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TinyML Speech Classification Embedded Ai Module for Hand Rehabilitation Device

TinyML Speech Classification Embedded Ai Module for Hand Reh...

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Mechatronics, Control and Robotics (ICMCR), international conference on

作者： Arkorn Numsomran Arjin Numsomran Jutarut Chaoraingern Triam Udom Suksa Pattanakarn Suvarnabhumi School Bangkok Thailand Department of Instrumentation and Control Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Robotics and AI Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

Recent advancements in artificial intelligence and machine learning have significantly improved healthcare, especially in the development of assistive technologies for rehabilitation. This paper introduces a method for hand rehabilitation that utilizes the capabilities of tiny machine learning to enhance speech classification in a rehabilitation device. The proposed method employs a CNN model capable of classifying speech signals that are indicative of different hand movement patterns. Patients emit these speech signals during prescribed hand exercises, which are crucial for their rehabilitation process. The main focus of this study is on training and deploying a speech classification system that can work in the resource-limited environment of TinyML platforms. We detail the process of capturing speech data, preprocessing it, and extracting the most features relevant to different hand movements. Our results show that using TinyML to help with hand rehabilitation works. The method we came up with shows how TinyML could change the way rehabilitative devices are controlled, and it also shows us what personalized and easy-to-use rehabilitation tools might look like in the future.

关键词： Hands Training Technological innovation Tiny machine learning Medical treatment Speech recognition Transforms Speech enhancement Feature extraction Robots

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OSOL: Open Linked data Generation Using Semantics Oriented learning Paradigms 15th

OSOL: Open Linked Data Generation Using Semantics Oriented L...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Aravind Krishnan, A. Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031889912

In the era of Web 3.0, there is an imminent need for a strategic framework for open linked data and meta tag generation for web pages as they would be useful in indexing which would serve as a roadmap for retrieval and also several other applications such as text summarization, text mining, etc. This paper proposes an open-linked data generation framework in which the XGBoost classifier classifies the web URL dataset. Category extraction and URL canonicalization are performed which helps in strategic maximal utilization of the term set. Strategic knowledge base repositories like CYC and NELL are used for entity enrichment in the model. The RBFNN classifies the web-based directories extracted from the current structure of the web using a focused crawler. Unlike most traditional frameworks, the OSOL uses Semantics-oriented reasoning, which is achieved through the SimRank measure with an empirically decided threshold. The Eagle optimization algorithm (a metaheuristic, nature-inspired algorithm) generates the optimal solution set. Shannon’s entropy helps in feature selection for classifying the dataset. This framework can achieve an overall mean precision percentage of 95.17%, a mean recall percentage of 96.75%, and an FDR value of 0.05, making it the best-in-class model for linked open data and meta-tag generation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： entity enrichment linked open data meta tag generation retrieval web-based directories

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machine learning Model Based on Deep Neural Networks for Emotion Detection Using Audio-Visual Modalities 3rd

Machine Learning Model Based on Deep Neural Networks for Emo...

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3rd international conference on Signal and data Processing, ICSDP 2023

作者： Jeevakumari, S. A. Amutha Mohanty, Gyanadipta Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819795772

Emotion identification is a challenging endeavor since emotions can manifest in so many different ways. Recent advances have been made in the field, particularly with the development of deep neural networks, which have shown to be astonishingly good at recognizing emotional states. This work set out to investigate the difficulties of model-level fusion in order to develop a sophisticated multimodal model capable of simultaneously assessing both video and audio inputs for Emotion recognition. To rigorously evaluate the performance of this novel approach, the Ryerson Audio-Visual database of Emotional Speech and Song (RAVDESS) dataset was chosen as the ideal testing ground. This diverse dataset encompasses a wide range of emotional expressions, making it an excellent benchmark for assessing the proposed model’s accuracy and robustness. By combining cutting-edge deep learning techniques with a multimodal approach, this study aims to contribute to the evolving landscape of Emotion recognition, with the potential to enhance applications in fields such as affective computing, human–computer interaction, and emotional well-being assessment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Facial emotion recognition Multimodal models Neural networks Speech emotion recognition

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Automated Helmet Detection and Fine Issuance System Using YOLOv8 & OpenCV

Automated Helmet Detection and Fine Issuance System Using YO...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Hirald Dwaraka Praveena Goruva Vardhan Babu Jinka Muni Sai Yateesh Degasikshi Guna Guduru Vamsi Krishna Dept. of ECE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Dept of ECE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In densely populated countries like India, traffic violations are a major concern, especially among motorcyclists, leading to accidents and significant loss of life and property. A prevalent issue is the failure to wear helmets. To address this, a system called “Helmet Detection for Motorcyclists using YOLOv8 and OpenCV” is proposed, leveraging deep learning to automatically identify and penalize violators in real-time through traffic cameras. The system employs the You Only Look Once (YOLO) algorithm, a deep neural network-based object detection model, to identify riders without helmets. Subsequently, it detects the number plate of violators, extracting its text using the EasyOCR(Optical Character recognition) library. This extracted data is cross-referenced with a pre-existing database to retrieve the owner's contact details. For automation, an SMS API(Application Programming Interface) is integrated to send fine notifications to the violator's registered mobile number. By encouraging helmet use, the system aims to minimize head injuries during accidents and improve road safety. This automated approach enhances traffic law enforcement, provides evidence of violations, and ensures efficient penalty issuance. Ultimately, the system supports safer roads by promoting compliance with traffic regulations and protecting motorcyclists' lives.

关键词： YOLO Deep learning Helmets Real-time systems Road safety Libraries data models data mining License plate recognition Injuries

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Multiple Manifold Regularization Based on Non-Negative Matrix Factorization for Multi-View Clustering

Multiple Manifold Regularization Based on Non-Negative Matri...

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Disruptive Technologies (ICDT), international conference on

作者： Mutyala Sirisha Ghufran Ahmad Khan Jalaluddin Khan Hemlata Pant Kaneez Zainab Zulfikar Ali Ansari Shamshul Haq Ansari Department of Computer and Applications Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur Uttar Pradesh India Department of Computer Science and Engineering B.N. College of Engineering and Technology Lucknow Uttar Pradesh India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) (SIU) Lavale Pune India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In recent years, non-negative matrix factorization (NMF) cluster analysis of multi-view data has shown outstanding results in data mining and machine learning. Multi-view data typically encompasses complementary elements from multiple perspectives. Finding a consensus solution that works for all the different views is a difficult task. This study proposes a novel NMF-based clustering method that utilizes various manifold regularizations for multi-view data to address the previously mentioned problem. In this method, the NMF will decompose the input data into the two non-negative matrices. Next, we employ the manifold scenario to preserve the geometric structure within the data. Lastly, we design the novel objective function by combining all the earlier mentioned terms and then optimize it using the iterative strategy method to achieve the optimal solution. The empirical analysis of real-world datasets indicates that the proposed method outperforms numerous existing techniques in clustering efficiency.

关键词： Manifolds Encyclopedias machine learning Linear programming Internet Performance analysis Online services Iterative methods Matrix decomposition Optimization

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A Hybrid Deep learning Model for Violence Detection

A Hybrid Deep Learning Model for Violence Detection

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： S Chandira Prabha S Kaviyadharshini A Annie Micheal Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Current violence detection systems often face challenges such as high computational costs, real-time processing difficulties, and limited precision in detecting violent activities in dynamic environments. This research addresses these issues by proposing a hybrid deep learning model that integrates MobileNetV2 for efficient spatial feature extraction and ConvLSTM (Convolutional Long Short-Term Memory) for capturing temporal patterns. The model effectively discriminates between aggressive and non-aggressive behaviours in real-time video streams. Using the Kaggle Violence dataset, which comprises 1,000 video clips of violent and non-violent activities, the system achieved an accuracy of 96%, showcasing its high performance. Additionally, an integrated alert system enhances practical application by sending real-time notifications via a Telegram bot, including details such as location, timestamp, and camera ID. This solution ensures timely responses to violent incidents, addressing the core issues of computational efficiency and real-time precision, while offering scalability and applicability in diverse scenarios.

关键词： Training Accuracy Computational modeling Streaming media Feature extraction Real-time systems data models Computational efficiency pattern recognition Streams

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machine learning Based Fault Distance Estimation of Underground Transmission Using Impedance Relay

Machine Learning Based Fault Distance Estimation of Undergro...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sri Chaithanya Mathi Pooja Naidu Tammisettti Shaik Rohid Akthar Manitha P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Department of Electronics and Communication Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Fast development of electric transmission infrastructure necessitates efficient methods of transmission and distribution for reliable cost effectiveness. Between the two main types of transmission, Overhead Transmission Lines involve extensive maintenance cost, use of extensive protection schemes, earthing, and lightning protection. In contrast, Underground Transmission Lines with their insulated and securely laid design are highly resistant to external factors such as lightning, wind, and open conductor faults, showing higher reliability and efficiency. However, faults such as symmetrical and unsymmetrical faults can occur due to conductors being bound together. The traditional fault distance estimation techniques are often inefficient for high-power transmission when using impedance-based fault distance relays. The constant variation in fault characteristics leads to impedance fluctuations, and therefore, fault distance calculations become inaccurate. The current research introduces a machine learning-based Fault Distance Estimation system for Underground Transmission Lines using Impedance Relays to address the above-mentioned challenges. Through data fed into the training machine learning model based on various fault scenarios, data mining is extracted for predicting precise impedance in faulty states and accurately determining fault locations by utilizing trained models with high-performance K-Nearest Neighbour (KNN), Decision Trees (DT), Support Vector Regression (SVR), Random Forest (RF), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANNs). Through these algorithms and approaches, implementations that emphasize optimal preprocessing, effective feature selection and tuning of specific hyperparameters determine their respective modeling efficiencies. The results show that the models generate very accurate distance estimations, allowing for more accurate fault locations and quick rectification. Not only does it bypass the deficiency of the method

关键词： Power transmission lines Costs Estimation Predictive models Fault location Conductors data models Impedance data mining Long short term memory

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machine learning Based Heatwave Modeling for Akola, Maharashtra

Machine Learning Based Heatwave Modeling for Akola, Maharash...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Nabanita Mandal Abhinav Gajria Hussain Rangwala Tanuja Sarode Computer Engineering Department Thadomal Shahani Engineering College Mumbai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Heatwaves are a serious challenge in India, claiming many lives yearly. The heatwaves occur when the temperature rises above the normal continuously for a few days. This paper proposes a Stacked LSTM model that predicts the maximum temperature. The study region is Akola in Maharashtra, India which is prone to heatwaves. It is observed that the Proposed Method gives less Mean Absolute Error as compared to other techniques like Multiple Linear Regression, Random Forest, and Support Vector Regression. After data collection, appropriate data pre-processing technique is applied. Model is built considering the pre-processed data which does not have any anomalies. The reason for improved result of Proposed model is its ability to handle the complex pattern present in the data and learn from it to give better predictions. Maximum temperature is selected as the target variable for prediction and the other features are humidity, dew point etc. are considered here.

关键词： Heating systems Support vector machines Predictive models Prediction algorithms Market research data models Long short term memory Tuning Random forests Overfitting

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Leveraging Sentinel-2 Multispectral data and machine learning Algorithms for Land Use Land Cover Mapping in Semi-Arid Regions

Leveraging Sentinel-2 Multispectral Data and Machine Learnin...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Narayan Vyas Koushik Sundar Amit Sharma Pradeep Shree Adhikari Department of Computer Science and Application Vivekananda Global University Jaipur India Citibank NA Jersey City NJ USA Department of Computer Science and Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Land use and land cover (LULC) classification is a fundamental approach for landscape pattern mapping and monitoring of natural ecosystems and anthropogenic activities and is, therefore, an essential base for resource management to support sustainability. The land cover classes were classified in this study using multispectral data. The study utilizes Sentinel-2 data captured in February 2024, and a median composite image was generated using the Google Earth Engine (GEE) platform. Three different classification algorithms, random forest (RF), classification and regression tree (CART), and naive bayes (NB), were used to produce LULC maps. The algorithms are efficient in capturing different land cover patterns with spectral diversity. The performance with essential measures such as overall accuracy (OA) and Kappa coefficient (KC) has been evaluated to assess classification outcomes. Among these classifiers, the RF classifier with the maximum OA (96.91%) and KC (96.10%) exhibited optimal results and robustness for LULC classification. CART followed this, with OA of 95.88% and KC of 94.81%. NB achieved an OA of 85.57% and KC of 81.93%. It confirms the validity of the suggested method for obtaining LULC maps through Sentinel-2 data and ML algorithms, achieving maximum accuracy in semi-arid regions.

关键词： Earth machine learning algorithms Accuracy Satellites Land surface Classification algorithms Internet Random forests Engines Classification tree analysis

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Automatic recognition of Hearing-Impaired Children's Kannada Speech with Limited Vocabulary

Automatic Recognition of Hearing-Impaired Children's Kannada...

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Smart Systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Chandrashekar H M Anushree T R Chaitra T P Meghana K Monisha T U N Sreedevi Dept. Electronics and Telecommunication Siddaganga Institute of Technology Tumakuru India All India Institute Of Speech and Hearing Mysore India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Hearing is essential in our daily lives, helping us to understand language through sound and contributing to proper speech development. Hearing loss affects many people worldwide, and children account for a large part which can hinder their communication and development skills. Hearing impairment can influence the progression of children's participation in activities suitable for their age, as well as their functional speech communication abilities and language proficiency. Kannada database developed in collaboration with the All India Institute of Speech and Hearing (AIISH), Mysore, is used for hearing-impaired (HI) children. The main aim of this work is to build an automatic speech recognizer (ASR) system with a limited vocabulary dataset using Raspberry Pi. data augmentation is used since the dataset is limited in number of words and number of children. Shorttime Fourier transform (STFT) spectrograms are extracted by varying frame sizes by incorporating augmentation to build an ASR model. A convolutional neural network (CNN) classifier is used to build an ASR model. The speech recognition accuracies for HI speech are compared with and without data augmentation. Improved recognition accuracy is obtained with data augmentation. Finally, real-time implementation for ASR is also carried out and it is observed that there is a scope for improvement of the developed ASR system.

关键词： Vocabulary Accuracy Working environment noise Speech recognition Auditory system machine learning data augmentation Real-time systems Convolutional neural networks Spectrogram

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