• Journal of Communications and Networks
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• v.18 no.3
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• pp.523-530
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• 2016

We use both theoretical analysis and simulations to study the impact of crosspoint-queued (CQ) buffer size on CQ switch throughput and delay performance under different traffic models, input loads, and scheduling algorithms. In this paper, we present the following. 1) We prove the stability of CQ switch using any work-conserving scheduling algorithm. 2) We present an exact closed-form formula for the CQ switch throughput and a non-closed-form but convergent formula for its delay using static non-work-conserving random scheduling algorithms with any given buffer size under independent Bernoulli traffic. 3) We show that the above results can serve as a conservative guide on deciding the required buffer size in pure CQ switches using work-conserving algorithms such as the random scheduling, under independent Bernoulli traffic. 4) Furthermore, our simulation results under real-trace traffic show that simple round-robin and random work-conserving algorithms can achieve quite good throughput and delay performance with a feasible crosspoint buffer size. Our work reveals the impact of buffer size on the CQ switch performance and provides a theoretical guide on designing the buffer size in pure CQ switch, which is an important step toward building ultra-high-speed switch fabrics.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• Nonlinear Functional Analysis and Applications
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• v.29 no.1
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• pp.131-164
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• 2024

In this research, we study a modified relaxed Tseng method with a single projection approach for solving common solution to a fixed point problem involving finite family of τ-demimetric operators and a quasi-monotone variational inequalities in real Hilbert spaces with alternating inertial extrapolation steps and adaptive non-monotonic step sizes. Under some appropriate conditions that are imposed on the parameters, the weak and linear convergence results of the proposed iterative scheme are established. Furthermore, we present some numerical examples and application of our proposed methods in comparison with other existing iterative methods. In order to show the practical applicability of our method to real word problems, we show that our algorithm has better restoration efficiency than many well known methods in image restoration problem. Our proposed iterative method generalizes and extends many existing methods in the literature.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.207-212
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• 2021

Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.343-351
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• 2022

A brain tumor forms when some tissue becomes old or damaged but does not die when it must, preventing new tissue from being born. Manually finding such masses in the brain by analyzing MRI images is challenging and time-consuming for experts. In this study, our main objective is to detect the brain's tumorous part, allowing rapid diagnosis to treat the primary disease instantly. With image processing techniques and deep learning prediction algorithms, our research makes a system capable of finding a tumor in MRI images of a brain automatically and accurately. Our tumor segmentation adopts the U-Net deep learning segmentation on the standard MICCAI BRATS 2018 dataset, which has MRI images with different modalities. The proposed approach was evaluated and achieved Dice Coefficients of 0.9795, 0.9855, 0.9793, and 0.9950 across several test datasets. These results show that the proposed system achieves excellent segmentation of tumors in MRIs using deep learning techniques such as the U-Net algorithm.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1936-1942
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• 2016

This paper presents a mobile color-compensating application using a RGB color compensation algorithm. The proposed application enables color vision deficiencies to differentiate images which cannot be distinguished by them. The suggested mobile application has two functions: converting images taken by camera and converting contents in the device screen. The proposed application is computationally inexpensive, since it does not require color space transformation. Simulation results show that the proposed application enables color vision deficiencies to receive information expressed by colors such as subway line maps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1733-1750
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• 2011

In the early stages of a software project, it is critical to understand the needs of the customers and elicit their customer requirements. Various requirements elicitation methods have been proposed. However, existing methods still have the limitations such as a limited number of target customers, limited expression of customers' opinions, and difficulty in collecting the customers' opinions continuously. A novel method for eliciting customer requirements is proposed by utilizing a social network service (SNS), which is a shared source of raw information of the customers' needs and opinions. The proposed method is validated to show its effectiveness in overcoming the limitations of existing methods.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3124-3137
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• 2022

We propose a spectral-efficient coordinated direct and relayed transmission (CDRT) scheme for a relay-assisted downlink system with two users. The proposed scheme is based on backscatter communication (BC) and non-orthogonal multiple access (NOMA) technique. With the proposed BC-NOMA-CDRT scheme, both users can receive one packet within one time slot. In contrast, in existing NOMA-CDRT schemes, the far user is only able to receive one packet in two time slots due to the half-duplex operation of the relay. We investigate the outage of the BC-NOMA-CDRT scheme, and derive the outage probability expressions in closed-form based on Gamma distribution approximation and Gaussian approximation. Numerical results show that the analytical results are accurate and the BC-NOMA-CDRT scheme outperforms the conventional NOMA-CDRT significantly.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.157-161
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• 2023

Reliability is one of the computable quality features of the software. To assess the reliability the software reliability growth models(SRGMS) are used at different test times based on statistical learning models. In all situations, Tradational time-based SRGMS may not be enough, and such models cannot recognize errors in small and medium sized applications.Numerous traditional reliability measures are used to test software errors during application development and testing. In the software testing and maintenance phase, however, new errors are taken into consideration in real time in order to decide the reliability estimate. In this article, we suggest using the Weibull model as a computational approach to eradicate the problem of software reliability modeling. In the suggested model, a new distribution model is suggested to improve the reliability estimation method. We compute the model developed and stabilize its efficiency with other popular software reliability growth models from the research publication. Our assessment results show that the proposed Model is worthier to S-shaped Yamada, Generalized Poisson, NHPP.