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Hybrid CNN-GRU Model for Predicting Dysarthric Speech using Deep Learning Approaches 3

Hybrid CNN-GRU Model for Predicting Dysarthric Speech using ...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chelliah, Suresh Kumar Arivazhagan, N. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331527549

This research study presents the novel method for dysarthria speech detection and classification. In order to create systems that are capable of accurately categorizing speech patterns that are impacted by dysarthria, it is vital to have speech detection and classification capabilities for dysarthria. The condition known as dysarthria, which is characterized by difficulties in articulation, phonation, resonance, and prosody, is a disorder that leads to a reduction in the intelligibility of speech. This method of classification necessitates the development of machine learning or deep learning models, more specifically a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), in order to accomplish the objective of automatically classifying dysarthric speech into categories such as severity or presence/absence of dysarthria. The primary purpose of dysarthria speech categorization is to bring about the development of a reliable instrument that can be used for identifying, categorizing, and managing dysarthric speech. As an added benefit, the classification of dysarthric speech allows for the adaptation of treatment methods and the acquisition of information about the underlying causes of the condition. The individuals who are affected by this speech difficulty are the target audience for this strategy, which ultimately aims to enhance the quality of life of those individual. The study addresses the challenge of classifying dysarthric speech caused by neurological impairments using a Hybrid CNN-GRU model, achieving accuracies of 70.3% (high-severity), 38% (severe-moderate), 34.5% (moderate), and 10.3% (mild dysarthria). © 2025 IEEE.

关键词： Convolutional neural networks

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An Overview of the Use of Artificial intelligence Techniques to Improve Power Quality 3rd

An Overview of the Use of Artificial Intelligence Techniques...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Baggam, Swathi Rani, R. Sheba School of Engineering Malla Reddy University Hyderabad India

ISBN: (纸本)9789819783540

An extensive analysis of the use of artificial intelligence (AI) approaches to enhance power quality in electrical grids is presented in this paper. In maintaining the power quality there might be many problems like frequency variations harmonic distortions and voltage instability. Conventional approaches to these problems are unable to handle the dynamic and intricate structure of contemporary electrical grids. By facilitating predictive modelling, real-time monitoring, and adaptive control, artificial intelligence (AI) approaches including machine learning, neural networks, and optimization algorithms present intriguing answers. In addition to classifying AI-based methods for improving power quality, this assessment summarises the body of research in the area and highlights problems, trends, and potential future possibility enhancement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Power grids

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Intelligent Image Captioning with InceptionV3, LSTM, and PySpark Integration 3

Intelligent Image Captioning with InceptionV3, LSTM, and PyS...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kurian, Thanu Mathew, Jimsha K. Syam Dev, R.S. Anand, P.K. Vignesh, A. Rakshith, B.S. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331515911

Image captioning is a challenging task in artificial intelligence that involves generating descriptive captions for images automatically. In this project, we propose a novel approach leveraging advanced technologies such as PySpark, LSTM, and InceptionV3 to develop an effective image captioning system. We harness the power of PySpark, a distributed computing framework, to efficiently process large-scale image datasets and extract high-level features from images using the InceptionV3 convolutional neural network (CNN) model. These features capture the semantic information present in the images and serve as input to the caption generation model. The caption generation model utilizes LSTM neural network as the decoder component. LSTM is well-suited for sequential data processing and is capable of generating coherent and contextually relevant captions based on the extracted image features. The InceptionV3 model acts as the encoder, extracting meaningful visual features from input images, while the LSTM decoder generates captions by decoding these features into natural language descriptions. This multimodal approach enables the model to understand and describe the content of images accurately. Through experimentation and evaluation on diverse image datasets, our proposed system demonstrates promising results in generating accurate and human-like captions for a wide range of images. By integrating PySpark, LSTM, and InceptionV3, our image captioning system achieves state-of-the-art performance, highlighting the effectiveness of leveraging advanced technologies in solving complex AI tasks. © 2025 IEEE.

关键词： Convolutional neural networks

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

3rd International Conference on Intelligent Systems and Sust...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

ISBN: (纸本)9789819783540

The proceedings contain 52 papers. The special focus in this conference is on Intelligent Systems and Sustainable Computing. The topics include: Supervised machine Learning Method for Bone Cancer Detection and Classification;heart Disease Identification Using Ensemble Learning and Deep Learning;Eigenvalue-Based MRAC for All-Quadrant Vector-Controlled Induction Motor Drives Using Fictitious Resistance;PCOS Screening Using machine Learning and Ensemble Learning Techniques in a Smart Way;MRI-Based Brain Tumour Detection and Classification Using Random Forest Algorithm;decentralizing Rides: A Blockchain Approach to Carpooling;a Privacy Preserving Decision Tree Classification Algorithm for Prediction of Health Care Monitoring System;drug Review Classification by Using Sentiment Analysis;dynamic Resource Management of Cloud Resources Using machine Learning Classification Approaches;deciphering Diabetes: Illuminating Prognostic Insights with an Interpretable machine Learning Framework;integrating Blockchain for Supply Chain Management of Agricultural and Food Products;a Comprehensive Survey on Traffic Light Detection Sensors for Vehicle Safety and Automatic Emergency Braking Systems Using Deep Learning Techniques;a Multi-objective Smart Grid Optimization: Integrating Swarm intelligence with Domain-Specific Heuristics for Superior Energy Efficiency;generative Adversarial Networks for Semi-Supervised Learning in Clinical Decision Support Systems;Efficient Temporal Data Mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems;optimized Task Scheduling in Diverse Distributed Systems with a Hybrid Genetic Algorithm Approach;scalable Real-Time Anomaly Detection in Financial Markets Using a Novel machine Learning Model;AI Enabled Swarm Application for Fire-Fighting Bots Over Web-Bluetooth APIs;disaster Management: Sentimental Analysis Using Tweets Based on Experience and Rehabilitation;movie Sense—Hybrid Movie Recommendation System with Sentimental An

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Multi-strategy Central pattern Generator and Reinforcement Learning Integration for Quadruped Locomotion 3rd

Multi-strategy Central Pattern Generator and Reinforcement L...

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3rd international conference on machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Zuo, Guoyu Wang, Yong Yu, Shuangyue Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819624676

Legged animals have the flexibility to change their gait locomotion pattern strategies under different environments and walking speeds, which is important for efficient locomotion. However, legged locomotion is currently studied as a fixed set of pre-programmed gait patterns, which limits locomotor flexibility. Moreover, the process of designing multiple coping strategies for multiple tasks is cumbersome. In this study, we introduce a novel framework for quadrupedal locomotion learning, enabling robots to swiftly and adaptably transition between gait patterns tailored to various environments. In addition, we introduce an environmental feedback framework to improve the quadrupedal robot's ability to perceive external environmental features. We obtain a strategy network through a two-stage training and deploy it to real robots. Finally, a multi-strategy controller is obtained, which enables the quadruped robot to realize flexible strategy shifts and, moreover, efficient traversal of various complex terrain environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Third international conference on Communications, Information System, and Data Science, CISDS 2024

Third International Conference on Communications, Informatio...

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3rd international conference on Communications, Information System, and Data Science, CISDS 2024

ISBN: (纸本)9781510688315

The proceedings contain 18 papers. The topics discussed include: client dependability evaluation in federated learning framework;FPGA-based hardware optimization and implementation of YOLOv4-tiny;MTOClus: multi-type objects clustering in heterogeneous information networks;FPFS: federated privacy-preserving feature selection with privacy techniques for vertical federated learning;renal tumor classification and detection based on artificial intelligence;stat-net: spatio-temporal aggregation transformer network for skeleton-based few-shot action recognition;dynamic feedback-based vulnerability mining method for highly closed terminal protocols;a RevVIT-based discrimination model for concrete crack images;and acquisition of adaptive knowledge in case-based reasoning for the online set-point control of industrial process.

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Cooperative Search and Tracking Framework for Multi-UAV Systems in Urban Environments 3rd

Cooperative Search and Tracking Framework for Multi-UAV Sy...

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3rd international conference on machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Wang, Zhenzhao Ma, Yunchang Li, Jinxu Li, Linxuan Liu, Zhihong College of Intelligence and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819624676

Cooperative search and tracking of multiple Unmanned Aerial Vehicle(UAV) systems is becoming a key application paradigm for realizing efficient coordination missions. This paper proposes a multiple UAV cooperative search and tracking framework. This framework includes four components: task assignment, motion planning, target detection and target tracking. Among them, task assignment uses auction algorithm to solve the distributed task assignment problem;motion planning uses deep reinforcement learning algorithm;target detection uses YOLOv8 algorithm and ByteTrack algorithm to achieve target recognition and tracking on the image plane;and target tracking utilizes position-based visual servo control to achieve target tracking control of UAV in space. We experimentally validate our proposed framework through XTDrone simulator. The experimental results show that each functional module in our framework plays its corresponding role and can effectively improve the search and tracking efficiency in complex environments of UAVs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motion planning

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A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial intelligence to Quantum machine Learning 3

A Comprehensive Survey on Breast Cancer Diagnostics: From Ar...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Reddy, Manideep Raya V S R S Kumar, Sunil Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Patanjali CPS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum machine Learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis. © 2025 IEEE.

关键词： Artificial intelligence Biopsy Breast Cancer Mammography Quantum machine Learning

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Malaria Detection in Microscopic Blood Smears using Deep Learning: A CNN-based Framework 3

Malaria Detection in Microscopic Blood Smears using Deep Lea...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Bura Vijay Chauhan, Shanvi School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

Still a life-threatening illness brought on by parasites spread by mosquito bites, malaria presents a major danger to world health. Effective therapy and disease control depend on timely and precise identification of malaria-infected cells in blood smears since delays can cause major health problems or death. In this work, we constructed a convolutional neural network (CNN) model to identify malaria in microscopic images of blood smears. The training set consisted of 220 photos of parasite-infected cells and 196 uninfected cells from a smaller dataset;the test set included 91 images of infected cells and 43 uninfected cells. Trained to identify cells as either "Parasite" or "Uninfected" depending on visual patterns, the model Our approach proved potential in helping malaria diagnosis since it achieved 97% validation accuracy and 87% testing accuracy. This study fits objectives of world health, poverty elimination, lower inequality, and creative solutions to solve health disparities. Although there is always room for development, the suggested CNN model shows promise as a means of enhancing diagnosis capacity, especially in environments with low resources where professional pathologists might not be easily accessible. This paper emphasizes the need of using machine learning to handle worldwide health issues. © 2025 IEEE.

关键词： Convolutional neural networks

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Smart AgroAid: AI-Driven Web-based Crop Disease Detection and Management System 3

Smart AgroAid: AI-Driven Web-based Crop Disease Detection an...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Dighe, Suyog Balu Kotkar, Akshay Nandu Modhave, Shravani Dattatraya Patekar, Tushar Sudhakar Jawale, M.A. Sanjivani College of Engineering Department of Information Technology Kopargaon India

ISBN: (纸本)9798331509675

Crop diseases pose a serious danger to global food security and cause large financial losses in the agricultural sector. In order to provide accurate diagnosis and treatment, this study introduces a web-based crop disease detection system that integrates environmental data and sophisticated picture processing. The system analyzes leaf pictures and uses convolutional neural networks (CNNs) to classify plant illnesses. To improve diagnostic precision, environmental elements including soil characteristics, temperature, and humidity are also taken into account. Farmers input leaf photos using the system's user-friendly web interface, and they will receive personalized treatment recommendations along with real-time disease diagnosis findings. This system's sturdy design guarantees scalability and accessibility, especially in rural and resource-constrained locations, and is inspired by earlier research combining ML and AI for agricultural applications. This approach aims to boost output, reduce crop losses, and promote sustainable agricultural practices by providing useful insights and automating disease detection. The multilingual solution ensures inclusion for varied farming communities by automating disease identification and offering actionable insights. © 2025 IEEE.

关键词： Artificial intelligence Crop Disease Detection Environmental Data Integration machine Learning Multilingual Support Sustainable Farming Web-Based System

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