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Development Of An Image Restoration Algorithm Utilizing Generative Adversarial Networks (GAN’s) For Enhanced Performance In engineering Applications: A Comprehensive Approach To Improving Image Quality And Clarity Through Advanced Machine Learning Techniques

Development Of An Image Restoration Algorithm Utilizing Gene...

引用

2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Manjunath, T.C. Pavithra, G. Samyama Gunjal, G.H. Ninawe, Swapnil S. Dept. of Electronics & Communication Engineering Rajrajeswari College of Engineering Bangalore India Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India Department of Computer Science & Engineering University Visvesvaraya College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331505134

Image restoration, a critical task in computer vision and image processing, focuses on recovering degraded or damaged images to their original, high-quality state. This paper introduces an innovative approach to image restoration using Generative Adversarial Networks (GANs). GANs, a prominent deep learning framework, consist of two neural networks—a generator and a discriminator—that compete to produce and evaluate realistic images. The generator creates images, while the discriminator distinguishes between real and generated ones, refining the generator's capability through adversarial training. Leveraging GANs' ability to learn complex image features, the proposed algorithm restores degraded images affected by noise, blur, and low resolution, producing high-quality, realistic results. Simulation outcomes demonstrate significant advancements in image restoration, showcasing GANs as a powerful tool for addressing challenges in this domain. The study underscores the potential of GANs in generating visually appealing restorations and advancing the state-of-the-art in image processing and restoration tasks. © 2024 IEEE.

关键词： Generative adversarial networks

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Performance Study of ZnO/SnO Thin Film Heterojunction Solar Cell 8

Performance Study of ZnO/SnO Thin Film Heterojunction Solar ...

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8th International Conference on computers and Devices for communication, CODEC 2023

作者： Mandal, Lipika Anwer Askari, Syed Sadique Asraful Sekh, Md. Aref Billaha, Md. Asansol Engineering College Dept. of Computer Science and Business Administration Asansol India IIIT Ranchi Dept. of Electronics and Communication Engineering Ranchi India Aliah University Dept of Electronics & Communication Engineering Kolkata India Jalpaiguri Polytechnic Institute Dept. of Electronics & Telecommunication Engineering Jalpaiguri India

ISBN: (纸本)9798350317176

The interesting properties of both Tin monoxide (SnO) and Zinc Oxide (ZnO) semiconductors allow to predict promising photovoltaic performance of n-ZnO/p-SnO heterojunction. In this work, one-dimensional numerical simulation is used to study and optimize the n-ZnO/p-SnO thin film based heterojunction solar cell. The effects of ZnO and SnO thickness on the performance of ZnO/SnO heterojunction solar cell is investigated. A highest efficiency of 15.85% is obtained for ∼1.75-2.0 μm of SnO absorber layer thickness. © 2023 IEEE.

关键词： Thin films

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Resource Allocation Optimization in 5G Networks Using Variational Quantum Regressor 1

Resource Allocation Optimization in 5G Networks Using Variat...

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1st International Conference on Quantum communications, Networking, and Computing, QCNC 2024

作者： Pathak, Param Oad, Vidhi Prajapati, Aditya Innan, Nouhaila Sardar Vallabhbhai Patel Institute of Technology Dept. of Computer Engineering India Vishwakarma Government Engineering College Dept. of Electronics and Communication India Dhirubhai Ambani Institute of Information and Communication Technology Dept. of Information and Communication Technology India Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M'sick Hassan II University of Casablanca Morocco

ISBN: (纸本)9798350366778

Quantum Machine Learning (QML) is a powerful tool for addressing complex telecommunications challenges. As 5G technology continues to evolve, our research provides a roadmap for integrating QML into the resource allocation framework, potentially improving the efficiency and performance of 5G networks. Through the application of the Variational Quantum Regressor (VQR), our study demonstrates the potential for quantum models to enhance predictive accuracy in resource allocation scenarios. Our VQR accurately aligns predicted quantum states with their intended targets, thereby reducing discrepancies. Such precision in regression analysis, particularly in the context of 5G networks, represents a significant breakthrough. A key highlight of our algorithm is its ability to substantially reduce the mean squared error to an impressively low value of 0.0081, indicating near-perfect prediction accuracy. This innovative approach is set against the backdrop of the evolving landscape of communication networks and computational systems, where the challenge of resource allocation is increasingly pronounced due to the rising demand for bandwidth-intensive and computation-heavy services. © 2024 IEEE.

关键词： Mean square error

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Leveraging Artificial Intelligence for Enhanced Data Generation in Addressing Imbalance in Binary Classification system 2

Leveraging Artificial Intelligence for Enhanced Data Generat...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Bandopadhyay, Srijita Dutta, Srimonti Haider, Imran Anuraag, Bhavaraju Zhu, Jerry Bazaz, Saad Ahmed University of Engg. & Management Dept. of Electronics and Communication Engineering Kolkata India Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India Vellore Institute of Technology Dept. of Electronics and Communication Engineering Vellore India University of Waterloo Faculty of Mathematics ON Canada National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350360523

This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority Over-sampling Technique (SMOTE), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) to enhance classification outcomes. Traditional classification models tend to favor the majority class, while the impact of imbalanced misclassification costs is often overlooked. The integration of SMOTE, GANs, and VAEs in binary classification, or SMOTE-GAN-VAE, addresses these challenges by generating synthetic instances, refining data representations, and capturing latent features. To evaluate the effectiveness of various data generation methods, a credit card fraud dataset is used. The performance metrics considered include F0.5-score, F1-score, and F2-score, which account for both precision and recall. The results indicate that SMOTE-GAN-VAE outperforms individual methods, such as SMOTE, GANs, and VAEs, demonstrating its potential to enhance data representation and classification accuracy, and outperformed the ß- VAE filtered approach employed in previous literature. © 2024 IEEE.

关键词： Generative adversarial networks

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Stripe Removal from Hyperspectral Food Images acquired by Handheld Camera using 2,1 Norm Minimization and SSTV Regularization

Stripe Removal from Hyperspectral Food Images acquired by Ha...

引用

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and communications Technology, IAICT 2023

作者： Shanthini, K.S. George, Sudhish N George, Sony Devassy, Binu Melit National Institute of Technology Dept. of Electronics and Communication Engineering Calicut India Norwegian University of Science and Technology Dept. of Computer Science Norway

ISBN: (纸本)9798350313635

Hyperspectral imaging offers the capacity to quickly and noninvasively monitor a food product's physical, chemical and morphological properties. Specim IQ is a handheld push broom camera with basic data handling and data analysis capabilities within the camera software. However, the recordings of the Specim IQ camera showed a line pattern (stripes) that was evident in all images. Stripes significantly reduce the visual quality of the images and lower the results of further processing. Hence an efficient destriping model is developed, which specifically addresses this issue. The proposed model uses a spatial gradient term to analyze the directional characteristics and group sparsity to describe the structural characteristics of the stripe component. In addition to this, a spatial spectral total variation regularization is used to ensure piecewise smoothness in the spatial and spectral domains and to remove Gaussian noise. The ensuing optimisation problem is solved using the alternating direction method of multipliers (ADMM). The proposed method is tested in real stripe noise environments, and the findings demonstrate that it outperforms some of the best approaches in terms of visual quality and quantitative evaluations. When compared with the other approaches, the proposed method attained the highest noise reduction (NR) and lowest mean relative deviation (MRD) values (NR=1.67, MRD=1.02%). © 2023 IEEE.

关键词： Noise abatement

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Wearable Sensor-Based Gait Analysis: Advanced Feature Extraction Techniques 2

Wearable Sensor-Based Gait Analysis: Advanced Feature Extrac...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Karthik Salian, Preethi K. Ranjith, H.D. Salian, Supriya Jayashree Shetty, K. Annapoorneshwari NMAM Institute of Technology Udupi Nitte India NMAM Institute of Technology Udupi Nitte India Dept. of Electronics and Communication Engineering Mangalore Institute of Technology and Engineering Moodabidri India Dept. of Computer Science and Engineering St. Joseph Engineering College Mangaluru India Dept. of Computer Science and Engineering NM Institute of Technology Bangalore India Mangalore India

ISBN: (纸本)9798350383867

Gait irregularities among adults are common in clinical settings and can result in severe consequences such as falls and injuries, leading to increased healthcare costs. Presently, gait evaluation primarily relies on subjective assessments using standardized clinical tools. Although motion capture systems like Qualisys and Vicon offer more objective analysis, they depend on costly, intricate equipment in specialized gait laboratories. Due to limited accessibility, involving factors like equipment scarcity, demand for technical expertise, in-person patient visits, complex procedures, and high costs, these methods remain largely inaccessible. Consequently, there is a pressing need for a more objective and cost-effective gait assessment solution. The proposed work introduces an economical automated feature extraction approach to address this issue. Leveraging Inertial Measurement Unit (IMU) sensors, comprising a three-axis gyroscope and accelerometer system captures precise foot position and orientation data crucial for comprehensive gait analysis. IMU sensors have gained popularity due to their adaptability in clinical environments and non-clinical settings like patient residences or community spaces. With the help of a microcontroller mounted on the barefoot, the IMU sensor collects raw gyroscope and accelerometer data for gait analysis. These data are wirelessly transmitted via Bluetooth to a computer for subsequent in-dept. analysis, enabling effective feature extraction for comprehensive gait assessment. © 2024 IEEE.

关键词： Accelerometers

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Enhancing Device Authentication in IoT Security Application using Artificial Intelligence: A Survey 3

Enhancing Device Authentication in IoT Security Application ...

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3rd International Conference on communication, Control, and Intelligent systems, CCIS 2024

作者： Brogya, Maame Serwah Dumfeh Ayuso, Enrique Shukla, Aasheesh Goyal, Amit Kumar Sain, Mangal Dongseo University Dept. of Computer Engineering Busan Korea Republic of Gla University Department of Electronics and Communication Mathura India Chandigarh University Department of Computer Science and Engineering Mohali140413 India

ISBN: (纸本)9798331528201

Technological advancement over the last decade has seen significant technological development, particularly in IoT, which is the most used and commercialized. This is due to its convenience, as it gives users remote control of their appliances via portable devices like smartphones and tablets, without needing to be physically present or close to those devices. As IoT technologies continue to grow and new innovative solutions for everyday applications are being developed there are more interconnected devices and large volumes of data being generated every second. However, the rapid growth of IoT has heightened security concerns, especially around device authentication. This paper reviews IoT authentication complexities and explores Machine Learning solutions under AI, comparing various algorithms and, providing a foundation for researchers and developers in the field. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Wireless Sensor Network Routing Through Memetic Algorithms: Enhancing Energy Efficiency and Data Reliability 3

Optimizing Wireless Sensor Network Routing Through Memetic A...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Aishwarya, D. Saranya, S. Sathiya, A. Manoharan, Samuel J. Dept. of Computer Science and Engineering School of Computing Sathyabama Institue of Science and Technology Chennai India Dept. of Computer Science and Engineering Dr. N.G.P Institute of Technology Coimbatore India Dept. of Electronics and Communication Engineering M. Kumarasamy College of Engineering Karur India Dept. of Electronics and Communication Engineering Sir Issac Newton College of Engineering and Technology Nagapattinam India

Memetic Algorithm Based Routing in Wireless Sensor Networks (WSNs) is an innovative approach for optimizing data transmission routes within a network of interconnected sensors. Traditional routing algorithms often fail to consider the dynamic and resource-constrained nature of WSNs, resulting in inefficient energy utilization and decreased network lifetime. By incorporating principles of natural selection, crossover, and mutation from genetic algorithms, along with local search methods, memetic algorithms offer a more adaptive solution. This research aims to implement a memetic algorithm to find the optimal routing paths while minimizing energy consumption and maximizing data delivery ratio. Preliminary results indicate a significant improvement in the network's overall performance metrics, including a longer lifespan and higher data reliability, compared to traditional routing methods. © 2023 Elsevier B.V.. All rights reserved.

关键词： Wireless sensor networks

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A Comparative Study on the Effectiveness of Various Machine Learning Paradigms in Image Recognition and Predictive Modelling

A Comparative Study on the Effectiveness of Various Machine ...

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2023 IEEE International Conference on Device Intelligence, Computing and communication Technologies, DICCT 2023

作者： Upadhyay, Deepak Gupta, Mridul Verma, Aditya Mittal, Saksham Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9781665474924

The efficiency of several machine learning algorithms in picture identification and predictive modelling is thoroughly compared in this research article. The necessity to find the best algorithms for certain use cases and the rising need for reliable and effective image recognition systems across a variety of areas are the driving forces behind the study. In order to assess how well these algorithms, perform in terms of accuracy, precision, and recall, the study uses a wide range of machine learning techniques, including classical and deep learning approaches. The study's findings show that convolutional neural networks, in particular, outperform conventional algorithms in image identification tests while standard and deep learning techniques perform similarly in tasks requiring predictive modelling. The study also emphasises the value of transfer learning and fine-tuning strategies in obtaining cutting-edge performance on picture datasets, and finally we will see results at the end. © 2023 IEEE.

关键词： Transfer learning

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Innovative Method for Enhancing E-Bikes Performance through riding mechanical force

Innovative Method for Enhancing E-Bikes Performance through ...

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2023 IEEE Renewable Energy and Sustainable E-Mobility Conference, RESEM 2023

作者： Yadav, Hritik Shrivastava, Neha Rathod, Devendra Singh Gupta, Tanisha Ies College of Technology Bhopal Dept. of Electronics and Communication Engineering Bhopal India Ies College of Technology Bhopal Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350311327

This paper presents a novel approach to charging E-bikes using their own mechanical energy generated during riding. Inspired by the method used in petrol vehicles where the battery charges itself while in use, we propose a similar system for E-vehicles, specifically E-bikes. Our method involves the addition of a secondary battery to the bike, which will charge itself while the vehicle is in motion, and when parked, it will charge the main battery. This innovative solution aims to improve the mileage of E-bikes, which is a significant drawback compared to petrol vehicles. © 2023 IEEE.

关键词： Vehicle performance

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