کہ انتظار تھا جس کا یہ وہ سحر تو نہیں
نحمدہ ونصلی علی رسولہ الکریم امّا بعد فاعوذ بااللہ من الشیطن الرجیم
بسم اللہ الرحمن الرحیم
معزز اسا تذہ کرام اور میرے ہم مکتب شاہینو!
آج مجھے جس موضوع پر اظہار خیال کرنا ہے وہ ہے:’ ’انتظار تھا جس کایہ وہ سحر تو نہیں ‘‘
صدرِذی وقار!
امید پردنیا قائم ہے، امید سے گلشن ہستی میں بہار ہے، امید سے رخ ِکائنات پر نکھار ہے، امید پر سب کا دارو مدار ہے، امید پیام ِمسرت ہے، امیدعیش وعشرت ہے، امید ضرورتِ وقت ہے، امید سے تعلق نعمت ہے۔
جنابِ صدر!
امید بر نہ آئے تو آفتاب مسرت گہناجاتا ہے۔ گلستانِ حیات میں خزاں آجاتی ہے۔ شجرسایہ دار کے نیچے خس و خاشاک اُگ آتے ہیں، یہ خودرو غیر مفید پودے فضا کو آلودہ کرتے ہیں، مایوسی و پژمردگی کے سائے بڑھنے لگتے ہیں، یاس و اُمید کا فقدان ہو جاتا ہے، نا امیدی کا مردار گدھ ماحول کو تعفن کرنے میں کلیدی کردار ادا کرتا ہے۔
صدرِمحترم!
آرزو پوری نہ ہو تو خواب پورے نہیں ہوتے ، قلوب و اذہان میں آسودگی نہیں آتی ، حالات سازگار نہیں ہوتے ، دل کے ارمان ادھورے رہ جاتے ہیں، زندگی کی بوقلمونیوں میں ٹھہراؤ آجاتا ہے، عزیز و اقربائ، احباب واصدقاء کا قرب مفقود ہو جاتا ہے، زیست کی رعنائیاں دم توڑتی ہوئی نظر آتی ہیں۔
جنابِ صدر!
کسان محنت کرنے اورکھیتی کشتِ زعفران نہ بنے ، منصف شب و روز محنت کرے اور درست فیصلہ نہ کر سکے۔ خطیب کا روح پرور خطبہ بھی خاطر خواہ نتائج برآمد نہ کر سکے ، مجاہد کی سخت کوشی بھی دشمن کی یلغار کو روک نہ سکے ،مدرس کی تدریس طلبا کے لئے سازگار اور سود مند ثابت نہ ہو، مصنف کی تصنیف نفع بخش...
"An analysis of the allegations of extremism and terrorism against religious institutions (Madrasas)". The priceless services done by the religious scholars for the preservation and uplift of religious and Islamic values in the subcontinent are indelible and unforgettable chapter of history. They geared up progress of religious institutions and the tilt of people towards them of the increasingly charming trend. The Heathen world is afraid of the emerging strongholds of Islam. The repercussions of this trend on society are becoming more and more prominent with the march of time. They are striving for the preservation and identity of the Islamic characteristics. After 9/11 incidents, the west is unable to understand how to detach the religious institutions from the embedded Islamic social integrity. The western media and foreign funded rulers have been endeavoring hard to defame religious institutions through there venomous propaganda against them. All this is visible to everyone. There is no parallel of the religious institutions educational boards (Wafaqs) in and outside the country even no such example is present in the whole Islamic world as well as in the subcontinent. Besides other baseless allegations, religious institutions are branded as terrorists and extremists. The west and America are much worried about the Islamic educational institutions and the Holy war (Jihad). The article encompasses the opinions of the regious as well as secular apostles. In a nutshell, all the allegations of extremism and terrorism are not only baseless but just a propaganda.
The non-destructive analysis of a Solid Pharmaceutical Product (SPP) is essential to verify the quality without destroying the product. This analysis may be performed using various image processing and signal processing techniques on images and multispectral data. Based on this analysis, an SPP may be classified as defective or non-defective. The SPP (categorized as defective) are exposed to three different environmental factors (humidity, temperature and moisture) over different time periods and the variations in data are analyzed to judge the effects of these factors on classification of an SPP. In this research, we have proposed two non-destructive methods to identify defective and non-defective SPPs using their surface morphology. In first approach, multiple textural features are extracted using microscopic images of the surface of the defective and non-defective SPPs. These textural features are Gray Level Co-occurrence Matrix, Run Length Matrix, Histogram, Auto Regressive Model and HAAR Wavelet. Total textural features extracted from microscopic images are 281. The features are reduced using three feature reduction techniques; Chi-square, Gain Ratio and Relief-F. We have formulated three feature sets, through experimentation, with 281, 15 and 2 features. We have used four classifiers namely Support Vector Machine, K-Nearest Neighbors, Naïve Bayes and Ensemble of Classifiers, to calculate the accuracy of proposed approach. The classifiers are implemented using leave-one-out cross validation and holdout validation methods. We tested each classifier against all feature sets and the results were compared. The results showed that in most of the cases, Support Vector Machine performed better than the other classifiers. In second approach, we have used multispectral data and applied wavelet transformations in conjunction with various machine learning techniques for the classification. The results showed that the spectrum extracted from Ultra Violet x wavelength range is more suitable for the classification between defective and non-defective SPPs. Furthermore, results also described that K-Nearest Neighbors classifier or Ensemble of Classifiers is a more appropriate classifier. In the last, the hybrid of the both approaches was tested. The analysis of the results showed that the hybrid approach is better than the individual ones. An accuracy of 94% is achieved using K-Nearest Neighbors when a combined dataset of SPPs affected by all of the three environmental factors is used.