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The extraction of association rules remains a crucial strategy in data analysis, particularly in the context of massive datasets. This method unveils complex relationships, correlations, and meaningful patterns within vast datasets, providing essential insights for decision-making and understanding behaviors. Our approach stands out through the use of clustering algorithms for intelligent data partitioning. This strategic choice establishes a robust foundation for efficient association rule extraction. By organizing data specifically through clustering techniques before applying the extraction algorithm, we aim to optimize the relevance and significance of the discovered rules.

Background Postpartum ovarian thrombosis is an uncommon condition. It appears with the nonspecific, predominantly right-sided abdominal symptoms and must be differentiated from other acute visceral conditions. If left untreated, postpartum ovarian thrombosis can have severe consequences, including sepsis, pulmonary embolism, and even death. Momentarily, there are no specific guidelines for postpartum ovarian thrombosis management. We present a case of postpartum ovarian thrombosis admitted to our hospital with symptoms of acute appendicitis. Case presentation  A 39-year-old Omani obese multiparous woman of Afro-Arab origin was admitted with acute symptoms, mainly abdominal pain, fever, and vomiting 1 week postpartum. Clinical picture and biochemical profile did not exhibit a recognizable pattern. Ultrasonography excluded retained products of conception. Computerized scan for abdomen and pelvis with oral and intravenous contrast reported a dilated tubular structure in the right adnexa extending up to the right renal hilum level with surrounding inflammation. Those findings were consistent with the thrombophlebitis of the right ovarian vein. Blood cultures and sensitivity showed group A β-hemolytic streptococci sensitive to penicillin G and clindamycin. The patient was treated successfully with antibiotics and therapeutic anticoagulants and discharged home 3 days later; follow-up was arranged. Conclusion This pathology is an exceptional entity in Oman. Therefore, awareness of this unique condition is required so that clinicians will be vigilant, exploring similar cases with imaging and avoiding unnecessary surgical interventions.

The fundamental goal of this work is to prepare and carry out diabetes prediction using various Machine Learning techniques and conduct output analysis of those techniques to find the best classifier with the highest accuracy. This study use the Pima Indian Diabetes Dataset and applied the Machine Learning classification methods like Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) for diabetes prediction. The performance of each algorithm is analysed to determine the one with the best accuracy. The dataset includes details like pregnancies, glucose levels, blood pressure, and other important health information. The focus of this study is to unify FP-Growth algorithm with ML algorithm in order to predict diabetes. The FP-Growth is used to extract the frequent items for data pre-processing before prediction. LR algorithm stands out with high accuracy, showing promise in predicting type 2 diabetes when using the risk factors identified by FP-Growth algorithm. The results help guide future research and make it easier to choose the best algorithms, especially ones that are fast, for medical decision support systems. LR algorithm stands out with high accuracy, showing promise in predicting type 2 diabetes when using the risk factors identified by FP-Growth algorithm.

In this paper the concepts of soft (1, 2)-strongly open maps and soft (1, 2)-generality open maps are introduced and their relations with soft (1, 2)-open maps and soft (1, 2)-continuous maps are stated. We show that every soft (1, 2)-strongly open map is soft (1, 2)-generality open map and our work in this paper are examined. Furthermore, these our concepts are used to discuss the notion of soft (1, 2)-semi Hausdorff spaces in this work.

Many communities use the Vulnerability Index-Service Prioritization Decision Tool (VI-SPDAT) to determine which individuals and/or households experiencing homelessness are most vulnerable and therefore prioritized for the limited housing resources available. Because of the tool’s widespread use and implications for housing, the present study examined the reliability and validity of the second version of the VI-SPDAT for Single Adults with a sample of individuals experiencing chronic homelessness in Charlotte, NC. Results suggest that the VI-SPDAT is strongest in measuring areas associated with psychological symptomatology and/or mental health, but that the scale had significant limitations in its internal consistency, ability to adequately measure the construct of vulnerability, and convergent, concurrent, and predictive validity. Furthermore, findings raise concerns related to the VI-SPDAT’s ability to adequately reflect the complex and dynamic behavioral, social, and medical needs of those experiencing chronic homelessness. Taken together, the findings from this study point to issues with the VI-SPDAT’s reliability and validity and provide actionable information to help inform areas that should be strengthened and/or modified to better capture the needs and vulnerability of individuals experiencing homelessness. It is hoped the findings from this study can inform local efforts for assessing the needs and functioning of individuals experiencing homelessness and, more importantly, provide information that can be used to ensure equitable allocation of services in the homeless service system.

In recent years, family courts have made increasing use of advisors and/or advocates (e.g., forensic psychologists, social workers, best interest attorneys, guardian ad litems, etc.) in determining outcomes in child custody disputes. Evidence suggests that recommendations from advisors and/or advocates carry considerable weight in judges’ custody determinations. As a result, the present study examined processes and mechanisms utilized by a custody advocacy program in formulating, determining, and operationalizing the best interests of children, as well as barriers and challenges experienced in doing so. The Custody Advocacy Program (CAP) examined in this study utilizes a unique structured model (within the context of custody advocacy and legal representation), consisting of a staff attorney, volunteer attorney, and a custody advocate (collectively, the “best interest team”). Given the exploratory nature of this study, an ethnography-inspired approach was utilized, with data drawn from interviews and systematic observations (of a CAP staff meeting and a trial). Interviews were transcribed and analyzed using a constant comparative method. Findings indicated that the structural model of CAP yielded several benefits including a representation of a diverse set of perspectives when determining custody recommendations, reduced personal biases, and specialized legal knowledge. Moreover, the study revealed how staff attorneys interviewed for the purposes of the study defined the concept of “best interests,” which consisted of children having a relationship with both parents, safety, health adjustment, satisfactory mental and physical health, and legal custody. Study findings also underscored challenges in custody advocacy, such as families experiencing barriers in accessing recommended services, family noncompliance to investigations and/or recommendations, and difficulty assigning weights to factors in cases when determining custody arrangements. Overall, study findings suggest that a structured model like CAP may be beneficial in improving how children’s best interests are represented and advocated for in high-conflict custody cases. Implications from this study include a recommendation for a judicial and legislative response that ensures that all aspects of best interests are addressed in state statutes.

سعت هذه الدراسة إلى وصف وتحليل التقارير الإخبارية عن الأزمة السورية في قناة الجزيرة والميادين خلال الفترة من يناير إلى مارس 2020. وذلك من خلال تحديد حجم ونوعية اهتمام القناتين بالأزمة السورية في التقارير الإخبارية والموضوعات التي رركزتا عليها في عرض هذه الأزمة واتجاهات التغطية مصادرها والشخصيات المحورية والأطراف الفاعلة فيها.وتبحث الدراسة إشكالية التغطية الإخبارية للأزمة السورية في الفضائيات العربية، وذلك بالتركيز على قناتين فضائيتين تقدمان مواقف متعارضة من الأزمة وهما قناة الجزيرة القطرية وقناة الميادين اللبنانية، وذلك للوصول إلى خلاصات حول تأثير تلك المواقف على التغطية الإخبارية التي يفترض أن تكون موضوعية بحكم المهنية التي يجب أن تتمتع بها وسائل الإعلام.واعتمدت الدراسة على منهج المسح الإعلامي مسح مضمون وسائل الإعلام، وعلى أداة تحليل المضمون (Content Analysis)، وذلك لتحليل عينة عشوائية من التقارير الإخبارية التي تم انتاجها وبثها عن الأزمة السورية بلغ حجمها 125 تقريرا تلفزيونيا في القناتين.وخلصت الدارسة إلى أن القناتين وظفنا التقارير الإخبارية لعرض مختلف أحداث الأزمة السورية، والتعبير عن مواقفهما منها. واعتمدت القناتان على المحررين العاملين بهما في انتاج هذه التقارير، بالإضافة إلى مراسليهم سواء من الداخل السوري أو من مختلف عواصم العالم المرتبطة بالأزمة، واعتمدت القناتان على محللين سياسيين، وخبراء استراتيجيين يدعمون توجه كل منهما في الأزمة. وكانت الموضوعات العسكرية أبرز الموضوعات التي تناولتها التقارير الإخبارية في القناتين، تلتها الموضوعات الإنسانية، ثم الموضوعات السياسية في قناة الجزيرة، والموضوعات الاجتماعية في قناة الميادين.وكشفت الدراسة أن الرئيس التركي والمسؤولين الأتراك كانوا أهم الشخصيات المحورية الذين تمت الإشارة إليهم في التقارير الإخبارية لقناة الجزيرة، فيما كان القادة والمسؤولين السوريين أهم الشخصيات المحورية في تقارير قناة الميادين، وتوصلت الدراسة إلى أن الحكومة السورية جاءت في المرتبة الأولى ضمن الأطراف الفاعلة في تقارير القناتين، تلتها \"روسيا\" في قناة الجزيرة، و\"تركيا\" في قناة الميادين. وجاءت محافظة إدلب في مقدمة المناطق الجغرافية في التقارير الإخبارية للقناتين ووظفت قناة الجزيرة الصورة الحية في عناصر الإبراز بشكل أساس، أما قناة الميادين. فقد اهتمت بإبراز العناوين.

Web 3.0 represents the third generation of web technologies for incorporating decentralisation and agility in web applications. As it integrates Social Media (SM) in the Internet of Things (IoT), the endless synergies established promise consumers greater connectivity and interaction as well as more seamless movement between physical spaces. Despite these advantages, this integration also increases issues of cyber vulnerabilities and cyberattacks that cause financial, political and social damage in such data-rich environments. Although machine learning underpins this transition, the data used may contain sensitive information that could be compromised by privacy and security breaches. Therefore, techniques that maintain the utility of such valuable data while protecting individuals’ privacy are increasingly being required, with federated learning-based privacy preservation ones the current standard. However, as federated learning approaches still require a central authority, they enable significant cyberattacks.In this thesis, a major contribution is the safeguarding of heterogeneous data, such as that in IoT-integrated SM networks (i.e., SM 3.0). It does so by introducing several new contributions, a novel privacy preservation IoT-integrated SM framework and extended and improved federated learning-based ones that intensively leverage the power of the privacy definition involved in differential privacy and the trustworthiness of blockchain modules to protect against privacy attacks. Apart from improving privacy preservation in conventional federated learning-based frameworks in future social platforms, in this work, accountability and trustworthiness are investigated, with blockchain-based systems integrated into the developed frameworks. Simultaneously, by applying privacy preservation techniques, a preserved and more statistical data version of IoT-integrated SM data is offered to machine learning-based applications to maintain high utility levels.The experimental results reveal the following two key properties; the proposed frameworks yield noticeable performance improvements with high privacy and comparable utility levels while also allowing enhanced recognition of user preferences in the SM 3.0 dataset developed as part of this work to address the unavailability of data sources as well as highly classifying a user’s actions based on observations of the surrounding environment in the IoT datasets. Furthermore, since the proposed frameworks attain differential privacy on the training data, they are considered the same as standard federated learning but with a greater level of privacy preservation. In future, these frameworks will be extended by examining their integration with other distributed learning ones.

The standing slow magneto-acoustic oscillations in cooling coronal loops are investigated. There are two damping mechanisms which are considered to generate the standing acoustic modes in coronal magnetic loops namely thermal conduction and radiation. The background temperature is assumed to change temporally due to optically thin radiation. In particular, the background plasma is assumed to be radiatively cooling. The effects of cooling on longitudinal slow MHD modes is analytically evaluated by choosing a simple form of radiative function that ensures the temperature evolution of the background plasma due to radiation coincides with the observed cooling profile of coronal loops. The assumption of low-beta plasma leads to neglect the magnetic field perturbation and eventually reduces the MHD equations to a 1D system modelling longitudinal MHD oscillations in a cooling coronal loop. The cooling is assumed to occur on a characteristic time scale much larger than the oscillation period that subsequently enables using the WKB theory to study the properties of standing wave. The governing equation describing the time-dependent amplitude of waves is obtained and solved analytically. The analytically derived solutions are numerically evaluated to give further insight into the evolution of the standing acoustic waves. We find that the plasma cooling gives rise to a decrease in the amplitude of oscillations. In spite of the reduction in damping rate caused by rising the cooling, the damping scenario of slow standing MHD waves strongly increases in hot coronal loops.

In this project, we investigate exact solutions of certain nonlinear evolution equation of dispersive and dissipative nature by using different methods such as hyperbolic method, series method and jacobi elliptic function method.In chapter (1), we derive Non-dimensional shallow water equations by using equation of continuity, equation of inotion and boundary conditions for surface water wave problem. Then shallow water equations have been used to derive korteweg de Vries (KdV) equation and we use Jacobian elliptic functions to get solitary waves solution of KdV equation. In chapter (2), we deal with different examples of nonlinear evolution and wave equation and we solve it with the aid of tanh method. We use Maple7 programming to solve some equations and to draw the graph of solutions. In chapter (3), we use series method to derive the solitary wave solution for a modified KdV equation, the kink wave solution for a combined KdV-mKDV equation and we do phase plane analysis of a coupled wave equation which gives justification for choosing the special value a parameter in the series method. Finally, in chapter (4) we used mapping method and modified mapping method to derive the Jacobian elliptic function solutions for the combined KdV-mKDV equation and the squared Jacobian elliptic function solutions for the (2+1)-dimensional Kadomtsev Petviashvili equation and we draw the graphs for some solutions.