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automated Pipeline for Sentiment Analysis of Political Tweets 21

Automated Pipeline for Sentiment Analysis of Political Tweet...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Das, Atrik Gunturi, Kushal Sai Chandrasckhar, Aditya Padhi, Abhinandan Liu, Qian Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781665424271

Social media such as Twitter have proven to he great resources of information regarding many events that proliferate the entire world. It also has the power to change the opinions of millions which is especially useful to sway the masses during political campaigns like presidential elections. Sentiment analysis can he performed on tweets 10 determine how people feel about certain political events which can be used to predict the behavior of people and propagate real-time change as the events play out. Therefore we decided to create a sentiment analysis and polarity detection pipeline generalized for all political data. The pipeline attempts to automate all the NLP tasks from data scraping to cleaning and pre-processing the dataset to make it ready for the classification tasks. The predictions are visualized via word clouds and a map color coded to reveal the sentiments of key nations around the world regarding the political event. This pipeline is tested from end-to-end with our own personal use case being to determine which candidate do countries around the world preferred during the 2020 US Presidential Elections: Trump or Biden. The pipeline provides fruitful results with a lest accuracy of 73.73 percent.

关键词： Political pipeline Twitter sentiment analysis Deep learning

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FAMC: A FPGA Implemented Automatic Modulation Classification via Deep learning for IoV Applications 21

FAMC: A FPGA Implemented Automatic Modulation Classification...

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21st IEEE India Council international conference, INDICON 2024

作者： Sar, Dipanjan Das, Debanjan Mahapatra, Rajarshi Udutalapally, Venkanna Iiit Naya Raipur Department of Electronics and Communication Engineering India Iit Kharagpur Centre of Excellence in Affordable Healthcare India Nit Warangal Department of Computer Science and Engineering India

ISBN: (纸本)9798350391282

The Internet of Vehicles (IoV) is revolutionizing intelligent transportation systems by enabling real-time data exchange and decision-making to enhance traffic safety and reduce congestion. However, effective spectrum management in dynamic vehicular environments presents a significant challenge, particularly for Automatic Modulation Classification (AMC) in the presence of noise and high mobility. Traditional AMC methods, reliant on manual feature extraction and classical algorithms, often struggle under these conditions due to limited accuracy and high computational demands. Existing deep learning solutions, while promising, face difficulties in real-time implementation on resource-constrained devices like FP-GAs. Our work introduces a novel deep learning-based AMC framework optimized for FPGA deployment named 'fAMC', addressing these limitations. The proposed 'fAMC' achieves a classification accuracy of 96.00% on the FPGA platform, with real-time processing capabilities of 15.7 frames per second. This demonstrates significant advancements in accuracy and efficiency, providing a robust solution for IoV systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Video Anomaly Detection: Opportunities and Challenges 21

Deep Video Anomaly Detection: Opportunities and Challenges

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Ren, Jing Xia, Feng Liu, Yemeng Lee, Ivan Federat Univ Australia Sch Engn IT & Phys Sci Ballarat Vic Australia Dalian Univ Technol Sch Software Dalian Peoples R China Univ South Australia STEM Adelaide SA Australia

ISBN: (纸本)9781665424271

Anomaly detection is a popular and vital task in various research contexts, which has been studied for several decades. To ensure the safety of people's lives and assets, video surveillance has been widely deployed in various public spaces, such as crossroads, elevators, hospitals, banks, and even in private homes. Deep learning has shown its capacity in a number of domains, ranging from acoustics, images, to natural language processing. However, it is non-trivial to devise intelligent video anomaly detection systems cause anomalies significantly differ from each other in different application scenarios. There are numerous advantages if such intelligent systems could be realised in our daily lives, such as saving human resources in a large degree, reducing financial burden on the government, and identifying the anomalous behaviours timely and accurately. Recently, many studies on extending deep learning models for solving anomaly detection problems have emerged, resulting in beneficial advances in deep video anomaly detection techniques. In this paper, we present a comprehensive review of deep learning-based methods to detect the video anomalies from a new perspective. Specifically, we summarise the opportunities and challenges of deep learning models on video anomaly detection tasks, respectively. We put forth several potential future research directions of intelligent video anomaly detection system in various application domains. Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection.

关键词： Anomaly detection Deep learning Video surveillance intelligent systems

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Terrain Recognition for intelligent Powered Ankle-Foot Prosthetics Using sEMG and Ensemble learning Models 21

Terrain Recognition for Intelligent Powered Ankle-Foot Prost...

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21st IEEE India Council international conference, INDICON 2024

作者： Kumar, Vidyapati Hrishikesh, M.V. Shijas, Muhamed Pratihar, Dilip Kumar Indian Institute of Technology Department of Mechanical Engineering West Bengal Kharagpur India

ISBN: (纸本)9798350391282

Accurate terrain recognition is crucial for improving the functionality of ankle-foot prosthetic devices, which are designed to enhance mobility in amputees. Current prosthetics cannot often identify and adapt to varied terrains, leading to instability and decreased user confidence. Most existing research focuses on mechanical enhancements rather than effectively integrating intelligent terrain adaptation. This study addresses this gap by developing an intelligent framework for terrain recognition using machine learning and sensor fusion techniques. The framework utilizes surface electromyography (sEMG) data from nine muscles which include thigh muscles and below-knee muscles, specifically the tensor fascia lata, rectus femoris, vastus medialis, semimembranosus, upper tibialis anterior, lower tibialis anterior, lateral gastrocnemius, medial gastrocnemius, and soleus muscles. Various machine learning models, including Extra Trees, Random Forest, XGBoost, Decision Tree, Artificial Neural Networks (ANN), and Support Vector Classifier (SVC), were trained and evaluated. The Extra Trees model demonstrated superior performance with an accuracy of 0.87, F1 score of 0.88, precision of 0.87, and recall of 0.87. This method significantly enhances the prosthetic device's ability to adapt to different terrains, ensuring improved stability and safety for the user. The findings of this research pave the way for future developments in smart prosthetics, offering substantial benefits to amputees in real-world environments. © 2024 IEEE.

关键词： Artificial limbs

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Rule-Based Representation learning for Traditional Chinese Medicine Knowledge Graph

Rule-Based Representation Learning for Traditional Chinese M...

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international conference on Software engineering Research, Management and Applications (SERA)

作者： Dongsheng Shi Feng Lin Yuxun Li Qianzhong Chen Yiying Lin Wentao Zhu Dongmei Li Xiaoping Zhang National Forestry and Grassland Administration Beijing Forestry University Engineering Research Center for Forestry-Oriented Intelligent Information Processing Beijing China National Data Center of Traditional Chinese Medicine China Academy of Chinese Medical Sciences Beijing China

Traditional Chinese medicine (TCM) has a unique advantage of preventive treatment of diseases, and adopting the concept of early intervention can effectively prevent diseases. Using knowledge graph is an effective way while the knowledge in the field of TCM is huge and messy. However, the structure of the TCM knowledge graph is often relatively sparse, which makes it highly limited. To this end, a rule-based compositional representation learning (RCRL) model is proposed. RCRL uses the implicit rules in the TCM knowledge graph, which solves the problem of poor representation learning due to the sparse structure of the TCM knowledge graph to a certain extent. Extensive experiments are conducted on the TCM knowledge graph and public datasets, and they are compared with other baselines. Experimental results show that RCRL is superior to other baselines, with improved learning accuracy and interpretability, and can be used for various downstream tasks.

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Novel Music Genre Classification System Using Transfer learning on a Small dataset

Novel Music Genre Classification System Using Transfer Learn...

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international conference on Software engineering Research, Management and Applications (SERA)

作者： Yi Wang Takaaki Goto Tadaaki Kirisima Kensei Tsuchida Toyo University Saitama Japan

Music is an essential "element" in our lives. With the rapid development of technology, the use of compact disks and tapes for listening various forms of music of different cultures has become obsolete. Because of the generation of considerable music data, the accurate classification of music data has become a critical topic of research, and the development of deep learning technology provides a solution for music classification. Transfer learning techniques are widely used in image recognition but are rarely used for music classification. In this study, a small music dataset is used for music classification using transfer learning.

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Guidance with Spherical Gaussian Constraint for Conditional Diffusion 41

Guidance with Spherical Gaussian Constraint for Conditional ...

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41st international conference on Machine learning, ICML 2024

作者： Yang, Lingxiao Ding, Shutong Cai, Yifan Yu, Jingyi Wang, Jingya Shi, Ye ShanghaiTech University China MoE Key Laboratory of Intelligent Perception and Human Machine Collaboration China

Recent advances in diffusion models attempt to handle conditional generative tasks by utilizing a differentiable loss function for guidance without the need for additional training. While these methods achieved certain success, they often compromise on sample quality and require small guidance step sizes, leading to longer sampling processes. This paper reveals that the fundamental issue lies in the manifold deviation during the sampling process when loss guidance is employed. We theoretically show the existence of manifold deviation by establishing a certain lower bound for the estimation error of the loss guidance. To mitigate this problem, we propose Diffusion with Spherical Gaussian constraint (DSG), drawing inspiration from the concentration phenomenon in high-dimensional Gaussian distributions. DSG effectively constrains the guidance step within the intermediate data manifold through optimization and enables the use of larger guidance steps. Furthermore, we present a closed-form solution for DSG denoising with the Spherical Gaussian constraint. Notably, DSG can seamlessly integrate as a plugin module within existing training-free conditional diffusion methods. Implementing DSG merely involves a few lines of additional code with almost no extra computational overhead, yet it leads to significant performance improvements. Comprehensive experimental results in various conditional generation tasks validate the superiority and adaptability of DSG in terms of both sample quality and time efficiency. Copyright 2024 by the author(s)

关键词： Gaussian distribution

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Deep Human-guided Conditional Variational Generative Modeling for automated Urban Planning 21

Deep Human-guided Conditional Variational Generative Modelin...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Wang, Dongjie Liu, Kunpeng Johnson, Pauline Sun, Leilei Du, Bowen Fu, Yanjie Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Beihang Univ Dept Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665423984

Urban planning designs land-use configurations and can benefit building livable, sustainable, safe communities. Inspired by image generation, deep urban planning aims to leverage deep learning to generate land-use configurations. However, urban planning is a complex process. Existing studies usually ignore the need of personalized human guidance in planning, and spatial hierarchical structure in planning generation. Moreover, the lack of large-scale land-use configuration samples poses a data sparsity challenge. This paper studies a novel deep human guided urban planning method to jointly solve the above challenges. Specifically, we formulate the problem into a deep conditional variational auto-encoder based framework. In this framework, we exploit the deep encoder-decoder design to generate land-use configurations. To capture the spatial hierarchy structure of land uses, we enforce the decoder to generate both the coarse-grained layer of functional zones, and the fine-grained layer of POI distributions. To integrate human guidance, we allow humans to describe what they need as texts and use these texts as a model condition input. To mitigate training data sparsity and improve model robustness, we introduce a variational Gaussian embedding mechanism. It not just allows us to better approximate the embedding space distribution of training data and sample a larger population to overcome sparsity, but also adds more probabilistic randomness into the urban planning generation to improve embedding diversity so as to improve robustness. Finally, we present extensive experiments to validate the enhanced performances of our method.

关键词： automated Urban Planning Conditional Variational Generative Model Human Guided Spatial data Mining

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Estimating Blood Glucose Levels Using Machine learning Models with Non- ?nvasive Wearable Device data

Estimating Blood Glucose Levels Using Machine Learning Model...

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21st international conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023

作者： Aziz, Sarah Ahmed, Arfan Abd-Alrazaq, Alaa Qidwai, Uvais Farooq, Faisal Sheikh, Javaid AI Center for Precision Health Weill Cornell Medicine-Qatar Doha Qatar Department of Computer Science and Engineering Qatar University Qatar Center for Digital Health and Precision Medicine Qatar Computing Research Institute Doha Qatar

ISBN: (纸本)9781643684000

In 2019 alone, Diabetes Mellitus impacted 463 million individuals worldwide. Blood glucose levels (BGL) are often monitored via invasive techniques as part of routine protocols. Recently, AI-based approaches have shown the ability to predict BGL using data acquired by non-invasive Wearable Devices (WDs), therefore improving diabetes monitoring and treatment. It is crucial to study the relationships between non-invasive WD features and markers of glycemic health. Therefore, this study aimed to investigate accuracy of linear and non-linear models in estimating BGL. A dataset containing digital metrics as well as diabetic status collected using traditional means was used. data consisted of 13 participants data collected from WDs, these participants were divided in two groups young, and Adult Our experimental design included data Collection, Feature engineering, ML model selection/development, and reporting evaluation of metrics. The study showed that linear and non-linear models both have high accuracy in estimating BGL using WD data (RMSE range: 0.181 to 0.271, MAE range: 0.093 to 0.142). We provide further evidence of the feasibility of using commercially available WDs for the purpose of BGL estimation amongst diabetics when using Machine learning approaches. © 2023 The authors and IOS Press.

关键词： Wearable technology

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data Mining on Extremely Long Time-Series 21

Data Mining on Extremely Long Time-Series

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Simmons, Scott Jarvis, Louis Dempsey, David Kempa-Liehr, Andreas W. Univ Auckland Dept Engn Sci Auckland New Zealand Univ Canterbury Dept Civil & Nat Resources Engn Christchurch New Zealand Univ Freiburg Freiburg Mat Res Ctr Freiburg Germany

ISBN: (纸本)9781665424271

The crux of machine learning for time-series revolves around the engineering of relevant features as inputs into a predictive model. Such features are designed to capture the informative characteristics of an input signal. For many real world applications it is required that these features are not only informative but also interpretable for domain experts. Recent work has led to the automation of time-series feature engineering. In this paper, we are demonstrating that for very large and complex time-series, the dynamics of time-series features itself are sources of discriminative and interpretable time-series features. This concept is evaluated for a predictive maintenance use case (time-series classification) and the estimation of a vibrational sensor signal from a co-located sensor (time-series regression). Both use cases show promising preliminary results for the efficacy of extracting time-series features from the dynamics of time-series features.

关键词： Time series

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