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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

4th EAI International Conference on Cognitive Computing and ...

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

ISBN: (纸本)9783031488870

the proceedings contain 39 papers. the special focus in this conference is on Cognitive Computing and Cyber Physical Systems. the topics include: Drug Recommendations Using a Reviews and Sentiment Analysis by RNN;optimizing Real Estate Prediction - A Comparative Analysis of Ensemble and Regression Models;estimation of Power Consumption Prediction of Electricity Using machine learning;medical Plants Identification Using Leaves Based on Convolutional Neural Networks;An Efficient Real-Time NIDS Using machine learning Methods;Maixdock Based Driver Drowsiness Detection System Using CNN;A Novel Approach to Visualize Arrhythmia Classification Using 1D CNN;exploring machine learning Models for Solar Energy Output Forecasting;the Survival Analysis of Mental Fatigue Utilizing the Estimator of Kaplan-Meier and Nelson-Aalen;an Optimized Ensemble machine learning Framework for Multi-class Classification of Date Fruits by Integrating Feature Selection Techniques;EEMS - Examining the Environment of the Job Metaverse Scheduling for data Security;text Analysis Based Human Resource Productivity Profiling;a Novel Technique for Analyzing the Sentiment of Social Media Posts Using Deep learning Techniques;comparative Analysis of Pretrained Models for Speech Enhancement in Noisy Environments;Use of Improved Generative Adversarial Network (GAN) Under Insufficient data;unraveling the Techniques for Speaker Diarization;LUT-Based Area-Optimized Accurate Multiplier Design for Signal Processing Applications;speaker recognition Using Convolutional Autoencoder in Mismatch Condition with Small dataset in Noisy Background;face Emotion recognition Based on Images Using the Haar-Cascade Front End Approach;textRank – Based Keyword Extraction for Constructing a Domain-Specific Dictionary;harmonizing Insights: Python-Based data Analysis of Spotify's Musical Tapestry;Enlighten GAN for Super-Resolution Images from Surveillance Car.

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Novel Hybrid Feature Selection Method and Globalization Technique for Text Classification 4th

Novel Hybrid Feature Selection Method and Globalization Tech...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Mekhzoumi, Dalila Boulkaboul, Sahar Amroun, Kamal CERIST Res Ctr Algiers Ben Aknoun Algeria Abderrahmane Mira Univ Bejaia Algeria

ISBN: (纸本)9783031821523;9783031821530

Feature selection in text classification refers to the critical process of identifying and selecting the most relevant and informative features such as words, phrases, or other linguistic elements from a text dataset. this process, which has been a research topic for decades and finds applications across various fields such as bioinformatics, image recognition, image retrieval, text mining, etc., is essential for optimizing classification accuracy and efficiency. Addressing the challenge of high dimensionality in text data, stemming from the abundance of features like words or n-grams, is crucial to mitigate computational inefficiency and overfitting. Furthermore, the presence of irrelevant or redundant features in text datasets poses another significant challenge, as these features can introduce noise or irrelevant information, thereby undermining the performance of classifier. In this paper, we proposed a new approach GL-SMCHI using improved features selection method to reduce the CHI value of high-frequency words, and globalization technique to incorporate both feature and class information when evaluating the importance of each feature. We compared the results obtained from our proposal against those of existing robust alternatives, the simulation results show that the proposed method outperforms the standard CHI squared, the improved feature selection methods, and the globalization method in term of F-Score and accuracy.

关键词： Features selection CHI-Squared machine learning Globalization Text classification Support Vector machines

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Leveraging DQN-Based Recommender Systems for E-Commerce in Smart Cities 4th

Leveraging DQN-Based Recommender Systems for E-Commerce in S...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Abakarim, Sana Qassimi, Sara Rakrak, Said Cadi Ayyad Univ Fac Sci & Tech L2IS Lab Marrakech Morocco

ISBN: (纸本)9783031821523;9783031821530

In the age of digital urban transformation, smart cities are emerging as ecosystems where technology, infrastructure and data converge to improve quality of life, sustainability and economic prosperity. In this context, e-commerce plays a central role, providing platforms for businesses to thrive by enabling seamless transactions, personalised shopping experiences and greater market reach. However, the dynamic and evolving nature of user preferences presents a significant challenge, requiring more adaptive and smart recommendation systems. this paper presents an approach by integrating Deep Q-Network (DQN), a reinforcement learning technique, into recommendation systems for e-commerce in smart cities. By comparing the proposed DQN model based recommender system with traditional models such as MLP, DeepFM, LSTM and CNN using metrics such as MSE, RMSE and NDCG@5, we demonstrate its superior performance in predicting user preferences and dynamically adapting to changes in user behaviour. the results highlight the potential of DQN models to revolutionise e-commerce recommender systems, delivering more personalised and adaptive user experiences in the interconnected environments of smart cities.

关键词： Deep Q-Network Recommender Systems Reinforcement learning Smart Cities E-Commerce machine learning Deep Reinforcement learning

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Deep learning-based Worm Detection Method for Polymorphic Networks 4

Deep Learning-based Worm Detection Method for Polymorphic Ne...

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4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Shen, Wuqiang Wang, Yechao Yao, Chaosheng Xie, Ning Guangdong Power Grid Corporation Guangzhou510220 China China Southern Power Grid Joint Laboratory on Cyberspace Security Guangzhou510220 China Shenzhen E-Link Information Technology Co. Ltd. Shenzhen510663 China

ISBN: (纸本)9798350361643

the maintenance of cybersecurity has become increasingly vital with the advancement of the information age, particularly in the detection of polymorphic network worms. Due to their ability to mutate, these worms can evade the capture of traditional detection techniques, for which the application of deep learning provides a new perspective. this paper delves into the exploration of deep learning, especially Convolutional Neural Networks (CNN), in the field of polymorphic network worm detection. By studying deep learning algorithms and understanding how they operate in complex pattern recognition tasks, this study constructs a detection framework based on CNN for the identification and disruption of worm propagation. In addressing network traffic data, a unique method of effective payload matrix processing is proposed to meet the data input requirements of CNN. this method can extract deep-seated features hidden in traffic data, enabling the effective identification of polymorphic worm attacks. through the establishment of an experimental environment and the validation of the proposed method using standard datasets, the experimental results confirm the accuracy and practicality of this approach in polymorphic worm detection. this work significantly enhances the capability of the network security defense system and lays a foundation for defending against future worm attacks. © 2024 IEEE.

关键词： Network security

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the Hidden Influence of Latent Feature Magnitude When learning with Imbalanced data 4th

The Hidden Influence of Latent Feature Magnitude When Learn...

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

作者： Dablain, Damien A. Chawla, Nitesh V. Department Computer Science and Lucy Family Institute for Data and Society University of Notre Dame Notre DameIN46553 United States

ISBN: (纸本)9789819787012

machine learning (ML) models have difficulty generalizing when the number of training class instances are numerically imbalanced. the problem of generalization in the face of data imbalance has largely been attributed to the lack of training data for under-represented classes and to feature overlap. the typical remedy is to implement data augmentation for classes with fewer instances or to assign a higher cost to minority class prediction errors or to undersample the prevalent class. However, we show that one of the central causes of impaired generalization when learning with imbalanced data is the inherent manner in which ML models perform inference. these models have difficulty generalizing due to their heavy reliance on the magnitude of encoded signals. During inference, the models predict classes based on a combination of encoded signal magnitudes that linearly sum to the largest scalar. We demonstrate that even with aggressive data augmentation, which generally improves minority class prediction accuracy, parametric ML models still associate a class label with a limited number of feature combinations that sum to a prediction, which can affect generalization. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Improving Biomedical Claim Detection using Prompt learning Approaches 4

Improving Biomedical Claim Detection using Prompt Learning A...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Chen, Tong Stefanidis, Angelos Jiang, Zhengyong Su, Jionglong Jiangsu Suzhou215123 China

ISBN: (纸本)9798350324303

Biomedical claim detection is an effective method to uncover negative effects arising from the treatment of disease and detect misinformation about medications from online platforms. Due to the power of pre-trained language models (PLMs), such as BERT, RoBERTa and T5, fine-tuned PLMs perform exceptionally well in biomedical claim detection. However, a gap exists in the text classification task between objective forms used in pre-training and fine-tuning for PLMs methods, preventing these models from taking full advantage of the information for biomedical claim detection. Motivated by the prompt learning approach, we propose a method, in which the classification task is transformed into a masked language modeling task that fully utilizes the mask learning capability of PLMs for better prediction of biomedical claim detection. In our method, a template with a mask representing the label is first constructed, and the mask is then filled and mapped to the corresponding label. We use three PLMs as backbone models, i.e., BERT, RoBERTa, and T5, with both hard and mixed templates which are fully and partially predefined templates. Experimental results using the BioClaim dataset demonstrate the superiority of the prompt learning methods over the BERT and RoBERTa classification baselines. Furthermore, the T5 model with mixed template consistently outperforms the rest of experimented models and achieves state-of-the-art performance with an increase of 5.3% on F1-score compared to previous research on this dataset. © 2023 IEEE.

关键词： learning systems

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Using EEG data to Classify Hand Motor Imagery in Stroke Patients 4

Using EEG Data to Classify Hand Motor Imagery in Stroke Pati...

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4th international conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Shen, Yiwei Liu, Junting Yang, Yuxuan Xie, Wenjun Hefei University of Technology School of Software Anhui Hefei China

ISBN: (纸本)9798331528850

EEG-driven brain-controlled prosthetics can greatly help people with movement disorders in their daily lives. Still, the task of accurately translating intentions into expected actions in the brain remains a technical challenge. In recent years, as a branch of machine learning, Deep Neural Networks (DNN) have made great progress in improving the recognition and classification ability of computer vision tasks. this study evaluate the information representation ability of 2 machine learning models and 16 NN models and their variants. the experiment is conducted on an open source EEG dataset of hemiplegic stroke patients, and we evaluate the thematic and cross-thematic performance of the above algorithm. We find that a single-layer GRU network remained an optimal choice in subject subject classification because it is able to effectively reduce model overfitting. For cross-subject analysis, some deep-learning models outperform all other methods due to the availability of training data and can function effectively in cross-subject decoding tasks. Independent Component Analysis (ICA) processing is also proved to be effective for improving the accuracy of classification. these improvements will provide important information for researchers in the field of Brain-computer interface (BCI) to select data processing strategies appropriate for their research tasks, it also lays a foundation for the future research on the interaction between the characteristics of bioelectric signals and the structure of neural networks. © 2024 IEEE.

关键词： Brain computer interface

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Deep learning based garbage recognition system 4

Deep learning based garbage recognition system

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4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Song, Zelong School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

the rapid increase in the amount of rubbish and the diversity of the substances in people's daily rubbish are two major problems of rubbish disposal at present, and the rubbish problem brings a serious burden to the environment as well as a waste of resources. At present, the main approach to deal with the waste problem is through waste separation. However, today’s traditional manual classification method is still used to screen the rubbish, which is labour-intensive with low sorting efficiency. this paper proposes a deep learning-based intelligent rubbish classification system, which can quickly achieve the positioning and recognition of rubbish in complex backgrounds for classification by incoming pictures and effectively improve the efficiency of rubbish classification. the system uses TensorFlow as the deep learning platform, and the combination of a convolutional neural network and a fully connected neural network is used to achieve the construction of the deep learning model. Meanwhile, Some nodes of the fully connected layer are discarded with a given probability in the training phase to avoid the overfitting of the system to optimise the system, and the system is also optimised by collecting various types of rubbish pictures and building inspection datasets, using data augmentation to improve the robustness of the model, the fast and accurate identification of different types of rubbish is achieved. the experimental results demonstrate that the intelligent rubbish classification system proposed in this paper has a high performance in terms of both accuracy and efficiency. © 2024 SPIE.

关键词： Efficiency

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the 4th international conference on data Science, machine learning 2022

The 4th International Conference on Data Science, Machine Le...

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the 4th international conference on data Science, machine learning 2022

ISBN: (纸本)9789819920570

the proceedings contain 39 papers. the special focus in this conference is on data Science, machine learning and Applications. the topics include: Monitoring the Farming Conditions Using IoT;Design of OTFS Modulation by Superimposed Pilot-Based Channel Estimation and Embedded Pilot-Aided Estimation;Morphology and EMD-Based Patch-Wise Image Fusion;PSO-Based Evolutionary Image Segmentation System for Analysis of Fatty Liver Level recognition;blake–Zisserman Model of Segmentation Method for Low-Contrast and Piecewise Smooth Image;Performance Enhancement of Wireless Systems Using Hybrid RIS Technique;Creation of a Platform for Artisans to Promote their Product Using Blockchain as NFT;face recognition Based Home Security System to Detect Usual/Unusual person Using IoT;opinion Mining-Based Fake Review Detection Using Deep learning Technique;automatic Detection and Cleaning of Manhole Blockages Using IoT;design and Implementation of a Smart Door Locking System with Automatic Appliance Switching;dynamic Game Difficulty Adjustment Based on Facial Emotion recognition;parallel Implementation of PageRank Based K-Means Clustering on a Multithreaded Architecture;ZACube-2 Mission Operations Analysis;a Novel Approach for Speech Emotion recognition with Facial Expression Analysis;effect of Environment on Students Performance through Orange Tool of data Mining;phishing Email Mitigation Technique Using Back-Propagation Neural Network for Cyber Space;crime Visualization and Forecasting Using machine learning;an Empirical Comparison of Classification machine learning Models Using Medical datasets;Analysis of the SEER data set for Lung Cancer Diagnosis for Stage Classification and Survival Analysis;risk Prediction of Chronic Kidney Disease Using machine learning Algorithms;sandalwood Tree Deduction Using Deep learning;phishing Attack Detection Using machine learning.

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Identification of Alzheimer's Disease Associated Genes through Explicable Deep learning and Bioinformatic 4

Identification of Alzheimer's Disease Associated Genes throu...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Sun, Xiangrong Shu, Wei Zhang, Yuxin Huang, Xiansheng Liu, Juxia Liu, Yuan Yang, Tiejun Guilin Medical University Department of Intelligent Medicine College of Intelligent Medicine and Biotechnology Key Laboratory of Medical Biotechnology and Translational Medicine of Guangxi Education Department Guilin China Guilin Medical University College of Intelligent Medicine and Biotechnology Department of Intelligent Medicine Guilin China

ISBN: (纸本)9798350324303

We utilized interpretable deep learning to identify key genes and potential biomarkers associated with Alzheimer's disease (AD). AD-related gene expression data from GEO datasets and Alzdata were collected. To create a training set, we performed differential expression analysis to delete low-expressed genes. To explore disease-related genes and potential biomarkers, we employed a pathway-centric deep learning network, which integrated gene expression data with established biological pathways, building upon the foundation of PASNet. Our study employed a pathway-related deep learning model based on PASNet combined with bioinformatic analysis for AD risk prediction, and achieved a good performance (AUC = 0.82, F1 score = 0.73). the model accurately classified AD patients based on gene expression data from the brain tissue while providing interpretability deep neural networks. Potential biomarkers (DYNC1I1, DNAJC1, SCRIB, TFEB, MAPKAPK3) and biological pathways (Senescence-Associated Secretory Phenotype, Transcriptional activity of SMAD2/SMAD3:SMAD4 heterotrimer, Toll-like Receptor Cascades) associated with AD were identified. We have employed a combination of bioinformatics analysis and deep learning models to identify a series of candidate genes associated with the diagnosis of Alzheimer's disease (AD). Moreover, we have validated their potential as biomarkers through the utilization of external datasets, and finally focus on the five genes DYNC1I1, DNAJC1, SCRIB, TFEB, and MAPKAPK3. © 2023 IEEE.

关键词： Risk assessment

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