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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment 14

Image based and Point Cloud based Methods for 3D View Recons...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vasikarla, Shantaram Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati India Indian Institute of Technology Dept. of Electrical Engineering Kanpur India California State University Dept. of Computer Science NorthridgeCA United States

ISBN: (纸本)9798350360134

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more. © 2024 IEEE.

关键词： Network coding

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Deep Learning-Driven Black Box Approach For Image Segmentation 1

Deep Learning-Driven Black Box Approach For Image Segmentati...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sudha, Nuthalapati Kasetty, Sai Bhargav Madhuri, K. Sai Nikitha, Jajala Margret, Issac Neha Rajakumar, K. Mahindra University School of Engineering Telangana Hyderabad India V.I.T - University School of Computer Science and Engineering Tamilnadu Vellore India BVRIT Narsapur Dept. of Computer Science and Data Science Telangana Hyderabad India VNR VIET Dept. of CSE - AIML and IoT Telangana Hyderabad India

ISBN: (纸本)9798350356816

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation. © 2024 IEEE.

关键词： Image segmentation

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A Novel Approach for Driver Drowsiness Detection using Deep Neural Networks 2

A Novel Approach for Driver Drowsiness Detection using Deep ...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Sharon, K.V. Natarajan, B. Madhumitha, P. Dhanamathi, A. Prabu, S. Saranya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Dept. of Artificial Intell. and Data Science Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India K. Ramakrishnan College of Engineering Dept. of Cse Tamil Nadu Tiruchirappalli621112 India

ISBN: (纸本)9798350383287

One major problem that contributes to many traffic accidents that cause injuries and fatalities all over the world is drowsy driving. This research introduces the novel approach for detecting driver drowsiness using deep networks and computer vision techniques aims to solve this issue and improve road safety. The Proposed system employs an InceptionV3-based Convolutional Neural Network (CNN) model to precisely determine whether the driver's eyes are open or closed. A sizable dataset of eye images taken in actual driving situations is used to train the CNN model. Whenever the indicators of drowsiness are detected, the proposed system will identify them in real time and promptly notify the driver. The proposed model not only has strong real-time capabilities but also exhibits superior accuracy based on stringent evaluation metrics, which makes a significant contribution to efforts aimed at promoting road safety. The performance and adaptability of the system are improved and further integrates alarm activation for drowsiness detection in a variety of driving conditions. © 2024 IEEE.

关键词： computer vision

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Personalized Stress Detection using a Lightweight Machine Learning Framework With Convenient Wrist-Worn Sensors

Personalized Stress Detection using a Lightweight Machine Le...

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2024 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2024

作者： Hasan, M. Mithun, S. Mahmud, M. Baral, S. Poudel, K. Middle Tennessee State University Computational And Data Science MurfreesboroTN United States University Of Memphis Dept. Of Computer Science TN United States University Of Memphis Dept. Of Electrical And Computer Engineering TN United States Middle Tennessee State University Dept. Of Computer Science MurfreesboroTN United States Univ. Of Tennessee Health Science Center Div. Of General Internal Medicine MemphisTN United States

ISBN: (纸本)9798350388572

Over 70% Americans experience daily stress, making real-time, accurate stress detection crucial for timely intervention, and promoting physical and mental well-being. In this study, we detect stress using machine learning from publicly available wrist-worn sensor data. We examined both population and individual levels, focusing on simple, computation friendly statistical features for reduced processing to facilitate adoptions in real-life mHealth applications. Our framework demonstrates that stress detection achieves over 96% accuracy at both the population and individual levels, with the Extreme Gradient Boosting classifier achieving the highest predictive accuracy. We found significant variability between individuals and achieved an average accuracy of 99.63% with area under the curve (AUC) 99.23%, F1-score 99.00%, precision, and recall 99.22%. This approach offers a promising solution for real-life stress detection due to its high accuracy and simplicity. © 2024 IEEE.

关键词： LightGBM Machine learning Personalization Stress detection Wrist-band sensors

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Diversity Seeking Techniques for Red-Teaming Large Language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lee, Seokhan Ku, Bonhwa Ko, Hanseok Dept. Computer Science and Engineering Korea University Seoul Korea Republic of Machine Learning Big Data Institute Korea University Seoul Korea Republic of Dept. Electrical and Computer Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798350368741

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines. © 2025 IEEE.

关键词： AI safety large language models red-teaming reinforcement learning toxicity

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Detecting Gastro-Intestinal Cancer from Wireless Capsule Endoscopy Images using Efficient Net Model 14

Detecting Gastro-Intestinal Cancer from Wireless Capsule End...

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14th International Conference on Information and Communication Technology and System, ICTS 2023

作者： Geetha, S. Sharmila, V. Sasikala, S. Balamurugan, S. Appavu Alias Balamurugan, N.M. Vit Chennai School of Computer Science and Engg. India Velammal College of Engg. & Tech Dept. of Ai and Data Science Madurai India Periar Maniammai Inst. of Sci. & Tech Dept. of Computer Science and Engg Thanjavur India Sri Venkateshwara College of Engg. Sriperumbudur Dept. of Computer Science and Engg Chennai India

ISBN: (纸本)9798350312164

Recently polyps and ulcers have become a fatal form of gastrointestinal problem, and several researchers have looked into algorithms to diagnose gastrointestinal cancer lesions. This research work investigates the application of CNN to detect Gastro-Intestinal (GI) cancer from wireless capsule endoscopy (WCE) GI images. Five distinct CNN architectures EfficientNet, LeNet, GoogleNet, MobileNet-V2, and ResNet-50 have been studied, with two different setups i) original data with transfer learning ii) original data with data augmentation, and transfer learning. Simulations ran on 37,790 images with five different CNN architectures proved that the EfficientNet model exhibited superior performance of about 99.15% accuracy over all other CNN models, on the augmented dataset. Furthermore, the results from the experiments show that the EfficientNet model significantly outperforms previous methods in terms of classification accuracy. © 2023 IEEE.

关键词： Diseases

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Exploring the Potential of IoT in Healthcare: Challenges and Opportunities 3

Exploring the Potential of IoT in Healthcare: Challenges and...

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

作者： Verma, Navneet Rana, Sumit Gaba, Shivani Nagpal, Shally Paniapt Institute of Engineering and Technology Dept. of Computer Science & Engg. Haryana Panipat India Paniapt Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Haryana Panipat India

ISBN: (纸本)9798350342338

Due to the propagation of IoT technologies Healthcare cannot remain outside of this paradigm. In latest years, there has been a lot of interest in the Internet of Things (IoT) technology to relieve the strain on healthcare systems brought on by the ageing population and the rise in persistent illnesses. One of the main obstacles preventing advancement in this field is standardization or global connectivity between the Internet of Things (IoT) and the medical surroundings. All from electrical engineers to data engineers have a significant problem in having to integrate everything in a global setting. The difficulties that healthcare IoT confronts, such as security, privacy, and less-energy procedures, are discussed, and suggestions are provided for future research paths. This paper is written to gather data on various Internet of Things architectures now used in Healthcare and analyse how well different academics have applied it. © 2023 IEEE.

关键词： Architecture Connectivity Healthcare Internet of Things Security

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Medication Management application for Directly Observed Treatment of TB Patients 6

Medication Management Application for Directly Observed Trea...

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6th International Conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Tania, Marzia Hoque Rahman, Md. Asib Ahmed, Kadir Uddin Centre for Big Data Research in Health Unsw Sydney Sydney Australia Buet Dept. Computer Science and Engineering Dhaka Bangladesh Independent Researcher Sydney Australia

ISBN: (纸本)9798350385779

Tuberculosis (TB) poses global health challenges, requiring efficient management and monitoring of patients undergoing Directly Observed Therapy (DOT). This paper introduces TB-meds, a proposed software application to automate essential aspects of TB patient management. TB-meds aims to optimize resource utilization, improve patient care, and enhance TB control efforts. The application streamlines medication distribution, facilitates medication adherence monitoring, sends timely clinical test requirement's notifications, and enables convenient treatment status updates. By automating these processes, TB-meds has the potential to enhance efficiency, improve treatment outcomes, and reduce transmission rates. Its user-friendly interface promotes seamless communication among healthcare providers and TB patients. With tasks automation, the workload of DOT providers can be reduced, and more time can be dedicated to patient education, nutrition counseling, and lifestyle guidance. Additionally, TB-meds proposes to revolutionize TB surveillance by enhancing data collection and analysis capabilities, enabling informed decision-making and targeted interventions. The implementation of TB-meds holds promise for transforming TB patient management, contributing to better health outcomes, and advancing TB control efforts. © 2024 IEEE.

关键词： Telemedicine

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Exploring the Intersection of Blockchain and Distributed Quantum Computing: Synergies, Challenges, and Future Directions 2

Exploring the Intersection of Blockchain and Distributed Qua...

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2nd International Conference on Microwave, Antenna and Communication, MAC 2024

作者： Singh, Madhusudan Rawal, Bharat Long Island University Brooklyn Department of Data Analytics New York United States Granbling State University Dept. of Computer Science LA United States

ISBN: (纸本)9798350350104

Distributed quantum computing has become a potential way to solve hard computing problems by combining the power of many quantum devices that are connected to each other. But it's hard to make sure that distributed quantum computer nodes can trust each other, stay safe, and work together well. The decentralized and open nature of blockchain technology holds significant potential in addressing these challenges and revolutionizing the domain of distributed quantum computing. This scholarly essay examines the potential of blockchain technology in facilitating the coordination, resource sharing, and data management within distributed quantum computing networks, with a focus on ensuring safety and efficiency. We provide an overview of the primary advantages, challenges, and potential applications of integrating blockchain technology into distributed quantum computing frameworks. This has the potential to result in a quantum computing environment that is both more stable and scalable. The objective of this scholarly essay is to elucidate the potential impact of integrating blockchain technology into distributed quantum computing. Distributed quantum computing networks may effectively coordinate, share resources, and manage data by using the decentralized, transparent, and secure nature of blockchain technology. © 2024 IEEE.

关键词： Quantum computers

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Comparative Analysis of Python-Based Machine Learning Algorithms for Stock Market Prediction: A Case Study on Forecast Accuracy and Methodological Insights 1

Comparative Analysis of Python-Based Machine Learning Algori...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Rathore, Rachna Swetha, B. Saritha, P.S. Kumar, Ajay Singh, Vijay Vir Shyamu Jiwaji University Dept. of Mathematics Gwalior India Meenakshi A.H. Edu. and Research Dept. of Mathematics Chennai India Dhanalakshmi Srinivasan College Dept. of AI and Data Science Coimbatore India Manglayatan University Dept. of Computing and IT Aligarh India Lingayas Vidyapeeth Dept. of Mathematics Faridabad India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

Predicting stock market movements is a complex task due to the high volatility and unpredictability of stock prices. In recent years, machine learning algorithms have proven to be effective tools for addressing this challenge. This study presents a detailed analysis of various Python-based machine learning techniques applied to stock market prediction. It examines the effectiveness of several algorithms in forecasting stock prices and evaluates their comparative performance. The findings reveal that utilizing machine learning methods can significantly improve the accuracy of stock market predictions. This research offers valuable insights for both researchers and practitioners aiming to leverage machine learning for stock market forecasting. By providing a comprehensive overview of different techniques and their applications, the study serves as a critical resource for those seeking to enhance predictive capabilities and make informed decisions in the dynamic and unpredictable environment of stock market analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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