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English Speech recognition using Convolution Neural Networks and IoT 5

English Speech Recognition using Convolution Neural Networks...

引用

5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Vijayakumari, R. Bannatti, Akshay Ashok Maranan, Ramya Kaur, Jaspreet Kamatchi, S. Department of Research and Innovation Saveetha School of Engineering Simats Krishna University Machilipatnam Chennai Kanchipuram India Simats India Gulzar Group of Institutions India Simats Krishna University Saveetha School of Engineering Machilipatnam Chennai TAMIL NADU Kanchipuram India

ISBN: (纸本)9798350383522

the middle era of speech popularity makes it viable for machines and those to communicate effectively and seamlessly. the demand for particular and dependable voice popularity structures is rising as Internet of things (IoT) applications are expanding quick. the use of convolutional neural networks (CNNs) to enhance speech recognition performance, especially for the linguistic English language, is presented in detail in this summary. the complex phonetics and phonology of the English language, in addition to its numerous accents, dialects, and regional editions, are all covered within the field of linguistics. For efficient human-pc interplay in a whole lot of IoT contexts, including smart houses, healthcare systems, and voice-controlled gadgets, accurate linguistic English speech reputation is crucial. For quite a few pc imaginative and prescient and audio processing obligations, convolutional neural networks (CNNs) have turn out to be effective deep mastering fashions. In this paper, we explore the use of CNNs in IoT contexts for linguistic English voice popularity. the collection of speech data, preprocessing, function extraction, model schooling, and reputation are the primary components of the recommended system. To ensure robustness and generality, we use huge-scale datasets of English speech to train the CNN fashions. Backpropagation and stochastic gradient descent are used to educate the version whilst optimizing the parameters to lessen the popularity blunders. this summary's end emphasizes the significance of unique English speech popularity in IoT programs. We can improve the effectiveness of speech popularity systems for the linguistic English language via using convolutional neural networks. By allowing greater intuitive and herbal interactions via speech commands and voice-managed devices, the suggested approach creates new opportunities for seamless human-gadget engagement in IoT environments. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Audio analysis Convolutional Neural Networks (CNNs) Deep learning Internet of things (IoT) IoT applications IoT devices Language processing Linguistic English language machine learning Natural Language Processing (NLP) Neural network models Speech recognition Speech-to-text Voice recognition

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Detecting Feet's Bending patterns With Smart Insoles

Detecting Feet's Bending Patterns With Smart Insoles

引用

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

作者： Yuchen Zhong Qijun Ying Xiaohui Cai dept. of Computer Science and Technology University of Science and Technology of China (USTC) Hefei China dept. of Data Science University of Science and Technology of China (USTC) Hefei China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Foot bending is a basic component of walking. Although bending is important for foot-ground interaction and ensures the speed and balance of the whole walking cycle, there is still no practical method for recording and analyzing foot bending under everyday scenarios. In this study we propose to use a smart insole, with two embedded Inertial Measurement Units, to acquire the movements of the fore-foot and the hind-foot and deduce the bending, represented by the bending point's position. We use the deep learning approach to estimate the bending position and its changes during different walking patterns. On the dataset containing 8 test subjects and 6 walking patterns, our method can achieve an overall mean absolute error accuracy of 2.58 cm. the smart insole can be with the subject all the day, bring in a new method for continuous foot bending monitoring.

关键词： Legged locomotion Measurement units three-dimensional displays machine learning algorithms Measurement uncertainty Inertial navigation Bending Recording Monitoring Foot

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Cross-dataset Evaluation of an Effective Face Anti-Spoofing Algorithm

Cross-Dataset Evaluation of an Effective Face Anti-Spoofing ...

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

作者： Xingyi You Xiaohu Zhao National and Local Joint Engineering Laboratory of Internet Applied Technology on Mines China University of Mining and Technology School of Information and Control Engineering Xuzhou China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Face anti-spoofing (FAS) is a kind of face-liveness detection technology that is the key to ensuring the reliability of face recognition results. In recent years, some deep learning-based methods have performed well in intra-tests. However, their limited ability to extract intrinsic features from face images often hinders generalization to attacks in unknown domains and leads to poor performance. In this paper, we propose a novel FAS method based on domain generalization, comprising an image encoder, two lightweight feature extractors (identity (I-FT) and style (S-FT), and a modulation factor. Initially, deep features extracted from the ResNet34 network are utilized to convert input face images into high-dimensional feature vectors. Subsequently, two lightweight feature extraction modules learn distinct hints related to identity and style information from the image encoder. Finally, an adjustment factor is adopted that adjusts the extracted feature parameters for enhancing model performance on domain generalization tasks for anti-fraud purposes involving faces. After the verification of a large number of experimental results, we find that the proposed method has good domain generalization ability and shows excellent performance on different datasets.

关键词： Deep learning learning systems Visualization Correlation Face recognition Modulation Feature extraction Vectors Reliability

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DL-StormCast: Deep learning and Near-forecast of Severe Convective Precipitation

DL-StormCast: Deep Learning and Near-forecast of Severe Conv...

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

作者： Guoqing Zhao Shenzi Yang Xianbiao Kang Leilei Deng Civil Aviation Flight University of China Guanghan China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Given the rapid onset, swift evolution, and short lifespan of severe convective precipitation events, accurately forecasting these within a two-hour window presents significant challenges. In the context of meteorological big data, this study introduces an integrated learning approach that enhances the efficiency of dual-polarization radar parameters in near-term forecasting of severe convective precipitation. By combining the Support Vector machine-Boosted Naive Trees (SVM-BNT) classification model with the Convolutional Long Short-Term Memory (ConvLSTM) prediction model, we achieve a notable improvement in forecast accuracy. the SVM-BNT model effectively transforms explanatory variables, converting prior knowledge into posterior probabilities, which enhances the classification of precipitation particles based on radar observation data. the resulting classified data then serve as refined inputs for the ConvLSTM model, enabling it to capture crucial spatiotemporal characteristics of precipitation more effectively. this integration reduces the Root Mean Square Error (RMSE) from 3.10 to 2.45 and the Mean Squared Error (MSE) from 10.65 to 7.80, demonstrating significant enhancements in prediction precision. these advancements have substantial practical implications, particularly for improving response strategies in severe weather events and optimizing resource allocation for disaster management.

关键词： Deep learning Visualization Precipitation Accuracy Weather forecasting Predictive models data models Spatiotemporal phenomena Forecasting Root mean square

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Research on Sensor Interference recognition of three-Phase Asynchronous Motor Based on Deep learning 5

Research on Sensor Interference Recognition of Three-Phase A...

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5th international conference on Internet of things, Automation and Artificial Intelligence, IoTAAI 2023

作者： Li, Hao Zhao, Shenghui Zhao, Liang School of Computer Science and Technology Anhui University of Technology Anhui Ma'anshan243002 China School of Computer and Information Engineering Chuzhou University Anhui Chuzhou239000 China

ISBN: (纸本)9798400716485

three-phase asynchronous motor is widely used in petrochemical industry, but they are generally installed in harsh environments. Traditional maintenance and fault diagnosis are difficult to detect the potential faults of three-phase asynchronous motors effectively. It is a common method to detect the state of three-phase asynchronous motor based on temperature and humidity. Considering the characteristics of temperature and humidity data, this paper introduces a CM-RSL model based on deep learning. the model comprises the Random Forest (RF) algorithm for detecting data anomalies resulting from electromagnetic interference, a Support Vector machine (SVC) classifier for identifying anomalies caused by sensor failure, and Long Short-Term Memory (LSTM). the CM-RSL model is used to analyze whether there is abnormal interference in the temperature and humidity data of the three-phase asynchronous motor, and then estimate the working condition of the connected motor. In the concluding phase, the proposed model underwent evaluation using long-term real-time temperature and humidity data from 32 three-phase asynchronous motors, collected through temperature and humidity sensors. the results demonstrate that the CM-RSL model effectively identifies the sources of disturbances leading to anomalies in the temperature and humidity data of three-phase asynchronous motors, achieving an accuracy rate exceeding 98%. © 2023 ACM.

关键词： Internet of things

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Advanced Feature Engineering and Sampling Technique for Effective Heart Disease Prediction System using machine learning Technique 5

Advanced Feature Engineering and Sampling Technique for Effe...

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5th IEEE international conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Awoyemi, John Oloruntoba Awoyemi, Bosede Olanike Ugwu, Tolulope Anthonia Oluwasola, Atolase Mercy Oladokun, Temitope Ugwu, Chukwuemeka Christian Federal University Oye-Ekiti Dept. of Computer Science Oye-Ekiti Nigeria Afe Babalola University Dept. of Economics Ado-Ekiti Nigeria Federal University of Technology Dept. of Information Systems Akure Nigeria International Brewery PLC-AB-Inbev Lagos Nigeria Federal University of Technology Dept. of Data Science Akure Nigeria

ISBN: (纸本)9798331542559

Predicting heart disease is crucial in healthcare, where accurate detection can significantly impact patient outcomes. A major challenge in prediction is imbalanced data, with underrepresented samples in the minority class leading to biased models. this study addresses this by combining advanced feature engineering with SMOTE (Synthetic Minority Oversampling Technique) to manage class imbalance. the objectives include refining data through feature engineering, building predictive models using machine learning algorithms, and assessing performance metrics. Algorithms such as Random Forest, SVM, Decision Tree, Logistic Regression, and K Nearest Neighbor are applied. Results show that robust feature engineering notably enhances performance, with Random Forest achieving the highest accuracy (0.9604), followed by SVM (0.954) and Decision Tree (0.947). the findings highlight the role of feature engineering and sampling in improving heart disease prediction models, with meaningful implications for healthcare advancements. © 2024 IEEE.

关键词： Algorithms Class imbalance Coronary data mining SMOTE

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Hybrid Prediction of Daily COVID-19 Infections Using machine learning and SIR Model Integration

Hybrid Prediction of Daily COVID-19 Infections Using Machine...

引用

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

作者： Suronapee Phoomvuthisarn Puripant Ruchikachorn Department of Statistics Chulalongkorn University Bankgkok Thialand

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

the COVID-19 pandemic has highlighted significant challenges in predicting the daily infectious individuals, a critical aspect for managing public health responses. Accurate prediction models are essential, but epidemic models, alongside traditional statistical and machine learning models require extensive data, which may have accurate predictions but may not be timely enough to handle a pandemic immediately in real-life scenarios. this paper explores a hybrid approach, utilizing machine learning to estimate the basic reproduction number (Ro) and integrating it into the SIR (Susceptible-Infected-Recovered) model for more reliable predictions during the early stages of a pandemic using limited data. this study evaluates the effectiveness of this approach using historical COVID-19 data from thailand and compares it to existing methods. the proposed hybrid model is evaluated against traditional SIR models with fixed Ro and various machine learning models, including Random Forest Regressor, RNN, LSTM, and GRU. Prediction accuracy is measured by mean absolute error (MAE). Our approach demonstrates superior performance in predicting daily infectious individuals during the first wave of COVID-19 in thailand, even with limited early-stage data, and effectively adapts to changing epidemic dynamics influenced by policy responses. Results indicate that the hybrid model significantly outperforms both traditional SIR models and other machine learning approaches, showcasing its robustness and adaptability in real-world pandemic management scenarios.

关键词： COVID-19 Training Adaptation models Accuracy Pandemics Predictive models data models Random forests Long short term memory Context modeling

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Analysis of machine learning Based Big data mining System for Enterprise Businesses

Analysis of Machine Learning Based Big Data Mining System fo...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Luo Dan University of Malaya Kuala Lumpur Malaysia

the primary objective of this paper is to conduct an in-depth analysis of a machine learning-based enterprise business big data mining system. Firstly, the introduction section presents the background, significance, and objectives of the research. Secondly, we provide an overview of the fundamental concepts of data mining and machine learning, including data mining methods and machine learning algorithms. Specifically, we discuss data mining methods such as the Apriori algorithm, clustering algorithm, and decision tree, as well as machine learning algorithms such as the Naive Bayes algorithm and K-nearest neighbors method. Furthermore, we introduce the principles and applications of the big data processing framework Hadoop. Subsequently, the design and implementation of the enterprise big data mining system are discussed. Finally, we conduct experiments on the enterprise big data mining system. We present a detailed description of the experimental process, including the introduction of experimental data, the experimental environment, and parameter settings, as well as specific experimental steps and procedures. Additionally, we showcase the experimental results and conduct evaluation and analysis. the research in this paper aims to provide valuable guidance and practical experience for enterprises in the field of business big data mining, promoting the intelligence and precision of decision-making, and enhancing the healthy development and competitive advantage of businesses.

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Research on Tibetan Voiceprint recognition Methods Under Low-Resource Condition

Research on Tibetan Voiceprint Recognition Methods Under Low...

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

作者： Qiong Ni Dingguo Gao Guo Ba School of Information Science and Technology Tibetan Information Technology Innovative Talent Cultivation Demonstration Base Tibet University Lhasa China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In recent years, research on Chinese and English voiceprint recognition has matured and has been widely ap-plied in various fields. However, research on Tibetan voiceprint recognition is relatively limited, primarily focusing on text-related voiceprint recognition. Research on text-independent Tibetan voiceprint recognition is almost nonexistent, and deep learning-based Tibetan voiceprint recognition has only just begun. Furthermore, existing models rely heavily on large datasets. To address these issues, this paper implements text-independent Tibetan voiceprint recognition using mainstream models and proposes a ResNet model that incorporates multi-scale attention to enhance model performance in low-resource environments. Firstly, diverse data is expanded using the pub-licly available Tibetan speech dataset ertz0y0o, resulting in a dataset of 61,457 audio samples from 112 speakers. Secondly, six sets of comparative experiments were conducted using main-stream models TDNN, ResNet34SE, and the proposed model ResNetSE_Multi on both the complete dataset and a low-resource dataset consisting of 20 % of the audio data randomly sampled from each speaker. Experimental results demonstrate that the proposed model achieves an accuracy improvement of 2% and 3% over the other two models in the low-resource scenario, indicating stronger learning capabilities and laying a foundation for practical applications in Tibetan voiceprint recognition.

关键词： Training Deep learning Accuracy Text recognition Focusing Speech recognition data models Spectrogram

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Combating Advanced Persistent threats for Imminent Low Earth Orbit Cognitive Communications Systems 16

Combating Advanced Persistent Threats for Imminent Low Earth...

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16th Annual IEEE international Systems conference (SysCon)

作者： LaMar, Suzanna Gosselin, Jordan J. Happel, Lisa Jayasumana, Anura Northrop Grumman Corp 15120 Innovat Dr San Diego CA 92128 USA Colorado State Univ Dept Elect & Comp Engn Ft Collins CO USA

ISBN: (纸本)9781665439923

With the proliferation of Low Earth Orbit (LEO) spacecraft constellations, comes the rise of space-based wireless cognitive communications systems (CCS) and the need to safeguard and protect data against potential hostiles to maintain widespread communications for enabling science, military and commercial services. For example, known adversaries are using advanced persistent threats (APT) or highly progressive intrusion mechanisms to target high priority wireless space communication systems. Specialized threats continue to evolve with the advent of machine learning and artificial intelligence, where computer systems inherently can identify system vulnerabilities expeditiously over naive human threat actors due to increased processing resources and unbiased pattern recognition. this paper presents a disruptive abuse case for an APT-attack on such a CCS and describes a trade-off analysis that was performed to evaluate a variety of machine learning techniques that could aid in the rapid detection and mitigation of an APT-attack. the trade results indicate that with the employment of neural networks, the CCS's resiliency would increase its operational functionality, and therefore, on-demand communication services reliability would increase. Further, modelling, simulation, and analysis (MS&A) was achieved using the Knowledge Discovery and data mining (KDD) Cup 1999 data set as a means to validate a subset of the trade study results against Training Time and Number of Parameters selection criteria. Training and cross-validation learning curves were computed to model the learning performance over time to yield a reasonable conclusion about the application of neural networks.

关键词： cognitive communications system wireless communications cybersecurity advanced persistent threat intrusion detection resiliency and machine learning

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