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RISS 인기검색어
- 검색키워드
- 저자 : Ali Jamshaid (검색결과 5 건)
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Rajesh Mishra,Hafsa Jamshaid,Sikander Abbas Basra,Gaoming Jiang,Zhe Gao,Pibo Ma,Michal Petru,Ali Raza,Miroslav Muller 한국섬유공학회 2020 Fibers and polymers Vol.21 No.11
Investigations on influence of stitch types in single jersey knitted fabric on flammability and comfort properties arecarried out in this paper. Seven different knitted structures were developed on single jersey machine. In this study the effect ofcombinations of knit, miss and tuck stitches on comfort and flammability properties of knitted fabrics are investigated. Flammability test/properties e.g. afterglow time, char length, and weight loss % are evaluated. Several tests for thermophysiologicalcomfort i.e. air permeability, thermal resistance and absorbency are also carried out. Results obtained show thatboth comfort and flammability properties are dependent on type of stitch, pattern of stitch, location of stitch and percentage ofany stitch used in the fabrics. Moreover, fabric physical parameters also affect the flammability and comfort properties. Thefindings of this study shall work as a guide for material or textile engineers in the design and selection of fabric for apparel aswell as high performance thermal protective clothing; as a result safety and occupational health of fire fighters will beimproved.
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Comparative Analysis for Chronic Disease Prediction via Deep Machine Learning Approaches
Rabia Javed,Tahir Abbas,Jamshaid Iqbal Janjua,Sadaqat Ali Ramay,M. Kashan Basit,Muhammad Irfan 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.12
Globally, chronic diseases have a significant impact on health. The diagnosis of chronic diseases has seen extensive usage of machine learning techniques. Early disease detection and treatment lower the risk of increasing disease severity and, consequently, related mortality. The major goal of this research is to provide a technique that increases classification accuracy while also shortening computing time. This comparative research shows the impact of distinct model architectures and features on disease prediction accuracy in addition to assessing the advantages and disadvantages of each technique. These discoveries have implications for personalized healthcare, allowing medical professionals to select the best models for various chronic conditions. Additionally, this research can direct the creation of better forecasting technologies, as well as influence healthcare legislation and budget allocation. In our study comparative analysis of the state-of-the-art approaches has been presented. Using a hybrid model combination of CNN and RNN could be more beneficial. In conclusion, our comparison research improves our comprehension of the potential of deep machine learning for chronic disease prediction, highlighting the significance of adjusting model selection to certain disease types. To progress the field of chronic disease prediction, future research should concentrate on improving these models, and further explore their applicability across various and larger datasets.
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A Novel Green Stabilization of TiO2 Nanoparticles onto Cotton
Muhammad Tayyab Noman,Muhammad Azeem Ashraf,Hafsa Jamshaid,Azam Ali 한국섬유공학회 2018 Fibers and polymers Vol.19 No.11
Facile embedding of TiO2 nanoparticles onto cotton fabric has been successfully attained by ultraviolet light irradiations. The adhesion of nanoparticles with fibre surface, tensile behaviour and physicochemical changes before and after ultraviolet treatment were investigated by scanning electron microscopy, energy dispersive X-ray and inductive couple plasma-atomic emission spectroscopy. Experimental variables i.e. dosage of TiO2 nanoparticles, temperature of the system and time of ultraviolet irradiations were optimised by central composite design and response surface methodology. Moreover, two different mathematical models were developed for incorporated TiO2 onto cotton and tensile strength of cotton after ultraviolet treatment and used further to testify the obtained results. Self-clean fabric through a synergistic combination of cotton with highly photo active TiO2 nanoparticles was produced. Stability against ultraviolet irradiations and self-cleaning properties of the produced fabric were evaluated.
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Estimation of Surgical Needle Insertion Force Using Kalman Filter
Syed Riaz un Nabi Jafri,Ali Jamshaid,Syed Minhaj un Nabi Jafri,Jamshed Iqbal 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.2
This paper presents a novel low-cost technique to measure the insertion force of a surgical needle on a testing surface to check needle strength. A combination of a load cell with a current sensor for a linear DC motor has been used to estimate the insertion force based on Kalman Filter (KF). The custom-designed and in-house fabricated system to estimate the insertion force comprises of the motor coupled with a vertically articulated arm. The needle to be tested is mounted at the end of the arm. Movement of the arm has been controlled electronically to produce the insertion force by the needle to a testing surface. The sensory measurement data generated during this process has been collected using Arduino based embedded electronic hardware. The KF based proposed strategy has been validated using the developed system by conducting various tests with diferent needles. Results in the form of penetration force and friction force have been experimentally observed and are then compared with standard force meters. Comparative analysis witnesses efciency of the proposed approach.
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