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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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Automatic Skull Shape Completion of Defective Skulls Using Transformers for Cranial Implant Design 2

Automatic Skull Shape Completion of Defective Skulls Using T...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Resmi, S. Singh, Rimjhimpadam Palaniappan, Kannappan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electrical Engineering and Computer Science University of Missouri MO United States

Cranioplasty is a surgical method that restores the aesthetic and protecting function of a damaged skull by implanting material into the damaged *** and accurate design of patient specific cranial implants is very much required in the process of *** time consumption for designing and manufacturing of patient specific cranial implant has become an obstruction for cranioplasty procedures. Hence, a fully automatic and fast design of cranial implant becomes very important. The cranial implant design processmainly comprises of two steps. The former step concentrates on the automatic skull shape completion of defective skulls to fill the gaps and the cracks created in the skull. While the second step computes the difference between defective input and the completed skull for generating the implant. Currently computer aided design is used for the skull shape completion task which is a time consuming process. The application of deep learning techniques may result to faster and accurate skull shape completionwhich can be used for the design of patient specific cranial implants. This work proposes a novel approach combining 3D U-Net with Transformers for the automatic skull shape completion *** are using a vision transformer in the encoder section of the 3D U-Net architecture to consider the volumetric skull reconstruction as sequence- to-sequence prediction problem and to efficaciously grasp the global contextual information. The work also compares its performance with the famous variants of 3D U-Net deep network model namely, 3D U-Net and 3D U-Net with attention. From the values resulted for the dice coefficient metric it is clear that the proposed 3D U-Net with transformer approach performs better than the other two models on test images. © 2024 Elsevier B.V.. All rights reserved.

关键词： computer aided design

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Psychological Wellbeingness Prediction Using Machine Learning with Hybrid Hyparameter Tuning Techniques

Psychological Wellbeingness Prediction Using Machine Learnin...

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Rajah, Puvanenthira Habib, Masruk Ravikumar, R.N. Nadarajan, Sivakumar Chougule, Bhagyashree Amit Kirubhakaran, M. Marwadi University Dept. of Computer Engineering Rajkot India Alliance University Dept. of Computer Science and Engineering Bangalore India Tatyasaheb Kore Institute of Engineering and Technology Dept. of Computer Engineering Maharashtra India Suriya Engineering College Dept. of Ai & Ds Erode India

ISBN: (纸本)9798350379716

This study investigates the application of machine learning (ML) techniques in predicting Psychological Well-being outcomes, emphasizing the use of ensemble methods like AdaBoost and Random Forest for enhanced accuracy. With Psychological well-being issues affecting a significant portion of the global population, traditional assessment methods are challenged by issues of accessibility, stigma, and subjectivity. Leveraging data from various surveys, this research compares different ML algorithms, including Logistic Regression and Support Vector Machines, in predicting psychological well-being conditions. The results highlight the superior performance of AdaBoost and Random Forest, particularly when hyperparameters are finely tuned, achieving accuracies up to 0.996. The study underscores the potential of ML in improving psychological well-being interventions by offering precise, unbiased, and accessible diagnostic tools. Future research directions include the integration of multimodal data and the development of models that can suggest personalized treatment plans while ensuring data privacy. This paper advocates for the integration of advanced computational techniques with clinical insights to revolutionize psychological well-being diagnostics and care. © 2024 IEEE.

关键词： Clinical research

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A comparative study for lung, colon and breast cancer diagnosis using different convolutional neural networks 7

A comparative study for lung, colon and breast cancer diagno...

引用

7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Akar, Hussein Diab, Jacqueline Abou Sbeity, Fatima Ali, Mohamad Abou Kassem, Abdallah Hamawy, Lara Lebanese International University Dept. of Biomedical Engineering Lebanon Lebanese University Dept. of Physics & Electronics Lebanon University of the Basque Country Dept.of Computer Science & Artificial Intelligence Spain Notre Dame University Dept. of Electrical &computer Engineering Lebanon

ISBN: (纸本)9798350325850

In modern times, the prevalence of diseases, particularly cancer, is rising rapidly. Diagnostic errors pose a critical safety concern in healthcare due to the complexity of medical decision-making, disease variability, and human cognitive limitations. This paper aims to compare different convolutional neural networks (CNNs) to find the most accurate model that can classify lung, colon, and breast cancer using histopathological images. A convolutional neural network is a subset of machine learning. It is one of the various types of artificial neural networks which are used for different applications and data types. A CNN is a kind of network architecture for deep learning algorithms and is specifically used for image recognition and tasks that involve the processing of pixel data. We created a baseline model that consists of a basic CNN architecture. It serves as a starting point for comparison with more complicated models. The baseline model's performance is then compared to the performance of various pretrained models (ResNet-50, ResNet-101, VGG-16, VGG-19, Inception V3, and DenseNet-121). The dataset used consists of 25,000 histopathological images of both the lung and colon and 277,000 histopathological of breast images. Results show that the ResNet-50 model is the most efficient among all the other models, scoring a validation accuracy of 98.3%. Additionally, ResNet-101, VGG-16, VGG-19, and DenseNet-121 also demonstrated strong performance, scoring 95.8%, 96.6%, 95.1%, and 92.4%, respectively. Meanwhile, Inception-V3displayed a fair result, scoring a validation accuracy of 73.2%. © 2023 IEEE.

关键词： Convolutional neural networks

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Towards Robust mmWave-based Human Activity Recognition using Large Simulated Dataset for Model Pretraining

Towards Robust mmWave-based Human Activity Recognition using...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Joshi, Vinay Xu, Shengkai Cao, Qiming Zhu, Yi Wang, Pu Xue, Hongfei Unc Charlotte Dept. of Computer Science Charlotte United States Purdue University Electrical and Computer Engineering West Lafayette United States Wayne State University Dept. of Computer Science Detroit United States

ISBN: (纸本)9798350362480

Human activity recognition (HAR) is crucial for real-world applications such as healthcare, surveillance, and smart homes. Among sensing technologies, millimeter wave (mmWave) sensors stand out due to their contactless nature, high sensitivity, and ability to operate in low-light environments while preserving privacy. However, the scarcity of mmWave sensing data limits the generalizability of mmWave-based HAR systems. To address this, we propose mmAP, a data augmentation and pretraining framework that synthesizes a large mmWave dataset using human mesh data, followed by pretraining a robust and general mmWave heatmap encoder using a multi-modal masked autoencoder framework using the synthesized data. We enhance the model's robustness with heatmap-specific data perturbations and perform task-specific fine-tuning on a small real-world dataset. The experiment results over the baseline demonstrate the effectiveness of the proposed mmAP framework. © 2024 IEEE.

关键词： Large datasets

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Exploring the Impact of Univariate Feature Selection Method on Machine Learning Algorithms for Heart Disease Prediction

Exploring the Impact of Univariate Feature Selection Method ...

引用

2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Islam, Mainul Islam, Rafiqul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Dept. of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

This study compares various machine learning techniques such as logistic regression, support vector machine (SVM), random forest, Naïve Bayes, and decision tree. Later, a feature selection approach that considers the Univariate Feature Selection (UFS) method was applied to select the best features based on statistical testing. In this research, we employed the ANOVA F-test to pick the most significant features, and we used a cutting-edge dataset for experimental analysis. The ANOVA F-test was used to exploit the target variable's dependency on the continuous variables. The results show that employing the UFS approach for picking features improves the performance of the algorithms. According to the experimental results, using UFS with the logistic regression technique yields 93.3% accuracy, whereas the standalone Naïve Bayes algorithm achieved the best accuracy. © 2023 IEEE.

关键词： Feature Selection

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Identification of Affective States from Malayalam Audios Based on Valence Arousal Prediction

Identification of Affective States from Malayalam Audios Bas...

引用

2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： George, Talit Sara George, Jini Pournami, P.N. National Institute of Technology Dept. of Computer Science and Engg. Calicut India Providence College of Engineering Dept. of Computer Science and Engg. Chengannur India

ISBN: (纸本)9798331540067

Affective state plays a key role in emotion recognition, influencing various applications from mental health evaluation to human-machine interaction. These states can be described using two primary aspects: valence, which indicates the pleasantness of the speech, and arousal, which relates to the intensity of the emotion. This study aims to automate the prediction of valence and arousal values from speech data, addressing the limitations of manual annotation and human error. The IIITSAINT-EMOMDB dataset, created from Malayalam films, uses the Circumplex Model of Affect to map emotions such as happy, sad, angry, and calm. The approach leverages machine learning models, including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), for prediction. Experimental results demonstrate that the traditional models like SVR and ANN are outperformed by the CNN model, which achieved a mean squared error (MSE) of 0.3 for valence and 0.1 for arousal. These findings highlight the power of automated emotion recognition in large-scale audio datasets, offering promising applications in affective computing and psychological analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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An Artificial Intelligence Approach in 5G New Radio Beam Enhancement 3

An Artificial Intelligence Approach in 5G New Radio Beam Enh...

引用

3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Upadhyay, Deepak Mittal, Saksham Jain, Ayushi Sharma, Ravi Bagla, Piyush Tripathi, Neha Tiwari, Pallavi Graphic Era Hill University Dept. of Computer Science & Engineering Uttarakhand Dehradun India National Institute of Technology Computer Science & Engineering Dept. Punjab Jalandhar India Dept. of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350300710

The paper presents a novel application of artificial neural networks in the context of 5G new radio beamforming. The research leverages the intricate mechanism of ANNs to demonstrate the efficacy of this approach in optimizing the beamforming process and improving the overall performance of the 5G new radio system. The utilization of ANNs allows for real-time adaptation and decision-making, thereby mitigating the limitations of conventional beamforming techniques. The results of the study are quite promising, indicating a substantial enhancement in the accuracy and efficiency of beamforming in 5G new radio. The proposed method is expected to have a significant impact on the development of advanced wireless communication systems. © 2023 IEEE.

关键词： Beamforming

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Proposal of Healthcare System for Pathological Symptom Detection Employing Voice Analysis

Proposal of Healthcare System for Pathological Symptom Detec...

引用

2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Liu, Chenyang Liu, Jiang Shimamoto, Shigeru Dept. Computer Science and Communications Engineering Graduate school of Fundamental Science and Engineering Waseda University Tokyo Japan

ISBN: (纸本)9798350350548

Mobile healthcare has attracted significant attention in recent years as people have become more health-conscious. Furthermore, there is an increasing demand for non-contact and non-invasive diagnostic methods to reduce the risk of infection and pain. This study proposes a healthcare system that detects pathological symptoms by utilizing speech audio. Existing methods for the detection of COVID-19 have relied on cough recordings. To investigate the potential application of this method in the real use case, this study utilizes speech audio instead of cough recording. Our target is to alleviate the burden on the medical field and help medical workers by implementing this mobile healthcare system. This method uses transfer learning to address the problem of limited data. We evaluate the accuracy with transfer learning and random forest classification utilizing the specific features extracted in such a spectrum and spectrogram. © 2024 IEEE.

关键词： Audio recordings

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An Ensemble ML Model to Predict the Wastage of Food: Towards Achieving the Food Sustainability

An Ensemble ML Model to Predict the Wastage of Food: Towards...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Masuk Shopnil, Md Masrur Husna, Asmaul Sultana, Shaheena Islam, Muhammad Nazrul Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh NotreDame University Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Food waste is a major issue in students' hostel or dormitories, where uneaten food often ends up in the garbage, resulting in economic losses as well as reducing the food sus-tainability. Although Machine Leaning (ML) has emerged as a promising tool for predicting future events in several files, but a very few has been focused to generate accurate predictions and reducing food waste by analyzing read data for a particular context or a country. Therefore, the objective of this research is to explore the factors of affecting food waste in students' hostel or dormitories and to develop an ensemble ML-based food waste prediction system for the student hostel or dormitories. In order to attain these objectives, this study firstly find out the 18 key features that ensure a comprehensive understanding of food waste patterns;then collected the data against these features over the last six months from various mess records of a students' hostel of an engineering university situated in Dhaka, Bangladesh. Secondly, six classification models were developed and analyzed their results and found that Random Forest and Decision Tree performed best accurate prediction (75.41%). Thirdly, the all possible combinations of the studies ML models were explored to find out the best possible ensemble combination;as such, the proposed ensemble ML model showed the highest F1-score of 86.88% that considers the Random Forest and AdaBoost;while all other possible combinations showed around 80-82% F1-score. This research thus contributes to the broader goal of reducing food waste and promoting sustainability in students' mess and other food service settings;as well as may assist the management team in making data-driven decisions about food production, purchasing, and resource allocation. © 2023 IEEE.

关键词： Forecasting

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