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Speech Emotion Recognition Using ML 7

Speech Emotion Recognition Using ML

引用

7th IEEE International Conference on Computational systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Niharika, S.M. Soumya, A. Rv College of Engineering Dept. of Computer Science & Engineering India

ISBN: (纸本)9798350343144

Due to its numerous uses in industries including human-computer interaction, healthcare, and entertainment, speech emotion recognition (SER) has attracted a lot of interest recently. The use of Convolutional Neural Networks (CNN) in combination with the Librosa library for SER is used to recognise emotions from audio data. The capacity to automatically recognise and categorise emotions from spoken words can revolutionise many applications since emotions are a crucial component of human communication. Here, CNNs-known for their skill in image analysis-are adapted to the spectrogram representation of audio, efficiently identifying distinguishing characteristics. To improve the reliability of emotion identification, Librosa, a Python library for music and audio analysis, offers crucial capabilities for preprocessing and feature extraction from audio data. The architecture of the CNN model, which includes of convolutional layers for feature extraction and pooling layers for dimensionality reduction, is required to build the model. To increase model generality, methods for data augmentation and transfer learning are employed. The two feature extraction capabilities of Librosa, chroma features and mel-frequency cepstral coefficients (MFCCs), are emphasised for their effectiveness in extracting emotional cues from audio data. It has been proved via considerable research that CNNs and Librosa together produce state-of-the-art results in a variety of emotion identification tasks, considerably improving SER accuracy. This study has broad repercussions, from enhancing the emotional intelligence of virtual assistants to helping mental health practitioners monitor patients' emotional health through speech analysis. In summary, the combination of CNNs and Librosa in SER offers a potential path for developing the discipline of emotional analysis in speech, resulting in more emotionally intelligent technology and a deeper comprehension of human emotions. © 2023 IEEE.

关键词： Feature extraction

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Online Transit Entanglement Routing in Quantum Networks: Architecture Design and Optimization

Online Transit Entanglement Routing in Quantum Networks: Arc...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ho, Wan-Ting Fang, Shing-Yan Hsieh, Wei-Chia Cheni, Li-Feng Kuo, Jian-Jhih Tsai, Ming-Jer National Tsing Hua University Dept. of Computer Science Hsinchu Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan

ISBN: (纸本)9798350351255

Quantum networks (QNs) gradually gain significant attention due to their higher security compared with classical networks. Conventional approaches in QN routing often aggregate multiple requests into a batch before determining their routing paths. However, this approach may overlook the limited lifetime of qubits, resulting in critical decoherence. In this paper, we present a new online entanglement routing architecture with an online optimization problem and propose a novel [1, O(log |V|)]-competitive algorithm supporting online requests with admission control, aiming to maximize the number of admitted requests. Finally, extensive simulation results show that our algorithm can outperform the existing approaches by up to 98%. © 2024 IEEE.

关键词： Quantum entanglement

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Faulty Classes Prediction in Object-Oriented Programming Using Composed Dagging Technique 3

Faulty Classes Prediction in Object-Oriented Programming Usi...

引用

3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Mahfuz, Nagib Shill, Pintu Chandra North Western University Dept. of Computer Science and Engineering Khulna Bangladesh Khulna University of Engineering and Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350345360

Class is one of the fundamental concepts of the object-oriented paradigm and has been scrutinized since the developers moved on from procedural programming design. In software fault prediction, the legalization of software metrics is essential. As a handful of software metrics suites exist, it is a very hard task to predict the defective classes flawlessly using a particular set of metrics suites. However, it is a rational approach to use only the object-oriented metrics that are directly relatable to the class definitions in the code that helps the developers foresee the errors in defining the classes and minimize the errors as much as possible. This paper utilized twelve object-oriented metrics selected from various metrics suites. The dagging ensemble model is merged with three well-known classification algorithms (Naive Bayes, Multilayer Perceptron, J48 Decision Tree) individually and applied to twelve java projects. The study depicts that the proposed ensemble method gives improved outcomes that are statistically significant when merged with Naive Bayes and Multilayer Perceptron. The proposed ensemble method shows improvements up to 12.5% in accuracy and 15% in F-Score. © 2023 IEEE.

关键词： Object oriented programming

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Activity Recognition from Channel State Information for Few-Sampled Scenarios

Activity Recognition from Channel State Information for Few-...

引用

2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Diaz, Guillermo Sobron, Iker Eizmendi, Iñaki Landa, Iratxe Velez, Manuel Dept. of Communications Engineering Bilbao Spain Dept. of Computer Languages and Systems Bilbao Spain

ISBN: (纸本)9798350370218

Human Activity Recognition (HAR) using channel state information (CSI) from wireless systems has been a field in progress for the last few years. In particular, amplitudes of WiFi-based CSI have been widely used for HAR in indoor scenarios. In contrast, CSI phases are less exploited due to higher sensitivity to timing errors in channel estimation. This paper further explores a recent phase processing method to obtain environmental information from CSI and improves classification using a Convolutional Neural Network in few-sampled scenarios. For this purpose, a new dataset has been generated with measurements of three receivers on different days and with different people, for activity recognition and people counting. In addition, we incorporate a transfer learning strategy using fine-tuning along with a data fusion model from different receivers to improve accuracy metrics by training the model and validating it on data taken on different days. Promising results with accuracies higher than 80 % have been obtained with transfer learning between days using 5 % of data for fine-tuning. © 2023 IEEE.

关键词： activity recognition CSI few-shot learning fine-tuning people counting phase

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A Collaborative Health Care System: A Pilot based on Blockchain 3

A Collaborative Health Care System: A Pilot based on Blockch...

引用

3rd IEEE International Conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Divya, P Bindu Jadon, Shruti People's Education Society University Dept. of Computer Science and Engineering Bengaluru India People's Education Society Dept. of Computer Science Bengaluru India

ISBN: (纸本)9798350354348

Blockchain-driven healthcare system that offers personalized, data-driven treatment options hopes to transform the medical industry. This system aims to increase diagnostic accuracy, treatment efficacy, and data security. It strongly emphasizes the value of interdisciplinary collaboration among medical professionals, enabling various professions to work together to provide comprehensive care for complex medical conditions. It also addresses the ethical and legal concerns that result from cross-specialty cooperation and the exchange of patient data, including as patient privacy and data security. © 2024 IEEE.

关键词： Differential privacy

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Modeling of Weight Perception Based on Subjectivity by Considering Individual Differences 13

Modeling of Weight Perception Based on Subjectivity by Consi...

引用

Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Kushida, Daisuke Nakajima, Kei Tottori University Faculty of Engineering Dept. Electrical Engineering and Computer Science Tottori Japan Graduate School of Sustainability Science Tottori University Dept. Engineering Tottori Japan

ISBN: (纸本)9798350373332

The minimum amount of perception that humans can perceive is called the stimulus threshold. This study aims to clarify the stimulus threshold of weight perception for the biceps brachii muscle based on a method of constant stimuli. In general, the stimulus threshold depends on the individual. The authors consider that physical characteristics are related to individual differences, construct a model of the relationship between physical characteristics and stimulus thresholds, and attempt to estimate stimulus thresholds from physical characteristics. The constructed model is evaluated via cross-validation and found to be able to estimate stimulus thresholds from physical characteristics with an 85% accuracy. © 2024 IEEE.

关键词： individual difference modeling stimuli threshold subjectivity weight perception

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Automated Road Extraction in Urban and Rural Regions from Remote Sensing Imagery with U-Net and ResNet-Based Architectures 1

Automated Road Extraction in Urban and Rural Regions from Re...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Palvi Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Dept. of Computer Science & Engineering Punjab India Chandigarh University Dept. of Computer Science & Engineering Punjab India University of Kufa Faculty of Computer Science and Mathematics Najaf Iraq National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350356816

Accurate road extraction from Remote Sensing Imagery (RSI) is crucial for various applications, including urban planning, navigation, and disaster response. This study investigates automated road extraction in rural, semi-urban and urban regions using deep learning architectures, specifically UNet and Deep Residual U-Net (ResNet) models. We leverage High-Resolution Satellite Imagery (HRSI) to train and validate our models, focusing on the distinct characteristics and challenges presented by urban and rural environments. Our methodology involves comprehensive data preprocessing and augmentation techniques to enhance model robustness. We evaluate the performance of both architectures using metrics and achieved an accuracy of 90.29%, recall of 80.95%, precision of 74.89%, and F1- score of 77.80% for the Deep Residual U-Net. Experimental results demonstrates that while both models perform well, the Deep Residual U-Net exhibits superior accuracy and generalization, particularly in complex urban landscapes. © 2024 IEEE.

关键词： Urban planning

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Implementing HR Management System with an AI Powered ChatBot 3

Implementing HR Management System with an AI Powered ChatBot

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susitra, D. Pratheesh, G. Karthiga, J.D. Dhanalakshmi, K. Shamreen Ahamed, B. Sylvia Grace, J. Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331527549

Human Resource (HR) management is one important section that increases the success of an organization to handle some important functions like employee data manipulation, attendance tracking, payroll processing, and performance evaluation. With the management of these processes becoming key to the success of small-scale startups, it poses a very big challenge for many of them to be able to manage these processes efficiently and cost efficient. By contrast, the old era of traditional HR systems was based on database driven architectures that are expensive in terms of operational cost, require storage space and complex infrastructural needs. These limitations often make traditional HR systems impossible for a startup that would prefer lightweight, affordable, and scalable solutions. Startups need robust platforms to streamline HR work streams, but often lack with financial resources for building complex database driven systems. In this paper, an AI powered HR management system is developed to simplify employee management processes for small scale startups. In this system. cache memory is used instead of traditional databases, thereby the operational cost of the system will be reduced for storage and retrieval of employee details. Automated Attendance Management enhanced with Face Recognition technology, Payroll Management, Leave Management, Feedback Submission modules are included. It is integrated with an AI powered chatbot, which facilitates a smooth interaction by the employees with query for information, submit leave requests and pull up all payroll information etc. A user-friendly dashboard for a consolidated view of employee profiles, attendance records, leave approvals, and payroll data can be served over an optimized and time efficient HR workflow. This system addresses the needs of the startups such as flexible usage, cost effective operations and improved resource management. © 2025 IEEE.

关键词： AI powered chatbot for Small-scale startups using cache memory attendance management face recognition HR Management System leave management payroll management

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

引用

2024 International Conference on Advancement in Renewable Energy and Intelligent systems, AREIS 2024

作者： Raji, V. Maheswari, S. Dharinya, S. Sree Chinniah, P. Kavin, K.S. Raja, Kiruba Thangam S.K.P. Engineering College Dept. of Information Technology Tiruvannamalai India Tamilnadu Erode India School of Computer Science Engineering and Information Systems Vit University Dept. of Software and Systems Engineering Vellore India ST. Joseph College of Engineering Dept. of Ece Tamilnadu Sriperumbudur India Government College of Engineering Dept. of Eee Tamil Nadu Tirunelveli India Vit University Dept. of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350387230

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Leveraging XAI and Breakthrough Machine Learning Techniques ...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Abhijna, M.B. Akul, P. Kodipalli, Ashwini Global Academy of Technology Dept. of Information Science and Engineering India Global Academy of Technology Dept. of Computer Science and Engineering India Global Academy of Technology Dept. of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350364866

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence. © 2024 IEEE.

关键词： Logistic regression

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