic behavior. Class and objects have been considered for the class labels and memory requirements checking with the proper stack call and constructor invocation. Inheritance has been considered for the code reusability testing. Dynamic behavior has been tested for the runtime allocation. Then clustering algorithm is used for the parameter preprocessing and grouping based on the parameters. For data filtration chi-square testing has been applied. Then logistic regression and ant colony optimization (LR-ACO) have been considered for the final classification. Then F-Measure, Odd Ratio and Power have been considered for the analysis of the classification based on LR-ACO. The result after LR-ACO shows better accuracy as comparison to the previous methods.

scholarly journals Test Case Reduction Using Ant Colony Optimization for Object Oriented Program

2015 ◽  
Vol 5 (6) ◽  
pp. 1424
Author(s):  
Sudhir Kumar Mohapatra ◽  
Srinivas Prasad
Keyword(s):  
Ant Colony Optimization ◽  
System Development ◽  
Object Oriented ◽  
Ant Colony ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Reduction Techniques ◽  
Maintenance Stage ◽  
The Cost

Software testing is one in all the vital stages of system development. In software development, developers continually depend upon testing to reveal bugs. Within the maintenance stage test suite size grow due to integration of new functionalities. Addition of latest technique force to make new test case which increase the cost of test suite. In regression testing new test case could also be added to the test suite throughout the entire testing process. These additions of test cases produce risk of presence of redundant test cases. Because of limitation of time and resource, reduction techniques should be accustomed determine and take away. Analysis shows that a set of the test case in a suit should satisfy all the test objectives that is named as representative set. Redundant test case increase the execution price of the test suite, in spite of NP-completeness of the problem there are few sensible reduction techniques are available. During this paper the previous GA primarily based technique proposed is improved to search out cost optimum representative set using ant colony optimization.

scholarly journals Image retrieval based on swarm intelligence

2021 ◽  
Vol 11 (6) ◽  
pp. 5390
Author(s):  
Shahbaa I. Khaleel ◽  
Ragad W. Khaled
Keyword(s):  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Water Drop ◽  
Optimization Method ◽  
Modern Technology ◽  
Ant Colony ◽  
Query Image ◽  
Fuzzy Method

To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.

scholarly journals Credit Scoring for Financial Services Institution using Ant Colony Optimization Algorithm under Logistic Regression Model

2019 ◽  
Vol 8 (4) ◽  
pp. 5957-5961
Keyword(s):  
Logistic Regression ◽  
Credit Risk ◽  
Regression Model ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Logistic Regression Model ◽  
Financial Institution ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Credit Analysis

Economic and trade activities are important in a country. All these activities are regulated by financial institutions, such as banks. The process of channeling funds to the public or known as credit is one of the tasks of the banking sector which aims to improve the people's economy. Credit granting is required for credit analysis, which is useful to determine the level of eligibility of a debtor to receive credit. The function of the credit analysis is to reduce the credit risk of prospective debtors who have failed to pay as well as to avoid financial institution losses or charges. The method used to analyze credit risk in this study is the Ant Colony Optimization algorithm in the Logistic Regression model. Past data held by each prospective debtor obtained from one financial institution in Indonesia is used as a feasibility parameter in this analysis. The results of the study showed that eight variables analyzed were five variables including the significant influence (age of debt ( 1 X ), family dependents ( 2 X ), value of the collection ( 4 X ), the number of credit limits ( 6 X ), and the term of the loan ( 8 X ) while the other three variables (the amount of savings ( 3 X ), income per month ( 5 X ), net income ( 7 X ) are not significant to the risk of default.

scholarly journals ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing

2019 ◽  
Vol 4 (5) ◽  
pp. 1239-1250
Author(s):  
Neelam Singh ◽  
Devesh Pratap Singh ◽  
Bhasker Pant
Keyword(s):  
Big Data ◽  
Ant Colony Optimization ◽  
Clustering Algorithm ◽  
Optimization Problems ◽  
Data Preprocessing ◽  
Ant Colony ◽  
Raw Material ◽  
Search Spaces ◽  
Domain Specific Knowledge ◽  
Search Speed

Big Data is rapidly gaining impetus and is attracting a community of researchers and organization from varying sectors due to its tremendous potential. Big Data is considered as a prospective raw material to acquire domain specific knowledge to gain insights related to management, planning, forecasting and security etc. Due to its inherent characteristics like capacity, swiftness, genuineness and diversity Big Data hampers the efficiency and effectiveness of search and leads to optimization problems. In this paper we explore the complexity imposed by big search spaces leading to optimization issues. In order to overcome the above mentioned issues we propose a hybrid algorithm for Big Data preprocessing ACO-clustering algorithm approach. The proposed algorithm can help to increase search speed by optimizing the process. As the proposed method using ant colony optimization with clustering algorithm it will also contribute to reducing pre-processing time and increasing analytical accuracy and efficiency.

scholarly journals Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Sahar Ebadinezhad
Keyword(s):  
Performance Evaluation ◽  
Ant Colony Optimization ◽  
Routing Protocols ◽  
Clustering Algorithm ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Ant Colony ◽  
Minimum Number

AbstractThis study focuses on Vehicular Ad-hoc Networks (VANETs) stability in an environment that is dynamic which often leads to major challenges in VANETs, such as dynamic topology changes, shortest routing paths and also scalability. One of the best solutions for such challenges is clustering. In this study, we present five novel routing protocols based on Dynamic Flying Ant Colony Optimization (DFACO) algorithm to achieve minimum number of clusters, high accuracy, minimum time and solution cost by selecting the best cluster-head which is obtained from a new mechanism of dynamic metaheuristic-based clustering. In this regard, major improvements are applied on classical DFACO by adjusting the procedure for updating the pheromone and tuning the evaporation rate that has a major role in DFACO. In this research two individual phases of experiments are conducted for performance evaluation of proposed routing protocols. The presented solution is verified and compared to classic Ant Colony Optimization (ACO), DFACO and ACO Based Clustering Algorithm for VANET (CACONET) algorithms in phase one; and compared to clustering algorithms such as Center Position and Mobility CPM), Highest-Degree algorithm (HD), Angle-based Clustering Algorithm (ACA) in phase two through NS-2 and SUMO simulation tools. Simulation results have confirmed the expected behaviour and show that our proposed protocols achieve better node connectivity and cluster stability than the former.

scholarly journals Object oriented quality prediction through artificial intelligence and machine learning: a survey

2020 ◽  
Vol 5 (17) ◽  
pp. 1-5
Author(s):  
Jitendrea Kumar Saha ◽  
Kailash Patidar ◽  
Rishi Kushwah ◽  
Gaurav Saxena
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Dynamic Behavior ◽  
Software Quality ◽  
Object Oriented ◽  
Quality Metrics ◽  
Quality Prediction ◽  
Quality Estimation ◽  
Future Directions

Software quality estimation is an important aspect as it eliminates design and code defects. Object- oriented quality metrics prediction can help in the estimation of software quality of any defects and the chances of errors. In this paper a survey and the case analytics have been presented for the object-oriented quality prediction. It shows the analytical and experimental aspects of previous methodologies. This survey also elaborates different object-oriented parameters which is useful for the same problem. It also elaborates the problem aspects as well the limitations for the future directions. Machine learning and artificial intelligence methods have been considered mostly for this survey. The parameters considered are inheritance, dynamic behavior, encapsulation, objects etc.

scholarly journals Semi-supervised associative classification using ant colony optimization algorithm

2021 ◽  
Vol 7 ◽  
pp. e676
Author(s):  
Hamid Hussain Awan ◽  
Waseem Shahzad
Keyword(s):  
Ant Colony Optimization ◽  
Classification Accuracy ◽  
Ant Colony ◽  
Associative Classification ◽  
Ant Colony Optimization Algorithm ◽  
Main Ingredient ◽  
Novel Approach ◽  
Training Approach ◽  
Classification Tasks ◽  
Class Labels

Labeled data is the main ingredient for classification tasks. Labeled data is not always available and free. Semi-supervised learning solves the problem of labeling the unlabeled instances through heuristics. Self-training is one of the most widely-used comprehensible approaches for labeling data. Traditional self-training approaches tend to show low classification accuracy when the majority of the data is unlabeled. A novel approach named Self-Training using Associative Classification using Ant Colony Optimization (ST-AC-ACO) has been proposed in this article to label and classify the unlabeled data instances to improve self-training classification accuracy by exploiting the association among attribute values (terms) and between a set of terms and class labels of the labeled instances. Ant Colony Optimization (ACO) has been employed to construct associative classification rules based on labeled and pseudo-labeled instances. Experiments demonstrate the superiority of the proposed associative self-training approach to its competing traditional self-training approaches.

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