Patient-adapted and inter-patient ecg classification using neural network and gradient boosting

Heart disease diagnosis is an important non-invasive technique. Therefore, there exists an effort to increase the accuracy of arrhythmia classification based on ECG signals. In this work, we present a novel approach of heart arrhythmia detection. The model consists of two parts. The first part extracts important features from raw ECG signal using Auto-Encoder Neural Network. Extracted features obtained by Auto-Encoder represent an input for the second part of the model, the Gradient Boosting and Feedforward Neural Network classifiers. For comparison purposes, we evaluated our approach by using MIT-BIH ECG database and also following recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for ECG class labeling. We divided our experiment into two scenarios. The first scenario represents the classification task for the patient-adapted paradigm and the second one was dedicated to the inter-patient paradigm. We compared the measured results to the state-of-the-art methods and it shows that our method outperforms the state-of-the art methods in the Ventricular Ectopic (VEB) class for both paradigms and Supraventricular Ectopic (SVEB) class in the inter-patient paradigm.Web of Science28325424

An efficient parallel method for mining frequent closed sequential patterns

Mining frequent closed sequential pattern (FCSPs) has attracted a great deal of research attention, because it is an important task in sequences mining. In recently, many studies have focused on mining frequent closed sequential patterns because, such patterns have proved to be more efficient and compact than frequent sequential patterns. Information can be fully extracted from frequent closed sequential patterns. In this paper, we propose an efficient parallel approach called parallel dynamic bit vector frequent closed sequential patterns (pDBV-FCSP) using multi-core processor architecture for mining FCSPs from large databases. The pDBV-FCSP divides the search space to reduce the required storage space and performs closure checking of prefix sequences early to reduce execution time for mining frequent closed sequential patterns. This approach overcomes the problems of parallel mining such as overhead of communication, synchronization, and data replication. It also solves the load balance issues of the workload between the processors with a dynamic mechanism that re-distributes the work, when some processes are out of work to minimize the idle CPU time.Web of Science5174021739

ACO for continuous function optimization: a performance analysis

The performance of the meta-heuristic algorithms often depends on their parameter settings. Appropriate tuning of the underlying parameters can drastically improve the performance of a meta-heuristic. The Ant Colony Optimization (ACO), a population based meta-heuristic algorithm inspired by the foraging behavior of the ants, is no different. Fundamentally, the ACO depends on the construction of new solutions, variable by variable basis using Gaussian sampling of the selected variables from an archive of solutions. A comprehensive performance analysis of the underlying parameters such as: selection strategy, distance measure metric and pheromone evaporation rate of the ACO suggests that the Roulette Wheel Selection strategy enhances the performance of the ACO due to its ability to provide non-uniformity and adequate diversity in the selection of a solution. On the other hand, the Squared Euclidean distance-measure metric offers better performance than other distance-measure metrics. It is observed from the analysis that the ACO is sensitive towards the evaporation rate. Experimental analysis between classical ACO and other meta-heuristic suggested that the performance of the well-tuned ACO surpasses its counterparts

Consumers’ Acceptance and Use of Information and Communications Technology: A UTAUT and Flow Based Theoretical Model

The world has changed a lot in the past years. The rapid advances in technology and the changing of the communication channels have changed the way people work and, for many, where do they work from. The Internet and mobile technology, the two most dynamic technological forces in modern information and communications technology (ICT) are converging into one ubiquitous mobile Internet service, which will change our way of both doing business and dealing with our daily routine activities. As the use of ICT expands globally, there is need for further research into cultural aspects and implications of ICT. The acceptance of Information Technology (IT) has become a fundamental part of the research plan for most organizations (Igbaria 1993). In IT research, numerous theories are used to understand users’ adoption of new technologies. Various models were developed including the Technology Acceptance Model, Theory of Reasoned Action, Theory of Planned Behavior, and recently, the Unified Theory of Acceptance and Use of Technology. Each of these models has sought to identify the factors which influence a citizen’s intention or actual use of information technology. Drawing on the UTAUT model and Flow Theory, this research composes a new hybrid theoretical framework to identify the factors affecting the acceptance and use of Mobile Internet -as an ICT application- in a consumer context. The proposed model incorporates eight constructs: Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influences, Perceived Value, Perceived Playfulness, Attention Focus, and Behavioral intention. Data collected online from 238 respondents in Saudi Arabia were tested against the research model, using the structural equation modeling approach. The proposed model was mostly supported by the empirical data. The findings of this study provide several crucial implications for ICT and, in particular, mobile Internet service practitioners and researcher

Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866

Congruences in ordered sets and LU compatible equivalences

summary:A concept of equivalence preserving upper and lower bounds in a poset $P$ is introduced. If $P$ is a lattice, this concept coincides with the notion of lattice congruence

Optimization of rules selection for robot soccer strategies

Mobile embedded systems belong among the typical applications of distributed systems control in realtime. An example of a mobile control system is a robotic system. The proposal and realization of such a distributed control system represents a demanding and complex task for real-time control. In the process of robot soccer game applications, extensive data is accumulated. The reduction of such data is a possible win in a game strategy. The main topic of this article is a description of an efficient method for rule selection from a strategy. The proposed algorithm is based on the geometric representation of rules. A described problem and a proposed solution can be applied to other areas dealing with effective searching of rules in structures that also represent coordinates of the real world. Because this construed strategy describes a real space and the stores physical coordinates of real objects, our method can be used in strategic planning in the real world where we know the geographical positions of objects.Web of Science11art. no. 1

A GaN-based wireless power and information transmission method using Dual-frequency Programmed Harmonic Modulation

Information transmission is often required in power transfer to implement control. In this paper, a Dual-Frequency Programmed Harmonic Modulation (DFPHM) method is proposed to transfer two frequencies carrying power and information with the single converter via a common inductive coil. The proposed method reduces the number of injection tightly coupled transformers used to transmit information, thereby simplifying the system structure and improving reliability. The performances of power and information transmission, and the method of information modulation and demodulation, as well as the principles of the control, are analyzed in detail. Then a simulation model is set up to verify the feasibility of the method. In addition, an experiment platform is established to verify that the single converter can transfer the power and information simultaneously via a common inductive coil without using tightly coupled transformers.Web of Science8498564984

Acceleration of particle swarm optimization with AVX instructions

Parallel implementations of algorithms are usually compared with single-core CPU performance. The advantage of multicore vector processors decreases the performance gap between GPU and CPU computation, as shown in many recent pieces of research. With the AVX-512 instruction set, there will be another performance boost for CPU computations. The availability of parallel code running on CPUs made them much easier and more accessible than GPUs. This article compares the performances of parallel implementations of the particle swarm optimization algorithm. The code was written in C++, and we used various techniques to obtain parallel execution through Advanced Vector Extensions. We present the performance on various benchmark functions and different problem configurations. The article describes and compares the performance boost gained from parallel execution on CPU, along with advantages and disadvantages of parallelization techniques.Web of Science132art. no. 73