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Optimizing Communication with DDQN-Based Adaptive Modulation Coding: Two Strategic Approaches

Optimizing Communication with DDQN-Based Adaptive Modulation...

引用

IEEE international conference on Signal processing, Communications and computing (ICSPCC)

作者： Lingling Zhang Jianan Zhang Weijie Tan Chengkai Tang School of Marine Science and Technology Northwestern Polytechnical University Xi'an China State Key Laboratory of Public Big Data Guizhou University Guiyang China School of Electronics and Information Northwestern Polytechnical University Xi'an China

In this paper, we present an adaptive modulation coding scheme based on DDQN algorithm. Taking the different need of communication system into account, we construct two communication strategies, which are energy efficiency prioritized strategy and reliability prioritized strategy. Simulation is conducted over the time varying underwater acoustic channel, with comparison to traditional fixed link and DQN adaptive method. The results show that based on the reliability priority strategy, the DDQN method can select the modulation mode that makes the BER lower in the current environment for data transmission, while based on the energy efficiency priority strategy, the DDQN method could reach higher system throughput and energy efficiency compared to the fixed MCS and DQN adaptive method.

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Alpha Node: Smart data Retrieval System Using Network Simulator (NS)-3

Alpha Node: Smart Data Retrieval System Using Network Simula...

引用

Intelligent Technologies (CONIT), international conference on

作者： Mustafa Husain Abbas Jabalpurwala Tamanna Vaikkath Rajeshwar Balla Sudhanshu Maurya Nitin Rakesh Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Today, networking is the core of modern communication and connectivity. The reality is that it is the underlying framework for data and resource exchanges across various types of computing devices. Alpha node (cluster head) selection, a crucial factor in optimizing networks of groups comprising nodes that can work cooperatively in a localized environment, takes the highest place in the clustered networks hierarchy. The behavior and competence of the alpha node, the most central and leading node of any cluster, profoundly impacts the network’s capacity to coordinate its activities and allocate resources effectively. This study investigates the integration of machine learning methods into the ns-3 simulation framework, i.e., a discrete event simulator with many complicated features. This paper aims to provide the theoretical basis and solutions for managing clustered networks, particularly tree networks. The research will concentrate on combining machine learning models and simulations employing a machine learning model that crystallizes all methods of alpha nodes with a particular eye on the efficiency and quality of alpha node selection. This is aimed at adding significant flow into the network optimization practice.

关键词： Knowledge engineering Machine learning algorithms Query processing Diversity reception Machine learning Prediction algorithms Magnetic heads

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Game data Visualization Using Artificial Intelligence Techniques 11th

Game Data Visualization Using Artificial Intelligence Techni...

引用

11th international conference on Recent Trends in computing, ICRTC 2022

作者： Mohapatra, Srikanta Kumar Sarangi, Prakash Kumar Sahu, Premananda Sharma, Santosh Kumar Sharma, Ochin Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering SRMIST DELHI-NCR Modinagar Ghaziabad India Department of Computer Science and Engineering CVRCE Odisha Bhubaneswar India

ISBN: (纸本)9789811988240

Games usually have a tendency to have the properties of massive use of a location and excessive complexity, making them notable benchmarks for evaluating numerous strategies which consist of AI ones. Broadly talking, techniques are utilized in video games in a position to make them larger exciting, immersive, smarter, and many others. Both of these prove to be excellent types of AI recreation. Since there are few viewpoints on contemporary results in the activity AI discipline from the standpoint of the fundamental program, in our work we have taken a scientific look at a number of commonplace that seems to be up to software program fields of pastime AI: viable agents in non-player character studies and recreation stage technological know-how in procedural content material fabric generation. Here we have proposed a model which can provide the different game visualization in the form of terms and conditions using different AI-based algorithms. In addition, AI for everyday tasks and hybrid genius for different games are explored. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Visualization

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A Testbench for Stereo-processing Acceleration Based on PYNQ and the StereoPi

A Testbench for Stereo-Processing Acceleration Based on PYNQ...

引用

IEEE international Workshop on Intelligent data Acquisition and Advanced computing Systems: Technology and Applications

作者： John Kalomiros John Vourvoulakis Stavros Vologiannidis Dpt. of Computers Informatics and Telecommunications International Hellenic University Serres Greece

Stereo processing accelerators are often designed and implemented using Field Programmable logic. In this paper, a new setup for the implementation and practical testing of hardware stereo-processing solutions is presented. A PYNQ Z1 board featuring a Zynq SoC is combined with a low-cost StereoPi camera. It is shown that the system is versatile, since it can exploit the optimized OpenCV capabilities of the Raspberry Pi Compute Module for image pre-processing and the hardware acceleration capabilities of the Programmable Logic embedded in the Zynq chip. A series of experiments prove the feasibility of the concept. A state-of-the-art implementation of the SGM stereo algorithm in hardware description language is used in order to prove the feasibility of the design. The system can process depth maps with the nominal refresh rate of 60 fps, using stereo image resolution 640×480 and up to 96 levels of disparity.

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Artificial Intelligence Tools for University Fundraising 5.0: A Comprehensive Analysis 5

Artificial Intelligence Tools for University Fundraising 5.0...

引用

5th IEEE international conference on Modern Electrical and Energy System, MEES 2023

作者： Cherniavska, Olena Belov, Alexandr Shmygol, Nadiia Jarvis, Marina Cherniavska, Oleksandra Tsalko, Tetiana Swiss Federal Institute of Technology Eth Zürich Chair of Technology and Innovation Management Zurich Switzerland Kyiv National University of Technology and Design Faculty of Management and Business Design Kyiv Ukraine Institute of Social and Economic Initiatives Department of Innovation Research and Development Poltava Ukraine Warsaw University of Technology Division of Management and Quality Warsaw Poland Zaporizhzhya Politechnik National University Departament of Management Zaporizhzhya Ukraine Department of Business Administration Tallinn Estonia Kyiv Education Training and Youth Centre Departament of Management Kyiv Ukraine Kyiv National University of Technology and Design Department of Management and Smart Innovation Kyiv Ukraine

ISBN: (纸本)9798350359787

University Fundraising 5.0 represents the latest paradigm shift in this domain, one that resonates with the European Union's transformative vision of Industry 5.0. This visionary approach to fundraising not only incorporates cutting-edge technologies but also emphasizes collaboration, sustainability, and social impact, aligning seamlessly with the broader European framework of Industry 5.0. We applied horizon scanning methods, such as AI-Human Collaboration through information scanning and technological analysis, to classify and functionally organize artificial intelligence (AI) tools that can be applied in universities. Through the analysis of 61 platforms and services related to AI and fundraising, along with a systematic classification of AI tools based on functionality, implementation, and other relevant criteria, this research offers valuable insights for universities in selecting the most suitable AI tools to meet their specific fundraising requirements and objectives. The analyzed platforms provide various solutions, including AI tools, cloud computing, machine learning, data analytics, data visualization, natural language processing, fraud protection, contact data management, social media community management, content optimization and personalization, as well as fundraising and donor database management tools for non-profit organizations. Study highlights the potential future prospects and implications of integrating AI technologies in university fundraising beyond donor interactions. It opens up avenues for further research on AI applications in areas such as grant management, corporate partnerships, and foundation collaborations. Exploring these perspectives can provide universities with a comprehensive understanding of the broader impact of AI on their overall fundraising strategies and financial sustainability. The research findings contribute to advancing the understanding of AI's potential in university funding and fostering innovation in higher educat

关键词： data Analytics

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A Systematic Review of data Science and Deep Learning Applications in Extracting Biological data

A Systematic Review of Data Science and Deep Learning Applic...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Murala Praveena J. Paramesh K. Bala School of Computing Dept. of CSE Bharath Institute of Higher Education and Research Chennai India

This article provides a thorough analysis of data sciences and deep learning(DL) characters in data science emphasizing which one is needed out of DL for finer prediction has been as well addressed. This study gives a detailed correlation of disparate DL approaches employed in the biological sector (BS). Because of the appearance of dominant computers, advanced DL algorithms, and broad data production out of disparate industrial tools, a promising tomorrow in establishing solutions to the intricate issues in the BS, which remained formerly besides the hold of analytical solutions (AS) or numerical simulations (NS) is noticed. DL tools could include each particular within the log data and each data linked to the target data. Despite their constraints, these remain unlimited by constraining presumptions of AS or by specific data and/or power processing requisites of NS. This exhaustive work could provide an exceptional reference for DL implementations in this sector. Centered upon the review performed, it has been observed that DL approaches provide immense power in resolving issues in the BS’ major fields incorporating prediction, classification, and clustering.

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A High Performance computing Platform for Big Biological data Analysis

A High Performance Computing Platform for Big Biological Dat...

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international conference on Applied System Innovation (ICASI)

作者： Chang-Wei Yeh Chieh-Wei Huang Chia-Lee Yang Yu-Tai Wang National Center for High-performance Computing National Applied Research Laboratories Hsinchu Taiwan

With the rapid progress of high-throughput sequencing projects, biological data is growing exponentially, creating a need for efficient and scalable algorithms and high-performance computing (HPC) platforms for big biological data analysis. However, traditional HPC platforms are inadequate for meeting the demand for rapid data analysis tasks in bioinformatics research. Analyzing biological sequence data is challenging due to its complexity and long computing time. This poses challenges for researchers to gain a deep understanding of biological functions. Thus, this research presents a large-scale biological data analysis platform enabled by an HPC infrastructure for genomic sequencing data and protein structure analysis. This study lists the characteristics of big biological data and traditional computing platforms, provides a reconfigurable HPC platform for biological data analysis applications. A practical case of HPC platforms for big biological data analytics in Taiwan is also presented. By using a reconfigurable HPC platform, the computational bottleneck can be removed, and data analysis can be accelerated. The platform enables researchers to gain profound insights into the deepest biological functions by addressing the challenges of big biological data analysis.

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Cloud API Oriented Analysis on Rolling Upgrade Dependability

Cloud API Oriented Analysis on Rolling Upgrade Dependability

引用

Electrical Engineering, Big data and algorithms (EEBDA), IEEE international conference on

作者： Zhihao Yu Min Fu University of Liverpool Liverpool England Macquarie University Sydney Australia

When cloud operators conduct rolling upgrade operation on AWS (Amazon Web Services) cloud applications, they interact with the cloud platform by indirectly calling rolling upgrade related cloud API functions provided in cloud platform, such as "UpdateAutoScalingGroup". Hence, rolling upgrade operation can be viewed as a series of rolling upgrade related cloud API calls. When calling those API functions in certain occasions, some errors might occur, e.g. the error of "ScalingActivityInProgress". These errors somehow account for the operation failures. This paper summarizes the possible errors for all the rolling upgrade related cloud API functions, and addresses when the errors would occur. We derive these research outcomes from an empirical study on these API functions. The empirical study is conducted by using AWS EC2 platform and AWS SDK for .NET. Our research provides a summarization for the error/failure scenarios of rolling upgrade related cloud APIs, and it can facilitate any other researches related to the dependability of cloud computing.

关键词： Electrical engineering Cloud computing Web services Big data

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Analysis and Implementation of Machine Learning Model for Detection of Parkinson’s Disease

Analysis and Implementation of Machine Learning Model for De...

引用

Sustainable computing and data Communication Systems (ICSCDS), international conference on

作者： Kumar Spandan Biresh Kumar Pallab Banerjee Pooja Jha Laxmi Kumari Pathak Mohan Kumar Dehury Amity Inst. of Information Technology Amity University Jharkhand Ranchi INDIA Amity School of Engg. and Technology Amity University Jharkhand Ranchi INDIA

Parkinson’s disease, which affects 2-3% of people under 65 years of age, is the second most prevalent neurological ailment. The neuropathological indicators of Parkinson’s disease are intracellular inclusions containing clumps of synuclein and neuronal death in the substantia nigra, which results in striatal dopamine insufficiency. The central and peripheral autonomic nerve systems contain numerous more cell types that are also involved, most likely beginning with the first stages of the disease. Although bradykinesia and other essential motor characteristics are required for clinical diagnosis, Parkinson’s disease is also accompanied by a wide range of non-motor symptoms that increase overall disability. Numerous routes and mechanisms, including synuclein proteostasis, mitochondrial function, oxidative stress, calcium homeostasis, axonal transport, and neuroinflammation, are involved in the underlying molecular etiology. This paper aims to implement the SVM algorithm to predict Parkinson’s disease in a patient.

关键词： Support vector machines Training Machine learning algorithms Computational modeling Supervised learning Prediction algorithms data models

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Machine Learning based Credit Card Fraud Detection

Machine Learning based Credit Card Fraud Detection

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Sustainable computing and data Communication Systems (ICSCDS), international conference on

作者： Y. Vijaya Lakshmi Y. Sahithi Priyanka A. Harika N. Rajitha D. Bhargavi Department Of Information Technology Vignan’s Nirula Institute of Technology and Science for Women Guntur India

Cashless transactions are increasing because electronic commerce technology is developing so quickly. Since everyone uses credit and ATM cards for transactions, fraud may also rise. The rise in fraud, which costs businesses money worldwide, the development of numerous procedures and strategies for spotting fraud, Analysis of user behaviors is necessary for fraud detection in order to identify users that act maliciously. Delinquent, fraudulent, intrusive, and account defaulting behaviors all fall under the umbrella of malicious behavior. This study analyzes credit card fraud detection by using machine learning algorithms, specifically the Random Forest and Decision Tree algorithm. The survey of current strategies utilized in credit card fraud detection were depicted. This study has employed the Principal Component Analysis (PCA) to perform feature selection and speed up the learning process. The comparison outcomes demonstrate that Random Forest (RF) outperforms Decision Tree (DT).

关键词： Technological innovation Machine learning algorithms Training data Credit cards Fraud Behavioral sciences Decision trees

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