Migrating Birds Optimization-Based Feature Selection for Text Classification

This research introduces a novel approach, MBO-NB, that leverages Migrating Birds Optimization (MBO) coupled with Naive Bayes as an internal classifier to address feature selection challenges in text classification having large number of features. Focusing on computational efficiency, we preprocess raw data using the Information Gain algorithm, strategically reducing the feature count from an average of 62221 to 2089. Our experiments demonstrate MBO-NB's superior effectiveness in feature reduction compared to other existing techniques, emphasizing an increased classification accuracy. The successful integration of Naive Bayes within MBO presents a well-rounded solution. In individual comparisons with Particle Swarm Optimization (PSO), MBO-NB consistently outperforms by an average of 6.9% across four setups. This research offers valuable insights into enhancing feature selection methods, providing a scalable and effective solution for text classificatio

Neural network algorithms for fraud detection: a comparison of the complementary techniques in the last five years

Purpose: The purpose of this research is to analyse the complementary updates and techniques in the optimization of the results of neural network algorithms (NNA) in order to detect financial fraud, providing a comparison of the trend, addressed field and efficiency of the models developed in current research. Design/Methodology/Approach: The author performed a qualitative study where a compilation and selection of literature was carried out, in terms of defining the conceptual analysis, database and search strategy, consequently selecting 32 documents. Subsequently, the comparative analysis was carried out, in turn being able to determine the most used and efficient complementary technique in the last five years. Findings: The results of the comparative analysis depicted that in 2019 there was a greater impact of research based on NNA with 11 studies. 27 complementary updates and techniques were identified related to NNA, where deep neural network algorithms (DNN), convolutional neural network (CNN) and SMOTE neural network. Finally, the evaluation of effectiveness in the collected techniques achieved an average accuracy ranging between 79% and 98.74% with an overall accuracy value of 91.32%. Originality/Value: Being a technique which is applied and compared in diverse studies, ANNs uses a wide range of mechanisms concerning training and classification of data. According to the findings of this research, the complementary techniques contribute to the progress and optimization of algorithms regarding financial fraud detection, having a high degree of effectiveness concerning on-line and credit card fraud

Comparative Analysis of Different Distributions Dataset by Using Data Mining Techniques on Credit Card Fraud Detection

Banks suffer multimillion-dollars losses each year for several reasons, the most important of which is due to credit card fraud. The issue is how to cope with the challenges we face with this kind of fraud. Skewed "class imbalance" is a very important challenge that faces this kind of fraud. Therefore, in this study, we explore four data mining techniques, namely naïve Bayesian (NB),Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF), on actual credit card transactions from European cardholders. This paper offers four major contributions. First, we used under-sampling to balance the dataset because of the high imbalance class, implying skewed distribution. Second, we applied NB, SVM, KNN, and RF to under-sampled class to classify the transactions into fraudulent and genuine followed by testing the performance measures using a confusion matrix and comparing them. Third, we adopted cross-validation (CV) with 10 folds to test the accuracy of the four models with a standard deviation followed by comparing the results for all our models. Next, we examined these models against the entire dataset (skewed) using the confusion matrix and AUC (Area Under the ROC Curve) ranking measure to conclude the final results to determine which would be the best model for us to use with a particular type of fraud. The results showing the best accuracy for the NB, SVM, KNN and RF classifiers are 97,80%; 97,46%; 98,16% and 98,23%, respectively. The comparative results have been done by using four-division datasets (75:25), (90:10), (66:34) and (80:20) displayed that the RF performs better than NB, SVM, and KNN, and the results when utilizing our proposed models on the entire dataset (skewed), achieved preferable outcomes to the under-sampled dataset

A modified migrating bird optimization for university course timetabling problem

University course timetabling problem is a dilemma which educational institutions are facing due to various demands to be achieved in limited resources. Migrating bird optimization (MBO) algorithm is a new meta-heuristic algorithm which is inspired by flying formation of migrating birds. It has been applied successfully in tackling quadratic assignment problem and credit cards fraud detection problem. However, it was reported that MBO will get stuck in local optima easily. Therefore, a modified migrating bird optimization algorithm is proposed to solve post enrolment-based course timetabling. An improved neighbourhood sharing mechanism is used with the aim of escaping from local optima. Besides that, iterated local search is selected to be hybridized with the migrating bird optimization in order to further enhance its exploitation ability. The proposed method was tested using Socha’s benchmark datasets. The experimental results show that the proposed method outperformed the basic MBO and it is capable of producing comparable results as compared with existing methods that have been presented in literature. Indeed, the proposed method is capable of addressing university course timetabling problem and promising results were obtained

On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems

Migrating Birds Optimisation (mbo) is a nature-inspired approach which has been shown to be very effective when solving a variety of combinatorial optimisation problems. More recently, an adaptation of the algorithm has been proposed that enables it to deal with continuous search spaces. We extend this work in two ways.Firstly, a novel leader replacement strategy is proposed to counter the slow convergence of the existing mbo algorithms due to low selection pressure. Secondly, mbo is hybridised with adaptive neighbourhood operators borrowed from Differential Evolution (de) that promote exploration and exploitation. The new variants are tested on two sets of continuous large scale optimisation problems. Results show that mbo variants using adaptive, exploration-based operators outperform de on the cec benchmark suite with 1000variables. Further experiments on a second suite of 19 problems show that mbo variants outperform de on 90% of these test-cases

WSN based sensing model for smart crowd movement with identification: a conceptual model

With the advancement of IT and increase in world population rate, Crowd Management (CM) has become a subject undergoing intense study among researchers. Technology provides fast and easily available means of transport and, up-to-date information access to the people that causes crowd at public places. This imposes a big challenge for crowd safety and security at public places such as airports, railway stations and check points. For example, the crowd of pilgrims during Hajj and Ummrah while crossing the borders of Makkah, Kingdom of Saudi Arabia. To minimize the risk of such crowd safety and security identification and verification of people is necessary which causes unwanted increment in processing time. It is observed that managing crowd during specific time period (Hajj and Ummrah) with identification and verification is a challenge. At present, many advanced technologies such as Internet of Things (IoT) are being used to solve the crowed management problem with minimal processing time. In this paper, we have presented a Wireless Sensor Network (WSN) based conceptual model for smart crowd movement with minimal processing time for people identification. This handles the crowd by forming groups and provides proactive support to handle them in organized manner. As a result, crowd can be managed to move safely from one place to another with group identification. The group identification minimizes the processing time and move the crowd in smart way

Güncel en iyileme algoritmalarının paralel ve birlikte uygulamaları ve performans analizleri

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.En iyileme yöntemleri yapılan işin en iyi yapılmasını sağlamak için kullanılırlar. Bu tekniklerin kullanılmasındaki temel hedef her zaman için en iyi çözümleri yakalayabilmektir. Uygunluk veya hata değeri tanımlanabilen her sistemin en iyi çözümünün elde edilmesinde en iyileme algoritmaları kullanılabilir. Sadece ait oldukları problemlere özgü olmaları ve yüksek hesaplama maliyeti içermeleri gibi sebepler nedeniyle mevcut geleneksel en iyileme algoritmalarının kullanımı çok sayıda parametre içeren gerçek dünya problemlerinin çözümünde bazen yeterli olmayabilir. Bu gibi durumlarda daha az işlem ile daha kısa sürede en iyi çözüme yakınsayabilen meta-sezgisel yöntemlerin kullanımı daha makul çözümler olarak karşımıza çıkmaktadır. Son 20 yıl içerisinde doğadan ilham alınarak çok sayıda meta-sezgisel en iyileme algoritması geliştirilmiştir. Buna paralel olarak bazı araştırmacılar mevcut algoritmalar üzerinde birtakım iyileştirmeler yapmışlar, bazıları da birden fazla algoritmayı bir arada kullanarak performansı daha yüksek melez yöntemler elde etmişler ve daha sonra bu yöntemleri kullanarak gerçek dünya problemlerine en iyi çözümler üretmişlerdir. Bu tez çalışmasında sistem kimliklendirme süreci, yapay sinir ağı eğitimi, sempozyum katılımcı listelerinin düzenlenmesi, slab kesme uzunluklarının planlanması gibi gerçek dünyaya ait problemlere birer en iyileme problemi olarak yaklaşılmış, seçilen güncel ve yaygın meta-sezgisel algoritmalar kullanılarak geleneksel yöntemlerin çözümleri ile rekabet edebilen çözümler üretilmiştir. Ayrıca, karar ağacı tasarım süreci hem kombinatoryal hem de nümerik en iyilemeleri içeren bir problem olarak ele alınmış, olası karar ağacı tasarımları arasında sistematik arama yapan yeni bir yöntem ile karar ağacı tasarımı gerçekleştirilmiştir. Önerilen yöntemle elde edilen test sonuçlarının aynı veri setinin kullanıldığı daha önceki karar ağacı çalışmaları ile elde edilen sonuçlardan daha iyi olduğu görülmüştür. Son olarak, yapay arı koloni ve göçmen kuşlar en iyileme algoritmaları kullanılarak yeni modifiye, melez ve paralel çalışma sistematikleri önerilmiştir. Önerilen yöntemlerin performans testlerinden elde edilen sonuçlar, onların daha iyi keşif ve yakınsama yeteneklerine sahip olduklarını ortaya koymuştur.Optimization methods are employed in order to make a job in an optimal way. The main aim of their usage is to get an optimal solution in every execution. Optimization algorithms can be applied to find optimal solutions for the systems whose fitness or error calculations can be defined. Sometimes, existing conventional optimization algorithms may be insufficient for the real world problems having many parameters because of the reason that they are problem specific and have higher calculation costs. Since metaheuristic algorithms can find near optimal solutions with less calculations requiring lower time, their usages seem more feasible for these cases. Within the past 20 years, so many metaheuristic algorithms which are inspired by the nature have been developed by researchers. In parallel to these studies, while some of the researchers were working on some enhancements for existing algorithms, some of them were working on their hybrid forms. Then, they tried to find more optimal solutions for real world problems by using these new enhanced and hybrid algorithms. In this dissertation study, some real world problems such as system identification process, artificial neural network training, preparation of symposium attendee lists, scheduling slab cutting lengths etc. are thought to be optimization problems. Some competitive solutions with respect to solutions of the conventional methods are generated to these real world problems by using some recent and common metaheuristic algorithms. In addition, thinking the decision tree construction process as a problem including both numerical and combinatorial optimizations, a novel decision tree construction method which makes a systematic search among possible decision tree designs is proposed to get optimal decision tree. It is seen that the results obtained by proposed method are better than those of previous studies using same data set. Finally, some modified, hybrid and parallel running strategies using artificial bee colony and migrating birds optimization algorithms are proposed. It is observed from the performance test results that proposed strategies have better exploration and exploitation capabilities

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested