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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Yadav, Surendra Sinha, Deepak Kumar Kumari, Sweta Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science Engineering Banglore India Atlas SkillTech University Department of Isme Mumbai India

ISBN: (纸本)9798350388800

In this research, we look at how well using neural network techniques may improve the processing of near field sensor arrays. As the introduction explains, near field sensor array technology is becoming more important across many fields, therefore there is a pressing need for cutting-edge techniques that improve spatial resolution, signal-to-noise ratio, and computational efficiency. The study presents a unique neural network technique for processing near field sensor arrays, capitalizing on the flexibility and learning characteristics of the network to boost performance. The suggested neural network technique is shown to be better in all measures by the findings. It outperforms more conventional approaches, which only managed a score of 0.78, because of its 0.92 spatial resolution. Furthermore, the suggested technique achieves a signal-to-noise ratio of 18.5, which is much higher than the greatest signal-to-noise ratio produced by conventional methods (15.2). The suggested method is more accurate, with significantly reduced mean absolute error (0.027) and mean squared error (0.001) values. In addition, the suggested method's processing speed is competitive, coming in at 3.8 ms whereas more conventional approaches take between 4.2 and 5.0ms. With an efficiency of 95% in computation, it outperforms the efficiency of conventional approaches, which ranges from 82% to 88%. Medical imaging, environmental monitoring, and wireless communication are just a few of the many potential applications highlighted by the results, validating the efficacy of the suggested technology. The findings call for further investigation and cooperation at the crossroads of sensor technology and deep learning, which should lead to novel processing techniques for near field sensor arrays that may be used in a wide variety of contexts. © 2024 IEEE.

关键词： Deep Learning Efficiency Neural Network Optimization Processing Sensor Spatial Resolution Strategy

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Rule-based database intrusion detection using coactive artificial neuro-fuzzy inference system and genetic algorithm

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International Journal of Business Intelligence and Data Mining 2022年第1期21卷 85-101页

作者： Brahma, Anitarani Panigrahi, Suvasini Samal, Neelamani Gountia, Debasis Department of Computer Science and Engineering VSSUT Sambalpur Odisha Burla India Department of Computer Science and Engineering CET Odisha Bhubaneshwar India Department of Computer Science and Application CET Odisha Bhubaneshwar India

Recently, a fuzzy system having learning and adaptation capabilities is gaining lots of interest in research communities. In the current approach, two of the most successful soft computing approaches – neural network and genetic algorithm with learning capabilities are hybridised to approximate reasoning method of fuzzy systems. The objective of this paper is to develop a coactive neuro-fuzzy inference system with genetic algorithm-based database intrusion detection system that can detect malicious transactions in database very efficiently. Experimental investigation and comparative assessment has been conducted with an existing statistical database intrusion technique to justify the efficacy of the proposed system. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Database systems

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Optimized Pedestrian Detection Leveraging YOLOv9: A Thorough Deep Learning Framework for Autonomous Vehicle System

Optimized Pedestrian Detection Leveraging YOLOv9: A Thorough...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Kumar, Hemant Mamoria, Pushpa Dewangan, Deepak Kumar Chhatrapati Shahu Ji Maharaj University Department of Computer Science & Engineering Kanpur India Chhatrapati Shahu Ji Maharaj University Department of Computer Application Kanpur India ABV-IIITM Department of Computer Science & Engineering Gwalior India

ISBN: (纸本)9798350379525

Pedestrian detection for an autonomous vehicle system is the crucial and foremost important aspect for accurate identification of pedestrian on the road scene environment. For this study a YOLOv9-C state-of-the-art deep learning model is used. YOLOv9 is renowned for its accuracy and efficiency in real time application. Model is trained on KITTI dataset. This dataset has a number of classes but for this study only pedestrian class is used. Dataset provides a number of images of urban scene environment where number of objects are there and trained model is perfectly detecting pedestrian in the images. In the YOLOv9 model an additional backbone layer is added for achieving high efficiency and time bound detection. The primary aim of this research is to look for the possible technique by which detection accuracy can be improved. Additional added layer helps in detecting more complex features of pedestrian in the images. After careful observation findings are shows that proposed model has achieved better accuracy in comparison with another models. Experimental results also show that there is a chance for more reliability and responsiveness of object detection if model is trained on diverse dataset. Although this study demands high computational resources which may be a barrier in some sense. © 2024 IEEE.

关键词： Deep learning

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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network 2

Identification of Age, Plumage, and Sex of Bird Species Usin...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Sourik Gupta, Richa Pandian, J. Arun Saranya, S. Mariappan, L. Thanga Vellore Institute of Technology Department of Computer Application Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350306415

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset. © 2024 IEEE.

关键词： Bird Plumage Identification Bird Species Identification Deep Convolutional Neural Networks Image Augmentation Pre-trained networks

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FViT: Finetuned Vision Transformer for Improved Brain Tumor Classification

FViT: Finetuned Vision Transformer for Improved Brain Tumor ...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Chandra, Lalini Chandra, Tej Bahadur Rathy, Anirud Rai, Anushka National Institute of Technology Department of Computer Application Chhattisgarh Raipur India Bennett University School of Computer Science Engineering and Technology UP Greater Noida India

ISBN: (纸本)9798331530259

Timely detection and treatment of brain tumors (BT) remain a critical challenge due to their rapid growth and complex morphologies. Recent deep learning (DL) approaches and large vision models (LVMs) have promising potential in early and accurate diagnosis of BTs. However, the optimal tuning of hyperparameters is crucial for increasing the model's efficacy and robustness. Large vision models have significant potential for improving the accuracy of diagnosing BT using MRI images. Additionally, we evaluated and compared the performance of recent DL models ResNet50, VGG16, and Long Short-Term Memory (LSTM) using BT MRI dataset. Through rigorous experimentation, our results highlight the superior diagnostic capabilities of the FViT model with an accuracy (ACC) of 99.68% and an F1-score (FS) of 99.68% for BT classification (using training-validation set). This result is also verified with high ACC of 99.31% and FS of 99.31%using testing set. The study demonstrates how well FViT model performs in classifying BT accurately, showcasing its potential in clinical settings. These findings emphasize the critical role of fine-tuning LVM models in advancing BT diagnosis through brain MRI images. © 2025 IEEE.

关键词： Diagnostic radiography

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Unveiling the Dynamics of Online Culinary Convenience: A Systematic Exploration 2

Unveiling the Dynamics of Online Culinary Convenience: A Sys...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Bhatt, Chandradeep Bisht, Anurag Sharma, Sanjay Gupta, Shefali Singh, Teekam Graphic Era Hill University Cse Department Dehradun India Graphic Era Hill University Computer Science and Engineering Dehradun India Sgrr Univeristy Computer Application Department Dehradun India Chitkara University Institute of Engineering and Tech Chitkara University Punjab India Graphic Era Deemed to Be University Computer Science and Engineering Dehradun India Sgrrits Computer Application Department Dehradun India

ISBN: (纸本)9798350306415

The industry for online food delivery has grown quickly, changing how customers access and use food services. Finding foods that are suitable for their diets and leanings among the dozen selections offered to customers and users is frequently difficult. For this study, existing research on customer behavior in online food ordering systems is reviewed in order to identify any gaps where intelligent advocacy systems can be very helpful. The proposed work focuses on utilizing sophisticated machine learning algorithms and approaches to create personalized food advice that takes into consideration user preferences, previous order history, and contextual data. The findings of the empirical study offer priceless insights into the efficiency of intelligent suggestion systems and their influence on user engagement. The results provide guidance for future advancement in the sector and help to understand consumer preferences as well as the difficulties that online meal ordering platforms have had to overcome. My suggested system includes an online food ordering option that makes it simple for clients. My system makes it simple to purchase food online from restaurants and use the mess service. This study looks into how the online food ordering landscape is changing and aims to offer opinions on how intelligent advising systems could improve user experience. The ultimate goal of this is to help companies devise plans to enhance customer service and spur expansion in the online food ordering sector. © 2024 IEEE.

关键词： DOM E-Commerce JWT machine learning

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring 5

DL-Based Somnolence Detection for Improved Driver Safety and...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

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HIJLI-JU-CLEF at MULTI-Fake-DetectiVE: Multimodal Fake News Detection Using Deep Learning Approach 8

HIJLI-JU-CLEF at MULTI-Fake-DetectiVE: Multimodal Fake News ...

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8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2023

作者： Sarkar, Sandip Tudu, Nripen Das, Dipankar Department of Computer Science and Application Hijli College Kharagpur India Department of Computer Science and Engineering Jadavpur University Kolkata India

This report presents the progress made in developing a system for participation in a shared task called MULTI-Fake-DetectiVE Task 1, which aims to detect and verify fake news in a multimodal environment comprising both textual and visual elements. The task primarily revolves around automatically identifying fake news by analyzing the combined use of text and images. Our system is structured into three distinct modules. The initial module is responsible for extracting textual information from images. The second module serves as a translation component, enabling the analysis of non-English text by converting it into English. Lastly, the classification module utilizes the outputs from the previous modules to predict the appropriate classes, allowing for accurate differentiation between various types of content. Our objective is to create a system that can predict these labels. To achieve this, we extract information from both the image and the text, specifically focusing on English language text and translating any textual data into English. Subsequently, both sets of data are utilized to train a classification-based model, which aims to predict the aforementioned labels. We were able to obtain a Weighted Average F1-Score of 0.393 by implementing a Multi-head attention mechanism. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep learning

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CiGeN 1.0: A Generative AI Enabled Medical Image Augmentation Pipeline for Deep Network Models

CiGeN 1.0: A Generative AI Enabled Medical Image Augmentatio...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Dash, Archana Nayak, Debasish Swapnesh Kumar Swarnkar, Tripti Siksha 'O' Anusandhan Deemed to be University Department of Computer Science and Engineering Bhubaneswar751030 India Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar752050 India National Institute of Technology Department of Computer Application Raipur492010 India

ISBN: (纸本)9798331530259

Deep Learning methods have gained a significant momentum in medical image classification tasks, including many ranges of datasets. However, these datasets often suffer from class imbalance, where the minority class (e.g., diseased cases) is underrepresented. This imbalance leads to biased predictions, affecting the model's performance. Generative Adversarial Networks (GANs) offer a potential solution by generating synthetic images for the minority class, helping balance the dataset. Methodology: In this study we further evaluated our Deep Learning Model CoVaD-GAN with separate set of datasets to test the dynamics behind it. We applied a Deep Convolutional GAN (DCGAN) to generate synthetic images for underrepresented classes in the chest X-ray (CXR) and Break His histopathological dataset. Augmented synthetic images were made a part of training set, and the model's performance was evaluated before and after augmentation using VGG-16, ResNet-50 with necessary metric measures as accuracy, sensitivity, and specificity were compared pre- and post-GAN augmentation. We also evaluated the quality of GAN-generated images using Fréchet Inception Distance (FID) and manual expert reviews. The results demonstrate significant improvements in model accuracy, sensitivity, and F1-scores after applying GAN-based augmentation. The use of GANs for data augmentation enhances the ability of deep learning models to generalize, particularly for minority classes. GAN-generated images were of high quality and contributed positively to model performance. The Proposed model (CiGeN 1.0) dynamically generalizes the other medical dataset introduced to it and derived a fair resulting diagnosis. © 2025 IEEE.

关键词： Chest X-rays CNN Deep Learning GAN Medical data augmentation

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Quantum Odyssey: Traversing the NISQ Era's Quantum Terrain 3

Quantum Odyssey: Traversing the NISQ Era's Quantum Terrain

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Arya, Swati Ansar, Syed Anas Aggarwal, Shruti Babu Banarsi das University Department of Computer Application Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350342338

The advent of quantum computing has ignited unprecedented anticipation within the realm of IT. This has transcended the limitations of classical computation and revolutionized various facets of technological advancement. At the forefront of this quantum revolution stands the Noisy Intermediate-Scale Quantum (NISQ) era, characterized by a delicate interplay of immense potential and persistent imperfections. This paper delves into the intricacies of NISQ-era quantum computing, unraveling its fundamental principles, challenges, and transformative implications for the IT landscape. Beginning with quantum computing basics-qubits, superposition, entanglement, and gates-the NISQ era's essence addresses error-prone operations and decoherence. Quantum algorithms for NISQ devices, like Grover's search, Shor's factorization, and Variational Quantum Eigensolver (VQE), are scrutinized for their computational potential. Central to NISQ's viability, quantum error correction and mitigation strategies are surveyed, stabilizing quantum computations amidst noise. Examining quantum hardware architectures and performance metrics unveils NISQ devices' potential. applications of NISQ quantum computing in cryptography, optimization, machine learning, and more are explored, showcasing quantum advantage's real-world potential. This paper embarks on a multidimensional exploration of NISQ quantum computing, illuminating its intricate facets, applications, and ethical implications. As quantum computing evolves from a theoretical notion to a tangible paradigm, the insights gleaned herein contribute to a holistic understanding of its role in shaping the next wave of computing. © 2023 IEEE.

关键词： Ethical Implications Noisy Intermediate-Scale Quantum (NISQ) Era Quantum Algorithms Quantum Computing Quantum Error Correction (QEC)

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