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Fuzzy Information and Engineering 2024年第2期1卷 229-230页

The 3rd international conference on Fuzzy Information and Engineering (ICFIE'09) will be held in Chongqing, a beautiful mountain city in China in September 26–29, 2009. The conference, sponsored by Fuzzy Information and Engineering Branch of ORSC, is to be organized by Chongqing University of Science & Technology, China. Based on the success of two previous conferences, the ICFIE'09 is a major symposium for scientists, engineers and practitioners in China as well as in the world to present their latest researches, results, ideas, developments and applications in fuzzy areas, such as fuzzy sets and systems, fuzzy information and knowledge engineering, operation researches and optimizations under fuzzy environments, fuzzy decision making and forecast, fuzzy data mining, artificial intelligence and machine learning, high performance computing and visualization, etc. It aims to bring together researchers, engineers and practitioners to exchange their topics on the latest achievements and innovations in a wide area from fuzzy information and engineering, to discuss thought-provoking developments and challenges, and to consider potential future directions.

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A machine learning based Insect Bite Classification

A Machine Learning based Insect Bite Classification

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Smart data Intelligence (ICSMDI), international conference on

作者： V Akshaykrishnan C Sharanya K Abhinav C K Aparna P V Bindu Department of Computer Science and Engineering Government College of Engineering Kannur Kerala India

Identifying insects by their bite marks can assist doctors in diagnosing victims and providing appropriate treatment. In recent years, researches using machine learning have been actively conducted and have produced excellent results in fields such as object detection, behaviour recognition, voice recognition, and cancer detection in medical field. This study has developed a classification application that can be used on mobile phones to solve the insect classification problems. Experiments were carried out on five insect species chosen for being the most common biting insects. Detailed study was conducted on different images with the help of Random Forest and Support Vector machine models. These models need different insect bite marks images to classify them. Random forests achieve a better performance and are usually much faster than Support Vector machines.

关键词： Support vector machines Insects Speech recognition Object detection Medical services Cancer detection Mobile handsets

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conference Committees

Conference Committees

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pattern recognition and machine learning (PRML), IEEE international conference on

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Face recognition based Door Lock System

Face Recognition based Door Lock System

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： D. Vaishali Akash Barath V S Santhosh Jerome A R K Sri Krishna Maanesh M Department of ECE SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

The proposed face recognition-based door lock model presents a new and effective way to enhance home security by using face recognition technology with an electrical locking mechanism. The system incorporates a camera module for capturing facial images, a machine learning algorithm for facial recognition, and an electric locking mechanism to ensure secure access. Real-time facial data is processed using the ESP32 microcontroller through a compatible module, enabling the identification and verification of authorized individuals. Upon successful recognition, the system triggers the locking mechanism of the door, bolstering security through biometric authentication. The project emphasizes security and ease of use, meeting the expectations of modern homeowners seeking innovative and simple security solutions.

关键词： Renewable energy sources Multi-factor authentication machine learning algorithms Microcontrollers Face recognition Refining Lighting User experience Robustness Real-time systems

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Role of Educational data mining and learning Analytics Techniques Used for Predictive Modeling

Role of Educational Data Mining and Learning Analytics Techn...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： Kanksha Kaur Omdev Dahiya School of Computer Science and Engineering Lovely Professional University Punjab India

The use of data mining techniques to answer important educational questions is done with educational data mining that may be related to predicting students' performance or assessment. The purpose of these remains to address the goal of meeting and fulfilling the learning objectives. learning analytics is a new approach for teachers to think about education and involves the visualization of data about students to improve learning. It is related to management strategies that prioritize quantitative measurements, which can sometimes be at odds with a teaching-focused approach to education. The purpose of this study is to perform a systematic literature review of the various techniques used in both educational data mining and learning analytics. The area of educational data mining utilizes data mining techniques to examine educational data. Meanwhile, learning analytics centers on utilizing data to gain knowledge about learning processes and student performance. The review will consider various techniques, including statistical methods, machine learning algorithms, and data visualization techniques, which are commonly used. The goal is to identify the most effective techniques for analyzing educational data and improving student learning outcomes. By performing a systematic literature review, this study will provide an overview of the current state of research in educational data mining and learning analytics. It will also identify gaps in the literature and suggest areas for future research. Ultimately, the findings of this study will be of great value to researchers, educators, and policymakers who are interested in using data to enhance the learning experience. For performing this, a few research questions have been designed to select relevant studies. In this study, the research articles from a decade, 2012 to 2022, are taken into consideration. From the overall search results, 41 studies have been taken into consideration that has addressed the scope and significan

关键词： Epidemics Systematics machine learning algorithms Statistical analysis Bibliographies Education data visualization

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Clustering Method Analysis for Gene Expression data using Fire Fly Optimization and Simple K-means Algorithm with machine learning

Clustering Method Analysis for Gene Expression Data using Fi...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： S. Sridharan K. Satheeshkumar R Rajesh Sugitha Deivasigamani Department of Computer Science and Engineering University College of Engineering Thirukkuvalai (A Constituent College of Anna University::Chennai) Nagapattinam India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

data mining is an endeavor to identify patterns, correlations, similarities, and links among massive datasets in order to uncover hidden, intriguing information. The goal of clustering is to arrange data sets that share similar attributes into groups, or clusters. The gene data are enormous, complex, and contain information on gene expression simultaneously, making it difficult to manage the unmanageable amount of data. The massive volume of data makes cluster analysis and interpretation difficult. Self-organizing maps are thought to be a helpful method for evaluating and visualizing high dimensional gene data for machine learning algorithms. An instrument for the best cluster mining in gene samples is the Firefly algorithm. This approach helps to improve visualization and suggests a fresh angle for identifying reliable clusters from the massive amounts of gene expression data. The results of this work yield the most accurate and optimal clusters from the large gene data sets.

关键词： Self-organizing feature maps machine learning algorithms Accuracy Sociology Clustering algorithms data visualization Gene expression

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Lateral Ego-Vehicle Control Without Supervision Using Point Clouds 3rd

Lateral Ego-Vehicle Control Without Supervision Using Point ...

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3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Müller, Florian Khan, Qadeer Cremers, Daniel Technical University of Munich Munich Germany

ISBN: (纸本)9783031090363

Existing vision based supervised approaches to lateral vehicle control are capable of directly mapping RGB images to the appropriate steering commands. However, they are prone to suffering from inadequate robustness in real world scenarios due to a lack of failure cases in the training data. In this paper, a framework for training a more robust and scalable model for lateral vehicle control is proposed. The framework only requires an unlabeled sequence of RGB images. The trained model takes a point cloud as input and predicts the lateral offset to a subsequent frame from which the steering angle is inferred. The frame poses are in turn obtained from visual odometry. The point cloud is conceived by projecting dense depth maps into 3D. An arbitrary number of additional trajectories from this point cloud can be generated during training. This is to increase the robustness of the model. Online experiments conducted on a driving simulator show that the performance of our model is superior to that of a supervised model trained on the same initial data set and comparable to the same model but trained on data collected with noise injection. © 2022, Springer Nature Switzerland AG.

关键词： Deep learning

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Using Convolutional Neural Network to Handle Word Shape Similarities in Handwritten Cursive Arabic Scripts of Pashto Language 3rd

Using Convolutional Neural Network to Handle Word Shape Simi...

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3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Shah, Muhammad Ismail Suen, Ching Y. Computer Science and Software Engineering Department Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783031090363

The Inter-Class Word Similarities in combination with Intra-Class Variations make it a difficult task for an OCR or any other machine learning system to recognize the handwritten characters and words with high accuracy, especially in the domain of cursive Arabic scripts. Convolutional Neural Network was originally designed to handle the problems of shape resemblance, position shift and distortion in the domain of handwritten characters and digit recognition. In this paper, we have used Convolutional Neural Network (CNN) to recognize multi class handwritten words written in cursive Arabic scripts of Pashto Language. The handwritten word images in Pashto database [3] contain a high level of shape resemblance, and position shift in terms of diacritic marks. Hence it has been proved to be a good source for properly analyzing the performance of any CNN based recognition system. The model has successfully handled the problem and has not been affected by the level of complexity of Inter-class resemblances. The CNN model has been well tested on three sub data sets of Pashto database. In each data set the number of classes and level of complexity, i.e., Shape Similarity, increase from one data set to another, i.e., 25, 40 and 68 classes of handwritten words. The average accuracies for the three data Sets are 97.26%, 96.25% and 95.84%, respectively. © 2022, Springer Nature Switzerland AG.

关键词： Convolution

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Researches advanced in Generative Adversarial Networks 3

Researches advanced in Generative Adversarial Networks

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3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Lei, Yilin Wang, Haozhen Xu, Zhiwen Cheltenham Ladies Coll Cheltenham Glos England Guangdong Expt Middle Sch Int Dept Guangzhou Peoples R China SCNU Int Dept Affiliated High Sch Chengdu Peoples R China

ISBN: (纸本)9781665417907

As a new unsupervised learning algorithm to model complex data in various applications, the Generative Adversarial Networks (GANs) have gained extensive recognition and become a research hot. Inspired by the two-person zero-sum game theory, the GANs methods have more powerful feature learning and feature expression ability than traditional machine learning algorithms, while still suffering from several challenges such as the non-convergence, vanishing, and exploding gradients, modal collapse, and instability problems at the model training stage. In this paper, we firstly introduce the theory and framework of GANs and analyze the causes for the above difficulties in detail. Then we survey a wide range of typical improved GANs models by summarizing their Advantages and limitations. Finally, we discuss the existing problems in the application and possible research areas in the future.

关键词： Deep learning Generative Adversarial Network Non-convergence Vanishing gradients

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3rd international conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning, 3DIT-MSP and DL 2021

3rd International Conference on 3D Imaging Technologies- Mul...

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3rd international conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning, 3DIT-MSP and DL 2021

ISBN: (纸本)9789811924477

The proceedings contain 24 papers. The special focus in this conference is on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning. The topics include: Grid False data Intrusion Detection Method Based on Edge Computing and Federated learning;comparative Analysis of Automatic Poetry Generation Systems Based on Different Recurrent Neural Networks;citrus Positioning Method Based on Camera and Lidar data Fusion;network Intrusion Detection Based on Apriori-Kmeans Algorithm;research on the Adaptive Matching Mechanism of Graphic Design Elements Based on Visual Communication Technology;research on Graphic Design of Digital Media Art Based on Computer Aided Algorithm;water Environmental Quality Assessment and Effect Prediction Based on Artificial Neural Network;full-Focus Imaging Detection of Ship Ultrasonic-Phased Array Based on Directivity Function;research on Visual Communication of Graphic Design Based on machine Vision;dynamic Grey Wolf Optimization Algorithm Based on Quasi-Opposition learning;innovative Design of Traditional Arts and Crafts Based on 3D Digital Technology;Insulator Detection Study Based on Improved Faster-RCNN;image-Based Physics Rendering for 3D Surface Reconstruction: A Survey;Global Analysis of Discrete SIR and SIS Systems;a Fast Heuristic k-means Algorithm Based on Nearest Neighbor Information;Color Restoration of RGB-NIR Images in Low-Light Environment Using CycleGAN;preface;longitudinal Structure Analysis and Segmentation Algorithm of Dongba Document;development of Mobile Food recognition System Based on Deep Convolutional Network;a Novel Space Division Rough Set Model for Feature Selection;Overview of SAR Image Change Detection Based on Segmentation;image recognition Methods Based on Deep learning.

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