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检索条件"任意字段=2nd IEEE Conference on Information Technology and Data Science, CITDS 2022"

共 8828 条记录，以下是101-110 订阅

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An Empirical Investigation into Machine Learning Approaches for Breast Cancer Classification 2

An Empirical Investigation into Machine Learning Approaches ...

引用

2nd ieee International conference on Recent Advances in information technology for Sustainable Development, ICRAIS 2024

作者： Duggal, Anoushka Muhuri, Samya Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9798350354461

Breast cancer was diagnosed in 2.3 million individuals worldwide in 2022, and was the cause of 670,000 deaths. It ranks as the second most common cancer overall and the most frequently diagnosed cancer in women. Since survival is highly dependent on early detection, the development of computational models for diagnosis is crucial. Among the various models available, we have chosen to focus on three supervised machine learning models in this study: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Feed-forward Neural Networks (FFNN). Their roles in breast cancer detection are investigated. We have pre-processed the data using normalization and feature selection to enhance these models' performances. They are trained on real-life documented patient data to predict breast cancer. It is determined that the FFNN is more accurate than both SVM and KNN. This paper provides a comprehensive analysis of the various machine learning models, highlighting their positive and negative aspects in diagnosing breast cancer. The results underscore the potential of advanced neural networks in medical diagnostics and pave the way for further research and development in this domain. © 2024 ieee.

关键词： Adversarial machine learning

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An Adaptive Event-Related Component Identification (AERCI)based Technique for SSVEP-BCIs 2

An Adaptive Event-Related Component Identification (AERCI)ba...

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2nd ieee International conference on Advances in information technology, ICAIT 2024

作者： Darshan, H. Swetha, K.R. Department of Computer Science & Engineering BGSIT Adichunchanagiri University Mandya India

ISBN: (纸本)9798350383867

SSVEPs (Steady State Visual-Evoked-Potentials) captures brain responses to the visual stimuli from outside sources, detectable through scalp recordings. Brain-Computer Interface (BCI) technique facilitates direct brain-to-device interaction, enabling users to engage with the external environment using brain signals. This study introduces Adaptive Event-Related Component Identification (AERCI), aimed at enhancing SSVEP reproducibility across trials and improving SSVEP data SNR by eliminating EEG background events and optimizing the feature extraction process. Notably, the feature extraction process is tailored to prioritize crucial data relevant for effective classification, with the primary objective of maximizing classification accuracy. This involves reducing the feature space dimensionality or eliminating redundant features. During classification, Power Spectral Density (PSD) is employed to forecast higher magnitudes for every stimulation frequency. © 2024 ieee.

关键词： Electroencephalography

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Facial Emotion Analysis using Deep Learning for Seamless Human-Computer Interaction 2

Facial Emotion Analysis using Deep Learning for Seamless Hum...

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2nd ieee International conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Sangeetha, S. Saini, Poonam Banupriya, R. Srimathi, S. Kiruthiga, S. Aarthi, R. Srm Arts and Science College Department of computer Applications and Technology Kattankulathur Changelpattu603203 India Sir Padampat Singhania University Dept. of Computer Science & Engineering Vil. Bhatewar Teh. Vallabhnagar Rajasthan Udaipur India Pgp College Of Engineering and Technology Dept. of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science & Technology School of Computing Department of Computer Science & Engineering Chennai India R.M.D Engineering College Department of Ece India

ISBN: (纸本)9798350372892

HCII, or human-computer intelligent interaction, is a new academic subject that tries to make computer use more intuitive. Some say machines need human-level social abilities before interacting with people. These skills include reading emotions. Due to its exponential expansion, facial emotion recognition is becoming essential to face recognition. According on photographs, machine learning can recognise expressions. This paper discusses expression recognition model training datasets. Deep learning for Facial Emotion Recognition (FER) is used in this research to teach robots to recognise and adapt to human emotions. An extensive review of deep learning models, including CNNs and CNNR, emphasises their ability to extract spatial and temporal data needed for emotion classification. FER pipeline components like data collection, preprocessing, and model architecture are examined in the paper. Transfer learning, pre-trained models, and data augmentation are studied to address low-labeled dataset concerns. Detailed model training, validation, and evaluation metrics emphasise performance assessment. This study uses the enlarged CohnKanade (CK+) dataset from a face emotion recognition experiment. The system averaged 92.81% accuracy. Eight emotions were successfully detected by the algorithm. The proposed method for emotion recognition has proven useful. © 2024 ieee.

关键词： Contrastive Learning

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Cloud Computing Based Network Education data Analysis Platform Using Improved Support Vector Machine with Deep Neural Network 2

Cloud Computing Based Network Education Data Analysis Platfo...

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2nd ieee International conference on data science and Network Security, ICDSNS 2024

作者： Liu, Hongshan Jilin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9798350373110

The rapid development of Cloud Computing (CC) technology, education system has transformed significantly from books to computers to recent multimedia teaching. Nowadays, education is accomplished in online platform to create an informal network for study groups, which are utilized by students to share information and enable their connection from anywhere in world. The CC education platform can more efficiently overcome issues discovered in the present teaching practice and give consistent ideas that would be helpful for the students. However, some learning platforms still face challenges in terms of data protection and executing a scalable online security mechanism. To overcome this problem, an online education platform based on Improved Support Vector Machine (ISVM) with Deep Neural Network (DNN) method for ensuring data analysis and improves network education in CC technology. The combination of these models predicts and analysis the requirements of students and teachers that improves communication between them to increase learning in cloud platform. The review results show great results of designed cloud computing network education platform. © 2024 ieee.

关键词： Cloud platforms

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Infectious Disease Detection and Drugs Recommendation Using Machine Learning 2

Infectious Disease Detection and Drugs Recommendation Using ...

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2nd International conference on Advances in Computation, Communication and information technology, ICAICCIT 2024

作者： Archana, T. Gupta, Sandeep Kr Hembrom, Kankan Mandal, Sayantan Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

This paper introduces a machine learning system that combines Logistic Regression, Decision Tree, and Random Forest algorithms to analyze patient data, focusing on symptoms and case histories to predict likely diseases and recommend effective treatments. The objective is to improve early disease detection and personalized medication recommendations, reducing side effects and enhancing clinical judgment. Extensive medical data trains these models, enabling integration with electronic health records to support clinical decisions, which strengthens healthcare delivery. This work contributes to the field by providing a model that increases diagnostic accuracy and offers a tailored treatment plan for individual patients, marking a step towards enhanced personalized healthcare. © 2024 ieee.

关键词： Electronic health record

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The Impact of Artificial Intelligence for Developing Supply Chain Collaboration Performance: An Empirical Investigation 2

The Impact of Artificial Intelligence for Developing Supply ...

引用

2nd ieee International conference on Emerging Research in Computational science, ICERCS 2024

作者： Jenefa, Leena Kanchana, Kanchana Jayakumar, M. Subburaj, L. Kanishkar, R. Ashok Kumar, S. School of Management Hindustan Institute of Technology and Science Chennai India Sarah Tucker College Department of Commerce Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Management Studies Chennai India

ISBN: (纸本)9798331534950

Organizations are increasingly looking into artificial intelligence to improve operational efficiency and performance. Artificial intelligence marketing makes use of data to forecast customer behavior and improve their experience. It bridges the gap between data science and execution by processing and analyzing massive volumes of previously unmanageable data. An AI marketing tool is a platform or application that employs AI technology to enhance data-driven decisions. These technologies include combine artificial intelligence, and usual processing, by using machine learning, and data analytics to fasten the procedure with automatic processes, then analyze data, and give efficient output for optimizing the result for helping the management to frame new strategy for their business campaigns. This research will provide empirical information on how to maximize the benefits of AI capabilities for generating long-term SCP. © 2024 ieee.

关键词： Artificial Intelligence Logistics Supply Chain efficiency Supply chain performance and Supply Chain collaboration

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Digital Media Image Enhancement technology based on data Analysis 2

Digital Media Image Enhancement Technology based on Data Ana...

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2nd International conference on data science and information System, ICDSIS 2024

作者： Yang, Gongbo Wang, Lin Jinan Preschool Education College College Of Information And Business Shandong Jinan China Jinan Preschool Education College College Of Wang Jinmei Ideological And Political Education Shandong Jinan China

ISBN: (纸本)9798350381665

In recent years, with the rapid development of computer and information technology, image processing technology is also developing continuously, and has become the object of mutual learning and discussion of computer science, medicine, biology, engineering, information science and other disciplines. In the image processing system, its processing process can be divided into three steps: the first is the original image preprocessing, the second is the image feature extraction, the third is the image identification analysis. Among them, the image preprocessing link, that is, the image enhancement. Therefore, before the analysis of the image, we must first improve the quality of the image, then we need to use image enhancement. Image enhancement is an important basic image processing method, its goal is to process an image, make it highlight some specific information, weaken or remove some unnecessary information, so as to obtain better visual effect and use, or transform it into a format more suitable for human or machine analysis and processing. Based on the above problems, this paper discusses how to construct the image super-resolution reconstruction model through the Convolutional Neural Networks (CNN) algorithm and the attention mechanism, and analyzes the performance of other algorithm models through experiments. The results show that the image reconstruction time based on CNN-AM is only 20.7 s, which is shorter than other algorithms. © 2024 ieee.

关键词： Image enhancement

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Observation Model of Electromagnetic Radiation Based on Polynomial Regression Fitting Algorithm 2

Observation Model of Electromagnetic Radiation Based on Poly...

引用

2nd ieee International conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Xu, Zile Yong, Yan Chen, Dan Xiao, Yang Bo, Siqi University of Shanghai for Science and Technology College of Science Shanghai China Jinan University College of Information Science and Technology Guangzhou China City Institute Dalian University of Technology School of Management Dalian China

ISBN: (纸本)9798350372892

Over the past years, solar activity has extremely impacted the Earth's environment and human society. With a view to getting a better understanding about the characteristics and trends of solar activities, this paper collects the data related to sunspots and electromagnetic radiation and builds the predictive models for research and analysis. First, we used Python crawlers and techniques of self-search to collect historical data of sunspots from websites like SILSO, NASA, and ESA. Hereafter, we identified and processed the missing values and outliers in the data. We applied the Kolmogorov-Smirnov test and a box plot to exclude outliers and applied the Newton interpolation method to fill in the missing values. Further analyses included graphical and correlation analyses referring to them as solar activity cycles, periods of maximum solar activity, those of minimum solar activity, and periods of length of solar activity. Finally, prediction of the epoch of the solar maximum and its duration was done through fitting nonlinear models. These techniques may be useful for the confident prediction of future changes in solar activity, thus being applied to base future research on the influence of solar activity. © 2024 ieee.

关键词： Solar irradiance

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A Study of Big data Analytics: Tools, Applications, and information Value Chain 2

A Study of Big Data Analytics: Tools, Applications, and Info...

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2nd ieee International conference on Advances in information technology, ICAIT 2024

作者： Pandey, Mukesh Bist, Ankur Singh School of Computer Science University of Petroleum & Energy Studies Uttarakhand Dehradun India Graphic Era Hill University Uttarakhand Bhimtal India

ISBN: (纸本)9798350383867

In the internet world, data is generated quickly, every second, from different sources. Therefore, various applications, including social networking sites, e-commerce websites, review websites, mobile apps, and various blogs, generate terabytes of random data in their state. So, it is a big problem to store and manage data. Extracting relevant information to make decisions on these big data sets requires a lot of effort at various levels. As a result, big data analysis research is necessary in the automobile industry. In this paper, we focus on the meaningful impact of a big data analytic approach to achieve faster and more accurate results in various areas such as education, healthcare, insurance, banking and finance, manufacturing, government, and marketing, and also play a big role in achieving sustainable development goals. In this paper, we propose a new information value chain framework for big data analytics processes. We also examine some important analysis tools that can handle huge amounts of data. © 2024 ieee.

关键词： AI Applications big data Big data Analytics Sustainable Development Goals tools Value Chain

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Design of Machine Translation information Processing System Based on AI technology 2

Design of Machine Translation Information Processing System ...

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2nd ieee International conference on data science and Network Security, ICDSNS 2024

作者： Li, Lu Yang, Bing Dalian Institute of Science and Technology Department of Foreign Language Liaoning Dalian China

ISBN: (纸本)9798350373110

Artificial intelligence (AI) has greatly improved the quality of machine translation. In order to promote translation teaching, this article proposed machine translation teaching strategies for teachers and machine translation self-learning strategies for students. Considering machine translation as a way to improve translation skills, it is also necessary to use it wisely to enhance basic English skills such as reading, writing, and vocabulary. In response to the issue of recognition accuracy in English reading recognition ability grading, this article combined CNN (Convolutional Neural Networks) algorithm and LSTM (Long Short-Term Memory) algorithm to construct a CNN-LSTM network model to learn the characteristics of historical reading articles in groups, analyze students' English reading ability, and assist students in machine translation. The experimental results showed that the accuracy of reading classification based on CNN-LSTM algorithm was between 88% and 96%. The system can learn a large amount of language data and continuously optimize the translation algorithm to improve the accuracy and fluency of translation. © 2024 ieee.

关键词： Neural machine translation

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