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Computational, Communication and Information technology (ICCCIT), International Conference on

作者： RVS Praveen Meenakshi Maindola Anandhi R J Nagarjuna Thandra Saloni Bansal Vishal Sharma Digital Engineering and Assurance LTIMindtree Limited Hyderabad Telangana India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of Information Science Engineering New Horizon College of Engineering Bangalore Dept. Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana Department of Computer Engineering and Applications GLA University Mathura Lovely Professional University Phagwara India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. Applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged.

关键词： Deep learning Autonomous underwater vehicles Accuracy Transfer learning Object detection Data augmentation Real-time systems Data models Environmental monitoring Convolutional neural networks

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Utilizing AI-powered Drones for Efficient Flood Rescue Operations

Utilizing AI-powered Drones for Efficient Flood Rescue Opera...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： M. Jaswanth Kumar Dhanush Bitra Rohan Titus Dill Jazz Cinu C Kiliroor Dept. of Electronics and Communication Indian Institute of Information Technology Kottayam Kottayam Kerala India Dept. of Computer Science Indian Institute of Information Technology Kottayam Kottayam Kerala India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

In times of severe earthquakes and floods, rapid and effective aid is crucial. Traditional methods often face challenges due to accessibility issues and the vast scale of *** proposed Autonomous Aerial Humanitarian Assistance and Disaster Relief (A2-HADR) System aims to transform disaster response through the use of drone technology. The proposed solution addresses the obstacles in rescue operations by employing drones equipped with Artificial Intelligence (AI) to provide immediate support. The advanced system utilizes state-of-the-art computer Vision technology to autonomously detect people and obstacles from heights of 50-100 meters at various angles and deliver essential supplies like food, clothing, and rescue gear to those in need. We have executed and assessed a range of advanced object detection algorithms, such as YOLOv8, YOLOv9, and Detectron2. After thorough evaluation, Detectron2 emerged as the most effective model among those tested, showcasing exceptional accuracy and resilience.

关键词： Deep learning computer vision Accuracy Disasters Computational modeling Object detection Disaster management Transforms Artificial intelligence Drones

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Comparative Study of Motion Correction Algorithms for Contrast-Enhanced Ultrasound Imaging

Comparative Study of Motion Correction Algorithms for Contra...

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IEEE India Conference (INDICON)

作者： Shubham Debabrata Ghosh Kuntal Chowdhury Manoj Mathur Dept. of Electronics and Communication Engineering Thapar Institute of Engineering & Technology Patiala India Department of Computer Science & Engineering Thapar Institute of Engineering & Technology Patiala India Department of Radiodiagnosis Government. Medical College Patiala India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Due to its speed, safety, and affordability, ultrasound imaging is preferred in medical diagnosis; nonetheless, speckle noise and limited scan lines cause detail loss. Contrast-Enhanced Ultrasound (CEUS) enhances tissue perfusion and tumor detection using microbubbles but is prone to motion artifacts, particularly in dynamic settings like Dynamic Contrast-Enhanced Ultrasound (DCE-US), where patient or probe movement degrades image quality. Motion artifacts in traditional DCE-US are usually corrected by removing the affected frames, which leads to a loss of useful data. To maintain image integrity and improve clinical analysis, recent advances have concentrated on motion correction algorithms. Using quantitative metrics like Root Mean Square Error (RMSE), Correlation Coefficient (CC), and qualitative assessment through visual inspection of Maximum Intensity Projection (MIP), this paper assesses three such algorithms: Block Matching Algorithm, Sub Pixel Registration, and 2-Tier image registration. The objective is to determine the most efficient technique for reducing motion artifacts and enhancing both qualitative and quantitative analyses in medical imaging.

关键词： Correlation coefficient Visualization Ultrasonic imaging Statistical analysis Dynamics Speckle Safety Root mean square Motion artifacts Tumors

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Influence of Quantum Computing on IoT Using Modern Algorithms 4

Influence of Quantum Computing on IoT Using Modern Algorithm...

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4th International Conference on Advanced science and engineering, ICOASE 2022

作者： Ageed, Zainab Salih Zeebaree, Subhi R. M. Saeed, Rezgar Hasan Nawroz University Computer Science Dept. Duhok Iraq Duhok Polytechnic University Energy Eng. Dept. Duhok Iraq Duhok Private Technical Institute Information Technology Dept. Duhok Iraq

ISBN: (纸本)9781665472227

Even while the Internet of Things (IoT) already affects our day-to-day activities, its future relevance and potential for transformation remain untapped. Security issues with present communications technology must be solved before secure end-to-end connection between services can be achieved. Internet of Things (IoT) is here to stay. In the next years, it will become an integral part of our everyday life. There is a good chance that sensor-based networks can recognize it as a direct service provider in our surroundings. Even if it's just in the form of a value-added service delivered through cellular networks, it's going to be helpful nevertheless. It is, nevertheless, subject to a wide range of security threats. The current level of security is insufficient for IoT applications in the future. A secure cryptosystem is needed for the Internet of Things. quantum-based security has seen a surge of attention recently. Additional quantum key distribution systems and network services are now supported by this solution. As a result of the quantum computing's unrivaled security level, it has become more popular in recent years. Because every measurement must affect the state of the quantum bit being sent, quantum physics dictates that this must be the case. Regardless of whether the sender or recipient is aware of the change, it is clear. It is thus no longer possible to listen passively. Polarized photons may encode a string of bits using protocols like BB84. Secure cryptographic keys may be generated over an unsafe channel utilizing various key distillation procedures. © 2022 IEEE.

关键词： Internet of things

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Optimization Techniques used in Heart Disease Prediction: A Systematic Analysis

Optimization Techniques used in Heart Disease Prediction: A ...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Priyanka Behki Rishi Pal Dept. of Computer Science and Engineering University Institute of Engineering Chandigarh University Gharuan Punjab India

Today, heart disease is one of the leading causes of death around the world, making it a top priority in healthcare systems everywhere. Predicting cardiac issues with precision is crucial for facilitating prompt medical attention. There is great potential for improved decision-making and data-driven forecasts in healthcare with the application of Particle Swarm Optimization (PSO) in conjunction with machine learning. By utilizing numerous algorithms, this method equips both patients and doctors to intervene early and avoid sickness. It's important to remember that the parameters you pick for your optimization strategy can have a significant impact on how well these models work. This research compares three popular optimization techniques—Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization—in the context of predicting cardiovascular disease. The fundamental goal of this study is to compare and contrast several optimization strategies, such as Feature Selection, Bayesian optimization, Evolutionary algorithms, and their hybrids, and other tried-and-true approaches from current scholarly literature. The overarching goal of these studies is to develop concepts for better heart illness diagnostics. The research also compares other Machine Learning methods developed to solve specific optimization problems. It is hoped that the knowledge gathered from this study would encourage further study into optimizing methods for cardiac illness diagnostics and the development of more efficient preventative measures.

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Deep Learning-Based Adaptive Joint Source-Channel Coding using Hypernetworks

arXiv

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arXiv 2024年

作者： Xie, Songjie He, Hengtao Li, Hongru Song, Shenghui Zhang, Jun Zhang, Ying-Jun Angela Letaief, Khaled B. Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Dept. of Information Engineering The Chinese University of Hong Kong Hong Kong

Deep learning-based joint source-channel coding (DJSCC) is expected to be a key technique for the next-generation wireless networks. However, the existing DJSCC schemes still face the challenge of channel adaptability as they are typically trained under specific channel conditions. In this paper, we propose a generic framework for channel-adaptive DJSCC by utilizing hypernetworks. To tailor the hypernetwork-based framework for communication systems, we propose a memory-efficient hypernetwork parameterization and then develop a channel-adaptive DJSCC network, named Hyper-AJSCC. Compared with existing adaptive DJSCC based on the attention mechanism, Hyper-AJSCC introduces much fewer parameters and can be seamlessly combined with various existing DJSCC networks without any substantial modifications to their neural network architecture. Extensive experiments demonstrate the better adaptability to channel conditions and higher memory efficiency of Hyper-AJSCC compared with state-of-the-art baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Using em technique for Juvenile crime zoning 5

Using em technique for Juvenile crime zoning

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5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Changalasetty, Suresh Babu Ghribi, Wade Badawy, Ahmed Said Bangali, Harun Ahmed, Abdelmoty M. Thota, Lalitha Saroja Baireddy, Ravinder Reddy Pemula, Rambabu King Khalid University College of Computer Science Dept of Computer Engineering Abha Saudi Arabia Annamacharya Institute of Technology and Science Dept of Computer Science and Engineering Hyderabad India Raghu Engineering College Dept of Computer Science and Engineering Visakhapatnam India

ISBN: (纸本)9781665403412

The primary elements for child criminal behavior are education levels and family background of children and others like friends, money problems, internet, drugs etc. In this research paper, we use expectation-maximization cluster method of data mining to segment juvenile crimes in India with a major risk element of education levels of children. This research will help to visualize the major zones of children involving in crimes and helps to take early preventive steps to mitigate juvenile delinquency © 2021 IEEE.

关键词： Data mining

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Intelligent Manufacturing Education and Tutoring Experimental Platform Studies Based on Reconfigurable Module

Intelligent Manufacturing Education and Tutoring Experimenta...

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Information and Communication engineering (JCICE), International Joint Conference on

作者： Dandan Lu Shiyan Liang Min Wang Yinling Wang Dongxuan Han Yao Cheng Dept. Engineering Technology Center Southwest University of Science and Technology Mianyang China Dept. College of Computer Science and Technology Southwest University of Science and Technology Mianyang China

Given the disconnection between the experimental content and the theory in the experimental project in the field of control engineering education, and the experimental process only stays at the level of virtual simulation and small development kit, we propose an intelligent manufacturing teaching assistant experimental platform based on the reconfigurable module. The “intelligence + reconfigurable modularization” experimental module is designed by simulating the industrial production line, and a high-fidelity experimental platform for control engineering education is assembled. We describe in detail the design and implementation process of the teaching assistant experimental platform and expound on the experimental project’s task design, technological revolution, and program design through a case study, which shows that the teaching auxiliary experimental platform is closely integrated with engineering practice. It is proved that students can acquire industrial technical ability from the experimental platform and improve their systematic cognitive ability in the manufacturing system.

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Federated Learning-based Techniques for Healthcare IoV: Challenges and Future Directions -A Knowledge Review

Federated Learning-based Techniques for Healthcare IoV: Chal...

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International Conference for Convergence of technology (I2CT)

作者： Pallati Narsimhulu Rashmi Sahay Dept. of CSE Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education Hyderabad India Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Federated Learning (FL) is a cutting-edge method for creating Machine Learning (ML) models using networked data sets. It supports advanced Artificial Intelligence (AI), keeps information neighborhoods, protects personal information, and allows for ownership. FL’s features make it a good candidate for IoT networks due to its inherent distributed infrastructure. On the other hand, FL provides several challenges, the most significant of which is training on IoT devices with mostly heterogeneous data sources. The diversity of devices and algorithms in complex Internet of Vehicles (IoV) networks significantly impacts the FL training process and renders traditional FL unsuitable for direct deployment. While numerous recent publications claim to lessen the negative impacts of variety in FL networks, the efficacy of these proposed ways has never been tested and assessed. In this study, we examine the impact of heterogeneity in FL in dept. and provide an overview of the actual challenges imposed by the system and statistical heterogeneity. We researched on cutting-edge algorithms, emphasizing their practical application in IoT networks. We also compared the popular FL algorithms on a dynamic IoV network. Our study shows that existing methods fail to alleviate the issue appropriately, emphasizing the significance of considering system and statistical diversity while developing FL systems. To properly investigate the consequences of the network heterogeneity, we tested 10%, 30%, 50%, 70%, and 90% stragglers. These conditions serve as a training ground, allowing us to see how various degrees of network heterogeneity impact a technique’s efficiency. The test-bed results show that the Scafold technique outperforms existing techniques in terms of training time reduced to 18% and accuracy increased to 21%

关键词： Training Reviews Heuristic algorithms System performance Soft sensors Time measurement Stability analysis

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Effective Noise-Aware Data Simulation For Domain-Adaptive Speech Enhancement Leveraging Dynamic Stochastic Perturbation

Effective Noise-Aware Data Simulation For Domain-Adaptive Sp...

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IEEE Spoken Language technology Workshop

作者： Chien-Chun Wang Li-Wei Chen Hung-Shin Lee Berlin Chen Hsin-Min Wang Dept. Computer Science and Information Engineering National Taiwan Normal University Taiwan United-Link Co. Ltd Taiwan Institute of Computer Science Academia Sinica Taiwan

ISBN: (数字)9798350392258

ISBN: (纸本)9798350392265

Cross-domain speech enhancement (SE) is often faced with severe challenges due to the scarcity of noise and background information in an unseen target domain, leading to a mismatch between training and test conditions. This study puts forward a novel data simulation method to address this issue, leveraging noise-extractive techniques and generative adversarial networks (GANs) with only limited target noisy speech data. Notably, our method employs a noise encoder to extract noise embeddings from target-domain data. These embeddings aptly guide the generator to synthesize utterances acoustically fitted to the target domain while authentically preserving the phonetic content of the input clean speech. Furthermore, we introduce the notion of dynamic stochastic perturbation, which can inject controlled perturbations into the noise embeddings during inference, thereby enabling the model to generalize well to unseen noise conditions. Experiments on the VoiceBank-DEMAND benchmark dataset demonstrate that our domain-adaptive SE method outperforms an existing strong baseline based on data simulation.

关键词： Training Perturbation methods Noise Stochastic processes Speech enhancement Phonetics Generative adversarial networks Generators Noise measurement Data mining

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