WEIBULL DISTRIBUTION BASED ON EDUCATION PARTLY INTERVAL CENSORED DATA

The work in this project is concerned with the applying of techniques for the assessment of survival analysis in data that include censored observations. Survival analysis has a lot of achievement in the medical, engineering, economic, education and other fields and it also known as failure time analysis. Partly Interval Censoring (PIC) is one of the techniques of the censoring that used in the survival analysis and it can help to treat many types of data especially the incomplete data. One of the most commonly lifetime distribution used in the reliability applications is Weibull distribution. In this project we use Weibull model based on modified education partly interval censored data as well as medical data and simulation data. Based on the medical data, we found that our model is comparable with Turnbull method. From the education data and simulation study for this particular case, we can conclude that our proposed distribution describes well the nature of the model as compared to the Turnbull method in terms of the value of scale and shape parameter estimates. Plots of survival distribution function against failure time are used to examine the predicted survival patterns for the two types of failures

Facial Beauty Prediction and Analysis based on Deep Convolutional Neural Network: A Review

Abstract: Facial attractiveness or facial beauty prediction (FBP) is a current study that has several potential usages. It is a key difficulty area in the computer vision domain because of the few public databases related to FBP and its experimental trials on the minor-scale database. Moreover, the evaluation of facial beauty is personalized in nature, with people having personalized favor of beauty. Deep learning techniques have displayed a significant ability in terms of analysis and feature representation. The previous studies focussed on scattered portions of facial beauty with fewer comparisons between diverse techniques. Thus, this article reviewed the recent research on computer prediction and analysis of face beauty based on deep convolution neural network DCNN. Furthermore, the provided possible lines of research and challenges in this article can help researchers in advancing the state – of- art in future work

The Effect of Car Ijarah / Lease Financing on the Pakistani Islamic Banking Sector's Performance

Purpose: This study aims to identify the effect of car ijarah / lease financing on the Pakistani Islamic banking sector's performance. It is one of the primary enhancements offered by Islamic financial institutions. Research Gap: Lack of studies that focus on how Car Ijarah / Lease financing affects Islamic banks is a research gap in this field. Design/Methodology/Approach: In the study, six independent variables were used: return on equity (ROE), net profit margin (NIM), Log of total financing (LTF), Ijarah real financing (IJRF), cost of total financing (CTF), total financing over total resources (TFTR), base percentage (BP), customer value index (CVI) and gross domestic product (GDP). Moreover, study analyzes the effect of Ijarah financing on Islamic bank performance in Pakistan from (2018-2022) using Variable and Arbitrary Effect Models Analysis (AFMA). The Main Findings: The study shows that Islamic banks are more profitable, liquid, better capitalized, and have lower credit risk than conventional banks. However, operational efficiency and ROE are negatively linked, while Ijarah's finance and cost total financing have a positive impact on NIM and GDP. Theoretical/Practical Implications of the Findings: Ijarah facilitates ROE development, making it attractive for banks, especially for development contributions. As a result, Ijarah financing is supportive of racial and religious growth. Thus, Islamic bank profitability and sustainable growth can be enhanced by Ijarah financing. Originality/Value: This paper presents an original study of the Islamic banking system's performance based on the study of Car Ijarah / Lease finance in Pakistan

Epidemiologija ozljeda na radu među osiguranim radnicima u Saudijskoj Arabiji od 2004. do 2016.

This is a retrospective analysis of annual reports on occupational injuries issued by the national social insurance agency of the Kingdom of Saudi Arabia (KSA) for the years 2004 through 2016. For each criterion we calculated an index based on the equation NY/Nref x100, where NY is the number of occupational injuries by a specific criterion in a specific year Y, and Nref is the number of injuries in the corresponding criterion in the reference year, i.e. 2004. We also calculated the number of injuries to number of workers ratio (Ni/Nw) for different occupations and economic sectors to get a clearer idea of the injury trends per worker. In terms of occupational injury rates (with respect to 2004), we observed increases in construction, financing & real estate (economic sectors), among engineers and technicians (occupations), in infections and secondary contusions (injury type), for upper and lower limbs (affected body parts), over falls and “other” causes. Most injuries occurred on Fridays, which is a weekend day in Saudi Arabia. We also observed increased recovery without disability (injury status). However, if we look at the number of occupational injuries per worker, we can see a decreasing trend over time for all occupations and economic sectors, most likely thanks to improved labour law and safety at work practices for insured workers. Our findings are similar to reports from other Persian Gulf countries and reflect current labour health and safety issues in the area.Retrospektivno smo analizirali godišnja izvješća o ozljedama na radu od 2004. do 2016., koje objavljuje državna agencija za socijalno osiguranje Kraljevine Saudijske Arabije. Za svaki smo kriterij izračunali odgovarajući indeks pomoću jednadžbe NY/Nref x100, gdje NY označava broj ozljeda na radu prema specifičnom kriteriju u pojedinoj godini Y, a Nref broj ozljeda u odgovarajućem kriteriju zabilježen u 2004., koja je uzeta kao referentna godina. Također smo izračunali omjer ozljeda i registriranih radnika (Ni/Nw) za različita zanimanja i gospodarske sektore kako bismo dobili jasniju sliku trenda ozljeda po radniku. Primijetili smo porast učestalosti ozljeda na radu (u odnosu na 2004.) u građevinskom I financijskom/nekretninskom sektoru, među inženjerima i tehničarima, u broju infekcija i sekundarnih kontuzija, u broju ozljeda gornjih i donjih udova, s uzrocima koji su najviše kategorizirani kao “ostali”. Većina se ozljeda dogodila petkom, koji je dan vikenda u Saudijskoj Arabiji. Također smo primijetili veću učestalost oporavka bez invaliditeta (status ozljede). No kad se pogleda broj ozljeda na radu po radniku, primjećuje se padajući trend za sva zanimanja i sve gospodarske sektore, ponajviše, vjerujemo, zbog poboljšanja zakonskih odredbi o radu i sigurnijoj praksi kod osiguranih radnika. Naši rezultati slični su onima iz drugih zemalja Perzijskoga zaljeva te odražavaju trenutačne probleme vezane uz zdravlje I sigurnost radnika

Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study

Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring

Artificial Intelligence and Internet of Things Enabled Intelligent Framework for Active and Healthy Living

Obesity poses several challenges to healthcare and the well-being of individuals. It can be linked to several life-threatening diseases. Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss. State-of-the-art technologies have the potential for long-term benefits in post-surgery living. In this work, an Internet of Things (IoT) framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight. The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients. It also attempts to automate the data analysis and represent the facts about a patient. The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system. The proposed IoT framework also benefits from machine learning based activity classification systems, with relatively high accuracy, which allow the communicated data to be translated into meaningful information

Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

Obesity is a critical health condition that severely affects an individual’s quality of life and well-being. The occurrence of obesity is strongly associated with extreme health conditions, such as cardiac diseases, diabetes, hypertension, and some types of cancer. Therefore, it is vital to avoid obesity and or reverse its occurrence. Incorporating healthy food habits and an active lifestyle can help to prevent obesity. In this regard, artificial intelligence (AI) can play an important role in estimating health conditions and detecting obesity and its types. This study aims to see obesity levels in adults by implementing AI-enabled machine learning on a real-life dataset. This dataset is in the form of electronic health records (EHR) containing data on several aspects of daily living, such as dietary habits, physical conditions, and lifestyle variables for various participants with different health conditions (underweight, normal, overweight, and obesity type I, II and III), expressed in terms of a variety of features or parameters, such as physical condition, food intake, lifestyle and mode of transportation. Three classifiers, i.e., eXtreme gradient boosting classifier (XGB), support vector machine (SVM), and artificial neural network (ANN), are implemented to detect the status of several conditions, including obesity types. The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods, achieving overall performance rates of 98.5% and 99.6% in the scenarios explored

Assessment of Risk to Healthcare Workers During the COVID-19 Pandemic: A Tertiary Care Facility Based Cross-sectional Study in Pakistan

Objective: To assess the risk of COVID-19 to healthcare workers (HCWs) in Tertiary care hospitals and its association with demographic factors. Study Design: Cross-sectional analytical study. Place and Duration of Study: Tertiary Care Hospital, Rawalpindi Pakistan, from May to Dec 2020. Methodology: Healthcare workers working in a designated COVID-19 Tertiary care hospital were included in the study. A modified "Risk assessment and management of exposure of HCWs in the context of COVID-19 tool" was distributed. HCWs were categorized as "high risk" and "low risk" of COVID-19 infection. Frequency and percentages were computed for demographic variables. Results: A total of 182 healthcare workers were included, and 167(91.7%) returned the study questionnaire. Most of them were nurses (n=65, 40.1%) working in the medical unit (n=99, 61.1%). Low risk HCWs were 73.5%( n=119) and only 26.5%( n=43)were high risk. Gender (p-value: .02) and type of HCWs (p-value: .01) were significantly associated with the risk of COVID-19. Conclusion: One-fourth of HCWs were at high risk of COVID-19 virus infection. Female gender and nurses were more likely to acquire COVID-19 infection

Sustained release of captopril from matrix tablet using methylcellulose in a new derivative form

The present study was aimed to evaluate the suitability of a newly synthesized polymer methylcellulose glutarate (MCG) for sustained release matrix system using antihypertensive drug captopril. Methylcellulose glutarate was first prepared using methylcellulose and glutaric anhydride with 1:0.5 ratio and confirmed with FTIR, NMR and MALDI. MCG was then employed in various amounts with fixed amount of captopril for the preparation of matrix tablets. Decreasing the amount of MCG had no considerable sustaining effect on in vitro drug release from the matrix system. MCG was also evaluated at different pH values and stirring speed and no appreciable difference in the release profiles was noticed. Moreover, dissolution data of optimum formulation followed zero-order kineticColegio de Farmacéuticos de la Provincia de Buenos Aire

Re-structuring university hospital’s internship program using kern’s six-step model of Instructional design

Abstract Background: Internship is a phase of training wherein a graduate learns in the context of practice, acquiring skills under supervision so that he/she may become capable of functioning independently. We are reporting the process of curriculum restructuring for strengthening the Internship Program at this university hospital. Methodology: We used Kerns’ six-step model to evaluate and restructure the internship curriculum. Step 01: Problem Identification & General need assessment- Thorough literature review revealed Internship as the crucial year of training that needs to be fashioned around the competencies required to make good doctor