Forecasting Stock Exchange Data using Group Method of Data Handling Neural Network Approach

The increasing uncertainty of the natural world has motivated computer scientists to seek out the best approach to technological problems. Nature-inspired problem-solving approaches include meta-heuristic methods that are focused on evolutionary computation and swarm intelligence. One of these problems significantly impacting information is forecasting exchange index, which is a serious concern with the growth and decline of stock as there are many reports on loss of financial resources or profitability. When the exchange includes an extensive set of diverse stock, particular concepts and mechanisms for physical security, network security, encryption, and permissions should guarantee and predict its future needs. This study aimed to show it is efficient to use the group method of data handling (GMDH)-type neural networks and their application for the classification of numerical results. Such modeling serves to display the precision of GMDH-type neural networks. Following the US withdrawal from the Joint Comprehensive Plan of Action in April 2018, the behavior of the stock exchange data stream and commend algorithms has not been able to predict correctly and fit in the network satisfactorily. This paper demonstrated that Group Method Data Handling is most likely to improve inductive self-organizing approaches for addressing realistic severe problems such as the Iranian financial market crisis. A new trajectory would be used to verify the consistency of the obtained equations hence the models' validity

Presenting a New Strategy to Extract Data Clustering Heartbeat Samples by Using Discrete Wavelet Transform

This paper presents the improvement of detection system that normal and arrhythmia electrocardiogram classification. This classification is done to aid the ANFIS (Adaptive Neuro Fuzzy Inference System). The data used in this paper obtained from MIT-BIH normal sinus ECG database signal and MIT-BIH arrhythmia database signal. The main goal of our approach is to create an interpretable classifier that provides an acceptable accuracy. In this model, the feature extraction using DWT (Discrete Wavelet Transform) is obtained. The last stage of this extraction is introduced as the input of ANFIS model. In this paper, the ANFIS model has been trained with Quantum Behaved Particle Swarm Optimization (QPSO). In this study, for training of proposed model, four sample data have been used which result in acceleration of training data. On the test set, we achieved an outstanding sensitivity and accuracy 100%. Experimental results show that the proposed approach is very fast and accurate in improving classification. Using the proposed methodology and telemedicine technology can manage patient of heart disease

Presenting a New Strategy to Extract Data Clustering Heartbeat Samples by Using Discrete Wavelet Transform

This paper presents the improvement of detection system that normal and arrhythmia electrocardiogram classification. This classification is done to aid the ANFIS (Adaptive Neuro Fuzzy Inference System). The data used in this paper obtained from MIT-BIH normal sinus ECG database signal and MIT-BIH arrhythmia database signal. The main goal of our approach is to create an interpretable classifier that provides an acceptable accuracy. In this model, the feature extraction using DWT (Discrete Wavelet Transform) is obtained. The last stage of this extraction is introduced as the input of ANFIS model. In this paper, the ANFIS model has been trained with Quantum Behaved Particle Swarm Optimization (QPSO). In this study, for training of proposed model, four sample data have been used which result in acceleration of training data. On the test set, we achieved an outstanding sensitivity and accuracy 100%. Experimental results show that the proposed approach is very fast and accurate in improving classification. Using the proposed methodology and telemedicine technology can manage patient of heart disease

Challenges and Barriers to Providing Care to Older Adult Patients in the Intensive Care Unit: A Qualitative Research

BACKGROUND: Enhancing the quality of care for elderly patients needs an understanding of the challenges and obstacles experienced by the intensive care unit (ICU) staff in providing care. AIM: To explore the most challenging issues experienced by ICU staff, in particular, nurses, in the care of elderly patients in the general adult ICU. DESIGN: A qualitative research design was employed. The Standards for Reporting Qualitative Research (SRQR) were followed. METHODS: Based on theoretical sampling, we carried out 34 in-depth semi-structured interviews from two medical adult ICUs. Data analysis was carried out using qualitative conventional content analysis. RESULTS: Data analysis led to the identification of three interrelated categories and 12 subcategories. Three main categories were factors related to nurses’ attitude in elderly care, factors related to the system of care, and factors related to the models of patient care delivery. These categories came under the main theme of "Inappropriate and unfair system for elderly care". CONCLUSION: The findings of this study increase scholarly understanding of challenges and barriers to providing care to elderly patients in the general adult ICU. We found that the provision of care to elderly patients is inappropriate and unfair. Various obstacles must be overcome to improve the care of these patients. For example, negative attitudes toward elder care, inappropriate environments, lack of resources, lack of knowledge and skills, a specialized model of care delivery, respect for humanity, care without considering patient age, and separating professional conflicts from patient care. These findings may be used by ICU’s caregivers and managers to improve the quality of care. IMPLICATIONS FOR PRACTICE: Various obstacles were documented that need to be overcome by hospital administrators, nursing managers, clinical nurses, nursing educators, nursing researchers to improve the care of elderly patients admitted to ICU

A Combined Hybrid Fuzzy Multiple Criteria Decision-making Approach to Evaluating of QM Critical Success Factors in SME's Hotels Firms

AbstractThe purpose of this study is to evaluate the importance of QM CSFs in Iranian SME's hotels firms. Primary criteria to evaluate QM CSFs are achieved by the literature survey. Through the FDM by industries and academia experts the importance CSFs were categorized in three main factors and 16 sub-factors. Two fuzzy MCDM methods are applied; fuzzy AHPand TOPSIS. The finding of this study indicated that human factors had the first rank from three perspectives and leadership as a sub-factor was the first rank from 16 sub-factors

The economic impacts of climate change on the rice production in Malaysia.

This study attempts to estimate the potential impacts of climate change on the rice production in Malaysia. The crop model ORYZA2000 was used to simulate rice yield of MR 219 variety in eight granary areas of Malaysia from 1999-2007. The model predicted a reduction in rice yield of 0.36 t ha -1 under the scenario of an increase in temperature by 2°C and at the current CO 2 level of 383 ppm. With the reduction in rice yield, the economic loss to the Malaysian rice industry was estimated at RM162.531 million per year. Under the scenario of increase of CO 2 concentration from 383 to 574 ppm and with 2°C rise in temperature, it can be predicted that there will also be a decline in rice yield by 0.69 t ha -1 and consequently the economic loss will be at RM299.145 million per year for the rice industry. With the above potential impacts, some adaptation and mitigation strategies to overcome the adverse effects of climate change on rice production were recommended