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Improvisation of QoS in SDN-Frame Work for UAV Networks Using Dijkstra Shortest Path Routing Algorithm 1

Improvisation of QoS in SDN-Frame Work for UAV Networks Usin...

引用

1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Mathapati, Mahantesh Nandihal, Priya Mishra, Priti Kotagi, Veeranna Dept.of Cse Bengaluru India Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India Information Science and Engineering Atria Institute of Technology Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Ise Bengaluru India

ISBN: (纸本)9798350316469

This work presents a unique SDN-based Dijkstra shortest path routing algorithm for QoS improvisation in UAV Networks. It discusses the various problems pertaining to the networking of unmanned aerial vehicles in disaster rescue operations and unique networking algorithmic solutions to the problems. Involving the SDN technology in networks provides faster service delivery than the traditional technology, as it isolates the control plane and data plane. This SDN technology has the centralised governing management service on the distributed network that affords improved security. But, SDN with Wireless Sensor Network (WSN) does not provide enhanced Quality of Services (QoS) in sophisticated cloud computing (CC) applications. Across the research community, end-users seek to attain a QoS while accessing the cloud-based services depending on their knowledge in using today's technology. Therefore, this research study is aimed to prioritize the cloud application data without any delay from the normal data and also to provide the fastest communication using the SDN framework applying shortest path routing. The traditional networking infrastructure's speed, scalability, and agility are increased by the key functionality of SDN, which is used to intellectual the network information from the applications. Moreover, massive size data are managed and customized by using OpenContrail, which is a more responsive and scalable network. The QoS provided by the SDN with cloud-based services using shortest path, stimulates this research to apply the integrated technology in UAV. UAV can transfer the data through cloud service using SDN, in order to maintain a communication with remote experts. The proposed Shortest Path (SP) direction-finding algorithm provides appropriate deployment & connectivity to the modules of group of UAVs, also decreases time delay within the network. The simulation results reveal that the proposed technique is suitable and more effective to set up an emergency

关键词： Cloud Computing OpenStack Quality of Service Shortest Path Algorithm Software-Defined Networking UAV

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Implementation of Pruned and Quantized Semantic Segmentation Neural Network Using Cambridge-Driving Labeled Video Database (CamVid) Dataset

Implementation of Pruned and Quantized Semantic Segmentation...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Deepak Upadhyay Sonal Malhotra Mridul Gupta Somesh Mishra Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

In This research investigates the streamlined implementation of Semantic Segmentation Neural Networks through advanced techniques: pruning and quantization. Leveraging the CamVid dataset, our study achieved remarkable reductions in computational complexity and memory usage. Pruning removed redundant connections, effectively reducing learnable parameters, while quantization significantly minimized memory footprint. Despite these optimizations, the networks maintained high semantic segmentation accuracy. The implications of our findings are pivotal, particularly in resource-constrained applications like autonomous driving and image analysis. By enhancing efficiency without compromising accuracy, this research facilitates the seamless integration of deep learning models into real-time systems. Our study not only advances the field of computer vision but also underscores the practical feasibility of deploying sophisticated neural networks in practical, resource-efficient contexts.

关键词： computer vision Quantization (signal) Image analysis Semantic segmentation Computational modeling Artificial neural networks Real-time systems

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Automated Classification of Chest CT Scans for Accurate Diagnosis and Risk Stratification: A Deep Learning Approach

Automated Classification of Chest CT Scans for Accurate Diag...

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Deepak Upadhyay Mridul Gupta Devansh Yadav Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

The performance assessment metrics of the topic of classification models, such as recall, accuracy, and F1-score were considered in this work. We examined how many types of events our model categorizes well and view the more detailed metrics for every class. A special account of the precision-recall trade-off even shows that there is an undeniable advantage, and there may be a negative side. The outcomes also show how good the model is. For example, a perfect accuracy score in the 2nd Class demonstrates that the algorithm can differentiate occurrences of such a category. The weighted average metrics consider the fundamental dissimilarity in a group better. The model has an average 0.91 F1-score, indicating that it makes a balanced decision between accuracy and recall. While I can be proud of these results, I understand that the presented weaknesses realized that additional study of potential feature engineering and other model architectures remain. This experiment will be valuable for future research and usage in real-life.

关键词： Measurement Deep learning Accuracy Computed tomography Classification algorithms

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Predictive Modeling of Fire Hazard: Integrating Environmental Factors with Machine Learning

Predictive Modeling of Fire Hazard: Integrating Environmenta...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Tanjim Taharat Aurpa Samiha Maisha Jeba Khaza Minhaz Bin Monib Maherun Nessa Ela Md Shoaib Ahmed Md Musfique Anwar Dept. of Data Science and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur Bangladesh Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Bangladesh Boise State University Boise Idaho USA Dept. of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In today’s fast-paced world, where technology is advancing rapidly, the tragic loss of lives due to fire accidents is not only heart-wrenching but also poses a significant risk to public safety. Early prediction of such risks can play a crucial role in educating people about fire hazards, enabling them to be better prepared in case they find themselves in such unfortunate circumstances. Our study employed various machine learning (ML) techniques and evaluated their effectiveness by analyzing different factors. These factors are derived from the diverse living conditions in Bangladesh and were gathered through surveys. To refine and identify the critical features for classification, we utilized feature selection methods like ANOVA-f and the chi-square test. Our approaches have achieved a success rate of up to 97.98%.

关键词： Surveys Accuracy Feature extraction Hazards Encoding Vectors Public security Bayes methods Analysis of variance Random forests

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Optimizing Atmospheric Water Generation Through Advanced Weather Prediction Using Solar Energy Data 9

Optimizing Atmospheric Water Generation Through Advanced Wea...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Dar, Eman Rana, Khadija Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Khalid, Aqsa Javaid, Arslan Altaf, Jawad Sattar, Basit Chuhan, Saad Hussain Atif Yaqub, Muhammad Qureshi, Ali Moiz Mubarak, Zaima Information Technology University Computer Engineering Department Lahore Pakistan Bahria University Lahore Campus Dept. of Computer Science Lahore Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan National College of Business Adm. & Economics Department of Computer Science Lahore Pakistan National College of Ireland Nci Department of Computer Science Ireland Umt Department of Ai Lahore Pakistan

ISBN: (纸本)9798350394474

This research focuses on enhancing the efficiency of Atmospheric Water Generation (AWG) systems through the development of an accurate weather prediction model. Leveraging solar energy data, the project aims to forecast key weather parameters such as solar radiation, temperature, and humidity. The objective is to optimize the performance of the AWG device across diverse weather conditions. By integrating advanced weather prediction, the study endeavors to enable proactive adjustments in the AWG operation, ensuring optimal water production. This research contributes to the sustainable utilization of solar energy for atmospheric water generation, addressing water scarcity challenges with a forward-thinking and adaptive approach. © 2024 IEEE.

关键词： Solar energy

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Orchestration-Aware Optimization of ROS2 Communication Protocols

Orchestration-Aware Optimization of ROS2 Communication Proto...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Mirco De Marchi Nicola Bombieri Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Computer Science University of Verona Italy

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The robot operating system (ROS) standard has been extended with different communication mechanisms to address real-time and scalability requirements. On the other hand, containerization and orchestration platforms like Docker and Kubernetes are increasingly being adopted to strengthen platform-independent development and automatic software deployment. In this paper, we quantitatively analyze the impact of topology, containerization, and edge-cloud distribution of ROS nodes on the efficiency of the ROS2 communication protocols. We then present a framework that automatically binds the most efficient ROS protocol for each node-to-node communication by considering the architectural characteristics of both software and edge-cloud computing platforms. The framework is available at https://***/PARCO-LAB/ros4k.

关键词： Protocols Service robots Scalability Operating systems Software Real-time systems Topology

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Support Chatbot based on ChatGPT for Customer Relationship Management in Educational Institutions 6

Support Chatbot based on ChatGPT for Customer Relationship M...

引用

6th International Conference on Computing and Informatics, ICCI 2024

作者： Mohamed, Islam Samih Abdelsalam, Mohamed Moawad, Ibrahim F. Dept. Business Information Systems Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt

ISBN: (纸本)9798350373875

Chatbot technology is considered one of the latest artificial intelligence tools used to achieve interaction between the organization and the customer. It relies on natural language processing, as it helps the organization know the desires and needs of the customer, in addition to collecting customer data and displaying the services provided by the organization. This research proposes an architectural model for supporting chatbots based on the ChatGPT model. It receives customer interaction inputs, understands questions, and responds quickly and appropriately. In addition, this model was designed and implemented using the Voiceflow platform. This model was named EDUBot to build communication elements and components to customize the structure of the virtual conversation and manage the dialogue. This model can be applied to the Educational City institution as a case study to improve customer service and help the institution manage customer relationships more efficiently. © 2024 IEEE.

关键词： Sales

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Involutional Neural Network Based Approach for the Detection of Melanoma Cancer

Involutional Neural Network Based Approach for the Detection...

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

This paper presents an extraordinary method to detected melanoma, the most lethal form of skin cancer, using Involutional Neural Networks (INNs). Melanoma's early detection is crucial for effecacius treatment, yet existing diagnostic methods often lack the requiring precision and scalability. INNs offer a solve through their advanced capability to adaptively process spatial information in images, a huge different from traditional convolutional techniques. Our reaserch applies INNs to melanoma detectionair, focusing on their unique involution operations that significant enhance image analysis for accurately classification. The performance of our model, assessed across different metrics including accurate, precision, recall, and F1-scores, demonstrates its superior diagnostic capability. This investigation not only underscores the potency of INNs in medical imagining but also opens up new avenues for their application across diverse analytical needs in healthcare. By advancing melanoma detection methods, we contribute to the broader goal of improving early cancer diagnose and treatment outcomes!

关键词： Measurement Training Accuracy Scalability Neural networks Melanoma Predictive models Stability analysis Reliability Thermal stability

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EEG-driven GAN for Alpha Rhythm Generation in Passive BCI

EEG-driven GAN for Alpha Rhythm Generation in Passive BCI

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Metrology for Extended Reality, Artificial Intelligence and Neural engineering (MetroXRAINE), IEEE International Conference on

作者： Ettore Cinquetti Guglielmo Zanni Gloria Menegaz Silvia F. Storti Dept. of Computer Science University of Verona Italy Dept. of Engineering for Innovation Medicine University of Verona Italy

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

Passive brain-computer interface (BCI) based on electroencephalographic (EEG) signals show promise for monitoring human vigilance in critical settings that continuously strain human attention. However, the lack of extensive public datasets limits progress in artificial intelligence research. To overcome this problem, we implemented generative adversarial networks (GANs) to augment existing EEG datasets. We focused on two datasets: a publicly available resting-state dataset and a custom one simulating industrial activities. After extracting time-variant alpha indices using continuous wavelet transform, we compared the generated data with real data using the L2 distance metric and autocorrelation function. We additionally assessed two forecasting models trained on original and augmented datasets, comparing their predictions. The integration of synthetic data led to an improvement in signal behavior prediction, as evidenced by a 32% reduction in mean absolute error for the resting-state dataset. Furthermore, a metric inspired by the Frechét Inception Distance was computed using the forecasting model to discern the distributions of embeddings for the real and generated data, with results showing a strong resemblance between the two. This study challenges the limitation of acquiring domain-specific EEG data and aims to develop a robust signal generation framework.

关键词： Measurement Computational modeling Predictive models Brain modeling Generative adversarial networks Electroencephalography Data models Forecasting Artificial intelligence Synthetic data

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A BrainNet (BrN) based New Approach to Classify Brain Stroke from CT Scan Images

A BrainNet (BrN) based New Approach to Classify Brain Stroke...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Dhonita Tripura Imdadul Haque Mithun Dutta Shaikat Dev Tanjila Jahan Shomitro Kumar Ghosh Md. Ashiqul Islam Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

Worldwide, brain stroke is known as the 2nd leading cause of death, and based on Indian history, three people have suffered every minute. There are mainly two different types of brain stroke: ischemic stroke and Hemorrhagic stroke used to train the proposed models. Ischemic stroke is the most common and it contributes mostly to 80% of the brain stroke and Hemorrhagic stroke contributes mostly to 20% of the brain stroke. In the proposed model, there has been used a hybrid model called BrainNet (BrN) as CNN(Convolutional Neural Network) and SVM(Support Vector Machine)to classify brain stroke disease. After applying the required proposed model, it has produced a smart score of 91.91% accuracy, and compared to the existing model it performs pretty well. The BrainNet (BrN) model is mainly designed based on a deep neural network with dataset collection, preprocessing, and feature extraction with the desired model and make the classification concerning SVM. With compare to the existing model, it is an acceptable performance that belongs to the collected dataset designed with Ischemic stroke and Hemorrhagic stroke disease within the total number of 2515 data.

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