101 research outputs found
Synthesis of Heteroaryl Urea Derivatives as Antimicrobial Agents
Get PDFAzoles are prominent scaffolds in the pharmaceutical arena. In fact, medicinal properties of azole and benzazole containing compounds include anticancer, antimicrobial and antioxidant. Some of the drugs Inthomycin C, Oxaprozin, Tiazofurin, Dacarbazine, Tipifarnib, Albendazole, Febendazole, Omeprazole possess azole/benzazole moiety. Realizing the importance of azoles and benzazoles, it is planned to conjugate these two ligands as heteroaryl substituted urea derivatives and to study their antimicrobial activity. The results pertaining to these aspects will be discussed. © 2020 Author(s).The authors K. Narendra Babu and V. Padmavathi are grateful to CSIR (Council of Scientific and Industrial Research), New Delhi for financial assistance under major research project
Electricity consumption forecasting using DFT decomposition based hybrid ARIMA-DLSTM model
Get PDFForecasting electricity consumption is vital, it guides policy makers and electricity distribution companies in formulating policies to manage production and curb pilfering. Accurately forecasting electricity consumption is a challenging task. Relying on a single model to forecast electricity consumption data which comprises both linear and nonlinear components produces inaccurate results. In this paper, a hybrid model using autoregressive integrated moving average (ARIMA) and deep long shortterm memory (DLSTM) model based on discrete fourier transform (DFT) decomposition is presented. Aided by its superior decomposition capability, filtering using DFT can efficiently decompose the data into linear and nonlinear components. ARIMA is employed to model the linear component, while DLSTM is applied on the nonlinear component; the two predictions are then combined to obtain the final predicted consumption. The proposed techniques are applied on the household electricity consumption data of France to obtain forecasts for one day, one week and ten days ahead consumption. The results reveal that the proposed model outperforms other benchmark models considered in this investigation as it attained lower error values. The proposed model could accurately decompose time series data without exhibiting a performance degradation, thereby enhancing prediction accuracy
Real time analysis of social media data to understand people emotions towards national parties
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Prediction of selected Indian stock using a partitioning–interpolation based ARIMA–GARCH model
No full textAccurate long-term prediction of time series data (TSD) is a very useful research challenge in diversified fields. As financial TSD are highly volatile, multi-step prediction of financial TSD is a major research problem in TSD mining. The two challenges encountered are, maintaining high prediction accuracy and preserving the data trend across the forecast horizon. The linear traditional models such as autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedastic (GARCH) preserve data trend to some extent, at the cost of prediction accuracy. Non-linear models like ANN maintain prediction accuracy by sacrificing data trend. In this paper, a linear hybrid model, which maintains prediction accuracy while preserving data trend, is proposed. A quantitative reasoning analysis justifying the accuracy of proposed model is also presented. A moving-average (MA) filter based pre-processing, partitioning and interpolation (PI) technique are incorporated by the proposed model. Some existing models and the proposed model are applied on selected NSE India stock market data. Performance results show that for multi-step ahead prediction, the proposed model outperforms the others in terms of both prediction accuracy and preserving data trend
γ‐C−H Bonds: Access to 3‐Alkylated Thiophene/Furan and Benzothiophene/Benzofuran Motifs
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Performance Comparison of Four New ARIMA-ANN Prediction Models on Internet Traffic Data, Journal of Telecommunications and Information Technology, 2015, nr 1
Get PDFPrediction of Internet traffic time series data (TSD) is a challenging research problem, owing to the complicated nature of TSD. In literature, many hybrids of auto-regressive integrated moving average (ARIMA) and artificial neural networks (ANN) models are devised for the TSD prediction. These hybrid models consider such TSD as a combination of linear and non-linear components, apply combination of ARIMA and ANN in some manner, to obtain the predictions. Out of the many available hybrid ARIMA-ANN models, this paper investigates as to which of them suits better for Internet traffic data. This suitability of hybrid ARIMA-ANN models is studied for both one-step ahead and multi-step ahead prediction cases. For the purpose of the study, Internet traffic data is sampled at every 30 and 60 minutes. Model performances are evaluated using the mean absolute error and mean square error measurement. For one-step ahead prediction, with a forecast horizon of 10 points and for three-step prediction, with a forecast horizon of 12 points, the moving average filter based hybrid ARIMA-ANN model gave better forecast accuracy than the other compared models
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