مقصد حیات
ہم جو بھی پڑھتے ہیں اس سے متاثر ضرور ہوتے ہیں۔اسی طرح ناول نگار بھی کسی زندگی کی عکاسی کرتاہے اور جس زندگی کی عکاسی کر رہا ہوتا ہے وہ مقصد سے خالی نہیں ہوتی۔ناول نگار قاری کیاحساسات کے بارے میں آگاہ ہوتا ہے۔اس لیے وہ انسانی نفسیات کو سمجھتے ہوئے ایک کہانی تخلیق کرتا ہے۔ایک استاد کی طرح اپنی بات کو قاری کے ذہن تک پہنچانے کا مقصد صرف اور صرف یہ ہوتا ہے کہ ایک خاص قسم کا فلسفہ اس کے ذہن میں زبردستی ڈالا جاتا ہے۔
ناول نگار نے بھی ایک مقصدی کہانی لکھی ہے۔ وہ اپنی اس کہانی کے ذریعے ہمیں کرپشن اور ملک سے غداری کرنے والوں کو بینقاب کررہے ہیں۔وہ سیکس انڈسٹری کی ایک واضح تصویر اور اس کے نتائج کو ہمارے سامنے پیش کررہے ہیں جوکہ ایک عمدہ قدم ہے۔یقیناًنو لکھی کوٹھی کے بعد ناطق کا یہ دوسرا انتہائی شاندار ناول ہے جو یاد رکھے جانے کے قابل ہوگا۔ہر موڑ قاری کو ایک نئی راہ سے متعارف کروارہا ہے شوبز کی دنیا ایک جھانسہ ہے۔ہمارے معاشرے میں چلتے پھرتے لوگوں کے بارے میں خلاصہ کیاگیا ہے۔جائیداد کا لالچ تو ایک الگ بات ،اس پہ نسل در نسل بات پر اس طرح مٹی ڈال دی جاتی ہے کہ وہ ایک معمہ بن جائے اور حل نہ ہوپائے۔ناطق نے بہت ہی جانب داری کا مظاہرہ کیا ہے اور محسوسات کے مطابق ایک مقصدی کہانی لکھی ہے۔
Peaceful coexistence is a major challenge in a multi-ethnic region like the Midwest. After the creation of the region in 1963, ethnic distrust dominated the region's body politics. The Biafran invasion of Midwest remained one invent that heightened ethnic distrust in the region. Although, scholars have examined the invasion, the need to re-examine it arises from the fact that the event made ethnic antagonism among the groups in the region more intense than ever before. It is against this backdrop that this paper examines the Biafran invasion of Midwest and its implications on inter-group relations in the region. Relying on primary and secondary sources, the paper is of the opinion that the intense group antagonism and suspicion emanated from the fact that the Biafran incursion into Midwest caused division among the groups in the region. The groups that were loyal to the Nigerian Government opposed the groups that supported Biafra. The paper further argues that the ethnic tension was also as a result of the assumption by the non-Igbo groups in the region that Biafrans were in the region to promote the interest of the Igbo groups.
Subspace based algorithms belong to one of the most explored face recognition algorithm categories which follow a holistic approach for feature extraction. These methods operate directly on the pixel intensities of a facial image and extract features. The basic trait of these algorithms is that they reduce dimensionality to reduce the computational complexity of feature extraction while keeping the statistical separation between different classes. Therefore these algorithms are the economical choice for feature extraction. These algorithms are based on the key concept that most of the information in a facial image is highly redundant and that the discriminating features reside in a subspace of the face image. Therefore these algorithms aim to extract these features by reducing the redundant and non-discriminating information. The choice of a classifier is the key factor in designing an efficient pattern classification system. This choice very closely relates to the data on which it is going to be applied. Another important issue is the irrelevancy in reported results of different classifiers. The evaluation criterion which is set for evaluating a specific classifier plays a significant role in determining the true potential of a proposed classifier. There is a need to evaluate these reported classifiers using the same evaluation criterion to judge the suitability of each classifier for a specific imaging condition. For face recognition, a surfeit of classifiers has been proposed to date but none of them alone is capable enough to cater with all the inherent variations of the facial image data. Therefore there is a need to explore combinations of classifiers known as ensemble classifiers. As different classifiers extract complementary features of the object to be classified, therefore combining the properties of individual classifiers in an ensemble classifier does result in increased classification accuracy. The overall suitability of this ensemble classifier depends on the memory and computational complexities of the constituent base classifiers. VI In this thesis, a newly reported and highly cited face recognition algorithm Laplacianfaces is initially explored for its true potential by varying its internal and external parameters for different face recognition tasks. Based on the outcome of this initial analysis, other famous subspace face recognition algorithms are also evaluated by using distance metrics both from the image space and mahalanobis space. This evaluation was performed by using the evaluation methodology employed in Face Recognition Vendor Tests (FRVT) and FERET evaluations. These algorithms are evaluated against various probe sets from three different and famous facial databases namely FERET, ORL and YALE. This study hence provides enough testing variables to judge the performance of algorithms against different imaging conditions or facial variations. Based on this exhaustive comparative analysis, a group of six most accurate and most economical classifiers are selected. Ensemble classifiers with combinations ranging from two to six of these best selected base classifiers are evaluated against the same testing conditions. The ensemble classifiers are constructed by combining base classifiers using two simple ensembling techniques namely re-ranking and weighted scoring approach. The average performance of this ensemble classifier also called unified classifier is found to be well ahead of that for the individual constituent base classifiers. The work reported in this study proves the effectiveness of ensemble classifiers for face recognition tasks. The results of the proposed unified classifier in comparison to the best performing subspace algorithms demonstrate that the unified classifier has a global performance and can handle different variations effectively.