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共 769 条记录，以下是411-420 订阅

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Sentiment Analysis Applied to Arabic Tweets Using machine learning and Deep learning*

Sentiment Analysis Applied to Arabic Tweets Using Machine Le...

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international conference on Cloud Technologies and Applications (CloudTech)

作者： Mohammed Benali Abdeljalil Elouardighi LM2CE laboratory FEG Hassan 1 university Settat Morocco

Natural language processing is a field of study that treats multiple tasks, such as text generation, language modeling, and classification. the extraction of valuable information from text documents allows for better decision making in political decisions and analyzing public opinion; those techniques are also deployed in support systems. this work focuses on Arabic sentiment analysis for Twitter comments in Modern Standard Arabic and Moroccan dialectal Arabic. the process starts by extracting data from Twitter using APIs. this dataset is used to train various machine learning and deep learning models. the data must be properly trained, which requires the right pre-processing. We used emojis to annotate the data, and word sequences are used to create word vectors, and these vectors are then passed through dense layers to generate word embeddings, which is specifically tailored to our dataset. Sentiment analysis is undertaken to extract opinions from Arabic text using both Deep learning (DL) and machine learning (ML) models. Enabling a comparative analysis of performance between Deep learning and machine learning algorithms applied to Arabic textual data. the results will be presented above and evaluated on the basis of many factors and indicators. this work has shown very satisfactory and promising results, which is very interesting and encouraging to keep working on this subject.

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ENNG-Mix: Enhanced Semi-Supervised Anomaly Detection with Pseudo-Anomaly Generation

ENNG-Mix: Enhanced Semi-Supervised Anomaly Detection with Ps...

引用

6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Xin Guan Yuxin Ma Wei Ma Faculty of Electronic and Information Engineering LNTU Huludao Liaoning China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Anomaly Detection (AD) is a machine learning and data mining technique for identifying ***, or instances in data that are different or unusual from most otherdata. the goal is to discover samples that are inconsistent with expected behaviour, which may be anomalies or outliers. By identifying anomalies in a timely manner, it can help organisations prevent losses or potential risks. In this paper, we propose a new algorithm that generates supplementary pseudo-anomalies because of a constrained number of labelled anomalies and vast unlabelled data. Our proposed algorithm, called Enhanced Nearest Neighbour Gaussian Mixing (ENNG-Mix), efficiently integrates information from both labelled and unlabelled data to generate pseudo-anomalies. We compare the performance of this new algorithm with commonly used enhancement techniques such as Mixup and Cutout. We evaluate ENNG-Mix by training various existing semi-supervised and supervised anomaly detecti-on algorithms on raw training data along with the generated pseudo-anomalies. through extensive experiments on 57 benchmark datasets in ADBench, reflecting different data types, we demonstrate that ENNG-Mix outperforms other data enhancement methods. It yields significant performance improvements compared to a baseline trained only on the original training data. Notably, in ADBench, ENNG-Mix improves the yield by 17.6%, 10.8% and 9.2% compared to Classical, CV and NLP datasets, respectively.

关键词： Training signal processing algorithms Training data machine learning signal processing Benchmark testing Data augmentation Anomaly detection

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machine learning-Based Human Activity Recognition Using Miniature Inertial and Magnetic Sensors

Machine Learning-Based Human Activity Recognition Using Mini...

引用

6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Yanchu Liu Liming Sun Yong Liu School of Electronic Engineering Heilongjiang University Harbin Heilongjiang School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

this study conducts a comparative analysis of techniques for recognizing human activities through inertial and magnetic sensors, focusing on extracting eleven time-domain and twelve frequency-domain features from signal segments. these features, normalized and reduced in dimensionality through Principal Component Analysis (PCA) to 30 per segment, were input into various machine learning models including Linear Regression (LR), SVM, KNN, Decision Tree, XGBoost, LightGBM, and CatBoost for classification. the models' performances were evaluated using five metrics: Precision, Recall, F1 Score, Accuracy, and macro and weighted averages. Employing strategies like judicious dataset splitting and early stopping to prevent overfitting, and using five-fold cross-validation for generalization, the study highlights the effectiveness of these methods in enhancing classification accuracy swiftly, notably achieving over 91% accuracy with CatBoost. By leveraging GPU technology, training time was significantly reduced. this research underscores the practical value and application potential in human activity recognition using a blend of feature extraction methods and machine learning algorithms, showcasing the superiority of ensemble models like CatBoost in achieving high classification accuracy.

关键词： Training Measurement Accuracy Computational modeling Magnetic sensors Feature extraction Data models Human activity recognition Time-domain analysis Principal component analysis

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A Novel Anomaly Detection Framework for Big Network Traffic Data

A Novel Anomaly Detection Framework for Big Network Traffic ...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Hailong Li Honggang Wang Xiao Peng Ge Zhang Big Data Center State Grid of China Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Monitoring network traffic to identify malicious applications is an active research topic in network security. In the era of big data, with the increasing number of access devices, networks will become more and more dense and require more rapid response. Traditional anomaly detection methods cannot achieve both detection accuracy and latency. To study a high-performance network traffic anomaly detection method is imperative. In this paper, We proposed a novel anomaly detection framework based on big data analytics for network traffic to enhance the detection of sophisticated cyber threats. the framework is divided into two stages: online and offline. the online stage uses a distributed algorithm utilizing customized network traffic characteristics. In the offline stage, the multi-modal method of accurate classification is adopted, and the identification result is used as an expert system of online algorithm to realize data authentication. By integrating advanced machine learning algorithms and big data parallel anomaly detection techniques, the proposed framework aims to strike a balance between accuracy and efficiency in detecting emerging cyber threats. the framework has been tested on both open datasets and real-world datasets. A large number of experiments have been conducted to validate the feasibility and practicality of the framework.

关键词： machine learning algorithms Accuracy signal processing algorithms Telecommunication traffic Big Data signal processing Feature extraction Real-time systems Sparks Anomaly detection

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A New Approach for Estimating Sleep Efficiency from Electromyography signal

A New Approach for Estimating Sleep Efficiency from Electrom...

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international conference on Electrical Information and Communication Technology (EICT)

作者： Sadia Nawsheen Nijhum Mohammad Anamul Haque Chowdhury Md Bashir Uddin Department of Biomedical Engineering Khulna University of Engineering & Technology Khulna Bangladesh

the diagnosis of many sleep disorders mostly depends on the accurate estimation of sleep efficiency (SE) that results from the calculation of total sleep time (TST). By using electromyography signal and machine learning techniques, this study aims to develop a method for estimating SE by accurately predicting TST and total wake time (TWT). A dataset of 97 individuals suspected to have sleep issues was employed, with 81 cases serving as training data and 16 as testing data. After pre-processing, nine different features with 511 unique combinations were utilized to find out the best indicator of sleep duration. A total of 12 machine learning algorithms were applied to evaluate performance metrics such as sleep time accuracy, sensitivity, specificity, and F1 score. In addition, TST, TWT, and hence SE were automatically calculated from the classified sleep/wake state. the overall optimal result was obtained by the model ‘Decision Tree with a depth of 3’ for the feature combination of the ‘fast Fourier transform Magnitude Sum’ and the ‘Mean’. Epoch-by-epoch analysis resulted in sleep time accuracy, sensitivity, specificity, and F1 score of 91.16%, 89.09%, 62.19%, and 85.32%, respectively. In addition, this new approach resulted in an overall relative detection rate of 98.7% for estimating SE. this approach accomplished better results than the existing studies and, hence, would be applicable in diagnosing sleep-related problems, especially sleep apnea.

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MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal transplantation 6

MEDIMP: 3D Medical Images with clinical Prompts from limited...

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6th international conference on Medical Imaging with Deep learning (MIDL)

作者： Milecki, Leo Kalogeiton, Vicky Bodard, Sylvain Anglicheau, Dany Correas, Jean-Michel Timsit, Marc-Olivier Vakalopoulou, Maria Paris Saclay Univ MICS CentraleSupelec Inria Saclay Paris France Inst Polytech Paris CNRS LIX Ecole Polytech Paris France Paris Univ Necker Hosp Dept Adult Radiol Paris France Hop Necker Enfants Malad APHP Dept Nephrol & Kidney Transplantat Paris France Paris Univ Necker Hosp Dept Urol HEGP Paris France Paris Cite Univ UFR Med Paris France

Renal transplantation emerges as the most effective solution for end-stage renal disease. Occurring from complex causes, a substantial risk of transplant chronic dysfunction persists and may lead to graft loss. Medical imaging plays a substantial role in renal transplant monitoring in clinical practice. However, graft supervision is multi-disciplinary, notably joining nephrology, urology, and radiology, while identifying robust biomarkers from such high-dimensional and complex data for prognosis is challenging. In this work, taking inspiration from the recent success of Large Language Models (LLMs), we propose MEDIMP - Medical Images with clinical Prompts - a model to learn meaningful multi-modal representations of renal transplant Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE MRI) by incorporating structural clinicobiological data after translating them into text prompts. MEDIMP is based on contrastive learning from joint text-image paired embeddings to perform this challenging task. Moreover, we propose a framework that generates medical prompts using automatic textual data augmentations from LLMs. Our goal is to learn meaningful manifolds of renal transplant DCE MRI, interesting for the prognosis of the transplant or patient status (2, 3, and 4 years after the transplant), fully exploiting the limited available multi-modal data most efficiently. Extensive experiments and comparisons with other renal transplant representation learning methods with limited data prove the effectiveness of MEDIMP in a relevant clinical setting, giving new directions toward medical prompts. Our code is available at https://***/leomlck/MEDIMP.

关键词： Contrastive learning Natural Language processing LLM MRI Renal transplantation Medical Prompts

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Sentiment clustering using the unsupervised machine learning approach

Sentiment clustering using the unsupervised machine learning...

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international conference on Recent Trends in Advance Computing (ICRTAC)

作者： Abinash Tripathy Utpal Chandra De Bibhuti Bhusan Dash Sudhansu Shekhar Patra Binod Kumar Pattanayak Trilok Nath Pandey SCA KIIT University Bhubaneswar Odisha India Department of Computer Sc. & Engg ITER Bhubaneswar Odisha India School of Computer Science & Engineering Vellore Institute of Technology Chennai India

these days, social media is crucial for disseminating opinions and remarks on various subjects. Users can utilize these remarks to help them process and make decisions in the future. Since the majority of the reviews are text or emojis, processing is necessary to acquire the necessary data. the reviews obtained from different source for processing is not labeled. When the reviews are labeled, the processing is done by using confusion matrix and the parameter are obtained from that for evaluation of the performance. But, when the reviews are not labeled, classification of the reviews are not possible as the original polarity is not available, which need to be compared with predicted polarity. thus, unsupervised approach is adopted, where clustering approach is adopted. In this paper, three different unsupervised machine language are used namely, Affinity propagation, K-means, and Mini Batch K-means. the performance of the machine learning is checked by using performance evaluation parameters.

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Research on Unknown Distributed Denial-of-Service Attacks Intrusion Detection Based on Deep learning

Research on Unknown Distributed Denial-of-Service Attacks In...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Liumei Zhang Zhaoyang An Yuanjiao Hu School of Computer Science Xi'an Shiyou University Xi'an China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

the increasing prevalence of Distributed Denial-of- Service (DDoS) attacks poses a serious threat to network security, emphasizing the crucial need for effective intrusion detection methods. the continuous evolution of attack strategies has exposed the limitations of traditional feature engineering and statistical analysis in detection mechanisms. To address this challenge, this study proposes an innovative DDoS attack detection framework based on deep learning. At the core of this framework lies a Temporal Convolutional Network (TCN) structure, cleverly integrated with an attention mechanism to enhance the model's ability to identify and extract crucial information. this integration not only aids in capturing long- term dependencies and global patterns in time-series data but also significantly improves detection accuracy and system robustness. To validate the effectiveness of the proposed method, comprehensive experimental evaluations were conducted using the CICDDoS2019 dataset. the results demonstrate a notable achievement, with our model achieving a high classification accuracy of 98.14%. In comparison to traditional machine learning methods, the model exhibits significantly reduced false positive and false negative rates.

关键词： Deep learning Adaptation models Accuracy Attention mechanisms Statistical analysis Computational modeling Intrusion detection signal processing Convolutional neural networks Long short term memory

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Electromagnetic signal Adversarial Example Generation Based on Perturbation Region Selection

Electromagnetic Signal Adversarial Example Generation Based ...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： ShangYong Zhang XiaoLei Wang Yi Zhang LuLu Wang Juan Chen QingHua Song National Innovation Institute of Defense Technology Beijing China Academy of Military Sciences Beijing China China Rongtong Scientific Research Institute Group Co Beijing China

With the widespread application of deep learning techniques in the field of electromagnetic signal recognition, the generation of adversarial examples for electromagnetic signals has gradually become a hot research topic in the field. Existing techniques for generating adversarial examples for electromagnetic signals suffer from issues such as high perturbation magnitude, low timeliness, and poor stealthiness, which makes it difficult to meet the practical application needs effectively. this thesis proposes an adversarial sample generation method based on region selection, called Region Selection-FGSM (RS-FGSM). this method selects the perturbation regions based on the magnitude of the gradient of the electromagnetic signal, and generates adversarial examples by applying FGSM perturbation within the selected regions. Experimental results demonstrate that the proposed method outperforms the FGSM method in terms of perturbation magnitude, timeliness, and stealthiness.

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System Modeling and machine learning in Prediction of Metastases in Lung Cancer

System Modeling and Machine Learning in Prediction of Metast...

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16th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2023

作者： Swierniak, Andrzej Kozlowska, Emilia Fujarewicz, Krzysztof Borys, Damian Wilk, Agata Smieja, Jaroslaw Suwinski, Rafal Department of Systems Biology and Engineering Silesian University of Technology Akademicka 16 Gliwice44-100 Poland The 2nd Radiotherapy and Chemotherapy Clinic M. Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Wybrzeze Armii Krajowej 15 Gliwice Poland

the aim of this paper is to present goals and preliminary results of our project devoted to system engineering approach in prediction of metastases in lung cancer. More specifically we consider existing and develop new methods of system modeling, machine learning, signal processing and intelligent control to find biomarkers enabling prediction of risk of tumor spread and colonization of distant organs in non-small-cell lung carcinoma basing on clinical data and medical images. the results could bring us knowledge about the dynamics and origin of metastatic dissemination of lung cancer. By dynamics, we understand when and where a tumor will disseminate, and by origin we mean dissemination path (directly from original tumor or through lymphatic nodes). this information is very valuable for clinicians, as it could guide the personalized treatment of lung cancer patients. the results will elucidate important issues concerning prediction of individual progress of cancer and treatment outcome in oncology. they will provide both theoretical and simulation tools to support decision making and diagnostics in oncology, on the basis of individual patient state. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Lung cancer

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