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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Ramya, D. Manigandan, S.K. Velmurugan, J. Vishnukumar, A. Umarani, V. Reddy, P. V. Sai Ram Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Information Technology Chennai India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Chennai India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Business Systems Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350365269

Accurate brain tumors segmentation is essential for precise diagnosis, planning, treatment, and monitoring of the tumor. However, the variations in tumor size, shape, and location, automating this process can be challenging. Segmentation using manual method is both time-taking and can produce inconsistent results. A promising solution to automated brain tumor segmentation is through deep learning approach that use convolutional neural networks. The models that have used U-Net shown considerable promise in enhancing the precision and effectiveness of automated Brain tumor segmentation. Still, there is a scope for improvement in accurate segmentation of brain MRI images. The segmentation of Brain MRI Images greatly helps in earlier diagnosis of Brain tumor effectively. The proposed model using ResNet, ResNext, and Inception as a backbone of U-Net with BraTs 2020 dataset could potentially reduce the workload of radiologists and provide more consistent results across experts. Future research can explore the use of other architectures or hybrid models to enhance brain tumor segmentation performance. © 2024 IEEE.

关键词： Tumors

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Enhancing Team Sports Strategy and Performance Using Machine Learning: A Case Study on Soccer 3

Enhancing Team Sports Strategy and Performance Using Machine...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Narayana, K.Venkata Kumari, Nalli Vinaya Ghantasala, G.S. Pradeep Rajesh Sharma, R. Ananthanagu, U. Gupta, Deepika Mlr Institute of Technology Department of Physical Education Hyderabad India Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India Hpsebl Solan India

ISBN: (纸本)9798331518592

This study aims to improve performance in team sports, with a specific focus on football, using machine learning techniques. The research explores different machine learning methods, such as Random Forest, Decision Tree, and Support Vector Machine (SVM), by analyzing a dataset that includes player statistics, match results, and in-game events. The Random Forest model performed the best, with 88% accuracy in predicting match outcomes and identifying key performance indicators. The study highlights how machine learning can help coaches and analysts gain useful insights to enhance team performance and strategy. © 2024 IEEE.

关键词： Adversarial machine learning

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Primeval Identification and Diagnosis of Alzheimer's Disease Utilizing 3D CNN Models 10

Primeval Identification and Diagnosis of Alzheimer's Disease...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sujitha, K.L. Lynsha Helena Pratheeba, H.P. Rajeshwari, R. Ramaraj, Kottaimalai Murugan, Pallikonda Rajasekaran Thilagaraj, M. Mvj College of Engineering Department of Computer Science and Engineering Bengaluru India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350384369

An early-onset neurocognitive impairment, Alzheimer's disease (AD) affects individuals. The symptoms of this illness include diminished memory, strange conduct, and linguistic difficulties. Since AD is the source of 60-80% of instances of dementia, it is the single most prevalent cause of dementia. An AD that is neurodegenerative begins with moderate cognitive impairment (MCI) and progressively worsens. It damages brain tissue, impairs ability to think and remember, and makes it difficult to carry out basic duties. The development of more sophisticated therapies for AD depends on swift detection. For this reason, stopping and managing the progression of AD depends greatly on early detection. The primary goal is to create a comprehensive framework for medical image categorization according to different phases of Alzheimer's disease and early identification of the condition. This work employs a deep learning approach, more precisely convolutional neural networks (CNN). Simple CNN architectures that handle 2D and 3D structural brain magnetic resonance imaging (MRI) form the basis of the suggested methodology. The limited quantity of datasets presents the primary obstacles for medical images. Thus, in order to optimize the size of the dataset and avoid the issue of overfitting, methods to augment data are used. Techniques for resampling, such as downsampling and oversampling are employed to address imbalanced dataset collections. © 2024 IEEE.

关键词： Convolutional neural networks

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A Study on Word Embeddings in Local LLM-based Chatbot Applications

A Study on Word Embeddings in Local LLM-based Chatbot Applic...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Hariprasath, S. Paramarthalingam, Arjun Sundaramurthy, Shanmugam Cirillo, Stefano Department of Computer Science and Engineering University College of Engineering Kakuppam Villupuram India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu603203 India Department of Computer Science University of Salerno Salerno Italy

ISBN: (纸本)9798331533137

Large Language Models (LLMs) have revolutionized natural language processing, but their deployment often requires substantial computational resources and constant network connectivity. There is a growing need for efficient, locally-runnable LLMs with improved word embeddings for various applications, including chatbots. This study focuses on developing a Large Language Model (LLM) capable of running locally without network connections to generate solutions for given queries using improved word embeddings. In this work, two pre-trained models are used and further trained separately to enhance embedding results. The system incorporates additional features such as document-based chat functionality. Our approach develops a dynamic and adaptive NLP system that can assimilate new data from various sources when connected to a network, ensuring up-to-date word embeddings that capture the latest semantic nuances and language trends. The study compares FastText, GloVe, and Word2Vec models, finding that fastText outperforms the others in text generation within the specified time frame. While fastText may require more time to generate larger content, its efficacy in producing better results makes it the preferred choice for this application. ©2024 IEEE.

关键词： Semantics

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Kidnapped Vehicle Using Particle Filters-Self-Driving Car Nanodegree

Kidnapped Vehicle Using Particle Filters-Self-Driving Car Na...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Dhull, Sunil Thakur, Payal Sangwan, Yashwant Singh Singh Talwandi, Navjot Kumar, Rajender Kumar, Surender Panipat Institute Of Engineering & Technology India Chandigarh University Dept Of Computer Science Engineering Punjab India Govt. College Department Of Computer Science Haryana Hisar India Chitkara University Institute Of Engineering And Technology Chitkara University Punjab India Guru Teg Bahadur College Department Of Computer Science Sangrur Punjab Bhawanigarh India

ISBN: (纸本)9798350306446

Detecting and tracking stolen vehicles is an important task with self-driving cars. This study focuses on the use of particle filters to detect and track stolen vehicles. Particle filters are a probabilistic estimation method that can effectively handle the uncertainties and nonlinearities associated with vehicle tracking. The study examines the application of particle filters in the Nanodegree project to self-driving cars, specifically for the detection and tracking of stolen vehicles. The results demonstrate the efficiency of particle filters to accurately estimate the location and routes of hijacked vehicles, enabling effective recovery operations Vehicle occupied scenarios refer to situations in which a self-driving vehicle loses its location awareness The vehicle can measure its position observations based on a sensor. Using small- scale filters, this research aims to improve the ability of self- driving vehicles to reconstruct reality, accurately restoring its position in cases where they are unpredictable, ultimately contributing to the robustness and reliability of autonomous vehicle systems. © 2024 IEEE.

关键词： Monte Carlo methods

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Intelligent Recognition of Optical Modulation Formats in Dynamic Communication Networks 1

Intelligent Recognition of Optical Modulation Formats in Dyn...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Gnanakumar, D. Anusooya, V. Muthupradeepa, R. Gobalakrishnan, S. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur India Thandalam India

ISBN: (纸本)9798350352931

Machine learning-based optical modulation format recognition is essential for dynamic optical networks. Convolutional Neural Networks (CNNs) can analyze signal space diagrams directly from raw data. Specifically, CNNs excel in image classification due to their ability to extract features and learn autonomously. In this study, signal space diagrams for various modulation formats (BPSK, QPSK, 8-QAM, 16-QAM, 32-QAM, and 64-QAM) are generated using an electrical constellation visualizer in Optisystem software, with the Optical Signal-to-Noise Ratio (OSNR) varying from 10 to 40 dB and the noise figure ranging from 5 to 25 dB. The proposed VGG-16 CNN architecture achieves 100% accuracy in modulation format recognition (MFR) for all six formats, utilizing minimal training data and fewer epochs. This approach can significantly enhance next-generation dynamic optical communication networks by enabling automatic detection and recognition of modulation formats. © 2024 IEEE.

关键词： Convolutional neural networks

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Web Based Application to Recognize Diseases and Providing Consultant to Patients 3

Web Based Application to Recognize Diseases and Providing Co...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Ajitha, P. Sathish Kumar, P.J. Navaneethakrishnan, Sundara Rajulu Sivasangari, A. Surendran, R. Sathyabama Institue of Science and Technology School of Computing Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Dhanalakshmi Srinivasan University Trichy Department of Computer Science and Engineering Samayapuram Campus Trichy India Sathyabama Institue of Science and Technology School of Computing Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

One of the most important aspects of civilization is human wellness. The best and most accurate condition analysis seeks to offer the necessary assistance as soon as practical. The clinical research factor necessitates knowledge from other fields, such as computer science and statistics, as this comprehension is frequently difficult. The need to pursue novel approaches places a heavier burden than normal on these fields. The variety of new strategies allows for a comprehensive investigation while avoiding specific elements. As a solution to this issue, a systematic evaluation of human illnesses with system mastering is proposed. This study centers on current techniques for system learning advancement applied to the prognosis of human diseases in the medical field in order to discover exciting trends, make light predictions, and assist in decision-making. This initiative includes a scientific chatbot that uses synthetic intelligence to answer patients' questions. This article explains a one-of-a-kind set of device learning algorithms that are used in healthcare programmes to provide excellent preferred choice assistance. This article is intended to cover a research gap in the development of practical choice aid structures for scientific applications. © 2024 IEEE.

关键词： Clinical research

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Detecting IoT Network Breach Using the Random Forest Classifier

Detecting IoT Network Breach Using the Random Forest Classif...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Sasikala, K. Vasuhi, S. Saveetha Engineering College Department of Computer Science Engg India Madras Institute of Technology Department of Electronics Engg India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541170

Intrusion detection faces particular issues due to the interconnection and resource constraints of Internet of Things (IoT) devices. A complete collection of network traffic statistics pertaining to IoT security situations may be found in the CIC-IDS 2018 dataset. In this work, the Random Forest classifier, a flexible machine learning technique renowned for its high accuracy in handling heterogeneous data is used to address the critical problem of IoT network security. The intrusion detection system is developed using features taken from the CIC-IDS 2018 dataset, and it achieves an astounding accuracy rate of 99.96%.This remarkable outcome shows how well the Random Forest classifier works to identify and classify different types of network intrusions, including those that affect Internet of Things devices. The study enhances the security stance of Internet of Things networks by offering valuable perspectives on the application of machine learning methods for dependable intrusion detection in IoT environments. © 2024 IEEE.

关键词： Network intrusion

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Mobile Edge Computing for Multi-Services Digital Twin-Enabled IoT Heterogeneous Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1845-1853页

作者： Liu, Weiqi Hossain, Mohammad Arif Ansari, Nirwan Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States Middle Tennessee State University Department of Engineering Technology MurfreesboroTN37132 United States New Jersey Institute of Technology Advanced Networking Laboratory Department of Electrical and Computing Engineering NewarkNJ07102 United States

A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computIng reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computIng resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： Mobile edge computing

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Optimized deep transfer learning techniques for spine fracture detection using CT scan images

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Multimedia Tools and Applications 2025年 1-34页

作者： Kanna, G. Prabu Kumar, Jagadeesh Parthasarathi, P. Bhardwaj, Priya Kumar, Yogesh VIT Bhopal University Bhopal India Department of Computer Science and Engineering Kathir College of Engineering Coimbatore India Department of CS Bannari Amman Institute of Technology Sathyamangalam India School of Computing DIT University Dehradun India Department of CSE School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India

Spine fractures pose an important health concern that requires a quick diagnosis to protect people from any long-term complications. This paper aims to propose an automatic prediction system that uses deep transfer learning techniques for spine fractures and minimizes the time required for classification compared to traditional techniques. The CT scan images of the cervical spine have been collected in the form of JPEG format and subsequently segmented using several phases such as mask exploring, detecting, zooming, and cropping the affected area at 6 different angles followed by the computation of contour features to analyze their geometric as well as intensity-based parameters. Ten advanced deep learning models along with ADAM optimizer are trained where InceptionResNetV2 obtained the highest accuracy of 99.70% while the lowest loss, as well as root mean error square score, has been obtained by ResNet50V2 with 0.0493 and 0.2220.36 respectively. In the case of precision, recall, and F1 score, DenseNet169 computed the highest precision and recall values of 99.99% and 98.495% respectively while Xception did well in terms of F1 score with 98.32%. These models perform better because InceptionResNetV2’s architecture is well-suited for capturing intricate spatial hierarchies, DenseNet169 benefits from dense connectivity for feature reuse, and ResNet50V2’s optimized residual blocks contribute to minimizing error rates. Apart from this, less diversity and class imbalance are also seen within the dataset which affects model performance. These limitations highlight the need for future work to expand the dataset and ensure a balanced distribution of classes to enhance the robustness and clinical relevance of the proposed AI-based system. However, despite these challenges, the results underscore the transformative potential of deep learning in improving clinical diagnosis of spine fractures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part

关键词： computerized tomography

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