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A Transpose-SELDNet for Polyphonic Sound Event Localization and Detection

A Transpose-SELDNet for Polyphonic Sound Event Localization ...

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International Conference for Convergence of technology (I2CT)

作者： Spoorthy V Shashidhar G. Koolagudi Dept. of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangalore India

Human beings have the ability to identify a particular event occurring in a surrounding based on sound cues even when no visual scenes are presented. Sound events are the auditory cues that are present in a surrounding. Sound event detection (SED) is the process of determining the beginning and end of sound events as well as a textual label for the event. The term sound source localization (SSL) refers to the process of identifying the spatial location of a sound occurrence in addition to the SED. The integrated task of SED and SSL is known as Sound Event Localization and Detection (SELD). In this proposed work, three different deep learning architectures are explored to perform SELD. The three deep learning architectures are SELDNet, D-SELDNet (dept.wise Convolution), and T-SELDNet (Transpose Convolution). Two sets of features are used to perform SED and Direction-of-Arrival (DOA) estimation tasks in this work. D-SELDNet uses a dept.wise convolution layer which helps reduce the model’s complexity in terms of computation time. T-SELDNet uses Transpose Convolution, which helps in learning better discriminative features by retaining the input size and not losing necessary information from the input. The proposed method is evaluated on the First-order Ambisonic (FOA) array format of the TAU-NIGENS Spatial Sound Events 2020 dataset. An improvement has been observed as compared to the existing SELD systems with the proposed T-SELDNet.

关键词： Location awareness Visualization Direction-of-arrival estimation Convolution Event detection Deep architecture Estimation

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Impact of Machine Learning in Music Genre Classification using CNN

Impact of Machine Learning in Music Genre Classification usi...

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International Conference on Computing and Networking technology (ICCNT)

作者： Jorige Venkatesh Karthik Kannan M. Ayyadurai M G Sathish Dept. of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

The rapid expansion of available music tracks has made the classification of music genres increasingly crucial. To achieve accurate genre predictions, machine learning techniques have been widely employed and proven highly efficient. In this study, we focused on enhancing the accuracy of music genre classification systems by training a Convolutional Neural Network (CNN) model using Keras. Music genre classification serves as a valuable tool for music learning, offering a reliable method of categorization. Given the immense amount of data and the inherent complexity in identifying music genres, we leveraged the power of deep neural networks to classify ten distinct genres. To evaluate the effectiveness of our approach, we compared the performance of the CNN model to other existing models using the GITZAN dataset. Our results demonstrated the success of the CNN model in accurately classifying music genres, surpassing previous studies while requiring less data. This research contributes to the field of music genre classification by providing an advanced and efficient solution using deep learning techniques.

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Secure and Privacy-Preserving IoT-Based Arrhythmia Detection with Federated Learning 15

Secure and Privacy-Preserving IoT-Based Arrhythmia Detection...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Goranthala, Vishnu Prasad Sankar, K. Siva Malar, C. Brintha Noorul Islam Centre for Higher Education Kumaracoil Thuckalay Dept. of Computer Science & Engineering TamilNadu Thuckalay Kanyakumari District629180 India Noorul Islam Centre for Higher Education Kumaracoil Department of Information Technology TamilNadu Thuckalay Kanyakumari District629180 India

ISBN: (纸本)9798350370249

Arrhythmias pose a significant challenge for early diagnosis and treatment, necessitating advanced detection techniques. This paper introduces the Secure and PrivacyPreserving IoT-Based Arrhythmia Detection (SPIoT-AD) model, a federated learning (FL) based approach designed to enhance arrhythmia detection from ECG signals within IoT healthcare frameworks. Utilizing a secure multi-layered architecture that processes data at the edge, the methodology ensures data privacy and real-time monitoring, adapting to individual patient profiles. The model showcases exceptional performance metrics, demonstrating an accuracy of 99. 85 %, sensitivity of 98. 84 %, specificity of 99. 93 % and with a precision of 98.63% in the classification of multiple arrhythmia cases. Further, it also demonstrates a highest classification accuracy of 99.97% in the detection of normal cases. These results demonstrate the model's potential as a reliable tool for arrhythmia detection and classification, setting a foundation for future integration into clinical workflows, ensuring security and privacy of patient data. © 2024 IEEE.

关键词： Differential privacy

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Efficient Diagnosis of Coronary Artery Disease: A Hybrid Convolutional-XGBoost Approach

Efficient Diagnosis of Coronary Artery Disease: A Hybrid Con...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Arkashree P. Mishra Suvasini Panigrahi Dept. of Computer Science & Engineering Veer Surendra Sai University of Technology Burla India

Cardio Vascular Diseases (CVDs) are the prime reason of death globally. Particularly, the cases of coronary artery disease (CAD) have been rising in the post Covid era, especially amongst middle-aged people. Thus, the before time detection of the disease might evade the growing number of fatalities and morbidities. Two most common and accurate methods used to detect CAD are angiography and electrocardiography (ECG). While the former is invasive and a risky procedure, the latter one although non-invasive lacks correctness. Over a decade, non-invasive Machine Learning (ML) as well as Deep Learning (DL) methods have shaped the healthcare scenario by facilitating early diagnosis of the diseases through the application of various computer based algorithmic models over clinical data and images. A hybrid Convolutional- Extreme Gradient Boosting (Convolutional XGBoost) model has been proposed to accurately detect CAD by outperforming some of the existing methods.

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Student's Performance Prediction Using Machine Learning Algorithms- A Comparative Study

Student's Performance Prediction Using Machine Learning Algo...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kadu, Varun Mishra, Pawan Kumar Dandhare, Sarthak Patni, Jagdish Chandra Chandel, Palash Pathak, Soham Bahadure, Nilesh Symbiosis Institute of Technology Siu Nagpur Campus Pune India Graphic Era Deemed to Be University Computer Science & Engineering Dehradun India Alliance University Alliance School of Advance Computing Dept. of Cse Karnataka Bengaluru India Nagpur Campus Pune India

ISBN: (纸本)9798350373783

Predicting and enhancing student performance has been a crucial topic of concentration in education amid the quick development of technology and the increasing need for higher-quality instruction. The use of machine learning technology to forecast pupils' academic success is covered in detail in this study report. This research project examined several prediction models that reliably predict students' performance according to a range of extracurricular and academic attributes. An overview of machine learning methods is given in this paper to automate the process of predicting students' academic performance and extracting outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Optimizing Cross-Chain DeFi and Smart Contracts in Stochastic Integration

Optimizing Cross-Chain DeFi and Smart Contracts in Stochasti...

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International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Jongho Seol Jongyeop Kim Abhilash Kancharla dept. of information technology Middle Georgia State University Macon GA USA dept. of information technology Georgia Southern University Statesboro GA USA dept. of computer science The University of Tampa Tampa FL USA

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

This research presents a sophisticated technological framework for Cross-Chain Decentralized Finance (DeFi) and Smart Contract systems by seamlessly integrating Markov Models, Brownian Motion, and Stationary Processes. Focused on enhancing the adaptability and efficiency of financial interactions across interconnected blockchain networks, this framework establishes the foundational elements necessary for dynamic system modeling. The incorporation of Markov Models captures state transitions, Brownian Motion models random fluctuations, and Stationary Processes ensure statistical stability. The paper explores the technological implications of these stochastic processes, addressing challenges in system interoperability, latency, and security within decentralized financial ecosystems. Envisioning a future where decentralized systems are optimized and resilient, the research investigates advancements in blockchain protocol design, consensus mechanisms, and transaction validation strategies. The proposed framework, influenced by the dynamic and statistical nature of Brownian Motion and Stationary Processes, underscores the need for robust data structures, real-time data feeds, and decentralized oracle networks. This research invites collaboration from the blockchain, smart contract, and stochastic modeling communities to contribute to the ongoing exploration and refinement of this powerful technological framework, poised to reshape the landscape of cross-chain financial technologies.

关键词： Brownian motion Adaptation models Biological system modeling Smart contracts Ecosystems Finance Stochastic processes

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Predicting Customer Behavior Using Neural Network Optimized by Grasshopper Algorithm 2

Predicting Customer Behavior Using Neural Network Optimized ...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Loaeibi, Zoheir Alqammaz, Abdullah Sara, Massar Boostani, Reza School of Engineering Shiraz University Electrical and Computer Engineering Faculty Cse and It Dept. Shiraz Iran Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798350373363

Digital marketing, as an essential branch of science, could provide value for commercial organizations and strengthen the interaction with customers via increasing the electronic services. In digital marketing, real-time analysis of customer behaviors is considered as a key program, which could encourage novel advertisement concentration. Each deal that is assigned to a costumer, is a part of individual behavior and is a sample of feature vectors based on a set of interactions is modelled for creating the customer behavior. In this research, using data mining techniques and metaheuristic algorithms, the patterns of customer behavior are identified and predicted. Neural network as a classifier with acceptable performance is used for complex tasks. In the train phase, neural network is usually worked with back propagation algorithm. This algorithm suffers from falling into the local optima and needs to adjust the weights and using proper initialization weights. To optimize both weights and structure of the neural network, we adopt grasshopper evolutionary algorithm to adjust the weight in an iterative manner along with the adjusting the number of neurons in the hidden layer. In the proposed method, the probability of falling in the local optima is reduced and also the suggested algorithm could provide acceptable performance compared to the other rivals in a rich benchmark of digital marketing. © 2023 IEEE.

关键词： Optimization

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Multi-Task Deep Learning Model for Autonomous Driving: Object Detection, Semantic Segmentation, and dept. Estimation

Multi-Task Deep Learning Model for Autonomous Driving: Objec...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Wang, Yih-Chen Lin, Jhe-Li Chen, Yen-Lin Huang, Chieh-Sheng Lai, Ming-Liang National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan Elan Microelectronics Corporation Artificial Intelligence Research and Development Department Hsinchu Taiwan National Taipei University of Technology Graduate Institute of Intellectual Property Taipei Taiwan

ISBN: (纸本)9798350324174

In the field of autonomous driving, many models based on deep learning methods have been constructed to solve computer vision tasks related to this domain, such as object detection, semantic segmentation, and dept. estimation. Each model can provide different information about the surrounding environment of a vehicle to assist in driving. However, for applying in the real world, more detailed information about the surrounding environment is essentially required. In this study, we propose a model based on the concept of multi-task learning. This model is an encoder-decoder architecture mainly consisting of an encoder using the hard-parameter sharing technique and three decoders for individual tasks. Therefore, this model can handle object detection, semantic segmentation, and dept. estimation at the same time. Our proposed multi-task model has been verified to perform well on the public dataset Cityscapes and has higher generalizability than other models. © 2023 IEEE.

关键词： Semantic Segmentation

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Prediction of foot risk classification for Type II Diabetic through image analysis 6

Prediction of foot risk classification for Type II Diabetic ...

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6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Sheikh, Mehewish Musheer Balachandra, Mamatha Narendra, V.G. Maiya, Arun G Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Computer Sceince & Engg Manipal India Manipal College of Health Professions Manipal Academy of Higher Education Dept. of Physiotherapy Manipal India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

The global prevalence of diabetes mellitus has increased. The use of electronic platform devices has grown in popularity due to their low cost and ease of use. Despite their benefits, however, there remain concerns about their accuracy and precision. The objective of the study is to determine the accuracy and precision of the Win-track platform. In a cross-sectional study, 49 male patients' data were collected. Based on the pressure asserted, the data were further classified into different stages from 1 to 4. The study used four different types of classifiers (Logistic, Multi-layer Perceptron, Simple Logistic Regression and Meta-logit Boost) to check the accuracy. The result shown for all the classifiers was positive with Meta-logit Boost giving the higher Mathews correlation coefficient (MCC) (stage 1=1, stage 2=1, stage 3=0.904 and stage 4=0.912) and highest correctly classified instances and lowest incorrectly classified instances (95.91% and 4.08% respectively) with least amount of time taken for execution (T = 0.02ms). With respect to the accuracy obtained, it is suggested to use the Win-track platform in hospitals and clinics. © 2022 IEEE.

关键词： Image classification

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A Comparative Analysis and Study of a Fast Parallel CNN Based Deepfake Video Detection Model with Feature Selection (FPC-DFM)

A Comparative Analysis and Study of a Fast Parallel CNN Base...

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Advanced Computing and Communication Technologies for High Performance Applications

作者： Athirasree Das Linda Sebastian Dept. of Computer Science and Engineering St. Joseph's College of Engineering and Technology Palai Kerala India Dept. of Computer Science and Engineering College of Engineering Kidangoor Kerala India

Deep learning is an efficient and practical method that has been widely applied in numerous fields. Videos created with swapped faces in a video, altered facial emotions, changed genders, fraudulent video content generation, and altered facial features are referred to as “DeepFake” videos. These videos are created utilizing deep learning technology called generative adversarial networks. Fake videos are used to stir up political agitation, commit acts of terrorism, and demand money. A fast Parallel CNN-based deepfake video detection model with feature selection is the new model we presented in this project (FPC-DFM). In order to identify Deepfake videos, the FPC DFM architecture uses feature extraction, feature selection, and prediction. The FPC-DFM model extracted features using three convolutional models: EfficientN et, VGG16, and ResNet as well as Principal Component Analysis (PCA)-based feature selection and Support Vector Machine-based classification. We offer a new architecture for capturing the video frame features that will be utilized to determine if the video is real or fake by utilizing deep learning techniques. In comparison to other pre-trained models like VGG16-TL, EfficientNet-TL and Resnet50-TL and our results demonstrated that FPC-DFM has the best performance and the highest accuracy of 96.50%.

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