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All Journal Medical Journal of Indonesia International Journal of Engineering, Science and Information Technology Assyfa Learning Journal
Sandeep Gupta
Pt.B.D. Sharma, PGIMS, Rohtak-12400I Haryana
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Hypokalaemic periodic paralysis (A Case Report) Ghalaut, Partap S.; Ghalaut, Veena S.; Gupta, Sandeep
Medical Journal of Indonesia Vol 5, No 3 (1996): July-September
Publisher : Faculty of Medicine Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (187.3 KB) | DOI: 10.13181/mji.v5i3.869

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[no abstract available]
Media Representations and Ageing: The Influence of Bollywood on the Upper Economic Class in Urban Mumbai and Implications for Media Literacy Madhukullya, Samikshya; Gupta, Sandeep
Assyfa Learning Journal Vol. 3 No. 2 (2025): Assyfa Learning Journal
Publisher : CV. Bimbingan Belajar Assyfa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61650/alj.v3i2.694

Abstract

Mumbai's urban upper economic class faces unique pressures to maintain youthfulness, shaped by pervasive societal norms and the influential portrayals of ageing in Bollywood media. This study aims to evaluate how Bollywood's representations impact perceptions of ageing among Mumbai's affluent, and to explore the implications for media literacy and educational strategies. Employing a quantitative survey approach, data were collected from 32 upper-class residents of Mumbai using Google Forms and analyzed with SPSS to assess attitudes toward ageing, the influence of media, and gendered expectations. The findings reveal that Bollywood's youth-centric narratives significantly contribute to negative perceptions of ageing, reinforcing ageist stereotypes and intensifying the desire to appear youthful, with notable differences between male and female respondents. These results underscore the urgent need for inclusive and diverse media representations, as well as the integration of media literacy interventions in educational and community settings to foster critical engagement with age-related stereotypes. The study concludes that promoting media literacy and age-inclusive content can play a pivotal role in challenging societal biases, supporting intergenerational understanding, and informing policy and curriculum development in urban India.
Cotton Disease Prediction Using Deep Transfer Learning: Comparative Analysis of Resnet50, VGG16 and Inceptionv3 Models Gupta, Sandeep; Hamid, Abu Bakar Abdul; Nyamasvisva, Tadiwa Elisha; Jain, Vishal; Tyagi, Nitin; Mun, NG Khai; Ather, Danish
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1116

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Cotton is among the most critical crops in the world textile industry, but it is highly susceptible to a vast array of infections that have a tremendous impact on output and fiber quality. Traditional cotton disease diagnosis is mostly based on manual inspection by farmers and experts and is time consuming, labor intensive and inaccurate due to similarity of symptoms. The high rate at which artificial intelligence, especially computer vision and deep learning (DL), have advanced has provided effective alternatives to auto-detecting plant diseases. As a subdivision of the DL approach, transfer learning allows adapting existing convolutional neural networks to the agricultural domain using smaller datasets to guarantee higher performance. This work introduces comparative analysis of three popular deep transfer learning (DTL) models ResNet50, VGG16, and InceptionV3 that are used in the classification of cotton leaf diseases. The training, validation, and testing were performed on a dataset of 1,991 labelled images that included four categories of normal and diseased cotton leaves and plants. All models were optimized and assessed with standard measures, such as validation and test accuracy. The experimental results show that InceptionV3 had the highest accuracy of 95.28, VGG16 had 85.85, and ResNet50 had the lowest accuracy of 69.81. The high accuracy of InceptionV3 is also a testament to its ability in the extraction of multi-scale features, and the trade-off between accuracy and computational efficiency. The results affirm the feasibility of DTL frameworks to revolutionize precision agriculture by facilitating diagnosis of cotton diseases in a timely and reliable manner. This development can help in ensuring that farming activities are sustainable, pesticides are used efficiently and the economy does not suffer economic losses and helps in ensuring that productivity and environmental protection are maintained in cotton farming.
Co-Authors Ather, Danish Hamid, Abu Bakar Abdul Jain, Vishal Madhukullya, Samikshya Mun, NG Khai Nyamasvisva, Tadiwa Elisha Partap S. Ghalaut Tyagi, Nitin Veena S. Ghalaut