Browsing by Author "MUHAMMAD ASIF"

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    Analysis of sewing room problems of garments due to machine and handling factors and their possible precautions and practical solutions
    (UMT, Lhr, 2011) MUHAMMAD ASIF
    The project “Analysis of sewing room problems of garments due to machine and handling and their possible precautions and practical solutions” is regarding visiting textile mill and to highlight the important stitching faults which effected the production and specially to control the stitching faults. I will be focus on material handling, stitching problems due to machine and handling and also try to control these problems with suitable solutions in apparel industry.
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    DEVELOPING COMPUTER ASSISTED READING MATERIALS FOR TEACHING ENGLISH TO PAKISTANI MAINSTREAM SCHOOL DYSLEXIC STUDENTS OF ELEMENTARY LEVEL
    (UMT, Lahore, 2023) MUHAMMAD ASIF
    The present study aims to develop and gauge the impact of indigenously developed computer assisted reading materials, based on the reading problems, reading needs and effective teaching strategies as perceived by the English language teachers and guardians of Pakistani mainstream school dyslexic students, on their English language performance. Four lessons from Punjab Textbook Board‘s English of class VI were purposively selected and the materials were developed under the guidance of Mayer (2005)‘s Cognitive Theory of Multimedia Learning. Reading disability checklist was used for the screening of dyslexic students and the purposively selected thirty students were further divided in two (control and experimental) groups homogenously. The control group continued learning through conventional methodology whereas the experimental group was taught through these materials for six weeks.
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    Effectiveness of online teaching of business and technical English: a case study of virtual university of Pakistan
    (UMT Lahore, 2012) MUHAMMAD ASIF
    Online teaching (OT) is an emerging trend in Pakistan with approximately ten years background. During this brief time period online pedagogy has faced many challenges to establish itself as an equal counterpart of conventional classroom teaching. Whether it has achieved its desired outcomes or not is a debatable issue. This case study has been conducted to examine this very issue of the effectiveness of online teaching by narrowing it down to the subject of Business and Technical English (BTE) at the graduate and postgraduate levels at the one and only major online teaching institution of Pakistan i.e., Virtual University. The study explores how effective is the learning of Business and Technical communication Skills in English language that comes from online mode of instruction and to what extent it leads to proficiency in documentation for professional purposes. The study makes use of the Connectivist theory by Siemens (2004) to reach its desired findings. Data were collected from the different campuses of Virtual University of Pakistan, therefore, the sample is diverse belonging to various age groups, areas and languages. Factor analysis was used to estimate the effectiveness of Business and Technical English and for testing hypothesis, Mann Whitney U test was applied to get the required results. The results demonstrate that online teaching of Business and Technical English is considered very useful by the students of the Virtual University of Pakistan for improving their linguistic and grammatical competence to cope with the needs of the business world and job interviews. However, to improve certain limitations, the research also suggests some recommendations.
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    Lora wan based smart farming system using temperature, humidity, soil moisture sensors for drip irrigation to save the water to increase the yield of corn, sugarcane, lady finger
    (UMT Lahore, 2022) MUHAMMAD ASIF
    Smart agriculture is the use of technology like (IoT) Internet of things, LoRa, sensors, location system, and robots on your farm. The ultimate goal is to increase the yield and quality of crops while minimizing theβlabor cost. In smart farming use of IoT-related technology is rapidly increasing. In this thesis, a long-range, βlow-power, βand low-cost LoRa-based wide area network in a smart agriculture platform is introduced. Development of this platform includes a LoRaWAN gateway system which can be used to increase smart farm yield, accuracy, and quality. This smart agriculture farm development includes RAK Lora wan Gateway controlled by Raspberry pi. It will be poweredβby an external battery and use a requiredβnumber of LoRa Nodes with sensors. Different types of sensors will be available at different nodes to determine Soil Moisture, Nitrogen, phosphorous, and potassium in the soil. This proposed smart farming system has been evaluated on a real farm located in Pakistan, collecting environmental data (Soil moisture, Nitrogen, Phosphorus, Potassium, PH) related to the growth of farm crops over a weekly period. Using LoRaWAN smart farming system approximately 60% water, 40% Fertilizers, and 70% power consumption are easily saved and the most important gain in yield is 25%. A web-based tool is used to visualize collected data is also presented, to validate the LoRaWAN smart farming system.
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    The ilcrot predictor and deep neural networks for lysine crotonylation site detection
    (UMT Lahore, 2021) MUHAMMAD ASIF
    Lysine crotonylation is evolution conserved post translation method that plays very important part in several biological process. The biological process include cell differentiate, organismal development, chromosome organization, nucleic acid metabolism, RNA processing and gene expression. Lysine Crotonylation effects chromatin structure and to facilitate histone replacement. The information of lysine crotonylation sites in protein is very useful for research. The previous approaches such as affinity enrichment, liquid-chromatography fractionation, and mass spectrometry to identify lysine crotonylation sites were time consuming and tedious, because they used human engineered based feature extraction method. It is urgent to develop computational based methods to predict the possible crotonylation sites. Actually, computational prediction of PTMs sites has been paid more and more attention in recent years. In this study we report a novel predictor name ILcrot that is developed by using (PseAAC) Chou's Pseudo Amino Acid Composition with DNN that model combine training step and feature extraction to save lot of time and resulting in better prediction. In terms of accuracy, CNN outperformed all other DNNs, and all other calculated metrics outperformed all previously published predictors.