다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

An Introduction To Management Science Quantitative Approaches To Decision Making

High order fuzzy time series forecasting methods are more suitable than first order fuzzy time series forecasting methods in dealing with linguistic values. However, existing high order methods lack persuasiveness in dealing objectively with multiple – factor fuzzy time series, recurrent number of fuzzy relationships, and assigning weights to elements of fuzzy forecasting rules. In this paper, a novel two – factor high – order fuzzy time series forecasting method based on fuzzy C-means clustering and particle swarm optimization is proposed to resolve these drawbacks. Fuzzy C-means clustering is utilized in the fuzzification phase to objectively partition the universe of discourse and enable processing of multiple factors. Then, particle swarm optimization is utilized to assign optimal weights to elements of fuzzy forecasting rules. Daily average temperatures of Taipei and Taiwan Futures Exchange (TAIFEX) are used as benchmark data. Average forecasting error performance of 0.85% was obtained for Taipei Temperature forecast. Mean squared error performance of 199.57 was obtained for Taiwan Futures Exchange forecast. The forecasting results showed that the proposed method has higher forecasting performance than other existing methods.Keywords: fuzzy time series, fuzzy c-mean clustering, particle swarm optimization, forecasting, fuzzy relationships

/pdf/phytochemical-and-proximate-analysis-of-aspillia-kotschyi-58h35pvdct.pdf

Phytochemical and proximate analysis of aspillia kotschyi (sch.bipex, hochst) oliv

Based on the principle that the upgrading of any nation begins by raising the level of performance of its institutions that serve the community, including the Ministry of Healthcare and given the development in the field of technology, and the growing need to save the life of persons from different types of diseuses and determined the proteins that increase or prevent any diseuses, so it is found that the world has tended in recent years to intelligent data analysis techniques spatially deep neurocomputing in healthcare fields to predict high quality results in short time. The paper presents model to Codon-mRNA Prediction using Deep Optimal Neurocomputing Technique (DLSTM-DSN-WOA) and Multivariate Analysis. That model consists of five basic stages: The first stage is the process of collecting and preparing the data to make it in suitable form for the decision-making stage and included several steps, including the processing of missing values and condign the target, the second stage involved develop optimization algorithms called Whale Optimization Algorithm (WOA) to build optimal structure to one of the deep neurocomputing network (i.e., long short-term memory (LSTM)). That tool select after deep analysis achieve on optimization algorithms include PSO, BOA, WOA, COA, and FA; this analysis focus on determined for points: the main programming steps, main parameters, advantages, and disadvantages for each algorithm. . The WOA used to find the best structure of deep neurocomputing technique called LSTM that choose to develop after campier among multi techniques (i.e., Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bi-Directional Long Short-Term Memory (BiLSTM), and AlexNet, GoogleNet). That campier perform based on the programing steps and main parameters affect in each algorithm, because WOA as algorithm having many advantages and characteristics. The proposed model appears as pragmatic intelligent data analysis model for reduce the computation and time of handle huge real data. 

Codon-mRNA prediction using deep optimal neurocomputing technique (DLSTM-DSN-WOA) and multivariate analysis

Real-time video life streaming of events over a network continued to gain more popularity among the populace. However, there is need to ensure the judicious utilization of allocated bandwidth without compromising the Quality of Service (QoS) of the system. In this regard, this paper presents an approach based on spatio-temporal frame indexing that detects and eliminate redundancy within and across captured frame, prior transmission from the server to clients. The standard and local low motion videos were the two scenarios considered in evaluating the performance of the proposed algorithm. Results obtained showed that the proposed approach achieved an improvement of 5.13%, 15.8% and 5%, 15.6% improvement in terms of the buffer size and compression ratio. Though with a tradeoff of the frame-built time, where both the standard and local frame indexing outperforms the proposed scheme with 10.8% and 8.71% respectively

http://www.atbuftejoste.com/index.php/joste/article/download/844/pdf_547

A Spatio-Temporal based Frame Indexing Algorithm for QoS Improvement in Live Low-Motion Video Streaming

DeepPPF: A deep learning framework for predicting protein family

Fuzzy time series techniques are more suitable than traditional time series techniques in forecasting problems with linguistic values. Two shortcomings of existing fuzzy time series forecasting techniques are they lack persuasiveness in dealing with recurrent number of fuzzy relationships and assigning weights to elements of fuzzy rules in the defuzzification process. In this paper, a novel fuzzy time series technique based on fuzzy C-means clustering and particle swarm optimization is proposed to resolve these shortcomings. Fuzzy C-means clustering is adopted in the fuzzification process to objectively partition the universe of discourse. Then, particle swarm optimization is adopted to assign optimal weights to elements of fuzzy rules. Actual yearly enrollments at the University of Alabama and yearly deaths in car road accidents in Belgium are used as benchmark data. The forecasting results showed that the proposed method outperformed other existing methods. General Terms Fuzzy Systems, Pattern Recognition, Evolutionary Algorithms

/pdf/a-novel-hybrid-fuzzy-time-series-approach-with-applications-3jlxakjnbw.pdf

A Novel Hybrid Fuzzy Time Series Approach with Applications to Enrollments and Car Road Accidents

The advancement in the internet technology fosters the application of video streaming which is gaining more awareness amidst the populace. The application of video streaming includes internet broadcasting, video conferencing and video lecture among others. Â In video transmission, either through wired or wireless, a lot of images are needed to create an illusion of continuity. Therefore, there is need for effective utilization of storage space and allocated bandwidth.Â  In lieu of this, digital video standards and methods have been proffered by researchers and manufacturers in order to ensure compatibility among video codec from different manufacturers and to simplify the development of newer applications.Â  This paper presents a comparative study of the emerging and current video compression standards with a demonstration of some of the compression algorithms that are being employed in digital video compression.

http://www.atbuftejoste.com/index.php/joste/article/download/1005/pdf_648

A Comparative Analysis of Video Compression Standard and Algorithms: State of the Art

In this paper a hybrid forecasting technique that integrates Cat Swarm optimization Clustering (CSO-C) and Particle Swarm Optimization (PSO) with Fuzzy Time Series (FTS) forecasting is presented. In the three stages of FTS, CSO-C found application at the fuzzification module where its efficient capability in terms of data classification was utilized to neutrally divide the universe of discourse into unequal parts. Then, disambiguated fuzzy relationships were obtained using Fuzzy Set Group (FSG). In the final stage, PSO was adopted for optimization; by tuning weights assigned to fuzzy sets in a rule. This rule is a fuzzy logical relationship induced from FSG. The forecasting results showed that the proposed method outperformed other existing methods; using RMSE and MAPE as performance metrics.

A Hybrid Fuzzy Time Series Technique for Forecasting Univariate Data

Â Pilot Contamination (PC) is considered a major drawback of Time Division Duplexing (TDD) Massive Multiple-Input Multiple-Output (M-MIMO) systems because it limits their expected capacity. A Pilot Allocation Protocol (PAP) scheme is proposed in this letter to mitigate the effect of PC in order to improve the performance of users affected severely by PC. Specifically, the PAP technique is based on sectional pilot sequence initialization and sharing which allocate more than one user to share the same orthogonal pilot sequence within a cell which then creates intra cell interference between the users sharing the same pilot sequence but suppresses inter cell interference between users sharing the same pilot sequence in other cells and mitigate the effect of PC. Simulation results verify the performance gain of the proposed scheme over that of conventional schemes in a typical M-MIMO system.

https://www.atbuftejoste.com/index.php/joste/article/download/524/pdf_406

Shehu Mohammed Yusuf

Papers

DeepPPF: A deep learning framework for predicting protein family

A Novel Hybrid Fuzzy Time Series Approach with Applications to Enrollments and Car Road Accidents

A Comparative Analysis of Video Compression Standard and Algorithms: State of the Art

A Hybrid Fuzzy Time Series Technique for Forecasting Univariate Data

Mitigating the Effect of Pilot Contamination in Massive Multiple Input Multiple Output System Using Pilot Allocation Protocol