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All Journal Indonesian Journal of Electrical Engineering and Computer Science International Journal of Engineering, Science and Information Technology
Singh, Suraj
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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 Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Automated Alzheimer’s disease detection and classification based on optimized deep learning models using MRI Saini, Rashmi; Singh, Suraj; Semwal, Prabhakar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1333-1342

Abstract

Alzheimer’s disease (AD) is a devastating neurologic condition characterized by brain atrophy and neuronal loss, posing a significant global health challenge. Early detection is paramount to impede its progression. This study aims to construct an optimized deep learning (DL) framework for early AD detection and classification using magnetic resonance images (MRI) scans. The classification task involves distinguishing between four AD stages: mild demented (MD), very mild demented (VmD), moderate demented (MoD), and non-demented (ND). To achieve effective classification, three DL models (VGG16, InceptionV3, and ResNet50) are implemented and fine-tuned. A systematic evaluation is conducted to optimize hyper-parameters, with extensive experimentation. The results demonstrate superior classification performance of the customized DL models compared to state-of-the-art methods. Specifically, visual geometry group 16 (VGG16) achieves the highest accuracy of 95.85%, followed by ResNet50 with 89.38%, while InceptionV3 yields the lowest accuracy of 87.23%. This study highlights the critical role of selecting appropriate DL models and customizing them for accurate AD detection and classification across various stages, offering significant insights for advancing clinical diagnosis and treatment strategies.
Real-time Image Processing in Embedded Vision Systems for Autonomous Vehicles Venugopal, Vedanarayanan; Mohanty, Monalisa; Boregowda, Vinay Kumar Sadolalu; Singh, Suraj; Singh, Manpreet; Deepthi, Pochampalli; Deepak, Shashikant
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1495

Abstract

The most recent studies show that the benefits of the IT improvements associated to ads were substantial. These days, there is a lot of controversy over the optimal way to store, retrieve, and access personal and other data.Since systems are developing so quickly and clients may now access information virtually from anywhere, delivering personal or official information on a physical device has become outdated. This is how distributed computing has emerged and expanded to meet the needs of efficiency, security, unwavering quality, and laziness. The widespread use of Internet of Things devices promises to alter some aspects of our lifestyle. The delivery of human services services is being transformed by other individual Internet of Things devices, such as wearable wellness, wellness monitoring devices, and system-powered restorative devices. This invention promises to benefit the elderly and others with disabilities, enabling higher degrees of independence and personal fulfilment at an affordable price. According to the legally enforceable claim, the Internet of Things connects everything to the Internet, conducts data exchange, and transmits information via data-detecting devices including sensors, RFID, and global positioning systems. The Web of Things must be designed to detect, guide, and filter objects in order to provide clients with a variety of innovative data management services. The effects on transportation planning of autonomous cars, often known as self-driving, driverless, or robotic vehicles. Based on past vehicle technology experience, it examines the likelihood of such vehicles developing and being used quickly, their potential costs and benefits, how they will impact travel behaviour, and how they will influence planning choices like the best parking, roads, and public transportation options.
Co-Authors Boregowda, Vinay Kumar Sadolalu Deepak, Shashikant Deepthi, Pochampalli Mohanty, Monalisa Saini, Rashmi Semwal, Prabhakar Singh, Manpreet Venugopal, Vedanarayanan