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ICCDA 2021 - Proceedings of the 2021 5th international conference on Compute and data Analysis

ICCDA 2021 - Proceedings of the 2021 5th International Confe...

引用

5th international conference on Compute and data Analysis, ICCDA 2021

ISBN: (纸本)9781450389112

the proceedings contain 30 papers. the topics discussed include: a data-driven productivity assessment framework for collaborative research teams;finding clusters and patterns in big data applications: state-of-the-art methods in clustering environments;discovering risk factors of high intraocular pressure infection using data mining algorithms;prediction of chronic kidney disease risk using multimodal data;the applications of zero-failure data’ dependability analyzing to estimating mine sweeping;an extended bond pricing model and its algorithm based on the big data simulation;incorporating knowledge bases and databases to an effective repair of data errors;algorithmic fairness in applied machine learning contexts;and clustering of functionally related genes using machine learning techniques.

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machine learning and Deep learning based Intrusion Detection in Cloud Environment: A Review

Machine Learning and Deep Learning based Intrusion Detection...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： A. Vinolia N. Kanya V.N. Rajavarman Department of Information Technology Dr M.G.R Educational and Research Institute Chennai India Department of Computer Science and Engineering Dr M.G.R Educational and Research Institute Chennai India

Due to its open and dispersed nature, cloud computing (CC) faces several security-related difficulties. As a result, it is weak and open to breaches that compromise the security, reliability, and integrity of cloud resources and provided services. the most widely utilized element of computer system security and compliance procedures that protects cloud environments from numerous threats and attacks is the intrusion detection system (IDS). the goal of this article is to study how deep learning (DL) and machine learning (ML) networks are used by various methodologies at various stages of the intrusion detection process to get improved outcomes. the goal of this work is to discuss the state of the art for detectingintrusions usingavariety of techniques, including soft computing, data mining, and other approaches. the experimental findings demonstrate that unsupervised, deep learning-based techniques achieve superior accuracy of 99.95%.

关键词： Deep learning Cloud computing Computer hacking Soft sensors Fitting Intrusion detection Market research

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Sentient Sound waves: Elevating Emotional Communication with AI-Generated Speech Technology

Sentient Sound waves: Elevating Emotional Communication with...

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Emerging Technology (INCET), international conference for

作者： Rahul Roshan G Rohit Roshan Sohan M H S M Sutharsan Raj V R Badri Prasad Dept. of Computer Science and Engineering PES University Bangalore India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

this research-based project is about a new way to put feelings into computer-generated speech. It has two stages: text emotion detection and emotional speech synthesis. In the former part, labeled text data is used to build models to spot the emotions. this model learns to change pitch, volume, and rhythm based on the given emotion. During the latter phase, the text is changed into neutral speech using text-to-speech (TTS) methods. the model is used for practice to find the emotion in the text, tagging the text with that emotion. Also, a deep-learning model known as a tacotron is employed to make an emotional-sounding speech. the model perfectly uses machine learning strategies to blend feelings into speech. this opens up possibilities for turning plain text into speech with feelings of varied emotions. the proposed system makes a novice and important contribution to natural language processing for emotion identification and deep learning for emotional speech synthesis.

关键词： Deep learning Emotion recognition Solid modeling Computational modeling Speech recognition Tagging Turning

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Deep learning-Based Age and Gender Prediction through Feature Extraction from Facial Images using Convolutional Neural Networks

Deep Learning-Based Age and Gender Prediction through Featur...

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： Manasa S M Raghu Ramamoorthy Anitha Velu Angel Donny F Chaitrashree S Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Bengaluru India Department of Computer Science and Engineering The Oxford College of Engineering Bengaluru India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

In recent years, age estimation and gender classification have been focal points in the field of pattern recognition and computer vision. this paper introduces an automated approach for predicting age and gender by extracting features from human facial images. In contrast to conventional methods that operate on unfiltered facial images, our study demonstrates significant improvements in these tasks through the use of deep convolutional neural networks (CNNs). the employed feedforward neural network method enhances robustness for highly variable unconstrained recognition tasks, specifically targeting gender identification and age group estimation. the research methodology involves the analysis and validation of gender prediction and age estimation on two benchmark datasets: the Essex face dataset and the Adience benchmark dataset. Results indicate a substantial performance enhancement, showcasing the proposed approach's efficiency and state-of-the-art performance in both age and gender detection. the model achieves remarkable accuracy, positioning it as a notable advancement in the realm of age and gender prediction using deep learning techniques.

关键词： Training Accuracy Target recognition Face recognition Supervised learning Estimation Transportation Benchmark testing Feature extraction Convolutional neural networks

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Automated Intention mining with Comparatively Fine-tuning BERT 5

Automated Intention Mining with Comparatively Fine-tuning BE...

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5th international conference on Natural Language Processing and Information Retrieval (NLPIR)

作者： Sun, Xuan Li, Luqun Mercaldo, Francesco Yang, Yichen Santone, Antonella Martinelli, Fabio Sanda Univ Shanghai Shanghai Peoples R China Shanghai Normal Univ Shanghai Peoples R China Natl Res Council Italy Rome Italy Univ Molise Molise Italy

ISBN: (纸本)9781450387354

In the field of software engineering, intention mining is an interesting but challenging task, where the goal is to have a good understanding of user generated texts so as to capture their requirements that are useful for software maintenance and evolution. Recently, BERT and its variants have achieved state-of-the-art performance among various natural language processing tasks such as machine translation, machine reading comprehension and natural language inference. However, few studies try to investigate the efficacy of pre-trained language models in the task. In this paper, we present a new baseline with fine-tuned BERT model. Our method achieves state-of-the-art results on three benchmark data sets, outscoring baselines by a substantial margin. We also further investigate the efficacy of the pre-trained BERT model with shallower network depths through a simple strategy for layer selection.

关键词： Intention mining Language Models machine learning Natural Language Processing Online Discussion

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Application of data mining based on machine learning in automobile power prediction 21

Application of data mining based on machine learning in auto...

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5th international conference on Electronic Information Technology and Computer Engineering, EITCE 2021

作者： Zhang, Kexin Shanghai University of Applied Technology China

ISBN: (纸本)9781450384322

Automobile power prediction is the key to the automobile industry. Only by reasonably predicting the automobile power can we produce cars that meet the needs of consumers. Compared with traditional methods, machine learning model improves the accuracy of classification in power. machine learning models include BP neural network, random forest and KNN algorithm. In order to select the optimal vehicle power prediction model, this paper compares the advantages and disadvantages of these three machine learning models through design experiments. © 2021 ACM.

关键词： Automobiles

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Knowledge Visualization of Student Performance Based on Attendance by using Prescriptive Analytic

Knowledge Visualization of Student Performance Based on Atte...

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international conference on Artificial Intelligence and data Sciences (AiDAS)

作者： Shakiroh Khamis Mazida Ahmad Ahmad Subhi Bin Zolkafly Soong Hoong Cheng Hapini Awang Nur Suhaili Mansor Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Malaysia Institute of Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Kedah Malaysia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

In the contemporary educational landscape, student performance evaluation stands as a pivotal facet of pedagogical improvement. Recognizing the significance of attendance as a determinant of academic success has not been well evaluated. thus, this project embarks on a comprehensive investigation to analyze, visualize, and enhance the understanding of the intricate relationship between attendance and student performance. In this study, the visual analytic tool that combines data visualization and machine learning techniques to perform some visual analysis of students' attendance and its impact on student performance are presented. the research methodology of this project involves a comprehensive data Science Life Cycle method that consists of six steps. an in-depth analysis of attendance data collected from diverse Higher learning Institutions, encompassing different grade levels and disciplines. through rigorous statistical analysis and data mining techniques, aim to uncover meaningful patterns, correlations, and insights that illuminate the impact of attendance on student outcomes. Simultaneously, leveraging the robust features of Power BI, the project translates these findings into interactive, visually-rich dashboards and reports, facilitating a deeper understanding of the attendance-performance relationship. this holistic approach aims to empower educators, administrators, and students with the means to make informed decisions, optimize attendance strategies, and ultimately enhance academic outcomes in the ever-evolving educational ecosystem.

关键词： Performance evaluation Correlation Statistical analysis Visual analytics Ecosystems data visualization machine learning data science data mining

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Methods of Sentiment Analysis for Hindi and English Languages 5

Methods of Sentiment Analysis for Hindi and English Language...

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5th international conference on Computing Sciences (ICCS)

作者： Agrawal, Aarsh Bhardwaj, Vinay Lovely Profess Univ Comp Sci & Engn Phagwara India

ISBN: (纸本)9781665494458

Social media is widely regarded as one of the most important unstructured data. Analyzing and extracting meaning from such data is a time-consuming process. Because of the enormous data available on social media platforms, sentiment extraction has gotten a lot of attention. Microblogging is a relatively new phenomenon, with Twitter being the most widely utilized. It's one of the most comprehensive free and open data sources available. Today's society sees a lot of differing viewpoints on Twitter. Researchers can use opinion mining to obtain the present emotion and mood of the public. Sentiment Analysis is defined as the technique of extracting and finding the polarity of a given material to get insight into the hidden information, emotion, feeling contained within a text. the ultimate objective of sentiment analysis is to extract meaningful material from various sources of information. the first analysis of tweets was done using the Natural Language Processing (NLP) method. For further analysis of the opinionated data, two approaches are available: the Lexicon Based Approach (LBA) and the machine learning Approach (MLA) based on supervised learning. the LBA approach employs a resource dictionary, namely the Hindi SentiWordNet, and a Hybrid Based Approach (HBA) that joins the Lexicon based and machine learning for categorizing tweets as positive or negative

关键词： Sentiment analysis Social media data mining machine learning Twitter

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Research on prediction algorithm of shale gas reservoir production 2021

Research on prediction algorithm of shale gas reservoir prod...

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5th international conference on Innovation in Artificial Intelligence (ICIAI)

作者： He, Feng Xiao, Jianfeng Deng, Cai CNPC Chuanqing Drilling Engn Co Ltd Shale Gas Explorat & Dev Dept Chengdu 610051 Sichuan Peoples R China

ISBN: (纸本)9781450388634

China is rich in shale gas resources, and it is still in the initial stage of development with huge development potential. However, shale gas is different from conventional gas reservoirs in that the physical properties of the reservoir are very poor, and the storage method is special. Multi-stage fractured horizontal wells are often used for development. the development cost and technical requirements are high, and the development is difficult. Our model uses the most realistic and latest shale gas production data in the Weiyuan area. According to the reservoir information and fracturing information parameter values of each well, the commonly used machine learning methods are used to predict the test production of different wellheads respectively. the mean absolute error (MAE) is used to compare different machine learning prediction algorithms. Experiments and results show that the gradient-enhanced regression fitting effect is better, the accuracy of the predicted test production is relatively high, the generalization ability, robustness, and various types of data can be flexibly processed. At the same time, it takes less time to adjust parameters while ensuring the accuracy of the forecast. In addition, the use of big data analysis can help shale reduce mining costs, increase shale gas production, and optimize drilling parameters and fracturing parameters.

关键词： Shale Gas Test Production machine learning Weiyuan Area

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Intelligent energy management using data mining techniques at Bosch Car Multimedia Portugal facilities

Intelligent energy management using data mining techniques a...

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13th international conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th international conference on Emerging data and Industry 4.0, EDI40 2022

作者： Mosavi, Nasim Sadat Freitas, Francisco Pires, Rogério Rodrigues, César Silva, Isabel Santos, Manuel Novais, Paulo Algoritmi Research Center Department of Information Systems and Technology University of Minho Guimaraes Portugal Bosch Car Multimedia Braga Portugal

the fusion of emerged technologies such as Artificial Intelligence, cloud computing, big data, and the Internet of things in manufacturing has pioneered this industry to meet the fourth stage of the industrial revolution (industry 4.0). One major approach to keeping this sector sustainable and productive is intelligent energy demand planning. Monitoring and controlling the consumption of energy under industry 4.0, directly results in minimizing the cost of operation and maximizing efficiency. To advance the research on the adoption of industry 4.0, this study examines CRISP-DM methodology to project data mining approach over data from 2020 to 2021 which was collected from industrial sensors to predict/forecast future electrical consumption at Bosch car multimedia facilities located at Braga, Portugal. Moreover, the influence of indicators such as humidity and temperature on electrical energy consumption was investigated. this study employed five promising regression algorithms and FaceBook prophet (FB prophet) to apply over data belonging to two HVAC (heating, ventilation, and air conditioning) sensors (E333, 3260). Results indicate Random Forest (RF) algorithms as a potential regression approach for prediction and the outcome of FB prophet to forecast the demand of future usage of electrical energy associated with HVAC presented. Based on that, it was concluded that predicting the usage of electrical energy for both data points requires time series techniques. Where "timestamp" was identified as the most effective feature to predict consume of electrical energy by regression technique (RF). the result of this study was integrated with Intelligent Industrial Management System (IIMS) at Bosch Portugal. © 2022 Elsevier B.V.. All rights reserved.

关键词： data mining Energy consumption Forecasting Industry 4.0 machine learning Optimization Prediction

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