ایٹمی پروگرام کا اصل خالق کون
چئیر مین بھٹو شہید کی پھانسی کی اصل وجہ کیا تھی اور ضیاء الحق کو ایٹمی پروگرام کا کریڈٹ ؟
کبھی یہ سوچنا تم نے کیا کیا لوگو
یہ تم نے کس کو سرِ دار کھو دیا لوگو
یہ نظم اٹک سے تعلق رکھنے والے سابق سینیٹر شاعر دانشور احمد وحید اختر مرحوم کی ہے ۔
تو ہراساں نہ ہوا ظلم کے ایوانوں سے
تو نے جنگ لڑی وقت کے فرعونوں سے
سے قائد عوام اے قائدِ عوام
Epilepsy is a neurological disease in which people suffer from seizure attack and lose the normal function of brain. Almost 50 million people have epilepsy in the world due to which it has become the most common neurological disease. Early prediction of epilepsy helps patients to avoid epilepsy and live normal life. Many studies have been conducted for the early prediction of epilepsy. However, selection of the most appropriate classifier has always been a question that needs to be resolved. In this study, we are using six classifiers of machine learning which are KNN, Naïve Bayes, Linear Classification Model, Discriminant Analysis Model, Support Vector Machine and Decision Tree, to find the best classifier for the prediction of epileptic seizures, in term of accuracy. Dataset from “Kaggle†was used. Preprocessing and cross-validation of the data was carried out for training and testing of classifiers. The results depict that Naive Bayes classifier has a better average accuracy of 95.739% as compared to other classifiers. The future work of this study is to implement the suggested model in real time, so that the workload of medical members could be reduced.
The use of composite materials is rapidly growing in the construction of aerospace structures. Most of the aircraft parts made from composite materials, like wing skins, spoilers, fairings, and flight controls, are being used due to their reduced weight as compared to aluminum parts. New generation large commercial aircraft are being designed with all composite fuselage and wing structure. The main advantages of composites over conventional metallic materials are their high strength to weight ratio, corrosion resistance and tailored stiffness. Uni-directional composites have predominant mechanical properties in one direction and vary with the direction relative to the axis of structure. The aero-elastic properties, such as stiffness and dynamic stability, also depend on the stacking sequence of the plies. Fatigue and Damage Tolerance (F&DT) assessment of these advanced composite materials is an emerging field of research. In aircraft structural integrity analysis, the damage tolerance and fatigue life is investigated against a cyclic loading spectrum. The particular spectrum includes the stress/loading levels counted during a flight of certain duration. The occurrences of z-axis load factors ‘Nz’ may include higher gravitational acceleration ‘g’ levels. While maintaining a certain g level occurrence at higher angle of attack, wing structure vibrates with the amplitudes of its natural frequencies. The cyclic stress amplitudes of vibration depend upon the natural frequencies of vibrating structure, i.e. lower frequency gives higher amplitudes and vice versa. These fluctuating load amplitudes are superimposed on the higher ‘g’ level mean loads during fatigue analysis. These additional cycles are very critical in DTA studies.