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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Singh Rathore, Bhanu Pratap Dave, Rutul Dhanasekaran, K. Priyadarsini, K. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350322842

Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposure to radiation, or environmental factors. Early detection of brain tumors is essential for effective treatment and improved patient outcomes. To address this, a novel approach has been proposed for brain tumor detection using transfer learning with the VGG-16 convolutional neural network (CNN) architecture. By leveraging the pre-trained VGG-16 model on the ImageNet dataset, which provides a broad range of data for learning and representing complex features of various objects, meaningful features can be extracted from MRI scans and differentiate between tumor and normal tissue accurately. This refined VGG-16 model can provide a more precise and reliable diagnosis, which is crucial for successful treatment. This research study demonstrates that the utilization of VGG-16 model for brain tumor diagnosis can provide an efficient and accurate method for identifying brain tumors. Moreover, this approach can be applied to develop automated systems to assist medical professionals in diagnosing and treating brain tumors more effectively. © 2023 IEEE.

关键词： Convolutional neural networks

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Enhancing Literary Exploration: A Machine Learning Approach to Book Recommendations 7

Enhancing Literary Exploration: A Machine Learning Approach ...

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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Singh, Kritika Gupta, Sumita Dhir, Saru Gambhir, Sapna Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering Uttar Pradesh Noida India Amity University Amity School of Engineering & Technology Department of Information Technology Uttar Pradesh Noida India George Mason University College of Engineering and Computing Department of Computer Science United States

ISBN: (纸本)9798350350067

In today's era of rapid internet expansion, the need for efficient information retrieval tools is paramount. Recommendation systems serve as indispensable instruments, aiding users in navigating vast datasets swiftly and accessing relevant information. This paper presents a novel approach to book recommendation systems, offering readers a quick and intuitive method to discover their next read. Implementations encompass three algorithms: Popularity-based, Contentbased, and Collaborative-based recommendation systems. The primary focus lies in proposing a swift book idea system, facilitating users in promptly discovering fitting reading materials. By harnessing the power of these algorithms, readers can explore a diverse range of book options tailored to their preferences, enhancing their overall reading experience and satisfaction. Through this study, we aim to contribute to the advancement of recommendation systems, providing valuable insights and practical solutions for optimizing the book discovery process in the digital age. © 2024 IEEE.

关键词： Collaborative filtering

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Pedestrian-Based Adaptive Traffic Light Control Using YOLOv8 24

Pedestrian-Based Adaptive Traffic Light Control Using YOLOv8

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7th International Conference on Information Science and Systems, ICISS 2024

作者： Ortega, Gerson Itliong Ordas, Amiel Mir Odiamar Villaverde, Jocelyn Flores Juanatas, Roben Antalan Juanatas, Irish Castrojeres School of Electrical Electronics and Computer Engineering Mapua University Philippines College of Computing in Information Technologies National University Philippines Department of Information Technology FEU Diliman Philippines

ISBN: (纸本)9798400717567

The advancement of YOLO and similar computer vision models represents a technological era that could revolutionize numerous aspects of day-to-day life, including traffic management. This study explores the feasibility of utilizing YOLOv8 for pedestrian detection and counting to control traffic light timing sequences at isolated pedestrian crossings. By employing the Raspberry Pi 4 Model B with a camera module, real-time pedestrian video data can be captured and processed through YOLOv8. For the hardware, three LEDs and a Dot Matrix Module replicate conventional traffic light signals and timing, effectively simulating a traffic light system. The capabilities of the custom YOLOv8 model are evaluated through pedestrian detection accuracy, calculated by percentage error across twenty samples. Results reveal an average percent error of 7.33%, indicating 92.67% accuracy in pedestrian detection and counting. These findings underscore the model’s competence for pedestrian-based adaptive traffic light control, highlighting its potential to enhance practicality and safety at pedestrian crossings by dynamically adjusting signal timings based on real-time pedestrian activity. © 2024 Copyright held by the owner/author(s).

关键词： Pedestrian safety

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Deep Convolutional Neural Networks for Early-Stage Detection and Prognostication of Lung and Colon Cancer 2

Deep Convolutional Neural Networks for Early-Stage Detection...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Laxmikant, Kumar Arthi, A. Vinodhini, V. Natarajan, B. Bhuvaneswari, R. Selvam, Prabu Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Karpagam College of Engineering Computer Science and Technology Coimbatore India University College of Engineering Department of Electronics and Communication Engineering Ariyalur India VIT University School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350317558

Lung and colon cancers are significant health issues across the world, prompting the need for inventive methods of diagnosis. This study takes the lead in introducing advanced Deep ConvNets (CNNs) to enhance the accuracy of early detection. The model is trained on extensive datasets, resulting in impressive outcomes. During training, it achieves an accuracy of 92.54% and a loss value of 0.0161. These results are mirrored in testing, where the accuracy reaches 96.59% and the loss value is 0.0441. Beyond just numerical achievements, this research revolutionizes cancer diagnostics by providing a sophisticated tool for personalized and early detection. This study surpasses the mere influence on individuals and extends its reach to bring hope for society and the economy by tackling the pervasive weight of cancer. It distinguishes itself in the realm of advanced medical investigation, affirming the efficacy of the model and inspiring additional inquiry, encompassing the utilization of more extensive sets of data and implementation in diverse medical domains. © 2024 IEEE.

关键词： Colon Cancer Convolutional Neural Network Lung Cancer

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FluE: A Resource Fluid Equilibrium Strategy for AIGC Within Evolving computing Power Networks

FluE: A Resource Fluid Equilibrium Strategy for AIGC Within ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Zejun Qiu, Chao Ren, Xiaoxu Wang, Xiaofei Xiong, Zehui Yao, Haipeng Niyato, Dusit Tianjin University College of Intelligence and Computing Tianjin China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing China Singapore University of Technology and Design Singapore Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350351255

The presence of Artificial Intelligence Generated Content (AIGC) has garnered widespread interest. AIGC enables content creation by analyzing big data, leveraging the capabilities of extensive AI models, and substantial AI computing. computing power networks (CPNs) represent an excellent approach for offering pervasive AI computing resources to AIGC. However, these characteristics have posed unprecedented challenges to the CPNs helped AIGC, including the uncertainty of prompts' information value, the inability to model the continuity of computing resources, and the incapacity to represent complex multi-dimensional spaces. In this paper, we propose a computing resources equilibrium strategy based on the fluid model for AIGC helped by CPNs, namely FluE. This mechanism obtains information entropy by constructing an AIGC prompt tree to measure the information value of AIGC prompts. In addition, we model the continuity of computing resources by the fluid model. A fluid-stopping equilibrium strategy is formulated to obtain the average fluid level of computing resources based on the Laplace-Stieltjes transform. To solve the equilibrium strategy, we develop a diffusion-based algorithm for FluE to adjust the fluid policy dynamically to maximize resource rewards. Finally, the evaluations demonstrate improvements in average social welfare. © 2024 IEEE.

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Exploring Frontier Technologies in Video-Based Person Re-Identification:A Survey on Deep Learning Approach

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Computers, Materials & Continua 2024年第10期81卷 25-51页

作者： Jiahe Wang Xizhan Gao Fa Zhu Xingchi Chen Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan250022China College of Information Science and Technology&College of Artificial Intelligence Nanjing Forestry UniversityNanjing210037China Department of Mathematics and Theories Peng Cheng LaboratoryShenzhen518066China

Video-based person re-identification(Re-ID),a subset of retrieval tasks,faces challenges like uncoordinated sample capturing,viewpoint variations,occlusions,cluttered backgrounds,and sequence *** advancements in deep learning have significantly improved video-based person Re-ID,laying a solid foundation for further progress in the *** order to enrich researchers’insights into the latest research findings and prospective developments,we offer an extensive overview and meticulous analysis of contemporary video-based person ReID methodologies,with a specific emphasis on network architecture design and loss function ***,we introduce methods based on network architecture design and loss function design from multiple perspectives,and analyzes the advantages and disadvantages of these ***,we provide a synthesis of prevalent datasets and key evaluation metrics utilized within this field to assist researchers in assessing methodological efficacy and establishing benchmarks for performance ***,through a critical evaluation of the experimental outcomes derived from various methodologies across four prominent public datasets,we identify promising research avenues and offer valuable insights to steer future exploration and innovation in this vibrant and evolving field of video-based person *** comprehensive analysis aims to equip researchers with the necessary knowledge and strategic foresight to navigate the complexities of video-based person Re-ID,fostering continued progress and breakthroughs in this challenging yet promising research domain.

关键词： Video-based person Re-ID deep learning survey of video Re-ID loss function

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Biomedically Informed ECG Synthesis: Customizing Cardiac Cycle Phases with Diffusion Model

Biomedically Informed ECG Synthesis: Customizing Cardiac Cyc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Yuxin Ma, Jing Wang, Wei Wu, Zhihao Dong, Suyu Luo, Gongning Wang, Kuanquan Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology Faculty of Computing Harbin China

ISBN: (纸本)9798350386226

Cardiovascular diseases are a major global health challenge, with electrocardiography (ECG) being critical for diagnosis and monitoring. As artificial intelligence and automated ECG diagnostic technologies rapidly advance, the demand for large-scale ECG databases continues to grow. Generative ECG has become a mainstream method to enhance database size and diversity. However, existing methods typically generate ECG randomly or focus on limited physiological categories, lacking the ability to synthesize ECG with varying physiological features and cardiac cycles, which is crucial for various practical applications. In response to this need, we propose a novel approach introducing a diffusion model called DIFF-ECG to generate precisely customized ECG that accurately reflect diverse cardiac conditions. Segmentation-based quality assessments confirmed that the synthesized ECG accurately followed the specified cardiac cycle information, with our model significantly outperforming baseline diffusion and GAN-based methods. Therefore, our approach addresses the critical need for generating clinically relevant and customizable ECG, contributing significantly to the field of automated cardiac disease diagnosis. By enabling fine-tuning of cardiac cycle phases, our method significantly expands the application range of generative ECG, potentially improving the diagnostic accuracy for rare diseases and advancing personalized medicine. © 2024 IEEE.

关键词： Electrocardiography

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Early Detection of Glaucoma Utilizing Retinal Nerve Fiber Layer (RNFL) Investigation

Early Detection of Glaucoma Utilizing Retinal Nerve Fiber La...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Anandhi, S. Amarnath Kumaran, A. Sivakamasundari, P. Vijayakumar, K. Birnica, Y.J. Senthil Pandi, S. Rajalakshmi Engineering College Department of Cse Chennai India Erode Sengunthar Engineering College Department of Eie Erode Perundurai638057 India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chengalpattu603203 India St. Joseph's Institute of Technology Department of It Chennai India Ssn College of Engineering Department of Ece Chennai6031101 India

ISBN: (纸本)9798331543617

Early detection of glaucoma is essential for preventing permanent vision loss. Glaucoma, a degenerative eye illness that affects the optic nerve, causing vision degeneration without any obvious symptoms in the early stages. One hopeful method for early glaucoma detection is Retinal Nerve Fiber Layer (RNFL) analysis, which assesses the chunkiness of the Nerve Fiber Layer (NFL) in the Retina. Changes in RNFL thickness, particularly thinning, are an early indicator of glaucoma and can be detected before substantial visual injury occurs. Optical Coherence Tomography (OCT) is universally used for RNFL analysis, offering high-resolution images of the retina and enabling precise measurements of RNFL thickness. Through RNFL analysis, clinicians can identify patterns of nerve fiber thinning that correspond to early glaucomatous injury, permitting for timely involvement and treatment. This method can help with glaucoma therapy by reducing disease development and preserving patient's quality of life. Nonstop advancements in imaging technology and automated RNFL assessment tools are further enhance the accuracy and availability of early glaucoma detection. © 2024 IEEE.

关键词： Optical coherence tomography

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Calligraphy Alphabet Perception Using Artificial Intelligence 9

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Venkatesh, S. Jeevitha, D. Gnanaselvi, J. Anitha Srm Institute of Science And Technology Department of Data Science And Business Systems School of Computing Chennai Kattankulathur India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Fake Image Classification Engine Using Inception-ResNet-V1 Network 1

Deep Fake Image Classification Engine Using Inception-ResNet...

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1st IEEE International Conference on computing and Data Science, ICCDS 2024

作者： Kothandaraman, D. Narayanan, S. Sankara Mohamed Iqbal, M. Yekopalli, Ayesha Sri Krishnadevarayalu, S. School of Computer Science & Engineering VIT-AP University Andhra Pradesh Amaravati India SRM Institute of Science and Technology Department of Computing Technology Chennai India Sri Sivasubramaniya Nadar College of Engineering Department of Information Technology Tamilnadu Chennai India School of Electronics & Communication Engineering G. Pulla Reddy Engineering College GPREC AP Kurnool India

ISBN: (纸本)9798350365337

The goal of this project is to develop a real or fake facial image classification system using deep learning techniques. The main focus is on the InceptionResnetVl model pretrained on the VGGFace2 dataset and Kaggle DeepFake Classification Dataset for face classification. The application uses the facenet_pytorch library to perform face detection and preprocessing. Main features of the project include predicting the authenticity of a given facial image as either "real"or "fake". This is achieved by implementing the InceptionResnetVl model, which is fine-tuned for binary classification with a single output representing the probability of authenticity. The system is designed to run on the GPU when available, enabling faster computing. Grad-CAM (gradient-weighted class activation mapping) technique is used to ensure the explainability of classification decisions. This method creates class activation maps that visually highlight the regions of the input image that influenced the classification decision. The Grad-CAM printout is then superimposed on the original facial image, creating an interpretable visualization of the model's decision-making process. The Gradio user interface is used for the interactive presentation of the project. Users can upload their face and get real vs. false predictions and visual explanations generated by the Grad-CAM algorithm. The user interface also displays a confidence score for each forecast, giving users an idea of how reliable the model is. In general, this project provides a comprehensive system to classify real and fake faces, and the proposed method reaches the accuracy of 97% both in training and testing. © 2024 IEEE.

关键词： User interfaces

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