Akhan Akbulut

HMM based classification of sports videos using color feature

Video content classification is an important element for efficient access and retrieval of video in any media content management system. Categorizing the video segments can help to provide convenience and ease in accessing the relevant video content without sequential scanning. In this paper, we present a Hidden Markov Model (HMM) based classification technique for sports videos. Speed of color changes is computed for each video frame and used as observation sequences in HMM for classification. Experiments using more than 1 hour of 18 training and 18 testing sports videos of 3 predefined genres (golf, hockey and football) give very satisfactory classification accuracy.

Wireless sensor networks for space and Solar-system missions

Wireless sensor networks (WSNs) are multi-hop self-organizing networks which include a huge number of nodes integrating environmental measuring, data processing and wireless communications in order to apprehend, collect and process information to achieve defined tasks. A diverse set of applications for WSNs encompassing different fields have already emerged including environmental applications, inventory monitoring, military applications, intrusion detection, health applications, motion tracking, machine malfunction detection and etc. Among these application areas the use of WSNs can adapted to Space and Solar-system missions. In the last years, space-based WSNs have gained increasing attention from both the research communities and companies involved in space research. This paper outlines the usage of a space-based wireless sensor networks (SB-WSNs), which applies the concept of terrestrial wireless sensor networks to the space.

Agent based pornography filtering system

Internet is an infinite information repository that also contains harmful contents like; pornography, violence, and hate messages. It is very important to obstruct these kinds of contents from underage children not to adversely affect their development. Today, there are many commercial software products developed for this purpose. But the filtering capabilities of these commercial software are limited to text based and image based contents. Different techniques must be used to filter video based contents. This article describes an agent-based system which is developed for the detection of videos containing pornographic contents. Videos on the Internet can be divided into six groups as; anime, commercial, music, sitcom, sports, and porn related. The proposed system uses the Hidden Markov Model (HMM) based classification technique to classify the videos into these predefined categories with intelligent agents. Color features are extracted from each video frame and used as observation sequences in HMM for classification. According to the classification results, the videos, which are closely related to the category of sex and pornography, are filtered to the underage users. The test results obtained indicate that the classification has been satisfactory.

Energy and distance factor based routing protocol for wireless sensor networks using mobile agents

The wireless sensor networks are networks of compact micro-sensors for data acquisition or monitoring some environment characteristics, such as temperature, sound, vibration, pressure and motion. These sensors are embedded devices capable of data communication. In many applications, sensor nodes are distributed or deployed over a geo-graphically large area. Due to their structure, data of measured values must be transferred among stations through these sensor nodes. For this reason a successful, energy efficient, fault tolerant routing protocol should be implemented to pre-vent data loss and other challenges within limited energy levels. This paper presents an agent based routing algorithm for wireless sensor networks, based on the selection of the idea of active nodes. Our proposed routing algorithm is related with energy and distance factors of each nodes. The main objective is to increase the lifetime of a sensor network while not compromising data delivery. Critical tasks such as measuring, analyzing and monitoring of energy levels of nodes are handled by these autonomous mechanisms.

Deep Learning Approaches for Predictive Masquerade Detection

In computer security, masquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial factor for computer security. Although considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low false alarm rate is still a big challenge. In this paper, we present a comprehensive empirical study in the area of anomaly-based masquerade detection using three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Convolutional Neural Networks (CNN). In order to surpass previous studies on this subject, we used three UNIX command line-based datasets, with six variant data configurations implemented from them. Furthermore, static and dynamic masquerade detection approaches were utilized in this study. In a static approach, DNN and LSTM-RNN models are used along with a Particle Swarm Optimization-based algorithm for their hyperparameters selection. On the other hand, a CNN model is employed in a dynamic approach. Moreover, twelve well-known evaluation metrics are used to assess model performance in each of the data configurations. Finally, intensive quantitative and ROC curves analyses of results are provided at the end of this paper. The results not only show that deep learning models outperform all traditional machine learning methods in the literature but also prove their ability to enhance masquerade detection on the used datasets significantly.

Automatic energy expenditure measurement for health science

BACKGROUND AND OBJECTIVE It is crucial to predict the human energy expenditure in any sports activity and health science application accurately to investigate the impact of the activity. However, measurement of the real energy expenditure is not a trivial task and involves complex steps. The objective of this work is to improve the performance of existing estimation models of energy expenditure by using machine learning algorithms and several data from different sensors and provide this estimation service in a cloud-based platform. METHODS In this study, we used input data such as breathe rate, and hearth rate from three sensors. Inputs are received from a web form and sent to the web service which applies a regression model on Azure cloud platform. During the experiments, we assessed several machine learning models based on regression methods. RESULTS Our experimental results showed that our novel model which applies Boosted Decision Tree Regression in conjunction with the median aggregation technique provides the best result among other five regression algorithms. CONCLUSIONS This cloud-based energy expenditure system which uses a web service showed that cloud computing technology is a great opportunity to develop estimation systems and the new model which applies Boosted Decision Tree Regression with the median aggregation provides remarkable results.

Development of a Software Vulnerability Prediction Web Service Based on Artificial Neural Networks

Detecting vulnerable components of a web application is an important activity to allocate verification resources effectively. Most of the studies proposed several vulnerability prediction models based on private and public datasets so far. In this study, we aimed to design and implement a software vulnerability prediction web service which will be hosted on Azure cloud computing platform. We investigated several machine learning techniques which exist in Azure Machine Learning Studio environment and observed that the best overall performance on three datasets is achieved when Multi-Layer Perceptron method is applied. Software metrics values are received from a web form and sent to the vulnerability prediction web service. Later, prediction result is computed and shown on the web form to notify the testing expert. Training models were built on datasets which include vulnerability data from Drupal, Moodle, and PHPMyAdmin projects. Experimental results showed that Artificial Neural Networks is a good alternative to build a vulnerability prediction model and building a web service for vulnerability prediction purpose is a good approach for complex systems.

A cloud-based recommendation service using principle component analysis–scale-invariant feature transform algorithm

Cloud computing delivers resources such as software, data, storage and servers over the Internet; its adaptable infrastructure facilitates on-demand access of computational resources. There are many benefits of cloud computing such as being scalable, paying only for consumption, improving accessibility, limiting investment costs and being environmentally friendly. Thus, many organizations have already started applying this technology to improve organizational efficiency. In this study, we developed a cloud-based book recommendation service that uses a principle component analysis–scale-invariant feature transform (PCA-SIFT) feature detector algorithm to recommend book(s) based on a user-uploaded image of a book or collection of books. The high dimensionality of the image is reduced with the help of a principle component analysis (PCA) pre-processing technique. When the mobile application user takes a picture of a book or a collection of books, the system recognizes the image(s) and recommends similar books. The computational task is performed via the cloud infrastructure. Experimental results show the PCA-SIFT-based cloud recommendation service is promising; additionally, the application responds faster when the pre-processing technique is integrated. The proposed generic cloud-based recommendation system is flexible and highly adaptable to new environments.

Multi-tenant architectures in the cloud: A systematic mapping study

Unlike traditional web applications, cloud-based applications can provide services for large number of tenants using the same hardware and software by implementing multi-tenant architectures. 407 papers have been examined by using systematic mapping method to evaluate the publications related to this architecture, which have been used increasingly in the Software-as-a-Service (SaaS) model. The goal of the study is to determine which storage strategies were used most, which criteria were taken into account in selecting the preferred storage strategy, and the most searched topics under multi-tenant architecture model. Primary researches which are conforming to specified review rules have been obtained from electronic databases (IEEE, ACM, Springer, ScienceDirect, Wiley, Scopus) and classified by research topic and content.

DESIGN OF SPEAKER INDEPENDENT TURKISH SPEECH CONTROL SYSTEM IN NOISY VEHICLE ENVIRONMENT

This paper represents the results of speaker independent Turkish speech control experiments in vehicle environments. Almost there are unlimited numbers of words in the Turkish language, it is impossible to use words as the basic unit in the system. In this case, assuming to use the sub-units is more efficient, we choose to work with context-dependent (different vocabulary context in training and test) tri-phones as the smallest units and modelled with Hidden Markov Model (HMM). Also to limit the complexity of the tri-phone models decision tree based state clustering is used. Proposed speech recognition system is able to recognize the speech waveform by translating the speech waveform into a set of feature vectors using Mel Frequency Cepstral Coefficients (MFCC) that are the typical recognition parameters. In experiments on hands-free in-car speech recognition with the microphone far from the talker, this framework is found to be effective in terms of recognition rate and computational cost under various driving speeds. To examine the recognition performance of the system, tri-phone based acoustic model is tested with different decision tree pruning factors. System experiments results had shown that the word correctness of system tests is between 50-86 percent. This ratio is fulfills the safetycritical operations requirements of a vehicle.