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Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma Taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

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Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 257-271页

作者： Christopher Wiedeman Peter Lorraine Ge Wang Richard Do Amber Simpson Jacob Peoples Bruno De Man Department of Electrical and Computer Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA GE Research-Healthcare NiskayunaNY 12309USA Department of Biomedical Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Radiology Memorial Sloan Kettering Cancer CenterNew YorkNY 10065USA Biomedical Computing and Informatics Queen’s UniversityKingstonON K7L 3N6Canada

Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes,especially as the disease progresses into liver *** tomography(CT)is a frontline tool for this task;however,the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction *** hypothesized that image reconstruction with a highfrequency kernel could result in a better characterization of liver metastases features via deep neural *** kernel produces images that appear noisier but preserve more sinogram information.A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver *** pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases,and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using *** of 10,000 liver metastases were generated,scanned,and reconstructed using either standard or high-frequency *** data were used to train and validate deep neural networks to recover crafted metastases characteristics,such as internal heterogeneity,edge sharpness,and edge fractal *** the absence of noise,models scored,on average,12.2%(α=0.012)and 7.5%(α=0.049)lower squared error for characterizing edge sharpness and fractal dimension,respectively,when using high-frequency reconstructions compared to ***,the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical *** results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization,provided that noise is *** work should investigate the informationpreserving kernels in datasets with clinical labels.

关键词： Radiomics Deep learning Computed tomography Colorectal liver metastases Virtual clinical trials Image reconstruction

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Differentially private data publication with multi‐level data utility

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High-Confidence Computing 2022年第2期2卷 61-68页

作者： Honglu Jiang S.M.Sarwar Haotian Yu Sheikh Ariful Islam Department of Informatics and Engineering Systems The University of Texas Rio Grande ValleyBrownsvilleUSA Department of Computer science The University of Texas Rio Grande ValleyEdinburgUSA Department of Data Analytics The George Washington UniversityWashingtonUSA

Conventional private data publication mechanisms aim to retain as much data utility as possible while ensuring sufficient privacy protection on sensitive *** data publication schemes implicitly assume that all data analysts and users have the same data access privilege ***,it is not applicable for the scenario that data users often have different levels of access to the same data,or different requirements of data *** multi-level privacy requirements for different authorization levels pose new challenges for private data *** PPDP mechanisms only publish one perturbed and private data copy satisfying some privacy guarantee to provide relatively accurate analysis *** find a good tradeoffbetween privacy preservation level and data utility itself is a hard problem,let alone achieving multi-level data utility on this *** this paper,we address this challenge in proposing a novel framework of data publication with compressive sensing supporting multi-level utility-privacy tradeoffs,which provides differential ***,we resort to compressive sensing(CS)method to project a n-dimensional vector representation of users’data to a lower m-dimensional space,and then add deliberately designed noise to satisfy differential ***,we selectively obfuscate the measurement vector under compressive sensing by adding linearly encoded noise,and provide different data reconstruction algorithms for users with different authorization *** experimental results demonstrate that ML-DPCS yields multi-level of data utility for specific users at different authorization levels.

关键词： Differential privacy Compressive sensing Multi-level utility Privacy preservation

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A Hierarchical Architectural Model for IoT End-User Service Composition 19

A Hierarchical Architectural Model for IoT End-User Service ...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Montori, Federico Armandi, Vincenzo Bedogni, Luca University of Bologna Department of Computer Science and Engineering Italy Italy University of Modena and Reggio Emilia Department of Physics Informatics and Mathematics Italy

ISBN: (纸本)9781665431620

The Internet of Things is permeating our everyday life and the number of sensors and actuators around us is increasing at an exponential pace. Data generated by such heterogeneous devices is hard to organize, therefore, in pervasive scenarios like Smart Cities, there is an increasing need for service infrastructures that play the role of intermediary between citizen and things. Often, end users call for customized services that are tailored to their specific need rather than general-purpose ones. For this reason, in this paper we propose a service architecture based on End-User Service Composition (EUSC), through which individuals can aggregate primary sources of data to compose services. Furthermore, we investigate the requirements for service reusability and inherently leverage a hierarchical paradigm by introducing a specific class of composition languages. Finally, we show our Proof-of-Concept (PoC) middleware implementation, namely SenSquare, to show how this is achievable in a real deployment through visual programming, specifically illustrating how hierarchization is achieved. © 2022 IEEE.

关键词： Quality of service

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An efficient decentralized approach for mmWave MIMO Channel Estimation 4

An efficient decentralized approach for mmWave MIMO Channel ...

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4th International Balkan Conference on Communications and Networking, BalkanCom 2021

作者： Trigka, Maria Mavrokefalidis, Christos Berberidis, Kostas University of Patras Department of Computer Engineering and Informatics Greece

ISBN: (纸本)9781665402583

In the physical propagation environment, the channel matrices of neighboring users exhibit a joint sparsity structure due to the shared scatterers at the Base Station (BS) side. Based on this observation, we consider a multi-user Multiple-Input Multiple-Output (MIMO) system where the sparse channel estimation problem is tackled via an efficient fully distributed approach based on compressive sensing (CS). The involved users cooperatively estimate the sparsity support sets of the involved channels before individually estimate the channel coefficients, assuming that global and common sparsity support subsets exist. The performance of the proposed algorithm, named Weighted Distributed Simultaneous Orthogonal Matching Pursuit (WDiSOMP), is compared to Distributed Simultaneous Orthogonal Matching Pursuit (DiSOMP), local Simultaneous Orthogonal Matching Pursuit (SOMP) and a centralized solution based on SOMP in terms of the channel estimation under a multi-tasking scenario. © 2021 IEEE.

关键词： Channel estimation

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Penetration of Distributed Generation in Microgrids: Characteristics and Challenges

Penetration of Distributed Generation in Microgrids: Charact...

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International Universities Power engineering Conference (UPEC)

作者： Kypros Tillyros Nicholas G. Christofides Michael Komodromos Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus

The exploitation of sustainable distributed energy sources is associated with the energy resilience and power optimisation of power grids. This study divides the energy sector of urban areas into isolated and non-isolated topologies and attempts to review the application of microgrids within the two. In addition, it investigates methods to optimise power quality with the integration of multi-renewable generation to the system and discusses on the feasibility towards islanded operating microgrids. The proposed work is a result of a careful evaluation of the current literature on the topic. Consequently, the outcome of the given study is anticipated to facilitate future work on Microgrid implementation functioning in islanded mode.

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Financial Market Prediction Using Deep Neural Networks with Hardware Acceleration 12

Financial Market Prediction Using Deep Neural Networks with ...

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12th International Conference on computer and Knowledge engineering, ICCKE 2022

作者： Foroughi, Mohammad Hadi Bagherzadeh, Ali Rahmati, Dara Foroughi, Mehdi Gorgin, Saeid Shahid Beheshti University Computer Science and Engineering Department Tehran Iran University of Oslo Department of Informatics Oslo Norway Korea Republic of

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

Financial market prediction has long been a hard-To-solve problem. Recently many machine learning and deep learning approaches have been taken into account to predict various properties of financial markets, namely volatility, price, and trend prediction. This paper proposes a novel multi-Timeframe 1-D convolutional neural network to predict market movements. The model is trained with actual foreign exchange data in an offline setting. The trained model is then deployed on a Xilinx Zedboard Zynq-7000 SoC field programmable gate array (FPGA) to accelerate the calculation and also reducing the power consumption compared to the known approaches. We evaluate the performance of our model on test-set data new to the model;the results show that the proposed model outperforms commonly used models such as long short term memory (LSTM), multi-layer perceptron (MLP), and single-Time-scale convolutional neural networks (CNN) with an accuracy of 76.60%. Although deploying the model on FPGA causes a slight accuracy reduction of 2.56%, the evaluations show that the accuracy of the hardware model is still higher than other models. Moreover, according to the evaluation results, performing the inference phase on hardware dramatically reduces latency which is a vital feature for this kind of applications with a factor of roughly 380. Power consumption is also compared and reduced when compared to an Intel server with Xeon ES-2658 processor. © 2022 IEEE.

关键词： Multilayer neural networks

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Influence Maximization in Dynamic Social Networks Under Partially Observable Environments

Influence Maximization in Dynamic Social Networks Under Part...

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computer Science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Gkolfo I. Smani Vasileios Megalooikonomou Department of Computer Engineering and Informatics University of Patras Patras Greece

Influence and influence diffusion have been studied widely in social networks. Influence maximization is the problem of detecting a set of influential nodes in a social network, which represents relationships among individuals. Although, most of the existing works on this task focus on static networks, in this paper we study the problem of influence maximization in dynamic social networks where the changes that occur over time can be observed by periodically probing nodes to update their connections. The goal of this work is to probe a subset of nodes in a social network so that the actual influence diffusion process in the network can be best uncovered with the probing nodes. We propose three algorithms, MaxC, MaxDC and MaxT, to approximate the optimal solution with probing nodes, which achieve improvement on estimating the number of activated nodes over state-of-the-art-method by 2.88%, 4.95% and 5.39%, respectively.

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A Comprehensive Review of Redirected Walking Techniques:Taxonomy,Methods,and Future Directions

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Journal of computer Science & Technology 2022年第3期37卷 561-583页

作者： Yi-Jun Li Frank Steinicke Miao Wang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Department of Informatics Universität HamburgHamburg 20146Germany Peng Cheng Laboratory Shenzhen 518055China

Virtual reality(VR)allows users to explore and experience a computer-simulated virtual environment so that VR users can be immersed in a totally artificial virtual world and interact with arbitrary virtual ***,the limited physical tracking space usually restricts the exploration of large virtual spaces,and VR users have to use special locomotion techniques to move from one location to *** these techniques,redirected walking(RDW)is one of the most natural locomotion techniques to solve the problem based on near-natural walking *** core idea of the RDW technique is to imperceptibly guide users on virtual paths,which might vary from the paths they physically walk in the real *** a similar way,some RDW algorithms imperceptibly change the structure and layout of the virtual environment such that the virtual environment fits into the tracking *** this survey,we first present a taxonomy of existing RDW *** on this taxonomy,we compare and analyze both contributions and shortcomings of the existing methods in detail,and find view manipulation methods offer satisfactory visual effect but the experience can be interrupted when users reach the physical boundaries,while virtual environment manipulation methods can provide users with consistent movement but have limited application ***,we discuss possible future research directions,indicating combining artificial intelligence with this area will be effective and intriguing.

关键词： virtual reality locomotion redirected walking

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Performance Analysis of Resampling and Ensemble Learning Methods on Diabetes Detection as Imbalanced Dataset 5

Performance Analysis of Resampling and Ensemble Learning Met...

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5th International Conference on Vocational Education and Electrical engineering, ICVEE 2022

作者： Sari, Fiqey Indriati Eka Edlim, Frederick William Ramadhan, Fitrah Arie Muhtadin Navastara, Dini Adni Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665475815

ISBN: (纸本)9781665475815

Early detection of diabetes is essential to reducing a high mortality rate. Early detection can be made by studying the possibility of diabetes from the variables obtained in the data of diabetes patients. How to diagnose a patient with medical data becomes a challenge because these are usually imbalanced, where negative cases severely outnumber positive cases. For preprocessing the imbalanced data, this paper designs an algorithm using resampling techniques combined with an ensemble learning algorithm. There are some oversampling techniques ADASYN, ROS, and SMOTE. Whereas, the undersampling techniques are RUS, Tomek, and ENN. The combined techniques like SMOTE-ENN and SMOTE-Tomek are also used to handle highly imbalanced dataset diabetes. Then, the ensemble learning algorithm that is used is Random Forest, Bagging, AdaBoost, and XGBoost. Based on the experimental results, the best performance is using SMOTE-ENN with AdaBoost, with a recall score of 0.7330 even though the F1-Score of this model is 0.6459. AdaBoost Classifier also has good and stable results with various types of resampling. By using SMOTE-ENN, the recall score of the model increased by 0.1819 and the F1 score decreased by 0.2000 from the original model result. The higher sensitivity/recall is more important in medical diagnoses to correctly identify patients with disease than the F1 Score. © 2022 IEEE.

关键词： Learning systems

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