ذبح سے پہلے عمل تدویخ اور معاصر فقہی تحقیقات

Stunning is the process of rendering animals immobile or unconscious, with or without killing the animal, when or immediately prior to slaughtering them for food. In modern slaughterhouses a variety of stunning methods are used on livestock. Methods include: Electrical stunning, Gas stunning, Percussive stunning. There are three opinions of Islamic scholars about stunning. Those scholars; who do not allow stunning at all; are of the view that the method of rendering animals unconscious before slaughter is against the shairah method and Sunnah, and it is Makrooh e Teḥreemi. Before slaughtering, if an animal died due to stunning, then that animal is carcass and is not allowed to be eaten. But, if before slaughter, ḥayat e Mustaqirrah is present in animal and it is slaughtered in that condition then it is permissible to eat it. Certain scholars allow stunning in certain situations with some terms and conditions. The decisions of Mjam e Faqhiyyah of modern age are also based on conditional permission. Moreover, Mufti Muḥammad Taqi Usmani, Dr. Wahabah  Zoḥaili and Abdul Aziz Bin Baaz agree with conditional permission, while some other scholars allow all types of stunning without any condition; Mufti Muḥammad Abduho and his pupil Allamah Rasheed Raza Miṣri agree with later opinion.

N Enhanced Framework for Sentiment Analysis of Social Media Contents Using Supervised Learning Techniques

Sentiment analysis or opinion mining is proven to be very effective to analyze huge and complex amount of text of social media. Social media provides an online environment for the users to show their behaviors and emotions through tweets and post. Massive amount of personal information is placed on the World Wide Web due to huge usage of social media. Moods and emotions of the user are different from each other. Sentiment analysis of any written text especially social media content is applicable to extract the opinions, emotions and meaningful insights for better decision making. There are many challenges in the accurate and reliable sentiment analysis of available social media content. The challenges can be both technical and theoretical. Machine learning-based sentiment analysis techniques have issues such as huge lexicon, semantic gap, handling of negation, domain dependency and bi-polar words. Previously, many machine learning and data mining techniques have been proposed by several researchers to resolve these issues. However, the existing techniques have failed to provide satisfactory and reliable results for most of the available datasets. A novel methodology is proposed to overcome above mentioned issues using better and simplified way with less computational complexity and high reliability. Data acquisition, feature encoding, data preprocessing, feature selection, and classification are the various phases of implemented framework. Data gathering and preprocessing step is very critical in the analysis of data. The proposed research mainly contributes during data preprocessing, feature encoding, and classification phases. In feature encoding phase, a hybrid approach of bi-gram and tri-gram is used for word embedding. In the experiments, several benchmark datasets have been utilized to evaluate the effectiveness of the proposed framework. The results obtained from the proposed xii methodology show better or at least comparable results with maximum confidence. The outcome of the proposed work will be helpful to enhance the process of sentiment analysis of social media contents. The experimental results of the framework will be validated using WEKA simulation software.