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Big data Intelligence Empowered Specialized Disciplines Development pattern recognition in Power Industry Universities 5th

Big Data Intelligence Empowered Specialized Disciplines Deve...

引用

5th international conference on Big data and Security (ICBDS)

作者： Zhang, Yan Liu, PengCheng Xu, Hao Ge, Linlin Zhu, Zhentao Nanjing Inst Technol Sch Econ & Management Nanjing 211167 Peoples R China Nanjing Inst Technol Sch Elect Engn Nanjing 211167 Peoples R China Nanjing Univ Sch Informat Management Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9789819743865;9789819743872

Universities are concentrating on building industry characteristics to strengthen and expand their influence in the information age. Big data on intellectual output is a key representation of discipline construction. We created an algorithm to identify development features of disciplines, including temporal trend, research hotspots, and mutation characteristics. Using CNKI as data source, with the aid of scientific knowledge graph and social network analysis, 10786 core journal thesis published between 2012 and 2021 demonstrated the co-occurrence, keyword development trajectory, and mutation word development path. According to data mining, high-level intellectual output that is relevant to industry increased in quantity and proportion, and intimacy also improved. High levels of interdisciplinary interaction, a variety of disciplinary innovations, and in-depth disciplinary culture formation should characterize the pattern of disciplinary development. this study is an attempt of specialized disciplines development pattern recognition by big data intelligence, and the recognition algorithms can be used for feature recognition in multidisciplinary fields.

关键词： Big data mining and visualization Big data Intelligence Social network construction pattern recognition Specialized Disciplines in Power Industry Universities

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Employing machine learning Algorithms to Detect Phishing URL Websites 5

Employing Machine Learning Algorithms to Detect Phishing URL...

引用

5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Charishma, T.N.S. Koushik, A Sai Reddy, G Sai Ashmit Himabindu, M. Institute of Aeronautical Engineering Computer Science and Information Technology Hyderabad India

ISBN: (纸本)9798331518097

Phishing attacks have risen like wildfire in the era of new digital technology in the wake of exploiting holes in the electronic communication process while cheating a user to seek private information. this work provides machine learning-based resistance against phishing attacks by differentiating phishing websites using recognition of patterns and analysis on data feature. By training several machine learning models, including Random Forest, XGBoost, and Support Vector machine (SVM), on a dataset that included characteristics frequently found on phishing websites, we developed a system that could distinguish accurately between genuine websites and phishing attempts. this project brought in a nice innovation through the incorporation of these predictive models into a Flask-developed web application, thus allowing for an interactive, real-time phishing detection. the project shall ensure protection of financial security and personal information by giving users an easy interface to input website links so they can get instant feedback about whether the link is likely to be legitimate. this hybrid technique improves accessibility to non-technical users, brings better detection accuracy, and makes phishing detection more proactive, approachable, and successful in reality. © 2024 IEEE.

关键词： Phishing

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the Use of a DenseNet201 CNN and Other Deep learning Techniques for Exploratory data Analysis of Red Wine Quality 5

The Use of a DenseNet201 CNN and Other Deep Learning Techniq...

引用

5th IEEE international conference for Emerging Technology, INCET 2024

作者： Pritam, Nishant Gill, Kanwarpartap Singh Kumar, Mukesh Rawat, Ruchira Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun248002 India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350361155

this study delves into the exploration of red wine quality using a DenseNet201 CNN model and various other machine learning methodologies for Exploratory data Analysis (EDA). the dataset encompasses a diverse array of chemical and sensory attributes associated with red wines. Rigorous data preparation steps, including addressing missing values and outliers, are undertaken to ready the dataset for analysis. the simplicity and local pattern-capturing capability of the DenseNet201 CNN model are leveraged to unveil intricate connections within the dataset. the research evaluates the effectiveness of the DenseNet201 CNN model and several other machine learning approaches through appropriate metrics, thereby assessing their predictive capabilities and generalization to unfamiliar data. the objective of exploratory investigations is to explore and evaluate the potential benefits of combining feature engineering and dimensionality reduction techniques to enhance model interpretability and overall performance. the study's findings make significant contributions to understanding the complex relationships between chemical and sensory characteristics in red wines. these insights carry practical implications for professionals in viticulture, winemaking, and research in this field. Furthermore, the research underscores and utilizes the adaptability of machine learning in deciphering patterns within intricate datasets, opening avenues for further exploration in the realm of wine quality analysis. © 2024 IEEE.

关键词： Deep learning

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Improving wafer map classification in Industry 4.0 5

Improving wafer map classification in Industry 4.0

引用

IEEE 5th international conference on Industrial Cyber-Physical Systems (ICPS)

作者： Scheiber, Michael Nartey, Clement Zernig, Anja Kaestner, Andre KAI GmbH Villach Austria Flextron Int GmbH Althofen Austria Infineon Technol Austria AG Villach Austria

ISBN: (纸本)9781665497701

At the heart of Industry 4.0. lies the automation of manufacturing processes and interoperability of corresponding applications, in most cases utilizing Cyber-Physical Systems and Internet of things. Used within diverse industrial areas across the world, these concepts gained more importance in recent years. In the semiconductor industry, continuous improvement of all involved processes is achieved by utilizing these concepts in many different stages of the manufacturing process. this work proposes a machine learning framework that on one hand enhances wafer map classification in the testing stage of semiconductor devices, and on the other hand fulfills the requirements and demands of all involved stakeholders in adopted engineering processes. the core of the proposed system is the Wafer Health Factor, a machine learning framework that detects process deviations in analog wafer test data through pattern recognition. Additional integration of Radon-based features, as well as the use of an ensemble classification framework, boosts model performances in the presented real-world application scenario significantly. Moreover, we also show that the proposed framework performs well on two benchmark data sets from literature, even on small training data sets. We conclude, that the presented framework improves the performance of pattern recognition tasks in real-world applications and thus, enables the automatic and early detection of deviations within semiconductor manufacturing processes.

关键词： Arrowhead Tools analog wafer test data engineering process ensemble classification machine learning pattern recognition Radon transform

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Behavior Analysis and Safety Management of Workers in Limited Space Based on machine learning 5

Behavior Analysis and Safety Management of Workers in Limite...

引用

5th international conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2024

作者： Luo, Qifeng Ye, Jiehong Liang, Jingming Zhou, Qiang Guangdong Zhongshan Power Supply Bureau China Southern Power Grid Corporation Guangdong Zhongshan528400 China College of Electrical and Electronic Engineering Shandong University of Technology Shandong Zibo255000 China

ISBN: (纸本)9798331539818

the purpose of this study is to explore the design of a limited space worker behavior analysis and safety management system based on machine learning. By using the methods of machine learning, behavior recognition, process monitoring and safety warning, an innovative behavior analysis and safety management system for workers in limited space is proposed. the system learns the operator's behavior through training model to realize the prediction and risk assessment of the operator's behavior. By using fault diagnosis and anomaly detection methods, we can realize early identification and warning of potentially dangerous behaviors by analyzing operators' operational data. the process monitoring system monitors the operation process of the operator in real time to ensure the safety of the operation. Finally, combined with human movement recognition technology, it provides an effective solution for the safety management of workers in limited space. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review of Different machine learning Techniques for Fake Review Identification 5

A Review of Different Machine Learning Techniques for Fake R...

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5th international conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Mane, Kishor Dongre, Snehlata G H Raisoni College of Engineering Department of Computer Sci.& Engg. Nagpur India

ISBN: (纸本)9798350389609

Online reviews and ratings of the e-commerce products have grown to be an essential part of the contemporary customer experience in recent years, offering insightful information about the effectiveness and quality of products and services. A lot of users make their decisions about what to buy on Internet based on the reviews and ratings of e-commerce products. Consumers frequently believe that all evaluations of a products or service are authentic. Nevertheless, a lot of companies manipulate reviews by adding fraud reviews, either by using chatbots or content writer, tricking customers into buying faulty products. Furthermore, companies could fabricate unfavorable reviews of rival products on their website to harm their brand and lower their sales. these fake reviews hurt reputation of companies in addition to mislead customers. thus, there is a need for efficient methods to capture fraudulent reviews and improve the reliability of online reviews across variety of products and services. In fields like natural language processing (NLP), opinion mining, and machine learning, numerous researchers are experimenting with different approaches. this study offers a comprehensive survey addressing the difficulties that the existing fake review identification systems are facing. Various methods, such as CNN, RNN, RF, etc. have been used on various review datasets. In order to ensure the authenticity of online reviews, this paper finishes by noting gaps and obstacles in the field of fake review detection and highlighting the steps for new methodology. © 2024 IEEE.

关键词： Adversarial machine learning

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machine learning and Deep learning based Intrusion Detection in Cloud Environment: A Review 5

Machine Learning and Deep Learning based Intrusion Detection...

引用

5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

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

ISBN: (纸本)9781665474672

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%. © 2023 IEEE.

关键词： data mining

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CSGNet: A network with attention mechanism for automatic modulation recognition 5

CSGNet: A network with attention mechanism for automatic mod...

引用

5th international conference on Signal Processing and Computer Science, SPCS 2024

作者： Li, Xianyi School of Control Science and Engineering Shandong University Shandong Jinan250002 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In this paper, a new network called CSGNet is introduced for automatic modulation recognition, designed to improve recognition accuracy in complex electromagnetic environments. this innovative network leverages the strengths of both Convolutional Neural Networks (CNN) and Gate Recurrent Units (GRU) while mitigating their inherent weaknesses. Additionally, it incorporates a Self-Attention mechanism to further enhance its perceptual abilities. the primary advantage of this method is its superior recognition accuracy under low SNR conditions compared to traditional methods. the process starts by extracting basic I/Q data from the RadioML 2018.01a dataset as input. Subsequently, the CSGNet model performs an end-to-end modulation recognition task. the results demonstrate that this method excels at multiple SNR levels, highlighting the significant potential of deep learning in solving automatic modulation recognition challenges. this study underscores the effectiveness of integrating CNN, GRU, and Self-Attention mechanisms in enhancing the robustness and accuracy of modulation recognition systems. Furthermore, it demonstrates how deep learning techniques can be effectively applied to complex signal processing tasks. the findings suggest that CSGNet could be a valuable tool for improving communication systems, especially in challenging environments with low SNR. © 2025 SPIE.

关键词： Modulation Signal to noise ratio Feature extraction Deep learning Matrices data modeling machine learning data processing Neural networks Quadrature amplitude modulation

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machine learning Revolution in Early Disease Detection for Healthcare: Advancements, Challenges, and Future Prospects 5

Machine Learning Revolution in Early Disease Detection for H...

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5th IEEE international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Reddy, Kumbala Pradeep Satish, Mankala Prakash, A. Babu, S.Malli Kumar, P.Pavan Devi, B. Sunitha Telangana Hyderabad India CMR Institute of Technology Department of CSE Telangana Hyderabad India

ISBN: (纸本)9798350338287

this paper explored the integration of machine learning into healthcare has revolutionized early disease detection, offering a multidimensional approach to data analysis. Advanced algorithms, rooted in deep learning, process diverse datasets encompassing medical records, genetics, and imaging data, enabling subtle pattern detection. Deep learning, predictive analytics, natural language processing, anomaly detection, and personalized medicine have ushered in a proactive healthcare era, leading to better patient outcomes, reduced misdiagnosis, and cost-effective treatments. Systematic reviews underscore machine learning's impact across various medical domains. the future holds promise with enhanced data integration, interdisciplinary collaboration, explainable AI, real-time monitoring, global healthcare accessibility, ethical considerations, and continuous learning, ultimately reshaping healthcare for the better. © 2023 IEEE.

关键词： Cost effectiveness

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Multilingual Language Detection and Translation System with Multinomial Naive Bayes 5

Multilingual Language Detection and Translation System with ...

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5th international conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Vuddanti, Sowjanya Ariveni, Dhathri Sri Lekha Kethepalli, Niharika Sri Sandhya Manchimi, Lokesh Yajjavarapu, Jaswanth Bali Reddy College of Engineering Department of Artificial Intelligence and Data Science Lakireddy Andhra Pradesh Mylavaram India

ISBN: (纸本)9798350367171

Smooth communication becomes essential as the world unites different linguistic groupings. the proposed system for language recognition and translation addresses this difficulty. the proposed system uses a Google Translate-powered 'bridge' with a machine learning 'detective' to properly identify text language and translate between any supported combination. the Multinomial Naive Bayes classifier, a machine learning approach, is used by the language detection component to determine the language of input text. As the 'detective,' the Multinomial Naive Bayes classifier correctly determines the language of the input text with a remarkable accuracy rate of 97 %. the model can accurately classify language in a range of languages by preprocessing the text data and generating a representation of words in a bag. To translate text between languages, the text translation component uses the Google Translate API via the Google library. With the flexibility to choose the source and target languages provided by this component, users can translate text from any supported language to the language of their choice. Easy to use, it enables people to promote intercultural understanding and overcome language barriers in social media, business, education, and other domains, making the world more inclusive and connected. © 2024 IEEE.

关键词： machine translation

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