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Multilingual Sentiment Analysis using Deep-learning Architectures

Multilingual Sentiment Analysis using Deep-Learning Architec...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： Praveen Dominic Niranjan Purushothaman Anish Skanda Anil Kumar A Prabagaran J Angelin Blessy A John School of Graduate Studies MSc in Big Data Analytics Trent University Ontario Canada Visa Consolidated Support Services India Pvt. Ltd Bengaluru India School of Computing Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

machine learning techniques such as NLP (Natural language processing) play a key role in a context where mining social media data could add great value to governments of the world countries. the posts and tweets shared by the people on social media can be mined to infer the valuable ‘mindset’ of the people which is much required for any ruling government in the world. the objective of this study is to conduct sentiment analysis to mine the sentiment of the people regarding the ongoing war between Russia and Ukraine, using machine learning techniques. the idea is to analyze and infer if the countries have reacted in some way, considering the sentiment of their citizens, in the context of economic effects. the pipeline of the implementation associated starts with the data collection from social media such as Twitter and Reddit using Snscraper and the PRAW (Python Reddit API Wrapper). the larger posts from Reddit are handled by implementing suitable text summarization techniques. Sentiment analysis is performed for the social media data using the BERT transformer model. the non-English posts are translated to English using neural machine translation. Also, sentiment analysis is performed at various granularities such as the location and the people that are tagged using Named Entity recognition techniques. Finally, a comparative analysis of the world countries’ sentiment and their corresponding reliance on Russian oil is performed.

关键词： Sentiment analysis Analytical models Social networking (online) Oils Government Bit error rate machine learning

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A Novel Deep Ensemble learning Framework for Classifying Imbalanced data Stream 5th

A Novel Deep Ensemble Learning Framework for Classifying Imb...

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5th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Arya, Monika Hanumat Sastry, G. Bhilai Institute of Technology Chhattisgarh Durg India University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789811639449

machine learning has emerged as one of the most indispensable fields of this era in mining stream data. the stream data have the tendency to change their characteristics over time. mining imbalanced stream is a research demanding subfield of this area. In imbalanced data, one of the target classes has much less instances than other class. the imbalanced data may differ either in their ratio between majority and minority class or dimension or the number of classes. the performance of the classifier is affected by variety of imbalanced data set used for training the classifier. Because, the learning of classifier is different for different types of data sets. Imbalanced data can result due to rare events which can have negative impact on society. Most traditional data mining algorithm misclassifies the minority class of the imbalance data sets or considers it as noise. therefore, the decision is biased toward majority class and hence reduces the accuracy and overall performance of the algorithm. the algorithms for classifying imbalanced data sets thus demand high adaptability to changes in the majority and minority class ratios. the performance of traditional machine learning algorithms is enhanced by applying ensemble method and deep learning approaches. Purpose: this paper proposes a framework based on deep ensemble learning for classifying imbalanced stream of data. Methodology: the deep ensemble methods ensemble multiple base learners. While deep learning approach is applied to improve the performance by extracting lower-level features and feeding them forward for the next layer to identify higher level features. Results: In this method, the effect of highly imbalanced classes is reduced by uniting the ensemble method with deep learning. the accuracy of the classifier is improved in terms of not only accuracy but also categorical accuracy. Conclusion: In addition to accuracy, other performance measures like categorical prediction accuracy, training accuracy and pr

关键词： Deep learning

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A Review of Image Enhancement Methods Based on Stochastic Resonance of Neuronal Networks

A Review of Image Enhancement Methods Based on Stochastic Re...

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

作者： Ximin Sun Pingjuan Niu Yong Li Xiaoming Li Bingzhen Zhao Bin Zhang Dan Liu Bo Sun Tiangong University Laboratory of Metaverse in Electric Power Industry State Gride Ecommerce Technology CO.LTD Tian'jin China Laboratory of Metaverse in Electric Power Industry Tiangong University Tian'jin China Laboratory of Metaverse in Electric Power Industry State Gride Ecommerce Technology CO.LTD Tian'jin China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Stochastic resonance has always been a research hotspot in the field of image processing. the ability of the nervous system to detect signals is closely related to nonlinear and collective behavior in neural mediators. Neuronal noise is the most significant factor influencing this ability, and it is a natural endogenous fluctuation in brain activity. Experimental and theoretical studies have shown that noise is beneficial, not harmful, for the normal functioning of the nervous system. In this regard, it is generally agreed that noise of appropriate intensity can participate in the rhythmic activity of the brain, and that noise-induced oscillations enhance the performance of weak signal processing, especially when the frequency of the signal is approximately noise-induced rhythmic oscillations. this behavior in the biological nervous system can be explained by the concept of “stochastic resonance”. Correspondingly, the stochastic resonance mechanism of neurons is used to denoise the image, which has a good effect. In this paper, we first introduce an image processing method using random resonance of neurons. then, the commonly used evaluation indicators in the field of image processing are sorted out. Finally, the development trend of image processing using random resonance of neurons is prospected.

关键词： Reviews Stochastic resonance Noise Neurons Resonant frequency machine learning Market research pattern recognition Oscillators Image enhancement

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Resume Parsing and Skill Extraction using Custom pattern Matching algorithm and Gemini API

Resume Parsing and Skill Extraction using Custom Pattern Mat...

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international conference on Computing Communication Control and Automation (ICCUBEA)

作者： Atharva Joshi Shailaja Pede Prathmesh Kolekar Aditya Metkar Department of Computer Science Engineering (Artificial Intelligence and Machine Learning) PCCOE SPPU India Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

In the realm of contemporary recruitment, efficient resume parsing is essential for talent acquisition. this paper investigates resume parsing using advanced OCR technology and NLP methods. We begin with preprocessing, enhancing scanned resumes for legibility. Leveraging the Tesseract OCR engine, we convert images to machine-readable text. through NLP techniques with spaCy, we extract key information like personal details, education, experience, and *** approach integrates the Gemini Pro Vision API for semantic analysis and question-answering. this enriches our framework, enabling deeper insights and implicit attribute inference. Empirical validation across diverse datasets demonstrates the robustness of our methodology. this work sheds light on performance metrics and challenges in automated resume parsing, paving the way for future enhancements.

关键词： Visualization Technological innovation machine learning algorithms Resumes Optical character recognition Semantics Robustness data mining pattern matching Recruitment

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Multimodal 12-lead ECG data classification using multi-label DenseNet for heart disease detection 5

Multimodal 12-lead ECG data classification using multi-label...

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5th international conference on data Science and Information Technology, DSIT 2022

作者： Han, Xiao Tu, Enmei Yang, Jie School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Electrocardiogram(ECG) signal is one of the most effective diagnostic method for arrhythmia. Since cardiac dis-ease is a main cause of death and the allocation of medical resources is unbalanced, automatic recognition of arrhythmia by machine learning algorithm is of great significance. In this work, we develop a multimodal DenseNet to classify 12-lead ECG. Our method combines hand-crafted features with features from DenseNet. In addition, we propose to use softmax function and dynamic threshold optimization for our multi-level ECG classification task. We tested our model on the 2020 PhysioNet/CinC Challenge dataset. Using the official Challenge Score for evaluation, we achieve a Challenge Score of 0.602 on the validation set, which outperforms the champion model by an apparent margin. © 2022 IEEE.

关键词： Electrocardiograms

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SKTR: Trace Recovery from Stochastically Known Logs

SKTR: Trace Recovery from Stochastically Known Logs

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international conference on Process mining (ICPM)

作者： Eli Bogdanov Izack Cohen Avigdor Gal Data & Decision Sciences Technion–Israel Institute of Technology Haifa Israel Faculty of Engineering Bar-Ilan University Ramat Gan Israel

Developments in machine learning together with the increasing usage of sensor data challenge the reliance on deterministic logs, requiring new process mining solutions for uncertain, and in particular stochastically known, logs. In this work we formulate trace recovery, the task of generating a deterministic log from stochastically known logs that is as faithful to reality as possible. An effective trace recovery algorithm would be a powerful aid for maintaining credible process mining tools for uncertain settings. We propose an algorithmic framework for this task that recovers the best alignment between a stochastically known log and a process model, with three innovative features. Our algorithm, SKT R, 1) handles both Markovian and non-Markovian processes; 2) offers a quality-based balance between a process model and a log, depending on the available process information, sensor quality, and machine learning predictiveness power; and 3) offers a novel use of a synchronous product multigraph to create the log. An empirical analysis using five publicly available datasets, three of which use predictive models over standard video capturing benchmarks, shows an average relative accuracy improvement of more than 10% over a common baseline.

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Aspect Analysis Based on Statistical Description and Visualization of data 5

Aspect Analysis Based on Statistical Description and Visuali...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Tomar, Priyanka Sharma, Nonita Kamble, Shailesh D. Indira Gandhi Delhi Technical University for Women Information Technology Department Delhi India Indira Gandhi Delhi University for Women Artificial Intelligence and Data Science Delhi India

ISBN: (纸本)9798350398267

the trending machine learning algorithms have been applied in almost all spheres of life and able to give accurate results. However, none of the algorithms perform aspect analysis beforehand to see the nature and inferences of data. this research work presents the aspect analysis of heart failure data. As per the day today life scenario where each one is living a busy life, it's very important to know the health of our heart. this work will show case different types of models for cardiovascular diseases and its prediction through various heart patients' data and detect various heart disease. this work is developed based on the data visualization approach using python by plotting graphs between different types of attributes in dataset and analyzing the graphs which are plotted between various attributes of our data like age, cholesterol, trestbps, sex and fbs. Research work would visualize the pattern of heart diseases according to the age and gender of a heart patient and would like to present a profound and relevant result which will be helpful for the doctors to analyze and provide proper treatment to heart patients. © 2022 IEEE.

关键词： data visualization

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the Application of machine learning in Bitcoin Ransomware Family Prediction 2021

The Application of Machine Learning in Bitcoin Ransomware Fa...

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5th international conference on Information System and data mining (ICISDM)

作者： Xu, Shengyun ZheJiang Univ Finance & Econ Hangzhou Peoples R China

ISBN: (纸本)9781450389549

In recent years, ransomware attacks have become increasingly rampant, resulting in many large companies or financial institutions suffering heavy losses from ransomware attacks. Bitcoin, is a means of payment demanded by the Ransomware Family. By comparing and analyzing the characteristics of bitcoin transactions, we can predict the types of Ransomware Family. therefore, in this paper, the algorithm of machine learning is used to put forward the prediction method of Ransomware Family, so as to achieve the better effect of helping the attacked institutions to avoid being extorted effectively. In the traditional method, the judgment of Ransomware Family can only rely on human experience and subjective judgment, instead of accurate and batch analysis of Bitcoin transactions and prediction results. In this paper, a large number of known data sets of bitcoin's transaction features are used for analysis and modeling. First, we carried out descriptive statistical analysis to explore the differences between different Ransomware Families in bitcoin trading behavior. Next, we used a series of machine learning models to build the prediction model of Ransomware Family and conduct identification and classification, so as to help avoid financial losses from the Ransomware. Finally, we found that Ransomware family species were most significantly affected by year. In addition, it can be found that the accuracy of the Boosting model is the highest, and the test error is only about 3%.

关键词： Ransomware Bitcoin machine learning Classification

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machine learning Revolution in Early Disease Detection for Healthcare: Advancements, Challenges, and Future Prospects

Machine Learning Revolution in Early Disease Detection for H...

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Cybernetics, Cognition and machine learning Applications (ICCCMLA), IEEE international conference on

作者： Kumbala Pradeep Reddy Mankala Satish A. Prakash S.Malli Babu P.Pavan Kumar B. Sunitha Devi Department of CSE(AI&ML) CMR Institute of Technology Hyderabad Telangana India Department of CSE CMR Institute of Technology Hyderabad Telangana India

this paper explored the integration of machine learning into healthcare has revolutionized early disease detection, offering a multidimensional approach to data analysis. Advanced algorithms, rooted in deep learning, process diverse datasets encompassing medical records, genetics, and imaging data, enabling subtle pattern detection. Deep learning, predictive analytics, natural language processing, anomaly detection, and personalized medicine have ushered in a proactive healthcare era, leading to better patient outcomes, reduced misdiagnosis, and cost-effective treatments. Systematic reviews underscore machine learning's impact across various medical domains. the future holds promise with enhanced data integration, interdisciplinary collaboration, explainable AI, real-time monitoring, global healthcare accessibility, ethical considerations, and continuous learning, ultimately reshaping healthcare for the better.

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Micro-Expression Spotting and recognition Network based on Apex Frame

Micro-Expression Spotting and Recognition Network based on A...

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

作者： Bokai Li Wangwang Xu Houying Qin Lei Jin Jing Wang Zeliang Zhao School of computer science and information engineering Hefei University of Technology Hefei China The second hospital of anhui medical university Hefei China Shanghai ninth people's hospital Shanghai JiaoTong University School of Medicine Shanghai China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Micro-expression is an unconscious response made by people after receiving information, representing real emotions. Due to the short duration and low expression intensity, it is challenging to recognize micro-expression in the video. To this end, in this paper, we propose a novel approach based on the optical flow window to determine the position of the apex frame, which contains the most abundant emotional information. In addition, this paper proposes a Two-Stream Apex Network (TSAN), integrating complex and compelling spatiotemporal micro-expression features. the spotting network balances the micro-expression intensity by selecting the optical flow window, judges the expression states by the facial optical flow, and draws the intensity curve to obtain the apex frame. the recognition network uses the apex frame obtained by the spotting network, which is amplified and combined with the optical flow characteristics to get the micro-expression information. the spotting method can avoid the influence of frames without micro-expression in video and discard redundant features. the recognition method can better judge the category of micro-expression by combining multiple elements. this paper has extensively evaluated CASME II and CASME datasets with a comparison of state-of-the-art methods. the experimental results show that our localization method can effectively detect the position of micro-expression apex frame in the video, and the superiority of the recognition method has also been confirmed based on the apex frame.

关键词： Optical losses Location awareness Accuracy machine learning Feature extraction Spatiotemporal phenomena Character recognition Optical flow

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