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Copyright of Necmettin Erbakan University Journal of the Faculty of Political Science / Necmettin Erbakan Üniversitesi Siyasal Bilgiler Fakültesi Dergisi is the property of Necmettin Erbakan University Journal of The Faculty of Political Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Transportation & Logistics / Ulaştırma ve Lojistik Dergisi is the property of Journal of Transportation & Logistics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Celal Bayar University Journal of Social Sciences / Celal Bayar Üniversitesi Sosyal Bilimler Dergisi is the property of Celal Bayar University Journal of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Online Journal of Art & Design is the property of Online Journal of Art & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Developments in technology have significantly affected the marketing activities of businesses. With the integration of technology into marketing activities, sales increased and the attention of target markets began to be drawn more. Thanks to the opportunities brought by technology, businesses have had the chance to understand the personal needs and expectations of consumers more easily. Thus, personalization in marketing has begun to take place at the center of marketing. However, developments in recent years have brought the issue of hyper-personalization, which is one step beyond personalization of businesses, to the agenda. The effects of artificial intelligence, machine learning, internet of things have an important impact great in this. In this paper, gait-based gender recognition problem, which is an important example for hyper-personalized marketing activities, was attempted to solve with Convolutional Neural Networks (CNNs). Various networks underwent evaluation for this task, with one chosen as a foundation. Subsequent modifications were applied to this base network through experimentation with architectural choices and hyperparameters. Despite exhibiting promising performance akin to prior research, the experimental findings shed light on the impact of network structure and hyperparameters on performance. The experiments employed gait silhouette, a feature descriptor, as input. The resultant overall accuracy reached 97.45% with the proposed CNN architecture. This outcome offers valuable insights into utilizing gait feature descriptions for classification within the problem domain. [ABSTRACT FROM AUTHOR]

With the enormous growth rate in the number of movies coming into our lives, it can be very challenging to decide whether a movie is suitable for a family or not. Almost every country has a Movie Rating System that determines movies’ suitability age. But these current movie rating systems require watching the full movie with a professional. In this paper, we developed a model which can determine the rating level of the movie by only using its subtitle without any professional interfere. To convert the text data to numbers, we use TF-IDF vectorizer, WIDF vectorizer and Glasgow Weighting Scheme. We utilized random forest, support vector machine, k-nearest neighbor and multinomial naive bayes to find the best combination that achieves the highest results. We achieved an accuracy of 85%. The result of our classification approach is promising and can be used by the movie rating committee for pre-evaluation. Cautionary Note: In some chapters of this paper may contain some words that many will find offensive or inappropriateness; however, this cannot be avoided owing to the nature of the work

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The long jump is defined as an athletic event, and it has also been a standard event in modern Olympic Games. The purpose of the athletes is to make the distance as far as possible from a jumping point. The main purpose of this study was to determine the most successful machine learning algorithm in the prediction of the long jump distance of male athletes. In this paper, we used age and velocity variables for predicting the long jump performance of athletes. During the research, 328 valid jumps belonging to 73 Turkish male athletes were used as data. In determining the most successful algorithm, mean absolute error (MAE), root mean square error (RMSE), Mean Squared Error (MSE), R2 score, Explained Variance Score (EVS), and Mean Squared Logarithmic Error (MSLE) values were taken into consideration. The outcomes of the analysis showed that long jump performance can be determined by chosen independent variables. The 5-fold cross-validation technique was used for the performance evaluation of the models. As a result of the experimental tests, the Gradient Boosting Regression Trees (GBRT) algorithm reached the best result with an MSE value of 0.0865. In this study, it was concluded that the machine learning approach suggested can be used by trainers to determine the long jump performance of male athletes. [ABSTRACT FROM AUTHOR]

Copyright of Electronic Turkish Studies is the property of Electronic Turkish Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Make-up exam is an exam attended by students who fail the course at the end of midterm and general exams and are administered by many universities in our country. Although it is assumed that the students who fail the course will participate in these exams, some of the students may be affected by various internal (low midterm grade, desire to take the course again, need to take a vacation, etc.) and external motivations (distance between hometown and university, financial difficulties, etc.) does not take the make-up exams. Processes such as exam schedules, appointment of invigilators, copy of exam papers and toner, which are made by assuming that all students will take the exam, have caused tangible wastes. In this study, an extreme learning machine-based approach has been proposed in order to determine whether the students who they are expected to join the make-up exam will participate in the make-up exam. In the proposed approach, more than one extreme learning machine have been trained by using samples at a certain rate of the dataset, and the classification have been made with a voting-based approach in the testing phase. Furthermore, the ELM structures those join the voting, have been optimized by artificial bee colony algorithm. The training and testing of the proposed method have been conducted on 10 courses datasets. In the experimental studies, the performance of the extreme learning machine and the proposed method have been compared separately for each dataset, and it is seen that the performance of the proposed method is higher than the basic extreme learning machine in terms of classification accurac. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Marketing & Marketing Research / Pazarlama ve Pazarlama Araştırmaları Dergisi is the property of Pazarlama & Pazarlama Arastirmalari Dernegi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The fast span of the internet has accelerated the emergence of social media people in all over the world to affect each other in seconds through social media and spread their thoughts quickly. Social media provide an effective communication network individually and organizationally. Users can evaluate the services they have received as consumers or citizens on these platforms as positive or negative. As a result, an interaction network has been formed between the citizens and the organizations they receive services from. Reconsidering this interaction network, citizens frequently interpret municipal services. The services of the municipal corporations, the managing agency, are evaluated by citizens through social media. Considering the public relations aspect, individuals can personally communicate with municipalities and mayors, especially on Twitter. This situation provides a different communication ground between the citizen and the municipality and provides a solution-oriented interaction network. This study covers the Sakarya special; The shares of Twitter users about the Metropolitan Municipality, Adapazarı, Serdivan, Erenler and Hendek municipalities were examined, and the attitudes of citizens towards municipalities were prepared to be analyzed. The texts of tweets sent to municipal accounts were collected. These obtained data were first assigned to predetermined groups with Machine Learning methods, and then Emotion Analysis was performed with the Turkish Bert method, one of the Machine Learning and Deep Learning approaches. Citizens' attitude towards municipalities has emerged and has been evaluated with its positive and negative aspects. With this study, municipalities were provided with positive or negative feedback from citizens, ensuring citizen satisfaction and responding to requests faster. The principal aim to this paper municipalities change the negative attitudes of the citizens positively with the solution-oriented studies they will do. It is thought that through Twitter, municipalities will contribute to the society with topics such as improving their bilateral dialogue with citizens, finding solutions to requests, demands and complaints, and strengthening participatory democracy. [ABSTRACT FROM AUTHOR]

Copyright of Turkiye Klinikleri Journal of Forensic Medicine & Forensic Sciences / Türkiye Klinikleri Adli Tıp ve Adli Bilimler Dergisi is the property of Turkiye Klinikleri and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Nigde Omer Halisdemir University Journal of Engineering Sciences / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi is the property of Nigde Omer Halisdemir Universitesi (NOHU), Muhendislik Fakultesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Türk Radyoloji Seminerleri is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Türkiye Adalet Akademisi Dergisi is the property of Justice Academy of Turkey and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine Learning (ML) has become widespread in the food industry and can be seen as a great opportunity to deal with the various challenges of the field both in the present and near future. In this paper, we analyzed 91 research studies that used at least two ML algorithms and compared them in terms of various performance metrics. China and USA are the leading countries with the most published studies. We discovered that Support Vector Machine (SVM) and Random Forest outperformed other ML algorithms, and accuracy is the most used performance metric.

Copyright of Gazi Journal of Engineering Sciences (GJES) / Gazi Mühendislik Bilimleri Dergisi is the property of Gazi Journal of Engineering Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Gazi Journal of Engineering Sciences (GJES) / Gazi Mühendislik Bilimleri Dergisi is the property of Gazi Journal of Engineering Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of University Research / Üniversite Araştırmaları Dergisi is the property of Journal of University Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bartin University Journal of Faculty of Education is the property of Bartin University Journal of Faculty of Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using data from Bibliometrix and Web of Science, this study examined articles in the subject of language and linguistics that dealt with artificial intelligence (AI). The study used bibliometrics to uncover historical trends in AI in EFL. The study utilized Biblioshiny, a web-based tool in the bibliometrix package that analyses bibliographic database data, to examine downloaded Web of Science (WoS) data. The Bibliometrix R Package and Biblioshiny software created tables and graphs. The study searched the WoS website for studies with "Artificial Intelligence (AI)" in the title, abstract, and keywords to find bibliographic data. From 2013 to 2023, WoS focused on Language and Linguistics in Language Education. There were 1693 EFL AI papers. The study chose open-access publications to read the entire text. The present analysis examined 177 publications. Different bibliometric analysis techniques were employed to get the most usable data from research publications. Authors, publishing years, universities, countries, preferred journals, trendy topics, and keyword citation rates were all considered in the analysis. Findings showed an increase in publications over time and a growing interest in AI. Leading universities and prominent authors were identified. Depending on the country, different levels of engagement were observed. The distribution of data was provided via preferred journals. This study helps researchers and decision-makers evaluate AI research in language and linguistics.

Copyright of Pamukkale University Journal of Social Sciences Institute / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Pamukkale University, Social Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this research, we delve deep into the realm of Targeted Social Engineering Email Detection, presenting a novel approach that harnesses the power of Lambda Architecture (LA). Our innovative methodology strategically segments the BERT model into two distinct components: the embedding generator and the classification segment. This segmentation not only optimizes resource consumption but also improves system efficiency, making it a pioneering step in the field. Our empirical findings, derived from a rigorous comparison between the fastText and BERT models, underscore the superior performance of the latter. Specifically, The BERT model has high precision rates for identifying malicious and benign emails, with impressive recall values and F1 scores. Its overall accuracy rate was 0.9988, with a Matthews Correlation Coefficient value of 0.9978. In comparison, the fastText model showed lower precision rates. Leveraging principles reminiscent of the Lambda architecture, our study delves into the performance dynamics of data processing models. The Separated-BERT (Sep-BERT) model emerges as a robust contender, adept at managing both real-time (stream) and large-scale (batch) data processing. Compared to the traditional BERT, Sep-BERT showcased superior efficiency, with reduced memory and CPU consumption across diverse email sizes and ingestion rates. This efficiency, combined with rapid inference times, positions Sep-BERT as a scalable and cost-effective solution, aligning well with the demands of Lambda-inspired architectures. This study marks a significant step forward in the fields of big data and cybersecurity. By introducing a novel methodology and demonstrating its efficacy in detecting targeted social engineering emails, we not only advance the state of knowledge in these domains but also lay a robust foundation for future research endeavors, emphasizing the transformative potential of integrating advanced big data frameworks with machine learning models. [ABSTRACT FROM AUTHOR]

Copyright of RumeliDE Journal of Language & Literature Research / RumeliDE Dil ve Edebiyat Araştırmaları Dergisi is the property of RumeliDE Uluslararasi Hakemli Dil & Edebiyat Arastirmalari Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Necmettin Erbakan University School of Law Review is the property of Necmettin Erbakan University School of Law Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Diyanet Ilmi Dergi is the property of Diyanet Isleri Baskanligi Yayinlari and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Suleyman Demirel University Institute of Social Sciences is the property of Suleyman Demirel University, Institute of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Language identification (LI), which is a major task in natural language processing, is the process of determining the language from a given content. In this paper, a novel approach, which is based on the probability of the use of the characters that have the similar orders with respect to their UTF-8 values, was proposed. In order to evaluate and validate the proposed approach, four datasets, which contain texts in different numbers of languages, were employed. In the proposed approach, the features that were exacted by one-dimensional local binary pattern (1D-LBP) method were classified by various machine learning methods. Achieved LI accuracies in each of four employed datasets were 86.20%, 92.75%, 100% and 89.77%, respectively. The results showed that the proposed approach yields high success rates and it is an efficient way of language identification. [ABSTRACT FROM AUTHOR]

Copyright of Hitit Journal of Social Sciences is the property of Hitit University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Pamukkale University Journal of Social Sciences Institute / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Pamukkale University, Social Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Balkan & Near Eastern Journal of Social Sciences (BNEJSS) is the property of Balkan & Near Eastern Journal of Social Sciences (BNEJSS) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the developing technology, the need for the dissemination and protection of information is becoming increasingly important. Recently, attacks on information systems have increased significantly. In addition to the rise in the number of attacks, attacks of different types pose a great threat to systems. As a result of these attacks, institutions and users suffer serious damages. At this point, Intrusion Detection Systems (IDS) have a very important position. The pre-detection of these attacks on the systems and the preparation of the necessary reports can reduce the impact of the threats that may be encountered in the future. Recent studies are carried out so as to increase the performance of IDS. In this paper, classification was made using NSL-KDD dataset and SVM, KNN, Bayesnet, NavieBayes, J48 and Random Forest algorithms, and it was aimed to compare performance of these classifications by using WEKA. Consequently, it has been reached that the KNN algorithm had the best performance with an accuracy rate of 98.1237 %. In addition, the effect of increasing the number of folds and neighborhoods on the classification result has been examined comparatively.

Copyright of Pamukkale University Journal of Social Sciences Institute / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Pamukkale University, Social Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ankara University Faculty of Medicine / Ankara Üniversitesi Tip Fakültesi Mecmuasi is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ankara University Faculty of Medicine / Ankara Üniversitesi Tip Fakültesi Mecmuasi is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ankara University Faculty of Medicine / Ankara Üniversitesi Tip Fakültesi Mecmuasi is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ankara University Faculty of Medicine / Ankara Üniversitesi Tip Fakültesi Mecmuasi is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ankara University Faculty of Medicine / Ankara Üniversitesi Tip Fakültesi Mecmuasi is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently, the Extreme Learning Machine (ELM) becomes an interesting topic in machine learning area. The ELM has been proposed as a new learning algorithm for Single-Hidden Layer Feed forward Networks (SLFNs). The ELM structure has several advantageous such as good generalization performance, extremely fast learning ability and low computational process. Besides this advantageous, the ELM structure has some drawbacks. Firstly, the ELM encounters over-fitting problems because of using a least squares minimization in calculation of the output weights. The other drawback is about accuracy of the ELM. It depends on the number of hidden neurons. This situation is a big challenge in high-dimensional problems for some practical applications. In this paper, we propose a sparse ELM model to overcome the above-mentioned drawbacks. In the sparse ELM model, Forward-Backward Pursuit (FBP) algorithms based on greedy pursuit was used to obtain sparse representation of the output weights. The proposed method which is called FBP-ELM, has several benefits in comparing with the traditional ELM schemes such as avoiding over-fitting, low computational complexity and with adequate number of neurons in hidden layer. FBP-ELM shows its remarkable advantages when it is compared with the empirical studies on commonly used classification benchmarks. Moreover, a comparison with the original ELM and the other regularized ELM schemes such as Least-angle regression (LARS), Least absolute shrinkage and selection operator (LASSO) and Elastic Net is presented to show effectiveness of proposed FBP-ELM method. [ABSTRACT FROM AUTHOR]

The classification of observations is an important constituent of statistics and machine learning, either for analysis of data sets, or as a subgoal of a more complex problem. A novel machine learning technique, Support Vector Machines (SVM), has recently been receiving considerable attention in pattern recognition and regression function estimation problems. This paper uses standard logistic regression models for binary classification problems and compares them with SVM models with linear and non-linear kernel functions. An application with real data associated with giving birth to a low birth weight baby and patients with cancer of prostate are presented as an illustration. Based on the results of the numerical examples, it is determined that Support Vector Classification method produces remarkable results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Internet Applications & Management / İnternet Uygulamaları ve Yönetimi Dergisi is the property of Journal of Internet Applications & Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Competition Journal / Rekabet Dergisi is the property of Turkish Competition Authority and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Visionary E-Journal / Vizyoner Dergisi is the property of Suleyman Demirel University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)