Ahmet Murat Ozbayoglu

Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey

Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, “intelligence” becomes a focal point in IoT. Since data now becomes “big data,” understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding “context,” or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called “context-aware computing,” and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.

A Multi-agent System Model for Partner Selection Process in Virtual Enterprise☆

Abstract Virtual Enterprise (VE) is a collaboration model between multiple business partners in a value chain. VE information system deals with highly dynamic information from heterogeneous data sources. In order to manage and store dynamic VE information in the database, an ontology based VE model has been developed. To select winner enterprises in VE, a Multi Agent System (MAS) has been developed. Communication and data transition among agents and system entities are based on defined rules in VE ontology model. One of the most important contributions of agents in VE system is in partner selection step of VE formation phase. In this step several agents with different goals and strategies are collaborating and competing each other to win the negotiation procedure or maximize the profit for their assigned enterprise. Different strategies are developed for the agents depending on their appetite for winning the auction against maximizing the profit. Several simulations were run and the results are stored. These results are fed into a neural network in order to predict which enterprise will win the auction and what will be the profit margin. The motivation is to provide a forecasting agent for the customers about the outcomes of the auctions so that they can plan ahead and take the necessary action. Early results indicate such simulated multi-agent VE formations can be used in real systems.

Deep learning of micro-Doppler features for aided and unaided gait recognition

Remote health monitoring is a topic that has gained increased interest as a way to improve the quality and reduce costs of health care, especially for the elderly. Falling is one of the leading causes for injury and death among the elderly, and gait recognition can be used to detect and monitor neuromuscular diseases as well as emergency events such as heart attack and seizures. In this work, the potential for radar to discriminate a large number of classes of human aided and unaided motion is demonstrated. Deep learning of micro-Doppler features is used with a 3-layer auto-encoder structure to achieve 89% correct classification, a 17% improvement in performance over the benchmark support vector machine classifier supplied with 127 pre-defined features.

Developing a two level options trading strategy based on option pair optimization of spread strategies with evolutionary algorithms

In this study, a two level options trading strategy is modelled and optimized with Genetic Algorithms and Particle Swarm Optimization for profit maximization. In the first level, the trend is found and in the second level, options trading strategies for the particular trend are determined. The strike prices and expiration dates of the traded options are optimized and tested on 5 different Exchange Traded Funds (ETFs) (DIA, IWM, SPY, XLE, XLF). The performance of the proposed model is compared with Buy and Hold and commonly used technical analysis indicators and the results indicate using optimized options increased the overall profit with less drawdown risk.

Evaluation of Partner Companies Based on Fuzzy Inference System for Establishing Virtual Enterprise Consortium

Virtual Enterprise (VE) is one of the growing trends in agile manufacturing concepts. Under this platform companies with different skills and core competences are cooperate with each other in order to accomplish a manufacturing goal. Success of VE, as a consortium, highly depends on the success of its partners. So it is very important to choose the most appropriate companies to enroll in VE. In this study a Fuzzy Inference System (FIS) based approach is developed to evaluate and select the potential enterprises. The evaluation is conducted based on four main criteria; unit price, delivery time, quality and past performance. These criteria are considered as inputs of FIS and specific membership functions are designed for each. By applying fuzzy rules the output of the model, partnership chance, is calculated. In the end, the trustworthy of the model is tested and verified by comparing it with fuzzy-TOPSIS technique providing a sample.

An Experimental and Numerical Study of Two-phase Flow in Horizontal Eccentric Annuli

The aerated fluids have a potential to increase rate of penetration, minimize formation damage, minimize lost circulation, reduce drill pipe sticking, and, therefore, assist in improving the productivity. The technology of drilling using aerated fluids in the area of offshore drilling is very common. The use of compressible drilling fluids in offshore technology has found applications in old depleted reservoirs and in the new fields with special drilling problems. However, the drilling performed with gas-liquid mixture, calculating the pressure losses and the performance of cutting transportation is more difficult than single-phase fluid due to the characteristics of multi-phase fluid flow. In case configured drilling is directional or horizontal, these types of calculations are becoming more difficult depending on the slope of the wells. Both hydraulic behavior and mechanism of cutting transportation of the drilling fluids formed by gas-liquid mixture are not fully understood yet, especially there is a large uncertainty in selection of most appropriate flow regarding two phases. In this study, gas-liquid flow inside horizontal eccentric annulus is simulated using an Eulerian-Eulerian computational fluid dynamics model for two-phase flow patterns in an annulus, i.e., dispersed bubble, dispersed annular, plug, slug, wavy annular. A flow loop was constructed in order to conduct experiments using air-water mixtures for various in-situ air and water flow velocities. A digital high speed camera is used for recording each test dynamically for identification of the liquid holdup and flow patterns.

Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach

Abstract Computational intelligence techniques for financial trading systems have always been quite popular. In the last decade, deep learning models start getting more attention, especially within the image processing community. In this study, we propose a novel algorithmic trading model CNN-TA using a 2-D convolutional neural network based on image processing properties. In order to convert financial time series into 2-D images, 15 different technical indicators each with different parameter selections are utilized. Each indicator instance generates data for a 15 day period. As a result, 15 × 15 sized 2-D images are constructed. Each image is then labeled as Buy, Sell or Hold depending on the hills and valleys of the original time series. The results indicate that when compared with the Buy & Hold Strategy and other common trading systems over a long out-of-sample period, the trained model provides better results for stocks and ETFs.

Opinion mining of microblog texts on Hadoop ecosystem

Opinion mining started getting more traction due to the increasing popularity of Twitter and similar social network platforms that are producing fast and real-time responses to social events. It is a very challenging area since it is difficult, if not impossible, to identify general public sentiment towards events, entities, etc., using opinion mining techniques over huge numbers of tweets and messages automatically. In this study we present our opinion mining techniques on tweet data with early results. We apply sentiment scoring and clustering algorithms using Hadoop ecosystem for parallel processing. We classify tweets by tagging them as positive, negative, and neutral as a result.

Partner Selection in Formation of Virtual Enterprises using Fuzzy Logic

Virtual Enterprise (VE) is a temporary cooperation among independent enterprises to build up a dynamic collaboration framework for manufacturing. One of the most important steps to construct a successful VE is to select the most qualified partners to take role in the project. This paper is a survey of ranking the volunteer companies with respect to four evaluation criteria, proposed unit price, delivery time, quality and enterprises’ past performance. Fuzzy logic method is proposed to deal with these four conflicting criteria, considered as input variables of the model. As each criterion is different in nature with the other criterion, various membership functions are used to fuzzify the input values. The next step is to construct the logical fuzzy rules combining the inputs to conclude the output. Mamdani’s approach is adopted to evaluate the output in this Fuzzy Inference System. The result of the model is the partnership chance of each partner to participate in VE. A partner with highest partnership chance will be the winner of the negotiation. Implementation of this model to the illustrative example of a partner selection problem in virtual enterprise and comparing it with fuzzy-TOPSIS approach verifies the feasibility of the proposed approach and the computational results are satisfactory.