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共 3334 条记录，以下是611-620 订阅

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machine learning based Prediction of Dropout Students from the Education University using SMOTE 4

Machine Learning based Prediction of Dropout Students from t...

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Revathy, M. Kamalakkannan, S. Kavitha, P. Pallavaram Chennai India Pallavaram Chennai India

ISBN: (纸本)9781665401180

In past decade, there have been several students who have dropped out from the educational institutions and it is increasing rapidly. this has become one of the challenging factors for the educational institution. the students are getting into the institution andembarking their learning with several expectations and dreams. the expectations of the students have not fulfilleddue to various factors such as staff, management, parents, course chosen etc., that make them drop from their registered curriculum. However, this has become the main issue for all educational institutes wherein several researchers introduced the technique of data mining for analysing as well as predicting the student's dropout. therefore, this paper focuses on early finding of dropout variables as an advance by dimensionality reduction using feature selection and extraction methods. In feature extraction, there may be an occurrence of imbalanceddata that may affect the significance of machine learning (ML) techniques. thus, Synthetic Minority Oversampling Technique (SMOTE) is subsequently added with Principal Compound Analysis (PCA) whereas the oversampling of imbalanced data is managed to balanced dataset. Moreover, the 1,243 student's data have been collectedand analysed using proposed PCA-SMOTE to allow for a more accurate forecast in case of dropout. Accuracy performance related to PCA-SMOTE has been 97.6% that is evaluated through confusion matrix parameter and compared with existing ML to find out the exact students who are not satisfied with their fulfilment in the environment of education institute. © 2022 IEEE

关键词： Forecasting

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Improving Dynamic Hand Gesture recognition based IR-UWB using Offline data Augmentation and Deep learning 4

Improving Dynamic Hand Gesture Recognition based IR-UWB usin...

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4th IEEE international conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022

作者： Korti, Djazila Souhila Slimane, Zohra Belhadj Bouchaib University Department of Electonics and Telecommunication Ain-Temouchent Algeria

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

this paper presents a dynamic hand gesture recognition method that relies on combining a deep learning model with a traditional classifier, with the aim of eliminating the manual feature extraction phase. To this end, we proposed to use a multi-stream CNN-LSTM for automatic feature extraction in conjunction with a multi-class Support Vector machine (SVM) for classification. the proposed model consists of an efficient and lightweight architecture based on depth-separable convolution layers, which effectively reduces the computational cost and learning time while maintaining high recognition performance. the model is trained on the public UWB Gestures dataset and can automatically learn range-time information to identify different types of hand gestures. In order to achieve optimal results and avoid overfitting, we adopted offline data augmentation strategies to expand the training sample size. the results obtained showed that our proposed model achieves high performance on several measures, including precision, recall, F1-score and accuracy. © 2022 IEEE.

关键词： Feature extraction

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

4th International Conference on Intelligent Systems and Patt...

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

ISBN: (纸本)9783031821523

the proceedings contain 77 papers. the special focus in this conference is on Intelligent Systems and pattern recognition. the topics include: Tunisian Sign Language recognition and Translation Using Deep learning;Beyond Spatial: A Wavelet Fusion-Based Deep learning CAD for Skin Cancer Diagnosis;integrating machine learning and Deep learning Techniques for Enhanced Historical Script Classification;efficiency Meets Resilience: Accelerating Object Detection in Embedded Environments through Compressive Sensing;PMoET: Going Wider than Deeper Using the Parallel Mixture of Experts Transformer for 3D Hand Gesture recognition;Enhancing Forest Fire Classification with Feature Selection and machine learning Based on PRISMA Hyperspectral data;semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images Using SegFormer;handcrafted and Deep Features for Synthetic data Generation in Offline Handwritten Signature Verification;refining U-Net Architecture through Genetic Algorithms for Improved Skin Lesion Image Segmentation;An RNN-LSTM Approach for Algerian Accent Identification;ensemble Methods for Predicting Cyanobacteria’s Potential Toxicity in Water Dams;cross-Organ Investigation of Tumor Histological Features Similarities Using Transfer learning: A Case Study on Breast and Colorectal Tumors;behavior-Based Insider threat Detection Using a Deep Neural Network;holistic Ontology Alignment Using Ontologies Embeddings;learning Different Separations in Branch and Cut: A Survey;Cancer Classification in Breast Imaging via Enhanced CNN Deep learning Architecture;X-Ray Insights: A Siamese with CNN and Spatial Attention Network for Innovative Person Identification;hybrid Wiener Filter and Sharpening Filter for Image De-blurring;feature Extraction and Dimensionality Reduction to Evaluate Decision Tree Ensembles for Diabetic Retinopathy Detection;artificial Intelligence-Based on Automatic Detection of Diabetic Eye Diseases : A Systematic Review;automated Breast Cancer Detection: A

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

4th International Conference on Intelligent Systems and Patt...

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

ISBN: (纸本)9783031821554

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An Investigation into Big data of Emergency Rescue based on an Improved DDRfs 4

An Investigation into Big Data of Emergency Rescue based on ...

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4th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2022

作者： Deng, Jiabin Hu, Juanli Zhongshan Polytechnic School of Information Engineering Guangdong Province Zhongshan City528404 China

ISBN: (纸本)9798350333947

Post-validation data anomaly still exists in emergency rescue operations, such as spelling mistakes, abbreviated or shortened data formats during the pre-processing and data integration stages. If un-corrected or un-identified, resulting in a drop in data accuracy as the quality of data would be severely affected while performing data analysis or mining. In this essay, the authors propose a new methodology. We mixed the Fuzzy C-means Clustering Algorithm with Levenshtein Distance Method and created a new approach called De-duplicated Record for Similarity (DDRfS) to increase fuzzy search accuracy. Based on our tests, calculations in the data partition stage generated a much higher matching accuracy. © 2022 IEEE.

关键词： Big data

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Increasing Accuracy of Hand Gesture recognition using Convolutional Neural Network 4

Increasing Accuracy of Hand Gesture Recognition using Convol...

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4th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2022

作者： Park, Gyutae Chandrasegar, V.K. Park, Joonggun Koh, Jinhwan Gyeongsang National University Electronic Engineering Jinju Korea Republic of JD Co. Ltd Jinju Korea Republic of Gyeongsang National University Dept. of Electronics Jinju Korea Republic of

ISBN: (纸本)9781665458184

Human gestures play important roles in the interaction between humans and machines. these human gestures are becoming more important, yet complex gesture input and noise induced by external elements are important problems to solve in order to improve the accuracy of hand gesture recognition methods. Convolutional Neural Networks (CNN) are offered as a technology that can solve this problem in this research. CNN has the advantage of being able to learn image data, and this technology will greatly improve human-machine interaction accuracy. data was extracted using Vivaldi antennas with a frequency bandwidth of 7.4-9.0 GHz and gain characteristics of 8 dB in five sign language operations, and data that went through the preprocessing process was learned through CNN. the classification results of the proposed CNN showed about 90% accuracy. © 2022 IEEE.

关键词： machine learning

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HR Analytics in Predicting Attrition pattern Among Women in Private Education Colleges: Comprehensive Evaluation 4

HR Analytics in Predicting Attrition Pattern Among Women in ...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Nazir, Hena Vij, Renu Tiwari, Yukti Chandigarh University Department of AIT-Management Mohali India

ISBN: (纸本)9798350393248

Businesses may now effectively understand and lower employee turnover with the use of analytics for human resources (HR). With an emphasis on women's attrition trends, this research investigates the topic of private education institutions. Specifically, gender-specific dynamics in the workplace are emphasised in the first portion of the research, which examines the factors that cause women to leave these institutions. this is followed by the development of a comprehensive evaluation system that combines statistical analysis, machine learning algorithms, and historical data to estimate and understand the attrition patterns. Based on the investigation's findings, female employee turnover at private education institutes may be significantly predicted by a variety of criteria, including work-life balance, opportunities for professional progress, and workplace culture. Sophisticated statistical models, including logistic regression and decision tree analysis, are used in the study to estimate attrition trends and show these variables' relative relevance. through the implementation of HR analytics, organisations may enhance their ability to predict these concerns, formulate policies that consider gender parity, and foster a fairer and more supportive workplace culture. Apart from emphasising the importance of data-driven decision-making in retaining talent, this study provides HR professionals and organisational leaders with a valuable framework to enhance employee contentment and reduce attrition rates among female students enrolled in private Universities. © 2023 IEEE.

关键词： Decision trees

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Application of Image recognition for On-board Sailor Behavior Based on Broad learning System 4

Application of Image Recognition for On-board Sailor Behavio...

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4th international conference on data-Driven Optimization of Complex Systems, DOCS 2022

作者： Liu, Wenting Zuo, Yi Li, Tieshan Philip Chen, C.L. Dalian Maritime University Collaborative Innovation Center of Maritime Big Data & Artificial General Intelligence Navigation College Dalian China University of Electronic Science and Technology of China School of Automatic Engineering Chengdu China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Based on broad learning system (BLS), this paper monitors and identifies the behavior of crew members on ships. the main recognition scenarios include both of on deck and in the cabin, and the main recognition tasks include crew tracking and identification. In this paper, the video data is divided into frames and images are segmented. In the preprocessing of images, the filter is used to enhance the information and reduce the noise of the images. Finally, the recognition model is established by the BLS. In the experiment, we use BP neural network (BPNN) and support vector machine (SVM) as comparisons. the proposed method in this paper has achieved the best results in terms of recognition accuracy and training time. © 2022 IEEE.

关键词： learning systems

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

4th International Conference on Intelligent Systems and Patt...

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

ISBN: (纸本)9783031821493

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Plant Stress Classification with Deep learning Models Using STFT-Based 2D Representation of 6-Channel 500 kHz Plant Sound 4th

Plant Stress Classification with Deep Learning Models Using ...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Baek, Eu-Tteum Yoon, Hye-Seong AI&Bigdata Honam University Gwangju Korea Republic of

ISBN: (纸本)9789819787043

Plant stress is a physiological response triggered by various environmental factors, such as extreme weather conditions, diseases, or mechanical disturbances. Monitoring and classifying plant stress are crucial for assessing the overall health of vegetation and implementing timely interventions to ensure sustainable agricultural practices. However, estimating the magnitude of stress proves challenging due to a lack of comprehensive datasets and limited technological capabilities. therefore, in this study, we present a plant classification method leveraging a 2D representation based on Short-Time Fourier Transform of 6-channel 500 kHz sounds, recorded in scenarios such as tree cutting, drought, and other common environmental conditions. the experimental results demonstrate that our proposed method effectively predicts stress factors;however, due to a lack of a comprehensive dataset and limitations in data quality, the accuracy is slightly lower. these endeavors are expected to offer significant assistance in the analysis and judgment of plant stress. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： deep learning plant stress STFT

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