Saudi Cultural Missions Theses & Dissertations
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Item Restricted IMPROVING FORECASTING ACCURACY FOR TIME SERIES DATA USING FUZZY TECHNIQUES AND WAVELET TRANSFORM(Saudi Digital Library, 2025-07-09) Abdullah, Alenezy; Mohd. Tahir IsmailThis study focuses on improving the accuracy of stock market forecasting for the Saudi Arabia stock exchange (Tadawul) by employing advanced modeling techniques and adaptive learning approaches. The study utilizes the maximum overlapping discrete wavelet transform (MODWT) in conjunction with various mathematical functions to analyze daily stock price indices data from October 2011 to December 2019. Input variables, including oil price and repo rate, are carefully selected based on correlation analysis, multiple regression, and the Engle and Granger Causality test. The proposed models, such as MODWT-LA8-ANFIS, MODWT-LA8-FS.HGD, MODWT-LA8-HyFIS, and MODWT-LA8-FIR.DM, demonstrate superior forecasting performance compared to traditional methods like ARIMA, ANFIS, FS.HGD, HyFIS, and FIR.DM. The performance evaluation of the proposed model involves various statistical measures, including mean error (ME), root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MPE). The results highlight the effectiveness of these models in decomposing stock market patterns and accurately predicting stock market price volatility. This research contributes to the field of stock market forecasting and offers valuable insights for investors and financial analysts operating in the Saudi Arabia stock exchange.15 0Item Embargo ENHANCING LOCATION INFORMATION PRIVACY AND SECURITY IN IoBT USING DECEPTION-BASED TECHNIQUES(Florida Atlantic Uniiversity, 2024-09) Alkanjr, Basmh; Imadeldin, MahgoubIoBT stands for the Internet of Battlefield Things. This concept extends the principles of the Internet of Things (IoT) for military and defense use. IoBT integrates smart devices, sensors, and technology on the battlefield to improve situational awareness, communication, and decision-making in military operations. Sensitive military data typically includes information crucial to national security, such as the location of soldiers and equipment. Unauthorized access to location data may compromise operational confidentiality and impede the element of surprise in military operations. Therefore, ensuring the security of location data is crucial for the success and efficiency of military operations. We propose two systems to address this issue. First, we propose a novel deception-based scheme to enhance the location-information security of IoBT nodes. The proposed scheme uses a novel encryption method, dummy IDs, and dummy packets technology. We develop a mathematical model to evaluate our scheme in terms of safety time (ST), probability of failure (PF), and the probability of identifying the real packet in each location information update (PIRP). Then, we develop NetLogo simulations to validate the mathematical model. The proposed scheme increases ST, reduces PF and PIRP. We develop a scheme to protect the node's identity using dummy ID, silence period, and sensitive area’s location privacy enhancement concepts. We generate a pseudonym location for each node in the IoBT environment to protect the node's real location information. We propose a new metric called the average probability of linkability per dummy ID (DID) change to assess the attacker's effectiveness in linking the source node with its new DID following the silent period. We develop Matlab simulations to evaluate our scheme in terms of average anonymity and average probability of linkability per DID change. The results showed further privacy enhancement by applying the sensitive area concept. Tampering with location information, such as falsification attacks, can lead to inaccurate battlefield assessments and personnel safety risks. Thus, we design ANFIS and ensemble methods for detecting position falsification attacks in IoBT. Using the VeReMi dataset, our method achieved high detection accuracy while reducing false negative rate and computation complexity. Cross-validation further supports the reliability of our model.34 0