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All Journal International Journal of Engineering, Science and Information Technology
Deepak, Shashikant
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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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Quantum Machine Learning for Enhancing Signal Processing Applications Sairam, Kamineni; Deepak, Shashikant; Chakravarthi, Rekha; Mohanty, Saumendra Ku.; Rao, P.S. Raghavendra; Choudhary, Varsha; Punia, Ankit
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.v5i2.1375

Abstract

In neuroscience and therapeutic practice, electroencephalography (EEG) is a vital instrument for tracking and analysing brain activity. While traditional neural network models, like EEG-Net, have made significant progress in interpreting EEG signals, they frequently encounter difficulties due to the great dimensionality and complexity of the data. Quantum machine learning (QML) techniques offer new ways to improve machine learning models, thanks to recent developments in quantum computing. As a forward-looking approach, we present Quantum-EEG Net (QEEG Net), a novel hybrid neural network that combines quantum computing with the classical EEG Net architecture to improve EEG encoding and analysis. While the results may not always outperform conventional methods, it demonstrates its potential. In order to capture more complex patterns in EEG data and maybe provide computational benefits, QEEG Net integrates quantum layers into the neural network. Using the benchmark EEG dataset, BCI Competition IV 2a, we test QEEG Net and show that it consistently performs better than standard EEG-Net on the majority of participants and has other robustness to noise.
Advanced Power Electronics for Grid-Integrated Renewable Energy Systems Poonguzhali, S.; Mohapatra, Tapas Kumar; Boregowda, Vinay Kumar Sadolalu; Thakur, Ankita; Bhalla, Anubhav; Sairam, Kothakonda; 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.v5i2.1387

Abstract

Incorporating Renewable Energy (RE) into the grid necessitates efficient and dependable power conversion phases, especially given the rising need for enhanced controllability and adaptability from the system. Powered by sophisticated control and data technologies, power electronics converters are crucial for large-scale RE production. The deployment of power converters has revealed several issues within traditional power grids, such as decreased system stability. The paper presents grid integration with power electronics for large-scale RE production. Technical challenges and specifications are examined, emphasising grid-dependent wind, solar power, and energy storage solutions. The fundamentals of energy production and converting control for individual electrical conversions (e.g., generalised current management) and at the system layer (e.g., coordinated functioning of large-scale energy networks) are briefly addressed. Further studies are proposed to enhance large-scale RE generating capabilities by integrating additional power electronics technologies.
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 Bhalla, Anubhav Boregowda, Vinay Kumar Sadolalu Chakravarthi, Rekha Choudhary, Varsha Deepthi, Pochampalli Mohanty, Monalisa Mohanty, Saumendra Ku. Mohapatra, Tapas Kumar Poonguzhali, S. Punia, Ankit Rao, P.S. Raghavendra Sairam, Kamineni Sairam, Kothakonda Singh, Manpreet Singh, Suraj Thakur, Ankita Venugopal, Vedanarayanan