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An Evaluation of AI-Driven data Mining Techniques for Large-Scale data Processing 3

An Evaluation of AI-Driven Data Mining Techniques for Large-...

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

作者： Choudhary, Sheetal Khatoon, Naghma Singh, K. Ranjith Divya, K. Kumar, Santosh D. Gupta, Abhishek Kumar Himalayan University Department of Computer Science Itanagar India Usha Martin University Department of Coputing & Information Technology Ranchi India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India

ISBN: (纸本)9798350319125

Information mining and synthetic intelligence (AI) have gained a lot interest in recent years in the discipline of large-scale statistics processing. AI-driven statistics mining strategies are especially effective for identifying beneficial styles and functions in huge datasets. This paper presents an assessment of several typically used AI-pushed information mining techniques for processing big datasets and compares their performance in phrases of pace, accuracy, and scalability. Not unusual algorithms including precept component evaluation and clustering analysis are evaluated and in comparison to recent advances in AI-driven records mining which includes deep mastering and associative statistics mining. The overall performance of every technique is measured throughout 3 categories: speed, accuracy, and scalability. The effects of the evaluation show that deep learning and associative information mining outperform the conventional strategies in terms of accuracy and scalability whilst they're fairly slower than the other techniques. Therefore, these algorithms can be more suitable for positive types of massive-scale statistics processing tasks that require excessive accuracy and scalability. © 2023 IEEE.

关键词： component evaluation learning processing scalability

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UDS for data Communication in Temperature Monitoring 3rd

UDS for Data Communication in Temperature Monitoring

引用

3rd international conference on intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Mallikarjun, B.C. Prathima, H.S. Department of ETE Siddaganga Institute of Technology Karnataka Tumkur India

ISBN: (纸本)9789819784288

Electronic Control units (ECUs) are computer systems which are capable to process this data and send signals to the various systems in the vehicle to modify their behavior in order to optimize performance and ensure safety. Maintaining appropriate operating temperatures within ECUs is essential for preventing overheating, which can lead to system malfunction or failure. Overheating can cause the components of an ECU to degrade over time, leading to decreased performance, increased energy consumption, and potential system failure. This proposed work provides a temperature monitoring solution, i.e., Unified Diagnostic Service (UDS), that enhances the overall functionality of ECUs and contributes to vehicle safety and performance. The temperature monitoring solution would enable the ECUs to measure and monitor the temperature of the vehicles components and systems, which would help to reduce the risk of malfunctions and overheating, which can lead to catastrophic failures and safety risks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Catastrophic Electronic control units Temperature monitoring

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Research and Design of a Remote Bionic Signature Robot based on Internet of Things 3

Research and Design of a Remote Bionic Signature Robot based...

引用

3rd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Che, Xinyu Liu, Yizhen Sun, Lulu Lei, Kai Li, Shihuai Meng, Lingshuai Liaoning Institute of Science and Technology College of Electronic and Information Engineering Liaoning Benxi China

ISBN: (纸本)9781665490788

In the context of the COVID-19 pandemic and special scenarios, the normalization of online work, office and learning has brought the issue of digital signature to be solved. This paper presents an innovative research on a remote biomimetic signature robot based on IoT, utilizing Raspberry Pi 4B and single-chip microcomputer as the core devices. This project mainly consists of three modules: client module, server module, and transmission module. The client module designs the client app using the Vue framework, supporting component-based and module-based development. The server module adopts embedded development with single-chip microcomputer and Raspberry Pi. The transmission module develops a new algorithm suitable for this project by learning sha1, md5, base64 and other algorithms, and using secure encryption algorithms and data destruction to ensure the data security of the signature robot, avoiding data leakage and tampering issues. After testing, the remote signature robot is reliable and practical. Through this project, not only has the theoretical and practical ability of university students been strengthened, but also their self-learning ability and application innovation ability have been expanded, and their social service awareness has been enhanced, laying a solid foundation for their future career development. © 2023 IEEE.

关键词： Robots

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Application of Artificial Intelligence to Cluster Soil Behaviour from CPTu data 3rd

Application of Artificial Intelligence to Cluster Soil Behav...

引用

3rd international conference on Sustainable Civil engineering and Architecture, ICSCEA 2023

作者： Phu, Nhat Truyen Le, Pham Thanh Hieu Le, Ba Vinh Vo, Dai Nhat 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Linh Trung Ward Thu Duc City Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819974337

In this study, the authors used many artificial intelligence algorithms to cluster soil behaviour from CPTu data, that includes cone resistance (qc), frictional resistance (fs), dynamic pore pressure (u2), corrected cone resistance (qt) and friction ratio (Rf). The soil behavior type is following Robertson (1986). There are four model are built in this study include: i. Supervised learning with SVM algorithm by qc, fs, u2, qt and Rf;ii. Supervised learning with SVM algorithm by qt and Rf;iii. Unsupervised learning with Kmeans algorithm by qt and Rf with three clusters;and Unsupervised learning with Kmeans algorithm by qt and Rf with nine clusters. To satisfy "Imbalanced data" and Roberson’s chart shape, the raw data are being preprocessing within two steps before clustering with KMeans or continue to divide to 3 minor set for SVM algorithm that includes: training set - 50%, validation set - 20% and test set - rest. The result indicated the Supervised learning with SVM algorithm by qc, fs, u2, qt and Rf is the best model, while unsupervised learning with KMeans does not meet the requirements. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unsupervised learning

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A Comparative Analysis for the Detection of Hit Rate of Popular Music Videos in Social Network using Logistic Regression over Support Vector Machine Algorithm 3

A Comparative Analysis for the Detection of Hit Rate of Popu...

引用

3rd international conference on intelligent engineering and Management, ICIEM 2022

作者： Reddy, Mekala Bharat Kumar Vani, B. Babu, C. Nelson Kennedy Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

The objective of the work is to improve the innovative music videos popularity prediction in social networks based on hit rate using machine learning algorithms like Logistic Regression algorithm and Support Vector Machine (SVM) algorithm. Materials and Methods: SVM algorithm and Logistic Regression algorithm with sample size N = 10 was iterated 20 times for predicting innovative music videos popularity on social media. Result: The support vector machine algorithm produces a better accuracy of (87.6%) than logistic regression classifier (81.6%). The significance (p © 2022 IEEE.

关键词： Support vector machines

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An Integration of Wireless Communications and Artificial Intelligence for Autonomous Vehicles for the Successful Communication to Achieve the Destination 3

An Integration of Wireless Communications and Artificial Int...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Bhardwaj, Ankit Pattnayak, Jui Prasad Gangodkar, Durga Rana, Arti Shilpa, N. Tiwari, Prashant University of Connecticut The Math Company Department of Computer Science and Engineering StorrsCT United States JIS College of Engineering Computer Science and Engineering India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Uttaranchal University Uttaranchal Institute of Technology India SR University Department of Electronics and Communication Engineering Telangana Warangal India GLA University Institute of Business Management Mathura India

ISBN: (纸本)9798350399264

Multiple smart agents interacting with one another is a fundamental component of the IoT, sometimes referred to as social IoT, is the Internet of Things. such knowledgeable beings, like autonomous vehicles and robots, are furnished with powerful machine learning for mobile operation. Although it should go without saying that wireless networking is crucial in these situations, there aren't enough studies on the subject to offer a comprehensive knowledge of multi-Agent networks that operate wirelessly. We examine how wireless communications and multi-Agent systems interact in this essay interact while using disruptive reinforcement learning to model each artificial intelligence agent. The illustrative system is served by autonomous vehicles that drive across Manhattan's streets. As a result of receiving obtaining data via wireless transmission from other actors, the necessity to adapt policy exchange reinforcement learning is the first unique discovery. It is easy to see the benefit of using wireless communication. We also show how communication mistakes might negatively affect system performance. To draw an initial conclusion in favor of adopting small cell infrastructure, Direct vehicle-To-vehicle and vehicle-To-infrastructure communication is provided by multi-Agent systems in contrasted. Finally, using real-Time ALOHA, we investigate Over multi-Agent systems, multiple access communication is possible. Instant ALOHA rejects the retransmission mechanism, in contrast to conventional to assure timely wireless communication contributions to a multi-Agent system's learning process and acceptable performance, one should consider reliable packet delivery via re-Transmit after collisions. © 2023 IEEE.

关键词： Multi agent systems

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Effect of Weather Conditions and Transactions Records on Work Accidents in the Retail Sector - A Case Study 1

引用

3rd international conference on Optimization, learning Algorithms and Applications (OL2A)

作者： Borges, Lucas D. Sena, Ines Marcelino, Vitor Silva, Felipe G. Fernandes, Florbela P. Pacheco, Maria F. Vaz, Clara B. Lima, Jose Pereira, Ana I. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Regioes M Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (数字)9783031530258

ISBN: (纸本)9783031530241;9783031530258

Weather change plays an important role in work-related accidents, it impairs people's cognitive abilities, increasing the risk of injuries and accidents. Furthermore, weather conditions can cause an increase or decrease in daily sales in the retail sector by influencing individual behaviors. The increase in transactions, in turn, leads employees to fatigue and overload, which can also increase the risk of injuries and accidents. This work aims to conduct a case study in a company in the retail sector to verify whether the transactions records in stores and the weather conditions of each district in mainland Portugal impact the occurrence of work accidents, as well as to perform predictive analysis of the occurrence or non-occurrence of work accidents in each district using these data and comparing different machine learning techniques. The correlation analysis of the occurrence or non-occurrence of work accidents with weather conditions and some transactions pointed out the nonexistence of correlation between the data. Evaluating the precision and the confusion matrix of the predictive models, the study indicates a predisposition of the models to predict the non-occurrence of work accidents to the detriment of the ability to predict the occurrence of work accidents.

关键词： Predictive Analysis Correlation Analysis Weather Conditions Transactions Records

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Semantic Segmentation in Medical Image Based on Hybrid Dlinknet and Unet 3

Semantic Segmentation in Medical Image Based on Hybrid Dlink...

引用

3rd IEEE international conference on Computing, Communication, and intelligent Systems, ICCCIS 2022

作者： Samudrala, Suresh Mohan, C. Krishna Department of Computer Science and Engineering India India

ISBN: (纸本)9781665462006

Medical imagery segmentation has been widely using deep learning approaches, which are quickly evolving in semantic segmentation. Nevertheless, due to their poor performance, newly proposed methods like Fully Convolutional Network, U-Net, LinkNet and SegNet still require enhancement to offer better semantic segmentation while identifying breast cancer. Therefore, this article presents a hybrid encoder and decoder framework in histology images of tissue slides. The novel network is designed with DlinkNet and UNet with a fusion of medical imagery data. An Attention Gate Module (AGM) is inserted into the proposed network to enrich the network learning's capacity. This way, semantic features are extracted and semantically segmented in the tumors in the input image set. The effectiveness of the planned network is investigated concerning various parameters like Maximum Symmetric Surface Distance (MSSD 35.98), Accuracy (99.2%), Relative Absolute Volume Difference (RAVD 3.41), Jaccard index (0.83), Average Symmetric Surface Distance (ASSD 0.629), Sensitivity (91%) and Dice Coefficient (DICE) (0.928) with existing networks of FPN and SENet. © 2022 IEEE.

关键词： Semantic Segmentation

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Multivariate Deep learning for Reconstruction of Spatial Missing Climate data 3

Multivariate Deep Learning for Reconstruction of Spatial Mis...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Yao, Ziqiang Wu, Li Huang, Jianqia Wang, Xiaoying Department of Computer Technology and Application Qinghai University Xining810016 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Complete meteorological data is essential for meteorological research. However, due to sensor failure or occlusion, data loss always occurs. In order to deal with this problem, geoscience often uses Kriging and other traditional interpolation methods. Nevertheless, because the traditional method only considers the existing data, it cannot provide accurate reconstruction results when the spatial missing rate is large. Inspired by image inpainting algorithms, we propose a new deep learning method that combines other variables related to missing meteorological variables to provide more effective information for missing regions, and proves the good effect of the method through a series of experiments. To illustrate that our method can maintain good performance under different missing rates, we use the reanalysis data of the European Centre for Medium-Range Weather Forecasts to create a data set with three different missing rates of 30%, 50%, and 70% through the artificial mask. Under different missing rates, the average RMSE of the method based on deep learning decreased by 77% and 31% respectively compared with Kriging and DINEOF. In addition, by combining relevant variables, the accuracy of the deep learning model can be further improved by about 16%. © 2023 SPIE.

关键词： Kriging

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Boosting Accuracy of Fake Review Prediction Using Synthetic Minority Oversampling Technique 3

Boosting Accuracy of Fake Review Prediction Using Synthetic ...

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3rd IEEE international conference on Computing, Communication, and intelligent Systems, ICCCIS 2022

作者： Saxena, Bhawna Goyal, Shruti Kumari, Anjali Agarwal, Anushka Jaypee Institute of Information Technology Department of Computer Science and Engineering and Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9781665462006

In recent times prior to making a purchase, the vast majority read reviews about that product, and their decision is largely driven by the reviews. Deceitful online sellers often gather fake or spam reviews for their products or services, thereby reducing the effectiveness of online reviews. The review data is often imbalanced such that the fake reviews greatly outnumber the genuine reviews. An imbalance leads to a bias, as the model tends to mostly predict the majority class. To attain a high-quality classification outcome, the issue of imbalanced data should be resolved before applying the classification algorithms. This paper studies the performance of supervised machine learning classifiers pertaining to fake review detection. The approach put forward in this paper aims to improve the prediction accuracy of popular supervised learning classifiers Random - Forest, LightGBM, XGBoost, Naive Bayes, and Decision Tree on an imbalanced review dataset For boosting the accuracy of these classifiers, the Synthetic Minority Oversampling Technique is used for addressing the class imbalance problem. The performance of the classifiers has been studied by changing the oversampling parameters. The application of SMOTE showed a significant improvement in the classifier's prediction accuracy. © 2022 IEEE.

关键词： Decision trees

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