Jan'24
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The IUP Journal of Computer Sciences
ISSN: 2583-441X
A 'peer reviewed' journal indexed on Cabell's Directory,
and also distributed by EBSCO and Proquest Database
It is a quarterly journal that publishes research papers on state-of-the-art Computing techniques and Encryption techniques; Computational problems; Artificial Intelligence; Cloud Computing; Databases; Algorithms and data structures; Cybernetics; Firewall techniques; Chip designing, Logic circuits and Software engineering; Programming techniques; Computer architecture; Neural networks, Machine Learning etc.
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Nature-Inspired Harmony Search Algorithm: Latest Developments and Variants
Metaheuristic optimization algorithms involve various mathematical techniques inspired from nature and various day-to-day activities. These techniques provide the best solution at minimum cost and error along with maximum profit and utility. They can solve many tough optimization problems where conventional computing techniques do not work. Harmony search algorithm (HSA) is one such relatively recent technique. It is derived from the theory to obtain perfect harmony in music. It can handle diversification and intensification, which makes it a highly efficient metaheuristic algorithm. The paper reviews the latest developments and variants of the algorithm in HSA. It provides insights into the algorithm's applications to help the readers understand it and apply the knowledge obtained in further work.
Comparative Assessment of ResNet and VGG16 in Brain Tumor Classification
Brain tumor presents a complex challenge in modern medical treatment. During the initial stages of tumor growth, radiologists prioritize accurate and efficient analysis for which deep learning has emerged as a valuable tool. In particular, the deep residual network (ResNet), incorporating convolutional neural networks (CNNs) and VGG16, has demonstrated significant success in detecting and categorizing images of tumors. This advancement in deep learning holds the potential to aid radiologists in diagnosing tumors noninvasively. By enhancing comprehension of MRI images and improving training speed and accuracy, deep learning could revolutionize medical research. This study focuses on employing transfer learning with pretrained ResNet50 and VGG16 models to investigate multiclass brain tumor classification. The achieved accuracy rates are noteworthy: 78.32% for VGG16 and 80.10% for ResNet on brain tumor dataset.
Smart Tourism in India: Leveraging Technology for Enhanced Visitor Experience
India is embracing 'smart tourism' by leveraging technologies like Internet of Things (IoT), artificial intelligence (AI), big data analytics, mobile apps and augmented reality (AR) to provide personalized experiences for visitors. This technology revolution is transforming India's tourism industry, but challenges such as cyber security and cultural authenticity remain. An empirical study reveals significant connections between positive perceptions of AI, AR and technology's role in ecofriendly initiatives and sustainability within smart tourism. This study looks at future where technology seamlessly blends with India's unique tourism offerings, creating unforgettable experiences, while ensuring responsible and sustainable development. The analysis reveals significant correlations between respondents' opinions regarding various aspects of smart tourism such as AI integration, AR usage, technology in ecofriendly initiatives and sustainability. These technologies improve crowd management, resource optimization and visitor experience, transforming tourism perception and engagement.
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