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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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Design of a Deep learning-based Detection System for Criminal Activities 3

Design of a Deep Learning-based Detection System for Crimina...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Nojor, Verlyn V. Austria, Jarod Augustus C. Galit, Adrian A. Guevarra, Joaquin Tyrone B. Jogno, Kathleen Q. Venal, Maria Cecilia A. Somao-I, Mary Jane F. Technological Institute of the Philippines Computer Engineering Department Quezon City Philippines

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

In the last six years, Manila crime rate improvement was significantly low compare to other ASEAN cities. The Philippines' crime rate & statistics was recorded 8.93 in 2017 which has a percentage of 23.55% decline from 2016, then the Philippines' crime rate & statistics for 2018 was 6.47, a 22.96% decline from 2017. In 2019, there were more than 1,200 deliberate shooting deaths and more than 7000 cases of assault injuries in the nation. The project aims to design a deep learning-based detection system for criminal activities such as shooting and assault. Also, to design a web application that includes features such as a dashboard showing the number of confirmed detections, a notification page, and a live feed page for streaming the footage from the CCTV camera. The model is used for the system to work together with the Python framework. The system obtained an accuracy of 95% percentage of accuracy for crime detection along with a 100% accuracy percentage for the other project objectives. The design of the deep learning-based detection system has shown a significant potential to introduce a more active response to crime by supporting police officers in preventing violence and upholding a safer environment for all. © 2022 IEEE.

关键词： Crime

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Using EEG Brain Signals to Predict Children's learning Performance During Technology-assisted Language learning 3

Using EEG Brain Signals to Predict Children's Learning Perfo...

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3rd IEEE international conference on Human-machine Systems, ICHMS 2022

作者： Nakov, Nikolas Alimardani, Maryam Tilburg University Department of Cognitive Science and Artificial Intelligence Tilburg Netherlands

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

Brain-computer interface (BCI) systems can provide an objective evaluation of a learner's performance based on their brain signals. Having an accurate assessment of the learning process can facilitate the adaptation of the educational setting to improve the effectiveness of learning. The current study aimed at developing BCI classifiers that can accurately predict the learning performance of children by investigating temporal and spectral characteristics of their brain signals. EEG brain activity collected from children while learning a second language was analyzed by extracting two types of features: frequency-domain features using power spectral density (PSD) analysis and time-domain features using detrended fluctuation analysis (DFA). Two machine learning models (i.e., SVM and KNN) were trained with either PSD features or both PSD and DFA features. It was expected that adding a time-domain feature would improve the prediction accuracy of the models. However, the models with only frequency-domain features performed significantly better, suggesting that EEG temporal correlations may not reflect learning processes. Despite this, both approaches yielded high prediction performance, with SVM reaching 84% accuracy in predicting the learning performance of children. These findings highlight the potential of neurotechnology to be employed in learning analytics to refine traditional methods of academic performance measurement and ultimately achieve personalized learning. © 2022 IEEE.

关键词： Electroencephalography

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Time-Weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection 48

Time-Weighted Frequency Domain Audio Representation with GMM...

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48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Guan, Jian Liu, Youde Zhu, Qiaoxi Zheng, Tieran Han, Jiqing Wang, Wenwu Harbin Engineering University Group of Intelligent Signal Processing College of Computer Science and Technology China Harbin Institute of Technology School of Computer Science and Technology China University of Technology Sydney Centre for Audio Acoustics and Vibration Australia University of Surrey Centre for Vision Speech and Signal Processing United Kingdom

ISBN: (纸本)9781728163277

Although deep learning is the mainstream method in unsupervised anomalous sound detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as input can achieve comparable results with much lower model complexity and fewer parameters. Existing statistical frequency representations, e.g. the log-Mel spectrogram's average or maximum over time, do not always work well for different machines. This paper presents Time-Weighted Frequency Domain Representation (TWFR) with the GMM method (TWFR-GMM) for anomalous sound detection. The TWFR is a generalized statistical frequency domain representation that can adapt to different machine types, using the global weighted ranking pooling over time-domain. This allows GMM estimator to recognize anomalies, even under domain-shift conditions, as visualized with a Mahalanobis distance-based metric. Experiments on DCASE 2022 Challenge Task2 dataset show that our method has better detection performance than recent deep learning methods. TWFR-GMM is the core of our submission that achieved the 3rd place in DCASE 2022 Challenge Task2. © 2023 IEEE.

关键词： Deep learning

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Intelligent Flower Recognition System Based on Deep learning 3

Intelligent Flower Recognition System Based on Deep Learning

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3rd international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Gu, Fenfei Dept. of Big Data and Artificial Intelligence Anhui Xinhua University Anhui Hefei China

ISBN: (纸本)9781510657281

With the continuous expansion of the application fields of deep learning, human life is becoming more and more wonderful. This paper mainly uses the application of deep learning in the field of image recognition and uses deep learning algorithm to recognition the problem of flowers. Due to the huge number of flowering species, How to automatically recognition flowers through the system gives people more understanding of flowers while appreciating them, which is a topic with practical application value. This paper constructs a deep learning multi-classification model based on ResNet, and conducts training based on PyTorch framework on the data set of more than 3000 pictures of five kinds of flowers. After 50 rounds of iterative training, it finally achieves 91% average accuracy on the test dataset. Finally, a recognition system based on this model is realized by visualization technology, so as to facilitate users to effectively recognize the kind of flowers through this system. © 2022 SPIE.

关键词： Image recognition

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Exploring fusion model for the detection and localization of colorectal polyps 3

Exploring fusion model for the detection and localization of...

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3rd IEEE international Power and Renewable Energy conference (IPRECON)

作者： Geetha, S. Gopakumar, C. Chengannur Coll Engn Coll Engn Dept Comp Sci & Engn Karunagappally India APJ Abdul Kalam Technol Univ Thiruvananthapuram Kerala India Coll Engn Dept Elect & Commun Engn Karunagappally India

ISBN: (纸本)9781665491754

computer Vision (CV) and Artificial Intelligence (AI) have reported unprecedented growth in recent years and demonstrated potential in almost all domains. In the medical realm machine learning and deep learning techniques are widely used for detection as well as classification of a nomalies and segmentation of anomaly regions. Salient feature extraction is a challenging task in traditional machine learning techniques but deep learning models successfully extract better features with convolutional neural networks (CNN) and report better performance when the training dataset is tremendous. In the medical domain, the main challenge is the limited availability of annotated data to perform training. Colorectal polyp detection and localization is an arduous task due to the close resemblance of polyps with the surrounding colon regions and the irregular shapes of the polyps. But detection, localization, and segmentation of colon polyps are of great importance in the screening and removal of polyp regions for the prevention of colorectal cancer. Currently used U-Net and Mask-RCNN-based architectures for polyp segmentation are computationally expensive. In this paper, we explore a fusion model to overcome the downsides of both machine learning and deep learning techniques. The fusion model serves the similar purpose of segmentation with less computational overhead and can be used to assist clinicians in screening tests. In the proposed work, pre-trained deep CNNs are explored to extract salient features from image patches followed by ensemble machine learning techniques for the detection of colon polyps. A localization algorithm is applied on the detected image patches for localizing the position of the colon polyps.

关键词： fusion models colorectal polyp detection and localization ensemble machine learning techniques Random Forest XGBoost elliptical contour

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machine learning for Plastic Waste Detection: State-of-the-art, Challenges, and Solutions 3

Machine Learning for Plastic Waste Detection: State-of-the-a...

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3rd international conference on Communications, Information, Electronic and Energy Systems, CIEES 2022

作者： Tamin, Owen Moung, Ervin Gubin Ahmad Dargham, Jamal Yahya, Farashazillah Omatu, Sigeru Angeline, Lorita Universiti Malaysia Sabah Faculty of Computing And Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Education Research Centre Higashi-Hiroshima739-0046 Japan Taylor's University School of Computer Science Faculty of Innovation And Technology Selangor Malaysia

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

machine learning has advanced rapidly in recent technologies of artificial intelligence. The abundant and cheap computation in machine learning has created more knowledge and reliability of this method. Therefore, more researchers have relied on machine learning to solve plastic waste pollution, which contributes to global greenhouse gas emissions. Datasets are fundamental requirements to build the foundation of the plastic waste detection system. This paper provides an overview of several state-of-the-art deep learning models, focusing on the dataset used for waste classification and identification. The performance of each deep learning model in classification accuracy is also discussed. The challenges of implementing deep learning in plastic waste detection are identified;(i) Data deficiency for training, (ii) Quality of the existing plastic waste dataset, and (iii) Color spaces. Lastly, the proposed solution to each challenge is presented. © 2022 IEEE.

关键词： Object detection

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Leveraging Deep learning for COVID-19 Detection: A Comparative Study Using ResNet Models on X-Ray Images 3

Leveraging Deep Learning for COVID-19 Detection: A Comparati...

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3rd IEEE international conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Tuteja, Disha Sinha, Anurag Uprety, Deepak Chandra Banerjee, Dyuti Dewan, Ayushi Sharma, Chetna College Guru Tegh Bahadur Institute of Technology Department of Ece Delhi India Ignou Department of Computer Science New Delhi India Noida Institute of Engineering and Technology School of Computer Science in Emerging Technologies Department of Computer Science and Engineering (CS) Greater Noida 201306 India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence & Data Science (AI&DS) Vaddeswaram Green Fields Andhra Pradesh Guntur District 522302 India Rawal Institutions Faridabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331521691

The COVID-19 pandemic has rapidly spread across the globe, infecting millions of people, with no signs of slowing down. This continuous surge in cases presents a major challenge for healthcare systems worldwide. To combat the exponential rise in infections, medical researchers have primarily relied on RT-PCR testing to distinguish between COVID-19 positive and negative cases. However, this approach faces significant hurdles, particularly in low-income countries where these tests are often too expensive and inaccessible in rural areas. Another critical challenge is the shortage of skilled healthcare professionals, especially doctors and radiologists, whose expertise is in high demand for diagnosing and treating COVID-19 patients. As a result, healthcare resources have been stretched to their limits. To address these issues, innovative solutions are essential, and artificial intelligence (AI) and machine learning (ML) offer promising possibilities. In medical diagnostics, AI, ML, and deep learning (DL) have demonstrated remarkable accuracy. This study explores the application of DL models, specifically ResNet-34 and ResNet-50, for COVID-19 detection using X-ray images. The results indicate an impressive accuracy of 96.4% with ResNet-34. To comprehensively evaluate the performance of these models, the study employs various quantitative metrics, including Matthews Correlation Coefficient (MCC), F1 Score, Precision, and Recall. By contributing to both the effective detection of COVID-19 and addressing the shortage of healthcare professionals, this research provides a hopeful step forward in the ongoing fight against the pandemic. © 2025 IEEE.

关键词： AI applications Detection machine learning Pandemic Response Public Health Quantitative Analysis

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Deep learning-based Time Series Models for GDP and ICT Growth Prediction in India 3

Deep Learning-based Time Series Models for GDP and ICT Growt...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Kumari, Surbhi Singh, Sunil Kumar Mahatma Gandhi Central University Dept. of Computer Science and Information Technology Bihar Motihari India

ISBN: (纸本)9781665462006

Approximately 13% of the country's gross domestic product (GDP) depends on information and communication technology (ICT), and India's digital economy accounts for nearly ${\$}$200 billion in economic value annually. The literature has well established the function of ICT in stimulating economic growth. The work focuses on the relationship between the ICT use index and GDP based on the last 30-year time series multivariate data. The variables having a high correlation with respect to GDP has taken for the analysis. This work also forecasts the next 10-year growth in ICT use index and GDP w.r.t each other using machine learning and deep learning models which are linear regression(LR), random forest(RF), temporal convolutional network(TCN), Kalman forecaster(KF), Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (NBEATS) model and transformer model. There are eight performance metrics we have used for model evaluation. The transformer model has been suggested as the best predicting model with the least root mean squared error value of 0.326, mean absolute error of 0.51, mean absolute ranged relative error of 52.592, and so on. This paper also suggested policies to foster the country's economic growth using ICT. © 2022 IEEE.

关键词： Time series

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A Review on Speech Synthesis Based on machine learning 3rd

A Review on Speech Synthesis Based on Machine Learning

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3rd international conference on Artificial Intelligence and Speech Technology, AIST 2021

作者： Kumari, Ruchika Dev, Amita Kumar, Ashwni Indira Gandhi Delhi Technical University for Women Delhi India

ISBN: (纸本)9783030957100

Recently, Speech synthesis is one of the growing techniques in the research domain that takes input as text and provides output as acoustical form. The speech synthesis system is more advantageous to physically impaired people. In execution process, there arise some complications by surrounding noises and communication style. To neglect such unnecessary noises various machine learning techniques are employed. In this paper, we described various techniques adopted to improve the naturalness and quality of synthesized speech. The main contribution of this paper is to elaborate and compare the characteristics of techniques utilized in speech synthesis for different languages. The techniques such as support vector machine, Artificial Neural Network, Gaussian mixture modeling, Generative adversarial network, Deep Neural Network and Hidden Markov Model are employed in this work to enhance the speech naturalness and quality of synthesized speech signals. © 2022, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Design of Efficient Quadrature Scheme in Finite Element Using Deep learning 1

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3rd Biennial international conference on Future learning Aspects of Mechanical Engineering, FLAME 2022

作者： Chinchkar, Rohan Nath, Dipjyoti Gautam, Sachin S. Department of Mechanical Engineering Indian Institute of Technology Guwahati Assam Guwahati781039 India

ISBN: (数字)9789819930333

ISBN: (纸本)9789819930326

The advancement of computers has led to collection and handling of huge data from various resources. The inherent properties of computational mechanics application can be extracted from the appropriate data using different techniques of machine learning (ML). The present work enlights a method to employ machine learning in the field of finite element method (FEM) where evaluation of sufficient number of integrals has to be carried out. This calculation of integral by the standard Gauss–Legendre quadrature rule requires specific number of Gauss quadrature points for getting the desired accuracy which minimizes the computational cost. Whereas the element stiffness matrix is calculated numerically with required number of Gauss quadrature points for different element with respect to the material properties. Most of the auto-mesh software consider constant number of Gauss quadrature points for all the elements irrespective of their distortion and material behaviour. The main motivation of this work is to build an accurate and computationally efficient quadrature scheme with the help of standard Gauss–Legendre quadrature rule for computing elemental stiffness matrix. An efficient method is developed using a deep neural network to predict respective number of Gauss quadrature points for given element coordinates and the material properties. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Finite element method

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