New probabilistic models for managing user’s locations in PCS networks

Personal communication service (PCS) networks enable mobile terminals (MTs) to communicate regardless of their locations. However, in order to guarantee an efficient service delivery, MTs’ real time locations should be perfectly managed. Location management (LM) includes two fundamental processes; registration and paging. The former is concerned with the reporting of the current locations of the MTs, while the latter is used to locate a MT. Both processes incur signaling cost across the wireless channel. The more the location updates, the less the paging signaling cost, thus there is a tradeoff in terms of signaling cost. Due to the scarcity of PCS wireless bandwidth, it is important to reduce that signaling cost. As The blanket paging in current mobile networks wastes a lot of wireless bandwidth, we focus on the subject of paging and try to reduce the paging signaling cost under a predefined delay bound. Our contribution can be summarized as: (i) establishing a family of probability based paging strategies; (ii) introducing a novel topology for PCS registration area, which is called the hot spot topology (HST); (iii) based on HST, a novel LM strategy, which is called “Flower Based Strategy” had been introduced. Experimental results had shown that that the proposed paging strategies substantially lower the signaling cost of paging compared to the blanket one with a negligible increase in paging delay.     

Prediction of artificial soil’s unconfined compression strength test using statistical analyses and artificial neural networks

Laboratory prediction of the unconfined compression strength (UCS) of cohesive soils is important to determine the shear strength properties. However, this study presents the application of different methods simple–multiple analysis and artificial neural networks for the prediction of the UCS from basic soil properties. Regression analysis and artificial neural networks prediction indicated that there exist acceptable correlations between soil properties and unconfined compression strength. Besides, artificial neural networks showed a higher performance than traditional statistical models for predicting UCS. Regression analysis and artificial neural network prediction indicated strong correlations (R² = 0.71–0.97) between basic soil properties and UCS. It has been shown that the correlation equations obtained by regression analyses are found to be reliable in practical situations.     

Predicting the customer’s opinion on amazon products using selective memory architecture-based convolutional neural network

The Journal of Supercomputing - Opinion mining and sentiment analysis are useful to extract subjective information out of bulk text documents. Predicting the customer’s opinion on amazon...     

Parkinson’s Disease tremor classification – A comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.     

Hybrid credit ranking intelligent system using expert system and artificial neural networks

The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank’s loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it’s important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert’s knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.     

Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks☆

Control charts pattern recognition is one of the most important tools in statistical process control to identify process problems. Unnatural patterns exhibited by such charts can be associated with certain assignable causes affecting the process. In this paper, multi-resolution wavelets analysis (MRWA) is used to extract distinct features for unnatural patterns by providing distinct time–frequency coefficients. A reduced set of parameters is derived from these coefficients and used as input to an artificial neural network (ANN) classifier. Results show that the performance of the proposed technique in classifying shift, trend and cyclic patterns is superior to that of ANN classifier, which operated on coded observed data.     

Early detection of Alzheimer’s disease using local binary pattern and convolutional neural network

Multimedia Tools and Applications - Alzheimer’s disease, a progressive and irreversible abnormality of the human brain impairs memory and thinking skills. Gradually, it will damage the...     

Tangible versus graphical user interfaces for robot programming: exploring cross-age children’s preferences

This study explores children’s opinions and preferences regarding two isomorphic user interfaces that can be used for introductory programming activities, a tangible and a graphical one. The first system (tangible) comprises 46 cube-shaped blocks that represent simple programming structures and can be interconnected to form the programming code. The second system (graphical) presents on-screen the same programming space to the user (icons similar in appearance and operation with the tangible blocks). These two operationally equivalent user interfaces were given to three children groups of different ages (5–6, 7–8 and 11–12 years) to program the behavior of a Lego NXT robot. Children in dyads were let to interact with both systems, and during the activity, data were collected regarding children’s first-sight preference, enjoyment and easiness-to-use. The quantitative and qualitative analysis followed indicated that the tangible interface was more attractive especially for girls, and it was more enjoyable and finally characterized as easier to use only by younger children who were less experienced with computers. On the contrary, for older (11–12 years old) children, the tangible even though was more enjoyable, it was not considered as the easiest-to-use user interface. Taking into account the lack of empirical evidences related to the tangible user interfaces, this study discusses not only the potential usability advantages but also the disadvantages of tangible user interfaces for children.     

Adaptive L_2 control of nonlinear systems using neural networks

An adaptive neural network controller is developed to achieve output-tracking of a class of nonlinear systems. The global L2 stability of the closed-loop system is established. The proposed control design overcomes the limitation of the conventional adaptive neural control design where the modeling error brought by neural networks is assumed to be bounded over a compact set. Moreover,the generalized matching conditions are also relaxed in the proposed L2 control design as the gains for the external disturbances entering the system are allowed to have unknown upper bounds.     

Adaptive L2 control of nonlinear systems using neural networks

An adaptive neural network controller is developed to achieve output-tracking of a class of nonlinear systems. The global L2 stability of the closed-loop system is established. The proposed control design overcomes the limitation of the conventional adaptive neural control design where the modeling error brought by neural networks is assumed to be bounded over a compact set.Moreover,the generalized matching conditions are also relaxed in the proposed L2 control design as the gains for the external disturbances entering the system are allowed to have unknown upper bounds.     

Rating prediction by exploring user’s preference and sentiment

With the development of e-commerce, shopping on-line is becoming more and more popular. The explosion of reviews have led to a serious problem, information overloading. How to mine user interest from these reviews and understand users’ preference is crucial for us. Traditional recommender systems mainly use structured data to mine user interest preference, such as product category, user’s tag, and the other social factors. In this paper, we firstly use LDA+Word2vec model to mine user interest. Then, we propose a social user sentimental measurement approach. At last, three factors, including user topic, user sentiment and interpersonal influence, are fused into a recommender system (RS) based on probabilistic matrix factorization. We conduct a series of experiments on Yelp dataset, and experimental results show the proposed approach outperforms the existing approaches.     

Fourier and wavelet descriptors for shape recognition using neural networks—a comparative study

This paper presents the application of three different types of neural networks to the 2-D pattern recognition on the basis of its shape. They include the multilayer perceptron (MLP), Kohonen self-organizing network and hybrid structure composed of the self-organizing layer and the MLP subnetwork connected in cascade. The recognition is based on the features extracted from the Fourier and wavelet transformations of the data, describing the shape of the pattern. Application of different neural network structures associated with different preprocessing of the data results in different accuracy of recognition and classification. The numerical experiments performed for the recognition of simulated shapes of the airplanes have shown the superiority of the wavelet preprocessing associated with the self-organizing neural network structure. The integration of the individual classifiers based on the weighted summation of the signals from the neural networks has been proposed and checked in numerical experiments.     

The use of decision tree induction and artificial neural networks for automatic diagnosis of Hashimoto’s disease

Methods for classification of ultrasound thyroid images have been presented. These methods allow us to classify examined patients as either sick or healthy. Decision tree induction and a multilayer perceptron neural network have been used to build classification models. Test results showed that the proposed methods can provide a starting point for building a support system in the process of medical diagnosis. Better accuracy of classifiers was achieved for the normalized images. We have also found that, under adopted assumptions, the results obtained for them were statistically significant in contrast to original images. The proposed methods allow us to separate a fairly large group of incorrectly classified cases. According to the authors, this group may contain features of the early stage of Hashimoto’s disease.     

Design and performance analysis of tracking controller for uncertain nonlinear composite system using neural networks

Based on high order dynamic neural network, this paper presents the tracking problem for uncertain nonlinear composite system, which contains external disturbance, whose nonlinearities are assumed to be unknown. A smooth controller is designed to guarantee a uniform ultimate boundedness property for the tracking error and all other signals in the dosed loop. Certain measures are utilized to test its performance. No a priori knowledge of an upper bound on the “optimal” weight and modeling error is required; the weights of neural networks are updated on-line. Numerical simulations performed on a simple example illustrate and clarify the approach.     

A study of mobile internet user’s service quality perceptions from a user’s utilitarian and hedonic value tendency perspectives

Although a few studies have focused on mobile value from the distinctive feature of a mobile technology perspective, limited attempts have been made from a mobile user’s value tendency perspective. In this study, building upon prior research on productivity-oriented and pleasure-oriented nature of systems, we categorize mobile values as having utilitarian and hedonic use. Based on these two values, we conceptualize types of tendency of mobile users’ application use namely utilitarian tendency and hedonic tendency. The goal of this study is to examine the relationships between mobile consumers’ value tendency and their perceptions of mobile Internet service quality in terms of three different mobile quality dimensions (i.e., connection quality, design quality, and information quality). In addition, drawing upon the “digital divide” literature, the relationships between mobile users’ personal dispositions (i.e., maturity and socio-economic status) and their mobile value tendency are also tested. The empirical results of the study, the interpretation of the results, research contributions, and limitations are discussed.     

Medical thermograms’ classification using deep transfer learning models and methods

Multimedia Tools and Applications - Infrared thermal imaging and deep learning provide intelligent monitoring systems that detect diseases in early phases. However, deep learning models require...     

Modeling and predicting students’ engagement behaviors using mixture Markov models

Knowledge and Information Systems - Students’ engagements reflect their level of involvement in an ongoing learning process which can be estimated through their interactions with a...