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

Anticancer Plants in Islamic Traditional Medicine

Islamic Traditional Medicine (ITM) is a holistic and comprehensive medical school that has antecedents over 12 centuries ago

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

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Involvement of nitric oxide in granisetron improving effect on scopolamine-induced memory impairment in mice

AbstractGranisetron, a serotonin 5-HT3 receptor antagonist, widely used as an antiemetic drug following chemotherapy, has been found to improve learning and memory. In this study, effects of granisetron on spatial recognition memory and fear memory and the involvement of nitric oxide (NO) have been determined in a Y-maze and passive avoidance test. Granisetron (3, 10mg/kg, intraperitoneally) was administered to scopolamine-induced memory-impaired mice prior to acquisition, consolidation and retrieval phases, either in the presence or in the absence of a non-specific NO synthase inhibitor, l-NAME (3, 10mg/kg, intraperitoneally); a specific inducible NO synthase (iNOS) inhibitor, aminoguanidine (100mg/kg); and a NO precursor, l-arginine (750mg/kg). It is demonstrated that granisetron improved memory acquisition in a dose-dependent manner, but it was ineffective on consolidation and retrieval phases of memory. The beneficial effect of granisetron (10mg/kg) on memory acquisition was significantly reversed by l-NAME (10mg/kg) and aminoguanidine (100mg/kg); however, l-arginine (750mg/kg) did not potentiate the effect of sub-effective dose of granisetron (3mg/kg) in memory acquisition phase. It is concluded that nitric oxide is probably involved in improvement of memory acquisition by granisetron in both spatial recognition memory and fear memory.This article is part of a Special Issue entitled The Cognitive Neuroscience

Technetium-99m methoxyisobutylisonitrile scintigraphy in the assessment of cold thyroid nodules: is it time to change the approach to the management of cold thyroid nodules?

methoxyisobutylisonitrile (99mTc-MIBI) is recommended for evaluating thyroid nodule metabolism. In addition, it may help differentiate between benign and malignant nodules; however, the efficacy of this technique has not been fully elucidated. Therefore, it is not currently performed for routine clinical application. This prospective study was conducted to investigate the clinical significance of 99mTc-MIBI scintigraphy in the assessment of patients with cold thyroid nodules. Patients and methods This prospective study was conducted on 104 patients with cold thyroid nodules greater than 1 cm in diameter as detected on 99mTc-pertechnetate scintigraphy. Uptake of MIBI in thyroid nodules was compared with that in the surrounding normal thyroid tissue for both early and delayed images, and a score of 0–3 was assigned to each nodule as follows: 0, cold; 1, decreased; 2, equal; 3, increased. The thyroid scan was performed 20 and 40min after intravenous injection of 555MBq of 99mTc-MIBI. The patients underwent fine-needle aspiration cytology (FNAC). Detailed statistical parameters were determined on a per-nodule basis for each qualitative and quantitative scan analysis, as defined by histology. Results A total of 104 patients (93 women and 11 men; mean age 40.76±11.40 years, range 20–73) with a total number of 167 cold nodules were included in this study. When 99mTc-MIBI uptake was regarded as the criterion of malignancy in 99mTc-MIBI scintigraphy, the accuracy was between 69.46 and 92.21% on using seven different methods. In addition, FNAC findings indicated a sensitivity of 66.66%, a specificity of 100%, a negative predictive value of 95.72%, a positive predictive value of 100%, and an accuracy of 96.06%. Six malignant cold nodules were detected on a positive 99mTc-MIBI scan, which were determined as benign nodules on FNAC examinations. Conclusion The study demonstrated that 99mTc-MIBI scanning can be complementary to other diagnostic techniques in patients with cold thyroid nodules. In addition, because of its availability, rather low cost, simple protocol, and objective semiquantitative information, 99mTc-MIBI scanning seems to hold promise in routine imaging of cold thyroid nodules. Nucl Med Commun 35:51–57 �c 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins

CLONE-BASED MOBILE AGENT ITINERARY PLANNING USING SEPARATE TREES FOR DATA FUSION IN WSNS

ABSTRAC