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machine learning with unrestricted classification based on knowledge base 4

Machine learning with unrestricted classification based on k...

引用

4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Chen, Bin Zhang, Yong Yan, Jin Yu, Siliang Zhang, Xiaoqi Peng, Yu Jiangxi University of Finance and Economics Software and Internet of Things Engineering College Nanchang China

ISBN: (纸本)9781510686106

machine learning classification models are exceedingly large, complex and lack universality, not only consuming significant resources and posing difficulties in training, but also lacking in complexity, cannot achieve unrestricted classification. the academic community has attempted to solve the problem of unrestricted classification through lifelong learning and meta-learning, but these methods have limitations. the unrestricted classification of images is a complex task, inherently possessing infinite complexity. Consequently, systems designed for unrestricted classification ought to possess infinite complexity as well. therefore, the current large models without knowledge bases are actually insufficient in complexity for unrestricted classification. this paper addresses the issue of infinite complexity through the utilization of a knowledge base approach. Knowledge bases can be conveniently expanded and simply arranged to achieve infinite complexity. Based on the analogy principle, this article adopts two methods to build two image feature knowledge bases, and demonstrates the application of multimodal image unrestricted classification. this study conducts classification verification on a total of 270,000 images across 56 classes. the accuracy rates of both knowledge base construction methods are over 99%. © 2024 SPIE.

关键词： Contrastive learning

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Exploring machine learning Techniques On Yeast Dataset Classification 8

Exploring Machine Learning Techniques On Yeast Dataset Class...

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8th international conference on Computing in Engineering and Technology, ICCET 2023

作者： Datta, Shrayasi Choudhury, J. Pal Deartment of Information Technology Jalpaiguri Government Engg. College West Bengal Jalpaiguri India Deartment of Computer Sccience and Engineering Narula Institute of Technology West Bengal India

ISBN: (纸本)9781839539237

Generating information from different datasets using machine learning technologies is one of the crucial and pivotal jobs in the area of data mining. In the recent years, application of these data mining is seen in healthcare, academics and different organizations. A massive application is identified in the area of healthcare. An inseparable part of healthcare organizations is pharmacological industry-i.e. industry related to producing, discovering drugs, their composition and their reaction to human body. Yeast is considered one of the major components in producing drugs for human, for its similarity in cell structure with human cell. In this proposed work, a thorough study of application of machine learning algorithms along with different validation and ensemble techniques have been explored on Yeast dataset. the models are evaluated with mean accuracy. Several other performance measures f-score, Recall, RMSE etc. also has been considered to decide the best model for the said dataset. Results show that ensemble method bagged decision tree works best with a mean accuracy of 61%.the performance of Bagged extra tree is also best with respect to other performance metrics like f1-score, precision etc. © the Institution of Engineering & Technology 2023.

关键词： machine learning

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Crime Rate Prediction and Prevention: Unleashing the Power of Deep learning 4

Crime Rate Prediction and Prevention: Unleashing the Power o...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Sangeetha, S. Suganya, P. Shanthini, S. Murthy, G. N. Keshava Sathya, R. Kongunadu College of Engineering and Technology Department of Information Technology Tamilnadu Trichy India M.A.M College of Engineering and Technology Department of Information Technology Trichy India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Siddaganga Institute of Technology Department of Electronics and Instrumentation Engineering Kamataka Tumakuru India Kongunadu College of Engineering and Technology Department of Information Technology Trichy India

ISBN: (纸本)9798350300888

Around the world, crime is a serious issue that has an impact on many different communities. Given the rise in criminal behavior, it is essential to anticipate and stop crime before it happens. Using a chatbot to forecast crime rates and spread awareness using machine learning and deep learning is a creative way to tackle this issue. this project aims to create a chatbot that can forecast crime rates and increase public awareness of criminal activities. A sizable dataset of crime data, including details like location, time,and incident type, will be used to train the chatbot. machine learning-based techniques will be used to preprocess the data and evaluate it for associations and trends. Using natural language processing techniques, the chatbot will converse with users and give them pertinent information about crime in their neighborhood. Chatbot is used to analyse the crime rate in a particular area. the chatbot will include a deep learning algorithm that can forecast crime rates using past crime data. As a result, law enforcement officials will be able to respond before a crime occurs. the chatbot will also offer security advice and tips on avoiding becoming a victim of crime. this ability of the study is to increase public awareness of crime and inspire individuals to take action to avoid it is one of its most significant benefits. By giving precise and up-to-date information on crime, the chatbot can help individuals make wise decisions regarding their safety and well-being. this chatbot can help people stay safe by using the power of machine learning and deep learning to deliver insightful information on crime trends. © 2023 IEEE.

关键词： Support vector machines

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A Numerical Approach to Predict Breast Cancer Using Different machine learning Algorithms 6

A Numerical Approach to Predict Breast Cancer Using Differen...

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

作者： Kanauzia, Rohit Upadhyay, Soumya Gola, Kamal Kumar Kumari, Anupriya Kumar, Alok COER University Uttarakhand Roorkee India College of Engineering Uttarakhand Roorkee India

ISBN: (纸本)9798350304480

In current era, breast cancer is one of the serious issues for women as compared to all other cancers. Breast cancer occurs when cancer cells form in breast tissues. It can be fat or connective tissue in the breast and difficult to detect at early age. But, as technology grows, number of detection methods have been put forwarded to diagnose breast cancer at early stage such as Mammography. Apart from clinical application, the diagnosis of breast cancer is also been done by using new Data Mining and machine learning approaches. ML been exclusively used in medical application especially for the prediction-based detection. therefore, in proposed work we have opted novel machine learning approach to predict breast cancer disease. the implementation is done by using 4 machine learning algorithms which are (SVM, Naïve Bayes, Random Forest, XGBoost) classifier. the numerical analysis depicts that XGBoost algorithm is most efficient and effective because of the maximum achieved accuracy of 99.12%. © 2023 IEEE.

关键词： machine learning Support Vector machine

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Blockchain Trust based Authentication Protocol with Malicious Data Analysis Using Deep learning Architectures: Electronic Medical Record application 4

Blockchain Trust based Authentication Protocol with Maliciou...

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4th international conference for Emerging Technology, INCET 2023

作者： Krishnamoorthy, R. Kaliyamurthie, K.P. Bharath Institute of Higher Education and Research Department of CSE Chennai India

ISBN: (纸本)9798350335750

New opportunities for effective patient data management have emerged as a result of introduction of electronic health records (EHRs). By utilizing ML to mine digital patient record datasets, for instance, preventative rather than reactive medical practice is feasible. EHR is vulnerable to both insider and external threats due to sensitive nature of data, but security applications typically face the network's outer perimeter. Using deep learning methods, this study aims to enhance cloud data storage and malicious data detection. Blockchain trust based authentication is used to improve security-based cloud data storage in this study. After that, fuzzy rule Bayesian discriminant analysis is used to find malicious data. Utilizing results of malware analysis as well as detection and ML methods to evaluate difference in correlation symmetry, it was demonstrated that it was possible to detect harmful traffic on computer systems, thereby increasing network security. Data transmission rate, random accuracy, computation cost, communication overhead, mean average precision, and specificity are all examined in the experimental analysis for various electronic medical record datasets. © 2023 IEEE.

关键词： Authentication

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An application of Quantum Mechanics to Attention Methods in computer Vision 48

An Application of Quantum Mechanics to Attention Methods in ...

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48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Juntao Luo, Yihao Cheng, Peng Li, Zehan Wu, Hao Yu, Kun An, Wenbo Zhou, Jun Ams Institute of System Engineering Beijing China Yichang Testing Technique R&d Institute Yichang China Coolanyp L.L.C. Wuxi China University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781728163277

this work proposes the quantum-state-based mapping (QSM) for machine learning. QSM uses wave functions that describe microscopic particle systems as mappings. By QSM, original inputs or features extracted by neural networks are processed as quantum states to train wave function parameters. QSM has a low computational cost, almost no additional parameters, and is easy to integrate with other modules. We demonstrate the simplest form of the wave function as a mapping, that is, when a one-dimensional particle is in an infinitely deep potential well, in combination with advanced attention modules. Experiments show that QSM significantly improves the feature recalibration ability of attention module in transfer learning tasks. then, we tried to analyze the effectiveness of QSM. this work indicates that QSM has an important application value in interdisciplinary machine learning. © 2023 IEEE.

关键词： Attention method Image classification Quantum mechanics Transfer learning

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Deep learning Based Prediction of Commodity Prices Using LSTM 4

Deep Learning Based Prediction of Commodity Prices Using LST...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Deepa, P.B. Daisy, Josephine Mahendra Arts and Science College Tamil Nadu Namakkal637501 India St. Francis College Bangalore560034 India

ISBN: (纸本)9798350300888

Commodity price prediction using deep learning, specifically LSTM models, is a challenging task due to limited data, price volatility, and the complexity of capturing temporal dependencies. In recent years, various techniques have been explored to address these challenges, such as transfer learning, model interpretability, handling seasonality, and ensemble learning. However, existing systems still face issues related to model interpretability, uncertainty quantification, and adaptability to evolving market conditions. this article aims to analyze one of the machine learning methods and what applications it can have in the financial world. Specifically, it aims to explore a technique called deep learning. To do this, some methods are analyzed, which are most relevant and most used in the industry, which are found within the deep learning method. Once the selected methods are analyzed, it is intended to implement them in a programming environment, in this case Python is chosen, and then a practical case is developed and evaluated. the case in question is the forecasting of commodity prices, with particular focus on the price of Brent. Different technical analysis indicators will be used in this forecasting model, knowing that there is no solid statistical basis in their use, but in practice most traders use them and, in the absence of shocks, on a macroeconomic basis, the results obtained when using them are, in general, quite adequate. Finally, the results of the predictions obtained are compared under different models to calculate which model gives us the greatest advantage. © 2023 IEEE.

关键词： Long short-term memory

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Optimizing Deep learning Models to Address the Long Tail Problem and its application in Semantic Segmentation 24

Optimizing Deep Learning Models to Address the Long Tail Pro...

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4th international conference on Signal Processing and Communication Technology, SPCT 2024

作者： Sun, Jiarui Zhao, Yong School of Software and Microelectronics Peking University Beijing China School of Electronic and Computer Engineering Peking University Beijing China

ISBN: (纸本)9798400710636

In the semantic segmentation task, the long-tail problem causes the recognition performance of minority categories to be significantly lower than that of majority categories, affecting the overall effect of the model. this paper aims to optimize the FPN (Feature Pyramid Networks) model to better deal with the long-tail problem and improve the accuracy of semantic segmentation. First, by analyzing the category distribution of the dataset and identifying the severity of the long-tail phenomenon, a semantic segmentation dataset containing multiple categories is constructed. Secondly, for minority categories, data enhancement technology is used and resampling is performed to ensure that the model could be exposed to more minority category samples during training. A focus loss function is then integrated to prioritize hard-to-segment regions and underrepresented categories. Experimental results on standard benchmarks show that the proposed FPN model achieves a Dice coefficient of 88.1% and an F1-Score of 86.4%, significantly outperforming other models such as U-Net (Dice: 85.6%, F1-Score: 83.9%), DeepLab v3 (Dice: 87.4%, F1-Score: 85.6%), and DeepLab v1 (Dice: 84.3%, F1-Score: 82.8%). Notably, the minority class F1-Score for FPN reaches 72.1%, demonstrating its superior ability to tackle the long-tail problem. the results indicate that targeted methods, including dataset augmentation and loss function adjustment, can significantly enhance deep learning model performance, offering new insights for improving semantic segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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SHIP-D: SHIP HULL DATASET FOR DESIGN OPTIMIZATION USING machine learning

SHIP-D: SHIP HULL DATASET FOR DESIGN OPTIMIZATION USING MACH...

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ASME international Design Engineering Technical conferences / computers and Information in Engineering conference (IDETC-CIE) / 49th Design Automation conference (DAC)

作者： Bagazinski, Noah J. Ahmed, Faez MIT Dept Mech Engn Cambridge MA 02139 USA

ISBN: (纸本)9780791887301

machine learning has recently made significant strides in reducing design cycle time for complex products. Ship design, which currently involves years-long cycles and small batch production, could greatly benefit from these advancements. By developing a machine learning tool for ship design that learns from the design of many different types of ships, trade-offs in ship design could be identified and optimized. However, the lack of publicly available ship design datasets currently limits the potential for leveraging machine learning in generalized ship design. To address this gap, this paper presents a large dataset of 30,000 ship hulls, each with design and functional performance information, including parameterization, mesh, point-cloud, and image representations, as well as 32 hydrodynamic drag measures under different operating conditions. the dataset is structured to allow human input and is also designed for computational methods. Additionally, the paper introduces a set of 12 ship hulls from publicly available CAD repositories to showcase the proposed parameterization's ability to accurately reconstruct existing hulls. A surrogate model was developed to predict the 32 wave drag coefficients, which was then implemented in a genetic algorithm case study to reduce the total drag of a hull by 60% while maintaining the shape of the hull's cross section and the length of the parallel midbody. Our work provides a comprehensive dataset and application examples for other researchers to use in advancing data-driven ship design.

关键词： Shapes machine learning Naval architecture Optimization computer-aided design Computational methods Ships Hull Tradeoffs Design Parametrization Drag (Fluid dynamics) Cycles Genetic algorithms Waves

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application of Intelligent Computing (IC) in Human Resources (HR) Analytics: Towards better tomorrow 4

Application of Intelligent Computing (IC) in Human Resources...

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4th international conference on Intelligent Engineering and Management, ICIEM 2023

作者： Minocha, Amrisha Gupta, Priyanka Gopal, Sanghmitra Graphic Era University Department of Management Studies Dehradun India

ISBN: (纸本)9798350341126

these days Human resources management is highly influenced by constantly changing business environment and dynamic global workforce. this has led to better decision making promptly as and when required. To support the same intelligent computing has proved to be a game changer, with very high level of expertise in handling data in bulk, giving out actionable results in no time. Intelligent computing has strongly influenced many domains of the business and organization, primarily impacting HR processes and functions too. Using a systematic literature review the current study highlights the various types of intelligent computing methods/techniques, impacting different HR domain. 63 literatures have been scrutinized carefully that have corroborated the various intelligent computing methods. the article also provides a huge room for further research since as per the conclusion most of the studies have been conducted using only machine learning techniques and majority of the researchers have only drawn studied attrition as a problem in HR domain. © 2023 IEEE.

关键词： Decision making

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