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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6956 条记录，以下是801-810 订阅

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Effect of data Aggregation Protocols on Potency of Wireless Sensor Network 14

Effect of Data Aggregation Protocols on Potency of Wireless ...

引用

14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Nandal, Vijay Dahiya, Sunita ECE DCRUST Murthal India

The wireless sensor networks (WSNs) consists of nodes whose main task is to sense the particular phenomenon that is happening. Numerous uses for these networks exist, such as monitoring natural habitats, disaster preparedness, security, and military use. The size, processing power, and battery life of wireless sensor nodes are all incredibly small. Sensor networks may fail because the batteries they use have a very limited capacity. data aggregation is a crucial method in these type of networks. With the aid of data aggregation, we lower energy consumption by removing redundancy. In this paper, we discuss the influence on various parameters of the wireless networks when data aggregation is used. © Grenze Scientific Society, 2023.

关键词： Sensor nodes

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Employee Appraisal Prediction Using Classification Models 6

Employee Appraisal Prediction Using Classification Models

引用

6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Mascarenhas, Jonathan Savant, Siddesh Aswale, Shailendra Goa University Verna Information Technology PCCE India

ISBN: (纸本)9798350304480

This paper presents a study that leverages machine learning techniques to determine employee performance using available data in organizations. The objective is to detect the key causes of attrition and minimize them through data analysis. The methodology involves data acquisition, conditioning, visualization, and classification using classification models like Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Naive Bayes algorithms in Python. The results are evaluated using an accuracy score and confusion matrix, revealing that the Decision Tree XGBoost algorithm achieved the highest accuracy at 82.7%. However, Naive Bayes and Support Vector Machine models performed better in classifying true positives. This research provides valuable insights for organizations seeking to utilize data to understand and address attrition issues. © 2023 IEEE.

关键词： Attrition Classification Models data Analysis Machine Learning Supervised Learning

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A Novel Framework for Measuring the Popularity of Musicians 2

A Novel Framework for Measuring the Popularity of Musicians

引用

2nd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Lu, Houhan Electrical Engineering Institution Sichuan University Sichuan Chengdu610044 China

ISBN: (纸本)9798350339765

The directed network of musicians and its follower reveals how the influence of musicians passing through the network and the kinds of music change with time, and how others influence the style and characteristics of musicians. This paper presents a new framework to measure the influence of musicians by showing the processing of directed network data of musicians while demonstrating the basic process of solving this type of influence calculation. The framework we presented in this paper considers genre and time, respectively. Under this proposed framework, one can calculate the influence of a musician, determine specific characteristics of musicians, find out which genre a musician belongs and predict what type of music will dominate the future music market. © 2023 IEEE.

关键词： data handling

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The Smart Application of data Mining in the Detection of Fraudulent Transactions 3

The Smart Application of Data Mining in the Detection of Fra...

引用

3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Bodhankar, Aniruddha Aswal, Upendra Singh Arivazhagn, D. Pund, Sachin S. Kulkarni, Nandini Chaturvedi, Sudhir Kumar Dr. Ambedkar Institute of Management Studies and Research- Nagpur Department of Data Science & Information Systems Nagpur India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Symbiosis International University Department of Symbiosis School of Planning Architecture and Design Nagpur Nagpur India Business School Academy of Maritime Education and Training Deemed to Be University Department of Amet Chennai India Upes Department of School of Engineering Dehradun India

ISBN: (纸本)9798350319125

Records mining is a powerful analytical device used to find out styles, correlations, and traits in large facts sets. Smart application of data mining methods within the detection of fraudulent transactions has finished an extensive quantity of success. It normally entails statistical algorithms, facts evaluation, and machine getting to know techniques to stumble on suspicious sports associated with fraud. Using statistics mining techniques, it's far possible to music the behaviour of clients, discover anomalous transaction styles and developments in huge datasets, come across frauds and generate signals on suspicious activities. It also allows to identify which forms of merchandise are frequently centered, which price methods are used, which places are problem to danger, and which traders have capacity credit card fraud. The utility of facts mining within the detection of fraudulent sports entails principal techniques. The first method makes use of conventional techniques used by banks, credit groups and other retailers, which includes account reconciliation, annualized billing and complex question processing. Those methods are used to generate reviews that are used by investigators to perceive probably suspicious activities. The second technique entails the usage of superior analytical techniques such as neural networks, decision timber, and clustering algorithms. These strategies have the functionality to stumble on facts irregularities in several conditions that were formerly unseen or unknown. © 2023 IEEE.

关键词： developments entails reconciliation statistics transaction

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Performance Evaluation of KNN & Decision Tree in Prediction of Heart Disease 3

Performance Evaluation of KNN & Decision Tree in Prediction ...

引用

3rd international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Kumar, Aniket Mathew, Jobin Thomas Gupta, Neha Malhotra, Vivek Manav Rachna International Institute of Research Studies Faculty of Computer Applications Faridabad India

ISBN: (纸本)9781665494007

Heart disease has been the leading cause of death in recent decades. Every five minutes, one person dies unexpectedly from heart disease. Researchers from all over the world are assisting doctors in diagnosing heart problems. However, machine learning techniques can substantially minimize the number of tests required. The main objective of this paper is to predict the heart disease of a person well in advance to minimise the need for expensive treatment and medicines. The present papers have used some of the finest approaches of machine learning like decision tree and KNN classifier for prediction of heart diseases. These algorithms are quite useful in diagnosing cardiac illness without the use of machinery or labs, by figuring out the problem at a far lower cost. These algorithms are very widely utilized and helpful in other domains. The dataset was obtained from the Kaggle website and contain data of 303 patients and 76 attributes. The accuracy of the KNN along with Decision tree algorithms have been compared with existing work of researchers and found to be more in the detection of the heart disease. These algorithms certainly help in reducing the number of deaths which are happening all around the world. © 2023 IEEE.

关键词： Decision trees

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Research on Scheduling algorithms for AI data-Intensive Tasks in Edge Heterogeneous Environments

Research on Scheduling Algorithms for AI Data-Intensive Task...

引用

2023 IEEE international conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Liu, Luanqi Xu, Zixian Wang, Yingxun Wang, Xiang School of Computer and Information Engineering Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350309614

Edge computing has become a new computing service model by virtue of its low latency and high performance characteristics. It deploys cloud server resources at the edge of the network to help mobile devices to complete data-intensive, large-scale and complex computing tasks. The heterogeneity of resources and the mobility of devices in the edge environment make the edge heterogeneous environment often suffer from unbalanced distribution of resources and loads, which leads to the reduction of system computing performance and resource utilisation. The CPU-GPU heterogeneous computing system in the edge environment has higher performance than the isomorphic system, and the existing task scheduling strategy still limits the performance of the heterogeneous system. Aiming at the above problems, a task scheduling model for CPU-GPU heterogeneous systems is designed, and a genetic algorithm based on reinforcement learning is proposed to achieve efficient allocation of AI data-intensive tasks. The Q-learning algorithm is used to achieve adaptive adjustment of parameters to solve the complex and high-dimensional task scheduling problem. Simulation experiments are carried out by Edge-cloudsim platform, compared with other methods, the results prove that the QL-GA task scheduling method proposed in this paper can provide an efficient solution to the task scheduling problem in edge heterogeneous computing systems. © 2023 IEEE.

关键词： edge computing heterogeneous computing systems Reinforcement learning task scheduling

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AutoDL: Enriching PowerBI AutoML Features with Deep Learning 14

AutoDL: Enriching PowerBI AutoML Features with Deep Learning

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Shankar, Sneha Doshi, Sujay Bhuvaneshwari, R. Praveen Joe, I.R. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

PowerBI, a graphical analytics software developed by Microsoft, has incorporated various AI and ML tools to provide accurate predictions and insights for the data feeded into the software. PowerBI is equipped with the AutoML tool which automates the process of applying machine learning to obtain data driven insights. In recent years, the explosion of data has been phenomenal, and due to the rise in interconnectivity and the age of IoT it has led to the emergence of big data. Though AutoML, which uses machine learning, isn't equipped to handle big data. To tackle the presented challenge of volume and scalability, this study aims to provide a solution, AutoDL, with the usage of deep learning models and algorithms. In this study, we attempt to create a base automated deep learning pipeline providing better results on big data. The pipeline was tested on 2 big data sets and we received affirmative results. © 2023 IEEE.

关键词： Big data

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IoT, Heterogeneous data processing, and AI Automation Synergy for Improved Efficiency and Maintenance: Revolutionizing Hospital Operations

IoT, Heterogeneous Data Processing, and AI Automation Synerg...

引用

2023 IEEE international conference on Signal processing, Communications and computing, ICSPCC 2023

作者： Muqing, Niu Wenting, Liu Xiaole, Gao Zhiyuan, Zhao Jingjuan, Wang First Affiliated Hospital Henan University of Science and Technology Luoyang China Business School Henan University of Science and Technology Luoyang China

ISBN: (纸本)9798350316728

Modern healthcare is going through a big change because of the way that new tools are proposed. This paper works at how the Internet of Things (IoT), heterogeneous data transfer and processing, and Artificial Intelligence (AI) robotics could completely change how hospitals work. IoT technology has become a significant tool because it makes it possible to watch and collect data in realtime from hospital properties like medical equipment, wearables for patients, and smart sensors. This steady flow of different kinds of data brings both possibilities and problems. In our study, we talk about how significant it is to have strong data transmission and processing systems that can handle different data sources well. Intelligent automation that is led by AI is at the heart of this change. AI systems can work with the huge amount of data created by IoT devices and use machine learning, predictive analytics, and cognitive computing to improve different parts of hospital management. This paper gives case studies and real-world examples of how IoT, diverse data processing, and AI automation have been used successfully in hospitals to show how these ideas can be used in the real world. IoT technology, diverse data processing, and AI automation are assembled to change the way hospitals work by making them more effective and easier to maintain. This study offers the significance of using these tools to keep up with the varying needs of current healthcare. As hospitals continue to change, this relationship will play an even bigger role in making sure that patients get superior care while making the best use of their resources. © 2023 IEEE.

关键词： Efficiency

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Research on Pilot's Workload Based on Multisource data 21

Research on Pilot's Workload Based on Multisource Data

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Haibo Xu, Mengyue Li, Gen Pan, Ting Si, Haiqing Liu, Haibo Li, Yixuan Zhu, Yijin Nanjing University of Aeronautics and Astronautics College of General Aviation and Flight Nanjing210016 China

ISBN: (纸本)9798350329223

This study examines the mechanisms by which different working conditions impact pilot's workload. The study is conducted to explore the impact of clear weather and low visibility on pilot's workload using the human-machine environment synchronous cloud platform and EEG big data analysis technology. First, subjective and EEG signal data from pilots were collected. Subsequently, the power spectral density and sample entropy of the EEG signal data were extracted using frequency domain analysis and linear dynamic analysis. Finally, paired T-tests were conducted to analyze workload differences among participants under varying weather conditions in flight experiments. The results demonstrate significant effects of both clear weather and low visibility on various EEG indicators and subjective ratings of participants. Pilot's workload is significantly lower under clear weather conditions compared to low visibility conditions, thus confirming the effectiveness of EEG big data analysis and subjective ratings in evaluating workload. © 2023 IEEE.

关键词： Entropy

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Application of Artificial Intelligence in Machine-Assisted Translation Systems

Application of Artificial Intelligence in Machine-Assisted T...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Huang, Meilin Guangzhou Xinhua University School of Foreign Languages Dongguan China

ISBN: (纸本)9798350313413

Machine-assisted translation can help translators complete translation work with high quality, efficiency, and ease, but in the process of machine-assisted translation, cultural differences and context grasp will affect the translation results and decrease Machine-assisted translation accuracy. To improve the quality of machine translation, this paper proposes an artificial intelligence technology to perform artificial intelligence analysis on machine-assisted translation systems and improve machine-assisted translationlevel, shorten the time to make translation decisions. The translated data is then evaluated comprehensively. Finally, machine-assisted translation is performed using artificial intelligence and the final translation result is output. The results show that artificial intelligence can accurately perform language recognition and improve the level , and the accuracy is greater than 95%, which is better than the literal translation method. Artificial intelligence technology has a relatively high degree of optimization for assisted translation, and can optimize assisted translation. The artificial intelligence meets the requirements of machine-assisted translation, which is suitable for the continuous analysis of machine-assisted translation. © 2023 IEEE.

关键词： artificial intelligence technology Literal translation method machine-assisted translation Translation data level of translation

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