Alzheimer’s disease is a multifactorial and progressive neurodegenerative disorder that affects an individual’s memory and cognitive skills. It is a major cause of death around the globe and according to 2015 Alzheimer’s association report, the death percentage has increased to 71% since year 2000. The clinical symptoms of the disease appear at a stage when the loss has become irreversible. Modern brain imaging techniques have enabled us to non-invasively visualize the internal structures of the brain. Scientists believe that structural and functional changes due to Alzheimer’s disease begin in the brain more than 20 years before any clinical symptoms are observed. Early detection of the disease is crucial for the patient, care givers and relatives to cope with the situation. It will also help medical practitioners to discover new drugs. For this reason there is an imperative need of image based automated techniques to assist medical professionals in correct diagnosis of Alzheimer’s disease using brain images. In recent years, there is an intensive research focused on the identification of Alzheimer’s disease from brain images using machine learning methods. Structural brain images like MRI have been extensively used in this regard. In our research work, we have proposed an automated image processing based approach for the early identification of Alzheimer’s disease from MRI scans of the brain. The dataset selected consists of 236 age and gender matched individuals and the features selected are volume of GM, WM and CSF, and size of hippocampus. In addition to image features, genetic aspects of Alzheimer’s disease are also considered in classification task. Well known APOE risk gene data and 14 SNP data associated with Alzheimer’s disease are incorporated in the feature set. Seven different classification models from different algorithmic paradigms are used for identification of patients and controls. For evaluation of our scheme, we have used cross validation and 66% vi ii split test strategy. Classification results are obtained using image features, genetic features and combination of the both. It is observed that image features produced best classification of cases and controls. On the other hand, genetic data can be very useful in predicting the risk of disease well before any changes to brain are observed. The proposed approach is novel because it has been able to achieve higher accuracy/specificity/sensitivity values even using smaller feature set which is not the case of existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. Best results (100% accuracy, 100% specificity, 100% sensitivity) are achieved using volume of GM and size of left hippocampus with J48 classifier. Similarly APOE risk gene predicted the disease with 75% accuracy for all classifiers whereas SNP data achieved 86% accuracy with Naïve Bayes and SVM. The proposed approach will play a vital role in the domain of Computer Aided Diagnostics and Preventive Studies.
مولانا شوکت علی مرحوم اسی مہینہ کادوسرا المناک سانحہ مولانا شوکت علی خادم کعبہ کی وفات ہے، شوکت علی مرحوم ہندوستان کے اُن پرچند مسلمانوں میں ایک تھے جن کی شہرت نہ صر ف ہندوستان تک محدود ہے بلکہ دنیائے اسلام کے دوردراز گوشوں تک ان کانام عزت واحترام کے ساتھ لیاجاتاہے اوریہ واقعہ ہے کہ مرحوم بجا طور پر اس شہرت و احترام کے مستحق تھے، پچھلے چند برسوں کوچھوڑ کر بلاخوف تردید کہا جا سکتاہے کہ مرحوم کی زندگی قربانی، ایثار، ولولہ اورجوش عمل کے اعتبار سے مسلمانوں کے لیے قابل تقلید نمونہ تھی،جنگ طرابلس اورجنگ بلقان سے لے کراب تک ہندوستانی مسلمانوں کی اجتماعی اورسیاسی زندگی کے جتنے دور گذرے ہیں مرحوم کی خدمات اُن تمام دوروں میں اس قدر نمایاں اوراس قدر روشن ہیں جنہیں کسی طرح فراموش نہیں کیا جاسکتا’’علی برادران‘‘ہندوستان کی دوشخصیتوں سے مرکب ایک ایسی حقیقت کانام ہے جس کے زبان پرآتے ہی کرداروعمل اورشجاعت وبسالت کاایک سبق آموز نقشہ آنکھوں کے سامنے آجاتاہے۔ صد حسرت وافسوس کہ ہندوستان اپنے ایک جانباز،بہادر سپاہی اورپرانے خادم سے ہمیشہ کے لیے محروم ہوگیا۔انا ﷲ واناالیہ راجعون۔ حق تعالیٰ مرحوم کی خدمات کوقبول فرمائے اوردامانِ رحمت میں جگہ دے۔ [دسمبر۱۹۳۸ء]
Pedagogical innovation is critical in the twenty-first century, and each university, classroom or educational institution must take this point as an ultimate goal of their educational system. Professors are responsible for preparing students to tackle new challenges, which is why innovative teaching methods have become important for keeping spirit up.
This paper sheds light on pedagogical innovation at universities and give a review of its many forms, features, methodologies, and significance in the twenty-first century. It has been found that the types of pedagogical innovation are classified socially and technically. Moreover, the paper asserts that the innovative teaching methods do not consider all students at the same comprehension level according to their different abilities and skills.
Only about 4% of the Universe’s composition is known and understood, while the remaining 96% is the unknown and mysterious. Ever since the concrete discovery of the dark matter (≈ 26% of the total Universe) by Vera Rubin (mainly)1 in 1970s (otherwise known as the constant rotational velocity curves profiles) and of dark energy (≈ 70% of the total Universe’s energy density) by Adam Riess, Brian Schmidt, Saul Perlmutter and others in 1997, there have been many proposals for the both. The dark matter models contain quantum fields (mainly); scalar, vector and tensor; while other proposals rely on the modified gravity. The dark energy models could be categorized into two groups as: (1) the vacuum energy (non-dynamical); (2) the dynamical models. In the work presented in this thesis we do two things: (I) We explain the constant rotational velocity curves of the spiral galaxy in f(R) gravity. We showed that we can get the rotational velocity curves in f(R) gravity, in vacuum and in different 1Others include W. Kent Ford, Jr. and N. Thonnard. matter distribution scenarios of the galactic environment. We also used the Brans-Dicke theory to get information about the dark matter (particle nature) and its behavior in the galactic environment. (II) We describe the dark energy to be a combination of both non-dynamical vacuum energy and dynamical energy. The non-dynamical vacuum energy comes from the minimum energy of the scalar Higgs potential while the dynamical part comes from the evolving Higgs fields. We argue that all the dark energy models must have some prior connection with Particle Physics no matter which category they fall in. For this reason, we take the extension of the standard model of the Particle Physics by including two Higgs doublets in two different scenarios and the extension of the two Higgs doublet model with different vacuua to show that we can get the accelerated expansion of the Universe from scalar fields already present in Particle Physics in the quintessential regime while satisfying all Particle Physics constrains.