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Article Citation - WoS: 31Citation - Scopus: 50Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: a Case Study of an International Bank(Wiley, 2015) Ogwueleka, Francisca Nonyelum; Misra, Sanjay; Colomo-Palacios, Ricardo; Fernandez, LuisThe customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six-step procedure. The back-propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.Article Citation - WoS: 34Citation - Scopus: 59A Review of Soft Techniques for Sms Spam Classification: Methods, Approaches and Applications(Pergamon-elsevier Science Ltd, 2019) Abayomi-Alli, Olusola; Misra, Sanjay; Abayomi-Alli, Adebayo; Odusami, ModupeBackground: The easy accessibility and simplicity of Short Message Services (SMS) have made it attractive to malicious users thereby incurring unnecessary costing on the mobile users and the Network providers' resources. Aim: The aim of this paper is to identify and review existing state of the art methodology for SMS spam based on some certain metrics: AI methods and techniques, approaches and deployed environment and the overall acceptability of existing SMS applications. Methodology: This study explored eleven databases which include IEEE, Science Direct, Springer, Wiley, ACM, DBLP, Emerald, SU, Sage, Google Scholar, and Taylor and Francis, a total number of 1198 publications were found. Several screening criteria were conducted for relevant papers such as duplicate removal, removal based on irrelevancy, abstract eligibility based on the removal of papers with ambiguity (undefined methodology). Finally, 83 papers were identified for depth analysis and relevance. A quantitative evaluation was conducted on the selected studies using seven search strategies (SS): source, methods/ techniques, AI approach, architecture, status, datasets and SMS spam mobile applications. Result: A Quantitative Analysis (QA) was conducted on the selected studies and the result based on existing methodology for classification shows that machine learning gave the highest result with 49% with algorithms such as Bayesian and support vector machines showing highest usage. Unlike statistical analysis with 39% and evolutionary algorithms gave 12%. However, the QA for feature selection methods shows that more studies utilized document frequency, term frequency and n-grams techniques for effective features selection process. Result based on existing approaches for content-based, non-content and hybrid approaches is 83%, 5%, and 12% respectively. The QA based on architecture shows that 25% of existing solutions are deployed on the client side, 19% on server-side, 6% collaborative and 50% unspecified. This survey was able to identify the status of existing SMS spam research as 35% of existing study was based on proposed new methods using existing algorithms and 29% based on only evaluation of existing algorithms, 20% was based on proposed methods only. Conclusion: This study concludes with very interesting findings which shows that the majority of existing SMS spam filtering solutions are still between the "Proposed" status or "Proposed and Evaluated" status. In addition, the taxonomy of existing state of the art methodologies is developed and it is concluded that 8.23% of Android users actually utilize this existing SMS anti-spam applications. Our study also concludes that there is a need for researchers to exploit all security methods and algorithm to secure SMS thus enhancing further classification in other short message platforms. A new English SMS spam dataset is also generated for future research efforts in Text mining, Tele-marketing for reducing global spam activities.Article Citation - WoS: 9Citation - Scopus: 8New Computational Methods for Classification Problems in the Existence of Outliers Based on Conic Quadratic Optimization(Taylor & Francis inc, 2020) Yerlikaya-Ozkurt, Fatma; Taylan, PakizeMost of the statistical research involves classification which is a procedure utilized to establish prediction models to set apart and classify new observations in the dataset from every fields of science, technology, and economics. However, these models may give misclassification results when dataset contains outliers (extreme data points). Therefore, we dealt with outliers in classification problem: firstly, by combining robustness of mean-shift outlier model and then stability of Tikhonov regularization based on continuous optimization method called Conic Quadratic Programming. These new methodologies are performed on classification dataset within the existence of outliers, and the results are compared with parametric model by using well-known performance measures.Article Citation - WoS: 6Classification of Parasite Egg Cells Using Gray Level Cooccurence Matrix and Knn(Scientific Publishers india, 2016) Sengul, GokhanParasite eggs are around 20 to 80 mu m dimensions, and they can be seen under microscopes only and their detection requires visual analyses of microscopic images, which requires human expertise and long analysis time. Besides visual analysis is very error prone to human procedures. In order to automatize this process, a number of studies are proposed in the literature. But there is still a gap between the preferred performance and the reported ones and it is necessary to increase the performance of the automatic parasite egg classification approaches. In this study a learning based statistical pattern recognition approach for parasite egg classification is proposed that will both decrease the time required for the manual classification by an expert and increase the performance of the previously suggested automated parasite egg classification approaches. The proposed method uses Gray-Level Co-occurrence Matrix as the feature extractor, which is a texture based statistical method that can differentiate the parasite egg cells based on their textures, and the k-Nearest Neighbourhood (kNN) classifier for the classification. The proposed method is tested on 14 parasite egg types commonly seen in humans. The results show that proposed method can classify the parasite egg cells with a performance rate of 99%.Conference Object Citation - WoS: 1Gender Prediction by Using Local Binary Pattern and K Nearest Neighbor and Discriminant Analysis Classifications(Ieee, 2016) Camalan, Seda; Çamalan, Seda; Sengul, Gokhan; Şengül, Gökhan; Çamalan, Seda; Şengül, Gökhan; Information Systems Engineering; Computer Engineering; Information Systems Engineering; Computer EngineeringIn this study, gender prediction is investigated for the face images. To extract the features of the images, Local Binary Pattern (LBP) is used with its different parameters. To classify the images male or female, K-Nearest Neighbors (KNN) and Discriminant Analysis (DA) methods are used. Their performances according to the LBP parameters are compared. Also classification methods' parameters are changed and the comparison results are shown. These methods are applied on FERET database with 530 female and 731 male images. To have better performance, the face parts of the images are cropped then feature extraction and classification methods applied on the face part of the images.
