حیوانات کا تعارف
معنی و مفہوم"حیوان "کا لغوی معنی ہے:
"جاندار۔ ذی روح (۲) مویشی۔ چوپایہ (۳) نادان۔ بیوقوف۔ وحشی۔ جمع:۔ حیوانات"[1]
علامہ زمخشری ؒ حیوان کا لغوی مفہوم یوں بیا ن کرتے ہیں:
"والحيوان: مصدر حي، وقياسه حييان، فقلبت الياء الثانية واوا، كما قالوا: حيوة، في اسم رجل، وبه سمى ما فيه حياة: حيوانا"[2]
حیوان حَیَّ کا مصدر ہے اس کی اصل "حییان" ہے لیکن یا ء ثانی کو واؤ سے بدل دیا گیا ہے۔ جیساکہ عرب میں بعض لوگوں کا نام "حیوۃ" تھا۔ انہی معنی کے اعتبار سے ہر اس چیز کو جس میں حیات ہو حیوان کہتے ہیں۔
علامہ الجاحظ ؒ رقمطراز ہیں:
"أحيا من الحيوان؛ إذ كان الحيوان إنّما يحيا بإحيائها له"[3]
قرآن مجید میں حیوان کا لفظ زندگی کے معنوں میں استعمال ہوا ہے۔ قدیم عرب کے کئی قبیلوں کے نام حیوانات کے نام پر تھے جیسے اسد (شیر) ، قریش (آدم خور مچھلی) وغیرہ۔ مرنے والوں روحوں کو پرندے کی شکل میں پیش کیاجاتا تھ جو عرصے تک قبر کے ارد گرد اڑتا رہتا تھا۔ بعض حیوانات کو خاص دیوتاؤں سے منسوب کرکے ان کے گلوں میں قلاوہ ڈال کر ان کو حرام قرار دے دیا جاتا تھا۔ قرآن مجید ان قدیم باطل عقائد کی مذمت کی گئی ہے۔ [4]
اردو دائرہ معارف اسلامیہ میں حیوان کےمعانی بیا ن کیے گئے ہیں:
"لفظ حیوان کے سب سے زیادہ عام معنے، خواہ اسے صیغہ واحد میں استعمال کیا جائے یا صیغہء جمع میں، با لعموم ایک یا ایک سے زیادہ جانور ہیں بشمول انسان، جسے صیح تر الفاظ میں الحیوان الناطق کہا جاتا ہے"[5]
...
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.
Pervasivecomputingoffersenvironmentsinwhichuserneedsortasksarefulfilled without demanding their attention. This requires discovering a service or a set of services based on context (i.e. user presence, user activity, user location, temperature level,lightintensityleveletc.). Anatomicservicemaysometimesmeetthesimpleuser needs but meeting of complex user needs may lead to discovering a number of relevant services and composing them together. While the composed service may well serve userneeds,therecometimeswhenausermaywanttocustomizetheenvironmentbased on her preferences and this require adapting the composed service through parameter adjustment of one of its constituent services or a multiple constituent services. This makescontext-awarenessingeneralandcontextualservicecompositionandadaptation inparticularacorerequirementofpervasivecomputingapplications. Servicesavailable in the environment may be heterogeneous with regard to different discovery protocols (e.g., UPnP, SLP, JINI, etc.) being used fortheirpublication, discovery andinteraction. Context-aware service composition may involve discovery of heterogeneous services andtheadaptationofthecomposedservicemayinvolveinteractingwithheterogeneous constituent services. This raises the issue of service heterogeneity in context-aware service composition and adaptation. We have also proposed an approach following a separation of concerns, which allows adaptation decision logic (adaptation concern), a core part of context-aware applications, to be independently treated and managed as a separate unit of execution from the rest of application code. The proposed approach allows modelling of adaption concerns as declarative Event-Condition-Action (ECA) polices. This allows rapid development of context-aware applications and their dynamic modifiability. Another research issue that we address in this thesis is that of user involvement. Tothisend,wehaveproposedauser-centricapproachthatallowstheusertoparticipate in development of context-aware applications. To address aforementioned research challenges, we have designed and implemented a system whose detailed description is provided in the thesis. The system has been evaluated through usability,performance and scalability measures.