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Mapping Individual Software Engineering Practices on Essence

Thesis Info

Author

Sikandar Azam Shah

Supervisor

Muhammad Saud Khan

Program

MS

Institute

Riphah International University

Institute Type

Private

City

Islamabad

Country

Pakistan

Thesis Completing Year

2018

Thesis Completion Status

Completed

Page

viii, 71 . : ill. ; 30 cm. +CD

Language

English

Other

Submitted in fulfillment of the requirements for the degree of Master of Science in Software Engineering to the Faculty of Computing.; Includes bibliographical references and appendix; Thesis (MS)--Riphah International University, 2018; English; Call No: 005.1 SIK

Added

2021-02-17 19:49:13

Modified

2023-01-06 19:20:37

ARI ID

1676711675029

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پروفیسر عبدالحلیم ندوی

پروفیسر عبدالحلیم ندوی
افسوس ہے کہ اردو اور عربی زبان کے صاحب علم و قلم پروفیسر عبدالحلیم ندوی ۱۲؍ اکتوبر ۲۰۰۵؁ء کو دہلی کے اپولو اسپتال میں انتقال فرماگئے، اناﷲ وانا الیہ راجعون۔ ان کی تدفین جامعہ ملیہ اسلامیہ کے قبرستان میں ہوئی، مرحوم کئی سال سے مسلسل علیل ہونے کی بنا پر گوشہ نشین اور موتواقبل ان تموتوا کی عملی تفسیر ہوگئے تھے۔
پروفیسر عبدالحلیم کا وطن صاحب گنج دیوریا تھا جہاں ۱۹۲۶؁ء میں ان کی ولادت ہوئی مگر اب دہلی ہی کے ہوگئے تھے، عربی کی اعلا تعلیم کے لیے دارالعلوم ندوۃالعلماء میں داخلہ لیا، وہاں سے فراغت کے بعد جامعہ ملیہ اسلامیہ میں عصری تعلیم کی تحصیل کی اور علی گڑھ مسلم یونیورسٹی سے پی۔ایچ۔ڈی کی ڈگری لی، پھر قاہرہ یونی ورسٹی سے بھی کسب فیض کیا۔
مصر سے واپسی کے بعد جامعہ ملیہ اسلامیہ میں تدریس کی خدمت پر مامور ہوئے، پھر سنٹرل انسٹی ٹیوٹ آف انگلش اینڈ فارن لینگویجز حیدرآباد سے وابستہ ہوئے اور پروفیسر، صدر شعبہ اور فیکلٹی کے ڈین ہوئے، وظیفہ یاب ہونے کے بعد کچھ دنوں جواہر لال نہرو یونیورسٹی دہلی سے متعلق رہے اور ۱۹۸۳؁ء میں دمشق یونیورسٹی میں وزیٹنگ پروفیسر ہوئے اور جامعہ ملیہ دہلی میں پروفیسر ایمرٹس مقرر ہوئے، وہ ایک اچھے اور کامیاب استاد کی حیثیت سے نیک نام اور طلبہ میں مقبول تھے۔
ڈاکٹر سید عابد حسین نے ذاکر انسٹی ٹیوٹ جامعہ ملیہ اسلامیہ دہلی سے سہ ماہی ’’اسلام اور عصر جدید‘‘ کے نام سے ایک باوقار علمی رسالہ نکالاتو پروفیسر عبدالحلیم کو اس کا نائب مدیر مقرر کیا، مرحوم کو اردو اور عربی دونوں زبانوں میں تحریر و تقریر کا ملکہ تھا، اردو میں تاریخ ادب عربی کی تین جلدیں لکھیں اور عربی میں ہندوستان کے عربی مدارس پر مراکز التعلیمیۃ العربیۃ فی الھند لکھی، یہ دونوں کتابیں مقبول ہوئیں اور حوالے کے...

المقاصد عند الإمام الشاطبي

Scholars of the Usul -early and recent- have paid utmost importance to the Maqâsid al-Shariah or the Objectives of Shariah and tried to substantiate, associate and link them with its sources, Qur’an and Sunnah, then to derive from them the Legislator’s or the Shari‘s purpose, the revelation’s true purpose and how it benefits the creatures in terms of promoting what is in their interest and shunning away what is harmful. These objectives became the point of interest for the jurists and scholars who toiled to understand the texts and what they connoted and they continue to venture into its depths and extract its treasures. It becomes quite evident from their exegeses of Qur’an and the Prophetic Traditions and elucidations of the juristic principles and issues. Imam al-Shâtibī and Ibn ‘Ashoor are two such scholars in this field who studied the Objectives of Shariah and contributed to this discipline so greatly that they became reference points for those who came after them. The basic principles in the field that were inferred by them are still its pillars. The Objectives of Shariah are divided into four kinds with reference to their benefits: the necessities, needs, improvements and supplements. The necessities are further divided into protection of Deen, protection of self, of intellect, of possessions and of race among others. Prudence demands that all means should be employed to safeguard these necessities. Thus knowing the Objectives of Shariah and their promulgation among the masses is quite important for the society since it leads towards promotion of the beneficial and shunning away the harmful elements in it.

Automated Detection and Classification of Brain Tumor from Mri Images Using Machine Learning Methods

The focus of this thesis is to report an automated, efficient, and robust method of brain tumor detection and classification from Magnetic Resonance Images (MRI) images. Clinically, it is a challenging issue faced by the researchers working in this domain. In routine health care units, Magnetic Resonance (MR) scanners are being used to generate a massive number of brain slices, underlying the anatomical details. Pathological assessment from this medical data is being carried out manually by the radiologists or neurooncologists. Due to huge volume of brain anatomical data produced by MRI scanners, it is almost impossible to manually analyze every slice. Conclusively, if automated protocols are executed for auto-interpretation; not only the radiologist will be assisted but also a better pathological assessment process would be expected. Several methods have been suggested to address this problem, but still, accuracy, robustness and optimization is still an open issue to address. The development of such automated procedures is difficult due to complex organization of brain cells, several types of tumor, difference in medical traits of a specific ethnicity and many more factors. To achieve the target, research has been started from reviewing the most popular and prominent state-of-the-art methods. Based upon the reviewed literature, automated brain tumor detection and classification techniques have been reported with high computational cost, low classification rates, detection and classification of only one or a few of brain tumor types, lack of robustness, etc. Therefore, step wise research and experiments based upon empirical scientific methodology have been performed in order to achieve the objectives of brain tumor classification. In the first step, a research activity has been performed to report a colorization method with the aims to enhance the visualization, cell characterization and interpretation of brain cells. The high dimensional brain data scanned through MRI embodied in gray scale, if converted, represented, mapped and/or visualized in colored versions, irrefutably, more definitive and more accurate the pathological assessment process will be. Several methods have been reported to represent brain MRI data in color with high computational complexity. In this research activity, an efficient method of colorization using frequencies from visible range of color spectrum, has been proposed to embody the variations and sensitivity of the brain MRI images. The experiments have been performed on a locally developed dataset. Side by side visual comparison based on multiple MRI sequences of identical subjects by domain experts have proved the adequate success and fruitfulness of the story. The reported method of colorization as a protocol has also been deployed in Department of Radiology and Diagnostic Images, Bahawal Victoria, Hospital, Bahawalpur (BVHB), Pakistan. Radiologists are using this tool for visual interpretation and monitoring of the patients for their assessment and clinical decision making. In second step, an automated approach using Gabor filter and Support Vector Machines (SVMs), for the classification of brain MRI slices as normal or abnormal has been reported. Accuracy, sensitivity, specificity and AUC-value have been used as standard quantitative measures to evaluate the proposed algorithm. To the best of our knowledge, this is the first study in which experiments have been performed on The Whole Brain Atlas - Harvard Medical School (HMS) dataset, achieving an accuracy of 97.5%, sensitivity of 99%, specificity of 92% and AUC-value as 0.99. To test the robustness against medical traits based on ethnicity and to achieve optimization, a locally developed dataset has also been used for experiments and remarkable results with accuracy (96.5%), sensitivity (98%), specificity (92%) and AUC-value (0.97) were achieved. Comparison with state-of-the art methods proved the overall efficacy of the proposed method. In third step of the thesis, a method has been proposed to classify brain MRI image into brain related disease groups and further tumor types. The proposed method employed Gabor texture followed by a set of more distinguished statistical features. These features are then used by SVM to classify the brain disorder. K-fold strategy has been adapted for cross validation of the results to enhance generalization of SVM. Experiments have been performed to classify brain MRI images as normal or belonging to either of the common diseases, such as cerebrovascular, degenerative, inflammatory, and neoplastic. Neoplastic disease is further classified into glioma, meningioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, or sarcoma. Standard quantitative evaluation measures, i.e., accuracy, specificity, sensitivity, and AUC-value have been used to test performance of the developed system. The proposed system has been trained on complete dataset of HMS, so the trained model has the ability to deal with a wide range of brain abnormalities. Further, to achieve robustness, a locally developed dataset has also been used for experiments. Remarkable results on different orientations, sequences of both of these datasets as per accuracy (up-to 99.6%), sensitivity (up-to 100%), specificity (up-to 100%), precision (up-to 100%) and AUC-value (up-to 1.0) have been achieved. The proposed method classifies the brain MRI slices into defined abnormality groups. It can also classify the abnormal slices into tumorous or non-tumorous one. The major achievement of the developed system is its auto classification of tumorous slices into the slices having primary tumor or secondary tumor and their further types, which possibly could not be determined without biopsy. In fourth step of the thesis, results achieved through the proposed method of brain tumor classification have been validated on cross data set. The drive of this research activity is to verify the robustness of the reported approach. For this, the model has been trained completely on one data set, while tested completely on another one. A benchmarked dataset HMS and a locally developed dataset BVHB dataset has been used for this purpose. To ensure its robustness, complete HMS dataset was used to train the model and BVHB was used to test the trained model and vice versa. Standard evaluation measures, i.e., accuracy, specificity, sensitivity, precision and AUC-value have been used to evaluate the system. It has been established that the proposed method deals with multiformity and variability of brain MRI data. Overall, suppositions regarding robustness of the proposed method were attained with maximum measures as per accuracy as 92%, specificity as 92%, sensitivity as 93%, precision as 92%, and AUC-value as 0.93. The overall results achieved through the proposed method, manifests that it is robust, efficient and reliable. It has been trained on a large volume of multi-orientations, multi-sequences belonging to multi-datasets to deal with multiformity and to face variability.