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ایڈورڈ براؤن

براؤن، ایڈورڈ

            علمی دنیا میں نئے سال کا سب سے افسوس ناک سانحہ مشہور انگریز مستشرق پروفیسر ایڈورڈ جی براؤن کی وفات ہے، موصوف نے اس مہینہ کے آغاز میں غالباً ساٹھ پینسٹھ سال کی تخمینی عمر میں انتقال کیا، وہ پہلے کیمبرج میں فارسی کے لکچرر تھے، پھر ۱۹۰۲؁ء میں وہ عربی کے پروفیسر مقرر ہوئے، انھوں نے طب کی تعلیم بھی حاصل کی تھی، عربی میں وہ پروفیسر پامر کے شاگرد تھے، ان کی سب سے جامع، مسبوط اور مشہور تصنیف لٹریری ہسٹری آف پرشیا کی ضخیم جلدیں ہیں، موصوف نہ صرف علمی حیثیت سے بلکہ ایک بے تعصب عالم، ایک ہمدرد مشرق اور ایک شریف انسان ہونے کے لحاظ سے بھی نہایت بلند درجہ تھے، قومی تنگ ظرفی اور مذہبی عصبیت سے وہ قطعاً مبرا تھے، ان آنکھوں کو یہ عزت حاصل ہے کہ انھوں نے مرنے والے کی زیارت کی تھی، آئندہ معارف میں ان کے کچھ حالات سپرد قلم ہوں گے، ہندوستان میں ان کو ہم سے بہتر جاننے والے اشخاص بلکہ ان کے شاگرد موجود ہیں، کیا بہتر ہو اگر ان میں سے کوئی صاحب ہماری مدد فرمائیں اور براؤن پر ایک عمدہ مضمون لکھ کر عنایت فرمائیں اور اگر احباب پسند کریں تو معارف کا ایک نمبر صرف براؤن پر شائع کیا جائے کہ ان کے احسانات کا یہ ادنیٰ ترین معاوضہ ہے۔  (سید سليمان ندوی، جنوری ۱۹۲۶ء)

مغربی اور اسلامی نظریہ ثقافتی اور تہذیبی عالمگیریت تحقیقی جائزہ

The term globalization is not new to the modern world. It was a hope of humanity centuries ago to make the planate a global village. However there is a difference of interests of nations in doing so. In the present ages the word Globalization is considered as a tool and term used by western powers to rule the entire world. If we see the globalization from Islamic perspective we can find various contracatidions between the concepts of Islam and that of the western world about globalization. These differences are not limited to a single side of globalization, but are found in political, financial and cultural point of views as well. In this paper I have limited my topic to cultural globalization, where after a brief study of both terms I have come up with an analysis of both, their modern status and current situation. This paper consists of a detailed comparision of both concepts from different dimentions and their impact on human society.

Efficient Facial Expression Classification Using Machine Learning Techniques

The Non-verbal communication plays a pivotal role in daily life and contributes around 55% to 93 % in overall communications. Facial expression is a type of non-verbal communication and its contribution towards recognition is around 55%. It exhibits the physical intention, behavior, personality and mental state of a person. Facial expression analysis can be effectively used in video surveillance, emotion analysis, smart homes, gesture recognition, patient monitoring, treatment of depression and anxiety, lie detection, automated tutoring, psychiatry, paralinguistic communication, robotics, operator fatigue detection and computer games. Highly accurate solution is a major challenge in the development of Efficient FER system. Data collected using poor quality cameras and/or captured from distance suffer from low resolution problem. Region of interest is usually smaller than original image size and image collected in real world environment suffer from low resolution problem. It results in drastic decrease in classification accuracy of the facial expression recognition. Environmental and source light variations during image acquisition results in poor illumination which is also major cause of performance degradation. Furthermore, curse of dimensionality poses another challenge in the development of fast and accurate techniques. With the increasing demand of surveillance camera-based applications, the Very Low Resolution(VLR) problem happens in many FER application systems. Existing FER recognition algorithms are unable to give satisfactory performance on the VLR face image. In addition to VLR, Variable lighting conditions in uncontrolled environment is another factor which can cause unpredictable illumination affects that leads to poor FER performance. Furthermore, feature vector containing correlated and irrelevant information also causes performance degradation. In this dissertation problems mentioned above related to facial expression recognition have been addressed. A novel framework has been proposed to handle high and low resolution images with equal capability. Excitation component of Weber local descriptor (WLD) is employed to compute the salient features and DWT has been utilized for features extraction which resolves multi-resolution problem. Least number of features having high variances is used to perform classification. Experimental results have shown that this framework not only handle low resolution problem but also gives improved classification performance both in terms of complexity (i.e., number of features) and recognition accuracy as compare to existing techniques present in the literature using CK+, MMI and JAFFE data sets. Secondly, facial expression recognition being a multi-class classification problem is a challenging task and becomes more complicated in real world environment with data having variation in illumination conditions. In order to tackle this problem, illumination invariant technique has been developed based on HOG features. These HOG based illumination invariant features are further reduced using DCT. These highly significant features are passed to the classifiers for accurate facial expression recognition. Proposed frameworks can effectively handle illumination variance and very low resolution data during facial expression recognition. Detail experimentation have been conducted using well known standard datasets containing images with varying illumination, resolution, gender and ethnicity. Comparison of the system has been presented with other state-of-art techniques using CK+, MMI and Cross datasets. Comprehensive experimentation shows that the proposed technique produces significantly better results than existing state-of-the-art techniques present in the related work.
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