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Bioacoustics Signal Classification Using Hybrid Feature Space with Machine Learning 15

Bioacoustics Signal Classification Using Hybrid Feature Spac...

引用

15th international conference on computer and automation engineering, ICCAE 2023

作者： Haider, Usman Hanif, Muhammad Kobayashi, Hiroki Parajuli, Laxmi Kumar Shimotoku, Daisuke Rashid, Ahmar Safeer, Sonia Gik Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Topi Pakistan The University of Tokyo Information Technology Center Tokyo Japan

ISBN: (纸本)9798350396225

The vocal sounds emitted by animals and birds possess distinctive signatures. They are vital for acoustic monitoring to extract useful ecological data and track biodiversity. Recently, automated bioacoustics classification drew attention from the research community due to its diverse application. To that aim, we present a novel classification method for acoustic data by fusing optimally selected signal features. The proposed method extracts the distinctive statistical features, calculated using coefficients of Discrete Wavelet Transform (DWT), and fuses them with the descriptive features estimated using the Mel-Frequency Cepstral Coefficients (MFCC). The combined feature set is then passed to different machine learning classifiers, including Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN) for sound classification of different animals and birds. The evaluation results show that the proposed method improves the classification accuracy and achieved high precision on all classifiers. © 2023 IEEE.

关键词： Birds

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FG-PIH: A Fusion of Fresnelet Transform and Gradient Directional Pattern for Perceptual Image Hashing 2

FG-PIH: A Fusion of Fresnelet Transform and Gradient Directi...

引用

2nd international conference on Recent Trends in Microelectronics, automation, Computing, and Communications Systems, ICMACC 2024

作者： Meesala, Pavani Thounaojam, Dalton Meitei Computer Science and Engineering National Institute of Technology Silchar Computer Vision Laboratory Silchar India

ISBN: (纸本)9798350366570

Perceptual image hashing is pivotal in various image processing applications, including image authentication, content-based image retrieval, tampered image detection, and copyright protection. This paper proposes a novel approach for perceptual image hashing by combining the Fresnelet Transform with Gradient Directional Patterns. Using the FG-PIH technique, the proposed method achieves superior robustness against common image processing attacks while maintaining perceptual similarity for near-duplicate images. Experimental results on standard benchmark datasets demonstrate the effectiveness and efficiency of the proposed Fresnelet Transform-based perceptual image hashing scheme. Furthermore, comparative analysis against state-of-the-art methods underscores the competitiveness of our approach in terms of hash quality and computational complexity. © 2024 IEEE.

关键词： Hamming distance

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A Novel Approach for Heart Disease Classification using Cascaded XGBoost Algorithm 3

A Novel Approach for Heart Disease Classification using Casc...

引用

3rd international conference on automation, Computing and Renewable Systems, ICACRS 2024

作者： Rajitha, M. Megala, S. Santhana School of Computer Studies RVS College of Arts and Science Sulur Coimbatore India Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India Department of Computer technology Hindustan College of Arts and Science Coimbatore India

ISBN: (纸本)9798331532420

Heart disease remains a leading cause of mortality worldwide. Accurate and timely diagnosis is crucial for effective treatment and prevention. This research proposes a novel approach using a cascaded XGBoost model to improve the accuracy and efficiency of heart disease diagnosis. With gradient boosting, the cascaded XGBoost model effectively handles complex interactions within the dataset, leading to superior performance compared to traditional machine learning techniques like logistic regression, support vector machines, and decision trees. The proposed model was evaluated on a standard heart disease dataset, demonstrating significant improvements in terms of accuracy, precision, and recall. Furthermore, the model's ability to identify key factors contributing to heart disease provides valuable insights for clinical decision-making. This research highlights the potential of machine learning techniques, particularly cascaded XGBoost, in advancing the field of heart disease diagnosis and improving patient outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Community Detection in Signed Networks Based on Normalized Laplacian Matrix and Convex Non-negative Matrix Factorization 2

Community Detection in Signed Networks Based on Normalized L...

引用

2nd international conference on automation, Robotics and computer engineering, ICARCE 2023

作者： Zhang, Yibo Nan, Tian Zhang, Zhaozhi School of Information Science and Engineering Lanzhou University Lanzhou China State Grid Gansu Electric Power Company Material Company Lanzhou China

ISBN: (纸本)9798350308341

Signed networks can intuitively and flexibly describe the positive and negative interactions among entities in a complex network. Community structure detection is a basic research topic of signed network analysis, which is of theoretical significance and practical value to reveal the organizational structure, functional structure, and individual behavior of complex systems. However, it is still a challenging task to accurately detect the community structure in a signed network. In this paper, we design a community detection algorithm based on normalized Laplacian matrix and convex non-negative matrix factorization for signed networks. The proposed algorithm applies convex non-negative matrix decomposition to a signed network, and decomposes the adjacency matrix of the network into a community membership matrix and a node weight matrix. Meanwhile, the algorithm introduces normalized Laplacian graph regularization terms to reduce noise generated during the decomposition process. Experimental results verify that the proposed algorithm is effective in community detection. © 2023 IEEE.

关键词： Non-negative matrix factorization

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Predicting Coronary Heart Disease from Sleep-Stage-Specific Electrocardiograms Using Convolutional Neural Networks 10

Predicting Coronary Heart Disease from Sleep-Stage-Specific ...

引用

10th international conference on Control, Instrumentation and automation, ICCIA 2024

作者： Jahromi, Ali Fathi Naghsh, Shiva Safavi, Ali Akbar Sharafkhaneh, Amir University of Tehran School of Electrical and Computer Engineering Tehran Iran Shiraz University School of Electrical and Computer Engineering Shiraz Iran Baylor College of Medicine Department of Medicine HoustonTX United States

ISBN: (纸本)9798331509972

Coronary heart disease (CHD) is one of the leading causes of death globally, underscoring the importance of heart health monitoring and early prediction. This study explores utilizing deep convolutional neural networks (CNNs) to predict CHD by analyzing electrocardiogram (ECG) signals collected during overnight sleep. We aimed to compare the predictive value of ECG recordings from different sleep stages: Wake, N1, N2, N3/N4, and REM. The study utilized the Sleep Heart Health Study (SHHS) dataset to train a CNN-based classifier on ECG signals of two different lengths, 30 and 120 seconds, from each sleep stage. The proposed predictive model performed more efficiently when using ECG data from the Wake and N3/N4 stages compared to other stages. The model trained using 30-second ECG during the Wake stage achieved the highest accuracy (75.0%) The results suggest that analyzing sleep-stage-specific ECG Can potentially serve as a novel approach for early CHD prediction. © 2024 IEEE.

关键词： Convolutional neural networks

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Future Trends in HRM: The Role of Artificial Intelligence and automation in Shaping the Workplace

Future Trends in HRM: The Role of Artificial Intelligence an...

引用

2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Panicker, Aneesya Krishna, Somanchi Hari Garg, Shaifali Biswas, Prasanta Chatterjee Lourens, Melanie Kumar, K. Siva Gla University Institute of Business Management Uttar Pradesh Mathura India Vignana Bharathi Institute of Technology Department of Business Management Telangana State 501301 India Amity University Amity Business School Madhya Pradesh India Parul University Cdoe Vadodara India Durban University of Technology Deputy Dean Faculty of Management Sciences South Africa Bapatla Engineering College Computer Science and Engineering Andhra Pradesh Bapatla India

ISBN: (纸本)9798331508685

Artificial intelligence-based applications are integrated in the human resource management of firms for managing people within the region as well as international organisations. In the past years the growth of AI based applications proliferates the function of HR M that triggers to exciting new stream to study the effect of AI adoption on business level and individual outcomes. It evaluates the AI enabled HRM practises. The technology examines the firms with problem solving, decision making and utilisation of resources and opportunities for employees. The growing interest on AI based technologies for HRM for diffusion of innovation in large multinational enterprises. AI has the capability for reduction of administrative burden experienced by the HR Professional that allows them for precise decision making based on the data patterns. Adoption of artificial intelligence helps in effective employee management and decision making. So that employee of HR management can overcome the challenges. The study aims to analyse the future trends in HRM and the role of artificial intelligence and automation in shaping the workplace. © 2025 IEEE.

关键词： Resource allocation

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A GPU-Accelerated 3D Visual Simulation Platform of X-Ray Digital Imaging Based on Unity 15

A GPU-Accelerated 3D Visual Simulation Platform of X-Ray Dig...

引用

15th international conference on computer and automation engineering, ICCAE 2023

作者： Gao, Zhiyu Fu, Jian Lu, Junyi Gao, Xianliang Beihang University Dept. of Mechanical Engineering and Automation Beijing China Jiangxi Research Institute Nanchang China Ningbo Institute of Technology Ningbo China Shanghai Spaceflight Precision Machinery Institute Shanghai China

ISBN: (纸本)9798350396225

X-ray digital imaging is an advanced and widely used non-destructive testing technology, X-ray digital imaging system faces some problems in actual inspection such as high costs and radiation effects. computer simulation technology, which comprehensively applies mathematics, computer graphics and image processing technology, can solve these problems well and has its own advantages. At present, most of the research on X-ray digital imaging simulation has slow imaging speed, poor visibility and poor interaction. In this paper, we make a mathematical model for X-ray digital imaging and build a 3D visual simulation platform in Unity. By using GPU acceleration technology, the imaging speed is reduced to only a fraction of a second which can satisfy real-time interaction. This simulation platform has good demonstration and comfortable user experience based on the 3D visualization technology. This work will promote the applications of computer simulation technology in X-ray digital imaging technology. © 2023 IEEE.

关键词： Simulation platform

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Research on Circle Center Positioning Algorithm Based on Mean Shift Method 2

Research on Circle Center Positioning Algorithm Based on Mea...

引用

2nd international conference on automation, Robotics and computer engineering, ICARCE 2023

作者： Min, Yujin Chen, Zhuohang Zhang, Faquan School of Economics Wuhan Textile University Wuhan China School of Electronic and Electrical Engineering Wuhan Textile University Wuhan China

ISBN: (纸本)9798350308341

Aiming at the problems of long-time consumption and low accuracy in the circle center positioning algorithm, a circle center positioning algorithm based on Mean Shift is proposed. Firstly, the grid method is used to segment the contour of a circle, forming a large number of small arcs. Secondly, a dataset of selected center points is constructed using small arcs, which are likely to fall near the neighborhood of true circle center points. Finally, a fast Gaussian kernel function with adaptive bandwidth is introduced to solve the weight problem of sample points, and the Mean Shift clustering algorithm is used to calculate the optimal solution for the data density of the selected circle center sample. This algorithm eliminates bad data from the dataset, resulting in higher positioning accuracy. The validation experimental results show that the circle center positioning algorithm takes the least time, and has an accuracy improvement of 23% compared to the Hough transform and 16% compared to the circle fitting algorithm. © 2023 IEEE.

关键词： Clustering algorithms

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The smart power optimization for sensor based wearable IoT devices using AI based deep learning 2

The smart power optimization for sensor based wearable IoT d...

引用

2nd IEEE international conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Kumar, Anuj Sharma, Jyoti Goel, Mukulit Kumar, Manish Chaudhary, Vikas Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Delhi NCR Ghaziabad India KIET Group of Institutions Delhi NCR Ghaziabad Department of Information Technology Delhi NCR Ghaziabad India Ajay Kumar Garg Engineering College Ghaziabad Department of MCA Delhi NCR Ghaziabad India ABESEC College Ghaziabad Department of Computer Science and Engineering Delhi NCR Ghaziabad India

ISBN: (纸本)9798350368109

The intelligent power optimization for sensor-based wearable IoT devices with AI-based deep learning reduces the energy consumption of sensor-based wearable devices in IOT applications. The process employs deep learning algorithms based on artificial intelligence (AI) that can optimize the power consumption in such devices, making economical use of the battery and reducing frequent charging. Wearable IoT devices have long been a power-hungry type of wearable because they always need to sense and transmit data. This causes the battery life to shorten and your battery maintenance costs to increase. But at the same time, using deep learning-based algorithms of AI, they can understand and learn from their user's behavior so that it could save up to 40% power. This is done by real-time sensor data analysis and discovering user patterns, communication styles, and activity levels. This optimization process can actively change the power of different sensors and communication modules by identifying many patterns. This helps cut down power consumption and improves the overall performance of wearable IoT. In addition, energy harvesting from ambient sources such as body heat can be possible using this approach to extend battery lifetime even longer. © 2024 IEEE.

关键词： Smart sensors

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Variable Sampling Time Intelligent Control System For Driving the Hearth of an Industrial Furnace 15

Variable Sampling Time Intelligent Control System For Drivin...

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15th international conference on computer and automation engineering, ICCAE 2023

作者： Motorga, Roxana-Maria Muresan, Vlad Abrudean, Mihail Clitan, Iulia Sita, Valentin Unguresan, Mihaela Chifor, Laurentiu Technical University of Cluj-Napoca Automation Department Cluj-Napoca Romania Technical University of Cluj-Napoca Physics and Chemistry Department Cluj-Napoca Romania

ISBN: (纸本)9798350396225

In this paper a PID controller and an adaptive PID controller are designed and implemented in order to control the speed of the induction motors used to operate the rotary hearth furnace with constant load. The PID controller is further discretized and implemented with different values for the sampling time, leading to the online adaptation of the system which is ought to improve the obtained performances of the process. It is possible to implement the adaptive control strategy by means of artificial intelligence techniques which relay on the usage of neural networks - the neural networks are responsible with the generation of the coefficients of the PID controller, which are considered functions that depend on the sampling time. Even if the behavior of the induction motor is strongly nonlinear, the proposed control strategy can follow with high accuracy different setpoint speed values, fact proven through simulations. © 2023 IEEE.

关键词： Induction motors

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