This paper introduces a novel nonparametric hybrid cyber-intelligence-based statistical process control and anomaly detection framework in time series data. It is developed to overcome the shortcomings of the classical control schemes in dealing with complex, abnormal, and noisy input data, especially when it is autocorrelated. The proposed methodology combines three technical pillars: First, it utilizes a bidirectional long-short-term memory architecture (Bi-LSTM) to capture long-term time dependency and learn nonlinear patterns, leaving only true deviations as residuals that remove trends and noises from the market. Second, it adopts the Golden Eagle Optimizer (GEO) algorithm for optimal parameter selection. This intelligent algorithm tunes the smoother factor (l) and the control boundary (L) at a certain sample size to minimize the Average Run Length (ARL) of the nonparametric exponentially weighted moving average (NPEWMA-SR) scheme. Third, the framework is validated via R software. The framework was applied to Google's daily trading data using different sample sizes (10, 30, 60, 120, 250, 365, 600, 900, and 1245) days of 2026, to detect the shift in the system, within 2 trading days, achieving an In-control Average Run Length ARL0 = 499.6 and an Out-of-control Average Run Length ARL1 = 1.65 days. The system demonstrated high statistical stability, a very low false alarm rate, and the best statistical sensitivity among all sample sizes. These results prove its effectiveness across small, medium, and large samples, making it a powerful early warning system for monitoring market volatility.
Inefficient wastewater disposal and wastewater discharge problems in water bodies have led to increasing pollution in water bodies. Pollutants in the river contribute to increasing the biological oxygen demand (BOD), total suspended solids (SS), total dissolved solids (TDS), chemical oxygen demand (COD), and toxic metals render this water unsuitable for consumption and even pose a significant risk to human health. Over the last few years, water conservation has been the subject of growing awareness and concern throughout the world, so this research focused on review studies of researches that studied the importance of water quality of wastewater treated disposal in water bodies and modern technology to management w
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This work focuses on the implementation of interfaces for human machine interaction (HMI) for control and monitor of automatic production line. The automatic production line which can performance feeding, transportation, sorting functions.
The objectives of this work are implemented two SCADA/HMI system using two different software. TIA portal software was used to build HMI, alarm, and trends in touch panel which are helped the operator to control and monitor the production line. LabVIEW software was used to build HMI and trends on the computer screen and was linked with Micros
... Show MoreThe research aims at measuring the extent of the relationship and influence of the indicators of the Core competencies of the audit firms and offices in the Earning Quality of the private banks listed in the Iraq Stock Exchange under audit. The research community represents 38 banks. The sample of the research has been approved only 10 banks continue to issue their financial statements for the period (2007 – 2017), in addition to the audit offices assigned to audit these banks, which amounted to 14 companies and auditing offices. John's (1999) model revised by Kothari et al., (2005) was adopted to measure the Earning Quality by finding discretionary accruals and non-discretionary accruals, to measure the Core competencies indicators ,
... Show MoreThe research aims to analysis the future profits of companies listed on the Iraq Stock Exchange through analyzing the relationship between Actual Growth and Sustainable Growth, The study was applied to the sample of companies included in the Iraq Stock Exchange for the period (2010- 2014), The Internal Growth Rate has been used as a measure for the Actual Return Rate but the Sustainable Growth Rate has been measured under the Rose model, The research showed group of conclusions, the most important are:1. From the analysis of sustainable growth for the banks sample and the variables involved in its measure shows that investment in the investors amounts have achieved a significant return, which demonstrates the company's efficiency at gene
... Show MoreA substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co
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Bivariate time series modeling and forecasting have become a promising field of applied studies in recent times. For this purpose, the Linear Autoregressive Moving Average with exogenous variable ARMAX model is the most widely used technique over the past few years in modeling and forecasting this type of data. The most important assumptions of this model are linearity and homogenous for random error variance of the appropriate model. In practice, these two assumptions are often violated, so the Generalized Autoregressive Conditional Heteroscedasticity (ARCH) and (GARCH) with exogenous varia
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