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مولانا سید شاہ رضوان اﷲ قادری مجیبی

مولانا سید شاہ رضوان اﷲ قادری مجیبی
افسوس ہے کہ خانقاہ مجیبیہ پھلواری شریف پٹنہ کے سجادہ نشین مولانا سید شاہ رضوان اﷲ قادری مجیبی ۳۱؍ دسمبر ۲۰۰۳؁ء کو وفات پاگئے، اِنا ﷲ وَاِنا اِلیہ رَاجِعونْ۔
ابھی عمر کی جس منزل میں وہ تھے، یہ جانے کے دن نہیں ہوتے لیکن مشیت الٰہی میں کس کا دخل؟ موت کا تو وقت مقرر ہے، فَاِذَا جَآءَ اجلُھُمْ لَا یستَأخِرُونْ سَاعۃ وَّلَا یستَقْدِمُوْنَ۔[الاعراف:۳۴]
خانقاہ مجیبیہ کا علمی و روحانی فیض مدت دراز سے جاری ہے، شاہ صاحب اس کی قدیم روایات اور اپنے عالی مقام اسلاف کی خصوصیات اور خوبیوں کے حامل تھے اور خود بھی ایک صاحب فیض عالم اور ذاکر و شاغل بزرگ تھے، ان کی ذات سے ہزاروں طالبین و سالکین فیض یاب ہورہے تھے مگر اب تزکیہ و اصلاح اور ارشاد کا یہ سرچشمہ بند ہوگیا۔
ان کو تقویٰ و اخلاص، صوم و صلوٰۃ کی پابندی، سادگی و درویشی اور خوش مزاجی و خوش خلقی ورثے میں ملی تھی، راقم کو دو تین بار ان کی خدمت میں حاضری اور ان کی پاکیزہ سیرت اور مطہر زندگی کے جلوے دیکھنے کا اتفاق ہوا، ہر بار نہایت خندہ پیشانی سے ملے اور لطف و کرم سے پیش آئے، اﷲ تعالیٰ ان کے درجات بلند کرے اور پس ماندگان کو صبر جمیل عطا کرے، آمین۔
(ضیاء الدین اصلاحی، فروری ۲۰۰۴ء)

Implementation of Gaussian Process Regression in Estimating Motor Vehicle Insurance Claims Reserves

This study aims to calculate the allowance for losses by applying Gaussian Process regression to estimate future claims. Modeling is done on motor vehicle insurance data. The data used in this study are historical data on PT XYZ's motor vehicle insurance business line during 2017 and 2019 (January 2017 to December 2019). Data analysis will be carried out on the 2017 - 2019 data to obtain an estimate of the claim reserves in the following year, namely 2018 - 2020. This study uses the Chain Ladder method which is the most popular loss reserving method in theory and practice. The estimation results show that the Gaussian Process Regression method is very flexible and can be applied without much adjustment. These results were also compared with the Chain Ladder method. Estimated claim reserves for PT XYZ's motor vehicle business line using the chain-ladder method, the company must provide funds for 2017 of 8,997,979,222 IDR in 2018 16,194,503,605 IDR in 2019 amounting to Rp. 1,719,764,520 for backup. Meanwhile, by using the Bayessian Gaussian Process method, the company must provide funds for 2017 of 9,060,965,077 IDR in 2018 amounting to 16,307,865,130 IDR, and in 2019 1,731,802,871 IDR for backup. The more conservative Bayessian Gaussian Process method. Motor vehicle insurance data has a short development time (claims occur) so that it is included in the short-tail type of business.

Correlating Carotid Imaging and Phylogenetic Trees for the Pre and Post Analysis of Genetic Ischemic Strokes

Ischemic stroke is the most commonly occurring type of stroke and one of the most communal causes for disability and death in the world as per World Health Organization. Multiple factors such as hypertension, diabetes, arterial fibrillation, heart diseases, transient ischemic strokes, etc. contribute to ischemic stroke susceptibility. There is a compelling need for follow up checkups and post analysis to prevent further strokes. Apart from clinical tests, a lot of research is being carried out on computer based automated techniques and mechanisms for estimation of ischemic stroke risk. Ultrasound images of the carotid artery are used for development of noninvasive image based methods for stroke risk estimation however; carotid artery morphology, noise and artifacts in the ultrasound images can lead to false classification. Carotid intima media thickness is an indicator of future ischemic stroke. In this research, we have proposed an automatic ischemic stroke risk estimation approach using carotid intima media thickness from longitudinal carotid B-mode ultrasound images. Based on carotid intima media thickness, a classification scheme is proposed to associate the carotid artery stenosis with ischemic stroke risk. The proposed approach is tested and clinically validated on a data set of 100 longitudinal ultrasound images of the carotid artery. There is no significant difference between intima media thickness measurements obtained using our approach and the manual measurements by experts. The intra-observer error of 0.088, a Coefficient of Variation of 12.99%, Bland-Altman plots with small differences between experts (0.01 and 0.03 for Expert 1 and Expert 2, respectively) and Figure of Merit of 98.5% are obtained. The proposed approach makes the risk estimation process automatic and yet reduces the risk of subjectivity and operator variability for intima media thickness measurement. Additionally, some of stroke cases are suspected to be genetic as the patients do not suffer from the conventional risk factors. Extensive research has been conducted to investigate the unknown factors other than the conventional ones and their relationship with genetics. We have analyzed genotype data for stroke risk estimation. Nine classification models are used on the SNPs data to analyze and classify individuals. An accuracy of 88.16% is achieved by the proposed approach. Ischemic stroke risk has been correlated with genetic distances. For this purpose phylogenetic trees have been used. Analysis suggests that given two populations might be genetically close but they might be far with respect to ischemic stroke risk. Proposed research has addressed both the medical image analysis and genetic data analysis for stroke risk estimation. The proposed approach has achieved higher accuracy, specificity and sensitivity values when compared to existing approaches.
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