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In the electricity sector, new sides have emerged with the development of technology and the increasing the electric energy need. Today, electricity has become a product that is bought and sold in the market environment. Forecasting which is the first step of plans and planning have become much more important and have been made mandatory for the market participants by energy market regulators. In...
This paper presents a novel frame-pair based method for visual object tracking. Instead of adopting two-stream Convolutional Neural Networks (CNNs) to represent each frame, we stack frame pairs as the input, resulting in a single-stream CNN tracker with much fewer parameters. The proposed tracker can learn generic motion patterns of objects with much less annotated videos than previous methods. Besides,...
Solvents are used in a large number of industries especially in cleaning and cosmetic. Solvents are known to be harmful to human health. Classification of solvent in a product is important to determine the level of hazard that can people faced. In this study, three different solvents, methanol, acetone, and chloroform, are used to obtain binary gas mixtures in a laboratory environment. A gas sensor...
Passwords are frequently used in data encryption and user authentication. Since people incline to choose meaningful words or numbers as their passwords, lots of passwords are easy to guess. This paper introduces a password guessing method based on Long Short-Term Memory recurrent neural networks. After training our LSTM neural network with 30 million passwords from leaked Rockyou dataset, the generated...
Epileptic seizure source identification involves neurologists combing through a substantial amount of data manually, which sometimes takes weeks per patient. This paper presents a methodology for minimizing the amount of data a neurologist has to analyze to identify the seizure focus. The method keeps the neurologist as the final decision maker and aids in the decision making process. It has to be...
Reconfigurable manufacturing systems are susceptible to disturbances because of the characteristics associated with changeover of machine configuration and functionality. An Artificial Neural Network Driven Decision-Making System can mitigate these disturbances, if applied with extensive knowledge of the manufacturing system. This paper introduces a new concept into the paradigm of agile manufacturing...
This Paper reveals the information about Deep Neural Network (DNN) and concept of deep learning in field of natural language processing i.e. machine translation. Now day's DNN is playing major role in machine leaning technics. Recursive recurrent neural network (R2NN) is a best technic for machine learning. It is the combination of recurrent neural network and recursive neural network (such as Recursive...
In the last decade, numerous fake websites have been developed on the World Wide Web to mimic trusted websites, with the aim of stealing financial assets from users and organizations. This form of online attack is called phishing, and it has cost the online community and the various stakeholders hundreds of million Dollars. Therefore, effective counter measures that can accurately detect phishing...
Within the complex driving environment, progress in autonomous vehicles is supported by advances in sensing and data fusion. Safe and robust autonomous driving can only be guaranteed provided that vehicles and infrastructure are fully aware of the driving scenario. This paper proposes a methodology for feature uncertainty prediction for sensor fusion by generating neural network surrogate models directly...
The aim of this study is to develop a new method to distinguish between gait patterns of anterior cruciate ligament (ACL) deficient knees and healthy controls with bilateral ACL-intact knees via deterministic learning. The classification approach consists of two phases: a training phase and a classification phase. In the training phase, gait features representing gait dynamics, including knee rotations...
Early detection of small faults in closed-loop systems is a challenging issue in the fault diagnosis literature. The effect of faults in closed-loop systems will be obscured by a robust feedback control, especially when the controller is coupled with nonlinear uncertainty. In this paper, an approach for rapid detection for small faults in a class of closed-loop uncertain systems is proposed based...
Visual tracking is a significant but challenging field in computer vision. Although considerable progress has been made in recent years, robust tracking in complicated scenes remains an open problem. Trackers get confused easily when similar objects appear or heavy clutter occurs due to indistinguishable features. In this work, a more effective feature extraction method based on convolutional neural...
Despite recent rapid advances and successful large-scale application of deep Convolutional Neural Networks (CNNs) using image, video, sound, text and time-series data, its adoption within the oil and gas industry in particular have been sparse. In this paper, we initially present an overview of opportunities for deep CNN methods within oil and gas industry, followed by details on a novel development...
Web services evolve over time to fix bugs or update and add new features. However, the design of the Web service's interface may become more complex when aggregating many unrelated operations in terms of context and functionalities. A possible solution is to refactor the Web services interface into different modules that help the user quickly identifying relevant operations. The most challenging issue...
Many noninvasive continuous blood pressure measurements using photoplethysmography (PPG) are still inadequate in terms of accuracy and stability, which hinders the practical application of this method. This paper proposes a model based on ensemble method for BP estimation using PPG. A number of blood pressure calculation base-models is built on the same training data. These base-models are used to...
Face detection in unconstrained environments is a challenging problem due to partial occlusions with pose variations. Existing partial occluded face detection methods require training several models, computing hand-crafted features, or both. In this paper, our contributions are two-fold. First, we propose our Large-Scale Deep Learning (LSDL), a method that requires a single Convolutional Neural Network...
Sentiment analysis, also known as opinion mining, seeks to figure out points of view from documents. Sentiment classification is a specific task of sentiment analysis that divides documents into positive and negative sentiment polarities according to the attitudes expressed. Feature extraction is a significant part of sentiment classification. Traditional feature extraction methods mine statistical...
Wind speed forecasting has drawn a lot of research interests around the globe as it plays a key role in wind power plant operation. Accurate wind speed forecasting is vital for the integration of wind energy conversion system into existing electric power grids. The important factor of wind speed forecast is the choice of accurate prediction algorithm. Support Vector Machine Regression Model (SVM-R),...
In recent years, the strong growth in solar power generation industries is requiring an increasing need to predict the profile of solar power production over the day, in order to develop high efficient and optimized stand-alone and grid connected photovoltaic systems. Moreover, the opportunities offered by battery energy storage systems coupled with PV systems, require the load power to be forecasted...
The class imbalance problem occurs when instances in one class are more than that in another. It has been reported to severely hinder classification performance of many traditional classification algorithms and many researchers have paid a great deal of attention to this field. Different kinds of methods have been pro-posed to solve the problem these years, such as resampling methods, integrated learning...
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