SACM - Malaysia
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9660
Browse
107 results
Search Results
Item Restricted ENHANCED CRIMINAL BEHAVIOR DETECTION IN MOBILE PHONE DATA USING T-DBSCAN, CGAN, AND SEMI-SUPERVISED SELF-TRAINING(Saudi Digital Library, 2025-07) Okmi, Mohammed; Yee, Por Lip; Fong, Ang TanWith the rapid expansion of mobile telecommunication networks and the widespread adoption of smartphones, vast amounts of mobile phone data are being generated. These data contain digital traces that facilitate the analysis of criminal activities and the detection of suspicious behavior based on calling behavior and mobility patterns—both of which are widely used in criminal investigations. However, the inherent characteristics of mobile phone data, such as sparsity, noise, missing values, and lack of labeling, pose significant challenges to accurately clustering crucial locations (stay points) and compromise the precision of spatiotemporal information, which is vital for modeling criminal mobility patterns. These challenges are further exacerbated by variations in data formats and the presence of missing values due to low network coverage and privacy constraints. Consequently, analyses are often limited to a single behavioral aspect—such as mobility or calling patterns—hindering the ability to capture temporal variations in criminal behavior. To address these challenges, this research proposes t-DBSCAN (Temporal Density-Based Spatial Clustering of Applications with Noise), an enhanced version of DBSCAN. This method integrates mobile phone data with crime data to improve the clustering and identification of stay points, thereby aiding in modeling critical activities such as identifying crime hotspots and residential behaviors. Compared to baseline methods such as DBSCAN, K-means, and OPTICS (Ordering Points to Identify the Clustering Structure), the proposed t-DBSCAN achieves superior clustering performance, with a Silhouette Index (SI) of 0.9889 and a Davies-Bouldin Index (DBI) of 0.0213 for clustering home locations, and an SI of 0.8136 and a DBI of 0.1671 for identifying suspicious activities—indicating high intra-cluster cohesion and strong inter-cluster separation. Additionally, a Conditional Generative Adversarial Network (cGAN) is introduced to address missing values in mobile phone data by reconstructing user profiles that integrate both mobility and calling features. The proposed cGAN is evaluated against baseline probabilistic and deep generative methods using Jensen-Shannon Divergence (JSD) and Cosine Similarity (CS) metrics. It achieves a JSD of 0.0062 for stay duration, 0.0940 for trajectory length, and 0.1373 for visit frequency—significantly outperforming all baseline methods (JSD > 0.25). It also records the highest CS scores: 0.8521 for spatial and 0.7950 for spatiotemporal distribution, surpassing deep generative models (CS ≤ 0.82) and probabilistic methods (CS < 0.63). Furthermore, a Semi-Supervised Self-Training (SSST) approach is employed to enhance criminal behavior detection by leveraging both labeled and unlabeled data. The proposed SSST, implemented using machine learning methods such as SSST-Random Forest (SSST-RF) and SSST-Decision Tree (SSST-DT), demonstrates significantly improved classification accuracy compared to baseline supervised methods. Specifically, SSST-RF and SSST-DT achieve average accuracies of 87.82% and 82.91%, respectively, outperforming their baseline counterparts by +5.3% and +4.7%. In conclusion, this research successfully meets all its objectives and underscores the potential of mobile phone data mining as a powerful tool for enhancing crime pattern detection, criminal identification, and crime prevention strategies.5 0Item Restricted Building Awareness And Visibility for Smart Building Gateway Company(Saudi Digital Library, 2025) Alsufyani, Tahani Dhaifallah; ALhaimi, BasheerIn today’s rapidly evolving digital and competitive business environment, brand awareness and market visibility are critical to the survival and growth of small and medium enterprises (SMEs). This study explores how Smart Building Gateway (SBG), a Saudi-based SME in the building materials sector, can improve its digital presence and customer outreach through strategic digital marketing initiatives. Initially, SBG depended solely on traditional, word-of-mouth methods and lacked any structured online engagement.Using a qualitative action research methodology, the study was carried out in two intervention cycles. The first cycle focused on initiating basic digital activities via platforms like Instagram and WhatsApp. The second cycle expanded the digital strategy by launching new social media accounts (such as Twitter and Snapchat), producing targeted visual content, developing an official website, and recommending partnerships with digital marketing professionals.Data were gathered through semi-structured interviews, direct observations, and key performance indicators, such as online engagement and customer interactions. The findings revealed a significant improvement in brand visibility and customer engagement, highlighting the effectiveness of customized digital content and platform-specific strategies.This research offers practical guidance for SMEs seeking to enhance their digital footprint and provides academic insights into the role of digital marketing in improving business performance. It concludes that cost-effective, well-planned digital interventions can lead to increased brand awareness, stronger customer relationships, and long-term growth particularly in dynamic markets such as Saudi Arabia's building materials industry.11 0Item Restricted EARLY SCREENING USING ECHOCARDIOGRAPHY TO ASSESS LEFT VENTRICULAR REMODELING IN HYPERTENSIVE PATIENTS AT HAIL CARDIAC CENTRE, SAUDI ARABIA(Saudi Digital Library, 2025-08) Alsrur, Hamad Hamoud H; Zarihah, binti Mohd ZainBackground: Hypertension is a major global public health issue and a leading risk factor for cardiovascular morbidity and mortality. Early detection of left ventricular (LV) remodeling is essential to prevent progression to heart failure. Echocardiography, including advanced techniques such as Global Longitudinal Strain (GLS), provides detailed insights into subclinical myocardial dysfunction that may not be detected by conventional parameters. Objectives: This study aimed to assess early markers of LV remodeling among hypertensive patients at Hail Cardiac Centre, Saudi Arabia, and to evaluate the role of GLS compared with conventional measures such as left ventricular ejection fraction (LVEF) and left ventricular mass index (LVMI). Methods: A cross-sectional study was conducted on 341 hypertensive adult patients (aged 18–69 years) attending Hail Cardiac Centre between 2023 and 2024. Demographic, clinical, and echocardiographic data were collected, including LVEF, LVMI, relative wall thickness (RWT), and GLS using speckle-tracking echocardiography. Descriptive and comparative statistical analyses were performed to evaluate the association between hypertension-related variables and LV remodeling indices. Results: The mean age of patients was 53.2 ± 10.8 years, with a nearly equal distribution of males and females. LVEF values were within the normal range in most patients; however, GLS revealed subclinical dysfunction in some cases despite preserved LVEF, demonstrating its sensitivity for early detection of hypertensive-related changes. A considerable proportion of patients also exhibited increased LVMI and RWT, reflecting structural remodeling. GLS showed stronger associations with clinical variables compared to LVEF, underscoring its diagnostic significance. Conclusion: Echocardiographic evaluation of hypertensive patients demonstrates that conventional measures such as LVEF may appear normal, whereas GLS can uncover subtle dysfunction at an earlier stage. Incorporating GLS alongside LVMI in routine echocardiographic screening may improve early detection of LV remodeling and support timely clinical interventions in hypertensive patients. Remodeling, Subclinical Dysfunction.13 0Item Restricted FACTORS INFLUENCING STUDENT ENGAGEMENT WITH GAME ELEMENTS AMONG UNDERGRADUATE STUDENTS IN SAUDI ARABIA(Saudi Digital Library, 2025) Allehaidan, Ahmed Freeh M; Wan Mohd Nazmee Wan ZainonGamification and gamified systems have garnered significant attention in contemporary research. The concept of gamification encompasses various interpretations, including the incorporation of game-like elements into the design of user interfaces. However, it is essential to note that not all instances of gamification are restricted to software products. Despite increased research in the area, the various dimensions explored within the scope of gamification, along with the current advancements in gamification research, remain unclear. Therefore, this research aimed to examine the direct effect of Unified Theory of Acceptance and Use of Technology (UTAUT) components (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions), on the attitude toward using gamification as well as on students’ engagement (i.e., skill engagement and participant engagement). Significantly, this study also aimed to investigate the moderating role of students’ concentration on the relationship between their attitude toward using gamification and student engagement. The research model was underpinned by the UTAUT, self-determination theory, and flow theory to strengthen the study's argument. Data were gathered from undergraduate students in public universities in the Kingdom of Saudi Arabia. The theoretical model was tested using structural equation modelling (SEM) with the SmartPLS software. The research findings show the positive effect of gamification on student engagement and motivation. By incorporating game elements such as points, badges, and leaderboards, gamified learning environments effectively capture students' attention and encourage active participation in the learning process. The findings further demonstrated the significance of cultural adaptation and acceptance of gamified educational content in Saudi Arabia. When gamification elements were tailored to align with local values and norms, teachers observed higher acceptance and enthusiasm among students. The study concluded that gamification can improve students’ performance, productivity, engagement, and encourage participation. However, the study has a significant contribution to practicality as it will aid policymakers, the government, and institutions. Therefore, by embracing gamified learning experiences, policymakers can enhance student engagement, cater to diverse learning needs, and foster the development of essential skills.11 0Item Restricted ENHANCEMENT OF ZnO-BASED UV PHOTODETECTORS BY INCORPORATING Bi₂O₃, Ag, AND Ge NANOSTRUCTURES SYNTHESIZED USING LASER ABLATION IN LIQUID(Saudi Digital Library, 2025) Alharbi, Abdullah Marzouq; Ahmed, Naser Mahmoud and Rahman, Azhar AbdulThis research enhances the efficiency of UV photodetectors through the synthesis and integration of nanoparticles using laser ablation techniques. The study investigates the effects of bismuth oxide nanosheets (Bi2O3-Nsh), silver nanoparticles (AgNPs), and germanium nanowalls (GeNWs) on ZnO/Si-based UV photodetectors. By change nanoparticle size and shape, the research aims to improve responsivity, sensitivity, and overall performance. Comprehensive characterization of the synthesized nanoparticles and their integration into photodetector architectures was conducted to evaluate their effectiveness and potential for practical applications. Bi2O3 nanosheets (Bi2O3-Nsh) were synthesized using the laser ablation in liquid (LAL) method. The Bi2O3-Nsh were integrated into ZnO/Si photodetectors, and their crystalline structures, morphologies, and optical properties were characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray (EDX), and UV-visible spectroscopy analysis. The UV photodetection performance was assessed under 385 nm UV light at varying bias voltages. The ZnO/Bi2O3-Nsh/Si-based UV photodetectors demonstrated a strong response, with the I-V curve showing a significant change from 79 μA to 20 mA at 6 V. Additionally, the device exhibited the highest responsivity of 49.8 A/W, quantum efficiency of 161.61, sensitivity of 25000%, gain of 251, detectivity of 9.86 × 1010 Jones, and a noise equivalent power (NEP) of 1.01 × 10-12 W under 385 nm UV light at a bias voltage of 6 V. These results highlight the potential of Bi2O3-Nsh in enhancing xix ZnO/Si photodetectors. The research also synthesized AgNPs using a cost-effective laser ablation technique combined with RF sputtering. The AgNPs were encapsulated by zinc oxide on a silicon substrate to enhance photodetector efficiency while reducing costs. Three sample configurations (AgNPs/Si, AgNPs/ZnO/Si, and ZnO/AgNPs/Si) were characterized using FESEM, XRD, EDX, and UV-visible spectroscopy. The ZnO/AgNPs/Si photodetector exhibited the highest performance, with a peak responsivity of 132 A/W, quantum efficiency of 429.88, sensitivity of 31400%, gain of 315, detectivity of 18 × 1010 Jones, and an NEP of 0.556 × 10-13 W. These findings underscore the potential of AgNPs in enhancing UV photodetector performance and the feasibility of cost-effective synthesis methods. Furthermore, GeNWs were synthesized via pulsed laser ablation to improve UV photodetection. The GeNWs were integrated into ZnO/Si photodetectors in two configurations: ZnO/GeNWs/Si and GeNWs/ZnO/Si. The ZnO/GeNWs/Si configuration showed impressive performance, with a photocurrent of around 12.8 mA at 6 V, indicating significant enhancement in UV light absorption and carrier charge transport. The device exhibited a responsivity of 31.8 A/W, quantum efficiency of 103.43, sensitivity of 9600%, and detectivity of 4.90 × 1010 Jones. These results demonstrate the potential of GeNWs to enhance UV photodetector performance and the effectiveness of pulsed laser ablation. This research successfully addresses the objectives by using laser ablation techniques to synthesize and integrate Bi2O3-Nsh, AgNPs, and GeNWs into UV photodetectors8 0Item Restricted PARENTS’ KNOWLEDGE, ATTITUDE AND PRACTICE OF ORAL HEALTH AND ITS ASSOCIATION WITH THE ORAL HEALTH STATUS OF ARABIC PRE-SCHOOL CHILDREN IN KLANG VALLEY, MALAYSIA(Saudi Digital Library, 2025-07-22) Mohammed, Alsharif; Mohd, Nazan Ahmad Iqmer; Suriani, IsmailOral health diseases among pre-school children such as tooth decay is a global public health problem and influence the overall health of children. Parental oral health knowledge, attitude and practice have a direct effect on pre-school children, since the pre-school children may not be able to fully express their emotions orally. This study aimed to determine the level of parents’ knowledge, attitude and practice of oral health and its association with the oral health status of pre-school children in Arabic pre-schools in Klang Valley, Malaysia. A cross-sectional study was conducted among pre-schoolers with proportional stratified sampling from selected five Arabic pre-schools. 400 self-administered questionnaires in Arabic language were distributed among the Arabic parents. The questionnaire included five sections on sociodemographic characteristics, socioeconomic characteristics, parental oral health knowledge, attitude and practice, eating and oral hygiene habits of children and accessibility of dental services. The questionnaire had acceptable internal consistency (α=0.82). In this study, the internal consistency value was (α=0.78) and the test-retest reliability correlation coefficient showed a good reliability level (84%). SPSS version 23 was used to conduct Chi-Square test, Fisher’s exact test and binary logistic regression analysis for data analysis. Completed questionnaires were returned with a response rate of 95%. Among a total of 363 children with a median age of 4 years, 54.8% were females. Overall, 42.4% of the children had dental caries, 9.6% of them had gingivitis and 19.6% reported to have halitosis. Marital status, monthly income, accessibility to dental services, the gender of children, name of schools, start to brush the teeth and the number of times of brushing every day, visit the dentist regularly were factors significantly associated with the oral health status of children (p<0.05). Besides, parental knowledge, attitude and practice of oral health were significantly associated with the parent-reported oral health status of children (p<0.05). In conclusion, this study established that more than 50 % of the children found to have poor oral health. In order to mitigate these problems, effective oral health programs designed to change dietary habits and dental screening of children in this age group are necessary.11 0Item 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 Restricted PULSE LASER ABLATED TITANIA-HAEMATITE NANOCOMPOSITES FOR THE REMOVAL OF LEAD AND ARSENIC FROM WASTEWATER(Saudi Digital Library, 2025) ALSHAMMARI TEFLAH KHULAIF K; BAKHTIAR, HAZRI BINWater contamination by toxic heavy metals, particularly lead (Pb²⁺) and arsenic (As³⁺), poses a severe environmental and public health risks due to their persistence, bioaccumulation, and high level of toxicity. Conventional wastewater treatment methods, such as chemical precipitation and membrane filtration often suffer from high costs, energy demands, and secondary pollution. Therefore, there is a critical need for efficient, cost-effective, and sustainable materials for heavy metal removal. Nanocomposites, particularly those based on titania (TiO₂) and haematite (α-Fe₂O₃) offer promising adsorption and photocatalytic properties. The primary objective of this study is to synthesize titania (TiO₂)- haematite (α-Fe₂O₃) nanocomposites via pulsed laser ablation in liquid (PLAL) and evaluate their efficiency in removing Pb²⁺ and As³⁺ from wastewater. The nanocomposites were analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), high resolution transmission electron microscopy (HRTEM), Fourier transform infrared (FTIR) spectroscopy and UV-Vis spectroscopy to determine their structural, morphological, and optical properties. The adsorption efficiency of Pb²⁺ and As³⁺ was evaluated under varying parameters such as laser fluence, solvent pH, contact time, and initial metal concentration. The results indicated that the TiO2-(α-Fe2O₃) nanocomposites exhibited high adsorption capacity due to their large surface area, synergistic photocatalytic effects, and strong metal affinity. The observation of improved heavy metals removal efficiency of the proposed nanocomposites was ascribed to the synergy between TiO2 nanoparticles and α-Fe2O3 nanoparticles, indicating their wastewater treatment potential. The removal efficiency exceeded 90% for both metals under optimized conditions, demonstrating the potential of these nanocomposites as an eco-friendly and effective material for wastewater treatment. These findings affirmed the unique effectiveness of nanocomposites for water treatment implementation. This research contributes to sustainable water treatment technologies by introducing an environmentally friendly solution for heavy metal removal.7 0Item Embargo An Adaptable and Distributed Access Control Approach Based on Machine Learning Techniques in a BYOD Environment(Saudi Digital Library, 2028-07-10) Turkea, ALjuaid; Wahid, Ainuddin; Yamani, MohdTraditional access control systems, such as role-based access control (RBAC), attribute-based access control (ABAC), or relationship-based access control (ReBAC), may limit policy decision points due to the potential for status changes in response to minor changes in user and resource properties. Additionally, system administrators must rely on solutions that require complex rules with multiple conditions and permissions for decision control, which can lead to access control issues such as policy conflicts, decision-making bottlenecks, poor performance, and trust and privacy issues related to policy management. This thesis presents three security access control mechanisms to overcome these limitations. Firstly, it proposes a method of enforcing access decisions that is adaptable and dynamic, based on a multi-layer deep learning hybrid model (TabularDNN). The technique converts all input attributes from an access request into an allow or deny decision using multiple layers to ensure accurate and efficient access control. Furthermore, the proposed solution was evaluated using the Kaggle-Amazon access control policy dataset; the results indicated a 94\% accuracy rate, demonstrating enhanced access decision implementation by considering various resource and user attributes. Additionally, it ensures privacy through indirect communication with the Policy Administration Point (PAP). This mechanism improves flexibility and provides dynamic and adaptable access control, demonstrating the proposed method's efficiency and reliability. Secondly, this dissertation presents an access decision-making algorithm for access control-based supervised learning, enhancing policy decision points (PDPs) by converting the PDP problem into a binary classification for access requests. The research describes a vector decision classifier that uses machine learning methods, specifically implementing the random forest algorithm, to make accurate access decisions and enable dynamic, distributed PDPs. Performance was evaluated using the Kaggle-Amazon access control policy dataset, comparing the proposed mechanism to previous research benchmarks for performance, time, and flexibility. The method ensures privacy for access control policies by preventing direct communication between the PDP and PAP. The study showed that PDP-based machine learning could navigate multiple policies and large access requests with 95\% accuracy, a 0.15-second response time, and no policy conflicts. This method improves security by implementing a distributed access control system that is dynamic, adaptable, and flexible. Finally, it presents an adaptive policy adjustment based on anomaly detection methods using machine learning algorithms. This method conducts risk monitoring and anomaly detection and features an adaptive policy mechanism that dynamically adjusts policies based on detected anomalies. The UNSW-NB15 dataset was used to evaluate the solution's performance. The results highlighted a detection accuracy of 95\% with a response time of approximately 0.5 seconds. The adaptive policy adjustment achieves a 97\% accuracy rate. The mechanism improves insider threat detection and access control simultaneously while optimizing and simplifying the process of managing policies. This method effectively addresses the critical trust and privacy challenges associated with policy management in corporate environments.17 0Item Restricted Revitalizing Domestic Tourism in the Kingdom of Saudi Arabia: Strategies for Intensifying and Encouraging Domestic Travel(Saudi Digital Library, 2025-03) GHADWAN, SOAD SALEH M; Hikmahana, DhiyaDomestic tourism plays a significant part in enhancing the economic resilience of any country. This is especially important for countries such as Saudi Arabia, where economic diversification has been a top priority as dictated within its Vision 2030. Research has confirmed that domestic tourism enhances the economic well-being of a country by supporting sustainability and resilience against negative economic international events. Domestic tourism has also been argued to be an important factor in encouraging cultural cohesiveness and unity, among other benefits. Despite the importance of domestic tourism, its fullest potential has not been achieved in Saudi Arabia. This has been attributed to restrictive cultural and traditional practices, high costs associated with domestic travel, underdeveloped tourist activities, and poor marketing campaigns to create awareness of domestic attractions.This study was designed to examine the role and effect of tourism activities, tourism costs, marketing strategies, and cultural practices in order to develop policy and practical recommendations that can help boost domestic tourism in the country. The research is anchored on theoretical frameworks, including destination marketing and management, push and pull factors, tourism motivation theory, and price sensitivity theory. A pragmatism research paradigm is adopted with a quantitative research design. Further, the study was designed to follow a deductive research approach.Among the findings of this research include the fact that leisure accounted for the largest proportion of tourism activities that encouraged domestic travel. However, tourism activities were rated slightly below average, indicating the need to improve and develop tourism activities in the country. It was also identified that tourism costs in the country were judged to be moderately affordable, with significant room for improvement. The majority of the respondents in the study were also identified to have experienced at least one form of cultural restriction while visiting different tourist destinations. Furthermore, the most visible marketing medium was social media, which suffered poor effectiveness in creating awareness of tourism activities and destinations in the country.Strategies to improve domestic tourism in the country were identified to include targeting and segmentation of the target audience in the country in marketing efforts. Other recommendations included diversification of tourism activities, adoption of dynamic pricing, and integration of cultural variables within the tourism destinations in the country. Theoretically, the findings of this study provide deeper insight into the existing theories regarding domestic tourism. The study provides insights into the overall effect of cultural factors, costs, marketing strategies, and tourism activities that will be applied in the academic world. Practically, this study provides actionable insights that will help tourism authorities and other stakeholders enact measures that will improve domestic tourism in the country.7 0