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Hybrid CNN-GRU Model for Predicting Dysarthric Speech Using Deep learning Approaches

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

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

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

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

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).

关键词： Voice activity detection Deep learning Accuracy Transfer learning Speech recognition Speech enhancement Predictive models Feature extraction Robustness Convolutional neural networks

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Meta-learning for Text-Based Object Localization in Robotic Manipulation with DNN and CLIP

Meta-Learning for Text-Based Object Localization in Robotic ...

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

作者： Mariam Kashkash Mohsen Guizani Dept. Machine Learning MBZUAI Abu Dhabi UAE

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

As robotics increasingly intersects with human environments, text-based guidance allows robots to perform tasks flexibly and intuitively, bridging the gap between human intentions and robot actions. However, enabling robots to interpret diverse natural language commands, detect and manipulate a variety of objects, and generalize effectively across different datasets poses significant challenges. This paper presents a novel approach based on meta-learning for robotic manipulation tasks, specifically focusing on pick-and-place operations guided by natural language instructions. Model-Agnostic Meta-learning (MAML) framework is proposed to enable a robotic manipulator to generalize across varying tasks based on text-based commands. Utilizing a dataset of images paired with text instructions, a MAML-based model with a Deep Neural Network (DNN) and CLIP is proposed to localize objects mentioned in text commands within the images. The application of N-way K-shot learning allows the model to learn new tasks with minimal data efficiently. Experimental results reveal the impact of tuning the inner and outer learning rates, shedding light on their roles in enhancing the model's adaptability and accuracy. The performance of the approach is further validated using table-top and CLEVR datasets, where it achieves low loss values in object localization and task execution, demonstrating its effectiveness in generalizing across diverse tasks in a few meta-steps. The proposed model significantly reduces support and query losses, particularly in high N-way and K-shot configurations, highlighting its ability to learn effectively with minimal data.

关键词： Metalearning Location awareness Adaptation models Natural languages Artificial neural networks Predictive models data models Object recognition Robots Convergence

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Advanced Face Detection Using SVM and Haar-like Features: A Comprehensive Approach in AI and Image Processing

Advanced Face Detection Using SVM and Haar-like Features: A ...

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

作者： Devendra Somwanshi Archika Jain Kapil Joshi Vishnu Kant Salil Bharany Bura Vijay Kumar Department of EC Poornima College of Engineering Jaipur India Department of CSE Management & Gramothan (SKIT) Swami Keshvanand Institute of Technology Jaipur India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science and Artificial Intelligence SR University Warangal

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Face detection is a vibrant research field spanning artificial intelligence, image processing, video surveillance, human-machine interaction, identity authentication, machine learning, pattern recognition, and so on. While humans effortlessly recognize faces, computers face challenges due to facial variability. The complexity lies in factors like expressions, lighting changes, and head positions. Thus, solving the problem of face detection has an important academic value and provides important enlightenment. There are many aspects to face detection such as facial expression, head position, changing lighting conditions and many others. To deal with face detection problems various approaches are used such as template matching methods, knowledge-based methods, feature invariant methods, and appearance-based methods. The proposed approach for face detection utilizes Support Vector machine (SVM) and Haar-like Features. It begins by generating an integral image from a binary image measuring 19 x 19 pixels. Following this, the Haar feature extraction method is employed to obtain Haar values from both face and non-face isommages. A set of 200 face and non-face images from MIT database have been used. Features have been reduced five times to make different features sets of images. These features are passed to Support Vector machine as input parameters and processed to classify face and non-face datasets. Support Vector machine is used to create, train and test the neural network. Features are extracted on MATLAB and further processed in SVM using NeuroSolutions tool. The results are evaluated based on two scenarios as varying number of images and features.

关键词： Support vector machines Training Face recognition Lighting machine learning Feature extraction Magnetic heads Face detection MATLAB Faces

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Sparrow Mapping using Hyperspectral Remote Sensing and Deep learning

Sparrow Mapping using Hyperspectral Remote Sensing and Deep ...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： Thilagavathi K Kaavya K Muvin M Harei Sharan S H Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Biodiversity loss, driven by habitat destruction, poses a significant threat to ecological balance and the survival of endangered species. Accurate identification and monitoring of critical habitats are essential for effective conservation efforts. However, traditional field-based habitat mapping methods are often time-intensive, expensive, and spatially constrained. This work aim to address these limitations by integrating hyperspectral remote sensing with advanced deep-learning methodologies for precise habitat identification and mapping. This work integrates hyperspectral remote sensing with deep learning methodologies, specifically Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs), to analyse high-dimensional hyperspectral data. CNNs are utilized for extracting spatial features, effectively capturing both spectral and spatial variations in the imagery. ANNs are applied for spectral pattern recognition, enabling accurate classification of habitats based on their unique spectral signatures. The pre-processing pipeline includes resizing and grayscale conversion of hyperspectral images to optimize them for deep learning analysis. Validation using field data ensures the reliability of the model outputs. The methodology demonstrated promising results, with ANN achieving a superior accuracy of 99% and CNN achieving an accuracy of 84%. These results are intended to support conservation planning, resource allocation, and evidence-based decision-making. This work contributes to advancing biodiversity conservation and underscores the potential of deep learning in ecological monitoring and management.

关键词： Deep learning Accuracy Biological system modeling Habitats Vegetation mapping Predictive models Feature extraction Convolutional neural networks Monitoring Hyperspectral imaging

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Smart Water Management System For Rural And Urban Infrastructure Using Iot

Smart Water Management System For Rural And Urban Infrastruc...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： M Joshika R Ashwin S Hema Varshini A K Jubair Ahamed B Sakthikumar Department of ECE Sri Eshwar College of Engineering Coimbatore

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The IoT-based Smart Water Management System represents modern state-of-the-art technology which provides effective control of water resources for urban and rural areas. This system enables real-time assessment of water levels as well as stream flow and quality and usage pattern data by using advanced sensors and actuators and Internet of Things devices. The water delivery system benefits from precise wastage reduction through NodeMCU processing of data acquired from Ultrasonic Sensor and flow sensor inputs that monitor water levels and leak detection. Motor pumps and valves receive data through the Driver Circuit. Cookies send the data to cloud infrastructure to display vital information through web-based dashboards for remote monitoring purposes. The system employs machine learning algorithms for detecting anomalies while also predicting future water needs to adjust the water distribution. The system uses Java's animation function to display water tank levels chaotically which provides both improved visual interface and instant system performance monitoring. The method saves 30% of water resources while reducing human labor expenses and ensures more sustainable water management and energy-efficient solutions. The proposed plan shows successful implementation for eco-conscious water resource management and environmental preservation within industrial spaces and agricultural zones and smart urban areas.

关键词： Water Cloud computing Visualization machine learning algorithms Valves Sensor systems Real-time systems Internet of Things Leak detection Water resources

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Analyzing Handwriting Features for ADHD Detection in Children with Autism via machine learning

Analyzing Handwriting Features for ADHD Detection in Childre...

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Disruptive Technologies (ICDT), international conference on

作者： Ch. Prathima Anitha. T P Jayachandra reddy Yekkaluri Susmitha P Soni Reddy Nakkarasupalli Charitha Dept.of DS Mohan Babu University Tirupati India Department of CSE K.S.R.M College of Engineering Dept of CSE Annamacharya institute of technology & sciences Dept. of IT Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Attention deficit hyperactivity disorder (ADHD) develops in children's brains, portrayed by symptoms of inattentiveness, hyperactivity, and impulsivity. Early identification is very much essential, but traditional diagnostic methods might be subjective in practice and consuming time. Hence this work intends to introduce an end-to-end hybrid deep-learning system which fuses the techniques CNN and Bi-LSTM Networks to develop a system using handwriting to detect ADHD in children. The Bi-LSTM layers capture the sequential dependencies that exist in the handwriting data, capturing the temporal irregularities, whereas the CNN layers extract spatial features such as stroke patterns and pressure variations. The training and evaluation of the proposed model was done by synthetically balancing the dataset with accuracy, Confusion matrix, ROC curve and AUC for assessing performance. A method that has surpassed an accuracy of 90% in classification, this proposed system has been able to properly point out ADHD-related traits by hand. This method is scalable as it approaches to do fewer computations for the detection, thus making it a real data-driven alternative to traditional methods. Future work: Add attention mechanisms to increase the model's ability toward generalization, expand available datasets, and improve generalized capabilities.

关键词： Deep learning Training Hands Accuracy Attention mechanisms Fuses Feature extraction data models data mining Disruptive technologies

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pattern Synthesis of A 2 Element Patch Antenna Arrray Using Neural Networks with Tensorflow

Pattern Synthesis of A 2 Element Patch Antenna Arrray Using ...

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

作者： Bharat Reddy Gudibandi Om Harikirubalan Prashant Tomar B Hanumantha Rao School of Electronics Engineering VIT-AP university Amaravathi India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

This The growing need for highly directive and reconfigurable antennas in contemporary communication systems has fueled significant research in antenna array synthesis. This study introduces a new approach to antenna array synthesis employing a neural network model built in TensorFlow. This model predicts optimal parameter combinations to achieve desired radiation patterns at an operating frequency of 2.4 GHz. Utilizing a 3-layer architecture and incorporating ReLU activation functions in the hidden layers, the model effectively manages non-linear relationships within the data. Training is conducted on simulation data acquired from a 2xl patch antenna array designed in ANSYS HFSS software. The antenna elements are positioned at a distance of 0.28λ to examine the influence of deep learning on closely coupled arrays. Results confirm the model's accuracy in predicting optimal phase combinations for both validation and custom radiation patterns, emphasizing its potential to simplify antenna design and optimization. This research offers valuable insights into the use of machine learning in antenna array synthesis, advancing the development of high-performance antenna systems for future communication technologies.

关键词： Training Accuracy Patch antennas Design methodology Neural networks Predictive models Directive antennas Arrays Antenna arrays Antenna radiation patterns

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Comprehensive Comparative Study on data mining and machine learning Approaches for Fraud Detection in Financial Services 3rd

Comprehensive Comparative Study on Data Mining and Machine L...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Thanki, Dipak Gohil, Jaypalsinh A. Faculty of Computer Applications Marwadi University Gujarat Rajkot360003 India

ISBN: (纸本)9783031862984

It has been the case recently that there has been an escalation in the proposition of untrue activities within different monetary sectors most especially in banks. Consequently, it becomes prudent to adopt strong and resilient strategies intended to minimize loss of finances as well as promote people’s trust. Thus, preventive measures against fraud in the banking sector can be enhanced by utilizing data mining and machine learning techniques which are responsive instruments employed for such purposes. This paper looks at several data mining approaches meant for detecting banking frauds through certain algorithms, case studies and practices. To begin with, this article talks about various data mining techniques whose relevance to fraud detection is very essential. They include classification, anomaly detection, clustering and association rule mining methods. Additionally, it outlines how certain algorithms like logistic regression & decision trees together with k-means clustering & isolation forests help to recognize cheating behaviors in supervised machine learning model. The current information regarding data mining’s application for reducing bank fraud using ML techniques has been compiled in this assessment. It emphasizes how relevant it is for finance professionals to team up with data scientists in order to curb fraud development, identifies important areas that require further research, and proposes ways forward towards improved fraud detection systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： K-means clustering

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Recurrent Neural Networks based Bi-LSTM with Sentiment Analysis and Transfer learning approach for Medicine Recommendation System

Recurrent Neural Networks based Bi-LSTM with Sentiment Analy...

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

作者： Sandeep Kumar Hegde Rajalaxmi Hegde Thangavel Murugan Department of CSE NMAM Institute of Technology NITTE (Deemed to be University) Karnataka India College of Information Technology United Arab Emirates University Al Ain United Arab Emirates

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The implementation of an efficient medication recommendation system is indispensable for personalized medicine to improve patient outcomes by providing personalized treatment recommendations. This paper presents a comprehensive framework for medication recommendation based on transfer learning using deep learning methods like Recurrent Neural Networks (RNNs) and Bidirectional Long Short Term Memory networks (Bi-LSTM) along with sentiment analysis of user reviews for opinion mining. The proposed method implements a complex-sequential modeling architecture using an RNN-BiLSTM-driven deep network to model the complex sequential patterns and timing relationships among drug-related data, thereby preparing the system for learning and eventual adaptation based on past usage and feedback. Bi-LSTM retains context-information in both forward and reversed directions to attain better context understanding. Transfer learning implementation of the framework gains the knowledge from pre trained model pertaining to medical recommendation system towards ensuring high generalizability of the developed model. The proposed method essentially binds insights from historical users' data, user sentiments, and advanced deep learning to satisfy patients and treatment outcomes towards a more precise and personalized system for drug recommendations. The dataset required for conducting the experiment was gathered from the Kaggle repository. The experimental results have been validated by various measures such as ROC rate, precision, F1 Score, confusion matrix, and accuracy. The experimental results showed that the proposed method achieved a 97.6% accuracy using the measures considered, which is significantly better than traditional machine learning and deep learning algorithms.

关键词： Deep learning Sentiment analysis Adaptation models Accuracy Reviews Precision medicine Transfer learning data models Timing Recommender systems

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Chronic Kidney Disease: data Clustering and Prediction Using Various machine learning Algorithms

Chronic Kidney Disease: Data Clustering and Prediction Using...

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Disruptive Technologies (ICDT), international conference on

作者： Gaurav Dhuriya Aradhna Saini Prashant Johri Neha Yadav Attiuttama Artificial Intelligence and Machine Learning G. L. Bajaj Institute of Technology and Management Greater Noida Delhi - NCR India Computer Science and Engineering G. L. Bajaj Institute of Technology and Management Greater Noida Delhi - NCR India Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

A major worldwide health concern, chronic kidney disease (CKD) has a high mortality and morbidity rate and can lead to several other illnesses. Since there are no overt signs of infection in the early stages of chronic kidney disease (CKD), patients frequently miss it. Early-stage chronic kidney disease (CKD) patients have a greater chance of receiving prompt therapy to stop the infection from spreading. AI models can successfully help physicians accomplish this aim because of their quick and precise recognition execution. In this work, we propose an AI method for diagnosing chronic kidney disease. The University of California Irvine (UCI) AI collection provided a number of the missing properties in the CKD informative index. KNN ascription, which chooses a limited number of full examples with the most comparable predictions to handle each deficient example's missing data, was used to fill in the gaps. Overall, because patients may miss a few estimations for a variety of reasons, missing qualities are usually seen in clinical settings. Once the inadequate informational index was adequately rounded off, models were constructed using six AI computations: arbitrary woodlands, support vector machine, k-closest neighbor, innocent Bayes classifier, decision tree, and strategic relapse. The choice tree and irregular forests AI models outperformed the others with a 99.00% determination precision. Therefore, it is estimated that this procedure may apply to more complicated clinical data for sickness diagnosis by examining the misconceptions caused by the setup models.

关键词： Support vector machines Philosophical considerations Computational modeling Mortality Medical treatment Nearest neighbor methods Chronic kidney disease data models Bayes methods Indexes

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