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- 저자 : Paramjit Singh, Jamir Singh (검색결과 3 건)
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Paramjit Singh, Jamir Singh,Azlinda Azman,Shankar Durairaja,Mohd Syaiful Nizam, Abu Hassan,Farah Wahida, Suhaimi 한국정신간호학회 2024 정신간호학회지 Vol.33 No.1
Purpose: Decriminalisation of drug use is an alternative policy that many experts, including health experts, legal and enforcement experts, and stakeholders in non-government organisations (NGOs), uphold. In Malaysia, this policy was initiated in 2019 by the government to remove criminal penalties from various laws against drug possession for personal use. This study aimed to explore perceptions of drug rehabilitation officers and relapsed drug users towards decriminalising drug use that could be implemented in Malaysia. This qualitative research used an inductive approach. Methods: This study used a semi-structured, face-to-face individual interview guide. A total of 15 drug rehabilitation officers from the National Anti-Drug Agency (NADA) and 15 relapsed drug users were interviewed. Results: Thematic analysis generated three themes from raw transcripts: (1) potential outcomes of decriminalisation of drug use in Malaysia, (2) readiness of the public to accept decriminalisation of drug use, and (3) readiness of government and NADA in implementing decriminalisation of drug use. Conclusion: Findings of this explorative research provide important insight into the growing body of knowledge on decriminalisation of drug use policy in Malaysia.
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Hari Madhav,Paramjit Singh,Neetika Singh,Gautam Jaiswar 한국고분자학회 2017 Macromolecular Research Vol.25 No.7
The main object of this study is to analyse the effects of different functional groups on the thermal and microbial properties of nanocomposites of poly(methyl methacrylate) (PMMA). For this, amino functionalized PMMA were synthesized by using post polymer functionalization method. In this method, PMMA was treated with four different amino compounds to obtain functionalized PMMA. To determine the structural features, functionalized PMMA was characterized with Fourier transform infrared (FTIR) spectroscopy. These functionalized PMMA were used to prepare functionalized polymer nanocomposites with addition of nanoclay and Ag nanoparticles into the polymer matrix. These nanocomposites were further studied against thermal and antibacterial properties. Sophisticated analytical techniques i.e., thermal gravimetric analysis (TGA), differential thermal analysis (DTA), and derivative thermogravimetric curve (DTG) were used to characterize the thermal properties of nanocomposites. Antibacterial study was performed by using disc diffusion method against bacteria E. coli. The thermal results showed that, the maximum degradation temperature (Td) increases up to 85.5oC in air atmosphere in case of sample HM2. Antibacterial study showed that, the bacteria have least effect on the silver containing functionalized polymer nanocomposites.
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A HYBRID ALGORITHM FOR LUNG CANCER CLASSIFICATION USING SVM AND NEURAL NETWORKS
Pankaj Nanglia,Sumit Kumar,Aparna N. Mahajan,Paramjit Singh,Davinder Rathee 한국통신학회 2021 ICT Express Vol.7 No.3
The present research article focused on the factual findings of the potential usage of the combinational Feed-Forward Back Propagation Neural Network as a judgment making for lung cancer. In this context, Support Vector Machine is integrated with Feed-Forward Back Propagation Neural Network to create a hybrid algorithm that further helps in reducing the computation complexity of the classification. A set of 500 images are utilized in which 75% data is used for the training purpose and the rest 25% is used to achieve the classification. In the view of forgoing, a three-block mechanism is proposed for the classification in which the first block preprocesses the dataset, the second block extracts the features via the SURF technique followed by the optimization using Genetic Algorithm and the terminal block is for the classification via FFBPNN. The hybrid classification algorithm is named as Kernel Attribute Selected Classifier and the overall classification accuracy of the proposed algorithm is 98.08%. Herein, the objective of the study is to enhance the classification accuracy by applying a hybrid classification algorithm.
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