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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3282 条记录，以下是871-880 订阅

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Construction of Intelligent College Student Management Evaluation System Based on machine learning Algorithm

Construction of Intelligent College Student Management Evalu...

引用

Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Junling Lv Student affairs office Quanzhou preschool education College Quanzhou Fujian China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

With the in-depth development of blockchain, Internet, 5G and other technologies, major universities in China have also paid attention to the application of student management systems. Relevant researchers have also made in-depth investigations in the theory and practical application of information management system. In this paper, the intelligent student management system built by machine learning algorithm is used to collect and analyze the integrated data of teaching classroom information and student information of various faculties and departments on campus, and it is verified that the student management system built by machine learning algorithm has a remarkable effect on the improvement of teaching effect. This paper aims at the design and research of the student management evaluation system, and provides strategies for the creation and improvement of the student management evaluation system with the help of SVM (support vector machines) learning model. The study compared the results of 8 experts in related fields and the use of the evaluation system in S university, which proved the stability and input of the student management system.

关键词： Support vector machines machine learning algorithms Education Manuals learning (artificial intelligence) Vectors Stability analysis Internet Information management data mining

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Skin Cancer Detection Using Image Classification in Deep learning 3

Skin Cancer Detection Using Image Classification in Deep Lea...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Prasanna, D. Manishvarma, R. Mahendra Engineering College Department of Computer science and Engineering Namakkal India

ISBN: (纸本)9798331508456

The understanding of deep learning in skin cancer results in a dependable and faster method to diagnose malignant skin lesions at an initial stage. To improve the accuracy of detection, this research uses a number of steps in its methodology. First, we apply the Weiner filter to smooth the input images, minimizing the noise effects and improving the overall image quality. The next step is to use Adaptive Histogram Equalization (AHE) because it enhances the contrast of the image and thus helps in feature extraction. The feature selection process is accomplished through an Autoencoder that decreases dimensionality while preserving essential data characteristics. Finally, classification is executed using a Convolutional Neural Network (CNN), which performs inadequately for pattern recognition in medical images. The model shows a high accuracy of 96.3%, reflecting its effectiveness in diagnosing skin cancer. Such high accuracy proves the effective applicability of the proposed method as a diagnostic aid to dermatologists for timely and efficient reactions. Future works will emphasize external validity and thereby set the system into real practice settings. © 2025 IEEE.

关键词： Feature Selection

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Privacy-Preserving data mining Methods for Sensitive Information

Privacy-Preserving Data Mining Methods for Sensitive Informa...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Sukhvinder Singh L.H. Dari Jasim Vijay Dhote D. Suseela R. Venkatasubramanian Symbiosis Law School Nagpur Symbiosis International (Deemed University) Pune India College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science & Engineering IES College of Technology Bhopal India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science & Engineering New Prince Shri Bhavani College of Engineering and Technology India

This study explores innovative Privacy-Preserving data mining (PPDM) methods for handling sensitive information, addressing the critical balance between data utility and privacy. We identify prevalent issues arising from traditional data mining techniques and evaluate advanced PPDM approaches like differential privacy, homomorphic encryption, and federated learning using real-world datasets. Through a comprehensive analysis, our results indicate that hybrid models, which combine multiple PPDM techniques, are particularly effective in enhancing privacy without compromising data utility. The findings underscore the need for adaptable and robust PPDM strategies in the era of Big data, contributing valuable insights to both the academic and practical domains. Our research paves the path for future studies and developments in creating a harmonious ecosystem where data mining efficiency and privacy coexist, promoting an insightful yet private data-centric world.

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Mapping Social Connections: A Graph mining and Ensemble learning Approach to Link Prediction

Mapping Social Connections: A Graph Mining and Ensemble lear...

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Intelligent Technologies (CONIT), international conference on

作者： Devang Gangal Aniket Kadam Neeraj Salunke Amit Aylani Department of Computer Engineering Vidyalankar Institute of Technology Mumbai India

This research presents an innovative approach for predicting linkages in social network graphs using graph mining techniques. Link prediction is a key issue in network research and has a wide range of real-world uses, such as customised suggestions and targeted marketing. The most likely new interactions between social media users in the near future can be predicted using a social network’s graph. Our suggested approach makes use of the power of graph mining techniques to search for information on the system architecture and node attributes. This paper provides a variety of feature engineering methodologies that are used to convert this data into useful input. These traits are then fed into the machine learning model that is used to forecast links. We test the effectiveness of our method using real social network datasets. This study can further optimize our understanding of social network dynamics and aid in decision-making in a variety of contexts.

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Chatbot for Disease Prediction Using Classification Based machine learning Algorithms

Chatbot for Disease Prediction Using Classification Based Ma...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Ritika Chadha Aayush Sharma PMHAT Ottawa Canada

In today’s world, there is a plethora of medical data which if processed into information could alleviate the burden on the health-care systems and prevent readmissions to the hospitals. On another perspective, it could also make the patients aware of the criticality of their health conditions to take the required action. That warrants employing machine learning and data mining techniques to provide resolutions to system’s shortcomings and reducing overhead. Precise and prompt disease diagnosis based on the symptoms of the patients has become imperative. The proposed research is to create an automated conversational chatbot solution which takes in symptoms and suggests probable symptoms to the users for a complete and holistic understanding of their condition using Natural Language Processing. Following the aggregation of symptoms, the system predicts the disease based on the pre-labelled dataset using various classification-based machine learning techniques. The aim of the proposed system is for people to be aware of their health conditions and be able to determine when a doctor’s visit is absolutely necessary.

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Musical Instrument recognition Using machine learning Approaches

Musical Instrument Recognition Using Machine Learning Approa...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Chethan B K Bharath K N Kiran Puttegowda Manjula A V Sunil Kumar D S Anitha K Yathiraj G R Department of Computer Science Engineering (AI&ML) Vidyavardhaka College of Engineering Mysuru India Department of ECE Dayananda Sagar Academy of Technology and Management Bengaluru India Department of ECE Vidyavardhaka College of Engineering Mysuru India Department of ECE The National Institute of Engineering Mysuru India Department of Computer Applications East Point College of Higher Education Bengaluru India Department of ECE GSSS Institute of Engineering Technology for Women Mysuru India Department of CSE Coorg Institute of Technology Ponnampet India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This work focuses on musical instrument recognition in miscellaneous audio sources, a fundamental issue of the machine auditory system. We contrast traditional machine learning techniques against deep learning strategies in a clinical dataset solved under a supervised multi-label, multi-output classification paradigm. To enhance the performance of the system, we adjust the analysis parameters for window size, hop size, and label threshold levels. Furthermore, we explore the benefits of auxiliary feature sets as alternative data representations, specifically: Auditory features are based on two categories: (1) four-band spectral decomposition of stereo sound data, left, right, mid, and side channels and (2) the harmonic and residual model of the audio signal. Two methods for fusing the models trained on these representations are suggested and we use the best of these methods to increase recognition accuracy. These enhancements lead to a vast improvement; with a 14.25 % increase M-F score for the macro average and a 24.17 % improvement in the exact match ratio overall compared to the baseline.

关键词： Deep learning Integrated circuits Communication systems Instruments data visualization Auditory system Harmonic analysis Acoustics data models Background noise

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Research on UAV Image Ship recognition Based on Fine-grained Classification data Set 3

Research on UAV Image Ship Recognition Based on Fine-grained...

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3rd IEEE international conference on Frontiers Technology of Information and Computer, ICFTIC 2021

作者： Su, Chunqing Pan, Jun Jiang, Lijun Sun, Yehan Yu, Wei Cao, Yu Jilin University College of Geoexploration Science and Technology Changchun China

ISBN: (纸本)9781665406055

Ship identification is of great significance in coastal area monitoring and port military reconnaissance. The characteristics of different types of ships in high-resolution images are highly similar, and the internal details of the target are clearer. In order to effectively use the target details in the high-resolution image for target recognition, based on the UAV images of Xingcheng wharf and bingjiawan in Huludao City, Liaoning Province, the UAV ship image data sets with coarse-grained and fine-grained classification modes are established respectively. Build a deep learning platform based on tensorflow, and use SSD algorithm to identify the ships in the data set. The recognition accuracy of ship image data set based on coarse-grained classification pattern is 90.87%;The accuracy of ship image data set based on fine-grained classification model is 94.63%. The results show that: SSD algorithm can realize ship recognition of UAV image and Image data set based on fine-grained classification pattern can effectively use the detailed features of high-resolution image targets, so as to improve the accuracy of ship recognition. © 2021 IEEE.

关键词： Classification (of information)

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Computer Vision-based Survey on Human Activity recognition System, Challenges and Applications 3

Computer Vision-based Survey on Human Activity Recognition S...

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3rd international conference on Signal Processing and Communication (ICPSC)

作者： Manaf, Abdul F. Singh, Sukhwinder Punjab Engn Coll Deemed Univ Elect & Commun Chandigarh India

ISBN: (纸本)9781665428644

For surveillance purpose, lots of method were used by the researchers but computer vision based Human Activity recognition (HAR) technologies/systems received the most interest because they automatically distinguish human behaviour and movements from video data utilizing recorded details from cameras. But the extraction of accurate and opportune information from video of human's activities and behaviours is most important and difficult task in pervasive computing environment. Due to lots of applications of HAR systems like in medical field, security, visual monitoring, video recovery, entertainment and irregular behaviour detection, the accuracy of system is most important factors for researchers. This review article presents a brief survey of the existing video or vision-based HAR system to find out their challenges and applications in three aspects such as recognition of activities, activity analysis, and decision from visual content representation. In many applications, system recognition time and accuracy is most important factor and it is affected due to an increase in the usage of simple or low quality type cameras for automated systems. So, to obtain a better accuracy and fast responses, the usage of demanding and computationally intelligent classification techniques such as deep learning and machine learning is a better option for researchers. In this survey, we addressed numerous computationally intelligent classification techniques-based research for HAR from 2010 to 2020 for a better analysis of the benefits and drawbacks of systems, the challenges faced and applications with future directions for HAR. We also present some accessible problems and ideas that should be discussed in future research for the HAR system utilizing machine learning and deep learning principles due to their strong relevance.

关键词： Human Activity recognition Computer Vision machine learning Deep learning Challenges and Application of HAR Future directions

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Development of Network Public Opinion Analysis System in Big data Environment Based on Hadoop Architecture 3

Development of Network Public Opinion Analysis System in Big...

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Bi, Jiana Chen, Xiangjun Wang, Shuang School of Software and Big Data Changzhou College of Information Technology Changzhou China School of Physical Education Bohai University Jinzhou China

The rapid development of the Internet has given individuals the freedom to fully express their own opinions and value demands, and also accelerated the occurrence and evolution of online public opinions. In essence, online public opinion reflects social conditions and public opinions. In the era of big data, various kinds of data generated by the Internet every day are rapidly growing at the scale of PB level, which poses new challenges to the collation and analysis of online public opinion information. In order to comprehensively improve the authenticity and comprehensiveness of online public opinion governance, realize scientific guidance of public opinion, and optimize the way of online public opinion governance, this paper develops an online public opinion analysis system in the big data environment based on Hadoop architecture, and adopts data mining technology to analyze public opinion in three stages before, during and after the event. It evaluates the risks, predicts the development trend, puts forward early warning in time, and provides theoretical guidance for the governance reform of online public opinions. © 2023 The Authors. Published by Elsevier B.V.

关键词： Big data

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Recommendation System Using Emotion Detection

Recommendation System Using Emotion Detection

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Susindra S Sweta B Keerthikha Shri R K. Brindha School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems.

关键词： Industries Emotion recognition Technological innovation Sentiment analysis Decision making machine learning User experience Real-time systems Internet of Things Recommender systems

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