The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. Treatment (...) of liver diseases at an early stage is, therefore, an important issue as is designing a model to diagnose early disease. Firstly, an appropriate feature should be identified which plays a more significant part in the detection of liver cancer at an early stage. Therefore, it is essential to extract some essential features from thousands of unwanted features. So, these features will be mined using data mining and soft computing techniques. These techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. Different Regression algorithms are then applied to these methods individually to evaluate the result. Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618. (shrink)

Over the past century, private tutoring in many countries has increased dramatically. Moreover, the main disadvantage of PT is that has a byproduct and a characteristic on the educational system in developing countries in terms of contributing to conditions such as large class sizes, low public expenditures, and an inadequate number of universities. In Saudi Arabia, the spread of PT at school and university levels has yet to be addressed by researchers. One goal of this examination was to research the (...) spread of PT in relation to mathematics being taught at school and university levels. This investigation used quantitative data of the questionnaire to determine the reasons for the spread. A total of 1000 students responded from University of Ha’il. The edge plans given results that are associated and like the assessment that is performed with the whole data. The current study found that a good family financial situation and lack of parental supervision of the children were significant factors underlying the spread of private tutoring at school and university levels. (shrink)

Extropy, as a complementary dual of entropy, has been discussed in many works of literature, where it is declared for other measures as an extension of extropy. In this article, we obtain the extropy of generalized order statistics via its dual and give some examples from well-known distributions. Furthermore, we study the residual and past extropy for such models. On the other hand, based on Farlie–Gumbel–Morgenstern distribution, we consider the residual extropy of concomitants of m-generalized order statistics and present this (...) measure with some additional features. In addition, we provide the upper bound and stochastic orders of it. Finally, nonparametric estimation of the residual extropy of concomitants of m-generalized order statistics is included using simulated and real data connected with COVID-19 virus. (shrink)

The coronavirus outbreak around the world has caused public health concerns and changes in peoples’ behaviors and psychological distress. The pandemic impacts on human behavior, emotions, and cognition, leading to diverse reactions in relation to awareness of the disease. However, there is little understanding around the psychological impacts of the pandemic and strategies to overcome this impact. This study aimed to examine individuals’ reactions toward the COVID-19 pandemic in relation to their psychological hardiness, their degree of awareness toward the pandemic, (...) and precautionary measures taken. Individuals living in Saudi Arabia were invited to complete an online questionnaire which included demographic items, psychological responses to the pandemic, awareness of COVID-19, and measures of psychological hardiness. A total of 1272 individuals were recruited into the study, with the majority being female. Results indicated that the average psychological responses to the COVID-19 pandemic in the study sample were 75.85%. This indicates that the sample generally has a high level of positive psychological responses to the COVID-19 pandemic. The awareness of COVID-19 among Saudi was 91.50%. This indicates a high level of awareness among the study sample. (shrink)

Type 2 diabetes mellitus (T2DM) is a multifaceted chronic condition characterized by insulin resistance and hyperglycemia. This review synthesizes evidence on the influence of lifestyle factors such as diet, physical activity, and stress on the development and progression of T2DM. It highlights the intricate relationship between obesity, insulin resistance, and β-cell dysfunction, with a particular focus on the role of psychological stress in exacerbating disease outcomes. Through the analysis of recent studies, the review explores how lifestyle interventions, including dietary changes, (...) exercise, and stress management, can improve glycemic control and reduce complications. This comprehensive review underscores the need for integrated, personalized management strategies in T2DM care, emphasizing the importance of addressing both physiological and psychological factors to optimize outcomes and prevent complications. (shrink)

Background: Artificial intelligence (AI) is transforming healthcare systems by improving operational efficiency, simplifying patient care procedures, and improving diagnostic accuracy. Artificial intelligence (AI) technologies, like machine learning and natural language processing, present previously unheard-of chances to quickly and accurately evaluate enormous volumes of healthcare data, assisting with clinical decision-making and enhancing patient outcomes. Aim thorough examination and analysis of artificial intelligence's impact on healthcare leadership, with a particular emphasis on present nursing trends and their implications for the future. The study (...) tries to uncover the advantages, difficulties, and consequences of AI integration by looking at how AI technologies including clinical decision support systems, predictive analytics, robots, natural language processing, and telehealth are being used in nursing practice. Method: A comprehensive analysis including research articles published between 2015 and 2024 was carried out. To give a thorough overview of AI's present and future uses in healthcare, major themes and trends were found and summarized. Results: By stressing AI's role in improving diagnostic accuracy and patient outcomes, the study highlights the technology's major contributions to drug discovery, virtual patient care, and medical imaging. Human-centered design concerns, the necessity of educational changes, and ethical challenges surrounding the application of AI surfaced as crucial topics needing attention. Conclusion: AI has enormous potential to transform healthcare by enhancing operational effectiveness, optimizing the delivery of care, and increasing diagnostic precision. Still, ethical issues must be resolved, interdisciplinary cooperation must be promoted, and educational frameworks must be improved in order to provide healthcare workers with the necessary AI skills. (shrink)

Background: Context: Digital health interventions have become essential instruments in contemporary medicine, providing viable means of improving patient outcomes and healthcare provision. The goal of this study is to examine the impact, difficulties, and potential future directions of digital health interventions across a range of healthcare situations by synthesizing results from 12 carefully chosen studies. Aim: This study's objective is to thoroughly examine and summarize the body of research on digital health interventions, with an emphasis on acceptance and utilization as (...) well as implementation and efficacy. Method: Twelve pertinent papers published between 2016 and 2024 were found after a thorough search of electronic databases. Numerous digital health interventions were covered by these research, such as remote monitoring systems, telemedicine, and mobile applications. In order to find common themes, sub-themes, and patterns among the chosen research, thematic analysis was used in the data synthesis process. Results: A number of important themes emerged from the thematic analysis, including interoperability problems, patient engagement, healthcare provider acceptability, regulatory concerns, and usability issues. The sub-themes that fell under these categories emphasized the variables that affect the uptake and efficacy of digital health interventions. Conclusion: The synthesis of data emphasizes how digital health initiatives can increase clinical results, improve patient care, and streamline the delivery of healthcare. But in order to fully reap the benefits, major obstacles like interoperability problems, regulatory difficulties, and usability impediments need to be solved. The recommendations put forth aim to promote innovation and evidence-based practice in digital health by stressing user-centered design, improving digital literacy among medical practitioners, and encouraging interdisciplinary collaboration. (shrink)

The current study aimed to construct a multicultural psychological empowerment scale for Saudi women depending on the four dimensions of psychological empowerment, namely, meaningfulness, impact, self-efficacy, and self-determination. It was applied to a sample of Saudi women from various age categories, different social, educational, and employment status, and geographical regions. Pooled confirmatory factor analysis was using to determine the reliability and validity of the scale. As a result, the reliability and validity of the entire model were satisfied. Specifically, the composite (...) reliability values fell in the range between 0.804 and 0.883. Meanwhile, the convergent validity was achieved as these values produced from each construct were higher than the acceptable limit of 0.50. Also, the construct validity of fitness indexes was achieved. In its final form, multicultural psychological empowerment scale for Saudi Women included phrases distributed over its four dimensions. The study suggested systematic measurements of psychological empowerment for Saudi women from different categories, targeted with therapeutic, preventative and developmental visions, plans, and programs to determine the extent of their psychological empowerment. (shrink)

This study reveals that increases in the global population command an augmented demand for products and services that calls for more effective ways of using existing natural resources and materials. The recent development of information and communication technologies, which had a great impact on many areas, also had a damaging effect on the environment and human health. Therefore, societies are moving toward a greener future by reducing the consumption of nonrenewable materials, raw materials, and resources while at the same time (...) decreasing energy pollution and consumption. Since information technology is considered a tool for solving ecological difficulties, the green Internet of things is playing a vital role in creating a sustainable home. Extensive data analysis is required to obtain a valuable overview of the large and diverse data generated by the G-IoT. The gathered information will facilitate forecasting, decision-making, and other activities related to smart urban services and then contribute to the incessant development of G-IoT technology. Therefore, even if sustainable and smart cities become an actuality, the G-IoT approach and the knowledge gained through big data analysis will make cities more sustainable, safer, and smarter. The goal of this article is to combine innovation in technological development with the main focus on resource sharing in creating cities that improve the quality of life while reducing pollution and realizing more efficient use of the raw materials. In the practice of big data science, it is always of interest to provide the best description of the data under consideration. Recent studies have pointed out the applicability of the statistical distributions in modeling data in applied sciences. In this article, we introduce a new family of statistical models to provide the best description of the life span of the wireless sensors network’s data. Based on the proposed approach, a special submodel called new exponent power-Weibull distribution is studied in detail. The applicability of the proposed model is shown by analyzing the life span of the wireless sensors network’s data. (shrink)

Statistical distributions play a prominent role for modeling data in applied fields, particularly in actuarial, financial sciences, and risk management fields. Among the statistical distributions, the heavy-tailed distributions have proven the best choice to use for modeling heavy-tailed financial data. The actuaries are often in search of such types of distributions to provide the best description of the actuarial and financial data. This study presents a new power transformation to introduce a new family of heavy-tailed distributions useful for modeling heavy-tailed (...) financial data. A submodel, namely, heavy-tailed beta-power transformed Weibull model is considered to demonstrate the adequacy of the proposed method. Some actuarial measures such as value at risk, tail value at risk, tail variance, and tail variance premium are calculated. A brief simulation study based on these measures is provided. Finally, an application to the insurance loss dataset is analyzed, which revealed that the proposed distribution is a superior model among the competitors and could potentially be very adequate in describing and modeling actuarial and financial data. (shrink)

Background: This systematic review explores a diverse range of international and indigenous healthcare research studies, spanning topics from diabetes management to cardiovascular care. The synthesis aims to identify overarching themes, sub-themes, and emerging trends in healthcare practices, emphasizing the integration of digital technologies and advancements in nutrition therapy. Aim: The primary objective is to provide a comprehensive overview of healthcare literature, combining international and indigenous perspectives, to uncover commonalities and distinctions in approaches to patient care, treatment modalities, and research methodologies. (...) Method: A systematic and thorough review of 20 selected studies, encompassing international and Saudi Arabian research, was conducted. The studies, published between 2020 and 2024, were analyzed for their key themes, sub-themes, and trends, creating a detailed understanding of the current landscape of healthcare practices. Results: The synthesis revealed recurring themes, including the integration of digital technologies in diabetes care, advancements in medical nutrition therapy for obesity management, dynamic risk assessment in atrial fibrillation, and the potential of genetically modified food for nutritional security in Saudi Arabia. Sub-themes elucidated nuanced aspects of these overarching themes, offering a holistic perspective on contemporary healthcare strategies. Conclusion: International and indigenous research studies collectively underscore the importance of collaborative and context-specific approaches to healthcare. While global insights contribute to a broader understanding, indigenous studies emphasize the necessity of tailoring interventions to local needs. The synthesis not only highlights current achievements in healthcare but also identifies areas for further exploration and improvement. (shrink)

Hepatocellular carcinoma (HCC) presents a significant problem that necessitates a greater understanding of its underlying molecular complexity in order to improve diagnostics and therapies. Recent studies have shed light on the critical role that mitochondrial dysfunction plays in the development and course of HCC. Once thought to be primarily involved in the synthesis of cellular energy, mitochondria are now known to be key participants in the regulation of a variety of cellular functions that go beyond bioenergetics. The purpose of this (...) review is to elucidate the diagnostic and therapeutic implications of mitochondrial dysfunction in HCC by synthesising ideas from conventional and contemporary scientific literature. Within diagnostics, the discovery of mitochondrial biomarkers such as mutations in mitochondrial DNA and abnormalities in respiratory chain complex activities provides a state-of-the-art viewpoint for the timely identification and accurate description of HCC. In addition, non-invasive imaging methods that focus on mitochondrial function offer prospective means of improving clinical diagnostic accuracy. Moving on to treatment, a growing area of study investigates how mitochondria-targeted therapies might be used to treat HCC. Novel substances and repurposed medications with the potential to induce selective apoptosis in cancer cells, reduce oxidative stress, and restore mitochondrial homeostasis are highly promising. In the fight against hepatocellular carcinoma, an understanding of the complex interactions between mitochondrial dysfunction and HCC opens new diagnostic opportunities and paves the way for individualised treatment plans. (shrink)

This twelfth volume of Collected Papers includes 86 papers comprising 976 pages on Neutrosophics Theory and Applications, published between 2013-2021 in the international journal and book series “Neutrosophic Sets and Systems” by the author alone or in collaboration with the following 112 co-authors (alphabetically ordered) from 21 countries: Abdel Nasser H. Zaied, Muhammad Akram, Bobin Albert, S. A. Alblowi, S. Anitha, Guennoun Asmae, Assia Bakali, Ayman M. Manie, Abdul Sami Awan, Azeddine Elhassouny, Erick González-Caballero, D. Dafik, Mithun Datta, Arindam Dey, (...) Mamouni Dhar, Christopher Dyer, Nur Ain Ebas, Mohamed Eisa, Ahmed K. Essa, Faruk Karaaslan, João Alcione Sganderla Figueiredo, Jorge Fernando Goyes García, N. Ramila Gandhi, Sudipta Gayen, Gustavo Alvarez Gómez, Sharon Dinarza Álvarez Gómez, Haitham A. El-Ghareeb, Hamiden Abd El-Wahed Khalifa, Masooma Raza Hashmi, Ibrahim M. Hezam, German Acurio Hidalgo, Le Hoang Son, R. Jahir Hussain, S. Satham Hussain, Ali Hussein Mahmood Al-Obaidi, Hays Hatem Imran, Nabeela Ishfaq, Saeid Jafari, R. Jansi, V. Jeyanthi, M. Jeyaraman, Sripati Jha, Jun Ye, W.B. Vasantha Kandasamy, Abdullah Kargın, J. Kavikumar, Kawther Fawzi Hamza Alhasan, Huda E. Khalid, Neha Andalleb Khalid, Mohsin Khalid, Madad Khan, D. Koley, Valeri Kroumov, Manoranjan Kumar Singh, Pavan Kumar, Prem Kumar Singh, Ranjan Kumar, Malayalan Lathamaheswari, A.N. Mangayarkkarasi, Carlos Rosero Martínez, Marvelio Alfaro Matos, Mai Mohamed, Nivetha Martin, Mohamed Abdel-Basset, Mohamed Talea, K. Mohana, Muhammad Irfan Ahamad, Rana Muhammad Zulqarnain, Muhammad Riaz, Muhammad Saeed, Muhammad Saqlain, Muhammad Shabir, Muhammad Zeeshan, Anjan Mukherjee, Mumtaz Ali, Deivanayagampillai Nagarajan, Iqra Nawaz, Munazza Naz, Roan Thi Ngan, Necati Olgun, Rodolfo González Ortega, P. Pandiammal, I. Pradeepa, R. Princy, Marcos David Oviedo Rodríguez, Jesús Estupiñán Ricardo, A. Rohini, Sabu Sebastian, Abhijit Saha, Mehmet Șahin, Said Broumi, Saima Anis, A.A. Salama, Ganeshsree Selvachandran, Seyed Ahmad Edalatpanah, Sajana Shaik, Soufiane Idbrahim, S. Sowndrarajan, Mohamed Talea, Ruipu Tan, Chalapathi Tekuri, Selçuk Topal, S. P. Tiwari, Vakkas Uluçay, Maikel Leyva Vázquez, Chinnadurai Veerappan, M. Venkatachalam, Luige Vlădăreanu, Ştefan Vlăduţescu, Young Bae Jun, Wadei F. Al-Omeri, Xiao Long Xin.‬‬‬‬‬‬. (shrink)