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DeepGAN: Utilizing generative adversarial networks for improved deep learning

International Journal of Knowledge-Based and Intelligent engineering Systems 2024年第4期28卷 732-748页

作者： V, Edward Naveen A, Jenefa T.M, Thiyagu A, Lincy Taurshia, Antony Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Department of Computer Science and Engineering National Engineering College India

In the realm of deep learning, Generative Adversarial Networks (GANs) have emerged as a topic of significant interest for their potential to enhance model performance and enable effective data augmentation. This paper addresses the existing challenges in synthesizing high-quality data and harnessing the capabilities of GANs for improved deep learning outcomes. Unlike traditional approaches that heavily rely on manually engineered data augmentation techniques, our work introduces a novel framework that leverages DeepGANs to autonomously generate diverse and high-fidelity data. Our experiments encompass a diverse spectrum of datasets, including images, text, and time series data. In the context of image classification tasks, we conduct experiments on the widely recognized CIFAR-10 dataset, which consists of 50,000 image samples. Our results demonstrate the remarkable efficacy of DeepGANs in enhancing model performance across various data domains. Notably, in image classification using the CIFAR-10 dataset, our innovative approach achieves an impressive accuracy of 97.2%. This represents a substantial advancement beyond conventional CNN models, underscoring the profound impact of DeepGANs in the realm of deep learning. In summary, this research sheds light on DeepGANs as a fundamental component in the pursuit of enhanced deep learning performance. Our framework not only overcomes existing limitations but also heralds a new era of data augmentation, with generative adversarial networks leading the way. The attainment of an accuracy rate of 97.2% on CIFAR-10 serves as a compelling testament to the transformative potential of DeepGANs, solidifying their pivotal role in the future of deep learning. This promises the development of more robust, adaptive, and accurate models across a myriad of applications, marking a significant contribution to the field. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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From light sensing to adaptive learning:hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing

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Light(Science & Applications) 2025年第1期14卷 215-226页

作者： Bashayr Alqahtani Hanrui Li Abdul Momin Syed Nazek El-Atab Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)ThuwalSaudi Arabia Electrical Engineering Department College of EngineeringPrincess Nourah Bint Abdulrahman University(PNU)RiyadhSaudi Arabia

Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.

关键词： integrate volatile integrating

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Advanced functional connectivity analysis with integrated EEG signal enhancement and GTW-EEG-GAN for emotion detection

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International Journal of computers and Applications 2024年第12期46卷 1152-1165页

作者： Babu, Nirmal Varghese Kanaga, E. Grace Mary Division of Computer Science and Engineering School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Division of Data Science and Cyber Security School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techniques by employing Graph Neural Networks (GNNs) on functional connectivity matrices derived from EEG data to advance emotion recognition. Our approach integrates sophisticated preprocessing methods, including Integrated EEG Signal Enhancement (IESE) and novel augmentation techniques such as Gaussian Time Warping Generative Adversarial Network (GTW-EEG-GAN). This integration aims at enhancing data quality and diversity. Signal decomposition using EMD-Wavelet Hybrid Decomposition further refines feature extraction, enabling robust analysis of EEG signals. Functional connectivity metrics capture critical neural interactions intended for precise emotion characterization. The GNN architecture effectively processes these features, achieving significant accuracy improvements. Evaluations on the DEAP dataset demonstrate promising results, attaining 94.5% accuracy before hyperparameter tuning and further improving to 98.6% after tuning, highlighting the system's efficacy. The methodology showcases the integration of advanced signal processing techniques with AI, offering a comprehensive framework for future studies. The findings advocate potential applications in areas such as personalized mental health monitoring, adaptive learning environments, and responsive gaming experiences, demonstrating the broad impact and versatility of AI-driven EEG-based emotion recognition. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Emotion Recognition

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A High-Performance 220-290 GHz Micromachined Waveguide Switch Based on Interference Between MEMS Reconfigurable Surfaces

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IEEE Transactions on Terahertz Science and technology 2024年第2期14卷 188-198页

作者： Karimi, Armin Shah, Umer Yu, Suxian Oberhammer, Joachim Kth Royal Institute of Technology Micro and Nanosystems Technology Division School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden

This article presents a highly integrated novel silicon micromachined single-pole-single-throw waveguide switch based on two microelectromechanically reconfigurable switching surfaces (MEMS-RSs), which allows optimizing the switching performance by tuning the interference between the two such MEMS-RSs utilizing integrated electrostatic comb-drive actuators. The switch prototype is implemented with axially aligned standard WR-3.4 waveguide ports with a total footprint of 3 mm×3.5 mm×1.2 mm. The measured blocking (off) state insertion loss (isolation) and return loss, measured between two standard WR-3.4 waveguide flanges, are 28.5-32.5 dB and better than 0.7 dB, and the propagating (on) state insertion and return losses are 0.7-1.2 dB and better than 17 dB in the 220-290 GHz frequency band, respectively. The measured results were in excellent agreement with the simulation data, implying 27.5% fractional bandwidth, which is very close to a full waveguide band performance. For further investigations, two variants of the switching circuit with only a single MEMS-RS and without any MEMS-RSs have also been fabricated. The single MEMS-RS switch achieved the off-state isolation, on-state insertion loss, and return loss of only 11.5-12.5 dB, 0.8-1.3 dB, and better than 12 dB from 220 to 274 GHz, respectively, which clearly indicates the drastic performance improvement of the interference-based double MEMS-RS switch design. Moreover, measurement of the waveguide-only reference structure showed that the waveguide section alone attributed to 0.2-0.5 dB of the measured on-state insertion loss of the double MEMS-RS switch, and the rest is due to the introduction of the MEMS-RSs inside the waveguides. © 2011-2012 IEEE.

关键词： Millimeter waves

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Analytical review and study on various course recommendation systems

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International Journal of Wireless and Mobile Computing 2023年第2期25卷 160-170页

作者： Anupama, V. Elayidom, M. Sudheep Division of Computer Science School of Engineering Cochin University of Science and Technology Kerala Kochi India

In the educational system, online courses are significant in developing the knowledge of users. The selection of courses is important for college students because of large unknown optional courses. The course recommendation systems are provided with suggestions and improve course selection during the pre-registration stage. This survey presents the analysis of 50 research papers for course recommendation. The course recommendation systems are grouped under three categories, namely machine learning-based techniques, collaborative-based and data mining-based techniques. Besides, the classification of techniques, utilised tools, implemented software tools and performance metrics are considered for analysis. Moreover, the research gaps identified in the existing course recommendation system are discussed. The machine learningbased approach is mostly used for course recommendation among several approaches. Most existing course recommendation techniques use Java as the implementation tool and the Moodle log database. Also, F-measure, MAE, accuracy and RMS have been commonly used as performance metrics. © 2023 Inderscience Enterprises Ltd.

关键词： Collaborative filtering

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Etching-free pixel definition in InGaN green micro-LEDs

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Light(Science & Applications) 2024年第8期13卷 1655-1665页

作者： Zhiyuan Liu Yi Lu Haicheng Cao Glen Isaac Maciel Garcia Tingang Liu Xiao Tang Na Xiao Raul Aguileta Vazquez Mingtao Nong Xiaohang Li Advanced Semiconductor Laboratory Electrical and Computer Engineering ProgramCEMSE DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

The traditional plasma etching process for defining micro-LED pixels could lead to significant sidewall *** near sidewall regions act as non-radiative recombination centers and paths for current leakage,significantly deteriorating device *** this study,we demonstrated a novel selective thermal oxidation(STO)method that allowed pixel definition without undergoing plasma damage and subsequent dielectric *** annealing in ambient air oxidized and reshaped the LED structure,such as p-layers and InGaN/GaN multiple quantum ***,the pixel areas beneath the pre-deposited SiO_(2)layer were selectively and effectively *** was demonstrated that prolonged thermal annealing time enhanced the insulating properties of the oxide,significantly reducing LED leakage ***,applying a thicker SiO_(2)protective layer minimized device resistance and boosted device efficiency *** the STO method,InGaN green micro-LED arrays with 50-,30-,and 10-μm pixel sizes were manufactured and *** results indicated that after 4 h of air annealing and with a 3.5-μm SiO_(2)protective layer,the 10-μm pixel array exhibited leakage currents density 1.2×10^(-6)A/cm^(2)at-10 V voltage and a peak on-wafer external quantum efficiency of~6.48%.This work suggests that the STO method could become an effective approach for future micro-LED manufacturing to mitigate adverse LED efficiency size effects due to the plasma etching and improve device ***-LEDs fabricated through the STO method can be applied to micro-displays,visible light communication,and optical interconnect-based *** planar pixel geometry will provide more possibilities for the monolithic integration of driving circuits with ***,the STO method is not limited to micro-LED fabrication and can be extended to design other III-nitride devices,such as photodetectors,laser diodes,high-electron-mobility transistors

关键词： annealing protective quantum

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Machine Learning-based Immune Cell Classification for Enhanced Immunological Analysis 3

Machine Learning-based Immune Cell Classification for Enhanc...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Naveen Pranouv, S.S. Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509675

The automated classification of immune cells plays a vital role in advancing immunological research, diagnostics, and therapeutic monitoring. This paper leverages machine learning and image processing techniques to accurately classify various immune cell types from microscopic images by analyzing cellular features such as shape, size, and texture. The proposed Convolutional Neural Networks (CNN) based system integrates digital pathology workflows to provide a reliable and efficient tool for immune cell classification, reducing reliance on manual inspection. The model trained on a diverse dataset of labeled immune cell images to ensure high classification accuracy and adaptability across different cell types and imaging conditions. The proposed model provides a proactive solution to immunology research challenges, enhancing both the precision and efficiency of immune cell classification. © 2025 IEEE.

关键词： Convolutional neural networks

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A Review on Resource Provisioning Technique's in Cloud Environment 15

A Review on Resource Provisioning Technique's in Cloud Envir...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Ranjana, C. Anusha Bamini, A.M. Chitra, R. Division of Computer Science and Engineering Karunya Institute of Technology and Science India

ISBN: (纸本)9798331300579

Resource provisioning is the method of making sure that the cloud service can be efficiently resourced to the customers as demand increases. Resource provisioning is the procedure of selecting, deploying, and managing software like load balancers and database server management systems and hardware sources such as CPU, storage, and networks to guarantee utility performance. To do this in powerful manner, a CSP could want to take sure measures to effectively deliver on its SLAs. As an instance, the CSP may have procedures in place to feature servers or storage as call for increases. It goals to make sure that an organization can seamlessly get right of entry to the specified sources in an optimized and efficient manner. The general aim of aid provisioning is to permit the packages to utilize computational strength, garage, and offerings. Cloud aid provisioning requires the CSP to scale and manage consumer needs seamlessly the steps in resource provisioning are record Infrastructure, Configuring and booting, Provisioning API and Provisioning VMs. This paper suggests an evaluation between provisioning techniques utilized in cloud. The useful resource provisioning was formerly done with the aid of considering carrier level goals and with numerous scheduling methods Here we evaluate distinct strategies used in present day cloud computing environment. © Grenze Scientific Society, 2024.

关键词： Quality of service

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Neuroimaging-based Machine Learning for Early Alzheimer's Disease Prediction 3

Neuroimaging-based Machine Learning for Early Alzheimer's Di...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Jerome, Jim Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509675

Early detection of Alzheimer's disease (AD) is crucial for timely intervention and slowing its progression. This research leverages neuroimaging-based machine learning to classify cognitive impairment levels using MRI images. The proposed approach involves preprocessing MRI images, converting them into a numerical format, and training a BEiT (BERT for Image Transformation) model to classify individuals into one of four categories: No Impairment, Very Mild, Mild, or Moderate Impairment. A web interface, developed using Streamlit, enables user-friendly access, allowing real-time predictions by uploading MRI images. This system demonstrates the potential of integrating advanced machine learning techniques and neuroimaging for early Alzheimer's diagnosis, paving the way for personalized interventions and improved outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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A Confirmation Based Accident Detection System Using IoT for Smart Vehicles 3

A Confirmation Based Accident Detection System Using IoT for...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Ninan, Bettina Karunya Institute of Technology and Sciences Division of Computer Science and Engineering India

ISBN: (纸本)9798350384598

Accidents on the road continue to pose a significant threat to life and safety, necessitating innovative solutions to improve emergency response and minimize injuries. The proposed approach introduces an IoT-based Accident Detection System that integrates seamlessly with vehicle airbags, a confirmation button, GPS tracking, SOS calls, and a beeping system. The system harnesses a network of sensors to detect accidents accurately. When an accident occurs, it automatically deploys airbags to protect the passengers in the vehicle. A critical feature in the system is the manual confirmation button, which lets the occupants confirm the severity of the accident. If no confirmation is acquired within a set timeframe, the system proceeds to the next steps to save the occupant. The system uses GPS tracking with an accuracy of 96% to determine the location of the accident. Simultaneously, it initiates SOS calls to nearby hospitals and emergency services, ensuring seamless medical assistance. In addition, the system emits alerts to caution passersby and authorities about the *** system enhances emergency response, reducing the time between an accident occurring and medical attention. It not only minimizes injuries but also improves overall road safety. The proposed system aims to provide a brief overview of its multi-faceted capabilities, emphasizing its potential to save lives and reduce the severity of injuries in road accidents. © 2024 IEEE.

关键词： Automobile air bags

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