Browsing by Author "MUHAMMAD IRFAN"

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    Interpersonal communication barriers
    (UMT Lahore, 2017) MUHAMMAD IRFAN
    ABSTRACT This study aims to investigate the interpersonal communication barriers that may limit or hinder interpersonal communication in police department. Several empirical studies have investigated interpersonal communication within and between the professional organizations educational institutes and other workplaces; however, few have explicitly sought to uncover the possible problematic aspects linked to the interpersonal communication barriers in the police department. A sample of 25 policy officers of different ranks was selected to reveal communication barriers while disseminating and implementing security threat memos, which are composed in the English language. To find out the linguistic constraints and communication barriers, five security threat memos circulated by the higher authority over different time periods were given to the participants for reading. They were interviewed to express the barriers. Thematic analysis was used as a theoretical cover to analyze the data. Given the linguistic barriers, pronunciation, vocabulary and comprehension of the memos were among the most emerging challenges they are facing. The finding suggests providing basicEnglish language training to police department regarding how to communicate the exact message present in security threat memos. Future research can be undertaken to investigate the psychological and physical barriers that may limit or hamper interpersonal communication in police department.
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    Medical assistant using artificial intelligence
    (UMT, Lahore, 2021) MUHAMMAD IRFAN
    Nowadays, Healthcare facilities are supposed to be critical, and it’s a basic need in human life. Data related to healthcare is enormous in the biomedical community, which can help predict diseases in early stages and detect them with more accuracy. Artificial Intelligence plays a vital role in almost every field where data is involved, primarily in the medical field. Machine Learning algorithms are producing accurate results for many different kinds of data in the medical field. It is used to predict diseases, save many lives by early diagnosis or prevention of dangerous diseases caused due to severe stages of illness just because of untimely detection. Our research is based on improving healthcare facilities in societies and backward areas. It will ideally impact the community in terms of better diagnosis of the treatment and availability 24/7. Using Artificial Intelligence as Machine Learning Algorithms to make such a system that will diagnose patients' diseases by interacting with them in a way that they can get the feeling of being treated by a real doctor. Using Artificial Intelligence in the Field of Medical is an excellent initiative towards the foresight of better health care facilities. As the population increases, the need for healthcare facilities is also growing, especially in remote areas. However, healthcare facilities are very vulnerable and need much improvement. Highly qualified doctors with good experience and excellent records in their fields. They are not easily accessible by everyone due to their busy schedules and higher consultation fees that everyone cannot afford, too, in this inflation era. In this research, MEDICAL ASSISTANT using Artificial Intelligence is proposed to predict diseases and Xrays of knee and Chest. Thus, the user/patient will be able to diagnose diseases without visiting the actual Doctor. Models' accuracy is analyzed in terms of disease prediction on training and test data. We have used different models of machine learning.
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    Nuclear charge form factor of hellium in corelated nuclear shell model with hadron quark hybrid approach
    (UMT, Lahore, 2018) MUHAMMAD IRFAN
    Charge form factor reveals many features of nuclei and particles within the nuclei specially, in the higher momentum transfer region. In this work, we are concerned with 3He and 4He. In the high momentum transfer, regions it requires short range correlation effect (SRC), Masonic exchange current (MEC), D-state and quark degrees of freedom (QDF), to reproduce second minimum and third maximum in good agreement with the experimental data. Consideration of certain percentage of D-State wave function 3% for 3He and 4% for 4He Hadrons Quark Hybrid model (HQH) including interference terms produces improvement in the position of first minimum and second maximum. Contribution of QDF is found necessary with quark clusters of 3q, 6q, 9q when the inter nucleon distance becomes less than rc (critical radius). At a radius of 1.1 fm by using thermodynamic method 3q-quark clusters show higher probabilities up to 84.5% for He4 while, p6q = 12.6 %, p9q = 2.5% and p12q = 0.33 % respectively.