ڈاکٹر سید ہادی احسن
افسوس ہے کہ گزشتہ آخر مئی میں ڈاکٹر سید ہادی حسن صاحب سابق صدر شعبہ فارسی مسلم یونیورسٹی نے انتقال کیا، مرحوم یونیورسٹی کے لائق اور ممتاز اساتذہ میں تھے۔ قدیم و جدید فارسی زبان و ادب پر ان کو اسناد اور عبور حاصل تھا، ان کی فارسی دانی کا لوہا اہل زبان تک مانتے تھے، نہایت شستہ و رفتہ فارسی میں برجستہ تقریر کرتے تھے اور اپنی ساحرانہ خطابت سے سماں باندھ دیتے تھے اور اس حیثیت سے وہ یونیورسٹی کی آبرو تھے، یونیورسٹی سے ان کو والہانہ تعلق تھا، اس کے عملی کاموں میں سرگرمی سے حصہ لیتے تھے اور ان کی ذات سے یونیورسٹی کو بڑے فوائد پہنچے۔ میڈیکل کالج کے چندہ کی فراہمی میں ڈاکٹر ضیاء الدین مرحوم کے بعد ان کی کوششوں کو سب سے زیادہ دخل تھا، ان کی شخصیت بڑی دلآویز تھی، فارسی شعروادب اور شعراء کے تذکرے اور تراجم پر انگریزی میں متعدد تصانیف ان کی یادگار ہیں۔ اس زمانے کے فارسی کے اساتذہ میں ان کی جیسی قابلیت کی مثالیں کم ملیں گی، اﷲ تعالیٰ ان کی مغفرت فرمائے۔ (شاہ معین الدین ندوی، جولائی ۱۹۶۳ء)
Imbibing reading habits is an essential skill necessary in the acquisition of knowledge, which is highly demanding in the life of every student in the academic environment. However, as modern technological resources keep on advancing, the reading habits of students are also changing with reading on screen. This study examined the reading habits of college sophomore students as affected by technological resources. The survey instruments, distributed to 254 college sophomore students at the Ilocos Sur Polytechnic State College, allowed the respondents to assess their reading habits in terms of reading hours, reading purposes, reading content, and reading attitude, with the technological resources as their tool. The data gathered in the study was analyzed statistically using the simple frequency count and percentage, mean, and Pearson correlation. The findings revealed that the college sophomores achieved an average level in their English subjects. In addition, it was found that the technology resources were moderately adequate to be used for reading among the college sophomores. Despite this, the results still showed that college sophomore students spent 2-3 hours a day reading, primarily in preparation for their exams. In addition, they read the available reading content moderately often, and they perceive a positive attitude towards the use of technological resources for reading. Furthermore, it was revealed that there is a significant relationship between reading habits and academic achievements. The study's findings will be used to improve instructional approaches and to launch e-reading initiatives in colleges.
The success of pattern classification system depends on the improvement of its classification stage. The work of thesis has investigated the potential of Genetic Programming (GP) search space to optimize the performance of various classification models. In this thesis, two GP approaches are proposed. In the first approach, GP is used to optimize the performance of individual classifiers. The performance of linear classifiers and nearest neighbor classifiers is improved during GP evolution to develop a high performance numeric classifier. In second approach, component classifiers are trained on the input data and their predictions are extracted. GP search space is then used to combine the predictions of component classifiers to develop an optimal composite classifier (OCC). This composite classifier extracts useful information from its component classifiers during evolution process. In this way, the decision space of composite classifier is more informative and discriminant. Effectiveness of GP combination technique is investigated for four different types of classification models including linear classifiers, support vector machines (SVMs) classifiers, statistical classifiers and instance based nearest neighbor classifiers. The successfulness of such composite classifiers is demonstrated by performing various experiments, while using Receiver Operating Characteristics (ROC) curve as the performance measure. It is evident from the experimental results that OCC outperforms its component classifiers. It attains high margin of improvement at small feature sets. Further, it is concluded that classification models developed by heterogeneous combination of classifiers have more promising results than their homogenous combination. GP optimization technique automatically caters the selection of suitable component classifiers and model selection. Two main objectives are achieved, while using GP optimization. First, objective achieved is the development of more optimal classification models. The second one is the enhancement in the GP search strategy itself.